esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### E-Nose Detection  
E-nose was self-assembled by the agricultural products storage laboratory of Zhejiang A&F University in Hangzhou, China. As shown in Figure 1, it consisted of an electromechanical control unit, a sensor array, and a data acquisition unit. The volume of the air chamber was 350 mL. The sensor array consisted of 12 metal oxide sensors and one temperature and humidity sensor to monitor environmental temperature and humidity. The metal oxide sensors were purchased from Figaro Engineering Corporation, Japan. The models of the sensor were TGS2620, TGS826, TGS822, TGS832, TGS813, TGS816, TGS2600, TGS2610, TGS2611, TGS2615, TGS2602, and TGS2603, numbered S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, and S12, respectively. The temperature and humidity sensor HTU20 was purchased from the Humirel company, France, and numbered S13. The sensitive gases of each sensor are shown in Table S1.  
A dehumidifier was installed in the room to control the humidity and keep the ambient humidity constant. The relative humidity in the air chamber during sample testing is shown in Table S2. Detection with E-nose referred to the method of [PERSON] et al. (1999), with appropriate modifications. The prepared sweetpotato samples were put into 500 mL clean and odourless beakers, which were then sealed and placed in a constant-temperature environment of 30 \({}^{\circ}\)C for 15 min to obtain headspace gas. E-nose was preheated for 2 h in advance. When the sample was measured, the valves on both sides of the air chamber were closed, and air pump 1 was turned on. The headspace gas in the beaker was pumped out to the air chamber at a constant flow rate of 300 mL min\({}^{-1}\) for 90 s, then equilibrated for 30 s. After the sample was detected, sweetpotato and beaker were removed. The valves on both sides of the air chamber were opened, air pumps 1 and 2 were turned on at the same time, and the airflow rate was adjusted to the maximum. Fresh air filtered with activated carbon was pumped into the air chamber for 120 s for cleaning. When the response curve returned to the baseline, the experiment proceeded to the next sample detection. Thirty sweetpotato samples were detected at each time point.  
Figure 1: Composition and working principle of E-nose.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 3 Analysis of Resource Management in the MLSCS
### Primary Challenges in Resource Management
#### 3.2.3 Non-Uniform Distribution of Service  
First of all, the service distribution of MLSCS is non-uniform. For instance, in Figure 2, the users of the Iridium system are concentrated in densely populated, industrial, and commercially developed areas. Conversely, there are very few users in most other areas, such as the sea, deserts, and polar regions. Furthermore, during emergency events, users and traffic will concentrate in a small area within a short period, necessitating capacity enhancement and additional support.  
In terrestrial networks, areas with a sharp increase in service traffic can be accommodated by deploying more base stations. However, the capacity of SBSs struggles to meet the high-capacity requirements of emergencies. Furthermore, deploying more SBSs quickly in terrestrial networks is almost impossible. The only viable solution is to optimize resource scheduling within the system to meet these requirements.
## 3 Experimental TES System: Design, Construction, and Performance Evaluation of a Prototype
### Experimental Results and Discussion
#### 3.2.1 Analytic System Description  
To assess the TES system's performance, the stored energy (latent \(E_{L}\) and sensible \(E_{S}\)) are determined with Equation (2).  
\[E_{stored}=E_{L}+E_{S}=m_{PCM}\cdot\Delta H_{m}+\int\limits_{Ti}^{Tm}m_{PCM} \cdot Cp_{s}\cdot dT+\int\limits_{Tm}^{Tf}m_{PCM}\cdot Cp_{l}\cdot dT+\sum \limits_{j}\int\limits_{Ti}^{Tf}m_{j}\cdot Cp_{j}\cdot dT \tag{2}\]  
with \(m_{PCM}\) as the total PCM mass in the tank and \(\Delta H_{m}\) its enthalpy of fusion. \(Cp_{s}\) and \(Cp_{l}\) are respectively the solid and liquid PCM heat capacity; \(m_{j}\) and \(Cp_{j}\) stand for the total mass and heat capacity of the remaining storage components (plastic capsules, steel tank and water HTF). \(Ti\) and \(Tf\) are the initial and final temperatures.  
The efficiency is obtained with Equation (3).  
\[\eta_{stored/discharged}=\frac{E_{stored/discharged}}{E_{provided}}=\frac{E_{L} +E_{S}}{E_{L}+E_{S}+E_{losses}}\times 100 \tag{3}\]  
where the energy losses \(E_{losses}\) were determined as a function of the tank temperature from previous experiments performed under steady-state conditions at different temperature levels. The time needed to reach a fully charged state, \(\tau_{95\%}\), is defined when the stored energy is equal to 95% of its final value, reached at the end of the experiment. In the experiment previously presented in Figure 6, 4.4 kWh was stored (1.4 kWh of latent heat and 3 kWh of sensible heat), and 2.6 kWh was lost. While the HTF introduced 7.2 kWh of heat to the tank during this experiment, the energy balance deviation was about 3%. While the uncertainty on the thermocoule could rise by 10%, a deviation below this value is acceptable. Compared to a tank filled only with water, the storage energy density is increased by 26%, but should be about 34% without measurement instrumentation. Besides this, the thermal insulation could be improved (several thermal bridges were observed), together increasing the system efficiency, which was only \(\eta_{stored}\) = 63% in the experiment presented in Figure 6.
# An AC/DC LED Driver with Unity Power Factor and Soft Switching
## 2 Proposed Circuit Topology and Operation Analysis  
### Circuit Topology  
The proposed AC/DC LED driver is derived by integrating a buck-boost converter and a buck converter, as shown in Figure 2. Two MOSFETs serve as the active switches, \(S_{1}\) and \(S_{2}\). The diodes \(D_{S1}\) and \(D_{S2}\) are the intrinsic diodes of \(S_{1}\) and \(S_{2}\), respectively. The buck-boost converter is formed by \(L_{p}\), \(S_{2}\), \(D_{S1}\), \(D_{5}\), and \(C_{dc}\). The buck converter is formed by \(L_{b}\), \(S_{1}\), \(D_{S2}\), \(D_{6}\), \(D_{7}\), and \(C_{o}\). The low-pass filter is composed of \(L_{m}\) and \(C_{m}\). The control circuit is mainly a half-bridge controller that is widely adopted to control the active switches of a Class-D inverter. The power switches \(S_{1}\) and \(S_{2}\) are driven by two gated voltage, \(v_{GS1}\) and \(v_{GS2}\). They are nonoverlapping and complementary rectangular-wave  
Figure 1: An integrated circuit with a boost converter and buck converter [12].  
waveform. There is a short duration called deadtime. During the deadtime, both \(v_{GS1}\) and \(v_{GS2}\) are zero volts. Neglecting the deadtime, both \(v_{GS1}\) and \(v_{GS2}\) have a duty ratio of 0.5.  
As compared to the circuit topology proposed in Reference [12], the proposed one uses a buck-boost converter to serve the PFC stage while that of Reference [12] uses a boost converter. It is known that both converters operating at DCM can achieve nearly unity power factor. Nevertheless, when using a boost-typed PFC converter, it requires very high DC-link voltage to ensure unity power factor [14]. Although the proposed circuit needs to use two additional diodes, the voltage ratings of the circuit components could be lower than that of Reference [12].
## 2 Materials and Methods
### DPPH (2,2-Diphenyl-1-Picrylhydrazyl)-Assay  
The DPPH free radical scavenging activity was assessed in accordance with a modified version of a known protocol [12]. The extract solutions were diluted with methanol to obtain a series of concentrations ranging from 10 to 500 \(\upmu\)g/mL. The 0.06 mM DPPH solution was prepared by dissolving DPPH in methanol. The 100 \(\upmu\)L aliquot of the sample was mixed with 100 \(\upmu\)L of the DPPH solution and kept in the dark at room temperature. After 30 min, the absorbance of the solution was measured at 517 nm. BHT was used as a positive control, and methanol served as the blank control following the same experimental procedure. The radical scavenging activity was calculated using the equation as follows:  
\[\text{SC}_{50}\left(\%\right)=\left[1-(\text{A}_{1}-\text{A}_{2})/\text{A}_{ 0}\right]\times 100\%\]  
where A\({}_{0}\) represents the absorbance of the blank control (methanol and DPPH solution), A\({}_{1}\) is the absorbance of the sample with DPPH solution, and A\({}_{2}\) is the absorbance of the  
Figure 1: Jiangsu, Zhejiang, Fujian, and Guangdong Provinces are represented by the colors green, pink, orange, and purple, respectively. Sample collection sites are represented by the red dots.  
sample solution with methanol. The results were expressed as SC\({}_{50}\) (mg/mL), and each batch of samples was tested in triplicate.
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 6 Open Issues
### Physical Layer Issues  
Currently, there is a gap in acquiring data from extensive measurement campaigns. As a future work, one could could implement test-beds carry out real experiments in different propagation areas, especially in dense, urban, skyscraper-rich settings and over sea areas [46] in order to validate the accuracy of the learning algorithms, especially in scenarios with dynamically changing environments and ground nodes moving at high velocities, e.g., vehicles, with increased latency and resiliency requirements, while considering the interference in the propagation area and inspecting at real-world constraints, such as the energy efficiency of the learned trajectory. Nevertheless, these real-world problems typically comprise high-dimensional continuous state spaces, i.e, large number of states and/or actions, and make the corresponding problems practically intractable with current approaches. As more measured data is acquired, ML-based approaches can foster new developments in UAV channel modeling. Also, ML methods can be applied beyond PL prediction, such as the power-delay profile, correlation coefficient and matrices, as well as collinearity among others [47]. Furthermore, interference mitigation poses a major hurdle towards the efficient integration of UAVs in future networks. ML could improve the performance of multiple techniques that have been considered in ground networks, such as power control, UAV-user association, and seamless handover using ML solutions for predicting user mobility and network load, while the use of ML in forming the precoding matrices of massive MIMO-enabled UAVs can eliminate the interference and can increase the quality of the transmission [134]. Another area that can be enhanced by ML is the clustering of users and UAVs towards improved NOMA in the downlink and uplink, increasing the chances of successful interference cancellation maximizing the spectral efficiency of the network [135]. Recently, the authors in Reference [136] investigated the joint optimization of throughput provided by UAV BS and energy that they sell and buy for recharging from the gird. Therefore, in scenarios where multiple UAVs are deployed to serve as aerial BSs, the joint consideration of physical-layer metrics and energy and the application of ML techniques, such as DL to process heterogeneous data, can offer increased performance, as network lifetime prolongation is a critical aspect of UAV networks.
# The Architecture of an Agricultural Data Aggregation and Conversion Model for Smart Farming
## 1 Introduction
Primary ICT solutions in agriculture (semi-autonomous) are designed to monitor and manage sowing and planting, harvesting, packing and storage, and livestock care, but they are task-oriented, i.e., there is no possibility of integrating them into a more complex workflow for a full application. There is also a lack of interoperability, which requires users to carry out additional work, such as manually transferring output data from one system to another. Thus, the integration of different technologies and services, such as software components, various farm machines, mechanisms, vehicles, and other equipment, using a middleware solution with a common information model and application interface, can address the smart farming challenges described above. These improvements would also reduce the negative environmental impact of agriculture. To solve this problem, this paper describes the development of an innovative information system for agricultural machinery. The architecture of this agricultural data aggregation and conversion model allows for the collection and use of working data from different pieces of agricultural machinery which differ in their original file formats and cannot be combined and used in a common analysis system. File-format unification was carried out, enabling the conversion of the data into json format using a Python library. Then, utilising geographic data, spatial analysis methods employing mapping were used to match fields with different agricultural designations such as sowing and fertilising. Thus, digital layer formation was enabled to increase the model's functionality. Scientific contribution was defined through the application of different formats and data processing tools, expanding the functions offered by agricultural machinery. An additional added value of the developed model came from enabling different topographical functions in the representation of operational agricultural indicators. This paper is structured as follows. In Section 2, related projects and research studies focusing on smart farming development and data exchange tools are reviewed. Section 3 presents the structure and design of the architecture of the agriculture data aggregation and conversion model. Model application cases are presented in Section 4. A discussion and conclusions are provided in Section 5.
# Optimizing Time-Sensitive Traffic Scheduling in Low-Earth-Orbit Satellite Networks
## 1 Introduction
[17] addressed the resource constraints of satellite networks by designing a two-stage dynamic programming framework to achieve optimal data scheduling solutions. Additionally, [PERSON] et al. [18] introduced an injection time planning mechanism to enhance network throughput by optimizing injection scheduling. In terms of latency optimization, [PERSON] et al. [19] proposed a latency-optimal scheduling algorithm that approaches the global optimal solution within a limited number of iterations. [PERSON] et al. [20] developed the L2D2 mechanism, leveraging geographically distributed ground stations to reduce downlink latency. However, these studies did not account for time-sensitive services with varying latency requirements. Therefore, further research is needed to explore differentiated traffic management for diverse TSFs. To address the high-priority service guarantees and differentiated forwarding requirements for multi-level TSFs, existing studies have explored time-sensitive networking (TSN) and traditional Ethernet scheduling mechanisms. However, current algorithms have not fully considered the constraints of on-orbit resources or adequately leveraged the multipath and redundancy advantages of satellite networks. Moreover, there are still deficiencies in meeting the differentiated forwarding needs of multi-level TSFs, and scheduling mechanisms require further improvements to enhance traffic forwarding efficiency. To this end, this paper proposes a triad-priority cycle queue forwarding (TPC-CQF) mechanism, an enhancement of the CQF framework, which optimizes the scheduling capabilities for multi-level time-sensitive services through priority differentiation. This approach reduces packet loss rates while improving the throughput of LEO satellite networks without significantly increasing average latency. The main contributions of this paper are as follows:  
1. [label=(0)]
2. Designed a management framework for LEO satellite networks tailored to time-sensitive services, incorporating SDN-based management strategies. 3. Developed an efficient dynamic priority queue scheduling mechanism (TPC-CQF) to provide differentiated forwarding guarantees for multi-level time-sensitive flows. 4. Proposed a flexible scheduling strategy based on redundant time slots, fully utilizing the redundant time slots of time-sensitive flows to achieve efficient forwarding of high-priority TSFs.
# Towards Rack Utilization in Internet Datacenters: An Approach Based on Dynamic Programming
## 1 Introduction
Since the power supply of a rack is typically fixed, restricting the power budget of servers will allow more servers to be deployed in a rack. As the power of servers changes with the utilization of servers, the actual power of servers may exceed their power budget. To avoid this issue, an appropriate value should be set for the power budget of each server using the technique of power capping, which has been addressed in our previous work [13]. After power budgets are assigned for each server in a rack, there will probably be some remaining space of power, which is called power fragment of rack and will affect the rack utilization. Reducing power fragment involves the permutation of racks and servers in IDC, which is difficult to decide due to their vast amount. To solve it, we propose a server placement algorithm based on dynamic programming to address the arrangement of racks and servers with given power budgets, which is capable of making an approximately optimal solution in a short time. The main contribution of this paper is three-fold:  
* we analyse the potential reasons for inefficient rack utilization;  
Figure 1: Power margin. (**a**) Server’s power demand; (**b**) Setting power budget at power rating. * we propose a server placement algorithm based on dynamic programming to make a solution about the arrangement of racks and servers with given power budgets;
* we evaluate our algorithm based on real data from Tencent and synthetic data. The rest of this paper is organized as follows. The related work is discussed in Section 2. In Section 3, we introduce how to deploy servers in racks and analyse the potential reasons for inefficient rack utilization. In Section 4, we propose a server placement algorithm and evaluate it in Section 5 with the real traces from racks and servers in Tencent and synthetic data. Section 6 concludes this paper and points out our future work.
## 2 Carrier Aggregation as a Diversity Technique
The use of CA is certainly more complex than single-carrier transmission as it requires appropriate mechanisms to correctly execute the packet scheduling function, including the distribution of the user data across different CCs and the synchronisation of the scheduling information among CCs in a timely and efficient manner. However, commercial network equipment that supports CA must implement appropriate algorithmsto address these issues. Such algorithms are usually vendor-specific and do not need to be modified to implement the CA-based scheme considered in this work. A mobile operator willing to implement the CA-based scheme presented in this work would only need to reconfigure the transmission in a broad channel from single-carrier mode to CA mode to benefit from the performance improvements offered by the proposed CA-based scheme.
# Secure Transmission for Buffer-Aided Relay Networks in the Internet of Things
## Appendix C
\begin{split} F_{\gamma_{E2}^{SCJ}}\left(x\right)& =\sum_{s=0}^{M_{2n}}\left(\begin{array}{c}M_{2,n}\\ s\end{array}\right)\left(-1\right)^{s}e^{-\frac{\lambda_{RE}\mu^{\prime} \left(x-1\right)}{P_{R_{2}}}}\\ &\underbrace{\times\int_{0}^{\infty}\underbrace{\int_{0}^{\infty }\frac{\lambda_{RE}^{K}y^{K-1}}{\left(K-1\right)!}e^{-\left(\lambda_{RE}+\frac {\lambda_{RE}y^{\prime\prime}}{1+P_{I_{2}}z}\right)y}dy}_{\Lambda}\frac{ \lambda_{IE}^{K}x^{-1}e^{-\lambda_{IE}z}}{\left(K-1\right)!}dz}_{\Phi}.\end{split}  \tag{13}\]  
Now, by utilizing [40] (eq. (3.351.3)) and the binomial theorem, we have  
\[\Lambda=\left(1-\frac{\theta}{z+\omega}\right)^{K}=\sum_{t=0}^{K}\left( \begin{array}{c}K\\ t\end{array} \right)\left(-1\right)^{t}\left(\frac{\theta}{z+\omega}\right)^{t}, \tag{14}\]where \(\theta=\frac{\lambda_{RBSX}}{\eta_{2}\lambda_{BE}}\) and \(\omega=\theta+\frac{1}{\eta_{2}}\) for the analysis tractable. Hence, \(\Phi\) can be further written as  
\[\Phi=\sum_{l=0}^{K}\left( \begin{array}{c}K\\ t\end{array} \right)(-\theta)^{l}\frac{\lambda_{IE}^{K}}{(K-1)! }\underbrace{ \int_{0}^{\infty}\frac{x^{K-1}e^{-\lambda_{IE}x}dz}{\left(z+\omega\right)^{t} }}_{\Phi_{2}}. \tag{100}\]  
Obviously, to obtain \(\Phi\), we have to calculate \(\Phi_{2}\) first. For the item \(\Phi_{2}\), there are also two cases to consider, i.e., \(t=0\) and \(t>0\). For \(t=0\), with the help of [40] (eq. (3.351.3)), we have \(\Phi_{2}=(K-1)!\lambda_{IE}^{-K}\), which yields \(\Phi=1\). On the other hand, for \(t>0\), by changing variable \(z+\omega=\rho\) and using the binomial theorem, we have  
\[\Phi_{2}=e^{\omega\lambda_{IE}}\sum_{l=0}^{K-1}\left( \begin{array}{c}K-1\\ l\end{array} \right)(-\omega)^{K-1-l}\underbrace{\int_{\omega}^{\infty}\rho^{ l-t}e^{-\lambda_{IE}\rho}d\rho}_{\Phi_{1}}. \tag{101}\]  
By utilizing [40] (eq. (3.351.2)), [40] (eq. (3.352.2)) and [40] (eq. (3.353.1)), the item \(\Phi_{1}\) as \(t>0\) can be derived, as shown in Equation (32). Finally, the desired result in **Theorem 5** can be derived by pulling everything together.
## 3 Results and Discussion
### PM Emission and Fly Ash Analysis  
Several studies showed that the combustion of biomass fuels in small-scale heating appliances is a common source of both gaseous emissions (e.g., CO, CO\({}_{2}\), NO\({}_{\rm x}\), and SO\({}_{2}\)) and PM emissions. Results of gaseous emissions from poultry litter and fossil fuel co-combustion were discussed by several studies, but there were few results on the PM emission. Normally, PM can be classified by the following parameter: aerodynamic size, mass concentration, mass size concentration, number concentration, number size distribution, chemical concentration, and emission factor [31]. In this study, an emission factor of PM was calculated from the mass of PM per total heating value of fuel consumed. Under optimal working conditions--gaseous emissions, EA = 1.08, SA/TA = 0.44, SA height = 850 mm, and MR = 2.29, PM samples were collected and measured. Results indicated that emission factors of PM were 0.002 lb/MMBtu, 0.004 lb/MMBtu, and 0.1538 lb/MMBtu for the feeding rates of 15.6 lb/h, 17.2 lb/h, and 21.4 lb/h, respectively. At various feeding rates, all PM results were less than Maryland's permitting PM thresholds of 0.35 lb/MMBtu during poultry litter and natural co-combustion in the lab-scale biomass conversion process. A combination of optimal operating conditions as a primary measure and the cyclone as a secondary measure played a vital role in reducing PM during the poultry litter and natural gas co-combustion process.  
For a sustainable biomass-to-energy strategy, it is essential to close the material fluxes and integrate the biomass ashes within the natural cycles. Combustion of poultry litter on poultry farms for space heating will yield large quantities of ash that must be managed properly. Poultry litter ash (on average 0.1% and 10%) is one of the byproducts from poultry litter combustion. Practically, poultry litter ash has a smaller volume, less pathogens, and a higher nutrient content over untreated poultry litter. The  
Figure 7: Effect of mixing ratio (MR) on CO, NO\({}_{\rm x}\), and SO\({}_{2}\) emissions.  
soluble phosphate and potassium may remain in the ash, while the majority nitrogen is removed. Table 3 summarizes the methods for analysis along with concentration levels of major nutrients, including phosphorus, potassium, and nitrogen in the fly ash after the poultry litter co-combustion process. High amounts of phosphate (up to 10%) and potassium (up to 6%) in fly ash confirmed the fact that poultry litter ash can be recycled as a soil conditioner/amendment, with high amounts of phosphate and potassium serving as vital macronutrients. In addition, the ash is odorless, dry, and free of pathogens, making it more suitable than the fresh poultry litter for transport and storage [32].