esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 5 Conclusions  
In this paper, we focused on routing optimization based on the multiparameter fusion decision on the energy-constrained wireless sensor networks. The MPFDR algorithm comprehensively considers the residual energy, forward distance, single-hop transmission energy, buffer queue, and energy equilibrium degree for choosing the optimum next hop; hence, the algorithm can realize a multidirectional reflection of the network state. Meanwhile, combined with the defined routing strategy, the fusion contribution degree of each forward neighbor is obtained. Then the node with the maximum fusion contribution degree will be selected as the next hop.  
The simulation results show that our algorithm has better congestion control ability; that is, there are a lower packet loss rate and a lower transmission delay (average hops). The latency does not change dramatically with the increase of network size. Moreover, while ensuring low latency, the average energy consumption is adequately controlled. The result also shows that the MPFDR algorithm is more suitable for delay-sensitive applications, for example, substation environmental monitoring or medical equipment monitoring. In future research, we will focus on the application of the algorithm, mainly with the implementation of WSNs in power monitoring and industry 4.0 environments. For this, we will design a validation scheme, including three parts: (1) we will develop the WSN node based on the ZigBee protocol. (2) Scene 1: we randomly deploy nodes in a flat outdoor region, with a sink node located in the center, to verify performance in the line-of-sight environment. (3) Scene 2: we will deploy nodes in the building region to verify performance in a non-line-of-sight and object shielding environment. Through the scheme, we will attain our algorithm performance data in a real scenario and further analyze and improve the algorithm. In the future, we will study how to reduce transmission delays and improve transmission reliability, to meet the requirements of power accuracy control.  
Figure 5: (**a**) PLR versus different number of nodes; (**b**) AH versus different number of nodes.  
**Author Contributions:** Conceptualization, J.Y.; methodology, J.Y. and L.T.; software, J.C. and Z.L.; validation, J.Y.; formal analysis, J.Y., J.C. and L.T; investigation, J.Y.; resources, R.W. and L.T.; data curation, Z.L. and J.C.; writing--original draft preparation, J.Y. and J.C.; writing--review and editing, J.Y. and L.T.; visualization, J.C.; supervision, R.W. and L.T.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Fundamental Research Funds for the Central Universities 2016 MS45.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 2 Satellite Systems in Remote Sensing
### Satellite Sensors and Satellite Instruments  
Satellite sensors are divided into active and passive depending on the way they transmit signals, while microwave remote sensing instruments are combinations of active and passive remote sensing.  
**Active sensors** are equipped with radiation-transmitting equipment, such as a transponder, which allows the transmission of a signal directed towards a target (usually Earth) and the detection of the target-reflected radiation to be measured by the sensor, emulating a source of light. They are fully functional at any time, since they are relatively independent of atmospheric scatterings and sunlight. These devices usually employ microwaves due to their relative immunity to weather conditions and different techniques based on broadcasts (e.g., light or waves) and measures (e.g., distance, height, atmospheric conditions, etc.). Some active sensors are SAR, Lidar, Sounder, and Scatterometer.  
**Passive sensors** measure naturally emitted energy, such as reflected sunlight, because they do not streamline their own energy to the investigated area. They require appropriate weather conditions and sunlight. Multispectral and hyperspectral sensors are used to measure the desired quantity using various band combinations. Passive remote sensing devices include (1) spectrometers to distinguish and analyze spectral bands; (2) radiometers to measure the strength of electromagnetic radiation in some spectral bands; (3) spectro-radiometers to measure parameters regarding cloud features, sea color, temperature, atmosphere chemical traces, or vegetation; (4) imaging radiometers that provide a 2D array of pixels for image generation; and (5) accelerometers to detect changes in speed to distinguish between those caused by the influence of gravity and those caused by atmosphere air drag on the satellite.
## 5 Discussion and Conclusions  
To the best of our knowledge, there are no current papers that address the issue of energy consumption and latency reduction in a blockchain UAV network. The majority of the papers touch on the subject of improving security [19; 20] but do not take into account the amount of computing resources which is needed to complete these operations. Therefore, we think that this research is a very important topic to introduce to UAV networks that might suffer from less computational resources.  
In the following sections, we will explain some application scenarios where our proposed frameworks can be applied to help with the security and lessen the computational demand of the blockchain. Further on, we discuss some future research directions.
# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 2 The Proposed Methods
### Proposed Algorithm
#### 2.2.1 Proposed Structure of CNN
For a further understanding of deeper features, the convolution block was designed and set in the convolution path \(C_{i-j}\ (i=1,\ 2,\ 3;j=3,\ 4)\) and \(C_{3-2}\). Figure 3 depicts the structure of the convolution block. Firstly, the input blocks are convolved along principal and lateral branch paths to obtain two feature maps, Con-1 \(\times\) 1 and Con-2 \(\times\) 2, respectively, then to obtain the convolution blocks, the feature maps of the two different paths are stacked in the channel dimension, which can help the network learn about the many combinations of CU sizes. The convolution kernels of the main and side branch are \(1\times 1\) and \(2\times 2\), respectively. Meanwhile, the step size of both convolutions is set to 1, which aims to ensure that the feature maps of Con-1 \(\times\) 1 and Con-2 \(\times\) 2 have the same size to facilitate feature fusion. Those features contain rich texture information for learning CU partition. Secondly, the convolution kernels of the \(C_{i-1}\ (i=1,\ 2,\ 3)\) are set to half of the size of the current CU to obtain the global information of the image as much as possible. These features are closely related to the current CU partition. Finally, to fuse the feature maps of the two paths, the \(C_{i-4}\ (i=1,\ 2,\ 3)\) and \(C_{i-1}\ (i=1,\ 2,\ 3)\) channels are set to 64 and 128, respectively. In addition, a non-overlapping 2 \(\times\) 2 max pooling, computation with a step size 2 is used to filter the features in the main channel \(C_{i-j}\ (i=1,\ 2,\ 3;j=2,\ 3,\ 4)\). This drastically decreases the number of parameters and the risk of over-fitting while preserving the main features. **Concatenation Layer:** The fused feature maps obtained by the three branches are turned into three one-dimensional vectors respectively in the concatenation layer. The one-dimensional vector contains all the key information features of the current CU. **Fully connected layer**: In each of the three branches, the connection layer's three one-dimensional vectors are used as inputs to the fully connected layer. The input contains local features collected from various layers for each category. Furthermore, because QP is critical in the CU partition process, it is spliced into the fully connected layer of each branch as supplementary features. These above features will be integrated and categorized in this layer until the learning of all of the CTU partition rules is complete, and then the output results will be used to judge the current CU. Re LU and Sigmoid are used as the convolutional and output layer activation functions, respectively. Furthermore, the method proposed employs an early termination strategy  
Figure 3: Structure of the convolution block. to eliminate superfluous CU partition operations during coding. Table 1 displays the parameters of each layer of the proposed CNN, where full convolution, convolution, maximum pooling, and step size are abbreviated as FCon, Con, MP, and S, respectively.
## 3 Result
### Details of Short-Term Change in Chla Concentration  
Details of short-term change in Chla concentration can be detected by geostationary orbit satellite GF-4. As can be seen from Figure 7(a1,2), the Chla concentration, on 3 March 2017, is less than 14 \(\upmu\)g/L on the whole. At 11:12:34 s, the concentration of Chla was higher than 10 \(\upmu\)g/L along the coast of Liaodong Bay (the position shown in the red square in Figure 7(a3)) and Laizhou Bay (the position shown in the blue square in Figure 7(a3)), and lower than 5 \(\upmu\)g/L offshore of in Bohai Sea. After a few minutes, the Chla concentration changed, showing in Figure 7(a3), with the decrease in the south central part (the pink square in Figure 7(a3)) and increase in northeast part (red square in Figure 7(a3)). Furthermore, the change of Chla concentration in one minute can also been detected (Figure 7(b1-b3)),  
Figure 6: The distribution of Chla concentration in Bohai Sea. Figure 6a–f is the spatial distribution diagram of Chla concentration at different times after inversion using the empirical formula obtained, the unit is \(\upmu\)g/L. (**a**): The image Chla inversion concentration distribution map obtained on 9 June 2017; (**b**): The image Chla inversion concentration distribution map obtained on 10 June 2017; (**c**): The image Chla inversion concentration distribution map obtained on 30 June 2017; (**d**): The image Chla inversion concentration distribution map obtained on 12 July 2017; (**e**): The image Chla inversion concentration distribution map obtained on 12 January 2018; (**f**): The image Chla inversion concentration distribution map obtained on 6 September 2018.  
and the obvious increases appear in the top of northeast part and southwest (the red, blue, pink and green square in Figure 7(b3)) coastal line. However, on 6 September 2018 (Figure 7(c1-c3)), Chla concentration decreased, in southwest coastal line (the blue square in Figure 7(c3)) and increase in the northeast (the red square in Figure 7(c3)) and in the middle of the Bohai Sea (the pink square in Figure 7(c3)) in one minute, dramatically.
## 2 Basic Information
### The Digital Twin Concept  
A digital twin represents a digital replica of a physical entity [15] and belongs to the top 10 strategic technology trends for 2019 as proposed by the Gartner group [23]. The founder of this technology was the National Aeronautics and Space Administration (NASA) during the rescue mission of Apollo 13 in 1970. According to [PERSON], NASA chief flight director for Apollo 13, the simulators, supplementing the first digital twin were some of the most complex technology of the entire space program [24]. These simulators, running simultaneously with the damaged spacecraft, helped to bring the crew back to Earth alive. In particular, an analog simulation of the conditions in space and a virtual assembly enabled scientists on Earth to advise the crew by solving the problems in space.  
Digital twin technology has proven to be indispensable nowadays. IoT sensors enabled digital twins to become cost-effective, so they could become imperative to Industry 5.0 and play the main role in understanding, analyzing, and optimizing physical objects. In line with this, the digital twin integrates technologies such as artificial intelligence (AI) and machine learning (ML) [25]. The main domains of their applicability are forecasting problems, i.e., problems where one monitors changes in virtual models or predicts potential risks before applying new plans, which are necessary for Industry 5.0. Nowadays, digital twins solve various problems in medicine and sports.  
A scheme of the proposed digital twin is illustrated in Figure 1, from which it can be seen that this joins three different worlds, i.e., physical, analog, and digital, into a whole. The physical world (the environment) is sensed by the humans and their replicas (i.e., the digital twins) by sensors.  
Although a human senses the environment using five sensory organs (i.e., eye, ear, skin, nose, and mouth), a digital twin is capable of sensing only digital data acquired from digital sensors. Here, we are interested in digital sensors whose results are presented to humans in a user-friendly form (e.g., a display). Such senses can be processed by human brains and transmitted as data to the digital twin simultaneously.  
The human reacts to the senses with action, the selection of which is a consequence of the decision-making process in the mind (reactive behavior). On the other hand, digitalized data are processed by the digital twin's model that makes decisions on the basis of predictive analysis. This decision prescribes the so-called predicted behavior by the digital twin and is transmitted to the human. At the end, the human alone decides how to react to the sense: reactively or predictively.  
Figure 1: Scheme of the proposed digital twin model.
## 3 Results and Discussion
### Sensory Analysis  
The investigation of organoleptic properties of cooked meats showed that the highest scores for meat flavour intensity, flavour acceptability and overall acceptability, were given to the sample heated at 60 \({}^{\circ}\)C/4 h (Table 5). In respect to meat flavour intensity, this sample was significantly different from the sample heated at 60 \({}^{\circ}\)C/2 h. In terms of flavour acceptability, this sample was very different from the sample cooked at 75 \({}^{\circ}\)C/2 h. In terms of overall acceptability, this sample was superior to the samples cooked at 75 \({}^{\circ}\)C/1.5 h and at 75 \({}^{\circ}\)C/2 h. The highest perception of juiciness was noted in meat cooked at 60 \({}^{\circ}\)C/2 h, but a significant difference was shown, mainly compared to the samples cooked at 75 \({}^{\circ}\)C. The sample cooked at 65 \({}^{\circ}\)C/4 h received the highest scores for other sensory traits considered in the study, namely overall appearance, colour uniformity, aroma intensity and tenderness. No significant differences were observed between samples in respect to aroma intensity. The scores for juiciness, tenderness, flavour acceptability and overall acceptability slightly declined with increased treatment temperature. According to the analysis of variance, the temperature of cooking significantly affected tenderness, juiciness, flavour acceptability and overall acceptability. Time of treatment showed a significant effect on overall appearance, tenderness, juiciness, meat flavour intensity and overall acceptability. Interaction of cooking parameters influenced meat traits related to visual quality, namely overall appearance and colour uniformity. Our results regarding meat flavour intensity agree with those of [PERSON] et al. [11], who observed increasing intensity of meaty flavour of pork with increased temperature and cooking time using temperatures below 60 \({}^{\circ}\)C. [PERSON] et al. [56] pointed out that meat dish flavour is a complex phenomenon with origins from animal breed and handling before and after slaughter and then is related to meat processing, including heat treatment. The presence of precursors resulting from the type of meat cut and its origin, as well as
## 3 Robot Functionalities: People Perception, Human Following and Pick and Placing
### People Perception  
People perception is an important functionality required for robots operating in proximity of humans, in particular for robots in home and consumer environments expected to interact with humans. To obtain an efficient and complete people perception functionality, we apply and merge several techniques that allow the robot to (1) detect the presence of a person, (2) to locate the position of the person and to (3) identify the identity of the detected person.  
In this work, we employed a technique common to both detecting people and objects based on the Darknet YOLO [44] method. This method allows the detection in real-time of multiple people by returning a 2D bounding box of the detected people in the camera frame. The method receives RGB images from the RGB-D camera and uses the full images as input to a pre-trained neural network, resulting in an image that is divided into several regions and predicting possible bounding boxes with associated probabilities for each region.  
Upon detection, an estimate of the person's 3D position is computed. This is achieved by using the information registered by the RGB-D camera that supplies both RGB and depth images. These two images are synchronized (\(\approx\)12 Hz), then the corners of the detected bounding box are found in the depth image. The bounding box is shrunk to increase the density of interest points, and the center of this bounding box is defined as an approximation to the detected person's position [45].  
For people recognition, the used method is based on face recognition techniques [46]. The algorithm receives sampled frames from the RGB-D camera and feeds the image to a pre-trained neural network that detects all faces in an image by using the method Histogram of Oriented Gradients [47]. When all faces in an image are detected, each face has 68 face landmarks estimated [48] so that the face can be rotated and scaled to be centered in a square. This image is then fed to one other pre-trained neural network that returns an embedding of 128 measurements of each face [49] that can be used to compare and recognize faces. Then, for each image, the obtained embedding is compared with the  
Figure 1: The Mbot mobile robot at the ISR/IST ERL certified test bed performing a pick and place operation.  
previously known faces by applying a linear Support-Vector Machine (SVM) Classifier that returns the name of the person with closest embedding match, above a certain tolerance threshold. Algorithm 1 describes the implementation of this method while Figure 2 shows an instance from one of the face recognition trials.  
```
Result: Face detected from a group of known faces input:rgb_image, known_faces_list[] begin
2 frame = Sample(rgb_image);
3 face_list = face_detection_nn(frame);
4 forface in face_listdo
5 face_features = features_estimator(face);
6 face_centered = center(face, face_features);
7 embedding = face_encodings(face_centered);
8 face_estimate = classifier(embedding, known_faces_list[]);
9
10 end for
```  
**Algorithm 1**Face detection algorithm.
## 5 Contract-Based Energy-Efficient Resource Allocation for UAV-Assisted FL
### System Models
#### 5.1.1 Communication Model  
The channel states of VUs vary at different periods. Different from terrestrial channel models, the channel between the UAV and VU is affected by line-of-sight (LoS) and non-line-of-sight (NLoS) propagation modes. We can define the probability of having a LoS link between the UAV and VUs as \(P(\text{LoS,t})\), which can be represented as Equation (34):  
\[P(\text{LoS,t})=\frac{1}{1+a\exp\Bigl{(}-b\Bigl{(}\frac{180}{\pi}\theta(t)-a \Bigr{)}\Bigr{)}} \tag{34}\]  
where a and b are determined by the environment and \(\theta(t)\) is the elevation angle, which is equal to \(\arctan(\frac{\text{h}(\text{t})}{\text{r}(\text{t})})\), where h(t) is the height of the UAV and r(t) is the horizontal distance between the UAV and VU, as depicted in Figure 2. The probability increases with the enlargement of angle \(\theta(t)\).  
Assume that the path loss between the VU and the UAV is defined as \(g_{j}(t)\), and we can express it in Equation (35):  
\[g_{j}(t)=20\log\biggl{(}\frac{4\pi f_{c}d(t)}{c}\biggr{)}+P(\text{LoS,t})\eta _{\text{LoS}}+P(\text{NLoS,t})\eta_{\text{NLoS}} \tag{35}\]  
where \(f_{c}\) is the carrier frequency, d(t) is the distance between the UAV and the VU, and c is the speed of light. \(P(\text{NLoS,t})\) is the probability of having a NLoS link and is equal to \(1-P(\text{LoS,t})\). \(\eta_{\text{LoS}}\) and \(\eta_{\text{NLoS}}\) are the path loss coefficients. The values of \(a,b,f_{c},\eta_{\text{LoS}}\), and \(\eta_{\text{NLoS}}\) are referenced in [36].  
Assume that the UAV allocates the subchannels equally to all VUs and that the bandwidth of each VU is \(B\). The achievable data rate of \(c_{j}\) in iteration \(t\) is  
\[r_{j}(t)=B\log_{2}(1+\frac{p_{j}(t)g_{j}(t)}{N_{0}}) \tag{36}\]  
where \(p_{j}(t)\) is the transmission power and \(N_{0}\) is the noise power.  
When \(c_{j}\) broadcasts local model \(\omega_{j}(t)\), the real-time communication delay through the process is computed by  
\[T_{j}^{com}(t)=\frac{\omega_{j}(t)}{r_{j}(t)} \tag{37}\]  
The corresponding communication energy consumption is computed by  
\[E_{j}^{com}(t)=p_{j}(t)T_{j}^{com}(t) \tag{38}\]
# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 2 Materials and Methods
### Methods  
#### 2.3.1 Overall Technical Route  
Based on the FY-4B/AGRI Level1 data of China from July to August 2022, the precipitation retrieval model is established to estimate precipitation using RF. The algorithm's technical process is shown in Figure 1. Firstly, the FY-4B/AGRI Level1 data, topographic data and GPM IMERG product are pre-processed and the spatio-temporal matching dataset is established. Then, the dataset is divided into day and night datasets according to the SZ. The dataset of July 2022 is used for modelling (day dataset: 15,921,868; night dataset: 109,100,161), and 1/4 of the dataset is randomly taken as the training dataset and 3/4 as the validation dataset. In order to test the time extension of the model, the dataset of August 2022 is used as an independent testing dataset (day dataset: 13,962,058; night dataset: 10,658,703). In this study, sklearn in Python is used to realize RF model. In the first step, the precipitation identification model is established to determine whether each satellite pixel has rain using the RF classification model. The precipitation condition of GPM IMERG product, namely non-precipitation and precipitation, is as the target variable. And the trained model is applied to the independent testing dataset to determine the precipitation region. In the second step, non-precipitation regions in the dataset are removed, and the precipitation estimation model is established using the RF regression model. The \(\geq\)0.1 mm/h precipitation data in the GPM IMERG product are the target variable. And the trained model is also applied to the testing dataset to retrieve precipitation intensity. Finally, the GPM IMERG product is used as the reference data, and the retrieval results are  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Land Cover Types** & **Labels** \\ \hline Farmland & 10, 20 \\ Woodland & 12, 51, 52, 61, 62, 71, 72, 81, 82, 91, 92, 120, 121, 122 \\ Grassland & 11, 130, 140, 150, 152, 153 \\ Bare land & 200, 201, 202 \\ Artificial surfaces & 190 \\ Water bodies & 210 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Land cover types and corresponding labels.  
compared with those of the FY-4B/AGRI operational precipitation product to evaluate the retrieval effect of the model.