esa-sceva/satcom-chunk-collection huggingface dataset:
# Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in Complex Fire Scenarios
###### Abstract  
Fire-detection technology is of great importance for successful fire-prevention measures. Image-based fire detection is one effective method. At present, object-detection algorithms are deficient in performing detection speed and accuracy tasks when they are applied in complex fire scenarios. In this study, a lightweight fire-detection algorithm, Light-YOLOv5 (You Only Look Once version five), is presented. First, a separable vision transformer (SepViT) block is used to replace several Cross Stage Partial Bottleneck with 3 convolutions (C3) modules in the final layer of a backbone network to enhance both the contact of the backbone network to global information and the extraction of flame and smoke features; second, a light bidirectional feature pyramid network (Light-BiFPN) is designed to lighten the model while improving the feature extraction and balancing speed and accuracy features during a fire-detection procedure; third, a global attention mechanism (GAM) is fused into the network to cause the model to focus more on the global dimensional features and further improve the detection accuracy of the model; and finally, the Mish activation function and SIoU loss are utilized to simultaneously increase the convergence speed and enhance the accuracy. The experimental results show that compared to the original algorithm, the mean average accuracy (mAP) of Light-YOLOv5 increases by 3.3%, the number of parameters decreases by 27.1%, and the floating point operations (FLOPs) decrease by 19.1%. The detection speed reaches 91.1 FPS, which can detect targets in complex fire scenarios in real time.  
f 2022 202212 12, 12312[[https://doi.org/10.3390/app122312312](https://doi.org/10.3390/app122312312)]([https://doi.org/10.3390/app122312312](https://doi.org/10.3390/app122312312)) 202212221  
The remainder of the paper is organized as follows: Section 2 focuses on the studies related to fire-detection practices; Section 3 describes the model's framework and the implementation details; Section 4 verifies the algorithm's effectiveness through experiments; and Section 5 concludes with a summary.
## 2 Methodology  
### Stereo Matching  
Stereo matching [36] is the core procedure of the 3D reconstruction method for binocular vision. The goal of stereo matching is to find the corresponding homologous point pairs in the two views and to acquire the disparity. The disparity of the corresponding pixels is estimated by establishing a similarity measurement function to minimise the correlation value [37] in order to obtain the depth. According to epipolar geometry [36], stereo matching constrains and simplifies the complex 2-dimensional (2D) search problem into a simple 1-dimensional search. The matching process uses the \"winner takes all\" (WTA) criterion to estimate the disparity of each pixel. General similarity measurement functions in stereo-matching algorithms include the sum of absolute difference, sum of squared difference and zero normalised cross correlation (ZNCC).
## 3 Results and Discussion
### Adsorption and Desorption Experiments
#### 3.2.8 Reusability Study  
Reusability is one means of improving the economic efficiency of adsorbents when treating wastewater. The reusability of Cd-I-MA-_co-_AN is shown in Figure 10. In adsorption-desorption experiments, with the use of 1 mol L\({}^{-1}\) nitric acid on Cd-I-MA-co-AN, the adsorption rate began to decrease after ten cycles. Therefore, Cd-I-MA-_co-_AN has good reusability.
# Sequential Characteristics Based Operators Disassembly Quantization Method for LSTM Layers
## 4 Experiments
### Performance Analysis  
It is very important to measure the deployment performance of the model on different platforms, and it has a strong reference for improving the quantization scheme and FPGA hardware design. Since the speed of simulating NPU calculations on the PC side is slower, we deployed the proposed method to a real NPU, embedding the core calculations of the model on the neural network accelerator. Quantization and classification prediction are carried out on the neural network accelerator.  
The experiments on the PC were carried out on Windows 10. The CPU is Intel(R) Core(TM) i7-8700 K, 3.70 GHz, the GPU is NVIDIA GeForce GTX1070, and the deep learning framework is Torch 1.4.1 and Caffe. The experiment on NPU uses the TIANJI NPU3.0 neural network accelerator proposed by Xi'an Microelectronics Technology Institute [40]. This accelerator is implemented based on Xilinx ZCU102 FPGA, with self-controllable IP and the application development tool chain. It supports multi-model online switching and pipeline parallel acceleration [41]. It supports real-time detection and positioning applications of satellites, spacecrafts, and other targets and can meet the needs of visual ranging and intelligent obstacle avoidance applications.  
We test the running speed of LRCN models of different backbone networks on the CPU, GPU, and NPU, respectively. At the same time, the running speed of the backbone network itself on the NPU was also tested. Moreover, the specific speed of the LSTM layer was obtained by the difference between the execution speed of the AlexNet-LSTM or Vgg16-LSTM networks and the backbone network (AlexNet or Vgg16) on the NPU. Note that the execution speed here is the average model deployment time over a single time step. The details are shown in Table 9.  
Experiments show that the execution speed of the AlexNet-LSTM model on the neural network accelerator is 1.6 times faster than on the CPU and 1.3 times faster than on the GPU. The Vgg16-LSTM model is executed fastest on the GPU due to the small filter size and large model depth. The execution time is 39.7 ms. The execution time on the neural network accelerator is still 1.87 times faster than on the CPU. Moreover, from the data point of view, a single time step of the LSTM with the hidden state output feature dimension of 256 on the NPU is about 1 ms at 20 W power consumption.
## 3 Principals of Routing Protocols and Their Breakdowns
### Data-Based Routing Protocols
#### 3.4.7 L2-ABF [71]  
For UWSNs, a routing technique known as layer-by-layer angle-based flooding (L2-ABF) has been suggested. Without location information, the node in L2-ABF can compute its flooding angle and send the data packet to the next higher layer toward the surface sinks. Meanwhile, the nodes will choose the angle values based on their movement. A priority queue and a packet history buffer are used to regulate the number of forwarding nodes to tackle the problem of excessive collision and high energy consumption. L2-ABF uses the flooding approach to improve the wireless network's dependability and prevent flooding throughout the whole network by adjusting the angle of the flooding cone.
## 2 System Model and Problem Formulation
### Agent and Optimized Objective of Jammers  
The agent of a jammer can sense the communication channels, analyzes the communication preference of user, and guide deployment of jamming through reliable control link or wire link. The signal-to-interference-plus-noise ratio (SINR) of the user's receiver can be noted by  
\[\beta\left(f_{t},f_{t}^{j}\right)=\frac{g_{u}p_{u}}{\int_{f_{t}-b_{u}/2}^{f_{ t}^{f_{t}}+b_{u}/2}\left\{n\left(f\right)+\sum\limits_{j=1}^{n}g_{j}l_{t}^{j} \left(f-f_{t}^{j}\right)\right\}df}, \tag{1}\]  
Figure 1: System model.  
where the center transmission frequency at time \(t\) is denoted by \(f_{t}\). The parameter \(b_{u}\) is the user transmission bandwidth. The transmitted power of a user is denoted by \(p_{u}\), \(n(f)\) is the power spectral density (PSD) of noise, \(f_{t}^{j}\) denotes the selecting jamming center frequency by Jammer \(j\) at time \(t\). PSD of one jamming band is denoted by \(J(f)\), \(g_{j}\) is the channel gain from jammer to the user's receiver. Denoting the channel gain from user's transmitter to the jammer as \(g_{u}\). Set \(\mu(f_{t})\) as an indicator function for successful transmission as follows:  
\[\mu(f_{t},f_{t}^{j})=\left\{\begin{array}{cc}1,&\beta(f_{t},f_{t}^{j})\geq \beta_{th}\\ 0,&\beta(f_{t},f_{t}^{j})<\beta_{th}\end{array}\right., \tag{2}\]  
where \(\beta_{th}\) is defined as the threshold of SINR. When the SINR is below the threshold, \(\beta_{th}<\beta(f_{t})\), the transmission is seen as failed. The aim of jammer is to minimize the target function:  
\[\min_{f_{t}^{j}\in A_{j}}\ \ \Lambda=\sum_{t=0}^{\infty}\gamma^{t}\mu(f_{t},f_{t}^ {j}), \tag{3}\]  
where \(\gamma\) is the discount factor and \(\gamma\in(0,1)\).  
We set \(u_{t,i}\) as discrete spectrum sample value \(u_{t,i}=10\log(\int\limits_{i\Delta f_{j}}^{(i+1)\Delta f_{j}}R_{j}(f)df)\), \(\Delta f_{j}\) is the resolution of spectrum analysis of jammer, and \(R_{j}(f)\) is denoted by:  
\[R_{j}(f)=g_{a}U(f-f_{t})+\sum_{j=1}^{J}g_{a,j}j_{t}^{j}(f-f_{t}^{j})+n(f), \tag{4}\]  
where \(g_{a}\) is the channel gain from user's transmitter to the agent end, and \(g_{a,j}\) is the channel gain from jammer to jammer's agent end. With \(u_{t,i}\), we can rebuild the spectrum at time \(t\), find the user's communication frequency, and denote as \(u_{t,c}\). The total range of a jammer can jam at one time is \(B_{j}^{n}=\sum\limits_{i=1}^{n}b_{j}^{i}\), \(b_{j}^{i}\) is the jamming band for jammer \(i\), note that the \(B_{u}>B_{j}^{n}\).
## 3 Methodology
### LIDC dataset
For any lesion marked as a nodule \(>3\) mm, the XML file contains the coordinates of nodule outlines constructed by any of the 4 LIDC radiologists who identified that structure as a nodule \(>3\) mm. Moreover, any LIDC radiologist who identified a structure as a nodule \(>3\) mm also provided subjective ratings for 9 nodule characteristics (Table 2): subtlety, internal structure, calcification, sphericity, margin, lobulation, spiculation, texture, and malignancy likelihood. For example, the texture characteristic provides meaningful information regarding nodule appearance (\"Non-Solid\", \"Part Solid/(Mixed)\", \"Solid\") while malignancy characteristic captures the likelihood of malignancy (\"Highly Unlikely\", \"Moderately Unlikely\", \"Indeterminate\", \"Moderately Suspicious\", \"Highly Suspicious\") as perceived by the LIDC radiologists. The process by which the LIDC radiologists reviewed CT scans, identified lesions, and provided outlines and characteristic ratings for nodules \(>3\) mm has been described in detail by [PERSON] _et al._[36]. The nodule outlines and the seven of the nodule characteristics were used extensively throughout this study. Note that the LIDC did not impose a forced consensus; rather, all of the lesions indicated by the radiologists at the conclusion of the unblinded reading sessions were recorded and are available to users of the database. Accordingly, each lesion in the database considered to be a nodule \(>3\) mm could have been marked as such by only a single radiologist, by two radiologists, by three radiologists, or by all four LIDC radiologists. For any given nodule, the number of distinct outlines and the number of sets of nodule characteristic ratings provided in the XML files would then be equal to the number of radiologists who identified the nodule.
## 4 Theoretical Framework
### Infrastructure/System Construction  
The construction of the system will affect the current efficiency and change the subsequent expansion of the value proposition. In the early stage of system construction, it is required to communicate and cooperate with partners to meet the requirements of both parties [59]. Considering the stacking and development of technologies, investment and development of advanced technology applications are often required. The technical level of partners must be evaluated [23]. If an enterprise is unaware of new technologies and fails to match development cost with the systems of second-third parties, it may suffer a loss of resources. Therefore, when the enterprise is conducting the construction of the advanced system, the technology development platform with its partners has achieved the greatest intersection, which can effectively integrate various business demands. The system must be evaluated from the standpoint of service construction [19]. After combining the S-D logic, the microsystem's service development can develop different service models. Similar to the characteristics of microservices, the construction of microsystems is extensible and can unite services to achieve the purpose of portfolio expansion. If an enterprise constructs a microsystem architecture with a microservice orientation, it can also gain the advantages of relatively easy maintenance and integration [31]. In the Industry 4.0  
Figure 4: Theoretical framework.  
framework, real-time information updates can assist supply and demand parties in maintaining information transparency [11]. For manufacturing demands, many services must be provided or improved promptly. Therefore, it is bound to build a framework that truly meets the requirements in the early stages of development, dynamically communicating with the demand side, introducing the concept of microsystems and microservices and making timely corrections.
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 3 Implementation Details  
The proposed HCTC method was applied to the widely used RadioML2016a and RadioML2016b datasets [53]. The datasets were partitioned into 60% for training, 20% for validation, and the remaining 20% for testing after applying random sampling to each class. The performance of the model was evaluated using categorical cross-entropy as the loss function, and the Adam optimizer was employed to adjust the model parameters. The batch size for the implementation was 400, and the initial learning rate was set to 0.001. To prevent overfitting and ensure effective learning, the validation loss was monitored throughout the training process. If the validation loss did not improve over five consecutive epochs, its value was halved to encourage the model to escape from potential local minima. The training was terminated if the validation loss remained stagnant for 50 epochs, and the model with the best validation performance was retained.  
All the experiments were performed using an NVIDIA GeForce RTX 4070 GPU, leveraging Keras 2.10.0 with TensorFlow 2.10.1 as the backend framework to facilitate efficient computation. To benchmark the performance of the proposed model, it was compared with several commonly used AMR models. These included CNN1 [24], CNN2 [27], MC-NET [30], IC-AMCNET [33], ResNet [34], DenseNet [34], GRU [35], LSTM [25], DAE [26], MCLDNN [31], CLDNN [28], CLDNN2 [34], CGDNet [36], PET-CGDNN [37], and 1 DCNN-PF [32]. In addition, experiments were conducted with the proposed HCTC model using various batch sizes and model parameters.
## 4 AIMM Algorithm
### SVD Algorithm
By Laplace transformation of the above equation, we can acquire:  
\[R(s)=Laplace\big{\{}R_{j}(\tau)\big{\}}=\frac{-2\alpha_{\text{m}}\sigma_{j}^{2 }}{(s-\alpha_{\text{m}})(s+\alpha_{\text{m}})}=H(s)H(-s)V(s) \tag{37}\]  
where,  
\[H(s) =1/(s+a) \tag{38}\] \[V(s) =2\alpha_{\text{m}}\sigma_{j}^{2} \tag{39}\]  
Then change the above equation in time domain to obtain the differential equation as follows:  
\[j(t) =-\alpha j(t)+v(t) \tag{40}\]  
where the correlation function of white noise a is \(v(t)\):  
\[r_{v}(\tau) =2\alpha\sigma_{j}^{2}\delta(\tau) \tag{41}\]  
The discrete state transition matrix and state noise covariance matrix are as follows:  
\[\mathbf{F}_{2}= \begin{bmatrix}1&T&\frac{\tau^{2}}{2}&p_{1}\\ 0&1&T&q_{1}\\ 0&0&1&r_{1}\\ 0&0&0&s_{1}\end{bmatrix}  \tag{42}\]  
The elements of the state noise covariance matrix can be found in [27]. Since the [PERSON] model is of the fourth order and the [PERSON] and CS models are of the third order, it is necessary to expand the dimension of their state transition matrix and state noise variance matrix. All zero rows will appear in the traditional dimension expansion method, which leads to the fact that Cholesky in the CKF algorithm cannot be decomposed due to the loss of the positive definiteness of the matrix. Therefore, in order to enhance the stability of the calculation, SVD decomposition was considered to replace Cholesky decomposition in this paper. The specific process is as follows:Cholesky decomposition of the covariance matrix in the time update process was rewritten into SVD decomposition:  
\[\mathbf{P}_{k-1}=\mathbf{U}_{k-1} \begin{bmatrix}\mathbf{S}_{k-1}&0\\ 0&0\end{bmatrix} \mathbf{U}_{k-1}^{T} \tag{43}\]  
where \(\mathbf{S}_{k-1}\) is a diagonal matrix, \(\mathbf{S}_{k-1}=\text{diag}[s_{1},s_{2},\cdots s_{n}]\), and \(s_{i}\) is the singular value of the matrix. The cubature point can be expressed as:  
\[\mathbf{X}_{i,k-1}=\hat{\mathbf{x}}_{k-1}+\mathbf{U}_{k-1}\sqrt{\mathbf{S}_{k-1}}\xi_{i},\ i=1,2, \cdots m \tag{44}\]  
where \(\xi_{i}=\sqrt{m/2}[1]_{i}\). The measurement update is similar to the time update. \[\mathbf{P}_{k-1}^{-}=\mathbf{U}_{k-1}^{-} \begin{bmatrix}\mathbf{S}_{k-1}^{-}&0\\ 0&0\end{bmatrix} \mathbf{U}_{k-1}^{-}{}^{T} \tag{45}\]  
\[\mathbf{X}_{i,k-1}=\hat{\mathbf{x}}_{k-1}+\mathbf{U}_{k-1}^{-}\sqrt{\mathbf{S}_{k-1}^{-}}\xi_ {i},\ i=1,2,\cdots m \tag{46}\]  
This algorithm takes into account the representation of the Jerk model in the IMM model set and the reasonable decomposition of the covariance matrix in the CKF algorithm and enhances the stability of the algorithm.
# Effects of Grout Compactness on the Tensile Behavior of Ground Splice Sleeve Connectors
## 3 Tensile Bearing Capacity of Grounded Splice Sleeve  
There is a great difference in the processing technology and structural construction of grouted splice sleeves both at home and abroad, different performance, and different calculation theories were developed for different types of GSS [1; 3; 4; 7; 8; 9; 18; 19].  
In light of a lacking existing bond-slip model of rebar-grout in spliced sleeve, this section first analyzes the load transfer mechanism of GSS and then deduces the bond-slip model of the rebar-grout. Lastly, the formula is proposed for calculating the tensile bearing capacity of GSS.
## 3 Problem Definitions and Formulation
### The Mathematical Formulation of the Integrated Problem  
The mathematical model for this integrated problem \(P\) is formulated as follows.  
\[Z=Min\ f=Min\ \left(Max\{T2_{i\text{\emph{i}}k}\}\right)\forall\ i\in S,\ k \in K_{i},q\in Q \tag{1}\]  
Subject to  
\[BD_{j}>X_{ij}^{t}\cdot L_{i}\ \forall\ i\in S,\ j\in B,\ t\in T \tag{2}\]  
\[BL_{j}<X_{ij}^{t}\cdot Df_{i}\ \forall\ i\in S,\ j\in B,t\in T \tag{3}\]  
\[\sum_{i}^{n}X_{ij}^{t}=1\ \forall\ j\in B,t\in T \tag{4}\]  
\[p\cdot Y_{ipk}^{t}<\ q\cdot Y_{(i+1)\text{\emph{i}}k^{t}}^{t}\ \forall\ i\in S ;p,q\in Q;\ k\in K_{p},k^{\prime}\in K_{q},\ t\in T \tag{5}\]  
\[\sum_{j=1}^{n}\sum_{i=1}^{m}\sum_{q=1}^{Q}\sum_{k=1}^{K}Y_{iqk}^{t}X_{ij}^{t} \leq l\ \forall\ t\in T \tag{6}\]  
\[1\leq T1_{i\text{\emph{i}}k}\leq H\ \forall\ i\in S,\ q\in Q,\ k\in K \tag{7}\]  
\[1\leq T2_{i\text{\emph{i}}k}\leq H\ \forall\ i\in S,\ q\in Q,\ k\in K \tag{8}\]  
\[T1_{i\text{\emph{i}}k}\leq T2_{i\text{\emph{i}}k}\ \forall\ i\in S,\ q\in Q,\ k\in K \tag{9}\]  
\[BT_{i}=\left[Min\{T1_{i\text{\emph{i}}k}\},Max\{T2_{i\text{\emph{i}}k}\} \right]\ \forall\ k\in K_{i},q\in Q \tag{10}\]  
\[X_{ij}^{t}=\{0,1\}\ \forall\ i\in S,\ j\in B,\ t\in T \tag{11}\]  
\[Y_{i\text{\emph{i}}k}^{t}=\{0,1\}\ \forall\ i\in S,\ j\in B,\ t\in T \tag{12}\]  
Equation (1) is the objective function \(Z\) aimed at minimizing the makespan of the integrated problem \(P\). Equation (2) ensures enough berth length for the ship to berth. Equation (3) is a constraint guaranteeing enough water depth to accommodate an assigned ship. Equation (4) stipulates that only one ship is served at a time on a berth. Equation (5) is a constraint stipulating that QCs are sequentially assigned to ships according to increasing QC number and berth number. Equation (6) is a constraint ensuring that the total number of QCs assigned to ships in the same time period is equal to or less than the total number of QCs available. Equations (7) and (8) indicate the feasible value ranges for \(T1_{i\text{\emph{i}}k}\) and \(T2_{i\text{\emph{i}}k}\). Equation (9) defines the relationship between \(T1_{i\text{\emph{i}}k}\) and \(T2_{i\text{\emph{i}}k}\). Equation (10) defines the berth duration of a ship. Equations (11) and (12) define the value domains for the decision variables \(X_{ij}^{t}\) and \(Y_{i\text{\emph{i}}k}^{t}\).  
However, this mathematical model cannot be solved by commercial software as the variable \(t\) is unbounded. We solve them using a heuristic and simulation-based approach as described in Section 4.
## 1 Introduction
Considering such an influence on the micro-organisms of the mucous membrane, it can be assumed that something similar may take place in the dental biofilm on the tooth surface [23]. It has been shown that cigarette smoke also affects the subgingival microbiological composition in healthy individuals, and it is responsible for the depletion of beneficial bacteria. Differences in bacterial communities between smokers and non-smokers with moderate periodontitis showed that bacteria of the _Bacteroides_ genus are more common in non-smokers, while _Fusobacteria_, _Freibacteria_, _Streptococci_, and _Veillonella_ are more present in smokers [21]. A 2020 study which investigated the prevalence of periodontitis in healthy young adult smokers found that the levels of _Aggregatibacter actinomycetemcomitants_, _Porphyromonas gingivalis_, and _Fusobacterium nucleatum_ were actually very similar between smokers and non-smokers [24]. In addition, periodontal disease and red complex bacteria are strongly related to various systemic diseases [25]. A link to rheumatoid arthritis has also been reported [26]. Recently published studies have stated that periodontal pathogens have a well-documented effect on synaptic impairment, neurodegenerative function, and neuroinflammation through the induction of chronic low-grade systemic inflammation, which can have an effect on the manifestation of Alzheimer's disease [27]. Patients infected with _P. gingivialis_ have a higher risk of gastrointestinal cancer (odds ratio = 2.2), when compared to those who are uninfected [28]. Cardiovascular diseases can also be negatively affected by the disturbed oral microbiome. A significant number of very similar oral bacterial DNA were found in aortic valve specimens in patients suffering from periodontal disease [29]. When all these negative consequences from periodontal disease are added to those from smoking [30; 31; 32], the conclusion arises that cigarette smokers are a high-risk group that are susceptible to several serious and fatal diseases. The aim of this study was to compare the prevalence and abundance of periodonotophagems in classic cigarette smokers, IQOS consumers, and non-smokers, in relation to clinically diagnosed periodontitis. The null hypothesis was that HNB tobacco modifies the subgingival microbiome profile in such a way that IQOS users without periodontal disease are expected to have more periodonotophagems than non-smokers but less than classic cigarette smokers.