esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Analysis
### Example of Recognition Results  
A typical example of the results of this model for grapevine disease identification is shown in Figure 16, which can successfully identify diseases suffered by the fruit stalks of grapes, such as Botrytis cinerea of grapes; Can successfully identify diseases suffered by grapes, such as Botrytis cinerea, acid rot, black spot, powdery mildew, and anthracose; The possibility of successfully identifying diseases suffered by grape leaves, such as grape  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **mAP** & **ModelSize (M)** & **FPS** \\ \hline Faster RCNN & 94.57\% & 108 & 17.20 \\ SSD & 90.54\% & 100 & 20.12 \\ RetinaNet & 94.60\% & 140 & 16.40 \\ YOLOV5S & 95.99\% & 28.6 & 60.3 \\ Eff-B3-YOLOV3 & 98.60\% & 77.6 & 31.20 \\ GFCD-YOLOXS & 99.10\% & 37.1 & 48.57 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of different model checks for this paper’s dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Literature** & **Model** & **Precision** & **F1** & **Accuracy** \\ \hline Math [6] & Custom Models & 99.34\% & 99.44\% & 99.32\% \\ [PERSON] [7] & CNN + SVM & 99.08\% & 99.26\% & 99.25\% \\ [PERSON] [14] & GrapeNet & 86.29\% & 77.76\% & 79.05\% \\ Ours & GFCD-YOLOXS & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: On comparison of public datasets with other literature.  
leaf roll and grape downy mildew; Multiple targets of the same species to be detected, such as grape leaf roll disease, healthy grapes, can be successfully identified.  
Figure 15: Identification results of different models. (**a**) Recognition effect of the Faster RCNN model; (**b**) recognition effect of the SSD model; (**c**) recognition effect of the RetinaNet model; (**d**) recognition effect of the YOLOVSS model; (**e**) recognition effect of the Eff-B3-YOLOV3 model; and (**f**) recognition effect of GFD-YOLOXS model.
## 1 Introduction
The piston-cylinder assembly is one of the engine's key subsystems that determine the performances of vibration, noise and, hence, service lifespan. In earlier research, more than 80% of the total engine noise and vibration has been shown to stem from cylinder liners' dynamic response to combustion in the cylinder and the piston slaps. To fully understand the influences of fuels' combustion characteristics on engine vibrational responses, it is essential to make a thorough and detailed study of the dynamic behavior of cylinder liners and their generation mechanism. [PERSON] [9] and [PERSON] et al. [10] studied the dynamic response of cylinder liners by establishing the mathematical dynamic model and proposed preliminary predictions about the occurrence timing and observable numbers of piston slaps on liners. Nonetheless, due to the limited degree of freedom, the difficulty of defining boundary conditions and lack of structural characteristics, their predicted occurrence timing and observable number are not in good agreement with the experimental results. In order to obtain more accurate and reliable simulation results, some researchers have investigated the liner dynamics by using the finite element analysis technique. Structural analysis of cylinder liners has been reported using finite element analysis (FEA) [11; 12; 13]. The static deformation, forced response analysis and frequency response analysis of cylinder liners were undertaken based on finite element models, for impact force calculation and liner response analysis [13]. However, the majority of these finite element method (FEM)-based studies were performed based on a quasi-static model in which the modal characteristics and reaction upon the piston of cylinder liners were neglected. Obviously, such models may not be able to reflect the phenomena of actual contacts between piston and cylinder due to invertible elastic deformations under dynamic forces. In this paper, to gain an in-depth understanding of the dynamic responses of cylinder liners to in-cylinder combustion shocks and piston slaps, a finite element model is developed by taking into account structural modal characteristics, nonlinearities of assembly constraints and time-varying exciting forces. Based on the established dynamic model, the response characteristic of liner structure and its relationship with the combustion properties of biodiesel have been investigated. The paper is organized into five sections in addressing the subject. Section 2 details the development of the FEM-based model, which includes model validation against experimental measurements from a single-cylinder diesel engine. Section 3 investigates the dynamic responses of the cylinder liner to different excitation sources and fuel types. Section 4 introduces the design and construction of the experiment system. Section 5 examines the correlation between the numerical and experimental results and evaluates the liner's responses to the combustion of biodiesel and Section 6 summarizes the conclusions obtained through this initial work.
# UAV-Based Airport Lights Inspection Without GNSS Positioning Support
## 6 Conclusions  
The idea of UAV flights after daylight proves advantageous but a careful setup of the camera sensitivity is needed in order to retain the characteristic colours of lights. Background noise, that is, images of objects that do not make up the airport light system, is eliminated, significantly reducing the effort for extracting lights.  
The proposed procedure can be applied for the inspection of ALS and runway lights. The light-tracking operation enables the effective inspection of large sets of lights. Important for proper calculations is the availability of airport geodesic documentation. The precision of this documentation conditions the operations of determining camera positions. Although the tracking accuracy may be worse than in the case of PAPI lights, positioning must enable lights placed apart to be distinguished similarly to PAPI lights.  
UAV flight practice shows that the efficient execution of procedure steps requires experienced operators; a way to evade this demand is to automate the inspection. The optimization and automation of the inspection is an important goal for future studies.
## 3 Experimental Results and Discussion
### Strain Responses of Frame
The maximum strains of the upper and lower flanges at the ends of the bottom and top beams, subjected to the different intensity levels, are shown in Figure 23. The discrepancy of strains between the bottom and top beams was not remarkable when PGA was less than 0.7 g. However, the maximum strain at the bottom beam was slightly higher than that at the top beam after PGA = 0.7 g, because the slopes of the strain curves at the bottom beam increased sharply. This reveals that the strength requirement of the bottom beam, in the practical design of the structure, should be given more attention compared to the top beam under the higher intensity level. In addition, the maximum strains at the upper and lower flanges of beams at each story were approximately equivalent and symmetrical, and this regularity of strain distribution was almost identical for the three seismic waves. Figure 21: Time-history curves of strains at the bottom of the column under PGA of 1.0 g. (**a**) El Centro wave; (**b**) Kobe wave; (**c**) Taft wave. Figure 23: Maximum strains of the flange at the end of the frame beam subjected to the different intensity levels. (**a**) El Centro wave; (**b**) Kobe wave; (**c**) Taft wave. Figure 22: Maximum strains of the frame column subjected to the different intensity levels. (**a**) El Centro wave; (**b**) Kobe wave; (**c**) Taft wave. It could be seen from Figures 22 and 23 that the maximum strains in the frame column and beam were, respectively, 550 and 363 \(\mu\epsilon\) under the final loading PGA level of 1.6 g, approximately to rare earthquake intensity 9, which were far less than the measured yield strain of 1140 \(\mu\epsilon\) obtained by the material test for the utilized Q235 steel. This shows that no yielding or failure occurs in the frame column and beam, and the main structural members continue to remain elastic. This phenomenon can be attributed to the fact that most of the hysteretic input energy is significantly dissipated by the LYP steel energy absorbers, thus reducing the force acting on the parent structure induced by the earthquake or wind loads.
## 3 Materials and Methodology  
### Data Description and Sources  
This study relied entirely on freely available and accessible secondary data sources to ensure reproducibility. To evaluate the vulnerability of the transportation network in the CMC, we utilized the geographical road network (n = 1210) as the primary dataset, which was obtained from the Survey Department of Sri Lanka (SLSD). The dataset was then transformed into a topological network following the method described in this study. To analyze the impact of traffic flow, Annual Average Daily Traffic (AADT) data (n = 1210) for the study area were obtained from the 'GIS database of CoMTrans Urban Transport Master Plan-JICA' [47]. This database, which is the latest available for CMA, was used to develop a modeling framework to assess the impact of critical-link attacks on traffic flow changes. The CoMTrans study employed the coverage count method to collect traffic volume data over one week, capturing detailed hourly and daily variations. The CoMTrans report noted that while hourly and daily traffic variations are significant in CMA, seasonal variations are not [47]. Additionally, the study obtained GIS data regarding the road network, road type, average travel time, and road capacity from the same database.
## 4 Discussion
### Strengths and Limitations  
A key strength of this work was that the study employed a modified Delphi consensus process, which is a standard method of developing checklists or similar tools and has been widely used for designing AMS programmes in hospitals. We engaged local stakeholders, including senior management, frontline healthcare professionals, and public health specialists, in the consensus process, thus increasing elements of co-creation and a subsequent sense of ownership for the materials. This also meant that the checklist modifications were context-specific and relevant to health institutions. The representatives were recruitedbased on their level of expertise, level of interest, heterogeneity, accessibility, and the intended size of the panel, as recommended by the existing literature [38; 39]. Furthermore, our selection strongly considered representativeness as it engaged stakeholders across all partnership sites in all countries.  
This study, which commenced prior to the publication of the WHO AMS toolkit for LMICs, provides an additional step toward increasing the suitability and acceptance of standardised AMS tools in LMIC settings in several different ways:  
1. Our narrow focus on Sub-Saharan Africa (specifically Tanzania, Zambia, Uganda, and Ghana) allows for further local adaptation of the materials.
2. Our unique methodological approach uses elements of co-creation through the strong involvement of local hospital representatives during a Delphi consensus procedure.
3. We extended the number of checklist items to capture the more nuanced differences in the AMS elements. Additionally, we incorporate open-ended questions within the AMS checklist to allow for more reporting flexibility.
4. We provide an initial evaluation of the checklist's effectiveness by using it to measure the outcomes of an AMS intervention programme.  
The Delphi process was limited by not having an opportunity for face-to-face discussions about specific items. The small number of study sites could be considered a limitation; however, the similarity of the results across multiple countries suggests the results are transferable and that the approach can be implemented within other countries.
## 2 Materials and Methods
### Data Analyses  
A two-way repeated measures ANOVA was conducted to test the effect of operation method (three levels: \(\mathbf{M_{LS}}\), \(\mathbf{M_{BP}}\), and \(\mathbf{M_{DR}}\)) and in-vehicle location (five levels: \(\mathbf{L_{IWS}}\), \(\mathbf{L_{RWS}}\), \(\mathbf{L_{UCF}}\), \(\mathbf{L_{LCF}}\), and \(\mathbf{L_{CC}}\)) on each of the nine evaluation measures. The main and interaction effects of the independent variables on the dependent measures (accuracy, efficiency, rapidity, learnability, intuitiveness, safety, preference, memorability, and satisfaction) were examined. In cases of a significant interaction effect between the independent variables, the simple main effects for each independent variable were investigated. [PERSON]'s test was used to test whether the data met the assumption of sphericity. In cases where sphericity was violated, the degrees of freedom were corrected; the Greenhouse-Geisser correction was used when the Greenhouse-Geisser estimate of sphericity (\(\epsilon\)) was less than 0.75; else, the Huynh-Feldt correction was used [34]. In cases where an ANOVA identified a significant effect, post hoc multiple comparisons with Bonferroni corrections were conducted to determine the pairs with significant mean differences. The statistical analyses were conducted at an alpha level of 0.05 using IBM SPSS Statistics 26.0.  
Regarding the responses collected during the freehand design session (i.e., the pictures created by the respondents), an affinity diagram analysis was carried out on the group pictures with similar design characteristics. The frequency associated with each group, indicating the number of respondents who drew similar pictures, was recorded.
# Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
## 2 Materials and Methods
### Hyperspectral Data Acquisition  
Hyperspectral data of the wheat canopy were acquired using the FieldSpec(r)4 HiRes portable object spectrometer manufactured by ASD (Analytica Spectra Devices, Inc., Longmont, CO, USA) from the United States [20]. The instrument collects the spectral information in a view of 25\({}^{\circ}\) at the 350 nm\(\rightarrow\)2500 nm band [21]. The sampling interval is 1.4 nm in the band range of 350 nm to 1000 nm, and the sampling interval is 2 nm in the band range of 1000 nm to 2500 nm, and the spectral resolution is 3 nm [22]. Spectral reflectance data of the wheat canopy were covered over the flag-picking period (2 May), flowering stage (12 May), grain-filling stage (17 May), and maturity period (24 May) of wheat, including the key growth period of wheat. The experiment was carried out between 11:00 local time (LT) and 12:00 LT in sunny or cloudy weather. During the collection, the cloud coverage was less than 20%. The hyperspectral detector was in a vertical state, and the heights of the detector and the canopy were approximately 30 cm for the measurement of each study area. A 40 cm \(\times\) 40 cm whiteboard was utilized to reduce the influence of natural light on the spectrum [23]. We measured the spectral curve 10 times for each cell. After measurements, data were processed using View Spec Pro 6.0 software to extract the spectral reflectance, then the spectral reflectance value of each cell is averaged over the 10 spectral curves.  
Figure 1: Schematic diagram of study area and study field.
## 2 Materials and Methods and Results
### Phase 4: Live Trials
#### 2.3.2 Data Processing and Statistical Analysis  
Each pad had two load cells bolted to it, and each participant pushed on two pads (one for each shoulder), therefore four load cells were used in all trials. Voltages from each load cell were recorded using a custom Labview (National Instruments Corporation, Austin, TX, USA) program [16]; the voltage from each of the four load cells were summed in the data acquisition program to create a total voltage value. For each trial, we identified the peak voltage and the average voltage during the push as our dependent variables. The initiation and cessation of the push were determined visually; manual identification has been shown to be reliable in other multi-joint isometric tests [24].  
Descriptive statistics were generated for participants' age, sex, height, and body mass. For our dependent variables, peak voltage and average voltage, we used multiple methods to assess inter-trial reliability using a spreadsheet developed by [PERSON] [25], which calculates Intraclass Correlation Coefficients (3,1), and examined the change in the means between trials. Following the recommendations, we report both the raw reliability statistics as well as the log-transformed outcomes in the next section.
## Graphene Thermal Properties: Applications in Thermal Management and Energy Storage  
**[PERSON] 1,2, [PERSON] 1,3 and [PERSON] 1,2,*  
Nano-Device Laboratory (NDL) and Phonon Optimized Engineered Materials (POEM) Center, Department of Electrical Engineering and Materials Science and Engineering Program, University of California--Riverside, Riverside, CA 92521, USA; E-Mails: jrent002(@ucr.edu (J.D.R.); [EMAIL_ADDRESS] (D.L.N.)  
Quantum Seed LLC, 1190 Columbia Avenue, Riverside, CA 92507, USA  
E. Pokatilov Laboratory of Physics and Engineering of Nanomaterials, Department of Physics and Engineering, Moldova State University, Chisinau MD-2009, Moldova  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-951-827-2351.  
External Editor: [PERSON]  
_Received: 3 March 2014; in revised form: 30 September 2014 / Accepted: 13 November 2014 / Published: 28 November 2014_  
**Abstract:** We review the thermal properties of graphene, few-layer graphene and graphene nanoribbons, and discuss practical applications of graphene in thermal management and energy storage. The first part of the review describes the state-of-the-art in the graphene thermal field focusing on recently reported experimental and theoretical data for heat conduction in graphene and graphene nanoribbons. The effects of the sample size, shape, quality, strain distribution, isotope composition, and point-defect concentration are included in the summary. The second part of the review outlines thermal properties of graphene-enhanced phase change materials used in energy storage. It is shown that the use of liquid-phase-exfoliated graphene as filler material in phase change materials is promising for thermal management of high-power-density battery parks. The reported experimental and modeling results indicate that graphene has the potential to outperform metal nanoparticles, carbon nanotubes, and other carbon allotropes as filler in thermal management materials.  
**Keywords:** graphene; thermal conductivity; graphene applications; thermal phase change materials
## 2 Materials and Methods
### Hardware Design for DYU Air Box
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensor Types** & **Sensing Parameter** & **Measuring Range** & **Accuracy** & **Temperature/Humidity** & **Warning Range** \\ \hline BH1750 & Brightness & 1–65,535 lux & 1 lux & \(-\)40–85 \({}^{\circ}\)C/0–99\% & \(<\)10 lux or \(>\)10,000 lux \\  & Temperature & \(-\)40–85 \({}^{\circ}\)C & \(\pm\)0.5 \({}^{\circ}\)C & & \(<\)0 \({}^{\circ}\)C or \(>\)40 \({}^{\circ}\)C \\ BME680 & Humidity & 0–100\% & \(\pm\)3\% & \(-\)40–85 \({}^{\circ}\)C/0–99\% & \(<\)20 or \(>\)99 \\  & IAQ index & 0–500 & 1 & & \(\geq\)151 \\ SCD30 & CO\({}_{2}\) concentration & 400–10,000 ppm & \(\pm\)50 ppm & 0–50 \({}^{\circ}\)C/0–99\% & \(\leq\)450 ppm \\ PMS7003T & PM1.0 & 0–500 \(\mu\)g/m\({}^{3}\) & 1 ug/m\({}^{3}\) & \(-\)10–60 \({}^{\circ}\)C/0–99\% & \(\geq\)35 ug/m\({}^{3}\) \\  & PM2.5 & 0–500 \(\mu\)g/m\({}^{3}\) & 1 ug/m\({}^{3}\) & \(\geq\)10 ug/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main specifications and warning ranges of sensors. Figure 3: Schematics of heterogeneous sensors: (**a**) BME680 air quality sensor; (**b**) PMS7003T dust sensor; (**c**) SCD30 CO\({}_{2}\) gas sensor; (**d**) BH1750 light sensor. The TTGO ESP32 module was adopted as an MCU, Wi-Fi/Bluetooth communication platform and a local display device in charge of receiving and displaying the sensing data from BH1750, BME680 air quality, and SCD30 CO\({}_{2}\) gas sensors as well as the PMS7003T dust sensor and forwarding the observation data to the Thing5 peak IoT API through a neighboring 802.1X campus network. The pin assignment of the Wi-Fi module for the sensors is well defined in the datasheet. The programmable ESP32-based LX6 microcontroller was designed to feature the functions of both automatic warning and line notification, which could be implemented by the code programming as explained later. The main specifications of the BH1875 light sensor, BME680 and SCD30 CO\({}_{2}\) gas sensors, and PMS7003T dust sensors for the proposed wireless environment-monitoring system are listed in Table 2. Due to the limit of operation conditions for the SCD30 CO\({}_{2}\) sensor, the operating temperature for the proposed multi-sensor sensing module is suggested in the range of 0-50 \({}^{\circ}\)C. The suggested warning ranges of corresponding sensors are also predefined and tabulated in Table 2. The warning ranges of the light, temperature, humidity, dust, and CO\({}_{2}\) concentration levels as well as AQI parameter are listed in Table 2. Table 3 tabulates the AQI classification available in the datasheet of BME680 [22], which is in compliance with the AQI Code of Taiwan [26]. For the DYU-802.1X configuration, the authenticated SSID and password are checked in advance and are coded in the code programming later.