esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Automated Oral Cancer Screening Algorithm in Tobacco Users vs. Non-Tobacco Users
## 2 Materials and Methods
### Subjects  
This study was performed in 318 subjects from three clinical sites. Individuals attending Concorde College of Dental Hygiene Dental Clinics in Garden Grove, California, or West Coast University Dental Hygiene Clinics in Anaheim, California, as well as those referred to the University of California, Irvine Clinics with oral lesions suspicious of OPML or OC were recruited, as well as individuals with no visible oral lesions. Informed consent was obtained from all subjects prior to study begin. Study participants were first divided into \"smoker\" and \"non-smoker\" groups. They were classified as tobacco smokers if they self-reported consuming 10 or more cigarettes per day and 70 cigarettes per week over the past year or longer. Study participants were classified as tobacco non-smokers if they had not smoked any tobacco within the past year or more. Light smokers (less than 10 cigarettes per day within the past year), and individuals who had indulged in any form of vaping, cigar smoking, chewing tobacco or similar, snuff, or water pipe/hookah use within the past year were excluded from the study, as were individuals who self-reported consuming more than two glasses of beer or wine weekly, or who regularly imbibed any stronger forms of alcoholic beverages. These criteria were established to avoid some of the most common multifactorial causes of increased OC risk and of visible changes to the oral mucosa. For example, individuals who regularly consumed any more than minimal amounts of alcohol were excluded from the study, as this behavior potentiates OC risk in tobacco smokers, and in this study our goal was to single out tobacco use as a variable in the algorithm architecture. Inclusion/exclusion criteria are shown in Table 1.
## 7 Instrument Testing
### Noise of the Triaxial Flux Gate Probe  
The flux gate probe was placed in a zero-magnetic-field environment, and the background noise output by the probes was considered to be the sensor noise. The testing procedure is illustrated in Figure 25.  
A spectrum analyzer was employed to measure the power spectral density, and the value at the 1 Hz frequency point was recorded as the output voltage noise level of the flux gate probe at that frequency point. Different frequency points were selected. The output voltage noise at each frequency point was then divided by the sensitivity at that frequency point. The equivalent input magnetic field noise at each frequency point was obtained using Equation (4):  
\[B_{N}=\frac{U}{K_{B}} \tag{4}\]  
where \(B_{N}\) is the noise of the calibrated flux gate probe, \(U\) is the average output voltage value from ten readings of the calibrated flux gate probe, and \(K_{B}\) is the commutation coefficient of the calibrated flux gate probe. The test results, which indicate that the noise of the triaxial flux gate probe is \(\leq\)8 pT Hz\({}^{-1/2}\), are indicated in Table 5.
# Personalization in Digital Ecomuseums: The Case of Pros-Eleusis
## 5 User and Content Model  
A good understanding of the user is important for any recommendation system. In the case of our ecomuseum application, the user model was iteratively refined by identifying and examining the visitor characteristics that may be relevant.  
Visiting an ecomuseum that spans across a city is an activity that may take different forms according to user preferences and to external factors such as the weather or the time of day. The visitors may make the decision to walk between POIs or use their car, enter museums or monuments to explore, simply decide to lounge outside on a bench if the weather permits it, or even take a break at one of the many city restaurants or cafes. We identified the following user characteristics that may be relevant to visitor preferences, split into three main groups:  
**User Mobility**. A prominent characteristic to take into account for recommending POIs is accessibility. If a point of interest is not easily accessible for a specific user due to its location or lack of adequate support and facilities, it should be excluded from the recommendations. To model accessibility, we followed a simplified approach, taking into account user mobility, leaving them to record whether they can move anywhere or have limitations resulting from being in a wheelchair, having other issues, or even escorting young children in a stroller.  
**Interests.** Generic user interests relevant to the content offered are an important part of visitor preferences. Detailed interests in terms of preference for a historical period or topic are not straightforward to implicitly deduce, so we modeled such preferences by leveraging POI similarity. More specifically, the recommendation algorithm examines similarity with existing POIs that the user liked in order to recommend the next ones. In this case, the user model maintains the POIs the user has visited along with the rating the user provided for them. There are two user characteristics that could be directly declared by the user. One is their interest to interact with local city residents to better understand the local perspective, and the second is their interest in focusing on the highlights of the city, those monuments or places that are generally considered a \"must-see\".  
**Visit Context**. This category contains characteristics that may not be fixed but rather change depending on the visit context. These are relevant to the specific visit or even a specific time and may include available time for the visit, visiting alone or in a group, etc.
## 3 Results and Discussion
### Terawatt-Attosecond Hard X-ray Pulse Generation
For the robustness of the scheme, the shot noise effect on the X-ray pulse peak power and the pulse duration have been tested. The simulation was performed for 10 different seed values. Figure 6a shows the average power for uniform and tapered configuration. For the tapered undulator system, the average power after 13 UM stage amplification is 0.9 \(\pm\) 0.2 TW (red curve), while the average pulse duration is 100 \(\pm\) 15 attosecond FWHM. For the uniform undulator system, the average power is restricted to a maximum of 0.42 \(\pm\) 0.1 TW. Figure 6b shows the clean radiation spectrum for both the uniform (black line) and the tapered undulator (red line). For the uniform undulator, the FWHM pulse width is 130 as (inset) and the  
Figure 5: (**a**) Undulator parameter K for a stepwise tapering, and (**b**) the electron beam energy spread at the undulator entrance and exit for hard X-ray case. Figure 6: (**a**) The average radiation power and the power fluctuations for 10 different random seeds for a uniform and a tapered undulator. (**b**) Power spectrum of the radiation output for hard X-ray case (inset shows the temporal profiles of the radiation powers). spectrum bandwidth is 15 eV wide. From the frequency-time bandwidth relation, \(\Delta\
u\Delta t=0.441\), the bandwidth turns out to be 14 eV wide. In case of tapered undulator, the pulse width is 115 as, and the spectrum width is 20 eV wide. Using \(\Delta\
u\Delta t=0.441\), we obtain a bandwidth 15.8 eV wide. These estimates indicate that there is a chirp induced in the amplification. The chirp is larger in the tapered case than in the uniform case. The radiation spectrum of the tapered undulator is slightly broader compared to that of the uniform undulator. This spectral bandwidth increases due to the sideband growth in the tapered undulator case.
# CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements
## 4 Discussion  
### Developmental Aspects of CBM Sensor  
This study used open-source hardware and software platforms to develop CBM sensor for capturing key phenotypes, i.e., crop FW, DW, and height. Specifically, the system development incorporated the integration of a Raspberry Pi 4 board as the central processing unit, LeddarTech Vu8 module as the LiDAR unit, and Navio2 as the GNSS unit. The system design that was used in this study offers modularity of components. There are several alternatives to the onboard processing option, including different models or versions of Raspberry Pi and Arduino boards. Similarly, the sensing modules such as LiDAR and GNSS could be switched with other industry equivalents. Research in this space is beneficial to develop a specialized sensing system for the desired phenotypic traits in different crops.  
This provides a developmental platform to embed new analytical packages to simplify the process of high-throughput phenotyping which is otherwise challenging with closed-off-the-shelf available systems. Several studies have demonstrated the integration of sensing systems for phenotyping research. For example, the Raspberry Pi board with a camera to collect images for crop monitoring in sentry mode in field [34], to measure leaf area in field [35], to develop a plant imaging chamber [33] a seed germination phenotyping system [36], and the Microsoft's FarmBeats system for soil sensing [37].  
The CBM sensor that was developed in this study can operate for up to three hours on a portable 2500 mAh battery which can be swapped to extend the in-field operational time. In terms of data management, an onboard data storage system with cloud uploading was designed. An approximately 32 GB internal memory card was used in this study, of which 6 GB was invested in system files, packages, and the developed codes, and about 26 GB remains dedicated for data storage. The estimated data size is about 400 Kbytes/minute; therefore, the storage would last for up to approximately 50 days in a continuous mode of operation without cloud uploading; but when the data is regularly uploaded to the cloud, the storage space is automatically cleaned-up even with the local archiving, which provides unlimited practical storage. Furthermore, with all the processing operations that are performed on the server nodes, the local computation is readily avoided, preventing overheating or power drain. The current version of the CBM sensor depends on a WiFi network for internet connectivity. In a future version, a dedicated internet bridge hardware, i.e., subscriber identity module (SIM) card slot could be implemented to ease field use.
# High-Precision Roller Supported by Active Magnetic Bearings
## 4 Basic Performance Test of Precision Roller with Magnetic Bearings
### Step Movement Control Performance of Roller  
Another function that the roller must meet is the ability to accurately execute step movement position control to be able to calibrate the roller without disassembly of the entire system. The next section shows the performance of the roller to execute step movements of different heights in order to evaluate in advance how small the resolution of the step movement that the roller is able to perform.  
The magnetic bearings perform feedback control to constantly position the shaft core at the reference position, which is the center of the magnetic bearing stator core, by measuring the error between the actual position of the shaft core and the reference position and compensating for the error. The reference positions of the left and right radial magnetic bearings are moved in the radial direction to perform a step movement. The resolution and settling time for step movements of 10 and 1 \(\upmu\)m was measured with step movements being executed at constant intervals of 2 s. After five steps, the reference positions were moved back to the origin in five steps.  
In the 10 \(\upmu\)m movement test, an overshoot of approximately 20% occurred after the movement with a rising time of 0.047 s, and the movement to the reference position was performed with a 3% settling time of 0.47 s (Figure 9a). Position control with a resolution of approximately 1 \(\upmu\)m was also confirmed to be possible using magnetic bearings, which would be useful for the superimposed printing of precise patterns (Figure 9b).
# The Perpetual Vector Mosquito Threat and Its Eco-Friendly Nemeses
## 5 Eco-Friendly _Lysinibacillus sphaericus_ and _Bacillus thuringiensis_ subsp. _israelensis_
### Brief History of Lysinibacillus (Formerly Bacillus) sphaericus (Ls)  
The first mosquitocidal strain of Ls (Neide) was isolated from moribund 'cool weather mosquito' _Culsieta incidens_ ([PERSON]) larvae in Fresno, California [222]. Several other strains with varying levels of toxicities against _Culex_ and _Anopheles_ larvae were subsequently isolated [223; 224]. In particular, [PERSON] [225] isolated a highly toxic strain, Ls (Bs) 2362, in Nigeria that killed _Culex_ and _Anopheles_ larvae and showed that the lethality was due to the binary toxin, BinA/BinB, which was recently reclassified as Tpp1 Aa1/Tpp2 Aa1 [126; 220]. The Ls 2362 was commercialized in 2000 under the name VectoLex and is also currently used in combination with _Bacillus thuringiensis_ subsp. _israelensis_ (Bti) in VectoMax (Valent Biosciences, Libertyville, IL, USA)
## 2 Materials and Methods
### The Description of Study Sites
#### 2.1.2 The Sugarcane Plantation Site in Louisiana, USA  
The sugarcane plantation site (29.6341\({}^{\circ}\)N, 90.8349\({}^{\circ}\)W) is located at the Ardoyne Farm in Schriever, Louisiana, USA, and is managed by the USDA-ARS Sugarcane Research Unit. The HoCP 04-83\({}^{\circ}\) variety (reg. no. CV-181, PI 687221) of sugarcane plantation was selected in this site. This site (100 ha) has a long-term (50 yr) history of continuous sugarcane production. The study site is bordered to the north, east, south, and west by 1200, 1320, 360, and 2000 m of continuous sugarcane production fields. The field is graded to 0.2% toward the south. Sugarcane plant rows are spaced at 1.83 m apart. The soil type is Cancienne silty clay loam (Fluvacquenti Epiaquepts). The sugarcane plants usually green up in April and are harvested by December (Figure S3) [50].  
Figure 1: The longitude and latitude position information of the two sugarcane eddy covariance (EC) flux tower sites (red dot) and surrounding landscapes with one respective 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) pixel (red polygon). (**a**,**c**), the USR site, Brazil; (**b**,**d**), the Louisiana site, USA.
## 4 Results and Discussion
### Performance Evaluation and Result Analysis
In addition, although traditional language training models (such as BERT) can process contextual information data, they often have limitations when processing complex multimodal signals and long sequence data, especially in exploring the interactive relationship between different modalities and extracting key features. GPT-4 combines the self-attention mechanism and feature extraction capabilities to achieve more accurate processing in this regard, and through real-time feedback and dynamic adjustment, it greatly improves the adaptability and real-time performance of recommendations. The model in this paper can significantly surpass other mainstream models in various evaluation indicators. It is enough to prove its superiority and practical application value. The personalized learning path recommendation system based on GPT-4 proposed in this paper shows remarkable advantages in many aspects. First of all, using the powerful long-sequence modeling ability of GPT-4, the system can deal with students' complex and changeable behavior data more effectively and improve the accuracy of recommendation. Secondly, by fusing multimodal data, the system can fully understand students' learning status and needs and provide highly personalized learning paths. In addition, the system introduces incremental learning technology and a self-attention mechanism to realize real-time feedback and dynamic adjustment to ensure that the recommended content always matches the latest progress of students. The experimental results show that the personalized recommendation accuracy of the system is over 92%; the coverage rate and recall rate are over 91% and 93%, respectively; and the feedback adjustment time is controlled within 1.5 s, which is superior to other mainstream models. These advantages make this system not only improve the effect of personalized recommendation, but also enhance the diversity and innovation of the recommended content.
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 5 Experimental Results  
### Dataset  
In order to verify the effectiveness of the scheme in this paper, we established a dataset collected from Google Earth for the lack of publicly available datasets for on-board ship detection. The dataset contains 95 images under different imaging conditions with the resolution of 1 m. Training set includes 65 images and the remainder are used as test images. The image size of our dataset ranges from 1500 \(\times\) 1500 to 10,000 \(\times\) 10,000 pixels. In total, there are 1397 ships in the training set and 435 ships in the test set. We rotated the training image 36 times (per 10 degrees) to generate different directions, and cropped 12,965 training samples with the size of 1024 \(\times\) 1024 pixels from the augmented dataset. After data augmentation, 42,308 ships of different types, sizes and directions were used for training. We manually marked the ground truth of all ship targets to obtain the performance of our scheme. As shown in Figure 7, the mask of ship target was marked firstly, and then the minimum bounding rectangle of the connected area wascalculated. Finally, the short side of the minimum bounding rectangle was stretched to be the same as the long side to ensure the final bounding box was square. At the same time, we only focus on ships larger than 20 pixels in length, and small ships with long wake are not labeled as positive samples, since their exact location is difficult for human eyes to distinguish. Our dataset includes images under different conditions, such as calm sea, clutter sea, moving target, cloud, and fog interference, which is appropriate for simulating the practical application.
# Concise Robust Control of the Nonlinear Ship in Course-Keeping Control
## 5 Simulation Experiments and Result Analysis
### Course-Tracking Experiment and Analysis
It can be seen that although the PID method based on backstepping can realize the course control, the rudder angle variance and maximum rudder angle are too large to deal with the disturbance of wind and waves effectively. The concise robust control algorithm can select the appropriate frequency and door-closing slope according to the requirement in suppressing the wave interference, which can effectively reduce the maximum rudder angle and rudder angle variance, hence reducing the rudder wear and energy consumption.
## 4 Simulations on the Trapping System
### Thermal Simulations
#### 4.2.2 Ions Loading from Paul Trap to Electro-Optical Trap  
Studying the temperature variation of the trap is fundamental for estimating possible changes of bow-tie cavity length. In particular, in the transition phase from the Paul trap to the electro-optical trap, a local heating source (dissipated RF power) is turned off, thus possibly causing instabilities and long thermal drifts to the bow-tie cavity and the trapping interference pattern. If the temperature of the system undergoes a decrease of about \(2\,\mathrm{K}\), the calculations show that the corresponding variation of the optical cavity length would lead to a shift of the central minimum of the interference pattern equal to the waist of the trapping laser beam (\(40\,\mathrm{\SIUnitSymbolMicro m}\)). Therefore, the trapped ions would be slowly displaced until reaching the end of the trapping region. For better stabilizing the system temperature and avoiding shifts of the confining interference pattern, a plate made of copper was attached to the titanium base plate of the trap. This element was thermally connected to outside the vacuum chamber through a thick feedthrough fixed on one of its extremities. In this way, the plate worked as a \"cold finger\" that helped with dissipating the heat produced in the trap and, possibly, actively controlling the trap temperature. To this end, a vacuum-compatible thermistor was placed on the plate to monitor the trap assembly temperature.
## 2 Materials and Methods
### Adsorption Experiment  
Pre-measured solid BPA particles were dissolved in deionized water to obtain BPA master solution (1 mmol/L). The master solution was further diluted with deionized water to create BPA solutions in different concentrations, adjusted the pH value of the solution by adding an appropriate amount of HCl or NaOH. Each experiment was conducted in a beaker containing 50 mL of BPA solution, with an adsorbent dosage of 10 mg (0.2 g/L). A magnetic stirrer was used to agitate the BPA solution and adsorbent at a rotational speed of 500 revolutions per minute, while the temperature for adsorption was kept constant at ambient conditions (25 \({}^{\circ}\)C). At predetermined time intervals, 2 mL of the suspension was extracted from the beaker and subsequently filtered utilizing a polyethersulfone (PES) membrane filter with a pore size of 0.22 \(\upmu\)m. The residual organic compound concentration was analyzed by ultraviolet-visible-near-infrared absorption spectroscopy (UV-vis-NIR spectroscopy, UV-3600, Shimadzu, Japan). The rate of BPA adsorption \(\alpha\) in the solution by SWCNTs/Fe\({}_{\alpha}\)O\({}_{y}\) as the adsorbent and the adsorption capacity \(q_{t}\) as the unit mass of the adsorbent at a certain time are written as follows.  
\[\alpha=\frac{C_{0}-C_{t}}{C_{t}}\times 100\%, \tag{1}\]  
\[q_{t}=\frac{(C_{0}-C_{t})V}{m}, \tag{2}\]  
where \(C_{0}\) is the concentration of BPA solution at the initial time (mmol/L); \(C_{t}\) is the concentration of BPA solution at time \(t\) (mmol/L); \(V\) is the volume (L) of the solution; \(m\) is the mass (g) of the adsorbent; \(\alpha\) is the adsorption rate; and \(q_{t}\) is the adsorption capacity (mg/g).
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Statistical Test** & **Value of Test** & **GL1** & **GL2** & **Fraction F** & **Probability Level** & **Decision 0.15** \\ \hline
**Current (A)** & & & & & & \\ \hline Roy’s Largest Root & 264.3 & 6 & 5 & 220.2 & 0.0 & Reject \\ \hline Tartrazine & 786.6 & 3 & 8 & 30.3 & 0.00010 & Reject \\ \hline Erythrosine & 446.2 & 3 & 8 & 25.0 & 0.00020 & Reject \\ \hline Brilliant Blue & 1483.7 & 3 & 8 & 28.2 & 0.00013 & Reject \\ \hline Potential & 140.8 & 3 & 8 & 366.9 & 0.0 & Reject \\ \hline Mass & 0.014 & 3 & 8 & 34.3 & 0.000065 & Reject \\ \hline pH & 0.063 & 3 & 8 & 6.0 & 0.019 & Reject \\ \hline
**UV Light (B)** & & & & & & \\ \hline Roy’s Largest Root & 5.3 & 6 & 3 & 2.63 & 0.23 & Accept \\ \hline Tartrazine & 69.3 & 1 & 8 & 2.67 & 0.14 & Reject \\ \hline Erythrosine & 148.8 & 1 & 8 & 8.35 & 0.020 & Reject \\ \hline Brilliant Blue & 46.6 & 1 & 8 & 0.89 & 0.37 & Accept \\ \hline Potential & 1.8 & 1 & 8 & 4.75 & 0.061 & Reject \\ \hline Mass & 0.000040 & 1 & 8 & 0.01 & 0.92 & Accept \\ \hline pH & 0.051756 & 1 & 8 & 4.87 & 0.058 & Reject \\ \hline \multicolumn{1}{l}{**Current-UV Light (AB)**} & & & & & \\ \hline Roy’s Largest Root & 6.7 & 6 & 5 & 5.59 & 0.039 & Reject \\ \hline Tartrazine & 78.5 & 3 & 8 & 3.02 & 0.094 & Reject \\ \hline Erythrosine & 37.0 & 3 & 8 & 2.07 & 0.18 & Accept \\ \hline Brilliant Blue & 174.7 & 3 & 8 & 3.32 & 0.078 & Reject \\ \hline Potential & 0.35 & 3 & 8 & 0.92 & 0.47 & Accept \\ \hline Mass & 0.00015 & 3 & 8 & 0.36 & 0.79 & Accept \\ \hline pH & 0.091 & 3 & 8 & 8.58 & 0.007 & Reject \\ \hline \end{tabular}
\end{table} 
Table 2: The MANOVA analysis results showcase the impact of imposed current, UV light, and their combination on the degradation of dyes, cell potential, recovered mass, and pH. The statistical examination reveals significant differences in the variance of these parameters under the influence of imposed current, UV light exposure, and their combined effect.