esa-sceva/satcom-chunk-collection huggingface dataset:
# Generating Image Descriptions of Rice Diseases and Pests Based on DeiT Feature Encoder
## 4 Results and Analysis
### Image Description Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & **CIDEr** & **ROUGE-L** & **METEOR** \\ \hline AlexNet & 63.6 & 47.0 & 37.4 & 30.8 & 103.9 & 51.2 & 29.6 \\ VGG16 & 63.3 & 47.5 & 38.4 & 32.1 & 107.5 & 51.9 & 29.9 \\ ResNet101 & 62.9 & 45.8 & 36.4 & 29.9 & 104.3 & 51.4 & 29.5 \\ ResNet18 & 62.6 & 47.2 & 38.0 & 31.7 & 110.7 & 52.7 & 31.0 \\ InceptionV3 & 63.8 & 47.1 & 37.8 & 31.5 & 109.2 & 52.0 & 30.2 \\ MobileNetV2 & 65.0 & 47.9 & 38.0 & 31.0 & 107.9 & 52.2 & 30.1 \\ DeiT & 68.8 & 50.8 & 40.0 & 32.2 & 112.0 & 54.1 & 31.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation results on the actual field images. \begin{table}
\begin{tabular}{c c} \hline \hline
**Image** & **Predicted Results** \\ \hline
1. Rice bacterial streak & 1. Rice is affected by bacterial streak disease. The leaves of rice show yellow stripes. \\
2. & Rice is affected by bacterial streak disease, the disease is serious most of rice leaf surface whole leaf is witnessed and yellow. \\
3. & Rice is affected by bacterial stripe disease. \\ \hline
1. Rice is infected with bacterial blight and the leaves of rice show yellow pinstripes. \\
2. Rice is affected by bacterial blight, which has obvious yellow stripe in the middle of the leaves. \\
3. & Rice is infected with bacterial blight and has yellow pinstripes on its leaves. \\
2. Rice suffers from rice blast, irregular gray and white spot. \\
3. & Rice is afflicted with blast, the plant has a pale yellow gray-white spots. \\ \hline
3. Rice blast & 1. The body of the planthopper is semicircular. \\
2. The body of the planthopper is semicircular. \\
3. The planthopper has a pair of antennae. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction results of the image description models based on the DeiT feature encoder.
## 3 Results  
### Evaluation of Extracted Total Imperviousness  
Landsat derived imperviousness by LSMA approach was compared to corresponding Google Earth data (Figure 4) in 300 samples. Results showed good agreement between the estimated and the observed data (Figure 4) with a determination coefficient (R\({}^{2}\)) of 0.87.  
Figure 3: (**a**) Total imperviousness at Landsat pixel; (**b**) residential polygon; (**c**) residential imperviousness at Landsat pixel; (**d**) non-residential imperviousness at Landsat pixel; (**e**) residential imperviousness at suburb scale; (**f**) non-residential imperviousness at suburb scale. Numbers are in percentage (%). Figures has the same scale as Figure 1.  
RMSE of 9% and MAE of 7% also demonstrated a reliable accuracy for imperviousness estimated from Landsat 8 OLI/TIRS by the fully constrained LSMA approach in this study. The LSMA approach, however, overestimated and underestimated imperviousness in samples with imperviousness less than 20% and higher than 80% (Figure 4).  
Overestimation mainly occurs in samples with a high fraction of bare soil. This is mainly because bare soil is so similar to some impervious surface materials in terms of spectral behavior [34]. Evaluation indicators from this study were comparable with previous results (Table 1). For example, RMSE from this study is equal to or better than previous results [48; 49]. Moreover, MAE is lower than that from study conducted by [PERSON] and [PERSON] [10] and [PERSON] et al. [50]. This comparison indicates that the magnitude of the error is acceptable for this case study (SEQ region) which is a complex urban-rural frontier in Australia.
# Point-Sim: A Lightweight Network for 3D Point Cloud Classification
## 2 Related Work
### Point-Based Methods
#### 2.3.2 Attention-Based Methods  
SENet [20] introduces an efficient and lightweight gating mechanism that explicitly constructs correlations between channels. This consideration stems from the acknowledgment that pixels carry varying degrees of importance across different channels. These importance weights are then leveraged to amplify useful features while suppressing less relevant ones. CBAM [21] derives attention mappings separately along two dimensions, channel and spatial, within the feature mapping. Subsequently, these attention mappings are applied to the input for adaptive feature refinement of the feature map. With the demonstrated success of self-attention and transformer mechanisms in natural language understanding [22], there has been a proliferation of efforts in computer vision to substitute convolutional layers with self-attention layers. However, despite its accomplishments, self-attention incurs computational costs that scale quadratically with the size of the input image. PAT [23] employs a self-attention-like mechanism to capture correlation information between points and extract the most salient global features via Gumbel downsampling. Transformer [24] devises a point transformer layer and builds a residual point transformer block around it, thus enabling information exchange between local feature vectors and the generation of new feature vectors for all points. PCT [25] encodes input coordinates into the feature space to generate features and conducts feature learning through the offset attention mechanism. PoinTr [26] processes the point cloud into a series of point proxies, which represent features of local areas within the point cloud. These proxy points encapsulate neighborhood information, which is then inputted into a transformer for further processing. Subsequently, an encoder-decoder architecture is employed to accomplish the point cloud completion task.
###### Abstract  
The development of an effective evaluation method suitable for loess-tunnel excavation is necessary to avoid the collapse accidents caused by tunnel excavation and any secondary disasters. Although the Fenner formulas and the modified Fenner formulas are widely used in tunnel engineering, a defect still exists in these formulas because the Mohr-Coulomb (M-C) criterion exaggerates the tensile strength of the surrounding rock of the loess tunnel. A newly modified Fenner formula was derived based on joint strength to overcome this deficiency. First, the expressions of stress and the radius of the plastic zone of the surrounding rock of the loess tunnel and the expressions of radial displacement were derived based on the stress-equilibrium equation of the axisymmetric plane and the joint strength. Then, the difference in the modified Fenner formulas based on the two kinds of strength criteria for the loess tunnel were compared. The results showed that the radius of the plastic zone and the radial displacement of the loess tunnel determined by the modified Fenner formula based on joint strength were larger than those determined by the modified Fenner formula based on M-C strength. However, the plastic stress of the plastic zone determined by the modified Fenner formula based on joint strength was smaller. The comparative analysis reveals that the modified Fenner formula based on joint strength can evaluate the stress and plastic-displacement field of the surrounding rock of a loess tunnel more reasonably.  
loess tunnel; modified Fenner formula; joint strength; stress; radius of plastic zone
## 2 Methodology  
System development was achieved through an artificial vision system that analyzes Earth-sky dynamics. The prototype was designed with the intention of capturing sky dome images for detecting the position of the Sun and analyzing sky dynamics, avoiding the use of high-cost fish eye lenses, as well as expanding CMOS sensor life by avoiding overexposure to the sunlight. A robust and automatic methodology based on a digital image processing algorithm capable of identifying the position of the Sun in the images throughout the day and under different conditions has been developed. Having this information, the system will be able to estimate DHI and DNI values.
### Preparation of Nanostructured Thin Films and Sensor Fabrication  
PBM was performed using a Fritsch Pulverisette 7 planetary micro mill (premium line) (silicon nitride grinding jars, 80 mL capacity; zirconia grinding beads, 2 mm in diameter). In a typical experiment, ZnO powder (-7 g) was ground in approximately 10 mL of either EG or DI water solvent (a.k.a. colloidal grinding) with approximately 100 g of beads. The grinding speed and time were varied between 200 and 1000 rpm and 10 min and 60 min (in cycles of 5 min grinding, 5 min resting), respectively. After the completion of each grinding trial, a syringe and stainless-steel mesh were used to extract and separate the resulting PBM nanoink suspension from the grinding beads. Several films of each of the ground ZnO trials were prepared for characterization via doctor blading on substrates as follows: A few \(\mu\)L of ZnO nanoink was used to coat the surface by sweeping the blade across the substrate (masked with a single layer of Scotch tape) along with the suspension (blade angle 20-30\({}^{\circ}\)). The film thickness for all prepared sensors was approximately 60 \(\upmu\)m (based on nominal scotch tape thickness). The coatings were dried at -100 \({}^{\circ}\)C on a hotplate for approximately 5 min.  
For thin film surface imaging, a Nanonics MultiView 1000 atomic force microscope (AFM) with Olympus BXFM optical microscope was employed. Additionally, Raman scattering measurements of thin film samples were performed using a Renishaw inVia Raman microscope. Raman spectra were collected using 632.8 nm HeNe laser line under ambient conditions. Photoluminescence (PL) spectra were acquired using an Olympus FV1000 confocal laser scanning microscope with solid state laser line of wavelength 405 nm. Scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDX) were performed using a Hitachi S-2600. The thin film porosity and particle size analysis was performed using ImageJ (version 1.46r) [75].  
To create the gas sensor devices, two electrical contacts to the resulting films were made using silver paint and copper tape (see Figure 1a), followed by drying in a mechanical oven for 10-15 min at 75 \({}^{\circ}\)C.  
Figure 1: (**a**) Schematic diagram of thin film gas sensor devices: copper tape on glass substrates containing doctor bladed ZnO film served as the electrode contacts and silver paint were used for connecting sensor film to electrodes. (**b**) Illustration of the gas sensing apparatus used. The sensor was placed inside a quartz test chamber connected to gas cylinders (carrier and/or target gas) through MFCs. Cu wires were connected to the sample via a hermetic feedthrough for electrical measurements. The electrical response of the sensor was monitored using a precision source-measure system.
## 1 Introduction
### Specific Problem  
This study explores a machine learning approach to detect alcohol intoxication from gait data from smartphone sensors. This machine learning model could be the basis for a system that passively monitors users, detects intoxication, and triggers safety interventions such as notifications, vehicle disabling, or ride-hailing, preventing alcohol impairment-related mishaps such as alcohol DUI, injuries, and even death. While neural network approaches have emerged, due to the significant challenges with collecting intoxicated gait data, gait datasets are often too small to utilize such approaches. Challenges in collecting intoxicated gait data include difficulty obtaining IRB approval due to ethical concerns, difficulty in participant recruitment, high-level risks associated with such controlled experiments, and difficulty and expenses associated with acquiring necessary equipment such as breathalyzers. On such small datasets, traditional machine learning classification is preferred to avoid overfitting, and a comprehensive set of features have been proposed by prior works. Prior work by [PERSON] et al. [39] achieved 70% classification accuracy using traditional machine learning algorithms on 11 time- and frequency-domain features. However, until now, no work has systematically evaluated the performance of various categories of gait features for the task of alcohol intoxication detection using traditional machine learning algorithms.
# Thermostable D-Allulose 3-Epimerase for Long-Term Food-Compatible Continuous Production Systems
## 2 Materials and Methods
### Biological Characteristics of TcDAEase  
To simulate native conditions relevant to food-compatible applications, no metal chelation pretreatment was employed. The optimal temperature was determined by performing assays at 50-90 \({}^{\circ}\)C in 10 \({}^{\circ}\)C increments, while thermostability was evaluated by pre-incubating enzyme samples (without substrate) at 60, 70, and 80 \({}^{\circ}\)C in phosphate buffer, followed by activity measurement at intervals up to 8 h.  
The pH dependence was assessed using five buffer systems (50 mM): citrate-phosphate (pH 4.0-5.0), phosphate (pH 6.0-8.0), glycine-NaOH (pH 9.0-10.0), carbonate (pH 11.0), and KCl-NaOH (pH 12.0-13.0). Enzyme pH stability was tested by 6 h incubation at 60 \({}^{\circ}\)C across these buffers.  
To evaluate metal ion effects, divalent cations (1 mM final concentration; Fe\({}^{2+}\), Co\({}^{2+}\), Ni\({}^{2+}\), Cu\({}^{2+}\), Zn\({}^{2+}\), Ca\({}^{2+}\), Mg\({}^{2+}\), Mn\({}^{2+}\)) were added individually. Additionally, the Mg\({}^{2+}\) dose dependence was assessed at 0-25 mM concentrations to determine its catalytic enhancement profile.
Sensors_**2013**, _13_, 8303-8330; doi:10.3390/s130708303  
_Series of_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
Energy Consumption and Control Response Evaluations of AODV Routing in WSANs for Building-Temperature Control  
**[PERSON] *, [PERSON] and [PERSON]**  
Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University,  
15 Kanjanavanich Road, Kho Hong, Hat-yai, Songkhla 90112, Thailand;  
E-Mails: [EMAIL_ADDRESS] (W.T.); [EMAIL_ADDRESS] (C.L.)  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]RESS].  
_Received: 23 May 2013; in revised form: 12 June 2013 / Accepted: 18 June 2013 / Published: 27 June 2013_  
**Abstract:** The main objective of this paper is to investigate the effects of routing protocols on wireless sensor and actuator networks (WSANs), focusing on the control system response and the energy consumption of nodes in a network. We demonstrate that routing algorithms designed without considering the relationship between communication and control cannot be appropriately used in wireless networked control applications. For this purpose, an _ad-hoc_ on-demand distance vector (AODV) routing, an IEEE 802.15.4, and a building-temperature control system are employed for this exploration. The findings from our scenarios show that the AODV routing can select a path with a high traffic load for data transmission. It takes a long time before deciding to change a new route although it experiences the unsuccessful transmission of packets. As a result, the desirable control target cannot be achieved in time, and nodes consume more energy due to frequent packet collisions and retransmissions. Consequently, we propose a simple routing solution to alleviate these research problems by modifying the original AODV routing protocol. The delay-threshold is considered to avoid any congested connection during routing procedures. The simulation results demonstrate that our solution can be appropriately applied in WSANs. Both the energy consumption and the control system response are improved.  
**Keywords:** WSANs; routing; desirable control target; energy consumption; building temperature control system
# From Google Earth Studio to Hologram: A Pipeline for Architectural Visualization
## 2 Materials and Methods
### Model Selection in Earth Studio  
The first step involved the selection of appropriate 3D landmarks from Google Earth Studio's global library of photogrammetrically reconstructed urban environments. Several selection criteria were established to ensure optimal results for holographic reconstruction. The chosen construction needed to exhibit photorealistic 3D fidelity with accurate geometry and detailed surface textures, as the quality of Earth Studio representations varied depending on the geographic region. Additionally, geometric completeness was essential; the architectural volume of each subject had to be fully modeled without occluded facades or missing sections. Finally, a clear line of sight was required; selected buildings needed to be either taller than the surrounding cityscape or situated in open areas to allow unobstructed perspective capture from a wide range of viewing angles.
# Joint Balanced Routing and Energy Harvesting Strategy for Maximizing Network Lifetime in WSNs
## 3 Problem Statement and Motivation
### Problem Statement
In addition, the battery capacity of node 32 was first exhausted because the number of packets forwarded by the primary forwarding node adjacent to the data sink presents an uneven load distribution. The earliest exhausted node also causes the sub-area network to be unserved, thereby reducing the lifetime of the entire network. As a result, there are 35 nodes with good battery conditions, but these nodes cannot send data packets to the data sink through the primary forwarding node 32. As a result, the energy resource of the other nodes is not fully utilized in terms of the overall network underutilization. To improve the routing survivability, the highest traffic load can be transferred to several sub-trees with the least load, in order to speed up load balance among different nodes of the network.
## 5 Verification of Comprehensive Evaluation Model for Honing Processing Quality
### Effect of CBN Grain Size on Honing Quality
#### 5.4.2 The Relationship Between Surface Roughness  
It can be seen from Figure 9 that the average, maximum, and minimum values of workpiece tooth surface roughness decrease with the increase of abrasive mesh size when other process conditions remain unchanged, thus improving the honing tooth surface quality. The reason can be revealed according to the Ra expression of honing surface roughness.  
\[Ra=0.256(1+c)\bigg{(}\frac{1}{n_{w}t}\bigg{)}^{0.4}H_{v} \tag{18}\]  
In Equation (18) c curl compensation coefficient, \(n_{w}\) workpiece rotational speed, t honing machining time, and the maximum valley height of the workpiece surface after honing \(H_{v}\) are:  
\[H_{v}=0.97w^{1.2}(cot\theta)^{0.4}\Bigg{\{}\frac{v_{1}}{v_{2}}\sqrt{\frac{1}{ 2}\bigg{(}\frac{1}{\rho_{2}}-\frac{1}{\rho_{1}}\bigg{)}}\Bigg{\}}^{0.4} \tag{19}\]  
Figure 8: Comparison of total deviation of tooth profile before and after honing.  
In Equation (19), \(\theta\) abrasive grain top cone angle, \(v_{1}\) workpiece contact point velocity, \(v_{2}\) honing wheel contact point velocity, the radius of curvature of the workpiece tooth surface \(\rho_{1}\), the radius of curvature of the abrasive grain wheel tooth surface \(\rho_{2}\), and the average abrasive grain spacing \(w\) are simplified as:  
\[w=137.9G^{-1.4}\sqrt[3]{\frac{\pi}{32-S}} \tag{20}\]  
\(G\) is the honing wheel grit size, \(S\) is the organization number. The combined analysis of the above equations shows the surface roughness \(Ra\propto H_{v}\), and the maximum height of the valley on the surface of the workpiece after honing \(H_{v}\propto w^{1.2}\). And because of the average interval of abrasive grain \(w\propto G^{-1.4}\), therefore \(Ra\propto H_{v}\propto w^{1.2}\propto G^{-1.68}\), \(Ra\propto\frac{1}{G^{1.88}}\). That is, as the grain mesh size increases, the abrasive grain size decreases, and the average spacing of the abrasive grain decreases, resulting in a decrease in the maximum valley height \(H_{v}\), which in turn decreases the surface roughness value \(Ra\) and the surface roughness quality improves.
## 5 Mathematical Modeling and the Control of Attitude Dynamics
### Controller Design
#### 5.4.2 Pitch Attitude Controller  
From Figure 10, by including the torque from the three motors and considering the values of the moment of inertia of the selected propeller and octocopter along the \(y\)-axis and the value of the gyro gain, the open-loop transfer function of the pitch control loop without the controller is given as follows:  
\[\left(\frac{\theta}{\theta_{\text{desired}}}\right)_{\text{open\_loop}}=\frac{ 16.37\times 10^{-7}}{s[(1.226\times 10^{-10})s^{2}+(8.53\times 10^{-7})s+6.418 \times 10^{-6}]}. \tag{25}\]  
From the transfer function, the open-loop poles of the pitch control system are 0, \(-7.53\), and \(-6950\), and there is no zero. The pitch-attitude system is also a Type-1 system. Therefore, a PD controller is sufficient for pitch attitude control.  
Using the same procedure as in Section 5.4.1, the location of the PD controller's zero for the pitch control loop was found to be \(-62.7\). The value of the forward loop gain corresponds to a 10% overshoot from the root found to be 37.2. Therefore, from the standard form of the transfer function of the PD controller, KP = 2332.44, and KD = 37.2. However, with the designed PD controller using the root locus approach, we observed an overshoot  
Figure 12: Root locus for the roll attitude-control with the designed controller.  
Figure 13: Roll response of the octocopter with the designed proportional-derivative (PD) controller.  
of approximately 22% in the octocopter pitch response. Hence, we decided to design a PD controller by manually tuning the gains. With the manual selection of controller gains, the pitch response of the octocopter for the desired pitch-attitude command was obtained using MATLAB, as shown in Figure 14.