esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight Uav Swarm Detection Method Integrated Attention Mechanism
## 3 Materials and Methods
### Lightweight Model  
The essence of a lightweight model is to solve the limitations of storage space and energy consumption on the performance of traditional neural networks on equipment with low-performance hardware. Aiming at the problem that the traditional deep convolution neural network consumes a large amount of computing resources, this paper pays more attention to how to reduce the complexity of the model and the amount of computation, while improving the accuracy of object detection. Considering that the depthwise separable convolution method [4] can effectively compress the model size while retaining the ability of feature extraction, this paper uses it to simplify and optimize the UAV swarm detection network.  
A standard convolution both filters and combines inputs into a new set of outputs in one step. Depthwise separable convolution splits this into two layers, for filtering and combining. While minimizing the loss of accuracy, this approach can greatly simplify the network parameters and reduce the amount of calculation. The depth-separable convolution operation divides the traditional convolution operation into two steps: depthwise convolution and pointwise convolution. The depthwise convolution applies a single filter to each input channel. The pointwise convolution then applies a \(1\times 1\) convolution to combine the outputs of the depthwise convolution. The standard convolution operation and the depthwise separable convolution operation are shown in Figure 6(a),b, respectively. The depthwise separable convolution is used to replace the traditional convolution in the UAV detection network while reducing the network parameters and computation.  
The total number of convolution kernel parameters and the total amount of convolution operations are analyzed to determine the amount of internal product operations. If we assume that N groups of convolutions, having the same kernel size, \(D_{k}\times D_{k}\times C\), are taken to check the input image for convolution and that the required feature map size is \(W\times H\times N\), then the quantity of parameters and operation required by the two methods are shown in Table 3. The depthwise separable convolution method can compress the network size and reduce the amount of computation. In the process of obtaining a fixed-size feature map using convolution kernels of the same width and height, by expressing convolution as a two-step process of filtering and combining, we get a reduction in necessary computations of \(1/N+1/D_{k}{}^{2}\), which is the key to achieving light weight.  
Figure 7: Comparison of the two convolution methods.
## 3 An Approach to Generate the MEP Logic Chain from BIMs
### Logic Chain Completion of MEP Models  
Following the steps articulated above, the connection tables and identification rules can be obtained. The connection tables extracted from geometric data provide topological relationships between components. Each component is considered as a node and an edge is then developed between two nodes if their represented components are spatially connected. In addition to the topological relationship, attributes of specific node in different types of systems contribute to generate the logic chain. Identification rules extracted from prior knowledge or user-defined rules are then transformed into computer readable language. The logic query engine retrieves attributes' information according to the identification rules for establishing the logic chain. An example of an HVAC subsystem is discussed below reveals the mechanism of the MEP logical query engine, as shown in Figure 8.  
Figure 8: The workflow of logical relationships completion of MEP model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Definition** & **Syntax** & **Example** & **Explanations** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{\(E\)} & \multirow{2}{*}{Valve} & \multirow{2}{*}{\(E\) represents an entity type that defines the scope of an entity to which an inspection rule is applied.} \\ \cline{5-5}  & & & & \(E_{U}\) represents an inspection rule, and \(P\) is an attribute of the relevant component; then, \(R_{UIE}P\) is the specified attribute value of the upstream component examined by the inspection rule. \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{\(R_{UE}=(E_{U}.P,\theta,v)\)} & \multirow{3}{*}{(Valve.Name, contains, “XX”)} & \multirow{3}{*}{\(E_{U}\) represents an inspection rule, \(P\) is an attribute of the relevant component,} \\  & & & & then, \(R_{UIE}P\) is the specified attribute value of the upstream component examined by the inspection rule. \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{\((R_{UE},R,R_{DE})\)} & \multirow{3}{*}{(Valve.Name, contains, “XX”)} & \multirow{3}{*}{\(E_{U}\) represents an inspection rule, \(P\) is an attribute of the relevant component,} \\  & & & & \(R_{UIE}=(R_{UE}.P,\theta,v)\) is an inspection rule for all instances in UE that have values for property P that satisfy the predication. \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{\((R_{UE},R,R_{DE})\)} & \multirow{3}{*}{((Valve.Name, contains, “XX”), UpstreamOf, (Pipe.Name, contains, “XX”))} & If \(R_{UE}\) and \(R_{DE}\) are two queries for instances and R provides a relationship (UpstreamOf or DownstreamOf) of these two groups of instances, then \((R_{UE},R,R_{DE})\) is a complete inspection rule for the generated part of the MEP logic chain. \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description and examples of the syntax.
###### Abstract
There was an improvement in durability due to a 5-8% reduction in water absorption, although performance dropped when replacement levels were over 30%. Studies, such as those conducted by [PERSON] et al. [16], have evaluated the feasibility of incorporating granite dust as a partial replacement for natural sand in reinforced concrete, concluding that up to 15% of natural river sand can be substituted with granite dust in the construction of reinforced concrete structures. Furthermore, investigations by [PERSON] and [PERSON] [17] have shown that ECC blends with up to 10% sand replaced by granite dust exhibit improved compressive, flexural, and bond strength properties compared to control ECC blends. Additionally, granite dust has a positive influence on the microstructure of ECC, reducing the number of pores. [PERSON] et al. 's research has explored the use of waste granite dust particles as a substitute for finer sand particles in concrete mixtures, resulting in enhanced compression, failure, and bending properties of conventional concrete [18]. The inclusion of amorphous silica is essential for improving durability. Amorphous silica, especially in the forms of silica fume or nanoparticles, interacts with calcium hydroxide in cement to produce supplementary calcium silicate hydrate (C-S-H). This response enriches the microstructure by closing capillary holes, hence reducing permeability and improving the concrete's durability against environmental assaults such as chloride penetration [19]. This research intends to accomplish three primary goals due to the accumulation of building debris stemming from the conflict in Mosul. The primary objective is to integrate lime waste into the formulation of cement-lime mortar at ratios of 10%, 25%, 35%, and 50% as a partial replacement of cement to produce mortar with satisfactory properties for construction purposes, while simultaneously mitigating environmental waste and contributing to the reduction in cement consumption, which substantially affects the emission of significant volumes of carbon dioxide (CO\({}_{2}\)) into the atmosphere. The second purpose is to analyze the characteristics of cement-lime mortar including granite powder as a partial replacement for fine aggregates by assessing its physical and mechanical qualities. The third purpose is to establish nonlinear connections for predicting certain physical attributes and estimating compressive and flexural strength, using statistical analysis of the experimental findings from this study in conjunction with data gathered from prior research in this field.
## 4 Evaluation of Prediction Results  
### Prediction Accuracy Index  
According to the \"Hydrological Intelligence Forecasting Specification\" and combined with the actual situation, RMSE, MAE, MAPE and NSE are selected in this paper as the prediction accuracy evaluation.  
1. Root mean squared error (RMSE)  
The root mean square error is used to reflect the degree of deviation between the predicted and measured flood values, with smaller values indicating smaller deviations. The RMSE is calculated as shown in Equation (12).  
\[\text{RMSE}=\sqrt{\frac{1}{\text{n}}\sum_{i=1}^{\text{n}}\left[y_{\text{c}}( \text{i})-y_{0}(\text{i})\right]^{2}} \tag{12}\]
2. Mean absolute error (MAE)  
The mean absolute error reflects the degree of error between the predicted value and the measured value, and the smaller the value the closer the predicted value is to the measured value. MAE is calculated as shown in Equation (13).  
\[\text{MAE}=\frac{1}{\text{n}}\sum_{i=1}^{\text{n}}\left|y_{\text{c}}(\text{i} )-y_{0}(\text{i})\right| \tag{13}\]
3. Mean absolute percentage error (MAPE)  
The mean absolute percentage error is used to reflect the error between predicted and measured values as a percentage of the measured values. A MAPE of 0% indicates a perfect model, while a MAPE greater than 100% indicates a poor model. MAPE is calculated as shown in Equation (14).  
\[\text{MAPE}=\frac{1}{\text{n}}\sum_{i=1}^{\text{n}}\left|\frac{y_{\text{c}}( \text{i})-y_{0}(\text{i})}{y_{0}(\text{i})}\right|\times 100\% \tag{14}\]
4. Nash-Sutcliffe efficiency coefficient (NSE)  
\[\text{NSE}\ =1-\frac{\sum_{i=1}^{\text{n}}\left[y_{\text{c}}(\text{i})-y_{0}( \text{i})\right]^{2}}{\sum_{i=1}^{\text{n}}\left[y_{\text{c}}(\text{i})-y_{0} \right]^{2}} \tag{15}\]  
The Nash-Sutcliffe efficiency coefficient is used to assess the predictive power of the hydrological model and takes values from negative infinity to 1. In the case of a perfect model with zero variance of the estimation error, the resulting NSE = 1; NSE = 0 indicates that the model has the same predictive power as the mean of the time series in terms of the sum of squared errors; if the variance of the estimation error of the modeled time series is significantly larger than the variance of the observations, NSE becomes negative.  
In the above equation, \(y_{\text{c}}(\text{i})\) is the predicted value, \(y_{0}(\text{i})\) is the measured value, \(\overline{y_{0}}\) is the mean of the measured value and n is the number of samples.
## 2 Materials and Methods
### Site-Directed Mutagenesis  
After the transformation of chemically competent _E. coli_ zymo 10 beta cells with a codon-optimized synthetic gene encoding PCI_Lip (BioCat, Heidelberg, Germany) in the pET-28a(+) vector as described by [PERSON] and [PERSON] [24], the plasmid was isolated from overnight cultures using the FastGene Plasmid Mini Kit (NIPPON Genetics, Duren, Germany). The primer design and PCR reaction setup for site-directed mutagenesis were based on the QuikChange(tm) protocol (Stratagene, La Jolla, CA, USA). A list of primers used in this study can be found in Supplementary Table S1. For PCR, 1 \(\upmu\)L plasmid template, each 1 \(\upmu\)L forward and reverse primer, 1 \(\upmu\)L dNTPs, 10 \(\upmu\)L 5\(\times\) Phusion HF buffer and 0.5 \(\upmu\)L Phusion DNA polymerase (New England BioLabs(r), Ipswich, MA, USA) were mixed and adjusted to a total volume of 50 \(\upmu\)L. To perform the PCR at different temperatures, aliquots of 10 \(\upmu\)L volume were separated. The reaction was performed using a T100(tm) thermal cycler (Bio-Rad Laboratories, Feldkirchen, Germany) with the following program: initial denaturation for 1 min at 95 \({}^{\circ}\)C, denaturation for 45 s at 95 \({}^{\circ}\)C, annealing for 45 s at 60 to 73 \({}^{\circ}\)C, extension for 5 min at 68 \({}^{\circ}\)C (30 cycles), and final extension for 7 min at 68 \({}^{\circ}\)C. In order to digest the remaining template DNA, the PCR tubes were pooled, 1 \(\upmu\)L DpnI (Thermo Fisher Scientific, Dreiich, Germany) was added, and the mixture was incubated for 2 h at 37 \({}^{\circ}\)C. The DpnI-digested PCR products were then directly used for the transformation of chemically competent _E. coli_ 10 beta cells. The preparation of the competent cells and the transformation were performed as described by [PERSON] and [PERSON] [24]. The transformants were selected for kanamycin resistance on LB agar plates and respective colonies were picked and used for overnight cultures and plasmid isolation. For the confirmation of the mutations, the plasmids were sequenced by Eurofins Genomics (Ebersberg, Germany), and the results of the sequencing were analyzed with Geneious 9.1 (Biomatters, Auckland, New Zealand). Approved plasmids were used to transform _E. coli_ BL21 (DE3) as described above. For soluble protein expression, _E. coli_ BL21 (DE3) cells were transformed with the chaperon plasmid pG-KJE8 from TaKaRa BIO INC. (Kusatsu, Japan) for co-expression of the chaperones DnaK-DnaJ-GrpE and GroES-GroEL [25; 26]. Cultures were maintained as cryostocks with 36% \(v/v\) glycerol at \(-\)80 \({}^{\circ}\)C.
# Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility
## 4 System Design and Implementation
### System Deployment
#### 4.2.1 Physical Layer  
The physical layer of the proposed shared mobility system consists of the hardware components responsible for data collection and sensing on motorcycles. The following hardware components are used in the proposed system:  
1. Raspberry Pi (RPi) 4: The RPi 4 served as the edge computing device in the implementation, acting as the data collection component. It featured a 64-bit CPU running at 1.4 GHz, 4 GB of RAM, a 64 GB SD memory card, 4 USB ports, Ethernet and Wi-Fi access points, Bluetooth connections, 26 GPIO pins, and SPI, SCI, and I2C communication interfaces. The RPi 4 interfaced with various sensors is installed on the motorcycles, collecting driving parameters, location data, and relevant information during trips.
2. Sensing Unit: The sensing unit consists of several sensors, enhancing the data collection capabilities of the motorcycles. The following key sensors are used: * HC-SR04 Ultrasonic Distance Module: It is utilized for non-contact distance measurement between the motorcycle and nearby objects, with a range accuracy of up to 3 mm and a measurement range from 2 to 400 cm. * Force-Sensing Resistor (FSR): It is used for detecting impact force on the motorcycle caused by other objects with a resistance range from infinite/open circuit to 100 K\(\Omega\) and a force range of 0 to 100 Newtons.  
Figure 3: Prototype implementation of the proposed system.  
MPU-6050 Accelerometer and Gyroscope Module: It is employed for providing 6-axis motion tracking, capturing x, y, and z acceleration in addition to the angular velocities of the motorcycle during rides.
* Arducam USB Camera: It is deployed for the video recording of rides, offering a 4K 8 MP IMX219 autofocus capability with a resolution of 1080P.
## 3 Results
### Transformation of the Nanolayers to the Nano-Nets  
Figure 7 illustrates the XRD pattern of the product obtained by the decomposition of the supported LDH under critical conditions of pressure and temperature in presence of methanol and ethanol. The XRD pattern exhibited the characteristic diffractions peaks of cobalt ferrite (JCPDS 79-1744). The main diffraction peaks of the planes (220), (311), (400), (511), and (440) of cobalt ferrite were broad suggesting that their crystals are in the nano scale. In the same time, the characteristic peaks of the lamellar structure of LDH disappeared. It means that the layered structure of Co-Fe LDH was transformed to a clear phase of cobalt ferrite. The crystallite size of the produced cobalt ferrite was calculated from the XRD line broadening using the following Scherer relationship [30].  
\[\mathrm{D}=\mathrm{k}\lambda/\beta\mathrm{\ Co}\mathrm{s}\mathrm{\ \theta}\ [ \mathrm{k}=0.9,\,\lambda=0.15405\ \mathrm{nm}] \tag{1}\]  
Figure 5: TEM images of the CoFe\({}_{2}\)O\({}_{4}\)-supported Co-Fe LDH.  
Figure 6: EDX spectra of the CoFe\({}_{2}\)O\({}_{4}\)-supported Co-Fe LDH.  
According to Scherer equation, the particles crystallite sizes of cobalt ferrite are in the range of 7 nm. The nanostructure of this product was confirmed by TEM. The TEM images in Figure 8 showed that the produced cobalt ferrite is composed of nano-nets. These nano-nets consist of nanoparticles and short nanowires as shown in Figure 8c,d. Also, Figure 8c,d revealed that these nano-nets have nano-pores with diameters of 5-10 nm.  
Figure 8: TEM images and EDX spectrum of the CoFe\({}_{2}\)O\({}_{4}\) nano-nets.  
Figure 7: X-ray diffraction pattern of the CoFe\({}_{2}\)O\({}_{4}\)-supported Co-Fe LDH after thermal treatment.  
In addition, aggregations of the nanoparticles were observed in Figure 8a,b. These nanoparticles were also observed in the Co-Fe LDH before the thermal treatment and were due to CoFe\({}_{2}\)O\({}_{4}\). These nanoparticles have spherical shape with size less than 10 nm as shown in Figure 8b. These nanoparticles did not change after the thermal treatment. This means that these nanoparticles act as catalysts during the decomposition reactions of LDH structures, where the catalyst does not change during the reaction. By using EDX technique, cobalt, iron, and oxygen were clearly identified in the nano-nets of cobalt ferrite as shown in Figure 8e. It is known that the signals of the copper and the carbon belong to the carbon-coated copper grid for the TEM testing.
# Convergence Gain in Compressive Deconvolution: Application to Medical Ultrasound Imaging
## 3 Numerical Results
### Numerical Simulations
#### 3.1.2 In Vivo US Images  
We consider two real in vivo US images [19]: (a) _Mouse bladder_: The observed image is with the size \(400\times 256\) as shown in Figure 1. The number of homogeneous regions was set to K = 3 in this experiment, which is sufficient to represent the anatomical structures of the image. (b) _Skin melanoma_: The second in vivo image (of size \(400\times 298\)) represents a skin melanoma tumor, as shown in Figure 2.  
Since the ground truth of the TRF and the label map are not available for in vivo US data, the quality of the deconvolution results is evaluated using the contrast-to-noise ratio (CNR) [20]:  
\[\text{CNR}=\frac{|\mu_{1}-\mu_{2}|}{\sqrt{\sigma_{1}^{2}+\sigma_{2}^{2}}}, \tag{36}\]  
where \(\mu_{1}\) and \(\mu_{2}\) are the mean of pixels located in two regions extracted from the image, while and are the standard deviations of the same blocks. All code was written in MATLAB and performed on a ThinkPad computer equipped with Windows 7, 2.60 GHz and 2 GB of memory. Based on the same stopping criterion, \(\frac{\|x^{k}-x^{k-1}\|}{\|x^{k-1}\|}<10^{-3}\) is adopted.  
Figure 1: Results on _group1_ with CS ratio = 0.6.  
Figure 2: Results on _group2_ with CS ratio = 0.6.
## 3 Smoothing and Untangling Approach  
The proposed mesh smoothing and untangling approach consists of the following two key steps. First, each element of the input mesh is transformed individually and simultaneously towards its corresponding ideal element by using the transformation introduced in Section 2. In this step, each element is transformed separately without caring about the connection relationship with its neighbors. Therefore, a combination step is needed to stitch the disconnected elements. Here, the element stitching operation is performed by updating the position of each free node by a well-designed weighted means of its corresponding temporary node positions in the scaled transformed elements. The element stitching operation is actually using an explicit formula to approximate the partial derivative of the objective function with respect to the current node position to obtain the displacement of the node.  
As the proper choice of a quality metric is important for element stitching operation, the element quality metric used in this paper is first introduced.
###### Abstract  
This paper presents two categories of features extraction and mapping suite, a very high-resolution suite and an ultra-resolution suite at 2 m and 0.5 m resolutions, respectively, for the differentiation and mapping of land cover and community-level vegetation types. The features extraction flow of the ultra-resolution suite involves pan-sharpening of the multispectral image, color-transformation of the pan-sharpened image, and the generation of panchromatic textural features. The performance of the ultra-resolution features extraction suite was compared with the very high-resolution features extraction suite that involves the calculation of radiometric indices and color-transformation of the multi-spectral image. This research was implemented in three mountainous ecosystems located in a cool temperate region. Three machine learning classifiers, Random Forests, XGBoost, and SoftVoting, were employed with a 10-fold cross-validation method for quantitatively evaluating the performance of the two suites. The ultra-resolution suite provided 5.3% more accuracy than the very high-resolution suite using single-date autumn images. Addition of summer images gained 12.8% accuracy for the ultra-resolution suite and 13.2% accuracy for the very high-resolution suite across all sites, while the ultra-resolution suite showed 4.9% more accuracy than the very high-resolution suite. The features extraction and mapping suites presented in this research are expected to meet the growing need for differentiating land cover and community-level vegetation types at a large scale.  
WorkView; vegetation; machine learning; mapping; pan-sharpening; HSVs; textures; ultra-resolution; very high-resolution 26 May 2022  
28 June 2022  
30 June 2022