esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### DNA Extraction and qPCR  
The DNA extraction from Sugar Belle mandarin trees was accomplished by taking the midribs of the collected leaves (0.1 g fresh weight) and cutting them into small pieces with a clean razor blade. The leaf tissue was placed into 2 mL screw cap tubes and frozen in liquid nitrogen for 10 min. The frozen leaf tissue was processed using a Tissuelyzer II (Qiagen, Germantown, MD, USA) twice at 30 Hz for 1 min each, switching the position of the sample blocks (2 min total). The tubes of the homogenized leaf tissue were removed from the sample blocks and were centrifuged for 1 min at 6000\(\times\)_g_ to pellet the leaf powder. The DNA was obtained using the Plant DNEasy Minikit (#69106, Qiagen, Valencia, CA, USA) following the manufacturer's protocol.  
To confirm that the _D. citri_ from our psyllid colony were '_Ca_. L. asiaticus'-free, DNA was extracted from single psyllids (10 insects total) using Quick DNA Miniprep Plus kit (#D4068, Zymo Research, Irvine, CA, USA). Briefly, single psyllids were placed into a sterile 1.5 mL centrifuge tube with 95 \(\upmu\)L of solid tissue buffer (blue) and 10 \(\upmu\)L of proteinase K (provided in the kit), plus 95 \(\upmu\)L of water, then homogenized using a hand-held motorized pestle (F65000, BelArt, Wayne, NJ, USA). The homogenate was incubated at 55 \({}^{\circ}\)C in a water bath for 2 h. The remaining protocol was followed as given by the manufacturer except that the DNA was eluted into 35 \(\upmu\)L of warmed elution buffer to concentrate the DNA.  
Real-time quantitative PCR (RT-qPCR) assays were performed on both the plant and insect samples using an Applied Biosystems QuantStudioTM 3 real-time PCR detection system (ThermoFisher Scientific, Waltham, MA, USA) using TaqMan Universal PCR Master Mix (Life Technologies, ThermoFisher Scientific). The dye/quencher set was FAM/NFQ-MGB with carboxyhodamine (ROX) as the reference dye. the samples were analyzed with the degenerate genus-specific _rpoB_ primer/probe set [48]. Each well contained 10 \(\upmu\)L TaqMan, 0.6 \(\upmu\)L each forward and reverse primers, 0.3 \(\upmu\)L _rpoB_ probe (both primers and probe from IDT, Coralville, IA, USA), 3.5 \(\upmu\)L RNase-free water, and 5 \(\upmu\)L DNA template, for a total of 20 \(\upmu\)L. The RT-qPCR temperature program consisted of 2 min incubation at 50 \({}^{\circ}\)C, 10 min at 95 \({}^{\circ}\)C, and 40 cycles at 95 \({}^{\circ}\)C for 15 s and 60 \({}^{\circ}\)C for 1 min, respectively. The samples were assayed in duplicate and were expressed as the mean cycle threshold (Ct) number. Mean Ct values above 35 or \"undetermined\" were designated as negative for '_Ca_. L. asiaticus' infection. Pooled DNA from previously tested citrus plants or psyllids with Ct \(<\) 25 was used as the plate positive control. All of the plant and insect samples that were used for the experiment tested negative for '_Ca_. L. asiaticus' by RT-qPCR.
## 3 Experimental Scenario
### Performance Analysis of OST-SMC at Different Road Slopes  
The performance of the proposed method is analyzed at variable road slopes for high credibility and effectiveness. The experiment was tested for three different angles of declination which are 0.15 rad, 0.26 rad, and 0.4 rad, respectively, and their equivalent angle in degrees is 8.59\({}^{\circ}\), 15\({}^{\circ}\) and 23\({}^{\circ}\), respectively.  
From the velocity response, Figure 6(a), we can notice that the vehicle is maintaining its desired speed at different slopes. The zoomed part tells us when the car enters the declined road and leaves the declined road. In Figure 6(b), we can see that the vehicle acceleration has a similar trend with the velocity, meaning we can see that the vehicle acceleration is adjusted depending on the gradient of the road using the proposed method shown  
Figure 6: Vehicle pitch and pitch rate comparison under parameter variations and optimal conditions: how the vehicle tilts up and down during the whole scenario can be seen.  
in Figure 6(c). Thus, from the result we can see that the vehicle is cruising at its desired speed without any problem despite the changing road slope; eventually, this shows us the algorithm is robust for the practical variable slope of the road.
## 3 Sink-Based Centralized Transmission Scheduling
### Overview of the Proposed Method  
The key idea of SC-Sched is that the sink node performs transmission scheduling and uses asymmetric communication and WuR to directly activate sensor nodes involved in the communication just before the scheduled transmission timing. It works as follows.  
1. The sink node decides the routing and transmission timing of each sensor node. When the sink node tries to collect sensor data, it transmits a WuC to directly activate a pair of sensor nodes per hop on the route by asymmetric communication.
2. Sensor nodes stay in the sleep mode and monitor aperiodic queries with their WuRxs. A WuRx receiving a WuC that matches its own address activates its sensor node.  
Figure 2 is a conceptual diagram illustrating the components of the proposed method. Besides the asymmetric communication and wake-up control mentioned in previous works, the SC-Sched method introduces three new functions. Firstly, to reduce the active time of sensor nodes, the sink node directly activates a pair of sensor nodes per hop by integrating asymmetric communication and WuR.  
Figure 1: Communication paths: (**a**) symmetric multi-hop communication and (**b**) asymmetric communication.  
Second, to deal with frame loss, consecutive WuC transmission and an addition of retransmission slots are adopted. Finally, to reduce data collection time, by using different channels for WuC and data transmission, activation of sensor nodes and data transmission are made in parallel to mitigate the effect of delay generated when activating a pair of sensor nodes by a WuC per hop.  
In the following, Section 3.2 explains the node model, Section 3.3 describes the basic data collection procedure where a sink node utilizes WuCs to wake up sensor nodes per hop. Section 3.4 explains how to deal with frame loss caused by multipath fading. Section 3.5 further suggests shortening data collection time by using multiple channels.
## 3 Sensor Modules and Deployment
### Gateway Module  
Gateway module is composed of four wireless modules units, a STM32 processor and a gateway unit. The signals of the human target, which are detected by each sensor node, were all transmitted to the wireless module CC1100 in a specified interval. Because four wireless modules have four kinds of channels and they connect with the STM32 processor by General Purpose Input Output (GPIO), the STM32 processor can independently process the different data from sensor nodes, and it can also complete wireless data acquisition at the same time. Moreover, sensed data will be transmitted to a host computer through Ethernet interface for further processing and display. The whole structure and physical photo of the wireless gateway module are shown in Figures 4 and 5, respectively. Besides, the whole system is powered by a power unit.
# Context-Aware Wireless Sensor Networks for Smart Building Energy Management System
## 4 Proposed EMS for SEEB
### WSN Network Packet Delivery Evaluation  
In WSN testing, we used three different gateway hardware under two different network topologies. The goal of the test is to evaluate the gateway that has better performance for given network topology and load. In this phase, we tested three different gateways: First, XBee Gateway (Figure 3D), second, Raspberry Pi-based gateway (Figure 3C), third, Arduino Uno-based gateway (Figure 3E). These gateways have different CPUs, RAM capacity, operating systems, and communication resources. These resources and their configuration have an impact on the performance of the gateway. The following Table 4 illustrates the hardware configuration of these different gateways.  
We measured the packet arrival rate under different packet time interval. This test took 60 rounds. Each round lasts for 1 min. In the first round, we started with a packet time interval of 1 s, while in the last round, the packet time interval was 60 s. The test was also in two different network topologies: Full mesh topology and cluster tree mesh topology using 20 bytes packets.
# Super Twisted Sliding Mode Observer for Enhancing Ventilation Drive Performance
## 2 System Modelling
### Super-Twisted Slide Mode Observer (ST-SMO) Speed Estimator
#### 2.2.1 Super Twisting Algorithm (STA)  
The second-order SM technique, known as the STA, provides continuous control based solely on information from the SM surface. The algorithm's convergence is represented by the rotation of the phase diagram around the origin (\(S\), \(\hat{S}\)). This method employs a control law with two components: \(u_{1}\) and \(u_{2}\). These components represent the discontinuous time derivative and the continuous function, respectively, of the sliding variable, as defined by the following control rule [8]:  
\[u_{1}\ =\ -a_{1}|x|^{\frac{1}{2}}sin(x)+\Delta_{1}, \tag{7}\]  
\[u_{2}\ =\ -a_{2}sign(x)+\Delta_{2}, \tag{8}\]  
\[u_{est}\ =\ -a_{1}|x|^{\frac{1}{2}}sign(x)+\Delta_{1}-a_{2}\int sign(x)+ \Delta_{2}, \tag{9}\]where \(x\) denotes the SM surface, \(a_{1}\) and \(a_{2}\) represent control law gains, and \(\Delta_{1}\) and \(\Delta_{2}\) denote uncertainty terms. The uncertainty terms are bounded as follows:  
\[|\Delta_{1}|\leq\delta_{1}|x|^{\frac{1}{2}},\ |\Delta_{2}|\leq\delta_{2}, \tag{10}\]  
where \(\delta_{1}\) and \(\delta_{2}\) are positive constants. Therefore, the system demonstrates asymptotic stability when the gains meet the following specified conditions:  
\[\rho_{1}>2\delta_{1}, \tag{11}\]  
\[\rho_{2}>\rho_{1}\frac{5\delta_{1}\rho_{1}+6\delta_{2}+4\Big{(}\delta_{1}+ \frac{\delta_{2}}{\rho_{1}}\Big{)}^{2}}{2(\rho_{1}-2\delta_{1})}. \tag{12}\]  
All trajectories converge to the origin within finite time, where this convergence time is given by  
\[T\ =\ \frac{2V^{\frac{1}{2}}(x_{0})}{\lambda}. \tag{13}\]  
Here, \(V(x)\) represents a Lyapunov function, \(x_{0}\) denotes the initial state, and \(\lambda\) is a constant dependent on the gains \(a_{1}\) and \(a_{2}\).  
The stability of the STA is ensured by the Lyapunov stability theory, as discussed in [14].
## 2 Literature Review
### Cone Beam Computed Tomography and Conventional Radiology  
In maxillofacial and dental diagnostics, an important difference exists between two-dimensional and three-dimensional imaging. Orthopantomogram (OPG) radiograph is a two-dimensional radiological investigation, which may be adequate as a first level examination in some clinical conditions, such as mandibular apical-radicular dental lesions. As demonstrated by [PERSON] [27], deep learning techniques can also be applied to OPG to identify the IAC with excellent results. However, a three-dimensional study is required in case of more delicate circumstances, such as traumatology, extractions of impacted teeth, or implant surgery [28]. In such cases, the computed tomography (CT) is the main radiological examination. Both the conventional Multi-Detector CT (MDCT) and the more recent Cone Beam CT (CBCT) can be used in dental and maxillofacial surgical planning [29]. Compared to the first, CBCT guarantees an equal or superior image quality in the face with a reduced radiation exposure [30; 31]. It should be emphasized that conventional CT is not the first choice for some diagnostic purposes in dentistry and oral surgery, such as the evaluation of impacted teeth and periapical lesions [32] due to the better spatial resolution, CBCT should be preferred in these situations. The lower cost compared to medical CT, the wide availability in out-of-hospital healthcare facilities and greater patient comfort during examination are other noticeable characteristics of  
Figure 1: Position of the Inferior Alveolar Canal within the mandible, 3D annotated on a CBCT volume.  
CBCT [33]. Given these considerable advantages, the CBCT has therefore established itself as an important diagnostic tool in the oral and maxillofacial region [34; 35; 36; 37]. Furthermore, CBCT application and usefulness in anatomical studies of mandibular structures have been widely described [38; 39; 40; 41].
# An Urban Traffic Flow Fusion Network Based on a Causal Spatiotemporal Graph Convolution Network
## 1 Introduction  
Three factors are involved in traffic flow, i.e., flow, occupancy, and speed, and these factors determine the extent of road congestion. Usually, traffic flow is predicted for a short term (5-30 min) or a long term (more than 30 min). Previous popular statistical methods (e.g., linear regression) perform satisfactorily in short-term prediction but are ineffective in the long term because there a range of factors that impact traffic flow. In addition, the factors and their interdependencies vary over time.  
Traffic flow prediction is a kind of spatiotemporal prediction problem. For spatiotemporal prediction problems, the focus of research is how best to obtain the temporal correlation and spatial correlation. The development of traffic flow prediction methods has experienced several stages: statistical methods, machine learning methods and deep learning. Among these, deep learning methods have become a research hotspot in recent years because of their high efficiency of obtaining spatial and temporal correlations.
# Identification of Factors Influencing Fluoride Content in Tea Infusions: A Systematic Review
## 3 Results
### General Characteristics of the Qualified Articles  
In the eligible articles, a significant proportion of authors examined more than one type of tea [19; 28; 31; 32; 34; 47; 48; 49; 50]. Black tea was the most frequently studied, appearing in 20 articles [19; 28; 32; 34; 47; 49; 51; 52; 54; 55; 56; 57; 58; 60; 61; 62; 63; 64; 65]. Green tea was investigated in 14 studies [19; 28; 31; 32; 47; 49; 51; 52; 54; 55; 56; 57; 59; 60], followed by pu-erh tea in 7 articles [19; 28; 51; 52; 55; 57; 66], herbal tea in 5 articles [19; 31; 49; 55; 58], and white tea in 4 articles [28; 32; 47; 55]. Some authors additionally assessed oolong tea [52; 55]. Regarding the form of tea used, 15 studies analyzed loose leaves [19; 28; 31; 32; 34; 47; 50; 51; 52; 57; 58; 62; 63; 66; 67], 12 studies assessed tea in bags [19; 28; 31; 32; 49; 50; 51; 52; 54; 56; 58; 63], and tea powder was used in 5 studies [49; 54; 55; 55; 65; 68; 49]. Importantly, 11 articles compared multiple tea forms [19; 28; 31; 32; 49; 50; 52; 54; 58; 63]. Significant differences were observed in the brewing parameters. The water temperature during brewing ranged from 60 \({}^{\circ}\)C [68] to 100 \({}^{\circ}\)C [19; 28; 55; 66], and the brewing time varied considerably, from as short as 1 min [31; 56] to as long as 24 h [31]. Only three studies reported brewing according to the ISO 3103 standard [28; 63; 69], whereas none declared using brewing methods strictly based on manufacturer recommendations. Fluoride concentrations were most commonly determined using a fluoride ion-selective electrode (FISE), employed in 24 studies [19; 28; 31; 34; 49; 50; 51; 52; 53; 54; 56; 57; 58; 60; 61; 62; 63; 64; 66; 67; 68; 69; 70]. Alternative methods included ion chromatography [32; 57], molecular absorption spectrometry [10; 31], ion-exclusion chromatography [57], single quadrupole ICP-MS [47], and MIP-OES techniques [59]. Several studies also examined the influence of external factors on fluoride release, such as water hardness [32], the pH of the infusion [31; 55; 67], or the fluoride concentration in the brewing water [54; 56; 65]. Brewing time and water temperature consistently appeared as significant factors affecting fluoride levels [19; 28; 32; 49; 51; 52; 53; 54; 55; 56; 58; 60; 63; 64; 65; 66; 68]. In some cases, additives like milk or spices were also considered [34; 50]. The general information about the included studies is demonstrated in Table 1.
# Helicopter Blade-Vortex Interaction Airload and Noise Prediction Using Coupling CFD/VWM Method
## 3 Results and Discussions
### Sensitivity Study of BVI Noise
#### 3.3.1 Tip-Path-Plane Angle  
The BVI airload and noise characteristics at five tip-path-plane angles varied from \(-2^{\circ}\) to \(3^{\circ}\) (negative tilted forward) have been discussed with \(C_{T}\), \(\mu\), and \(Ma_{tip}\) kept constant.  
The tip-path-plane angle affects the wake axial movement, making an evident change to the miss-distance. Figure 9 shows the miss-distance varying with the azimuth angle at \(\alpha_{tpp}=-2^{\circ}\). Compared with the results in Figure 6, the miss-distance increases with a tilted forward angle. We see in Equation (14) that the amplitude of BVI noise depends on the interaction airload and its time derivative, thus the impacts on the sectional airload at 0.9 \(R\) are illustrated in Figure 10. As shown, with the rotor disk tilted forward, the impulse load amplitude decreases and the impulse characteristics become weaker due to the increase in the miss-distance.  
Figure 8: Peak noise level contours for six BVIs on the sphere surface.  
The predicted sound pressure varying with the tip-path-plane angle at #7, #3, and #9 are presented in Figure 11. At each observation location, the sound pressure exhibits similar waveform characteristics at different \(\alpha_{tpp}\), the amplitude of which quickly declines with the rotor disk tilted forward due to the increase in the miss-distance. Furthermore, in a strong BVI state, strong impulse characteristics appear, while in a weak BVI state, not only is the noise amplitude small but the frequency is also low.  
Figure 11: Predicted acoustic pressure at #7, #3, and #9 variance with the tip-path-plane angle.  
Figure 10: Sectional airload variance with the tip-path-plane angle at 0.9 \(R\). (**a**) Loading; (**b**) Time derivative of loading.  
Figure 12 shows the effects of the tip-path-plane angle on BVI noise directivity. At \(\alpha_{tpp}=2^{\circ}\), a high noise level appears at 120\({}^{\circ}\) in azimuth and the noise energy is relatively concentrated; this is largely from the parallel interaction #A3 for the small miss-distance. With the rotor disk tilted forward, the BVI noise weakens and the radiation direction is no longer concentrated. At this time, the blade thickness noise becomes more influential, radiating its acoustic energy to the front of the rotor near the rotor plane (Figure 12b).  
The effects of tip-path-plane angle on the peak BVI noise level are correspondingly shown in Figure 13. When the rotor disk is tilted forward, the peak noise level changes slowly with \(\alpha_{tpp}\) because the rotational noise dominates the acoustic field. However, when the rotor disk is tilted backward, BVI occurs and the peak noise level changes quickly with \(\alpha_{tpp}\). This indicates that the tip-path-plane angle has more influence in the BVI state but less influence in the non-BVI state.
## 5 Performance Analysis
### Evaluation and Simulation Results
#### 5.3.3 Security Robustness  
Figure 4 illustrates EdgeGuard's performance under varying percentages of malicious nodes for different types of attacks.  
EdgeGuard maintained an accuracy of over 90% even with up to 40% of the nodes being malicious, demonstrating strong resilience against data poisoning, model poisoning, and Sybil attacks.  
Figure 3: Communication efficiency.  
Figure 2: Model accuracy convergence.
## 3 Results and Discussion
### Integration of Renewable Energy Sources and Hybrid Microgrid Configurations
#### 3.2.4 Future Role of Sector Coupling and Energy Market Participation  
The future integration of RES in microgrids is expected to increasingly involve sector coupling, connecting the electricity sector with heating, cooling, and transportation systems to improve energy efficiency and support decarbonization efforts [36; 58; 83]. Sector coupling strategies enable flexible utilization of surplus renewable generation, such as directing excess electricity to electric vehicle charging or thermal storage systems, thereby enhancing overall system efficiency [47; 60].  
Additionally, microgrid participation in local and regional energy markets represents a promising opportunity for future development. Several studies emphasize the importance of establishing regulatory frameworks that enable microgrids to provide ancillary services and participate in peer-to-peer energy trading schemes [22; 23; 48]. Blockchain technology and decentralized transaction platforms are also being explored as mechanisms to facilitate transparent and secure energy trading within microgrid communities [34; 62].  
Future research should focus on developing business models that support sector coupling and market participation while ensuring the financial sustainability and operational resilience of microgrids [39; 43; 66]. These efforts should be complemented by the creation of advanced optimization tools capable of assessing the economic and environmental impacts of sector coupling strategies under various operational scenarios [70; 72; 77].
# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
## 4 Experimental Setup
### Experimental Data
The testing was conducted exclusively in the stateless mode (Section 2.5), where the full context was included in every prompt without retaining information from previous interactions. Listing 1 is an example of the input prompt. ```
1{
2\"context\":{
3\"free_slots\":3,
4\"total_slots\":5,
5\"offer.history\":{
6{\"status\":\"accepted\",\"price\":10},
7{\"status\":\"rejected\",\"price\":5}
8],
9\"current_offer\":{
10\"price\":8,
11\"reward\":12
12}
13},
14\"instructions\":\"Youarewarehouseagentoperatinginashared warehousesystem.Yourgoalstomamizewrofitswhileoffericientlyutilizingstorageslots.Basedontheprovidedcontext,decidewhatactiontotake.Options:['accept','reject','forward']\"
15}
```  
**Listing 1** Example prompt for AI decision-making in stateless mode. These prompts were constructed to maximize the agent's understanding of the current operational conditions (e.g., available storage slots and pricing history) and explicitly guide strategic decision-making. The structured design of these prompts ensured reproducibility and consistency across decisions. Moreover, by using stateless prompting, where each prompt includes all necessary context without reliance on previous interactions, the experimental setup avoided potential biases from sequence effects, thereby supporting robust comparative analyses between the randomized and LLM-based strategies. \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Outcome** & **Description** & **Probability (Pool)** & **Probability (No Pool)** \\ \hline Accept & Assigns a storage slot to the offer. & 0.5 & 0.5 \\ \hline Reject & Declines the offer outright. & 0.1 & 0.5 \\ \hline Forward & Sends the offer to the capacity pool. & 0.4 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Random agent decision outcomes.