esa-sceva/satcom-chunk-collection huggingface dataset:
# Embedded Deep Learning for Ship Detection and Recognition
## 3 Designing a Recognition Neural Network-DCNet
### DNet  
Region Proposal is one of the key points for a target detection network, such as Faster R-CNN [8] using RPN to generate better regions, and YOLOv1 [10] splitting an image into grid cells as region proposals to improve the detection efficiency. As shown in Figure 2, DNet divides the input image into 6 \(\times\) 6 grid cells as region proposals like YOLOv1 [10]. Each region proposal consists of eight predictions: \(x\), \(y\), \(w\), \(h\), \(c\), \(C\times 3\). The (\(x\), \(y\)) coordinates represent the center of the predicted box. \(w\) and \(h\) represent the width and height of the predicted box. \(c\) represents the IOU (intersection-over-union) between the predicted box and ground truth box. \(C\times 3\) represents the probability classes of bow, cabin and stern.  
Inspired by YOLOv1, DNet models the detection as a regression problem. Since the object and background are relatively simple, object features are relatively obvious, and the network is not as deep as VGGNet [10] and ResNet [22]. We pay more attention to the decreasing network model parameters. DNet resizes the image to 192 \(\times\) 192 as the input and we design five layers to extract features from an image; the last two layers predict the object probabilities and its coordinates.  
DNet predicts bounding boxes based on grid cells. A grid cell produces one bounding box predictor. We need one bounding box predictor to be \"responsible\" for each object, and choose the one based on which prediction has the highest current IOU (intersection-over-union) with the ground truth. To choose a proper predictor for each object, at training time, we design the loss \(L_{g}\) function as follows:  
\[L_{grid}=\sum_{i=0}^{S^{2}}(C_{i}-C_{i}^{*})^{2}, \tag{1}\]  
Figure 1: DCNet.  
Figure 2: Region proposal of DNet.  
where \(S^{2}\) is the number of the grid cells, \(C_{i}\) is the confidence value that the predicting box contain an object, and \(C_{i}^{*}\) is the IOU between predicted bounding box with ground truth. If there is no object in predictions, then \(C_{i}^{*}=0\).  
The final layer predicts both class probabilities and bounding box coordinates; we calculate the coordinates loss and classification loss only when the predictor is a proper one, the loss function is:  
\[L_{box}=\sum_{c\in\mathit{propers}}\sqrt{(x_{c}-x_{c}^{*})^{2}+(y_{c}-y_{c}^{* })^{2}+(w_{c}-w_{c}^{*})^{2}+(h_{c}-h_{c}^{*})^{2}}+(p_{c}-p_{c}^{*})^{2}, \tag{2}\]  
where \(p_{c}\) is the predicting class and \(p_{c}^{*}\) is the truth class. The loss of \(L_{box}\) is under the assumption that the predictor is a proper one. Therefore, it may not be ideal to weight the \(L_{grid}\) equally to \(L_{box}\). We use \(\lambda\) to weight the loss, and the final loss function is designed as follows:  
\[L=\lambda L_{grid}+(1-\lambda)L_{box}. \tag{3}\]
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 3 Methodology
### Traffic Information Detection Process  
The traffic information detection method based on Wi-Fi data is shown in Figure 3. Firstly, the Wi-Fi data preprocessing process is designed. Secondly, the calculation process of the space mean speed of the road section and the vehicle steering ratio information of the road entrance section is designed. Then, the traffic information prediction model of the road entrance section based on the LSTM network is constructed to achieve the prediction of the space mean speed and the vehicle steering ratio.  
Figure 3: Flow chart of traffic information detection method based on Wi-Fi data.  
Figure 2: Structure of LSTM neural network model.
# Deep Reinforcement Learning-Based Joint Scheduling of 5G and TSN in Industrial Networks
## 4 Markov Decision Process Modelling
### State Space  
Usually, the state is an abstraction of the current environment, and the agent acts as an intelligent decision-maker, and makes decisions only according to the state observed from the environment. In this article, the agent makes scheduling and resource allocation decisions based on the state of each application queue in gNB, channel quality of each UE and GCL setting for a time-triggered queue in DS-TT. Therefore, the state space \(\mathbf{S}\) is defined as:  
\[\mathbf{S}=\left\{\mathbf{G},\mathbf{L},\mathbf{D}\,I_{\text{GCL}}^{TT}\right\} \tag{8}\]  
Vector \(\mathbf{G}\) denotes the radio channel quality of all activate flows in a 5G system. Vectors \(\mathbf{L}\) and \(\mathbf{D}\) indicate the queueing packet number and queueing delay of each flow in 5G gNB, respectively. Meanwhile, variable \(I_{GCL}^{TT}\in\{0,1\}\) denotes the GCL state in DS-TT for a time-triggered application, \(I_{GCL}^{TT}=1\) means the GCL state of a time-triggered queue in DS-TT is \"OPEN\" and \(I_{GCL}^{TS}=0\) means \"CLOSED\" correspondingly.  
In order to facilitate the training process of neural networks, some simplification and normalization works are made for the elements in state space. Generally, the Signal-to-Interference-and-Noise Ratio (SINR) denotes the radio channel quality. However, the value of the SINR is consecutive and not suitable for training; it is preferable to choose the discrete variables with a finite value as the environment state. Therefore, we translate the SINR to the required RB numbers of flow \(i\) in each TTI. As shown in Equation (9), the maximum bits that can be transmitted in one RB is calculated [10].  
\[m_{i}=B\ \log_{2}(1+g_{i})-Q^{-1}(\varepsilon)\sqrt{V(g_{i})} \tag{9}\]  
where \(\varepsilon\) is the tolerable bit error rate, \(Q^{-1}(\varepsilon)=\frac{1}{\sqrt{2\pi}}I_{\varepsilon}^{\infty}e^{-\frac{ \varepsilon^{2}}{2}}dt\), \(V(g_{i})=\left(\log_{2}(e)\right)^{2}\)\(\left(1-\frac{1}{(1+g_{i})^{2}}\right)\), and \(B\) is the frequency bandwidth of one RB. According to Equation (9), the required RB numbers of flow \(i\) can be obtained from Equation (10).  
\[r_{i}^{expt}=\frac{s_{i}\times l_{i}}{m_{i}}\forall i\in\mathbf{F},l_{i}\in \mathbf{L} \tag{10}\]  
where \(r_{i}^{expt}\) indicates the required RB numbers of flow \(i\); \(s_{i}\) and \(l_{i}\) are the packet size and packet number in the queue of flow \(i\), respectively. Then, the radio channel state \(\mathbf{G}\) can be replaced by the required RB numbers of all flows denoted as \(\mathbf{R}^{expt}\). Furthermore, in order to reflect the relationship between queueing delay in gNB and EMLR, \(d_{i}\in\mathbf{D}\) is normalized as follows,  
\[d_{i}^{nor}=\frac{d_{i}^{gnb}}{d_{i}^{g0}} \tag{11}\]  
Finally, the state space can be expressed as  
\[\mathbf{S}=\left\{\mathbf{R}^{expt},\mathbf{L},\mathbf{D}^{nor},I_{GCL}^{TT}\right\} \tag{12}\]
## 2 Remote Sensing in Peatland Research
### Temporal and Spatial Pattern of Using RS in Peatland Studies
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Climate Regions** & **Number of Studies** & **Locations** & **References** \\ \hline \multirow{3}{*}{Subarctic, boreal} & \multirow{3}{*}{13} & Sweden (1), Arctic circle (1), & [17, 21, 22, 25, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38] & [17, 21, 22, 25, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38] \\  & & Canada (3), Finland (6), & [33, 34, 35, 36, 37, 38] \\  & & Alaska (USA) (1), others (1) & \\ \hline \multirow{3}{*}{Cool temperature} & \multirow{3}{*}{10} & Northern Hemisphere (2), & \\  & & Canada (7), Northern & [20, 24, 27, 39, 40, 41, 42, 43, 44, 45] \\  & & America and Scandinavia (1) & \\ \hline \multirow{3}{*}{Oceanic temperate} & \multirow{3}{*}{12} & Ireland (3), England (4), & \\  & & Wales (1), Argentina (1), & [23, 26, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38] & \\  & & Bolivia (2), Chile (1) & \\ \hline \multirow{3}{*}{Temperate} & \multirow{3}{*}{7} & Poland (2), Germany (2), & \\  & & Russia (2), France (1) & [18, 56, 57, 58, 59, 60, 61] \\ \hline \multirow{3}{*}{Subtropical, tropical} & \multirow{3}{*}{17} & Florida (USA) (1), Indonesia & \\  & & (11), Malesia (4), Ghana (1), & [62, 63, 64, 65, 66, 67, 68] \\ \cline{1-1}  & & Ecuador (1) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geographic characteristics of reviewed studies (prepared based on works provided in the Supplementary Material, Table S2). Figure 1: Cumulative number of reviewed studies during 2010–2021 and dates of launching selected satellite remote sensing systems (prepared based on works provided in Supplementary Material, Table S1). It is also visible that the analysis of multispectral data prevails among the used methods (Figure 2). As many as forty-seven analysed articles employed data from multi- and hyperspectral analyses, and only seventeen publications made use of passive remote sensing data (Figure 2A). The largest part of the analysed studies used readily available data from the Landsat and Sentinel-2 systems and the SPOT, MODIS, and ASTER systems (Figure 2B). The intensive development and availability of data from UAVs was also reflected in more recent research on peatland areas [33; 34; 46].
## 4 Case Study and Discussion  
### Basic Information for the IFGD Process  
To evaluate the identification performance of the developed approach, the mean absolute percentage error (MAPE) and root mean squared error (RMSE) are employed; they are defined as,  
\[\text{MAPE}(t)=\frac{1}{N}\sum_{i=1}^{N}\frac{\|y(t)-\hat{y}(t)\|}{\|y(t)\|} \tag{12}\]  
\[\text{RMSE}(t)=\sqrt{\frac{1}{N}\sum_{i=1}^{N}{(y(t)-\hat{y}(t))^{T}(y(t)- \hat{y}(t))}} \tag{13}\]  
where, \(N\) is the total number of observations, and \(y(t)\) and \(\hat{y}(t)\) are, respectively, predicted and real process response. As for the experimental setup, all simulations are implemented on a PC computer with a NVIDIA Quadro P620 GPU, a 2.60 GHz Intel i7-10750 processor and 32 GB of memory.  
In our case study, we focus on the desulfurization system (see Figure 5) at a coal-fired power station with a 600 MW unit in China, and different modeling approaches are tested based upon collected historical running data. Since variations in electric power loads may necessitate changes in boiler load, the boiler loads and thus the operation mode of the IFGD process in various seasons differs significantly. Furthermore, slight fluctuations of daily boiler load are known to exist. The dataset consists of four parts, and each part encompasses a weekly observed data in a season; they are from 10 March 2021 to 17 March 2021, May 2021 to 28 May 2021, August 2021 to 12 August 2021 and 18 November 2021 to 25 November 2021. All data were sampled every 10 min within a fixed time interval in a day. Every part in dataset is separated into three subsets for training (70%), testing (15%) and validation (15%). Among three subsets, the training set is used for model building; the validation set serves as the early stopping criterion as well as the reference dataset to choose the best combination of hyperparameters, while the testing set plays the role of evaluating the final identification performance of the established model. To better understand the IFGD process under study, Table 1 lists the key statistical characteristics of variable slurry pH and slurry flow for four seasons, and slash is utilized to distinguish between two variables, wherein the former corresponds to process input (limestone slurry flow) while the latter is the output (slurry pH).
## 4 Temperature Field Prediction Model Application
### Results and Comparison  
The simulation and monitored temperatures of the motorized spindle were compared to determine the accuracy of the model. Figure 10 shows this comparison after the heat transfer coefficient was optimized for 100 iterations. Figure 10a shows the results for 10,000 rpm and Figure 10b for 15,000 rpm. From Figure 10, it is clear that the predicted temperatures were very close to the experimental measurements for the latest time steps.  
To further validate the temperature field of the prediction model of the motorized spindle, it is necessary to compare the simulation temperatures and test temperatures of other locations on the motorized spindle, and to calculate the overall error in the model. In the test system, the temperature of water at the outlet was assumed to be consistent with that of the water jacket at the given location. The temperature of water at the outlet can be determined; therefore, the temperature of the water  
Figure 10: Comparison between the simulation temperature and experimental measurements. (**a**) Spindle speed of 10,000 rpm, (**b**) Spindle speed of 15,000 rpm.  
Figure 9: Isotherm of the motorized spindle at 15,000 rpm. (**a**) 0 iteration, (**b**) 100 iterations.  
jacket outlet could be used to determine the accuracy of prediction. The position of the outlet is shown in Figure 1. The relative mean error of the predictions of the model is  
\[AARE=\frac{1}{n}\sum_{t=1}^{n}\left|\frac{T_{t}-\widehat{T}_{t}}{T_{t}}\right|= \frac{1}{n}\sum_{t=1}^{n}\left|\hat{a}_{t}\right| \tag{12}\]  
The variance \(s^{2}\) and standard deviation s of the prediction model are  
\[s^{2}=\frac{1}{n}\sum_{t=1}^{n}\left(T_{t}-\widehat{T}_{t}\right)^{2}=\frac{ 1}{n}\sum_{t=1}^{n}a_{t}^{2} \tag{13}\]  
\[s=\sqrt{\frac{1}{n}\sum_{t=1}^{n}\left(T_{t}-\widehat{T}_{t}\right)^{2}}=\sqrt {\frac{1}{n}\sum_{t=1}^{n}a_{t}^{2}} \tag{14}\]  
Figure 11 shows the predicted and measured differences in temperature at the water outlet. Both the table and the figure show data pertaining to the temperature of the water jacket outlet for spindle speeds of 10,000 rpm and 15,000 rpm. Also shown in Figure 11 is the prediction error in the model. According to Formulae (12)-(14), when the spindle speed was 10,000 rpm, the relative mean prediction error was 1.5%, variance was 2.87 \(\times\) 10\({}^{-4}\), and the standard deviation was 0.017. At 15,000 rpm, the prediction error was similarly small, the relative mean prediction error was 3.6%, variance was 2.0 \(\times\) 10\({}^{-3}\), and the standard deviation was 0.045.
## 3 Heterogeneous Parallel Implementation  
### Description and Advantages of DCU Architecture  
A DCU (Deep Computing Unit) is a specially designed accelerator card used for heterogeneous computing. It connects to the CPU via the PCI-E bus and aims to enhance computational performance, particularly for computation-intensive program modules or functions. The DCU operates independently of the CPU, featuring its own processor, memory controller, and thread scheduler, enabling it to function as an autonomous computing system.  
The system architecture of the DCU includes the data pathway (DMA) between the CPU and DCU, compute unit arrays (CU), a multilevel cache system (including Level 1 and Level 2 caches), and global memory. Inside the DCU, there are multiple compute unit arrays, with each compute unit (CU) consisting of four SIMD units. Each SIMD unit includes multiple stream processing units responsible for performing basic arithmetic operations such as addition, subtraction, and multiplication. These stream processing units are highly optimized for high-speed data processing and parallel computing, greatly enhancing the processor's computational efficiency. Its streamlined and efficient instruction set enables the DCU to effectively accelerate computational processes in various computation-intensive scientific and engineering applications, making it a vital tool for enhancing processing capabilities.  
Advantages of DCU Architecture Compared to Existing Exascale Architectures: The DCU architecture uses the latest 7 nm FinFET technology and is designed based on microarchitecture for large-scale parallel computing. It not only has powerful double-precision floating-point computation capabilities but also excels in single-precision, half-precision, and integer computations. It is the only acceleration computing chip in China that supports both full-precision and half-precision training.
# Agriculture 4.0: Is Sub-Saharan Africa Ready?
## 2 Prospects and Challenges for Agriculture 4.0 Adoption in Sub-Saharan Africa
### Scale
The livestock sector has experienced its own \"revolution\", which has paralleled what has been referred to as \"Industry 2.0\", the mass production of goods to increase productivity [3]. Particularly in higher-income countries, livestock farming has shifted away from home-based animal husbandry towards intensive, often large-scale, farming [3]. Agriculture 4.0 technologies can be found in, for example, livestock monitoring and biosensing, which can be used to detect and identify infectious diseases, drug residues, and ovulation prediction [53]. Many of these technologies are linked to intelligent animal health monitoring systems, in which animals may wear the technology [3]. Agriculture 4.0 approaches have also been linked to ecologically friendly efficient farming approaches, that could include monitoring emissions from livestock [54]. In many SSA countries, the scale and intensity of the livestock sector differ considerably from the large-scale farming found in HICs. However, although in some higher-income countries per capita consumption of meat is plateauing or even falling, in SSA and other low- and middle-income countries (LMIC), consumption continues to increase, with pork, beef, and chicken being the most consumed meats [54], and with this increase in consumption comes increases in GHG emissions. This suggests that in the future, agriculture 4.0 technologies relevant to livestock will be increasingly important for SSA.
## 2 Related Work
### Multi-Degree-of-Freedom Target Motion Planning  
Multi-degree-of-freedom (multi-DOF) robots have been widely used in social production and life (for example, 6-DOF robot arm, human service robot, industrial production line etc.). An example of path planning in a complex environment is the palletizing activity of a six-degree-of-freedom manipulator. Although the scope of activity is small, it is prone to excessive calculations due to multiple dimensions in a single environment, causing the system to crash or stagnate. Meanwhile, under relatively complex background conditions, wrong planning occurs due to obstacle detection deviations. The result of this situation is devastating to the equipment. L Previous work on motion planning mainly focused on posture adjustment and Slam Mapping [34]. Firstly, the environment needs to be scanned, and a priori processing is performed in advance. Therefore, it is not suitable for motion planning that requires the position and gait of object instances in an unknown environment task.  
Motion and path planning of multi-degree-of-freedom targets has attracted increasing interest in recent years. DeepMapping [35] proposed a novel registration framework that uses DNN (Deep Neural Networks) [36] to simulate the highly non-convex mapping process, which can usually achieve more robust and accurate multi-point cloud global registration, thereby planning high-dimensional spatial moving objects. However, the data generated by it has a large amount of calculation and is inefficient for real-time tasks and requires high sensors. Real-time path planning [37] integrates the PRM (Probabilistic Roadmap) used for changing the environment with 3D sensor data, uses high-speed sensors to scan real-time obstacles, and then performs high-dimensional spatial motion planning. This method is safe and efficient but depend on hardware.  
In view of the previous local planning, HDPP designed a hierarchical discrete distribution decomposition framework to learn probability points and regional matching, which can simulate not only matching uncertainty, but also regional propagation. Compared with these algorithms, our approach can generate fewer key waypoints, and at the same time has a better migration planning effect in high-dimensional space planning tasks.
# Long-Term Stability of Class II Malocclusion Treated with Maximly Molar Distalization
## 1 Introduction  
Congenital tooth absence, early loss of primary teeth, and interproximal caries can lead to the mesial movement of the maxillary first molars, resulting in dental Class II malocclusions. There are several techniques for treating this anteroposterior discrepancy, which is certainly influenced by the patient's etiology, growth pattern, age, levels of cooperation, and, most importantly, chief complaint [1]. Most orthodontists tend to prefer nonextraction treatment when clinically appropriate; moreover, some patients completely refuse tooth extraction regardless of clinical recommendations [2, 3, 4].  
Distalization of the maxillary teeth up to the molar, canine, and incisor Class I relationship is the treatment for Class II malocclusion with no major skeletal involvement. This treatment does not involve any skeletal changes or modifications in the vertical dimension, nor does it affect the mandibular arch by distalizing the maxillary molars [5, 6].  
Both extraoral [7, 8, 9] and intraoral [10, 11, 12] appliances are utilized to distalize molar teeth. Generally, distalization appliances seem to correct Class II molar relationships primarily by dentoalveolar modifications rather than through maxillary growth inhibition [13, 14].  
Although mesial migration is a physiological phenomenon, this situation may imply a decrease in the long-term stability of molar distalization outcomes [15]. Because molar distalization involves movement in the opposite direction to physiological drift, long-term stability issues may arise. While a few studies [1, 10, 16, 17, 18] have evaluated the effects and short-term results of distalization, limited data exist regarding the long-term outcomes following comprehensive fixed orthodontic treatment [15, 19, 20, 21].  
The aim of this study was to evaluate the long-term stability of Class II malocclusion treatment achieved through maxillary molar distalization by using orthodontic models and lateral cephalometric radiographs. Our hypothesis was that the dentoalveolar corrections obtained through maxillary molar distalization followed by fixed orthodontic treatment would remain stable in the long term, and no significant relapse would occur.