esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 2 Materials and Methods  
### Cotton Boll Disease Image Collection  
The data collection area is located at the Cotton Planting Base of Binzhou National Agricultural Science and Technology Park, Shandong Province, China (37\({}^{\circ}\)34\({}^{\prime}\)53\({}^{\prime\prime}\) N, 118\({}^{\circ}\)3\({}^{\prime}\)49.5\({}^{\prime\prime}\) E). The data were collected during August-September 2024, between 10:00 and 18:00. The image data of cotton bolt diseases was mainly obtained through three methods: using an Intel D435i (RGB-D) camera mounted on a cotton ball disease detection trolley (as shown in Figure 1), manual photography, and online collection.  
The RGB-D camera was mounted at a height of 0.3 to 0.6 m above ground level, capturing images at a resolution of 640 \(\times\) 640. Additionally, manual image collection was conducted under various lighting conditions and angles. During the manual collection, a Redmi K60 phone was used as the shooting device, capturing images vertically at a distance of 20 cm from the target. The image resolution was 4096 \(\times\) 2304. All images were saved in JPG format. To enhance the model's robustness and applicability, additional cotton bolt disease images were collected online. When collecting cotton bolt disease images from the Internet, the selection criteria prioritize high-resolution, blur-free images captured at appropriate angles under moderate lighting. These conditions ensure clear visualization of bolt morphology and pathological characteristics. For quality control, a two-step process is implemented. Initial manual screening eliminated images failing predefined standards, followed by expert validation of disease diagnoses to ensure semantic consistency across the dataset. A total of 7100 cotton bolt images were collected, including 4000 images captured by the disease detection system, 3000 images manually captured, and 100 images collected online. Some of the images are shown in Figure 2.  
Figure 1: Cotton bolt disease detection vehicle (1—GPS receiver; 2—aluminum profile bracket; 3—electronic compass; 4—fill light equipment; 5—mobile power supply; 6—Jetson TX2; 7—display screen; 8—RGB-D camera; 9—Songling SCOUT2.0 chassis).
# Properties of a Lightweight Fly Ash-Slag Alkali-Activated Concrete with Three Strength Grades
###### Abstract  
Lightweight alkali-activated concrete (LAAC) is a type of highly environmentally friendly concrete, which can provide the benefits of both alkali-activated material and lightweight concrete. The study aimed to investigate the influence of different water/solid (W/S) ratios on the properties of normal-weight/lightweight fly ash-slag alkali-activated concrete manufactured at ambient temperature. The relative performance of the alkali-activated concrete (AAC) mixes with limestone and sintered fly ash lightweight aggregates as the coarse aggregates was also compared to the conventional ordinary Portland cement (OPC) concrete mix in terms of their compressive stress-strain relationship, splitting tensile strength and fracture parameters. The morphologies and microstructure of the four types of interfacial transition zones (ITZs) were characterized by scanning electron microscopy (SEM). Results indicated that the AAC had a higher tensile strength, stress intensity factor, brittleness and lower elastic modulus than its cement counterpart. With the decrease in the W/S ratio, the density, compressive and tensile strength, ultrasonic pulse velocity, fracture energy, brittleness and elastic modulus of the AAC increase. However, the influence of the W/S ratio on the mechanical properties of the LAAC with lightweight porous aggregates was less than that of the normal-weight AAC. Predictive models of the splitting tensile strength, fracture energy and elastic modulus of the AAC were also suggested, which were similar to those of the OPC concrete. Furthermore, the microstructure investigation showed that no wall effect occurred in the ITZ of the AAC. The ITZ structure of the hardened AAC was also more compact and uniform than that of the OPC concrete.  
lightweight aggregate; ambient curing; water-solid ratio; fly ash-slag alkali-activated concrete; mechanical properties +
Footnote †: journal: Applied Sciences
# Impact of Gait-Synchronized Vibrotactile Sensory Feedback on Gait in Lower Limb Amputees
## 3 Results  
### Demographics  
Of the eleven included subjects, ten (91%) were male and one (9%) female. The average age of the examined cohort was 55.82 (\(\pm\)12.50) years, and the mean time since amputation was 6.91 (\(\pm\)8.35) years. Five subjects (45.5%) were amputated at the level of the lower leg (below-knee amputation, (BKA)), two subjects (18.1%) had a knee disarticulation (KD) and four subjects (36.4%) were amputated at the thigh level (above-knee amputation, (AKA)). The left side was predominantly affected (63.6%), while the right side and bilateral amputations accounted for 18.2% each. Subject 10, who had undergone amputation on both sides, tested the feedback system bilaterally, while subject 11, also a bilateral amputee with a below-knee amputation (BKA) and above-knee amputation (AKA), exclusively tested the system on the left side (AKA). Trauma was the primary cause of amputation in the study population (45.5%), followed by vascular disorders (27.2%), accompanied by diabetes mellitus (13.6%). Other causes for amputation included tumor disease (9.1%), infection (9.1%), and chronic pain syndrome (9.1%). The average level of mobility (Medicare Functional Classification Level (K-Level)) before the intervention was 2.6 (\(\pm\)0.81). Four patients (36.4%) underwent standard lower limb amputation, two (18.2%) received an Agonist-Antagonist Myoneural Interface (AMI), two (18.2%) underwent Targeted Muscle Reinnervation (TMR) and two (18.2%) have an osseeintegrated prosthesis (OI). One of the subjects (subject 5) had undergone a Targeted Sensory Reinnervation surgery (TSR) prior to the study, while the remaining 10 subjects had not undergone any TSR surgery [30] (see Table 2).
# FTGM: Fast Transformer-Based Global Matching for Particle Image Velocimetry
## 3 Approach
### Global Matching and Flow Propagation  
Considering two feature sets \(\overset{\wedge}{F_{1}}\) and \(\overset{\wedge}{F_{2}}\) that have been enhanced by the Transformer block, their corresponding pixels should exhibit a high degree of similarity. To achieve this, we construct a similarity matrix \(C\) for the feature maps, a process that entails computing the feature similarity between each pixel in \(F_{1}\) with respect to all pixels in \(F_{2}\).  
\[C(i,j,u,v)=\overset{\wedge}{F_{1}}(i,j)\cdot\overset{\wedge}{F_{2}}(u,v)/ \sqrt{d_{m}}, \tag{3}\]  
where \((i,j)\) and \((u,v)\) refer to locations in \(\overset{\wedge}{F_{1}}\) and \(\overset{\wedge}{F_{2}}\), and \(1/\sqrt{d_{m}}\) is a normalization factor to counteract the effect of excessively large values resulting from the dot-product operation.  
To identify the correspondence between pixels in the features, we employ a softmax operator [38] on the last two dimensions of \(C\) to obtain a matching distribution,  
\[M=softmax(C), \tag{4}\]  
and then use this distribution as weights to perform a weighted average with the initialized 2D pixel grid coordinates \(G\) to derive a predicted grid coordinate,  
\[\overset{\wedge}{G}=M\cdot G, \tag{5}\]  
and by computing the difference between the grid coordinates, the optical flow can be derived:  
\[V=\overset{\wedge}{G}-G, \tag{6}\]  
Finally, we employ a self-attention layer to propagate high-quality flow predictions from matched pixels to unmatched ones by measuring the self-similarity of features:  
\[\overset{\wedge}{V}=Attention(\overset{\wedge}{F_{1}},\overset{\wedge}{F_{1}} )\cdot V-G. \tag{7}\]
# Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling
## 4 Evaluation
### Discussion  
The proposed task scheduling and task migration algorithms are designed to carefully assign and migrate users' tuples in terms of reliability. To this end, we considered various criteria (location, signal strength, mobility speed, and mobility properties) for tuple migrations. Table 1 shows the comparison of cloud task management schemes. Recent studies have shown that the live migration feature in IoT and IIoT computing environments is encouraging, since it helps increase the task throughput and reduces the overall downtime.  
However, a few studies thoroughly analyzed and evaluated IoT and IIoT resource management schemes, including live migrations, in terms of scalability and task reliability. For this matter, we developed task scheduling and migration algorithms to improve the scalability and task reliability. Even when the number of users increases, the tuples lost are below 1% on average compared to the previous method.  
The downside of the proposed orchestration technique is that it generates more network traffic for live migration data. The network traffic and other performance metrics (downtime and tuples lost) are in a trade-off relationship. To improve the task reliability, we went for the overall performance metrics rather than the network traffic. The performance results show that our scheme introduces more network traffic than the previous method.  
To mitigate network overheads, one can use state-of-the-art network chipsets (e.g., WiFi 6 [802.11 ax]). Since WiFi 6 is capable of a maximum throughput speed of 9.6 Gbps, the network transmission time for live migration data can be reduced. We conjecture that as network technology evolves, the network overheads can be drastically reduced.
# Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment
## 4 Results and Discussion
### Model Calibration
Moreover, Figures 4 and 5 reveal that when calibration is performed with an image that was obtained near the peak flow (3 November), the uncertainty of modeled extents increases with respect to the advanced recession phase (4 November) where water levels were less liable to change but the flood extent showed an increasing variation for the different Manning coefficient values. The low roughness coefficient values tend to fit well with the observed flood map in the first part of the event, while the water tends to recede faster respect to SAR-derived hazard maps during the recession phase. This is to be expected since changes in roughness would have an immediate and local impact on flood dynamics. The model responds to changes in channel friction by altering the speed of the flood wave and flow velocities consequently impacting the timing of flood wave propagation. If channel roughness were set too high, the flood wave would be delayed and if set too low, the flood wave would be too advanced, which explains the better performance of the variable spatio-temporal Manning coefficients with respect to others also in terms of water-depth evaluation.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 3 Analysis of Resource Management in the MLSCS
### Influencing Factors Associated with Resource Management in the MLSCS
To address these issues, the NCC must uniformly schedule the available spectrum based on the global electromagnetic situation. This scheduling, combined with optimizing resource usage in satellites and planning service areas, aims to enhance the overall performance of the MLSCS.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 5 Experimental Results
### Comparison with Other Methods
#### 5.1.2 Comparisons on M\({}^{3}\)Fd  
In addition, we selected 12 state-of-the-art image fusion algorithms to demonstrate the superiority of our YOLO-MEDet in fusion-based target detection tasks. These algorithms include SLBAF [42], EAEF [43], DAMSDet [44], RGB-X [45], DenseFuse [46], FusionGAN [47], RFN [48], GANMcC [49], DDcGAN [50], MFEIF [51], U2 Fusion [52], and TarDAL [53]. The experiments were conducted using the M\({}^{3}\)FD dataset, where the following models were evaluated separately: (1) a mono-spectral YOLOv5 model trained using only visible or infrared data, (2) 12 fused YOLOv5 models trained with data generated by the fusion algorithms described above, and (3) our YOLO-MEDet model. Table 6 records the performance metrics for each model on the M3 FD test set.  
As shown in Table 6, YOLO-MEDet achieved a [EMAIL_ADDRESS] score of 84.2% and a [EMAIL_ADDRESS]:0.95 score of 52.4%, outperforming all other models in these comparisons. The high robustness of YOLO-MEDet in multispectral fusion and target detection has been further validated by its performance compared to the top-performing fusion detection model, TarDAL, where YOLO-MEDet improved the [EMAIL_ADDRESS] by 3.0% and the [EMAIL_ADDRESS]:0.95 by 4.4%. Additionally, YOLO-MEDet demonstrated significant performance advantages over pixel-level fusion detection models such as RFN and MFEIF. These experimental results highlight that YOLO-MEDet not only significantly improves upon current fusion detection models but also sets a new benchmark for multispectral target detection on the M\({}^{3}\)FD dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Data** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** \\ \hline YOLOv5 & RGB & 81.7 & 50.1 \\ YOLOv5 & IR & 79.8 & 49.2 \\ SLBAF [42] & RGB + IR & 78.9 & 44.3 \\ EAEF [43] & RGB + IR & 80.1 & - \\ DAMSDet [44] & RGB + IR & 80.2 & 52.9 \\ RGB-X [45] & RGB + IR & 81.5 & - \\ DenseFuse [46] + YOLOv5 & RGB + IR & 81.6 & 50.7 \\ FusionGAN [47] + YOLOv5 & RGB + IR & 81.5 & 50.6 \\ RFN [48] + YOLOv5 & RGB + IR & 82.2 & 51.5 \\ GANMcC [49] + YOLOv5 & RGB + IR & 81.9 & 50.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different methods on the M\({}^{3}\)FD dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Data** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** \\ \hline IV-CRN [41] & RGB + IR & 72.3 & - \\ GAFF [13] & RGB + IR & 72.9 & 37.5 \\ CAPTM [37] & RGB + IR & 73.2 & - \\ YOLO-MS [20] & RGB + IR & 75.2 & 38.3 \\ MRD-YOLO [38] & RGB + IR & 76.5 & 40.9 \\ CFT [39] & RGB + IR & 78.3 & 40.2 \\ CMAFF [19] & RGB + IR & 79.2 & - \\ CSAA [40] & RGB + IR & 79.2 & 41.4 \\ ICAFusion [25] & RGB + IR & 79.2 & 41.4 \\ Ours & RGB + IR & 80.1 & 38.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._
## 1 Introduction  
### Background  
Today, many enterprises (such as Google, Baidu, etc.) have launched map services (Google Maps, Baidu Maps) based on road networks and public transport networks to plan feasible routes between two points based on these services with the main aim to find the shortest path or the lowest cost to achieve the fastest or most provincial route [1]. However, in reality, tourism users not only pay attention to path length and travel cost but also usually consider the utility value of the path [2]. For example, they may want to take a quiet route to free themselves and enjoy their personal time, or some users may want to take a route with good quality scenery so that travel is not boring or burdensome. They can even stop and go, visit multiple favourite attractions along the way, and improve the overall travel experience. Therefore, the fastest or most economical route does not meet the diverse needs of tourism users [3].  
At present, there are many studies on scenic tourism route planning. Most researchers describe the problem as an OP (orientation problem) or AOP (arc orientation problem). The OP [4] is a combination of selecting nodes and determining the shortest path between the selected nodes. This can be seen as a combination of two classical problems: the knapsack problem and the travelling salesman problem. The difference between AOP and OP is that the weight of AOP is assigned to an arc, and the search object in the path search process is an arc rather than a point. AOP and OP are NP (Non-deterministic Polynomial) hard problems [5]. In addition, due to the large scale of the road network, finding the exact solutions to these two problems is time-consuming. Therefore, in practical applications, in order to meet the needs of a rapid response, heuristic search methods are often used to find their approximate solutions [6; 7; 8].
# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 3 Results and Discussion
### Performance Evaluation Using Different Classifiers  
In this experiment, we compared the performance of the proposed method against different classifiers. For this purpose, we designed different experiments to test the accuracy of shot classification on standard convolutional neural networks (CNNs), SVM, KNN, Centroid displacement-based K-Nearest Neighbors (CDNN) and ELM classifiers. We also employed different feature descriptors and classifiers for shot classification and compared the classification accuracy with our framework. More specifically, we used Local Binary Patterns (LBPs) and Local Tetra Patterns (LTRPs) descriptors for feature extraction and trained them on SVM and Extreme Learning Machine (ELM) classifiers. For SVM classifier we employed different kernel functions like linear, quadratic, multi-layer perception (MLP), and radial basis functions (RBF) to analyze the classification performance. The analysis and evaluation of these experiments are presented in detail in this section.
## 2 Materials and Methods
### Data
#### 2.2.3 Environmental Factors Data  
Physiological mechanism factors (Table 2) were derived from the aforementioned MODIS products. Environmental mechanisms factor (Table 2) data were analyzed using the Fluxnet 2015 datasets. These datasets were standardized following the methods established by Fluxnet. The half-hourly data within the Fluxnet 2015 dataset were gap-filled using the MDS (marginal distribution sampling) method [69]. The 8-day vapor pressure saturation  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Site\_ID** & **Latitude** & **Longitude** &
\begin{tabular}{c} **Köppen–Geiger** \\ **Classification** \\ \end{tabular} & **Biome Type** & **Data Period** & **Reference** \\ \hline IT-Lav & 45.9562 & 11.2813 & continental & ENF & 2007–2009 & [46] \\ CZ-BK1 & 49.5021 & 18.5369 & continental & ENF & 2012–2014 & [47] \\ CH-Dav & 46.8153 & 9.8559 & polar and alpine & ENF & 2007–2009 & [48] \\ PA-SPn & 9.3181 & \(-\)79.6346 & tropical & DBF & 2007–2008 & [49] \\ FR-Fon & 48.4764 & 2.7801 & temperate & DBF & 2007–2009 & [50] \\ DK-Sor & 55.4859 & 11.6446 & temperate & DBF & 2012–2014 & [51] \\ US-UMd & 45.5625 & \(-\)84.6975 & continental & DBF & 2007–2009 & [52] \\ US-UMB & 45.5598 & \(-\)84.7138 & continental & DBF & 2012–2014 & [53] \\ PA-SPs & 9.3138 & \(-\)79.6314 & tropical & GRA & 2007–2009 & [49] \\ US-SRG & 31.7894 & \(-\)110.8277 & dry & GRA & 2012–2014 & [54] \\ DE-RuR & 50.6219 & 6.3041 & temperate & GRA & 2012–2014 & [55] \\ US-IB2 & 41.8406 & \(-\)88.241 & continental & GRA & 2007–2009 & [56] \\ DE-Gri & 50.95 & 13.5126 & continental & GRA & 2012–2014 & [57] \\ CH-Fru & 47.1158 & 8.5378 & polar and alpine & GRA & 2007–2009 & [58] \\ RU-Sam & 72.3738 & 126.4958 & polar and alpine & GRA & 2012–2014 & [59] \\ FR-Gri & 48.8442 & 1.9519 & temperate & CORN & 2012–2014 & [60] \\ DE-Kli & 50.8931 & 13.5224 & continental & CORN & 2012–2014 & [57] \\ US-Myb & 38.0499 & \(-\)121.765 & temperate & WET & 2012–2014 & [61] \\ US-LA2 & 29.8587 & \(-\)90.2869 & temperate & WET & 2012–2013 & [62] \\ DE-Akm & 53.8662 & 13.6834 & continental & WET & 2012–2014 & [63] \\ GL-NuF & 64.1308 & \(-\)51.3861 & polar and alpine & WET & 2012–2014 & [64] \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._deficit (\"VPD_F\"), air temperature (\"TA_F\"), soil temperature (\"TS_F_MDS\"), precipitation (\"P_F\"), CO2 mole fraction (\"CO2_F_MDS\"), net radiation (\"NETRAD\"), and remote sensing data were utilized for studying the extent of the impact on the LUEmax.
## 2 Finite Element Modeling of the Cutting Head Cutting Rock  
### Establishment of the Model  
When a tunneling-bolting combined machine is in operation, the cutting head either moves forward, which is called a translational movement, or it rotates around an axis, which is called a rotational movement. During this process, the trajectory of the pick approximates a helical curve. After the rock is broken, the rock in front of the cutting head is arc-shaped. To study the force of the pick during the cutting process, the rock model was, therefore, designed as an arc. The three-dimensional model is shown in Figure 2.  
The cutter consists of a cemented carbide-tipped head and a tool base. The head is brazed to the tool base. As these two components are brazed together, they are also integrated into the model. By defining the picks, pick holder, and drum as rigid materials, the model can be simplified. This saves computation time and has minimal impact on the results. The model uses an eight-node SOLID164 unit and a default constant stress unit formula to define the type of contact between the tool and the soft rock (erosive contact).
## 4 Conclusions
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature** \begin{tabular}{l l} \(A_{\text{c}}\) & Minimum flow cross-sectional area [m\({}^{2}\)] \\ \(A_{0}\) & Area of heat exchanger [m\({}^{2}\)] \\ \(c_{p}\) & Specific heat at constant pressure [kJ/(kg\cdot K)] \\ \(D_{\text{c}}\) & Collar outer diameter [mm] \\ \(F_{\text{s}}\) & Fin pitch [mm] \\ f & Friction factor \\ \(h\) & Heat transfer coefficient [W/(m\({}^{2}\)-K)] \\ j & Colburn j-factor \\ \(L\) & Slit length [mm] \\ \(\dot{m}_{ac}\) & Minimum area flow rate [kg/s] \\ \(P\) & Total pumping power per row [W/mm] \\ \(P_{\text{l}}\) & Longitudinal tube pitch [mm] \\ \(P_{\text{l}}\) & Transverse tube pitch [mm] \\ \(Q\) & Total heat duty per row [W/mm] \\ Re & Reynolds number \\ St & Stanton number \\ \(V_{m}\) & Maximum velocity [m/s] \\ \(W\) & Slit width [mm] \\ \(H\) & Slit height [mm] \\ \(\Delta P\) & Pressure drop [Pa] \\ \(\Delta T\) & Temperature difference [K] \\
**Greek Symbols** \\ \(\mu\) & Viscosity [kg/(m\(\cdot\)s)] \\ \(\rho\) & Density [kg/m\({}^{3}\)] \\ \end{tabular}
## 3 Simulation Results and Experimental Validation
### Experiment Setup
The Hall current sensor selected in this experiment is CSM3000 LTF. The current measurement range of CSM3000 LTF is 0- \(\pm\) 4500 A, and the accuracy can reach \(\pm\)0.3%. The \(\pm\)66 kV DC GISA outside is equipped with a three-column current measurement junction port, which leads the three-column current out to the measurement device and connects to the oscilloscope. The power frequency voltage of 57 kV (RMS) is applied. Setting the frequency of the alternating current allows the varistors to accumulate heat and then achieve a stable temperature rise. The maximum temperature is 355 K after loading for 16 min, and then, the internal temperature of the arrester is gradually reduced to room temperature after standing for about 5 h. The temperature change of each part of the arrester is observed by the optical fiber temperature sensor inside and is displayed on the temperature indicator outside. The \(\pm\)66 kV DCGISA core is a three-column parallel structure with 13 ZnO varistors and four spacers per column. The ZnO varistors and spacers of the arrester are arranged with 12 temperature measurement points. According to the order of potential from high to low, each column ZnO varistor and spacer is numbered. Arabic numerals are used for ZnO varistors, and Roman numerals are used for spacers. Taking column 2 as an example, the  
Figure 7: Laboratory setup of the current distribution experiment. number is shown in Figure 8. The temperature measuring points of the \(\pm\)66 kV DCGISA prototype are 1-6, I-I, 2-5, II-I, 3-6, and III-I.