esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Applications of HAR in Healthcare
### Personalised Health Recommendations  
HAR can be used to provide personalised recommendations on healthy habits. Maintaining a healthy lifestyle is individualistic because it is affected by things like physical habits, job, genetics, and social habits. The daily nutritional requirements for a professional athlete would be different from that of an office worker. Underlying health conditions can also influence the nutritional needs of the user. A personalised nutrition monitoring or recommender system based on HAR would ensure that these peculiarities and differences are considered when it comes to the nutrition needs of the user.  
A HAR-based exercise recommender system can be personalised based on the activities of the user. This can be useful for people with Parkinson's and other motor-generative diseases, as personalised exercises can be used to augment medication [117]. For athletes, this system can be used to emphasise weak spots in their training regimen. Personal trainers can also used this for more personalised exercise recommendation. These health recommendations can also change in response to changes in the user's pattern of activities. This allows for dynamic health recommendations that change with changes in the need of the user.
## 4 Discussion
### Shortcomings and Future Research Plans  
In this study, SBAS-InSAR technology was employed to investigate the slope-deformation law of the deep excavation channel section in the China South-North Water-Diversion Middle-Route Project. The spatial distribution and temporal evolution of uplift and subsidence deformations were analyzed in the study area. By considering the geological engineering conditions, the deformation mechanism of the study area was revealed. Additionally, the relationship between three-dimensional surface deformation and the soil water content change was analyzed. The research results provide valuable scientific and technological support for the prevention and control of channel slope-deformation disasters in the study area. However, the research results also have some limitations: (1) the analysis was based on a limited dataset of only 11 periods, which restricts a comprehensive understanding of the temporal deformation characteristics in the study area; and (2) the study did not specifically investigate the physical properties of soil expansion, which hinders a thorough understanding of the surface deformation mechanism of the channel slope in the study area. In future research, we plan to use additional freely available SAR data sources to further study the temporal deformation law of the study area. Moreover, we aim to focus on studying the physical characteristics of soil expansion to gain deeper insights into the deformation mechanism of the channel slope.
## 2 Architecture of the Proposed SDAMs
### Software Architecture
#### 2.2.2 ARM-DSP Interaction Process  
The function of the ARM-DSP interaction process is to enable data transmission between the NS3 process and the DSP process.  
TI's AM57 xx family processors are the ARM application processors built to meet the intense processing requirements of modern embedded products. In an AM57 xx processor, different cores in the chip can exchange messages and trigger interrupts with each other via the MAILBOX module. Each core can be configured with the MAILBOX module and read or write messages by DBUS. There are three methods to realize ARM and DSP communication based on the MAILBOX module:  
1. The DSP process is called by the OpenCL interface, and the DSP process is considered as an accelerator for the OpenCL interface.
2. The DSP process runs the SYSBIOS system by TI's RTOS SDK, and then the ARM process and the DSP process communicate via the IPC API message mechanism.  
Figure 6: The processing flow of the NS3 process.  
3. The ARM process and the DSP process communicate by the MAILBOX register directly.  
The above methods have different efficiencies and complexities. Method 1 has the lowest complexity and highest efficiency, while Method 3 has the highest complexity and the lowest efficiency. In this paper, we apply Method 2, which exhibits satisfactory tradeoff between efficiency and complexity.  
The Message-Q module and the CMEM module provided by TI are employed to realize the ARM and DSP intercore communication via IPC. Message-Q supports transmitting and receiving variable-length, structured messages. It provides APIs for message transmitting and receiving, which are suitable for message exchange between two heterogeneous processing cores. The CMEM module manages one or several consecutive physical block memories and provides address exchanges. In this paper, the Message-Q module is adopted to deliver custom-defined, structured messages such as message type, memory address, and data length. The CMEM module is adopted to address exchange and data copy. The combination of the above two modules more efficiently achieves large amounts of data exchanges between the ARM and DSP cores.  
To detect the message in Message-Q or socket, there are two methods: blocking or nonblocking. Single-thread, nonblocking polling detection or dual-thread blocking detection can be adopted in the ARM-DSP interaction process to implement the data arrival detection in NS3 or DSP. However, the nonblocking polling methods occupy CPU resources for a long time, so we choose the dual-thread blocking detection method. The processing flow of the ARM-DSP interaction process is shown in Figure 7.
## 4 Role of URLLC in Operating IoT
### On-Device Artificial Intelligence and URLLC  
Traditionally, communication networks are designed with the concept of achieving high data rates with centralized management of resources. To accomplish the upcoming extreme latency and reliability requirements, the communication network architecture is now being pushed to be more non-centric and proactive. Most of the IoT devices are designed to be remotely controlled or to operate in a limited non-complex environment. However, some of the machines/applications require machine learning (ML) or artificial intelligence (AI) in order to be more effective and efficient to achieve the goals of the applications.  
Clearly, the customary machine learning approach based on the centralized architecture, as shown in Figure 9, is not very suitable for delicate latency applications [21]. However, most of the IoT devices have limited resources [36], and such devices may not be able to carry out ML or AI-based algorithms effectively while meeting the latency requirements. Consequently, researchers are investigating decentralized approaches such as distributed ML or AI on edge that involve collective problem solving [37]. Even with on-device machine learning, devices require a significant amount of storage and computational ability, which most of the IoT devices lack.  
Most of the AI algorithms usually require a large data set to provide effective results. In URLLC, however, it is a challenge to provide such a big data set for the mission-critical IoT devices with reliability and low latency [38].
## 2 Thermo-Coupled \(J_{2}\)-Flow Model with Evolving Elastic Potential
### The Meanings of Constitutive Quantities  
The finite elastoplastic \(J_{2}\)-flow model established in the above are represented by three constitutive quantities, namely, the evolving elastic potential \(\overline{W}\) (cf., Equation (2)) and the yield limit \(r\) (cf., Equation (7)) as well as the Prager modulus \(c\) (cf., Equation (10)). Of them, the former characterizes nonlinear elastic behavior evolving with plastic flow and hence the stress-softening behavior at unloading, while the latter two characterize features of plastic flow. As has been known in theory of elastoplasticity, the strain-induced anisotropy may be represented by the von Mises function Equation (6) with the evolution of the back stress \(\mathbf{\alpha}\) (cf., Equation (9)). Perhaps more essentially, both the back stress \(\mathbf{\alpha}\) and the temperature-dependent yield limit \(r\) will give rise to thermo-induced plastic flow leading to the shape memory effect, as will be seen in Section 5. The form of the potential \(\overline{W}\) should be well chosen, so that unloading stress-strain curves may be simulated. Furthermore, the yield limit \(r\) and the Prager modulus \(c\) should be such that they joins the potential \(\overline{W}\) to characterize the development of plastic flow.  
In the succeeding sections, both the stress softening effect at unloading and the shape memory effect at heating will be derived from the proposed model. Toward this objective, we are going to show that the constitutive quantities in the foregoing may be determined from suitable uniaxial data for the stress-softening effect at unloading and for the strain recovery effect at heating, in such a sense that the predictions from the proposed model can exactly reproduce these data.
## 3 Method
### Spatial Autocorrelation Method  
The method of extracting dispersion curves from microtremor signal can be traced back to the 1960s. [PERSON] proposed the theory of spatial autocorrelation (SPAC) [24]. Since then, research based on microtremor has gradually developed from theory to practical application, and other methods of extracting dispersion curves, such as the frequency-wavenumber (FK) spectrum method [25] and the method of multi-channel analysis of surface waves (MASW) [26], have also been developed. In recent years, through improving the algorithm, researchers have proposed multi-channel analysis of passive surface waves (MAPS) [27], pseudo-linear-array analysis of passive surface waves (PLAS) [28] and phase-weighted stacking (PWS) method [29], and have achieved good results in some typical cases.  
According to the SPAC method, the natural microtremor signal in a certain period of time is a sample function \(X(t,\xi(x,y))\) of a stationary random process, and its spectrum can be expressed as follows:  
\[X(t,\xi(x,y))=\iint\exp(i\omega t+iK\xi)dZ(\omega,K) \tag{1}\]  
where \(\omega=2\pi f\) is the angular frequency; \(K=(k_{x},k_{y})\) is the wave-number; and \(Z\) is the orthogonal random process.  
The SPAC method generally adopts circular array, with one station at the center of the circle and other ones at the circumference. After normalization of the power spectral density of the incident noise field, with maintenance of the circumference radius and change of the frequency, the spatial autocorrelation coefficient of average azimuth is as follows:  
\[\rho(\omega,r_{0})=J_{0}(r_{0}\cdot\omega/c(\omega)) \tag{2}\]  
where \(J_{0}\) is the Bessel function of class I of zero order. It can be seen that the spatial autocorrelation coefficient obtained on circular array with radius \(r\) is related to the frequencyand changes in the form of Bessel function of class I of zero order. Therefore, the dispersion curve of phase velocity \(c(\omega)\) can be obtained by calculating the spatial autocorrelation coefficient \(\rho\) between the center point of the circular array and the points at the circumference and fitting it with the zero-order Bessel function.  
Extended spatial autocorrelation (E-SPAC) keeps the frequency and changes the circumference radius, and, thus, (2) is expressed as follows:  
\[\rho(\omega_{0},r)=J_{0}(r\cdot\omega_{0}/c(\omega)) \tag{3}\]  
From the above formula, the relationship between the autocorrelation coefficient and the distance \(r\) can be obtained, which makes it possible to process irregular array. Compared to the SPAC method, the layout of field arrays is more flexible [30]. In this paper, the more widely used E-SPAC method is adopted for extracting surface-wave dispersion energy with a higher resolution from microtremor signal.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 2 Characteristics of Snow and SAR
### Interactions of Snow and SAR
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Snow Type** & **Dry Snow** & **Wet Snow** \\ \hline \multirow{2}{*}{Backscattering source} & Volume scattering from snowpack, & \\  & Surface scattering at snow/ground & \\  & Surface scattering at snow/ground & \\  & Interface & \\ \hline Dominant factors influencing & Surface below snow (SAR frequency & \\ scattering mechanism & \(<\)–10 GHz), & Liquid water content (most important), \\  [113] & Grain size (SAR frequency \(>\)–10 GHz) & Surface roughness \\ \hline Backscattering coefficient & High & Low \\ \hline \multicolumn{3}{c}{The relationship between snowpack parameters and the amplitude of backscattering} \\ \hline Snow wetness & –[114] & +[92; 99; 115] \\ \hline Snow grain size & +[106; 116] & insignificant [117] \\ \hline \multirow{2}{*}{Snow depth/thickness} & +[116; 118] & \\  & +coarse-grained snowpack [96] & –[117] \\ \multicolumn{3}{c}{–fine-grained snowpack [96]} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effects of wet and dry snow on the SAR signal based on the snow physiology. “+” refers to positive correlation, ”\(-\)” to negative correlation. The number in brackets refers to the reference number.
## 2 Mechanism Introduction and Modeling Analysis
### Relationship between the Installation Angle of the Drive Cam and the Dropping Seedling Duration  
To compensate for the time difference in completing the fall of the potted seedlings at the different positions, it was necessary to set the compensation angle \(\gamma_{i}\) based on the angular difference arrangement of the drive cam, and then make the pot seedlings at different positions fall into the planting seedling groove at the same time interval by opening the seedling plate in advance or backward. Based on the installation position of the drive cam at the No. 1 position, the installation angle of other drive cams on the driving shaft is \(\beta_{i}\), the rotational angular velocity of the cam driving shaft is \(\omega\), and the starting rotation time of the seedling carrying plate at the No. 1 position is time 0, so the dropping seedling duration at the No. 1 position in the seedling receiving cylinder can be obtained  
\[T_{i}=\frac{\beta_{i}}{\omega}+t_{i} \tag{22}\]With a constant forward speed of machine \(V\), the time difference between the two adjacent pot seedlings determines the plant spacing. Let the position of the machine at time 0 be 0, then the landing position of the pot seedling at the No. 1 position in the seedling cylinder is  
\[D_{i}=T_{i}V=(\frac{\beta_{i}}{\omega}+t_{i})V \tag{23}\]  
If the drive cams are arranged at equal angular intervals, then  
\[\left[\begin{array}{c}\beta_{1}\\ \beta_{2}\\ \beta_{3}\\ \beta_{4}\\ \beta_{5}\end{array}\right]=\left[\begin{array}{c}0^{\circ}\\ 72^{\circ}\\ 144^{\circ}\\ 216^{\circ}\\ 288^{\circ}\end{array}\right] \tag{24}\]  
Taking the dropping duration \(t_{1}\) of the pot seedlings at the No. 1 position as the standard, the difference in the dropping duration of pot seedlings between the No. 1 position and the No. 2 position is  
\[\Delta t_{12}=t_{1}-t_{2}>0 \tag{25}\]  
The installation compensation angle of the drive cam at the No. 2 position is  
\[\gamma_{2}=\omega\Delta t_{12}=\omega(t_{1}-t_{2}) \tag{26}\]  
The installation angle of the drive cam at the No. 2 position is  
\[\beta_{2}=72^{\circ}+\gamma_{2}=72^{\circ}+\omega(t_{1}-t_{2}) \tag{27}\]  
Similarly, the installation angles of the drive cams at the No. 3, 4, and 5 positions can be obtained  
\[\left[\begin{array}{c}\beta_{3}\\ \beta_{4}\\ \beta_{5}\end{array}\right]=\left[\begin{array}{c}144^{\circ}+\omega(t_{1}- t_{3})\\ 216^{\circ}+\omega(t_{1}-t_{4})\\ 288^{\circ}+\omega(t_{1}-t_{5})\end{array}\right] \tag{28}\]  
After measuring the dropping duration of the pot seedlings at each position, the installation angles of the drive cam at the No. 2-5 positions can be obtained by solving Equations (27) and (28) based on the dropping duration of pot seedlings at the No. 1 position.
## 3 Proposed Methods
### The Structure and Working Principle of LSERNet  
The overall structure of the proposed LSERNet is shown in Figure 2. The network consists of a convolutional layer, a pooling layer, a LSERB module, an average pooling layer, a fully connected layer, and the softmax layer. LSERB (local squeeze-and-excitation residual block) is based on the ResNet50 network structure. It is composed of the local channel attention LSEB (local squeeze-and-excitation block) combined with RB (residual block). This module is similar to the ResNet50 network structure and contains four layers, each of which is composed of RB modules. Layers from 1 to 4 consist of three, four, six, and three RB modules, respectively.  
Figure 1: SE structure.  
Prior to the channel attention convolution, we locally divide the image feature information, input it into the channel attention module, respectively, and use the residual module for the convolution operation, to assign weights to the different regions. Then, the obtained information is integrated, and the final output is obtained by the softmax operation.
## 4 Discussion
### Crop Growth Patterns Estimated by Planet Dove Imagery  
Ratooning and spring planting of sugarcane had similar crop growth patterns (Figure 4). The only major difference in land cover condition between ratooning and spring planting was whether or not there was tillage after harvesting and if it was completely exposed to bare land, and the NDVI-based crop growth pattern is considered to be similar because the growing periods were the same. The NDVI time series clearly represented the summer planting of sugarcane. NDVI time series of summer planting did not always maintain the lowest NDVI value from harvest to re-planting. This process is due to the growth of weeds or residual sugarcane stocks in the agricultural field left after harvest,  
Figure 9: Dependence of classification accuracy on the number of variables (dates).  
and these being embedded in the soil by tillage just before planting. Pineapple had a lower NDVI value than that of pastures, possibly because of the difference in vegetation cover ratio. On Kumejima Island, 50% of purple yarn is planted in spring (March to May), 30% in summer (June to August), and 20% in autumn (September to November); it is harvested 5 or 6 months after planting. Since the purple yarn fields are usually transposed in one or two years, the crop growth patterns of 2017 and 2018 may have been unclear due to a mixture of other crop type and practice patterns. These results adequately represent the agricultural calendar of the crop types and practices according to the interviews with farmers, and indicate that Planet Dove images can reveal crop growth patterns.  
The NDVI time series generated from Planet Dove images contains noise components due to individual differences in sensors [85]. The Planet Dove's broadband sensor has a smaller NDVI range and a negative bias compared with the Landsat-8/OLI sensor. However, the relationship between NDVIs is linear, and the coefficient of determination is very high (top-of-atmosphere reflectance: 0.96; surface reflectance (6 SV): 0.99). The variations in NDVI calculated from Planet Dove were within the range of approximately \(\pm 0.1\)[85]. This means the variations had no considerable effect on our crop growth pattern observations because they were smaller than the range of temporal NDVI variations observed in this study (Figure 4).
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 4 Data Security Sharing Modeling
### Constraints for Secure Data Sharing
#### 4.2.5 Data Utility Constraints  
Shared data must be valuable to the data consumer to avoid redundant transmission.  
\[\forall D_{j},A_{i},\;\;\;U\big{(}D_{j},A_{i}\big{)}\geq U_{min}\]  
Here, \(U\big{(}D_{j},A_{i}\big{)}\in[0,1]\) represents the utility value of \(D_{j}\) for \(A_{i}\), and \(U_{min}\) refers to the minimum utility value.
# Effects of Decision Variables Selection on Sewer Optimization Problem
## 2 Methods  
### Sewer Network Hydraulic Optimization  
The cost of a sewer network consists of the costs of the pipe, manhole, excavation, and pumping station if there is one. The objective function of a sewer network optimization problem without pumping stations can be defined as in Equation (1):  
\[Min\ C=\sum_{i=1}^{N}L_{i}C_{p_{i}}+\sum_{j=1}^{M}C_{m_{j}}, \tag{1}\]  
where \(C\) is the sewer system cost, \(N\) is the total number of sewer pipes, \(i\) is the considered pipe, \(C_{p_{i}}\) is the unit cost of the sewer pipe construction, \(L_{i}\) is the length of the considered pipe, \(M\) is the total number of manholes, \(j\) is the considered manhole, and \(C_{m_{j}}\) is the cost of the manhole construction. \(C_{p_{i}}\) and \(C_{m_{j}}\) are defined as a function of the diameter \(d_{i}\) of the pipe and the manhole height \(h_{m}\), respectively. These variables are illustrated in Figure 1.  
The sewer network optimization problem has constraints for hydraulic, operational, and availability reasons. The constraints of this problem can be defined by Equations (2)-(10).  
Figure 1: Schematic representation of the sewer network profile.  
Subject to the following:  
\[d_{i}\in D,\ (\forall_{i}=1,\ldots,\text{N}) \tag{2}\] \[d_{i}\geq D^{\prime},\ (\forall_{i}=1,\ldots,\text{N})\] (3) \[V_{i}\geq V_{min},(\forall_{i}=1,\ldots,\text{N})\] (4) \[V_{i}\leq V_{max},\ (\forall_{i}=1,\ldots,\text{N})\] (5) \[\beta_{i}\geq\beta_{min},(\forall_{i}=1,\ldots,\text{N})\] (6) \[\beta_{i}\leq\beta_{max},(\forall_{i}=1,\ldots,\text{N})\] (7) \[S_{i}\geq S_{min},(\forall_{i}=1,\ldots,\text{N})\] (8) \[E\geq E_{min},(\forall_{i}=1,\ldots,\text{N},\ \forall_{m}=1,\ldots, \text{M})\] (9) \[E\leq E_{max},\ (\forall_{i}=1,\ldots,\text{N},\ \forall_{m}=1, \ldots,\text{M}), \tag{10}\]  
where \(d_{i}\) is the diameter of the sewer pipe, \(D\) is the discrete set of available commercial sewer pipe diameters varying between a minimum diameter value (\(minD\)) and maximum diameter value (\(maxD\)), \(D^{\prime}\) is the set of upstream pipe diameters, \(V_{i}\) is the flow velocity of the considered pipe, \(V_{min}\) and \(V_{max}\) are the allowable minimum and maximum velocities, \(\beta_{i}\) is the relative flow depth value in the considered pipe, \(\beta_{min}\) and \(\beta_{max}\) are the allowable minimum and maximum relative flow depth values, \(S_{i}\) is the slope of the considered pipe, \(S_{min}\) is the allowable minimum slope value, \(E\) is the cover depth value, and \(E_{min}\) and \(E_{max}\) are the allowable minimum (\(minCD\)) and maximum (\(maxCD\)) cover depth values. In addition to the constraints, in line with the acceptances of the uniform turbulent flow, [PERSON]'s equation is used for hydraulic calculations.