esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, time-series two-band enhanced vegetation index (EVI2) derived from 8 day composite 250 m MODIS reflectance data were used for crop yield estimation at the county level in Southern, Western, and Central Ontario agricultural regions. The regions are characterized by major crops of distinct phenological patterns, such as winter wheat and forage crops versus soybean and corn. A fuzzy decision tree classifier was used to identify these major annual crops to create crop-specific masks for extraction of county-level EVI2. The classification algorithm was able to identify pure pixels of major crops in the study area, with relatively large errors compared with high resolution crop classification maps and the reported county-level harvested area of crops. County-level crop yields were strongly correlated with EVI2 extracted at peak crop growth stages. Crop-specific masks were found to be able to decompose the compounding effects of different phenological patterns. Although accuracy of crop yield estimation using a general cropland mask was not considerably lower than that using crop-specific masks, the model may be impacted by other factors such as areal proportions of different crops. Cautions should be taken in using the general cropland mask when actual cropping condition (e.g., difference in relative areal proportions) is not known. The multiple linear regression model using peak stage average EVI2 and the year as independent variables can be used to map spatial-temporal variability of crop yields across different years with satisfaction; however, model performance may be poor in some years when important yield limiting factors are not captured by EVI2 at the peak growth stage. Further refinement of crop-specific masks and incorporation of additional information on growth conditions may help improve the performance of crop yield estimation at regional scale.  
Conceptualization, J.L., J.S., B.Q., and T.H.; data curation, J.L.; formal analysis, J.L.; funding acquisition, J.S., B.Q., T.H., and T.M.; investigation, J.L.; methodology, J.L., B.Q., and Y.Z.; project administration, J.S. and T.H.; resources, J.S.; validation, J.L., T.D., and Q.J.; writing--original draft, J.L.; writing--review and editing, B.Q., J.S., T.H., Y.Z., T.D., Q.J., and T.M.  
This study was supported by Agriculture and Agri-Food Canada (AAFC) under the Sustainability Metrics project (#J-000486), the Bio-products Targeted Call project (#J-001598), and the Integrated Soil and Land Use data project (#J-001383).  
The authors declare no conflict of interest.
# DB-Net: Detecting Vehicle Smoke with Deep Block Networks
## 2 Related Works
### Traditional Image Process Based Methods  
Existing works on the traditional image-process-based approach mainly focus on designing interpretable and efficient feature descriptors. [PERSON] et al. [7] proposed a feature extraction method based on similarity and dissimilarity matching measures of local binary patterns and combined the proposed method with local binary patterns for the texture feature modeling of the smoke. [PERSON] et al. [8] introduced a higher-order linear dynamical system descriptor for extracting dynamic texture features in video smoke detection. [PERSON] et al. [14] presented a robust volume local binary count pattern for smoke sequence recognition; the volume local binary count pattern is an extension of the 2D texture local binary count to the spatiotemporal domain, which combines texture and dynamic features. [PERSON] et al. [15] developed an adaptive scale local binary pattern and a discriminative edge orientation histogram for obtaining texture and gradient-based features, respectively, and used a multi-feature fusion-based technique for detecting vehicle smoke. Overall, due to the hand-crafted feature descriptor only extracting the shallow feature of the smoke, traditional image process-based methods may be limited by the representation abilities of the shallow feature.
## 4 Invariant Multi-Mode Vectors Pushover Analysis
### The Spectrum-Based Pushover Analysis (SPA) Method  
The proposed spectrum-based pushover analysis (SPA) method was developed by [PERSON] et al. [67] to establish a quick and effective seismic assessment method for tall buildings. The spectrum-based pushover analysis (SPA) procedure consists of several steps, as follows:  
1. Modelling of the building and applying the seismic gravity loads.
2. Eigenvalue analysis of the structure to compute the (\(\omega_{n}\)), \(T_{i}\), (\(\alpha i\), \(\beta i\), \(\lambda i\)), values.
3. Compute the force vector of different modes: calculate \(s_{n}*=m\Phi_{n}\), where \(s_{n}*\) represents the distribution of incremental lateral forces over the height of the structure for the \(n\)th stage of the multi-stage pushover analysis.
4. Obtain the design spectrum and compute the target displacements. Obtain the design spectrum for the sites of interest or the mean spectrum of a series of ground motion data. Compute the target roof displacements of the structure for different modes \(u_{ir0}\), the total roof displacement \(u_{r0}\), and contributions using Equations (26) and (27): \[u_{ir}=\alpha_{i}*u_{r0},\] (39) \[u_{ir0}\approx\left(\sum_{i=1}^{N}(u_{ir0})^{2}\right)^{0.5},\] (40)
5. The spectrum-based pushover analysis procedure (SPA):
6. In the first stage, perform a nonlinear static pushover analysis using incremental lateral forces \(S_{1}*=m\Phi 1\) until the roof displacement reaches \(u_{1r}=\alpha_{1}u_{r0}\). Obtain the peak response for this stage, noted by \(r_{1}\). In the second stage, apply incremental lateral forces \(S_{2}*=m\Phi_{2}\) until the roof displacement equals \(u_{2r}=\alpha_{2}u_{r0}\). Note that the initial condition in this stage is the same as the state at the last step of analysis in the last stage. Obtain the peak response \(r2\) for this stage. Repeat Step 5.2 for the remaining modes considered. For each \(i\)th step, the target displacement is \(uir=\alpha u_{r0}\), and the initial condition remains consistent with the state at the end of the previous stage.
7. Envelop the peak response for each step: determine the maximum response from all stages using: \[r=max\{r_{1},r_{2},\ldots,r_{n}\},\] (41) The value of \(r\) represents the seismic responses estimated by the SPA procedure.
## 1 Introduction
Hence, the mechanism of content addressing based on a hierarchical NRS makes an uncooperative ICN cache system implicitly form a hierarchical structure. As another crucial component of a caching system, an ICN router is supposed to be designed with multiple factors including IP compatibility, a communication interface with an NRS, a transparent caching function based on the chunk level rather than the packet level [13], and an ICN transport protocol stack to ensure reliable service for users. However, few studies have comprehensively considered these aspects when designing ICN routers. In summary, the contribution of our work is as follows:  
* We designed a simple uncooperative IP-compatible-ICN cache system based on an NRS that implicitly forms a hierarchical and nested structure because of the behavior pattern of data requests. Furthermore, we propose a modeling approach to analysis this hierarchical cache system, which considers the correlation of arriving requests between adjacent cache nodes under different caching strategies. * We designed a cache-supported ICN router with a spilt architecture as a complete service unit in our cache system, which comprehensively takes both forwarding and caching service into consideration. Furthermore, we proposed an implementation scheme of a high performance ICN router with multi-terabyte cache capabilities in a multi-core environment of a general \(\times\)86 server. * We conducted extensive experiments to verify the accuracy of our analysis model for the cache hit ratio at different levels and performed a thorough evaluation of hierarchical cache system (HCS) performance. Experimental results demonstrated the significant gains of the hit ratio and access latency of HCS in comparison to other kinds of cache systems under different caching strategies. The rest of this paper is organized as follows. Section 2 provides related research to our work. The design and model analysis of the HCS is presented in Section 3. Section 4 presents the design and implementation details of cache-supported router. Our experimental results are shown in Section 5. Finally, we conclude the paper and describe our future work in Section 6.
## 5 Optimization Design and Control Methods for Non-Conventional Lanes  
### Traffic Space Design Optimization  
Optimization of traffic space design is the foundation for implementing non-conventional lanes, directly affecting their operational efficiency and safety. Traffic design for non-conventional lanes needs to break through traditional design concepts and comprehensively consider multiple factors such as spatial constraints, traffic flow characteristics, and driving behavior.  
[PERSON] et al. [74] established a combined optimization model for geometry and signals for exit lanes for left-turn intersections, integrating the physical layout of the intersection and signal control parameters in a unified optimization framework. [PERSON] et al. [75] developed specialized simulation models for U-turn movements at unsignalized intersections. Traffic space design optimization for non-conventional lanes involves several key elements: determining lane width (considering passage requirements of different vehicle types), designing turning radii (affecting vehicle traveling speed and safety), calculating transition section length (ensuring smoothness of lane function conversion), arranging signs and markings (guiding drivers to travel correctly), and so on. Taking contraflow left-turn lanes as an example, parameters such as the starting position of the borrowed section, borrowing length, and recovery section settings need to be determined; for outside left-turn lanes, factors such as weaving section length and lane offset position need to be especially considered.  
Future research should strengthen the development of robust design methods, enabling non-conventional lanes to adapt to a wider range of traffic flow fluctuations. Simultaneously, with the development of autonomous driving technology, traffic space design parameters also need to consider the driving characteristics of intelligent vehicles, researching new standards for traffic space design in human-machine mixed driving environments. Additionally, parameterized and modular design methods should be developed for rapid application and adjustment in different scenarios.
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 4 Discussion  
In view of the increasing demand for cellulases and xylanas and the consequent need for higher productivity, it becomes very vital to source for novel enzymes and enzyme-producing microbial strains [9]. In that regard, for a thriving industrial production of cellulases and xylanas enzymes, utmost consideration is given to the selection of microorganism and the process strategies [9]. The genus _Streptomyces_ has been implicated over the years as a contraption of several metabolites and biomolecules, including hydrolytic enzymes [21; 33]. However there has been scant report on hemicellulose potential of _Streptomyces albidoflaws_[34; 35]. Microbial hydrolytic enzyme production is a multivariable controlled process involving the composition and microbial cell culture conditions. Therefore, quantifying the influence of these variables is vital for an enhanced enzyme titer and productivity [10].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Carbon Source** & **Cellulase Activity (U/mL)** & **Xylanase Activity (U/mL)** \\ \hline _Monosaccharides_ & & \\ \hline Arabinose & 0.061 b \(\pm\) 0.01 & 0.30 a \(\pm\) 0.01 \\ Galactose & ND & 10.35 a \(\pm\) 0.65 \\ Glucose & ND & 2.55 a \(\pm\) 0.36 \\ Mannose & 1.33 b \(\pm\) 0.014 & 5.92 a \(\pm\) 0.57 \\ Xylose & ND & 8.02 a \(\pm\) 0.65 \\ \hline _Disaccharide_ & & \\ \hline Cellobiose & ND & 4.39 a \(\pm\) 0.09 \\ \hline _Polysaccharide_ & & \\ \hline Avicel® & ND & 1.66 a \(\pm\) 0.13 \\ CMC & 0.070 b \(\pm\) 0.01 & 2.58 a \(\pm\) 0.15 \\ Xylan & ND & 14.15 a \(\pm\) 0.07 \\ \hline _Lignocellulose_ & & \\ \hline Wheat straw & 0.01 b \(\pm\) 0.002 & 14.85 a \(\pm\) 0.56 \\ Saw dust & ND & 2.29 a \(\pm\) 0.35 \\ Control * & ND & 0.17 \(\pm\) 0.01 \\ \hline \hline \end{tabular} a  
\end{table}
Table 1: Carbon source inducers of cellulase and xylanase production.
# Study on the Evolution Law of Overlying Rock Collapse Induced by Mining Based on BOTDR
## 3 Similar Simulation Experiment
### Experimental Design  
This experiment is conducted using the 7242 working face of Suntuan Coal Mine as the research background, aiming to investigate the collapse of overlying strata and the evolution of strain during the mining of the 72-coal seam. The dimensions of the similar simulation test platform are 300 cm \(\times\) 30 cm \(\times\) 140 cm. The geometric similarity ratio of the model is \(\alpha\)L = 100:1 (i.e., 1 m in the actual mine corresponds to 1 cm in the model), and the model bulk density ratio is \(\alpha\) = 1.6:1. To accurately replicate the on-site conditions, core samples were collected from the field to analyze the mechanical properties of different rock layers. Based on the strength characteristics of the in situ rock, the similar materials for each layer were scientifically proportioned to ensure that the physical and mechanical properties of the model closely match those of the actual mining site.  
The model materials primarily consist of fine sand, lime, and gypsum, where fine sand serves as the main component, while lime and gypsum act as cementing agents. During the layer placement process, mica sheets are laid between the strata to ensure uniformity and stability.  
The total model length is 300 cm, with 30 cm of coal pillars reserved on both sides, corresponding to an actual working face advancement distance of 240 m. The 72-coal seam is excavated in increments of 5 m per step. The total model height is 140 cm, representing a simulated overlying rock thickness of 140 m. The strata above the model are simulated by applying a distributed load using weighted plates on the model's top surface.
## 2 System Model
### Joint Effect of Turbulence and Spatial Jitter  
Since the received signal consists of two components, \(I_{T,n}\) and \(I_{P,n}\), each with its own PDF, the resultant irradiance, which incorporates both turbulence and spatial jitter effects, \(I_{n}\), is described by a joint PDF, calculated as [37; 41]:  
\[f_{I_{n}}\left(I_{n}\right) = \int f_{I_{P,n}}\left(\frac{I_{n}}{I_{T,n}}\right)\frac{f_{I_{T,n }}\left(I_{T,n}\right)}{I_{T,n}}dI_{T,n} \tag{5}\]  
After the appropriate substitutions and some mathematical manipulations, the corresponding expression is given as [41]:  
\[f_{TP,M,I_{n}}\left(I_{n}\right)=\frac{A_{n}B_{n}g_{n}^{2}}{2A_{0,n}g_{n}}\sum \limits_{k}^{\omega}C_{k,n}B_{n}^{-\frac{\omega+k}{2}}G_{1,3}^{3,0}\left( \left.\frac{B_{n}I_{n}}{A_{0,n}g_{n}}\right|\begin{array}{c}\xi_{n}^{2}\\ \xi_{n}^{2}-1,a_{n}-1,k-1\end{array}\right) \tag{6}\]
## 3 System Model and Problem Formulation
### Energy Model  
Here, we will jointly consider two aspects of costs: communication cost and aggregation cost. We will use the following radio communication model [25; 26] to calculate the energy consumption for sending and receiving data. The energy model can be respectively represented by the Formula (2) and Formula (3):  
\[E_{T}\big{(}u,v\big{)}=\big{(}\alpha+\beta*d_{uv}^{~{}~{}2}\big{)}\times\omega \big{(}u\big{)} \tag{2}\]  
\[E_{R}\big{(}u,v\big{)}=\alpha\times\omega\big{(}u\big{)} \tag{3}\]  
where \(\alpha\) is the energy consumed by each sending node to send each bit of data, or each receiving node to receive each bit of data. \(\beta\) is the energy consumption in the amplification circuit for forwarding each bit of data. \(d_{uv}\) is the distance between node \(u\) and node \(v\). \(\omega(u)\) is the data amount transmitted from node \(u\), so for edge \(e=(u,v)\), the communication cost \(t(e)\) of edge \(e\) is given by:  
\[t\big{(}e_{uv}\big{)}=\big{(}2\alpha+\beta\times d_{uv}^{~{}~{}2}\big{)}\times \omega\big{(}u\big{)} \tag{4}\]  
Here we discuss two node types: aggregation points and non-aggregation points. If node \(v\) is a non-aggregation point, it will merely consume energy to transmit data and receive data; if not, it should further consume energy to aggregate its own data and the data from its child nodes. That is to say, besides communication cost, in this paper we also include an aggregation cost model which is presented in [8]. We use Equation (5) to represent the cost for fusing the data of node \(u\) and \(v\):  
\[f\big{(}e_{uv}\big{)}=q\times\big{(}\omega\big{(}u\big{)}+\varpi\big{(}v\big{)} \big{)} \tag{5}\]  
where \(q\) indicates average unit aggregation cost and it is dependent on the type of data to be aggregated and data correlation. \(\omega(u)\) and \(\varpi(v)\) are data amount from \(u\) and \(v\) own data amount before aggregation.
# Secure PD-NOMA with Multi-User Cooperation and User Clustering in Both Uplink and Downlink PD-NOMA
###### Abstract  
The power domain non-orthogonal multiple access (PD-NOMA) scheme has gained tremendous interest with the multiple access behavior for fifth-generation (5G) wireless communication. Although the overall performance is improved through accurate power distribution among users' signals, it depends on the user clustering strategy. Moreover, the PD-NOMA communication is not completely secured due to its broadcast nature, which is still a major problem. This paper presents a novel low-complexity short code-based technique utilized by the registered users and the 5G base station (gNodeB) for communication. By doing so, the PD-NOMA scheme is made secure from unregistered users or eavesdroppers. We proposed a three-step user clustering strategy that selects the best cluster among all the possible clusters to improve the overall performance. The proposed clustering strategy achieves a low outage probability in PD-NOMA systems. Moreover, it uses a multi-user decode and forward cooperative relaying scheme with PD-NOMA (Cop-PD-NOMA) to increase the coverage range of the gNodeB. In the multi-user Cop-PD-NOMA, the strong users (near users) are used as relay stations to aid the weak users (far users) by the decode and forward (D&F) technique. The proposed work provides a secure PD-NOMA network and the most effective user clustering approach during validation. The bit-error-rate (BER) comparisons demonstrate that multi-user cooperation outperforms single-user cooperation in Cop-PD-NOMA communication.  
PD-NOMA; multi-user cooperation; user-clustering; secure PD-NOMA 2022111253. [[https://doi.org/10.3390/electronics11142153](https://doi.org/10.3390/electronics11142153)]([https://doi.org/10.3390/electronics11142153](https://doi.org/10.3390/electronics11142153))
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 4 Experimental Evaluation  
### Experimental Setup  
We have implemented and simulated PMHRP in discrete-event network simulator 2 (NS2) [47]. Generally, NS2 and its default routing protocols do not support 3D space propagation models. Therefore, we patched the space propagation model and also the GEO routing protocol [48] in NS2 version 2.35 running on ubuntu16 to support the 3D UAV network environment. The seven routing-protocol-based experiments are conducted with the simulation parameters given in Table 3, and the results gathered include the three methods of the proposed PMHRP, three variants of the existing GEO, and one famous topology-based AODV routing protocol. All routing protocols are executed with the same parameters to compare the performance of routing protocols in a 3D UAV network. Different scenarios have been simulated based on the varying average speeds of 20, 40, and 60 m/s by utilizing the random waypoint mobility model. The 2D RWP supports planner structure has only two coordinates: \(x\) and \(y\) regions. On the other hand, the 3D RWP mobility model tries to imitate unmanned aerial vehicle movement and position as closely as possible because it creates a 3D environment by utilizing three coordinates \((x,y,z)\) instead of a planner structure. Therefore, the 2D RWP mobility model or other 2D mobility models are not as close as the 3D RWP mobility model for UAV networks. The 3D RWP mobility model improves the performance of the routing protocols, which supports the UAV environment. To the best of our knowledge, the traditional AODV routing protocol works better only for planner network environments; therefore, its performance may slightly  
Figure 5: Routing and data forwarding.  
decrease in a UAV environment. In the following Section 4.3, we will analyze benchmark routing protocols in more detail. Furthermore, in order to check the stability of routing protocols, the different data flows are configured for different communication connections via the NS2 traffic generator. This traffic generator is a reliable software based on the Pascal language and is used in multiple studies, such as [49; 50]. This software generates TCL traffic patterns compatible with NS2 end-user code.