esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Long-Range Underground Monitoring
### Classification of Technologies by Parameter  
16 Market Maturity/TRL  
All considered LPWAN technologies offer products on the market and have been successfully tested in various use cases as described above; however, not all have been tested in underground mining environments. Each technology is therefore at least at the TRL 5-6 level. In terms of underground mining environments, only LoRa, DASH7, and MIOY have been tested, with LoRa being tested in the most diverse applications compared to the other technologies. DASH7 and MIOY also were successfully tested in mining environments, but only less independent industry research is available with those technologies. The three technologies therefore are categorized in TRL 7-9.  
17 Approval  
The considered LPWAN technologies operate in a Sub-GHz ISM band (in Europe, usually 868 MHz). The regulation requires devices transmitting in these frequencies to comply with duty cycling restrictions, which limits the amount of data that can be sent.  
18 Required Infrastructure and Intrusiveness  
The communication nodes in LPWAN usually have a low energy consumption and can be operated with batteries over a long time. Gateway connections nodes are connected to the network, since they are a bridge for the information from the LPWAN network. These devices are usually externally powered, and network connection is done via an ethernet cable. However, due to the long range, a sparse installation of gateways (compared to technologies like Wi-Fi) is possible.  
Coverage and Robustness  
LPWANs have not been extensively evaluated regarding underground coverage and signalling behaviour. However, in some studies as depicted in Table 3, Near- and/or Non-LOS could be achieved with the LPWAN technologies. The penetration of rock or even rock masses has not been covered by research yet but could potentially be achieved due to the low frequencies used.
## 2 Materials and Methods  
### Literature Search and Inclusion Criteria  
The structured literature search included the databases IEEE Xplore, Scopus, Web of Science, and Google Scholar. They were chosen for their broad coverage of peer-reviewed articles in computer science, telecommunications, and educational technology. In addition, relevant white papers and standards were screened (e.g., 3 GPP technical releases) to ensure that theoretical discussions aligned with real-world telecommunications advancements. Cornerstone literature was collected using the queries in Table 3 and filtered to include only sources published from 2020 to 2024.  
The PRISMA criteria were used to identify which studies will be used in the current research [27]. The process which was used to find, screen, and include these studies is presented in the diagram of Figure 1.
## 2 Methodology
### Exergo-Economic Analysis
#### 2.4.3 Cost of Destroyed Exergy and Lost Exergy  
The destroyed exergy cost rate (Equation (19)) and lost exergy cost rate (Equation (20)) are indispensable values to develop the thermo-economic study of the WHR system. The destroyed exergy cost rate of a device is not contemplated in the cost balances, which is the reason because it is related to an unknown cost [56]. In these balances, an average input cost with a variable quantity of exergy destruction or exergy loss is supposed.  
\[\dot{\text{C}}_{\text{D,K}}=\text{c}_{\text{J,k}}\dot{\text{E}}_{\text{D,k}} \tag{19}\]  
\[\dot{\text{C}}_{\text{L,K}}=\text{c}_{\text{L,k}}\dot{\text{E}}_{\text{L,k}} \tag{20}\]  
For the k device of the WHR system, the cost related to the input flow is estimated by using Equation (21), and the cost of the product using Equation (22).  
\[\text{c}_{\text{I,K}}=\frac{\dot{\text{C}}_{\text{I,K}}}{\dot{\text{E}}_{\text{ I,K}}} \tag{21}\]  
\[\text{c}_{\text{P,K}}=\frac{\dot{\text{C}}_{\text{P,K}}}{\dot{\text{E}}_{\text{ P,K}}} \tag{22}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **SORC** & **Component** & **SORC** \\ \hline ITC1 & \(\text{c}_{\text{10}}\big{(}\dot{\text{E}}_{\text{x11}}-\dot{\text{E}}_{\text{x10 }}\big{)}-\) & & \(\text{c}_{\text{10 RC}}\big{(}\dot{\text{E}}_{\text{x2 ORC}}-\dot{\text{E}}_{ \text{x1 ORC}}\big{)}+\) \\ ITC1 & \(\text{c}_{\text{1 AT}}\dot{\text{E}}_{\text{x1 AT}}=\) & \(\text{T}1\) & \(\text{c}_{\text{WFT1}}\dot{W}_{\text{T1}}=\dot{Z}_{\text{T1}}\) \\  & \(\text{c}_{\text{3 AT}}\dot{\text{E}}_{\text{3 SAT}}+\dot{Z}_{\text{ITC1}}\) & & \(\text{c}_{\text{10 RC}}=\text{c}_{\text{2 ORC}}\) \\  & \(\text{c}_{\text{3 AT}}\dot{\text{E}}_{\text{3 SAT}}=\text{c}_{\text{2 AT}}\dot{ \text{E}}_{\text{32 AT}}+\) & & \(\text{c}_{\text{10 RC}}\big{(}\dot{\text{E}}_{\text{x3 ORC}}-\dot{\text{E}}_{ \text{x2 ORC}}\big{)}\text{c}_{\text{2A}}\dot{\text{E}}_{\text{2A}}=\) \\ B1 & \(\text{c}_{\text{WFT1}}\dot{W}_{\text{B1}}+\dot{Z}_{\text{B1}}\) & ITC3 & \(\dot{Z}_{\text{ITC3}}\) \\  & \(\text{c}_{\text{WBI}}=\text{c}_{\text{WFT1}}\) & & \(\text{c}_{\text{1A}}=0\) \\  & \(\text{c}_{\text{1 AT}}\big{(}\dot{\text{E}}_{\text{x2 AT}}\dot{\text{E}}_{\text{ x1 AT}}\big{)}+\) & & \(\text{c}_{\text{40 RC}}\dot{\text{E}}_{\text{x4 ORC}}=\) \\ ITC2 & \(\text{c}_{\text{iORC}}\dot{\text{E}}_{\text{x1 ORC}}=\) & B2 & \(\text{c}_{\text{40 RC}}\dot{\text{E}}_{\text{x3 ORC}}+\text{c}_{\text{WFT1}}\dot{W}_ {\text{B2}}+\dot{Z}_{\text{B2}}\) \\  & \(\text{c}_{\text{40 RC}}\dot{\text{E}}_{\text{x4 ORC}}+\dot{Z}_{\text{ITC2}}\) & & \(\text{c}_{\text{40 RC}}\dot{\text{E}}_{\text{x3 ORC}}+\text{c}_{\text{WFT1}}\dot{W}_ {\text{B2}}+\dot{Z}_{\text{B2}}\) \\  & \(\text{c}_{\text{1 AT}}=\text{c}_{\text{2 AT}}\) & & \(\text{c}_{\text{WBI}}=\text{c}_{\text{WFT1}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Exergetic cost balances of system components.
# Fuel-Water Emulsions as an Alternative Fuel for Gas Turbines: A Project Summary
## 2 Subject and Methodology of Research
### Fuel Mixtures
#### 2.5.2 Fuel-Water Emulsion  
The fuel-water emulsions used in the tests consisted of a standard mixture of Jet A-1 and oil, surfactant and demineralized water. All of them contained 2% (by weight) of emulsifier. The percentages of ingredients in the emulsion refer to the total weight of the emulsion mixture. The emulsion was prepared in two stages. In the first stage, a surfactant was added to the mixture of kerosene and oil, followed by a 5 min mixing process with a mechanical mixer. In the second stage, demineralized water was added to the mixture and the mixing process was repeated. The fuel-water emulsion obtained in this way is macroscopically milky-white in color (Figure 8a), while microscopically, it is a mixture, with clearly visible water drops constituting the discrete phase of the emulsion fuel (Figure 8b). More microscopic photos of the emulsion prepared by the described method are available in [52; 53].  
An emulsion containing from 3% to 12% water (by mass) was used to study combustion in a miniature gas turbine. The emulsions used were characterized by approximately no SMD (Sauter Mean Diameter) sensitivity to changes in the amount of water included in their composition. Moreover, there was no significant impact of the turbine fuel system on the microstructure of the fuel-water emulsion used [27].
# Three-Stage Deep Learning Framework for Video Surveillance
## 3 Proposed Method
### Proposed Overall Architecture
#### 3.2.4 Proposed Transformer Architecture  
The modified Transformer encoder block introduces a novel approach optimized for the processing of video data. Compared to the conventional Transformer architecture, this new design focuses more deeply on analyzing the intricate relationships between video frames. Key improvements include the application of layer normalization and multi-head attention in the initial stages, along with a more complex, dense projection layer configuration. These modifications enable the model to process and integrate the features of each frame more efficiently.  
The first dense projection layer consists of four dense layers and three dropout layers, which help to prevent overfitting and enhance the model's generalization capabilities while also extracting more complex features. The process of combining the input and output of this layer, followed by additional layer normalization, smooths the information flow and increases the network stability, contributing to the model's ability to learn and integrate finer details from the input data.  
Figure 5: Architecture of proposed Transformer block.  
Subsequently, the second dense projection layer, comprising two dense layers and one dropout layer, further refines and compresses the features extracted by the first layer. This layer plays a crucial role in effectively condensing and extracting essential information from the final output, thereby optimizing the network's predictive performance. The use of these two stages of dense projection layers allows for the efficient handling of the complex nature of video data, enabling more precise analysis and learning, and is expected to yield superior performance in areas such as video analysis, object tracking, and action recognition.  
Overall, the innovative structure of this Transformer encoder block focuses on deeply analyzing and processing the complex characteristics of video data, offering superior performance compared to traditional methods.
## 4 Conclusions  
This research work studies deeply the main factors which have a significant impact on the morphology of micro-holes processed by the femtosecond laser helical drilling system. The results of the experiments demonstrate the following.  
1. The deflection angle of the wedge prism can change the diameter of the micro-hole. With the appropriate translation distance of the wedge prism, cylindrical holes or even inverted cone holes can be obtained. The size of the micro-hole is mainly affected by the two factors above. Other factors, such as the descent distance of the laser focus spot during drilling, will affect the inlet size due to the shape of Gaussian pulse.
2. The inlet and outlet morphologies of the micro-hole are mainly influenced by the rotation speed of the Dove prism, laser power, and laser repetition frequency, by changing the overlap rate of the light spot or the average power density. However, the change of instantaneous power density in the processing area is actually the fundamental reason for the variation in orifice morphology.
3. The higher instantaneous power density helps to improve the morphology of outlets, resulting in sharper cutting edges and thinner recast layers. When the rotation speed of the Dove prism is fixed according to the optimal dynamic balance state of the hollow motor, higher laser power and lower laser repetition frequency should be the preferred drilling parameters, such as 10 W and 100 kHz.
4. The efficient one-step drilling method is able to obtain a micro-hole with a recast layer less than 5 \(\upmu\)m within 3-5 s, which can satisfy the requirements for aircraft application of efficiency and quality.  
Conceptualization, M.L. and L.D.; methodology, Y.C.; validation, M.L. and Y.C.; formal analysis, D.L.; investigation, X.S. and D.L.; resources, M.L.; data curation, L.D. and D.L.; writing--original draft preparation, M.L.; writing--review and editing, Y.C.; supervision, X.S.; project administration, M.L. All authors have read and agreed to the published version of the manuscript.  
Financial support for this research was provided by Shenzhen Science and Technology Program (JSGGZD20220822095401003), Guangdong Province Key Research and Development Projects (2023B0909010006).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors gratefully acknowledge [PERSON] for his data validation and theoretical analysis in the revised manuscript, which directly enhanced the scientific rigor of this work.  
Author [PERSON] was employed by the company China Aviation Harbin Dong'an Engine Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Spatial Resolutions of On-Axis and Off-Axis Transmission Kikuchi Diffraction Methods
## 4 Conclusions  
The spatial resolutions of the on-axis and off-axis TKD methods were investigated by the improved DIC technique. We considered only the effective parts of the patterns and eliminated the noisy parts during the calculation to reduce the deviation. Moreover, average filtering was used to remove the low-frequency noise. The spatial resolution of the on-axis TKD was better than that of the off-axis TKD at the same accelerating voltage and sample thickness, which was attributed mainly to the high intensity and thick source region in the on-axis TKD. Furthermore, the spatial resolutions of both configurations were improved with an increase in accelerating voltage or a decrease in sample thickness. The optimal resolutions were achieved at the voltage of 30 kV and sample thickness of 74 nm, 6.2 nm for the on-axis TKD and 9.7 nm for the off-axis TKD.  
Conceptualization, Y.Z.; methodology, Y.S., Y.Z., Y.W., J.Z. and B.Y.; software, J.X.; formal analysis, Y.S.; investigation, Y.S.; writing--original draft preparation, Y.S.; writing--review and editing, Y.S.; supervision, Y.Z. and [PERSON]; project administration, Y.Z.; funding acquisition, Y.Z. This research was funded by the National Key R&D Program of China (2018 YFB0704400), CAS Key Foundation for Exploring Scientific Instrument (YJKYYQ20170041), International Partnership Program of Science (GJH21721), Shanghai Technical Platform for testing on inorganic materials (19 DZ2290700), and Shanghai Action Projects for Scientific and Technological Innovation (17142201500). The authors are grateful for the technical assistance provided by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. The authors declare no conflict of interest.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 5 Task Offloading Algorithms in Our Model  
In our algorithm design, the system time is discretized into steps to perform offloading decisions on the generated time steps. Obviously, the exact time a vehicle will arrive/depart or a task arrives is unknown during the run-time. Performance comparisons are made using four different algorithms. The first three algorithms are greedy heuristics and are used during the simulation experiments. They make decisions according to the available data at each step, i.e., run in an online mode. They are namely _Aggressive-Wait_, _Aggressive Tx-Order_, and _Adaptive_ offloading algorithms. The available data include vehicle-UAV associations, existing links with their bandwidths, and rough vehicle departure times. The final algorithm is named the _Quasi-Optimal_ offloading algorithm and an oracle-type decision-maker since it operates by extracting better solutions from recorded past data for benchmarking. Since this algorithm cannot process all the possible solutions from all the data recorded due to combinatorial explosion, it is quasi-optimal by processing events in time windows. The notations used in these algorithms are listed in Table 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Definition** \\ \hline \(w_{i}\) & \(w_{i}\) if \(K_{i}\) is assigned to \(P_{j}\) \\ \hline \(\Theta_{decision}\) & The maximum number of candidate solutions that the algorithm will attempt to solve within a time-window \\ \hline \(ER\) & Event for system event received \\ \hline \(TP\) & Controller task pool \\ \hline \(TA\) & Task to be assigned \\ \hline \(List_{D}\) & Penalty point list \\ \hline \hline \end{tabular}
\end{table}
Table 3: Notations in algorithms.
# Effect of Leaf Grade on Taste and Aroma of Shaken Hunan Black Tea
## 4 Conclusions  
This study aimed to examine the impact of different grades of fresh leaves on the taste and aroma of shaken Hunan black tea. The researchers used sensory evaluation along with SP, HPLC, and HS-SPME/GC-MS techniques. The findings revealed that 'Zhuyeqi' leaves with one bud and two leaves were the optimal grade for processing shaken Hunan black tea. The sensory evaluation showed that the taste of shaken Hunan black tea transformed from bitter and astringent to mellow, while the aroma changed from delicate and sweet to floral and ultimately pure as the grade of fresh leaves decreased. The analysis of SP and HPLC indicated a decrease in the content of polyphenols, catechins, alkaloids, and gallic acid in shaken Hunan black tea, along with an increase in amino acids as the fresh leaf grade decreased. Caffeine and EGCG were identified as the primary contributors to its bitter and astringent taste. Through GC-MS analysis, the researchers identified 63 differential volatile components in different grades of shaken Hunan black tea. Some of the key components influencing the sweet aroma were geraniol, jasmonate, \(\beta\)-myrrene, citral, and trans-\(\beta\)-ocimene, while methyl jasmonate, indole, and nerolidol were identified as key constituents contributing to the floral aroma. These findings have theoretical significance for the production and processing of shaken black tea. Nevertheless, the optimum technology and the chemical changes during processing for shaken Hunan black tea with one bud and two leaves require further research.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 5 Discussion
### Multiple Pallet Recognition Performance in Warehouse  
In a warehouse, goods are placed in a very dense arrangement, making it difficult to segment pallets. Moreover, pallets are often made with plastic, so diversity and notable color features are the most important types of information for this pallet recognition algorithm. However, color information is impacted by illumination conditions, and the classifier cannot precisely categorize pixels. To resolve this issue, the labeled template is used as another classifier to identify the pallet and reduce the interference of incorrect classification.  
One reason for misrecognition could be that most of the missed pallets in the test were not completely photographed, with matching scores less than the threshold, as shown in Figure 7a,c. Since the size of the template is determined according to the distance, some pallets will not be recognized when the distance between multiple pallets is large, as shown in Figure 7a. Another reason is that the color spaces of different labels overlapped in dark illumination, thus leading to incorrect classification.  
The main approaches to improve the accuracy of yolov5 is to increase the number of pallet samples, and moreover, the number of pallet multi-angle images should be added to the samples to ensure the robustness of the algorithm. The work of sample collection and labeling requires more workload.
## 2 Background and Related Works  
The stability of spacecraft in orbit is an important guarantee for the completion of space missions. In order to avoid the extreme impact and huge cost after failure, the technology of Prognostics and Health Management (PHM) of spacecraft has been proposed to deal with it. PHM predicts the future product reliability by evaluating the deviation or degradation degree of the product from the expected normal operating state, and measures, records, and monitors the deviation and degradation degree of the normal operating state in real time [20]. CMG is an inertial actuator to help spacecraft complete attitude adjustment, and is the \"fulcrum\" generated by a spacecraft in the weightless environment in space [21]. Anomaly detection of CMG is a key part of the health management of the whole spacecraft system.  
The performance of spacecraft anomaly detection is often affected by working conditions, and the setting of anomaly detection model parameters will be quite different under different working conditions. In addition, for data-driven anomaly detection methods, the detection model learned under one working condition usually cannot be well generalized to another [22]. Therefore, it is very necessary to propose targeted anomaly detection strategies for spacecraft under different working conditions. In addition, due to the special nature of spacecraft data, very little data is available on CMG. The particularity of anomaly detection data also determines that it is difficult to obtain the annotated data. Therefore, it is better to obtain partial prior knowledge using transfer learning.  
Based on the above analysis, we designed the anomaly detection process shown in Figure 1. The Sinc-LSTM time series reconstruction network is designed by combining SincNet with LSTM. Before Sinc-LSTM is trained by CMG data, it is pre-trained in two stages by two open access datasets (\(S_{1}\) and \(S_{2}\), which will be introduced in the Section 3). Before CMG data (\(S_{3}\)) is input into the network, the peak density clustering method is used to segment the working conditions. The reconstructed time series data (\(S_{3}^{R}\)) is obtained after the network is trained by CMG data from each working condition. Subtracting \(S_{3}^{R}\) from the original data yields a sequence of differences. The result of anomaly detection can be obtained by threshold judgment on the difference sequence after Gaussian smoothing. In this section, the related work is briefly introduced to analyze the advantages of the proposed method.
## 4 Results
### Early Warning System  
Early Warning Systems [32; 33; 34; 35] are in part social processes with various levels of complexity but require the collection and collation of accurate real-time data on ground conditions. The system structure developed here consists of two main parts, hardware and software. The hardware is an iMETOS IMT280 monitoring station which has the function of monitoring, through continuous transmission, all environmental conditions, namely the temperature, humidity, wind speed, wind direction and the visual imagery at locations with a high risk of forest fire. Each station has a rain gauge, and all the sensors enable the calculation of evapotranspiration by visualizing with the air temperature and relative humidity sensor, global radiation sensor, anemometer, circuit board control and communication equipment (USB 3G) and an internet protocol (IP) camera with 20 Mp image resolution, camera holder and rotary motor, circuit board to control the camera's rotation mode, NUC PC computer to run image analysis and hte processing software and 3G USB to transmit fire information via the internet. Change in the values of smoke and fire obtained from the image provides the basis for reporting the characteristics of the fire generated. Images are taken continuously at 20 frames per second.  
Figure 9: Correlation between the forecasted forest fire risk area and the actual forest fire area.