esa-sceva/satcom-chunk-collection huggingface dataset:
# Chinese Lip-Reading Research Based on ShuffleNet and CBAM
## 3 Experiment
### Experiment Settings  
(1) Data Preprocessing:  
Firstly, we collect all the videos and put them in different folders according to the class label. We use python libraries os and glob to list and fetch all files respectively and return videos and labels as NumPy arrays. Then we use the OpenCV library to read 29 frames from each video and crop the lip area by detecting 68 landmarks of the face with the dlib library. Afterward, all the frames are resized to 112 \(\times\) 112, normalized, and converted to grayscale. Video data augmentation also proves necessary to overcome the problem of limited diversity of data, including Random Crop, Random Rotate, Horizontal Flip, Vertical Flip, and Gaussian Blur.  
(2) Parameters Settings:  
We utilize the open-sourced libraries Tensorflow and Keras, which provide high-level APIs for easily building and training models. The model is trained on servers with four NVIDIA Titan X GPUs. We split the dataset into the training dataset and the test dataset using a ratio of 8:2 and set the epoch and batch size to 6032 using the Adam optimizer with an initial learning rate of 3 \(\times\) 10\({}^{-4}\). The frontend and backend of the network are pretrained on LRW. Dropout is applied with a probability of 0.5 during the training and finally the standard Cross Entropy loss is used to measure how well our model performs.  
Figure 11: (**a**) Pinyin with similar lip movement (**b**) corresponding lip movement.
## 3 The Main Parameters and Indices of MOT
### Application Requirement Analysis  
Conventional performance indices can accurately reflect the trapping capability of the MOT for gaseous atoms and can be used to assess the performance of the MOT device. However, using MOTs to obtain cold atomic groups is just the initial step for various cold atom interferometers. To evaluate MOT performance, we should not only consider conventional performance indicators but also analyze the development requirements of various cold atom interferometers. Thus, we can propose application performance indicators that are more suitable for practical use.  
The cold atom interferometer primarily includes an atomic clock, an atomic gravimeter, an atomic gravity gradiometer, and an atomic gyroscope [47; 48; 49; 50; 51]. The development of these devices has focused on three main aspects.
# Stable Throughput Analysis of Heterogeneous Hybrid FSO/RF Networks with Cognitive Radio Capability
## 3 Stable Throughput of Reference System (REF)  
With REF model, both RO pair and HY pair have licensed RF \(f_{1}\) and \(f_{2}\), respectively. It is assumed that each transmitter has no CR and uses its frequency only. Therefore, there is no interference signal caused by borrowing the frequency, and accordingly, \(v_{j,n,0}=v_{j,n,1}\) holds.
###### Abstract  
Communication using millimeter wave (mmWave) and terahertz bands between unmanned aerial vehicles (UAVs) is a crucial technology for the realization of non-terrestrial networks envisioned for Beyond 5G. While these frequency bands offer remarkably high-speed transmission capabilities of tens of Gbps and above, they possess strong directivity and limited communication range due to the requirement of high-gain antennas to compensate for substantial propagation loss. When a UAV employs radio of such a high-frequency band, the available communication time can be less than one second, and the feasibility of leveraging this ultra-narrow zone, which is only accessible for a short duration in a confined space, has not been investigated. This paper presents the theory behind the ultra-narrow zone in frequencies beyond mmWave and explores the data transfer characteristics at 60 GHz between two UAVs. We demonstrate the transmission of 120 MB of data within approximately 500 milliseconds utilizing the instantaneous proximity opportunity created as the UAVs pass each other. Additionally, we evaluate data transfer while the UAVs maintain a fixed distance, to sustain the 60 GHz link, successfully transmitting over 10 GB of data in the air with a throughput of approximately 5 Gbps.  
unmanned aerial vehicle (UAV); drone; mmWave; 60 GHz band; ultra-narrow spot; device-to-device (D2D) communications; proximity opportunities; non-terrestrial network Article
# Fast and Robust Asynchronous Rendezvous Scheme for Cognitive Radio Networks
## 5 Performance Evaluation
However, as the \(G\) increases, the average TTRs of both asymmetric FRARS and EJS schemes are similar to each other. Moreover, it is clear that the average TTRs for both EJS and FRARS schemes significantly decrease when the \(G\) increases. Finally, for the jamming attack in an asymmetric scenario, Figure 10 shows the rendezvous probability of the asymmetric EJS and FRARS schemes under the CDJA attacks when the ratio \(|m_{i}|/|M|=0.5\) and \(M=\{40,100\}\). Under the CDJA attack, it is clear that the asymmetric EJS system's rendezvous probability decreases dramatically regardless of the value of \(M\). As the number of common channels increases to \(G=20\), the probability of rendezvous increases slightly, and it does not exceed \(15\%\). However, the rendezvous probability of the FRARS schemes for both the \(M=40\) and \(M=100\) cases are \(100\%\) within the maximum time slots that was the duration of the runs for this experiment. The reason for this is that in FRARS scheme, since the repetition of the CH sequence of the sender is independent of the previous repetition, it is almost impossible for the jammer to estimate the CH sequence of the sender.
## 3 System Model
### Problem Formulation  
For the problem of computational task offloading, the variable \(x_{n,k}\in\{0,1\}\) is defined, in order to express whether to offload the task \(u_{n,k}\) to the edge cloud \((x_{n,k}=1)\) or not \((x_{n,k}=0)\).  
Based on the aforementioned descriptions, the goal of the presented problem is to minimize the total energy consumption consumed by mobile devices, can be shown below:\[\begin{array}{ll}\min_{x_{n,k}}&\sum E_{n,k},\\ s.t.&\text{C1:}\sum_{n=1}^{N}x_{n,k}f_{n,k}^{c}\leq c_{s},&\forall k\in K\\ &\text{C2:}\ T_{n,k}\leq D_{n,k},&\forall n\in N,\forall k\in K\\ &\text{C3:}\ x_{n,k}\in\{0,1\},&\forall n\in N,\forall k\in K\end{array}. \tag{7}\]  
The constraint condition (C1) guarantees the total computation resources required to offload tasks must be less than total capacity of the edge cloud during each time slot. The constraint (C2) shows that the task \(u_{n,k}\) must be completed within the time limit. The constraint (C3) indicates that the decision variable for task offloading is a binary data.  
The objective function of our presented problem is defined as follows:  
\[E_{n,k}=\begin{cases}E_{n,k^{\prime}}^{l},&x_{n,k}=0\\ E_{n,k^{\prime}}^{c},&x_{n,k}=1\end{cases} \tag{8}\]
###### Abstract  
Moving objects in the environment have a higher priority and more challenges in growing domains like unmanned vehicles and intelligent robotics. Estimating the motion state of objects based on point clouds in outdoor scenarios is currently a challenging area of research. This is due to factors such as limited temporal information, large volumes of data, extended network processing times, and the ego-motion. The number of points in a point cloud frame is typically 60,000-120,000 points, but most current motion state estimation methods for point clouds only downsample to a few thousand points for fast processing. The downsampling step will lead to the loss of scene information, which means these methods are far from being used in practical applications. Thus, this paper proposes a motion state estimation method that combines spatio-temporal constraints and deep learning. It starts by estimating and compensating the ego-motion of multi-frame point cloud data and mapping multi-frame data to a unified coordinate system; then the point cloud motion segmentation model on the multi-frame point cloud is proposed for motion object segmentation. Finally, spatio-temporal constraints are utilized to correlate the moving object at different moments and estimate the motion vectors. Experiments on KITTI, nuScenes, and real captured data show that the proposed method has good results, with an average vector deviation of only 0.036 m and 0.043 m in KITTI and nuScenes under a processing time of about 80 ms. The EPE3D error under the KITTI data is only 0.076 m, which proves the effectiveness of the method.  
moving object, point cloud; motion segmentation; motion state estimation +
Footnote †: journal:  
0000-0001-1989-6900-3300][PERSON]
# Technical Performance of an Inflatable Solar Dryer for Drying Amaranth Leaves in Kenya
## 3 Results and Discussion  
### Estimated Performance of the ISD Solar System  
Figure 3 presents solar radiation of Nairobi on 8-11 December 2016, together with the estimated course of SOC and voltage of the battery and resulting air mass flow of the two fans of the ISD. The fans were assumed to be switched off daily from 18:00 to 08:00, which was indicated by constant SOC and zero voltage in the simulation. When the solar system was switched off, the lowest battery SOC was noted as 76.3%. During operation, battery voltage and air mass flow of the fans varied from 12.3 to 13.1 V and from 0.72 to 0.76 kg/s, respectively. Total energy consumption during an assumed drying run of 72 h was 1587 Wh. Using a solar battery with an energy storage capacity of 912 Wh and two PV modules with a total capacity of 200 W\({}_{\mathrm{P}}\), the solar system was sufficient to power the fans without any interruption. As for comparison, measured data from air mass flow were added. A difference between the measured and the simulated air mass flow ranged between 0.03 and 0.10 kg/s. It could correspond to turbulent airflow at the outlet and any small leaks along the ISD.
# Saturation, Allowed Transitions and Quantum Interference in Laser Cooling of Solids
## 2 Theory
### Improved Cooling Efficiency Using Optical Saturation
Figure 3 presents a theoretical plot of the cooling efficiencies given by Equation (10) at two pump intensities, namely \(I=3\times 10^{2}\) W/cm\({}^{2}\) and \(I=3\times 10^{4}\) W/cm\({}^{2}\). The first thing to notice is that at long wavelengths (>1 \(\upmu\)m), the cooling efficiency increases at the higher pump intensity because the background impurities are easier to saturate than Yb\({}^{3+}\) near their own broad resonance. Such long wavelengths are highly detuned from the absorption peak of Yb\({}^{3+}\) (Figure 2). Naturally, saturation of the background reduces parasitic heating, leading to a significantly higher cooling efficiency in the long wavelength range. Closer to the absorption resonances of Yb\({}^{3+}\), at wavelengths of less than \(\sim\)1 \(\upmu\)m, the saturation intensity of the coolant ions drops, falling to a value below that of the background because the detuning is small. This raises the heat load and lowers the cooling efficiency at short wavelengths. Between these opposing trends, there is an intersection point in the plots versus wavelength of the saturation intensities (Figure 2) on the \"red\" side of the Yb\({}^{3+}\) absorption band. The same is true of the intensity-dependent cooling efficiency plot (Figure 3). In Figure 3, this crossing point can be seen around 1010 nm, identifying a wavelength \(\lambda_{cr}\), where the saturation intensities of the coolant ion and the background impurities are equal. The efficiency curves for all the input intensities cross at \(\lambda_{cr}\), so the cooling efficiency is intensity-independent there. As the experimental saturation intensity of the background impurities equals that of Yb\({}^{3+}\) at \(\lambda_{cr}\), it can be determined by inserting \(\lambda_{cr}\) into Equation (11), yielding the result \(l_{b}=l_{r}(\lambda_{cr})\).
# A Novel Wavelet-Based Algorithm for Detection of QRS Complex
## 3 Methodology
### Determination of Candidate Extremum Pairs  
Figure 7 illustrates an extremum pair that is produced by a QRS peak in d3'. It includes a positive and a negative extreme point. Let the coordinates of the first and second extreme points, M\({}_{1}\) and M\({}_{2}\), be \((x_{1},~{}y_{1})\) and \((x_{2},~{}y_{2})\), respectively. A candidate extremum pair corresponding to a possible QRS peak should meet the following conditions.  
Condition 1: An extremum pair consists of a positive extreme point followed by a negative extreme point or a negative one followed by a positive one. This condition makes an extremum pair represent a positive or negative peak in the QRS wave.  
Condition 2: The positive and negative extreme values, \(y_{1}\) and \(y_{2}\), of a candidate extremum pair are larger and smaller than the positive and negative thresholds defined in Equations (6) and (7),  
Figure 7: Illustration of an extremum pair in d3’.  
respectively. This condition makes a candidate extremum pair represent a peak with a large enough amplitude in the QRS wave.  
Condition 3: The distance between the first and second extreme points of a candidate extremum pair, \(x_{2}-x_{1}\), is less than 45 samples. This condition makes a candidate extremum pair represent a sharp enough peak in the QRS wave. This distance ranges from about 5 to 20 samples for narrower R waves produced by normal, paced, atrial premature beats, etc., and ranges from about 21 to 44 samples for wider R waves mostly produced by ventricular premature contraction beats.  
According to the above three conditions, we can determine the candidate extremum pairs in d2', d3', and d4'.
# Quality of Service Based NOMA Group D2D Communications
## 1 Introduction  
With the advancement in mobile communication research, the usage of cellular technology has spread beyond voice and simple data transfer to high data rate, delay sensitive, and loss tolerant multimedia applications. Despite the fast growth of fourth generation (4G) systems, the current spectrum resources are still scarce to meet the ever increasing subscribers' demands for bandwidth and resource hungry applications, with vigorous requirements of seamless connectivity, anywhere and anytime. These trends compelled wireless researchers from the academia and industry to define new paradigm technologies and structures to achieve the goals of fifth generation (5G) systems [1; 2].  
In order to realize the concept of 5G into reality, many enabling technologies are proposed, among which millimetre waves, massive multiple-input multiple-output (MIMO), full-duplex (FD), heterogeneous deployments and software-defined networks have captured the attention of both academia and industry [2; 3]. Nevertheless, the role of multiple access scheme always remains a vital factor in cellular networks in order to enhance the system capacity in a cost effective manner, while utilizing the bandwidth in such a way that overall spectral efficiency will be increased [4].  
Non-orthogonal multiple access (NOMA) is considered as a promising multiple access candidate for future fifth generation (5G) wireless systems due to its potential of improving spectrum efficiency [5; 6]. Unlike conventional orthogonal multiple access (OMA), NOMA superimposes message signals of different users in power domain and send this conglomerate signal using the same time,frequency or code resource [7; 8]. Successive interference cancellation (SIC) technique is employed at each receiver to cancel the intra-user interference [9].  
Apart from NOMA, another emerging technique to enhance the spectral efficiency is device-to-device (D2D) communications [10], which has the ability to improve the spectral efficiency of the conventional cellular network by sharing same spectrum resources among cellular users and D2D pairs [11; 12; 13].  
By introducing aforementioned two concepts and their potential to improve spectrum efficiency, it is natural to investigate the application of NOMA scheme to D2D communications.
# Portable Protein and Fat Detector in Milk Based on Multi-Spectral Sensor and Machine Learning
## 2 Materials and Methods
### XGBoost Algorithms  
A complex nonlinear relationship exists between milk composition and the corresponding spectral data, and the model trained using the XGBoost machine learning algorithm can fit this nonlinear relationship flexibly and facilitate accurate predictions. The XGBoost algorithm [28] is a gradient-boosting-based machine learning algorithm that builds a stronger model by integrating several weak models to solve predictive regression problems. It has several advantages compared to other machine learning models, including high prediction accuracy, high speed, and good generalization ability.  
The model is continuously iterated, and each iteration generates a weak learner. The model is trained based on the residuals of the previous round, which facilitates the continuous improvement of prediction accuracy. Finally, all weak learner outputs are accumulated to build a more accurate model.  
The objective function of the XGBoost algorithms is given as follows:  
\[Obj=\sum_{i}^{n}l(y_{i},\hat{y_{i}})+\sum_{t}^{T}\Omega(f_{t}), \tag{2}\]  
where \(l(y_{i},\hat{y_{i}})\) is the loss function, \(y_{i}\) is the true value, \(\hat{y_{i}
## 3 Proposed Scheme
### Metaheuristic-tabu Search Algorithm
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# Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids
## 4 Results and Discussion  
### Case 1: Implementation of S-MPC  
The non-optimal and optimal control (S-MPC) were compared for 96 h (four days) in this case. In other words, in case 1, the emphasis was on the optimization of S-MPC and its effect on the power flows of the system (Figure 6a) and the SOC of the battery (Figure 6b). Non-control methods employ simplistic control strategies or heuristic rules, disregarding the system's dynamic nature. In addition, they do not have any constraints, so there are disadvantages associated with this strategy, such as poor SOC management,  
Figure 5: Flow chart of the proposed method.  
potential deviations from desired SOC levels, and an inability to adapt to changing system conditions. As shown in Figure 6b, the SOC of the battery went below the critical value (20%), since the non-optimal method had no constraints. In contrast, the S-MPC method is an alternative to the non-optimal control method. S-MPC's ability to dynamically select the appropriate model and control strategy based on the system's current state is one of its key advantages. S-MPC optimizes control actions to achieve the desired performance objectives, particularly in effectively managing the SOC of a battery.  
The S-MPC controller was designed to select the optimal model and control strategy based on the current operating mode and performance objectives. S-MPC enables enhanced power flow control and EM by effectively adapting to the changing dynamics of the hybrid MG, utilizing distinct models for each state. Case 1's implementation of the S-MPC controller successfully optimized the hybrid MG system's power flows. It substantially reduced energy imports and increased energy exports, resulting in a more efficient use of resources and enhanced energy flow management. The improved control strategy enabled the MG to operate closer to its optimal performance, enhancing its dependability and reducing the operational costs. However, it is important to note that developing and implementing the S-MPC controller for the hybrid MG system presented obstacles due to the complex switching conditions and multiple operating modes. To guarantee the selection of the optimal model and control strategy, the switching logic had to be meticulously designed. The controller's increased complexity required more computational resources than traditional MPC methods. The model's computational time was almost 405 s.