esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Crossing Cache Placement in Cache-Enabled Device to Device-Aided Cellular Networks
## 3 Offloading Probability Analysis
### Offloading Probability in D2D Mode
Furthermore, (b) is derived from a change in variable \(x=(\frac{v}{r^{\alpha}\xi^{\frac{1}{k}}})^{2}\) and (c) results from the integration in [22], \(r_{0},r_{0}\to 0\) for the D2D transmitters caching other contents except content \(k\), \(Z_{1}(\xi)=\frac{2\xi}{\pi-2}\)\({}_{2}F_{1}\big{[}1,1-\frac{2}{\alpha},\)\(2-\frac{2}{\alpha},\)\(-\xi\big{]}\), \(Z_{2}(f_{0})=\xi^{\frac{2}{\alpha}}\frac{2\
u_{1}-\frac{2}{\alpha}}{\alpha-2}\)\({}_{2}F_{1}\Big{[}1,1-\frac{2}{\alpha},2-\frac{2}{\alpha},-f_{0}-^{\frac{ \alpha}{2}}\Big{]}\), and \({}_{2}F_{1}[. ]\) denotes the Gauss hypergeometric function. \[ \begin{array}{ll}\mathcal{L}\ _{I_{u}}&(\pi^{\alpha}\xi P_{u}^{-1})=E_{\{\[PERSON]_{u,k} ^{\prime}\cup\[PERSON]_{u}^{\prime}\},\{h_{l}\}}\Big{[}\exp(-r^{\alpha}\xi p_{u}^{- 1})\sum_{i\in\{\[PERSON]_{u,k}^{\prime}\cup\[PERSON]_{u}^{\prime}\}\setminus\{i_{0}\}} \ P_{u}h_{i}r_{i}^{-\alpha}\Big{]}\\ &=E_{\{\[PERSON]_{u,k}^{\prime}\cup\[PERSON]_{k}^{\prime}\}}\Big{[}\prod_{i\in\{\[PERSON]_{u,k}^{\prime}\cup\[PERSON]_{k}^{\prime}\}\setminus\{i_{0}\}}E_{h_{i}}[\exp(-r^{ \alpha}\xi h_{i}r_{i}^{-\alpha})]\Big{]}\\ &\stackrel{{(a)}}{{=}}e^{(-2\pi\
u\lambda_{u}(q_{k}\Omega\Gamma_{ \xi}^{\prime\prime})\int_{\frac{\
u}{1+\pi^{\alpha}\xi^{-1}\
u^{\alpha}}dv+(1-q _{k})\int_{\
u}^{\infty}\frac{\
u}{1+\pi^{\alpha}\xi^{-1}\
u^{\alpha}}dv)}}\\ &\stackrel{{(b)}}{{=}}e^{(-\pi\
u\lambda_{u}\
u^{2}\xi^{\frac{2} {\alpha}}(q_{k}\Omega\int_{\frac{\
u}{1+\pi^{\alpha}\xi^{-2}}}^{\infty}(\frac{ 1}{1+\pi^{\frac{\alpha}{2}}})dx+(1+q_{k})\int_{\{\rho_{k}^{\prime}\xi^{\prime }\}}^{\infty}\frac{\
u}{\pi^{\alpha}\xi^{\prime\prime}}\frac{(1-\frac{ \alpha}{2})}{(1+\pi^{\frac{\alpha}{2}}})dx))}\\ &\stackrel{{(c)}}{{=}}e^{(-\pi\
u\lambda_{u}\
u^{2}(q_{k}\Omega Z _{1}(\xi)+(1-q_{k})Z_{2}(\xi_{0})))}\end{array}  \tag{7}\]  
Similar to (7), we have  
\[ \begin{array}{ll}\mathcal{L}_{I_{b}}\ (r^{\alpha}\xi p_{u}^{-1})=e^{-\pi \lambda_{b}(\frac{p_{b}}{p_{u}})^{\frac{2}{\alpha}}}r^{2}Z_{1}(\xi)\end{array}  \tag{8}\]  
By substituting (7) and (8) into (6), we obtain  
\[ \begin{array}{ll}P(SIR_{k}^{d2d}\ \geq\xi)=\frac{\lambda_{u,k}^{\prime}}{P_{k} \ (C_{1}q_{k}+C_{2})}\Big{(}1-e^{-\pi R^{2}(C_{1}\ q_{k}+\ C_{2})}\Big{)}\end{array}  \tag{9}\]  
Here, \(C_{1}=\
u\lambda_{u}(\Omega Z_{1}(\xi)-Z_{2}(\xi_{0})+\Omega)\) and \(C_{2}=\lambda_{b}(\frac{P_{b}}{p_{u}})^{\frac{2}{\alpha}}(Z_{1}(\xi)+1)+\
u\ \lambda_{u}Z_{2}(\xi_{0})\). Then, from (3), (5) and (9), the offloading probability in the D2D mode can be derived as follows:  
\[ \begin{array}{ll}P_{2}=\sum_{k=1}^{K}\ \frac{p_{k}(1-q_{k})\lambda_{u,k}^{ \prime}}{(C_{1}q_{k}+C_{2})}\Big{(}1-e^{-\pi\lambda_{u,k}^{\prime}R^{2}\Omega }+e^{-\pi\lambda_{u,k}^{\prime}R^{2}}\Big{)}\Big{(}1-e^{-\pi R^{2}(C_{1}q_{k} +C_{2})}\Big{)}\end{array}  \tag{10}\]
###### Abstract  
Unlike terrestrial radio, the speed of sound in the ocean is relatively slow, which results in mobile underwater M-ary spread spectrum communication typically exhibiting significant and variable multipath effects along with strong Doppler effects, leading to rapid carrier phase shifts in the received signal that severely impact decoding accuracy. This study aims to address the issue of rapid carrier phase shifts caused by significant time-varying Doppler shifts during mobile underwater M-SS communication. This paper innovatively proposes a method for updating matched filters based on correlation cost factors. By calculating the correlation cost factors for each received symbol, the method guides the direction of Doppler estimation and updates the matched filters. After identifying the optimal match, the received symbols are shifted, correlated, and decoded. Simulation and sea trial results indicate that this method demonstrates higher computational efficiency and improved decoding accuracy compared to traditional Doppler estimation matched filters under low signal-to-noise ratio conditions, and exhibits greater robustness under complex motion conditions.  
Doppler effect; underwater acoustic communication; M-ary spread spectrum system (M-SS); carrier phase shift; correlation cost factor (CCF) Article
## 4 Analysis of Results and Discussion
### Interpretation of STRC Shear Walls Failure Modes Prediction Model  
To analyze the significant variables of the STRC shear walls failure modes, this study employs the SHAP [12] method for global interpretation of the Blending-CRKL model. Figure 11 shows the ranking of the important features of the STRC shear walls failure modes prediction model. The horizontal coordinates indicate the absolute value of the Shapley value of each feature, and the larger the value indicates, the more important the feature is. From Figure 11, it can be seen that the shear-span ratio (\(\lambda\)) plays the most important role in recognizing the failure modes of STRC shear walls, followed by the horizontal bar index in the boundary element (\(\rho_{\text{l}c,x}f_{y4}\)/\(\ell_{c}\)) and the vertical bar index in the boundary element (\(\rho_{\text{l}c,x}\)/\(\ell_{y1}\)/\(\ell_{c}\)). The influence of the steel tubular diameter-to-wall thickness ratio (\(D\)/\(\ell_{w}\)) on the failure mode is very small. As shown in Figure 12, different failure modes have different important characteristics. In flexural and flexural-shear failure, the most important feature is shear-span ratio (\(\lambda\)), while in shear failure, the most important feature is the vertical bars index in boundary element (\(\rho_{\text{l}c,x}\)/\(\ell_{y1}\)/\(\ell_{c}\)). Therefore, the reinforcement configuration of the boundary elements plays a crucial role in the shear and flexural shear failure of STRC shear walls.  
Figure 10: STRC shear wall failure modes predictability metrics: (**a**) precision; (**b**) recall; (**c**) F1-score; (**d**) accuracy.
## 2 Classical P&O and dp-P&O
### dp-P&O Modification  
The dP-P&O method performs an additional power measurement (\(p_{x}\)) in the middle of the perturbation period (\(T_{MPPT}/2\)). It can be assumed that any power difference (\(dp_{2}\)) between the power before the next perturbation (\(p_{k}\)) and \(p_{x}\) is solely due to external causes since the operating point remains the same. In contrast, the power difference (\(dp_{1}\)) between the perturbation point (\(p_{k-1}\)) and \(p_{x}\) contains the change caused by the action of the algorithm and the irradiance change. Based on this premise, the power difference obtained from the MPPT operation can be calculated as follows.  
\[dp=dp_{1}-dp_{2}=(p_{x}-p_{k-1})-(p_{k}-\ p_{x})=2p_{x}-p_{k}-p_{k-1}. \tag{1}\]  
However, under rapid irradiance changes, the operating point may fluctuate without the MPPT operation because of parasitic resistances in the converter. This fluctuation could confuse the controller since the premise of the process would not be valid.
## 4 The Proposed Protocol
### The Architecture  
As shown in Figure 2, three entities are involved in our protocol; the explanations of the components follow.  
* _Manufacturer (full node)._ The manufacturers of the IoT devices such as smart bulb, thermostat, or the other. It stores and releases a new firmware update for their devices. This entity is also a node in the blockchain network. It will publish the firmware update metadata to the smart contracts in the blockchain network. The set of manufacturers \(M\) is denoted as: \[M=\{m_{1},m_{2},...m_{n}\}\]
* _IoT gateway (lightweight node)._ The IoT gateway can be a Wi-Fi router in a smart home or another device. The information of the IoT devices will be stored in the IoT gateway (e.g., device manufacturer, device type, latest firmware version). The IoT gateway will download the new firmware update for the IoT device from the manufacturer or from another gateway. The IoT gateway is also a blockchain node. When the manufacturer stores the firmware update metadata to the smart contracts, it will receive a notification and save the information to the local database. The set of IoT gateways \(G\) is denoted as: \[G=\{g_{1},g_{2},...g_{n}\}\]
* _IoT devices._ The sensors or embedded devices. In our cases, it will be the OCF devices. IoT device will request and download the new firmware update from the IoT gateway. The set of IoT devices \(I\) is denoted as: \[I=\{i_{1},i_{2},...i_{n}\}\]
# Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing
## 2 Materials and Methods
### Data-Efficient Artificial Neural Network Model Design and Training
The virtual dataset was generated from Gaussian function profiles using identical CSAM process parameters to those employed to make the physical test tracks and comprised of 804 training data points (i.e., 67 geometric sampling points from 12 simulated Gaussian function profiles). Both Technique 1 and 2 were independently employed in the preparation of training data and then simultaneously utilised in the development of the proposed DANN model. For the development of the DANN model, this study iteratively changed the hidden layer architecture with the different number of hidden neurons (i.e., 1-15 neurons) per hidden layer and of hidden layers (i.e., 1-2 layers) to determine the optimal architecture using Mean Squared Error (MSE) as a performance evaluation function on an independent testing dataset. This range of hidden structures and evaluation function were selected given the limited data availability and frequent use in relevant studies respectively [29; 30]. Following the 75-25 division method for the training-testing dataset, 12 single-track profiles were fabricated using the experimental methods described in Section 2.1 to form the testing dataset. The experimental process parameters for these testing profiles were randomly determined within the boundary of each parameter in Table 1 with the aid of the default random number generator in MATLAB version R2018a, summarised in Table S2 of the Supplementary Materials. Due to the limited training data, Bayesian regularised back-propagation was selected as the training method, eliminating the need for a validation dataset [43]. With this training method, hyperbolic tangent sigmoid and linear activation functions were selected for hidden and output layers, respectively, and all input and output variables were scaled to [\(-\)1 1] for improving a training process [44]. The training of a DANN model was performed using the Deep Learning Toolbox in MATLAB version R2018a. Each architecture candidate was retrained 100 times to avoid local optima convergence due to initially allocated weights and biases. A purely data-driven ANN model was also developed for comparison in predictive performance, using the same methods presented above for the DANN model. The difference was two-fold: (1) the number of input variables was four without the approximated polar length by the Gaussian function model, and (2) only the original training dataset prepared from the experimental single-track profiles was used.
## 3 Sink-oriented Dynamic Location Service
### Query and Data Dissemination  
A sink queries the network to collect the location of source nodes. Query and data dissemination are achieved using the following procedures. After creating the anchor through the anchor nodes selection stage, the sink can identify the location of the source node by sending a query when the sink wants to receive events. To do this;  
1. The sink will register with the nearest grid node, known as the sink agent (see Figure 4, sink and selected sink agent).
2. The sink agent sends four location query packets (East, West, South, and North) to find the location of the source node.
3. The sink can obtain the location of source nodes from the location response at intersecting anchor nodes (Figure 4).  
Furthermore, once the sink agent obtains the source node's location information, it does not obtain it again. If one query message fails, the other two queries will serve as backups to retrieve the source node's location information. This also shortens the query response time, because the sink will utilize the first response.  
Once the sink agent receives the location information of the source node, the sink agent forwards it to the sink. The sink then finally can communicate with the source node using the GPSR protocol.  
Since our protocol provides the location of the source nodes, instead of the location of the mobile sinks, the anchor system, which is the location server, can maintain the location information of the source nodes, although the sinks move fast and continuously. Algorithm 2 explains how to find location of source nodes at sinks.  
**Algorithm 2. Pseudo-code to find location of source nodes at sinks.**  
**Procedure:**  
01: initially startNode, middleNode, endNode = 0;  
02: sink finds the nearest grid node as sink agent  
/* sink floods a query within a local area about a cell size. */  
03: startNode = sink agent;  
04: **for** (sink agent's each neighbor grid node) /* to four-direction */  
05: endNode = neighbor grid node;  
06: **while** (endNode!= border node)  
07: middleNode = forward (startNode, endNode);  
/* finding the nearest neighbor node to reach the destination node */  
08: **if** (middleNode == grid node)  
09: **if** (exist a new source node's location)  
10: **getLocation** (sink agent); /* reply to the sink */  
11: **endif**  
12: startNode = middleNode;  
14: **if** (startNode == endNode)* 15:endNode = startNode.neighbor grid node; /* i.e.) if the message is from the east, startNode's neighbor grid node is same direction. */
* 16:endif
* 17:endwhile
* 18:endfor
# Frequency Division Multiplexing of Terahertz Waves Realized by Diffractive Optical Elements
## 2 Materials and Methods
### Manufacturing  
Phase distributions of all structures have been designed accordingly to the previously described matrices in the form of gray-scale images presented in Figure 1. The size of the matrices as well as the pixel dimensions allowed for creating 3D models corresponding to the 2D bitmaps from simulations. The value of each pixel of the matrix is an integer in the range from 0 to 255, representing different tones of gray. The bitmaps have been extruded with respect to the pixel values. The real thickness \(h\) (dependent on the refractive index \(n\) of the material used for manufacturing) of each structure is proportional to the introduced phase delay \(\phi\) and can be calculated from the following formula:  
\[h(x,y)=\frac{\lambda}{n-1}\frac{\phi(x,y)}{2\pi}. \tag{3}\]  
Hereby, the created models had the dimensions of 74.06 mm \(\times\) 74.06 mm \(\times\) 1.67 mm. An additional thin layer of a substrate has been added to the model to provide stiffness of the structure. Manufactured DOEs have also been surrounded with the 10 mm thick frame for easier handling and damage protection. The substrate is flat, therefore it introduces a constant phase delay to the whole structure which does not influence its behavior (apart from slightly increased absorption loss). The total structure size is 94.06 mm \(\times\) 94.06 mm \(\times\) 8.00 mm (this thickness results from the frame size, not the structure itself).  
Two structures have been manufactured using Selective Laser Sintering. This method is based on sintering of a polymer in the form of powder by laser light [35,36] with a high vertical resolution of 0.1 mm. The structures have been manufactured from PA2200 (powder form of PA12). This material is commonly used in 3D printing and is characterized with above average optical properties in the THz spectral range [30,33,40]. The measurements performed using the THz Time Domain Spectroscopy (THz-TDS) method have shown that PA12 is characterized by a refractive index of 1.56 (at 300 GHz) accompanied by a low absorption coefficient of 0.9 cm\({}^{-1}\) that corresponds to high transparency [41]. DOEs with focal lengths \(f=200\) mm and \(f=400\) mm have been selected for the experimental evaluation. The manufactured structures can be seen in Figure 4.  
Figure 3: Calculated positions of the focal spots for structures with the focal lengths \(f=300\) mm (**top**) and \(f=400\) mm (**bottom**). Intensity distributions are shown with the main optical axis in the middle of left edge of the illustrated area. Colors denote the frequency of the illuminating radiation as in the legend.
# Optimizing HAWK Signature Scheme Performance on ARMv8
## 2 Background
### HAWK Signature Scheme [11]
#### 2.1.3 HawkVerify  
Create \(h_{pub}\) using the public key and combine it with the message to create hash M. Extract the salt value from the \(sig\) and combine M and salt to generate \(\mathbf{h}\). Restore s with the calculated \(\mathbf{h}\), \(\mathbf{Q}\) and \(s_{1}\), and calculate \(\mathbf{w}\) through the restored s. Check whether the signature value is valid by checking whether the calculated \(\mathbf{w}\) meets certain conditions.  
```
0: A message \(m\), a public key \(pk=Q\) and a signature \(sig=(salt,s_{1})\)
0: A bit determining whether \(sig\) is a valid signature on \(m\)
1:\(h_{\text{pub}}\gets H(Q)\)
2:\(M\gets H(m\|h_{\text{pub}})\)
3:\(\mathbf{h}\gets H(M\|salt)\)
4:\(s\leftarrow\text{Decompress}(s_{1},\mathbf{h},\mathbf{Q})\)
5:\(\mathbf{w}\leftarrow\mathbf{h}-2s\)
6:if\(\text{len}_{\text{bits}}(salt)=\text{salten}_{\text{bits}}\) and \(s\in R_{n}^{2}\) and \(\text{sym-break}(w)\) and \(\|w\|_{Q}^{2}\leq 4\cdot\sigma_{\text{verify}}^{2}\cdot 2n\)then
7:return 1
8:else
9:return 0
```  
**Algorithm 3** HawkVerify.
## Maps for Upscaling Studies  
### Super-Sites  
A number of sites across the Arctic developed into super-sites for satellite-derived land cover-based applications. Maps have been developed that served as input for applications, such as upscaling of soil properties and fluxes. This includes the Kuparuk basin on the Alaskan North Slope, the Usa basin in northeastern European Russia and the Lena Delta in northeastern Russia. All of these multi-purpose maps rely on Landsat MSS and/or TM data. Their location is shown in Figure 1, and their properties are summarized in Table 1.  
Figure 1: Location of the discussed sites with multi-purpose land cover maps from Landsat data. More details are provided in Table 1.  
Initial developments have been part of a set of studies with a focus on the Kuparuk River Basin. The applicability of satellite data products (mostly Landsat based) for carbon cycle studies [22; 41; 42] and subsurface thermal properties ([43], Active Layer Thickness (ALT) over permafrost) was investigated. A multi-purpose land cover map has eventually been developed using Landsat MSS data in 1997 [39; 44]. It was based on an unsupervised (ISODATA) classification method using the green, red and near-infrared bands. Twenty classes have been eventually distinguished, which have been interpreted for methane flux up-scaling (see Table 1). Tundra is largely distinguished by periglacial features and terrain units within them. The twenty classes are usually grouped into six units [45]. The dominating group is'moist graminoid, prostrate shrub Tundra' [46]. SPOT and AVHRR NDVI (Normalized Difference Vegetation Index) values have also been investigated with respect to a potential application estimating carbon dioxide fluxes [41]. The approach used to generate the maps was subsequently transferred to the entirety of Northern Alaska [46] and compared to the AVHRR product by [PERSON] [20], but only a 55.4% agreement was determined. Most affected are wetland/moist tundra classes and shrublands. Probable reasons are the difference in spatial resolution and the respective acquisition time. The spatial detail of wetlands and moist tundra in wetland complexes cannot be sufficiently captured by AVHRR. The acquisition time of the satellite datasets also differed with respect to the phenological stage, which has important implications for the sensor's capabilities to identify shrublands. The majority of methane flux up-scaling investigations in the high latitudes started only several years later, although [PERSON] et al. [41] demonstrated the capability of land cover data in tundra
_Applied_  
_sciences_  
_Article_  
**The Influence of Boundary Layer Caused by Riblets**  
**on the Aircraft Surface**  
**[PERSON], [PERSON] *, [PERSON] * and [PERSON]**  
College of Aerospace Engineering, Harbin Engineering University, Harbin 150001, China; [EMAIL_ADDRESS] (H.L.); [EMAIL_ADDRESS] (L.X.)  
* Correspondence: [EMAIL_ADDRESS] (Z.W.); [EMAIL_ADDRESS] (J.C.)  
Received: 4 May 2020; Accepted: 21 May 2020; Published: 26 May 2020  
**Featured Application: The riblet structure can effectively reduce the frictional resistance when the fluid flows through the surface of the object. For aircraft, placing riblets or grooves on the surface of the wing can reduce drag and increase the maximum angle of attack of the aircraft, which can increase the lift of the aircraft.**  
**Abstract:** Drag reduction of riblets is one of the most important problems in drag reduction of non-smooth surfaces. In the past two decades, the use of riblets arranged along the flow direction to reduce frictional resistance has received considerable attention. In this paper, we study the plates with the triangular concave grooves, triangular protrusion riblets, trapezoidal concave grooves, trapezoidal protrusion riblets, and circular concave grooves. The numerical simulation method is used to calculate five kinds of plates with grooves and riblets under multiple working conditions. The results showed that the plates with grooves and riblets generated vortices inside the grooves, which separated the incoming flow from the wall surface, and by increasing the thickness of the boundary layer, greatly reducing the average velocity gradient of the wall surface, compared with the smooth flat plate, the friction resistance is reduced. But, lateral riblets and grooves cause additional pressure resistance, which is one order of magnitude higher than the friction resistance. Then, the triangular concave grooves are arranged on the suction and pressure sides of the NACA0012 airfoil, respectively. We calculated the aerodynamic parameters of the both airfoils, and the standard NACA0012 airfoil from the \(-8^{\circ}\) attack angle to their respective stall attack angles. The results showed that the NACA0012 airfoil with triangular concave grooves on the suction side reduced the aerodynamic characteristics of the standard NACA0012 at a small angle of attack, but the stall angle of attack of the standard NACA0012 airfoil was improved, because the grooves ensure that some gas can flow normally on the suction side and delay the separation of the boundary layer. The NACA0012 airfoil with triangular concave grooves on the pressure side did not effectively improve the aerodynamic characteristics: lift-drag ratio decreased and stall angle of attack decreased, but it can increase the lift slightly.  
**Keywords:** riblets; airfoil; numerical simulation; drag reduction