esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
_Euglena gracilis_ is a green microalga known as a potential candidate for the production of commercial products that can be used in the field of food, fiber, feed, fertilizer, and fuel [1]. As an example in the food industry, these cells can produce many high-value metabolic products, including vitamins, amino acids, pigments, unsaturated fatty acids, and carbohydrates [1, 2, 3]. Especially, _Euglena gracilis_ can produce unique substances, such as paramylon and wax esters, that cannot be biosynthesized by other microorganisms. Paramylon is a linear \(\beta\)-1,3-glucan that has potential as a functional product due to its immune-enhancing, cholesterol-reducing, and antioxidant activity [4, 5, 6, 7, 8]. _Euglena_ also converts paramylon into wax esters in the cytoplasm under anaerobic conditions, and these wax esters are composed entirely of saturated carbon chains, including myristyl myristate (C14:0-C14:0), which is an ideal raw material for a drop-in bio jet fuel [9, 10]. In addition, _Euglena gracilis_ possesses the ability to survive in extreme conditions, such as low pH environments, which facilitates its dominance in cultivation systems [11].  
Various mutagenesis techniques and screening methods can be used to develop strains of _Euglena_ with increased biomass productivity, harvesting efficiency, or metabolic flux, and these strains may be suitable for various industrial uses [4; 12]. Targeted genetic engineering and random mutation methods can be used to alter the traits of cells [8]. For example, a recent study used the CRISPR-Cas9 method to create a non-motile mutant of _Euglena_ that enabled increased harvest efficiency due to natural precipitation [13]. On the other hand, random mutagenesis has been widely used to generate microalgae with different properties. Random mutagenesis, which can be achieved using ultraviolet (UV) radiation or exposure to ethyl methanesulfonate (EMS), has the advantages of being simple, fast, and inexpensive [12; 14]. Previous studies have induced random mutagenesis in _Euglena_ using Fe ions, UV radiation, and heavy particles and then screened for mutants with improved growth rate, increased lipid content, and increased paramylon content [4; 13; 15; 16]. The chemical mutagen EMS is an alkylating agent, which reacts with purine bases, particularly adenine (A) and guanine (G), leading to incorrect base pairing during DNA replication [17]. In principle, EMS can easily affect DNA in _Euglena_. However, very few previous studies have used EMS mutagenesis to develop useful strains of _Euglena_.  
In this study, we introduced a series of EMS treatment processes in _Euglena gracilis_ and then screened for mutants that exhibited differences in phenotypic characteristics. This procedure led to the identification of one mutant that was completely deficient in chlorophyll and another mutant that had greatly increased motility. We then characterized the growth rate, paramylon content, and odor analysis.
## 1 Introduction
### Related Work
The path planning problem considered in this paper is for a UAV, whose path is 3D. However, most graph search based approaches and tangent graph based approaches focus on the 2D path planning problems. Additionally, the considered application scenario, i.e., securing the wireless communication between the solar-powered UAV and a ground node in the presence of eavesdroppers in urban environments, introduces some new requirements on the UAV path. Specifically, the UAV should ensure that the ground node can successfully decode the sent data while the eavesdroppers cannot decode the sent data. The rest of this paper is organized as follows. Section 2 presents the system models and states the problem of interest. Section 3 presents the proposed path planning method. Computer simulations are shown in Section 4. Finally, conclusions are given in Section 5.
## 3 System Model and Problem Formulation
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(\mathbb{U},\mathbb{C},\mathbb{V}\) & Set of total VE, V2I VE, V2V VE \\ \(\mathbb{S},\mathbb{K}\) & Set of total RRHs, resource blocks \\ \(W^{total}\) & Total available bandwidth \\ \(p_{tr}^{0},p_{k}^{0}\) & Transmission power of RRH, V2V VE \(v\) at the \(k\)th RB \\ \(N\) & Noise power spectral density \\ \(\gamma_{k}^{c}\gamma_{k}^{v}\) & SINR of \(c\)th V2I and \(v\)th V2V VE at the \(k\)th RB \\ R & Total system capacity that means sum achieved data rate of total VE \\ \(P\) & Total system energy consumption \\ \(P_{\mathbb{S}}\) & Power consumption of total RRHs \\ \(P_{fronthaul}\) & Power consumption of total fronthaul links \\ \(EE\) & System energy efficiency \\ \(\gamma,\gamma_{0}\) & SINR of VE, SINR constraint \\ \(\tau,\tau_{max}\) & Outage probability of system, Outage probability constraint \\ \(p_{max}\) & Maximum transmission power of VE \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of symbols and their description. with values of 1 for association and 0 for nonassociation. The power consumption model of the fronthaul links is  
\[P_{fronthaul}=\sum_{s=1}^{S}\Bigl{(}\pi_{fronthaul}+\varphi\cdot t_{s}\Bigr{)} \tag{5}\]  
where \(\pi_{fronthaul}\) is the circuit power from the fronthaul transceiver and switch, \(\varphi\) is the power consumption per bit/s and \(t_{s}\) is the traffic associated with RRH \(s\). The power consumption model of the total system is  
\[P=P_{S}+P_{fronthaul} \tag{6}\]  
The system energy efficiency can be defined as  
\[EE=\frac{R}{P} \tag{7}\]  
Our main goal is to maximize the energy efficiency of the system, which can be defined as  
\[\max_{\{C,V,K\}}EE, \tag{8}\] \[\text{s.t.C1}:\gamma\geq\gamma_{0},\quad\forall c,v,k\] \[C2:\tau\leq\tau_{max}\] \[C3:0<p_{k}^{\eta}\leq p_{max},\quad\forall v,k\] (9) \[C4:\sum_{k\in\mathbb{K}}f_{c,k}\leq 1,f_{c,k}\in\{0,1\}\]  
where \(\gamma\) is the SINR of VE; \(\gamma_{0}\) is the SINR threshold; \(p_{max}\) is the maximum transmission power of VE; \(\tau\) is the outage probability of the system; and \(\tau_{max}\) is the maximum outage probability constraint; \(f_{c,k}\) is the allocation indicator with values of 1 for allocation to V2I VE \(c\) at \(k\)th RB and 0 for non-allocation. Of these constraints, C1 ensures that VEs satisfy the SINR constraint; C2 ensures that the outage probability in the system does not exceed the threshold outage probability constraint; C3 ensures that the transmission power of V2V transmitters does not exceed the maximum power level because of the critical interference problem; C4 denotes that each V2I VE can be allocated to an RB. It means that each V2I VE can be allocated to only one RB, and other V2I VE cannot share the allocated the RB.
## 3 Result and Discussion
### Dissolution of Polymers in Water  
Amphiphilic polymeric dispersants with carboxyl groups are usually dissolved in alkaline aqueous solutions because neutralization of the carboxylate group would facilitate the dissolution of the polymer [22]. When the alkaline solutions, which contain 1.2 times the excess molar of KOH compared to that of the carboxyl groups in the polymers, were added to the polymers, BL-02 and BL-04 were completely dissolved after 15 min of ultrasonic irradiation. However, the other polymers, BL-01, BL-03 and BL-05 were not dissolved at all. They were observed to be cloudy even after irradiation for more than 1 h, as summarized in Table 2. These three polymers had low acid-values of around 150 or less, which would be low acid-values compared to a commercially available amphiphilic-polymer-dispersant, and thus, neutralization may not be enough to dissolve the polymers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Monomers** & **Mol Ratio** & **Mn** & **Mw** & **Mw/Mn** & **Acid Value** \\ \hline BL-01 & St, DPE, MAA, & 13/1/8 & 2679 & 3107 & 1.16 & 129 \\ BL-02 & St, DPE, \(n\)-BMA, MAA & 3.9/1/9.1/8 & 2445 & 4077 & 1.67 & 146 \\ BL-03 & St, \(\alpha\)-MeSt, MAA & 6.8/2/5 & 1509 & 1737 & 1.15 & 143 \\ BL-04 & St, \(\alpha\)-MeSt, MAA & 8/2/13 & 2685 & 3165 & 1.18 & 271 \\ BL-05 & St, \(\alpha\)-MeSt, MAA & 24/2/14 & 3950 & 4752 & 1.20 & 152 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of polymers.  
Figure 2: 1H NMR chart of BL-01.
## 3 Numerical Results and Discussion
### Baselines  
To evaluate the performance of the proposed dual-driven MIMO detector, we selected several commonly used methods as baselines. These baselines include a linear detector known as the MMSE detector and model-driven detectors such as the OAMP algorithm and the OAMP-Net network. Descriptions of these baselines are presented in the following section.  
_Linear detector_: MMSE detector [22]. The MMSE detector estimates the transmitted signal, denoted as \(\widehat{\mathbf{s}}\), using the following equation:  
\[\widehat{\mathbf{s}}=\left(\overline{\mathbf{H}}^{\text{T}}\mathbf{H}+\frac{ \sigma_{\text{n}}^{2}}{2\mathbf{I}_{2N_{\text{t}}}}\right)^{-1}\overline{ \mathbf{H}}^{\text{T}}\overline{\mathbf{y}}, \tag{16}\]  
where \(\widehat{\mathbf{s}}\) represents the estimated result of \(\overline{\mathbf{s}}\).  
_Model-driven detectors_: The OAMP algorithm and the OAMP-Net network. The detection processes of the OAMP algorithm and the OAMP-Net network can be found in (5), (6) and (9), (11), respectively.
## 3 TVWS-DTV Coexistence: Field Measurements
### Field Tests Considering Long Distances
#### 3.2.3 Presence of Interfering Signal in the Licensed Frequency Range for the Test  
As previously mentioned, conducting field tests considering long distances involved requesting a _service_ _special para fins cientificos e experimentais_ (SEFICE) license, which is the Specialized Radiofrequency Station Installation License, from Anatel [43]. The license requested by Inatel considered four UHF channels for downlink and four channels for uplink, totaling a maximum width of 24 MHz per link. The frequency band planned for the transmission of the fixed TVWS station comprised the UHF channels 27, 28, 29, and 30. These channels were available according to the documents on the Anatel website.  
It is important to highlight that during the field tests, a DTV signal was identified occupying the UHF channel 30, which was not licensed in the Anatel system. The presence of the DTV signal, considered as \"interfering\" for the tests, was mitigated by using a  
Figure 12: Relief profile and link information obtained by simulation for each MS position. (**a**) MS_Loteamento. (**b**) MS_DeltaBlack. (**c**) MS_CafezalManoela. (**d**) MS_Pedralva. The colored lines along the terrain represent signal power as a function of distance, where green indicates higher power levels, yellow represents intermediate levels, and red corresponds to the lowest power levels.  
bandpass filter at the reception side, referred to as Filter\({}_{\text{RX}}\), configured to receive channel 28, which is the transmission channel of the fixed TVWS station. However, the undesirable signal was also used in some additional tests, as it represents a real field situation and allows exploring other test conditions and scenarios.  
Although the use of the unlicensed DTV signal facilitated measurements, serving as a second interfering signal in some tests, the presence of this signal on a frequency that was supposed to be free highlights the importance of employing systems for electromagnetic spectrum monitoring. This function aims to complement the database information related to licensed primary signals and available frequencies to ensure the proper operation and harmonious coexistence of TVWS and DTV systems.
## 4 The Proposed ECHR Algorithm
### Selection of the Root Node  
In each round of performing the ECHR protocol, the first step is to select the root node. Generally, the \(BS\) is assumed to be deployed at any location inside or outside of the monitoring area. According to the radio transmission model described in Section 3, transmitting a packet through a long path consumes greater energy. Since high energy consumption is not suitable for a power-limited network, the root node selection method is essential. In each round, we compute the root node weight of each node \(n_{i}\) by:  
\[\alpha_{i}=\left(q_{i}\right)^{\tau_{1}}\times\left(\frac{\left\|O\left(s_{i} \right)\right\|}{\left\|C\left(s_{i}\right)\right\|}\right)^{\tau_{2}}\times \left(\frac{1}{d\left(s_{i},BS\right)}\right), \tag{4}\]  
where \(q_{i}\) is the residual energy of \(s_{i}\), \(d\left(s_{i},\,BS\right)\) is the Euclidean distance between node \(s_{i}\) and the \(BS\), and \(\tau_{1}\) and \(\tau_{2}\) are the weighting coefficients for the residual energy factor and the coverage factor, respectively. After the weights of all nodes are computed, we can form a set of root node weights \(\alpha\) by:  
\[\alpha=\begin{bmatrix}\alpha_{1}\\ \alpha_{2}\\ \vdots\\ \alpha_{n}\end{bmatrix}. \tag{5}\]  
Next, we can select the \(H\)-th node of the network to be the root node via:  
\[H=\arg\max\alpha=\operatorname*{arg\,max}_{i\in S}\alpha_{i}\;, \tag{6}\]  
where \(S\) is a set of sensor nodes in the network.  
In each round, the root node broadcasts a beacon message with a packet format that includes its ID, residual energy, and level, toward other sensor nodes. Nodes that receive the beacon message of the root node are called the first level nodes. The first level nodes broadcast the beacon message, and the nodes that receive the beacon message from first level nodes are called the second level nodes. With the hierarchical broadcasting, each node is able to establish its level and receive the information of the neighboring nodes. After all sensor nodes broadcast the beacon message, each node is able to establish the neighbor set of its neighboring nodes.
# Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network
## 6 Conclusions  
This study improved the convergence performance of an existing optimization algorithm through an elite-guided strategy and a reverse learning mechanism. Simulation experiments were conducted using three benchmark test functions to compare the elite snake optimizer (ESO) with eight mainstream intelligent optimization algorithms. The results indicated that ESO exhibited a higher convergence accuracy and improved stability. Furthermore, ESO was used to initialize the weights and biases of the BP neural network, resulting in the creation of the ESO-BP model. The ESO-BP model was compared with traditional CNN, RNN, LSTM, and a baseline BP neural network in terms of prediction accuracy and stability using the C-MAPSS dataset. The findings showed that the ESO-BP model enhanced the prediction accuracy and stability of BP across all the sub-datasets of C-MAPSS. In conclusion, the ESO-BP model has theoretical significance in improving intelligent optimization algorithms and practical value in predicting the health index of turbofan engines.  
In the future, intelligent optimization algorithms can be employed to initialize the parameters for other neural networks and applied across various domains in machine learning. The primary concern is the unacceptable time cost required for intelligent optimization algorithms to search for the global optimum in high-dimensional spaces, attributed to the large number of weights and biases. Accordingly, we will develop effective pruning methods to eliminate specific dimensions of the connection weights, reducing time costs and streamlining the networks. This is a promising research direction.  
Conceptualization, X.Z. and W.D.; methodology, X.Z. and W.D.; software, N.X.; validation, N.X., X.Z. and W.D.; formal analysis, N.X.; investigation, J.W.; resources, G.Z.; data curation, N.X. and W.D.; writing--original draft preparation, X.Z. and N.X.; writing--review and editing, X.Z. and G.Z.; visualization, N.X.; supervision, X.Z. and G.Z.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
Yunnan Philosophy and Social Sciences Planning Pedagogy Project (AC21012), Humanities and Social Sciences Research Project of Yunnan Provincial Institute and Provincial School Education Cooperation (SYSX202008), the Special Project of \"Research on Informatization of Higher Education\" of China Society of Higher Education in 2020 (2020 XXHYB17).  
Not applicable.  
The data presented in this study are openly available in NASA Prognostics Data Repository at 10.1109/PHM.2008.471141, accessed on 20 September 2023, reference number [38].  
The authors declare no conflicts of interest.
## 2 Loss Mechanism Hypergraph of Single-Stage Planetary Gear Transmission
### Loss Mechanism of Single-Stage Planetary Gear Transmission
#### 2.2.2 Power Loss Mechanism of Single-Stage Planetary Gear Transmission System  
As shown in Figure 2d, power is transmitted to the helical gear planetary drive via the sun gear, which is in direct contact with the frame. The frame is in contact with the bearings of the planetary gear and ring gear, resulting in bearing power losses \(P_{\text{R1}}\), \(P_{\text{R2}}\), and \(P_{\text{R3}}\). Both the sun gear-planetary gear meshing pair and ring gear-planetary gear meshing pair are in contact at the teeth surface, and gear power losses \(P_{G1}\) and \(P_{G2}\) are generated. Finally, power is transmitted via the ring gear to the output  
Figure 2: Schematic representation of herringbone planetary gear transmission system. (**a**) Herringbone planetary gear structure; (**b**) cross-sectional view of planetary gear; (**c**) schematic of transmission structure; (**d**) hypergraph of power loss mechanism (1, 2, 3, and 4 denote the frame, sun gear, planetary gear, and ring gear, respectively; R\({}_{1}\), R\({}_{2}\), and R\({}_{3}\) represent bearing contacts and G\({}_{2\text{-}3}\) and G\({}_{2\text{-}4}\) represent tooth surface contacts, respectively).  
shaft. Power losses can be divided into two categories: power loss due to bearing contact and power loss due to gear meshing.  
Gears of the planetary gear transmission system are mounted on a rotating shaft, which is typically supported by a roller bearing and ball bearing. Power loss due to friction of the roller bearing is  
\[P_{R}=1.05\times 10^{-4}\text{nM} \tag{1}\]  
where \(n\) is bearing speed and \(M\) is bearing friction torque.  
Power loss associated with gears meshing in the lubricating oil-gas mixture, including power loss due to friction on the tooth surface and windage power loss, is  
\[P_{G}=P_{m}+P_{\omega} \tag{2}\]  
where \(P_{m}\) is tooth surface friction power loss and \(P_{\omega}\) is windage power loss.
# Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information
## 2 Materials and Methods  
### Data Sources and Study Area  
#### 2.1.1 ADS-B  
ADS-B (Automatic Dependent Surveillance-Broadcast) is a modern sensor network delivering weather data [24]. ADS-B devices transmit and receive data using a 1090 MHz Mode S (mode-selective) data link. Initially designed for aviation surveillance, ADS-B operates globally, allowing aircrafts to autonomously transmit crucial flight parameters like their position, altitude, and velocity. Unlike traditional radar systems that rely on interrogation-reply cycles, ADS-B functions continuously, updating automatically at 1-2 Hz. Platforms like Flightradar24 ([[https://www.flightradar24.com/](https://www.flightradar24.com/)]([https://www.flightradar24.com/](https://www.flightradar24.com/)) (accessed on 1 August 2024)) and FlightAware ([[https://flightaware.com/](https://flightaware.com/)]([https://flightaware.com/](https://flightaware.com/)) (accessed on 1 August 2024)) utilize ADS-B as a primary data source. The current focus in research is on hardware development, signal processing, network optimization, and surveillance enhancement. Alongside standard aviation metrics, ADS-B captures real-time weather data, such as wind and temperature. This information exchange enables enhanced situational awareness for both airborne and ground-based traffic control. Notably, ADS-B's high update frequency and cost-effectiveness drive its adoption for weather monitoring applications, facilitating precise and timely weather alerts in the aviation sector.  
Therefore, in this study, weather data (T, WD, and WS) derived by the Aviation Meteorological Office (AMO) of the Korea Meteorological Administration (KMA) via ADS-B were used to verify the trajectory-based weather variables extracted from the model. The relevant conversion details can be referenced in the World Meteorological Organization (WMO) guide documents [25; 26].  
The study on the production of weather data based on Mode S Enhanced Surveillance (EHS) began in 2007 by the Royal Netherlands Meteorological Institute (KNMI) [27]. Previous research indicated that the wind and temperature information calculated using Mode S EHS data has better quality than that using existing aircraft observation data [28; 29; 30]. Therefore, in this study, research was conducted based on realistic aircraft trajectories of actual flight data: 2000 aircraft trajectories from 19 March 2024 (Figure 1). These 2000 aircraft trajectories were defined and used as the number of API requests because each requires a unique API request to retrieve specific weather data along its trajectory. This approach ensures that the system is thoroughly tested for its ability to handle multiple simultaneous requests and provide accurate weather information tailored to individual aircraft trajectories.
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 4 Experimental Results
### Performance Evaluation
#### 4.4.1 Parking Space Detection Rates  
Table 5 lists the parking space detection rates for both indoor and outdoor settings. This table shows that the number of detected spaces was substantially lower than the number of actual spaces in indoor settings. The primary reason for this is uneven lighting. This problem causes a lower contrast between parking space marking lines and the road and leads to the omission of some parking spaces. In addition, columns in parking lots often cause the detected parking spaces to include errors in detection. In outdoor settings, a direct relationship was found between the detection rate of parking spaces and the integrity of the parking space markings. Because the marking lines are constantly exposed to weather, they are likely to be fragmented. This disrupts the parking space structure and affects parking space detection. In addition, if sunlight casts shadows on the junctions between marking lines, the system is unable to detect the corner and results in a detection failure.
# Explore Long-Range Context Features for Speaker Verification
## 6 Experiments
### Training Setting  
As shown in Figure 2, the ResNet-50 architecture are used in our experiments. The initial number of channels is set as 32. To maintain a similar number of parameters with ResNet50, \(t\) is set as 1.5 when \(s\) is set as 8 in the following experiments according to Equation (3) in the HS-ResNet. Moreover, experiments show that inserting the DSSA module between stages 3 and 4 achieves better improvements. Thus, for convenience, the DSSA module is applied between stages 3 and 4 if not specially specified.  
In the training stage, a mini-batch size of 32 is used to train models in all experiments. Softmax with cross-entropy loss is used to train models of the Vox series, and circle loss is used to train models of the Cn series if not specially specified. Stochastic gradient descent (SGD) with momentum 0.9, weight decay \(1\times 10^{-3}\) is used. The learning rate is set to 0.1, 0.01, and 0.001 and is switched when the training loss plateaus. Each speech sample in the training stage is sampled for L frames from each speech sample. The chunk size L is randomly sampled from the interval [L1; L2], and the interval is set to [200, 400], [300, 500], and [400, 600] in the three training stages.