esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Micro UAV Charging Techniques
## 3 Battery Charging Techniques for UAVs
### Charging from a Power Line  
Power lines are abundant in both rural and urban areas. These power lines can be used as a source of power and electricity supply. Several research studies have investigated tethering UAV technology to power lines to deliver the required energy to UAVs. The technique is viable to efficiently transmit data and power to UAVs. In [44], authors proposed the recharging of UAVs through power lines. An important technology to implement this technology is known as power line perching. It is based on landing a UAV on a current carrying conductor to recharge itself. It involves two aspects. Firstly, the process of guiding the UAV to the location of the power lines. Secondly, the charging process to deliver  
Figure 5: Dynamic soaring maneuver [44].  
Figure 6: Tethered UAVs [50,51].  
power to the UAVs. In addition, the conversion of high voltage energy from power lines into low voltage for the UAV's battery must be taken into account. In the context of landing maneuvers, it is worth noting that navigational electronics reading will be highly affected by the EM field. Researchers from the Massachusetts Institute of Technology (MIT) proposed a solution to overcome this challenge [52; 53]. The authors developed an internal measurement unit within the UAV which can guide it according to the EM field of power lines. The proposed mechanism involves a particle filter and synchronous demodulation. Though this proposed mechanism is promising, it involves high computational power and complex signal processing.
## 6 Results
### Latency Results
These results are representative of the network space  
Figure 4: Average and maximum latency increase values and execution times for networks with nodes distributed according to area specified in Table 6.  
studied, and, since these values are the worst cases, the study of more networks could possibly change them. However, due to the number of nodes and distribution areas, it is impossible to cover all possible scenarios. Even though scaled networks perform differently in terms of latency deterioration, the exponential trend in the execution time remains the same as shown in Figure 4c,d.
## 3 Overall Design Architecture  
The available protocols are not useful for video transmission on WMSN because they do not satisfy all the requirements of WMSN nor are they designed for other network architectures, e.g., ad-hoc networks. Also, available protocols in each layer do not provide proper services to proposed custom layers; therefore, a new architecture should be designed. This new architecture is called EQV-Architecture.  
The proposed architecture provides energy efficiency and video Quality by customizing three layers of the wireless sensor networks communication protocol stack. The customized layers are application layer, transport layer, and network layer. Some new policies, algorithms, and protocols in these layers are presented. These three layers and their relations have been shown in Figure 1, and are discussed briefly in the following subsections.
## 4 Discussion
### Coverage of Data Quality Aspects
#### 4.2.2 Consistency-Related Implementations  
The reference DQ framework distinguishes two classes of indicators in the range and value violations domain [6]. The first corresponds to inadmissible values, meaning data values that are not permissible. The second refers to uncertain values, which are unlikely data values because they are outside the expected ranges. Despite their entirely different meaning, their computation is identical, and a distinction between the two can only be achieved through the metadata. Thus, during our evaluation, when a package had a function to check for values outside a defined range, we have included it as fulfilling both indicators. This is justifiable particularly for rule-based programs, where users may simply apply rules in the intended context.  
Regarding contradictions, some value combinations may be impossible by the very nature of the targeted variables (e.g., follow-up examination date must be after the baseline examination date; date of birth must always be the same). Others may be very unlikely to happen (e.g., if the gender is male, no pregnancies should be listed, but maybe a study participant did undergo gender transitioning). The DQ framework classifies the former as logical and the latter as empirical contradictions. Although this distinction is solely based on the semantics of such rules, the method is the same for both contradiction types. Hence, we counted both in our assessment if a package is capable of checking these types of rules.
# A Strategy-Driven Semantic Framework for Precision Decision Support in Targeted Medical Fields
## 5 The Emergency Strategy Case Study: Needs Analysis and Formalization
### Emergency Strategy Overview
#### 5.2.2 Processing Phase Enhancement  
The processing phase has been expanded with new algorithms to support the emergency strategy. The input process now includes a broader range of evidence, encompassing not only symptoms reported by the patient but also vital signs (e.g., blood pressure) and additional symptoms identified by the medical expert during the examination (e.g., rigid abdomen). These inputs, along with the patient's indicators--specifically age, gender, and pre-existing conditions--are incorporated into the patients' KG instance and categorized as evidence symptoms.  
A new algorithm performs logical inference during runtime to handle the conditional edges introduced in the KG (See \"Algorithm A\" in Section 5.3.3). For each conditional edge, if its logical rule evaluates to true, it is dynamically added to the patient's KG instance, ensuring that it reflects the patient's specific profile. The output of this stage is named personalized subgraph or PSG in short (see Section 5.3.3 for more details).  
With the patient's PSG prepared, the disease identification and community inference processes proceed similarly to the original framework, with a slight modification to Algorithms 2 and 3: diseases are now prioritized in two stages: First, diseases marked with _risk?_ = _true_ are ranked, followed by all other diseases (see \"Algorithm 2\" in Section 5.3.3).  
Figure 2: The enhanced knowledge graph.  
Community rankings are subsequently determined based on disease scores, calculated by the number of evidence symptoms linked to a disease, including conditional edges evaluated as true (see \"Algorithm 3\" in Section 5.3.3).  
Algorithm 4 remains as described in [4; 5]. The framework identifies a symptom for each community to confirm or rule out a life-threatening condition. The inference process concludes with a ranked list of hypotheses, prioritizing any identified life-threatening diseases at the top.  
Figure 3 illustrates the stages of the processing phase within the emergency strategy.  
Figure 3: The interactions within the framework among the patient, the medical expert, and the KG during the processing phase in the emergency strategy.
## 3 Results and Discussions
### Uniform Twist Control with Single-Harmonic Inputs
Under the 3/rev control with the optimal phase angle of 90\({}^{\circ}\), pitch up deformations are generated at approximately 90\({}^{\circ}\), 210\({}^{\circ}\), and 330\({}^{\circ}\), while pitch down deformations are generated at approximately 30\({}^{\circ}\), 150\({}^{\circ}\), and 270\({}^{\circ}\). The lift loads alternately increase and decrease along the circumferential direction. The negative-lift region on the advancing side decreases, which reduces the lift loads at the front and rear region on the advancing side. The profile drag loads at the tip on the advancing side are alleviated as well. Generally, power reductions are achieved while using the uniform twist control with single-harmonic inputs essentially due to the improved load distribution. However, the effects on the rotor power mitigation are limited due to the simplicity of the single-harmonic input and spanwise uniform twist control.
# Effect of Fly Ash on the Properties of Ceramics Prepared from Steel Slag
## 4 Conclusions  
* Based on the steel slag ceramic, fly ash was used at a maximum addition amount of 20 wt %. Sample 4 with a fly ash content of 15 wt % exhibited a sintering shrinkage of 7.36% at the optimum sintering temperature, and its bending strength and water absorption were 43.37 MPa and 0.03%, respectively, which is better than the requirements of the Chinese national standard GBT 4100-2015 for ceramic tile. The range of sintering temperature widened from 1130-1135 \({}^{\circ}\)C to 1135-1150 \({}^{\circ}\)C, allowing the addition of up to 50 wt % of solid waste.
* With an increase in the fly ash addition amount, the diffraction peak of the quartz phase in the sintered sample gradually decreased. The main phases of the sample without added fly ash are the quartz, diopside, and augite phases. When the amount is 15 wt %, the diffraction peak of the anorthite phase increased. The main crystal phases were quartz, diopside, augite, and anorthite phases. When the addition amount increased to 20 wt %, the diffraction peak of the quartz phase disappeared.
* The pore size of the sintered ceramic samples increased with an increase in the fly ash addition amount, which is an important factor affecting the strength of the sample, and the size of the lath-shaped crystals was roughly the same. The addition of fly ash leads to an increase in the Al2O3 content, which increased the viscosity of the liquid phase; this affected the structure of the glass phase and increased the optimal sintering temperature.  
[PERSON] and [PERSON] have been leading the research project, obtaining funding acquisition and conceptualization as well as writing--review & editing. [PERSON] has designed, conducted the experiments and written this work. [PERSON] and [PERSON] have been writing--review & editing.  
This research was funded by the National Natural Science Foundation of China (Grant No. 51474028), Ministry of Science and Technology of China (Grant No. 2017 YFC0210301) and People-benefit project of Ningbo Science & technology (Grant No. 2015C50058).  
Authors are thankful to Datang Corporation and Shandong Iron and Steel Group for providing fly ash and steel slag.  
The authors declare no conflict of interest.
# Detection of _Camellia oleifera_ Fruit in Complex Scenes by Using YOLOv7 and Data Augmentation
## 3 Results and Discussion
### Influence of Data Augmentation  
Traditional data augmentation combined with mosaic augmentation were used to augment the dataset and improve the detection ability of the model in complex scenes. The DA-YOLOv7 model was established based on the data augmentation and the YOLOv7 network. The performance indicators of the DA-YOLOv7 model and the YOLOv7 model were compared as shown in the Table 3. It can be seen that the DA-YOLOv7 model had better mAP, Precision, Recall and F1 score than the YOLOv7 model. These indicators were increased by 0.29%, 0.53%, 2.41% and 1.48%, respectively.  
To further compare the generalisation, DA-YOLOv7 and the YOLOv7 models were used to detect the _Camellia oleifera_ fruit images in complex scenes on the test set. As shown in Figure 10, those in the blue circle were wrong detection, and those in the green circle were missed objects. Both models detected the sidelight objects. For the scenes with backlight, slightly and heavy occlusion, the YOLOv7 model was difficult to detect the right objects while the DA-YOLOv7 model can well detect them.  
In summary, the use of the traditional data augmentation combined with mosaic augmentation can enable the model to learn more features of _Camellia oleifera_ fruit in complex field scenes, thereby improving the learning ability of the model and enhancing the generalisation ability of the model. The optimal _Camellia oleifera_ fruit detection model is the DA-YOLO v7 model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{6}{c}{**Evaluation Index**} \\ \cline{2-7}  & **mAP** & **Precision** & **Recall** & **F1 Score (\%)** & **Detection Results** & **Numbers** \\ \hline YOLOv7 & 95.74 & 94.21 & 93.13 & 93.67 & \begin{tabular}{c} Detected objects \\ Right objects \\ Wrong objects \\ Missed objects \\ \end{tabular} & \begin{tabular}{c} 1588 \\ 1327 \\ 261 \\ 74 \\ \end{tabular} \\ \hline DA-YOLOv7 & 96.03 & 94.76 & 95.54 & 95.15 & \
\begin{tabular}{c} Detected objects \\ Right objects \\ Wrong objects \\ Missed objects \\ \end{tabular} &
\begin{tabular}{c} 1560 \\ 1340 \\ 220 \\ 61 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison between DA-YOLOv7 model and YOLOv7 model.
# The Exoskeleton Balance Assistance Control Strategy Based on Single Step Balance Assessment
## 1 Introduction
Nowadays, most research on exoskeleton control focuses on walking assistance rather than balance assistance. In recent years, there are more and more studies focused on how to assist the user when they lose balance. Current balance assistance control strategies can be classified into three categories including adaptive variable stiffness control [17; 18], joint trajectory tracking control [19; 20], and joint torque control [1; 21]. Joint trajectory tracking control is more suitable for gait rehabilitation rather than balance assistance, because the planned trajectory may conflict with human intention and increase the risk of falls. Current adaptive variable stiffness is only used for balance recovery during standing. Current joint torque control is an open-loop control that only provides assistance torque with a certain time when users lose balance, but the amplitude and action time of assist torque still need to be optimized. The main contributions of this paper are proposing a real-time balance assessment method that is suitable for both standing and walking, and then establishing an active balance assist control strategy that is based on the \"assist-as-needed\" principle that exoskeletons only provide assist power when balance loss is detected. The assist torque profile is calculated according to the degree of balance and fuzzy logical control (FLC). This paper is organized as follows: Section 2 proposes the real-time balance assessment method. Section 3 proposes the active balance assist control strategy. Section 4 makes a detailed analysis of the balance control experiments. Section 5 concludes the paper and suggests the future work.
# Defining Blood Plasma and Serum Metabolome by GC-MS
## 5 Workflow of GC-MS Analysis of Blood Metabolome
### Gas Chromatography
#### 5.2.2 Thermal Conditions  
After injection, the sample, in whole or in part, enters the chromatographic column, which is in the oven operated in either isothermal or temperature-programmed modes.  
Figure 5: Schematic illustration of major units in the GC system. Abbreviations in the figure: MS—mass spectrometry, GC—gas chromatography, 2D—two-dimensional chromatography, WCOT-wall-coated open tubular column, PLOT-porous-layer open tubular column, SCOT-support-coated open tubular column, FSOT-and fused silica open tubular column.  
In isothermal mode, a constant column temperature is maintained during the entire run. This solution is optimal for separating relatively simple mixtures, the components of which have similar RTs. Naturally, for the analysis of a complex sample, the components of which have very different boiling points, it is problematic to choose one temperature that will be maintained throughout the run and provide good separation in an adequate time. Temperature programmed gas chromatography (TPGC) involves heating the column at a controlled rate during the run and then returning to the starting point. Temperature increases speed up the movement of analytes through the column, yielding decreased RTs and duration of analysis (Figure 5).  
The temperature gradient allows lower detection limits and improved peak shapes (especially late eluting peaks). The disadvantage of TPGC is a higher noise level at high temperatures compared to isotherm and the need to cool the column between analyses [113].  
Interestingly, the isothermal analysis may provide higher internal efficiency than temperature-programmed analysis when analyzing the same complex mixture. However, the higher separation power of isothermal GC is achieved at the expense of extremely long run times, which ultimately last about 1000 times longer than TPGC runs [114]. With the same analysis time, the temperature-programmed option allows 2-3 times more peaks to be resolved [115]. In this regard, various temperature gradients are used to reduce the analysis time and competently resolve the signals [116].  
Moreover, separation efficiency can be increased using thermal gradient gas chromatography (TGGC). The essence of this dynamic method is that each section of the column heats up independently, achieving a focusing effect, improving the shape of the peaks, and reducing the noise level. According to theoretical models on normal alkanes, peak performance and resolution in a 100 cm open tubular column operating in TGGC mode are 10-13% higher than in TPGC [117; 118]. The need to accurately and rapidly produce and vary thermal gradients along the column significantly complicates the widespread adoption of this method. However, new technologies implying the simultaneous use of resistive heating and convective cooling leave hope for its popularization [118; 119].
# Zeroize: A New Method to Improve the Utilization of 5G Networks When Running VoIP over IPv6
## 5 Conclusions  
VoIP services provide many features that make them extremely wide-reaching in the telecommunication sector. The integration of VoIP and IPv6 over 5G networks is unavoidable. This integration leads to wasting up to 85.7% of the bandwidth and airtime  
Figure 8: Bandwidth reduction.  
Figure 7: Ch-Capacity (LPC codec).  
of a 5G network. This study contributes to the efforts to solve this issue by proposing the Zeroize method. The proposed method succeeds in using the source IPv6 address field in the IPv6 protocol header to carry the packet's digital voice data and reduce or eliminate the VoIP packet payload. The sender client extracts the voice data from the packet and places it in the source IPv6 address field. At the receiver, the VoIP client extracts the voice data from the source IPv6 address field and places it at the end of the packet as a normal payload. The Zeroize method was compared with the S-RTP, SVF, and ZSP methods by using three different codecs (LPC, G.726, and G.723.1). The Zeroize method reduced the wasted bandwidth by 20% compared with the S-RTP method with the G.723.1 codec. The proposed method is a promising solution to reduce the wasted bandwidth and airtime of 5G networks when running VoIP over the IPv6 protocol. The effect of altering the original value of the source IPv6 address field and software-defined networking-based networks will be investigated in future works.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
## 3 Results and Discussion
### Physicochemical Characterization of SHGCs
#### 3.1.5 Total Lipid Content  
Total lipid content varied slightly among goat cheese samples, ranging from 38.6% to 40.2%. However, significant differences were observed between SHGC-C and SHGC-PP, as well as between SHGC-C and SHGC-U (\(p\) = 0.0431). These values are higher than those reported by [PERSON] et al. [71] for \"Canestratto\" goat cheese (23.6%) and \"Gouda-style\" goat cheese (32.9%), as well as by [PERSON] et al. [66] for various Italian goat cheeses at the end of ripening: \"Flor di Capra\" (30.3 \(\pm\) 0.2%, \(w/w\)), \"Caprino di Cavalese\" (33.1 \(\pm\) 0.5%, WW), \"Caprino di Valsassina\" (32.7 \(\pm\) 0.4%, WW), and Capritilla (32.8 \(\pm\) 0.7%, WW).  
Although _P. palmata_ (1.6% DW) and _Ulva_ sp. (2.3% DW) contain relatively low lipid levels, the lipid content in supplemented cheeses increased compared to the control (SHGC-C). This could be considered beneficial, as lipids play a crucial role in cheese organoleptic properties, particularly in enhancing taste, flavor, and texture [72]. Furthermore, while the overall lipid content in seaweeds is low, it is of high nutritional quality. Seaeweeds are recognized as a valuable source of essential fatty acids, particularly \(\omega\)-3 fatty acids, as highlighted by several studies [73].
## 5 Conclusions  
In summary, we arrived at several conclusions with regards to the altitude of the sensor, minimum chemical size and forward overlap percentage for reconstruction of the image. To determine the suggested altitude and minimum size of the chemical that can be detected in the acceptable range of accuracy, several experiments were performed in real ground conditions. This research focused on the applicability of the proposed method in the real world. In the first set of experiments, the altitude of the sensor was varied from 40 cm to 150 cm, and the accuracy of the model was determined. From the analysis, it was found that the suggested altitude should be between 90-120 cm above ground, and in combination with forward overlap rates of close to 70%, it led to the best reconstruction detail and accuracy of the model where accuracy was more than 75%. In the second set of experiments, the minimum size of the chemical sample was varied from 0.25 cm to 1 cm. Accuracy of more than 70% in trace detection of chemicals with minimum chemical size of 0.5 cm was identified at suggested altitudes of 90-120 cm. This accuracy of the model cannot be achieved at coarser resolution at higher altitudes since the sensor resolution had linear effect, while other parameters had a non-linear effect. The non-linear effects of forward overlap and altitude on processing time might pose a constraint on using forward overlap rates higher than 70% if processing time is the main limitation. For various altitudes, the suggested speed of the vehicle were calculated. Therefore, to implement hyperspectral imaging system on the unarmed vehicle for real application, the suggested altitude and speed of the sensor should be around 90 cm and 10.5 cm/s at which the detection limit would be equal or more than 0.5 cm with accuracy greater than 70%.  
[PERSON], [PERSON] and T.N. contributed to the overall study design and supervised all research. [PERSON] finished the experiment, including the sample preparation and hyperspectral image acquisition. [PERSON], [PERSON] and [PERSON] contributed to the data analysis and the manuscripts. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Acknowledgments:** The authors thank and acknowledge Foundation of Innovation for Happiness, Thailand for providing resources and support throughout the project.  
**Conflicts of Interest:** The authors declare no conflict of interest.