esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Thermal Energy Storage (TES)
### Thermal Energy Storage in Buildings  
There is urgent necessity for the development of new technologies for energy conservation in the building sector. The application of TES systems is a promising technology with many advantages, including the increasing of energy and economic efficiency in buildings, as well as the reduction of electrical energy consumption and, furthermore, the reduction of environmental pollution and CO\({}_{2}\) emissions [11].  
The systems of TES that are applied in buildings can be either active or passive. Active TES systems are characterized by forced convection heat transfer and, in some cases, also mass transfer, such as a heat exchanger. Active systems mainly aim to control the indoor conditions of a building, for example providing free cooling. Additionally, an active TES system can be applied for peak shaving purposes in heating, ventilation, and air conditioning systems [7].  
On the other hand, passive TES systems aim to create or maintain comfort conditions in the building, exploiting naturally available energy sources such as the solar energy or the wind, along with architectural design of building components, such as shading effect using blinds, etc. Passive TES systems minimize the use of mechanically-assisted heating or cooling systems. The main idea is to maximize the comfort conditions of the building envelope without using external energy sources. Some examples of passive TES systems are the use of ventilation facades and the PCM wallboards [12,13].
# An Automatic Fault Diagnosis Method for the Reciprocating Compressor Based on HMT and ANN
## 2 The Proposed Method and Its Theories  
### Pressure-Volume Diagram  
When the piston reaches the top-dead-center (TDC), a new working cycle begins. At first, the piston moves from TDC to the bottom-dead-center (BDC), and the gas remaining in the cylinder expands. Until the pressure declines to the suction pressure, the suction valves open, then the suction process begins. When the piston reaches the BDC, the suction process is over and the suction valves close, then the piston moves back to the TDC and the compression begins. Until the pressure increases to the discharge pressure, the discharge valves open, and the compression is over and the discharge process begins. At last the piston reaches the TDC, the discharge process is over and the discharge valves close, the next cycle begins. Therefore, a working cycle is composed of four processes, i.e., expansion, suction, compression, and discharge. The compressor repeats this cycle over and over again. In the meantime, the pressure in the cylinder also changes with the piston movement periodically. The pressure-volume (\(p\)-\(V\)) diagram can exactly exhibit how the dynamic pressure over a working cycle changes against the working volume. The working volume is defined as [PERSON] et al. (2018):  
\[V = V_{0}+\frac{\pi D_{t}^{2}}{4}\cdot r\cdot\left[(1-cos\theta)+\frac{1}{ \lambda}(1-\sqrt{1-\lambda^{2}\sin^{2}\theta})\right] \tag{1}\]As Figure 1 shows, the \(p\)-\(V\) diagram is a closed 2D digital diagram comprising of the four thermodynamic process curves. According to the previous description, the entire \(p\)-\(V\) diagram can be divided into four sectionalized curves, as shown in Figure 1. From Figure 1, it can be seen that the characteristics of the four sectionalized curves are different, and the influence of various faults on shape change of the curves are also different. In practice, the changes of gradients of the expansion and the compression curves and the changes of fluctuations of the discharge and suction curves are the most obvious under faulty conditions.  
To capture the shape change accurately, two different feature extraction methods based on DIP were proposed according to the curve characteristics. In this study, the sectionalized curves were all saved as digital images first, and the feature extraction was also employed on these digital images. The hit-or-miss transform was applied to extract the comprehensive gradients of compression curve and expansion curve. Additionally, vertical projection transform was used to extract the fluctuation of discharge curve.
## 2 Related Work
Considering the linear characteristics of the monitoring targets and the significant distance differences between nodes and the sink, this method employs a hybrid transmission approach combining single-hop and multihop transmissions to reduce routing overhead. Furthermore, the next hop cluster head in multihop transmissions is determined based on the cluster head's energy, distance, and load factors, further balancing energy consumption among nodes. Overall, the proposed method effectively reduces the difficulty and cost of network deployment. Through comparison with existing classical methods, the results demonstrate that the proposed method in this paper has approximately doubled the network's energy efficiency, effectively balanced network energy, and extended the network's lifespan.
## 4 Results and Discussion
### Clustering Results Analysis
#### 4.3.2 Comparative Analysis of Intercity Trip Chains from a Spatial Perspective
So, the waiting times at departure hubs of Tianjin to Shijiazhuang were 10 min less than the opposite direction. Since passengers went directly to their destinations after arriving at the hub, the waiting times at arrival hubs were similar. For intercity travel to Beijing, which has a larger scale compared to other cities, the travel distance within the destination city is typically between 5 and 10 km, while other destinations are generally under 5 km. Table 9 outlines the characteristics of the most common trip chains for various directions in 2021. The data shows that travelers for the predominant trip chains across all directions primarily travelled on weekdays, with male travelers dominating. Both departure and arrival locations were residential. This pattern aligns with typical business travel trends, where weekday mobility is workforce-driven and gender disparities persist in professional activity participation. Based on Beijing's mature metro network, the main mode of travel from Beijing was the subway, while road travel was predominant from other cities, and urban travel within the destination city was primarily by road [70]. For the Tianjin to Beijing direction, travelers were mainly aged 30 to 34, while in other directions, the age distribution was predominantly 19 to 24, reflecting Beijing's dominance as a core \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Attributes** & **Beijing–**Tianjin** & **Tianjin–**Beijing** & **Beijing–Shijiazhuang** & **Shijiazhuang–**Beijing** & **Tianjin–**Shijiazhuang** & **Shijiazhuang–**Tianjin** \\ \hline Weekday & 3 & 3 & 3 & 3 & 3 & 3 \\ Gender & 1 & 1 & 1 & 1 & 2 & 1 \\ Type-O & 1 & 1 & 1 & 1 & 1 & 1 \\ Mode-O & 4 & 1 & 4 & 1 & 1 & 1 \\ Mode-D & 1 & 1 & 1 & 1 & 1 & 1 \\ Type-D & 1 & 1 & 0 & 0 & 0 & 1 \\ Age & 6 & 6 & 7 & 7 & 5 & 5 \\ MOT-O & 8 & 10 & 13 & 7 & 8 & 10 \\ Ds-O & 2 & 1 & 2 & 1 & 1 & 1 \\ Wt-O & 5 & 6 & 10 & 7 & 9 & 11 \\ Wt-D & 7 & 5 & 7 & 7 & 6 & 7 \\ Ds-D & 1 & 2 & 1 & 2 & 1 & 1 \\ Percentage & 55.7\% & 47.7\% & 52.6\% & 45.4\% & 47.6\% & 67.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Characteristics of the trip chain with the largest proportion of different travel directions in 2019.  
job market attracting career professionals. Except for the Tianjin to Beijing direction, where departure times were between 12 PM and 1 PM, all other directions had departures in the morning for commuting. Due to the large urban scale of Beijing, the distances for urban trips departing from Beijing are typically between 5 and 10 km, while for other directions, it is generally under 5 km. For urban trips arriving in Beijing, the distance is also typically between 5 and 10 km, with other directions being generally under 5 km.
# Critical Study Quality Management for the Anti-Seepage System in Macau's Landfill Area
## 4 Quality Control Plan
### Emphasizing the Importance of Implementing Quality Control in All Project Phases  
In the landfill anti-seepage system project, quality control should extend beyond the construction phase to encompass all stages of the project. From initial preparation to design, construction, and environmental monitoring, rigorous quality control is essential.  
During the initial preparation phase, conducting geological surveys and environmental assessments is crucial. Quality control plays a significant role in ensuring the accuracy and completeness of these data. This guarantees reliable bases for subsequent design and construction. By diligently reviewing and verifying the gathered information, potential errors and inaccuracies can be eliminated, thus minimizing risks during later stages.  
In the design phase, quality control becomes vital in ensuring the scientific and rational nature of the design plans. It involves a thorough examination of each detail, ensuring that technical indicators and quality standards are explicitly specified. Through meticulous supervision, any potential design flaws or oversights can be identified and rectified, enhancing the overall functionality and efficiency of the anti-seepage system.  
During the construction process, quality control becomes even more crucial. It involves meticulous material selection and acceptance, guaranteeing that only high-quality materials are used in the project. Not only does this contribute to the overall durability and effectiveness of the system, but it also ensures that all construction activities comply with established standards. Having a robust construction quality management system in place helps identify and rectify any deviations from the approved designs or standards, thus ensuring that the project is completed to the highest quality and safety standards.  
The implementation of an environmental monitoring system is another key aspect of quality control. With such a system in place, real-time tracking of environmental changes becomes possible. This allows project managers to proactively address any emerging environmental issues, ensuring minimal impact on the surrounding ecosystem. Regular monitoring and analysis enable prompt identification of any changes in water quality, potential leaks, or adverse impacts on nearby habitats. By promptly addressing such issues, the project can minimize environmental damage and demonstrate its commitment to sustainability.  
Emphasizing the importance of implementing quality control in all project phases is essential. Doing so helps prevent issues from magnifying or compounding in later stages. By addressing potential concerns upfront, the project team can identify and mitigate risks before they escalate, thus ensuring that the project achieves its expected quality objectives. Continuous monitoring and steadfast adherence to established quality control measures guarantee the longevity, effectiveness, and safety of the landfill anti-seepage system.
# Reproducibility and Data Storage for Active Learning-Aided Systematic Reviews
## 7 Discussion  
With this paper we aim to provide foundational insights into reproducibility and data storage within the context of screening prioritization using AI during the screening phase. However, there are also some clear boundaries to what we investigated. We recognize that other crucial phases of systematic reviews, such as database searching and data extraction, as well as the performance and biases of AI-aided screening, are beyond the scope of this study. These limitations highlight important avenues for future research.  
One significant area that merits deeper investigation is the potential for biases introduced by AI tools used in the screening process. While we have outlined procedures to enhance transparency and mitigate some risk of bias, the complete evaluation of bias, particularly how AI influences the selection and exclusion of studies, remains to be addressed. Future work should focus on developing methodologies to effectively detect and correct for these biases. For instance, the NLF procedure (Noisy Label Framework) can be applied to address noisy labels, the inaccuracies in labels provided by experts, enhancing the overall reliability of the screening process [47]. Similarly, the SAFE procedure, a heuristic for deciding when to stop screening, includes a quality check to correct for potential false exclusions [48]. Nonetheless, biases will be inherent in any screening process, be it fully human or AI-aided. This is one more reason it is important to store data related to a review in an accessible and reusable manner, so that biases of finished reviews can be investigated.  
Moreover, in future studies, it will be crucial to explore more robust methods for assessing inter-rater reliability (IRR) in the context of AI-aided systematic reviews [49]. Traditional metrics like Cohen's Kappa are often challenged by the data's nature in these reviews, specifically when data are missing not at random (MNAR) due to selective paper exclusion by AI mechanisms. Alternative metrics are proposed and designed to accommodate the complexities introduced by MNAR conditions, e.g., [50], which may provide more accurate assessments of IRR in AI-enhanced research settings.  
In our study, we referred to the use of inclusion and exclusion criteria; however, it is important to note the distinct roles these criteria might play at various stages of the systematic review process. Inclusion criteria are typically applied during the preliminary screening phase to select studies based on predefined attributes. Conversely, exclusion criteria might be used during the same phase to exclude studies based on attributes such as publication year, language, and article type. However, exclusion criteria can also be applied to further refine and finalize the selection of relevant studies after the screening phase.
## 2 Related Works
### Machine Learning Method for MER  
For music emotion recognition, commonly used machine learning approaches include SVM, the Gaussian mixture model, the KNN method, naive Bayes, and so on. The representative works using traditional machine learning methods are summarized in the first half of Table 1. Specifically, the authors in [11] use rhythm patterns as music features and use KNN and a self-organizing map (SOM) to predict emotion for a set of kid songs. [PERSON] et al. [13] proposed a hierarchical Gaussian mixture model to automate the task of mood detection, where three types of music features--intensity, timbre, and rhythm--are extracted to represent the characteristics of a music clip. The hierarchical framework can emphasize the most suitable features in different detection tasks. [PERSON] et al. [34] executed music emotion classification based on the lyrics using three machine learning methods: naive Bayes, the hidden Markov model, and SVM. The results showed that the classification performance of the SVM method was optimal. [PERSON] et al. [35] used SVM to evaluate musical emotions based on three novel lyric features: slang presence, structural analysis features, and semantic features. [PERSON] et al. [36] collected physiological signals from wearable devices, trained four classification models, and found that the KNN model achieved the best performance. [PERSON] et al. [37] introduced source separation into a standard music emotion recognition system and extracted a combined 84-dimensional feature vector for music emotion recognition. The combined feature vector consists of a 72-dimensional acoustic feature vector and a 12-dimensional chroma feature vector. Lastly, the SVM classifier is employed for emotion prediction, and the experimental results have verified that source separation can effectively improve the MER performance.  
Each of these traditional machine learning methods has inherent strengths and limitations. On the one hand, machine learning methods explicitly extract features from music data, allowing for a clear understanding of which features are crucial for music emotion recognition tasks. On the other hand, machine learning methods also have a significant drawback: manually extracted features may not cover the full spectrum of relevant characteristics, and there is a lack of further feature extraction beyond the initial selection.
## 4 Principles of Fuel Cell  
There are various types of FC systems. However, the principle of their function is similar. For a fuel cell system, three pillars are required: an anode, a cathode, and an electrolyte. FCs are categorized by the type of electrolyte material used. An FC can be composed of hundreds of individual cells, but each has the three same fundamental components. The electrolyte is located between the cathode and the anode. Figure 2 depicts a schematic of a polymer electrolyte FC (PEMFC) operation diagram [38]. This FC type is also known as a proton exchange membrane FC. The PEMFC is what is most commonly used in mobile power applications, such as vehicles. While the electrolyte material used varies depending on the type of FC, the general function of the FC is as follows--fuel (pure hydrogen) is fed into the anode compartment of the fuel cell while air or pure oxygen is fed into the cathode side of the  
Figure 1: Fuel cell vehicle with on-board storage [29].  
FC. On the anode side of the cell, electrons are separated as the gas tries to make its way through the electrolyte membrane. The membrane acts as a filter to separate the electrons and the hydrogen ions while only allowing the hydrogen ions to pass through. In the cathode compartment, the hydrogen ions that passed through the membrane combine with the oxygen atoms from the air supply to produce H\({}_{2}\)O as a by-product; heat is also produced as a by-product [39]. Unlike internal combustion engines, where the fuel is mixed with air and fuel, there is separation of the fuel and the oxidant with no combustion of the fuel in an FC. Therefore, FCs do not produce the harmful emissions that internal combustion engines produce.
# Stability Evaluation of TBM Pilot Tunnels to Rear Blasting Using the Protection Shield
###### Abstract  
Recent studies have increasingly investigated construction methods for tunnel excavation because of growing underground space development. Although the New Austrian tunneling method (NATM)--a representative tunnel excavation method--can be applied to various ground conditions, as well as having good constructability and economic feasibility, it suffers from problems such as vibration and noise. By contrast, excavation using a tunnel boring machine (TBM)--a representative mechanical construction method--affords advantages such as stable excavation, minimized ground and environmental damage, noise, and vibration. However, it cannot be applied easily to various ground conditions, and it suffers from problems such as high construction costs and delays owing to equipment defects. Therefore, the simultaneous pilot tunnel excavation using the TBM--which affords advantages such as constructability, economic feasibility, and minimized noise and vibration--and rear enlargement blasting using NATM was investigated in this study. A protection shield was installed to minimize accidents occurring from equipment defects (a disadvantage of TBM) and analyze the decrease in the effect of NATM blasting, which depends on the use of the protection shield and the separation distance through 3D numerical analysis.  
t +
Footnote †: journal: Applied Sciences
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
## 2 A Planar Antenna for LSA Bands  
This section presents a planar antenna (Ant#1) for the 1.5 and 2.3 GHz LSA bands.
# LSTM Network-Assisted Binocular Visual-Inertial Person Localization Method under a Moving Base
## 3 Results  
In this section, in order to verify the effectiveness of the algorithm, experiments were conducted in real indoor and outdoor scenarios. There are two main parts: experiments and analysis of inertial measurement errors under a moving base; experiments and analysis of visual-inertial human localization under a moving base.  
To verify the effectiveness of the algorithm, the trained 6-dimensional IMU data are solved to obtain the localization trajectory, and the trajectory of visual-inertial SLAM is used as the ground truth trajectory. The RONIN-LSTM method without position loss function and our method with position loss function are compared, and two evaluation criteria are used: absolute trajectory error (ATE) and relative trajectory error (RTE). The absolute trajectory error is positioned as the overall root mean square error (RMSE) between the estimated trajectory and the real trajectory, the relative trajectory error is defined as the average RMSE over a fixed time interval, the time interval is one minute in the evaluation of this paper, and for sequences less than one minute, the position error of the last frame is calculated and scaled.  
To evaluate the superiority of the proposed algorithm more intuitively, two sets of representative training results are selected in this paper: the first set has better training results and the second set has worse training results. As shown in Figure 8, the blue line is the trajectory generated by the algorithm, the yellow line is the ground truth trajectory, and the green line is the trajectory generated by the RONIN-LSTM method. From the figure, we can see that the trajectory generated by our algorithm in the first picture (a) basically overlaps with the ground truth, and the trajectory generated by the RONIN-LSTM algorithm deviates more seriously. In the second picture (b), both methods deviate, but the trajectory of our algorithm obviously deviates less than that of RONIN-LSTM. As can be seen in Table 2, both images show a significant reduction in ATE and RTE after training using the position loss function.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**01**} & \multicolumn{2}{c}{**02**} \\ \cline{2-5}  & **ATE** & **RTE** & **ATE** & **RTE** \\ \hline Our algorithm & 0.68 & 0.76 & 2.28 & 1.95 \\ \hline RONIN-LSTM & 2.65 & 1.05 & 3.23 & 2.22 \\ \hline Precision improvement & 74.34\% & 27.62\% & 29.41\% & 12.16\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: ATE and RTE for both methods.  
Figure 8: Trajectories of the two methods on the dataset.
# Development of an Energy-Efficient Smart Socket Based on STM32F103
###### Abstract  
Many efforts have recently been dedicated to developing smart sockets that seek to provide insights into the reduction of standby energy waste coupled to electric appliances. However, not all technical solutions consider the techno-economic benefits in the development. This research presents a hardware solution based on STM32F103 (STM32F103 devices use the Cortex-M3 core, with a maximum CPU speed of 72 MHz) for the development of an energy-efficient smart socket to address the standby energy waste of household electric appliances and associated economic losses. Input-output analysis on monitored voltage and current was employed to assess the performance and examine the precision of the developed system. As it was targeted at facilitating easier operation, the smart socket was developed to be compatible with other remote controllers of household electric appliances. Experimental results indicated that the developed system could measure voltage values accurately to avoid overvoltage for security protection. The measuring unit could monitor current values with high precision to support the energy-saving control. A functional testing was conducted on the prototypes with a lifecycle assessment employed to validate the economic attractiveness of the developed system. Results indicated that the system is user friendly and cost-effective as no extra wiring required and network environment independent. Indeed, indirect fruits, such as lifespan extension and safety enhancement, could also be achieved for appliances.  
s mart socket; energy-efficient; lifecycle assessment; safety design; STM32F103 22 October 2018; 8, 2276; doi:10.3390/app8112276