esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hybrid Network Implementation Using LoRaWAN and LoRaMESH
### LoRaWAN Module Behavior in Testbed
#### 3.15.2 Behavior of the Network with the Packets Sent by the EPC  
When packets are sent by the EEC, it means that the packet leaves the gateway towards the SMs. In this scenario, it is possible to see in the Figure 20 the results obtained for the LoRaWAN protocol performance analysis considering the sending of the three types of packets that the EEC will send to the SMs.  
As can be seen, the difference between RSSI (a) and SNR (b) in relation to the SF configuration and the package size is approximately \(-20\) dBm for RSSI, and 3 dBm for SF 7 to 9 and 1 dBm for the larger SF for SNR. However, the RSSI is better for the smaller package, with the larger size it has a difference of approximately \(-20\) dBm. On the SNR the difference is well reduced for all packet sizes in the SF7 to 11 nodes, with the SF12 the difference increases to almost 10 dBm. As far as ToA (c) is concerned, all packet sizes have a similar ToA for the smaller SF configurations, however, as the SF increases between SF 10 and 12, the difference in ToA reaches 25 ms, 100 ms and 150 ms respectively. The delay (d) behaves more evenly between all the SFs having a difference of approximately 1 s between the two smallest packets, and 2 s between the two largest packets. This way it is possible to notice that for the smaller SFs any type of size can be sent that the ToA won't be affected, but in the case of delay, the bigger the SF configuration the bigger the delay between the packets will be.  
Figure 19: LoRaWAN behavior (**a**) RSSI, (**b**) SNR, (**c**) ToA and (**d**) Delay of packets sent by SMs.
# Knowledge-Graph-Based Integrated Line Loss Evaluation Management System
## 5 Construction of a Mapping of Management Cases for Comprehensive Line Loss Evaluation
### Comparative Analysis
A comparison of the aforementioned case mapping visual analysis display with the existing comprehensive line loss management of Southern Power Grid during the same period reveals that knowledge mapping can provide managers with more comprehensive and accurate data support following the intelligent integration and analysis of data sources. In comparison with the conventional approach of analyzing data from a single source, the comprehensive evaluation methodology enables the integration of multiple data sources, thereby facilitating a more comprehensive understanding of the nature of the line loss problem among managers. The utilization of knowledge mapping enables the simultaneous updating of data and knowledge in real-time, thereby facilitating intelligent line loss evaluation management. This approach reduces the necessity for manual intervention, enhances management efficiency and accuracy, and significantly accelerates the determination of comprehensive evaluation management of line loss in comparison to traditional methods. Furthermore, the knowledge graph can leverage data mining and predictive analysis techniques to identify potential line loss risks and issue warnings in advance.
# Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver
###### Abstract  
A multi-constellation, multi-frequency Global Navigation Satellite System (GNSS) receiver is capable of simultaneously receiving signals from multiple satellite constellations across various frequency bands. This allows for increased observations, thereby enhancing navigation accuracy, continuity, effectiveness, and reliability. The spread spectrum code structures used in satellite navigation signals differ among systems. Compatible code generators are employed in multi-constellation, multi-frequency GNSS receivers to support tasks such as signal acquisition and tracking. There are three main types of spread spectrum code structures: Linear Feedback Shift Register (LFSR), Legendre sequences, and Memory codes. The Indian Regional Navigation Satellite System (IRNSS) released the L1-SPS (Standard Positioning Service) signal format in August 2023, which utilizes the Interleaved Z4-linear ranging code (IZ4 code) as its spread spectrum code. Currently, there is no universal code generator design compatible with the IZ4 code. In this paper, a proposed universal code generator is based on the hardware structure of the IRNSS IZ4 code generator. It achieves compatibility with all LFSR-based spread spectrum codes and enables parallel generation of multiple sets of GNSS signal spread spectrum codes, thereby improving hardware utilization efficiency. The proposed structure is implemented and validated using FPGA design, and resource consumption is provided as part of the validation results.  
GNSS; IRNSS IZ4; universal code generator; FPGA Article Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver
## 3 RNAi Traits for Rootworm Control  
### RNAi as a New Mode of Action for Rootworm Control  
Commercial traits used to control WCR have traditionally relied on insecticidal proteins identified from _B. thuringiensis_ and other bacterial species [118; 119; 120]. When insecticidal proteins are expressed in transgenic maize, rootworm larvae exposed through root tissue feeding are significantly impacted in terms of mortality and stunting. Mortality is often the result of midgut epithelial cell disruption after receptor recognition results in surface membrane binding of the protein and a subsequent cascade of molecular events [121]. This effect in rootworm larvae translates directly into reduced root damage and yield protection.  
As previously described, WCR is highly susceptible to environmental RNAi [122]. This has made it possible to target rootworm genes essential for survival through RNAi-mediated gene suppression as a new method to control rootworm damage complementary to insecticidal proteins. The next generation of rootworm-protected maize hybrids combine the MON87411 event, expressing Cry3 Bb1 and _Dv5 ntf7_, an RNAi trait, with the DAS-59122-7 event, expressing Cry34 Ab1/Cry35 Ab1, creating the first rootworm-active pyramid containing three modes of action [32]. A review of the efforts to identify efficient transgenic maize RNAi traits is provided below and a summary of the most successful genes evaluated in WCR is provided in Table 1.
## 2 Materials and Methods
### Pattern Recognition Methods
#### 2.2.2 Random Forest (RF)  
Random forests (RFs), or random decision forests, are an ensemble learning method for classification, regression, and other tasks. An RF operates by constructing multiple decision trees at different training times, and outputting the class representing the mode of classes (classification) or the mean prediction (regression) of individual trees [31].  
The RF algorithm incorporates growing classification and regression trees (CARTs). Each CART is built using random vectors. For the RF-based classifier model, the main parameters were the number of decision trees, as well as the number of features (\(N_{F}\)) in the random subset at each node in the growing trees. During model training, the number of decision trees was determined first. For the number of trees, a larger number is better, but takes longer to compute. A lower \(N_{F}\) leads to a greater reduction in variance, but a larger increase in bias. \(N_{F}\) can be defined using the empirical formula \(N_{F}=\sqrt{M}\), where \(M\) denotes the total number of features [32].  
RF can be applied to classification and regression problems, depending on whether the trees are classification or regression trees. The regression model is shown in Figure 2. Assuming that the model includes \(T\) regression trees (learners) for regression prediction, the final output of the regression model is  
\[H(\mathbf{x})=\frac{1}{T}{\sum_{i=1}^{T}{\rm{h_{i}}}(\mathbf{x})}, \tag{3}\]  
where \(T\) is the number of regression trees, and \({\rm{h_{i}}}(\mathbf{x})\) is the output of the \(i\)-th regression tree (\({\rm{h_{i}}}\)) on sample \(\mathbf{x}\). Therefore, the prediction of the RF is the average of the predicted values of all the trees.
## 5 Evaluation
### Overhead Evaluation
#### 5.5.2 Downstream Overhead  
In the case of loss recovery, the packets sent downstream by the MTNs are recovery packets. As shown in Figure 8, as the cache size increases, more chunks are cached, and the downstream overhead generated by all combinations decreases. Under the same cache strategy, compared with AARM and Equal, using DCAM to allocate the cache resources of the MTNs can achieve smaller downstream overhead. Among them, the DCAM-CAPC combination is always more advantageous than other combinations. For example, when the cache size is 900, the downlink overhead achieved by the DCAM-CAPC combination is 27.75%, 30.56% and 25.19% lower than that of DCAM-LCE, DCAM-Prob, and DCAM-ProbCache, respectively.  
Figure 8: Comparison of downstream overhead. (**a**) Comparison of downstream overhead under LCE; (**b**) comparison of downstream overhead under CAPC; (**c**) comparison of downstream overhead under ProbCache.  
Figure 7: Comparison of upstream overhead. (**a**) Comparison of upstream overhead under LCE; (**b**) comparison of upstream overhead under CAPC; (**c**) comparison of upstream overhead under Prob; (**d**) comparison of upstream overhead under ProbCache.  
The experiment results in this section reflect the advantages of DCAM in reducing the overall overhead in the process of reliable multicast loss recovery, and reflect that the allocation mechanism is more reasonable than AARM and Equal.
# An Urban Traffic Flow Fusion Network Based on a Causal Spatiotemporal Graph Convolution Network
## 4 Experiment
### Data Processing and Parameter Setting  
We removed redundant detectors to ensure that each detector represented one junction and that the distance between detectors was not less than 3.5 miles. Finally, there were 307 detectors in the PeMS04 set and 170 detectors in PeMS08. The detectors aggregated the traffic flow data at junctions every 5 min, and so the data of 288 points were collected for each junction every day. The missing values were filled by linear interpolation. In addition, the data were processed by zero-mean normalization to bring the average to 0.  
In the experiment, we tested our model by using the pytorch framework. We tested order \(k\in[0,1,2]\) of the LPF convolution module and achieved satisfactory results. Experi  
Figure 4: Visual comparison of data fluctuation of three elements of 24-h traffic flow at intersection 120 in the randomly selected data set.  
mentation revealed that the optimal results were obtained for \(k=2\). Meanwhile, the kernel size of the causal gated convolution was set to 3. In terms of data, we adopted the concept of \"sliding window\" to divide the data. In the LPF convolution module, the space axis was convoluted as per the time axis slice. The \"sliding window\" was employed to predict the traffic flow of the \(\tau+1\)-th time point by using the traffic flow of the previous \(0\sim\tau\) time points. The \(\tau+1\) data constituted a training set sample, the \(\tau\) data were input \(x\) and the \(\tau+1\) data were labelled \(\hat{y}\). To obtain the next sample, we moved the \"sliding window\" to the next time point where the traffic flows of the \(1\sim\tau+1\) time points constituted the new input data \(x\), and the traffic flow of the \(\tau+2\)-th time point became the new label \(\hat{y}\). These data and labels formed the training set and the test set. The \"sliding window\" used the average value of multiple data for prediction, avoiding outliers and differences caused by single data. In this way, the impact on the prediction results was minimized and our data were more stable. In this study, the size of our \"sliding window\" was set to \(\tau=12\). For the prediction range, the list of continuous time nodes may be selected at will; the prediction ranges in this study were set to 5 min, 15 min, 30 min, and 45 min, including two time marker points for short-term prediction and long-term prediction, respectively.  
Mean absolute error (MAE) and root mean square error (RMSE) were used as metrics and baselines for measuring and evaluating the performance of different methods in the experiment. We compared our method with the following typical methods: (1) historical average (HA); (2) auto-regressive integrated moving average (ARIMA); (3) Vector auto-regressive (VAR); (4) long short-term memory (LSTM) network; and (5) gated recurrent unit (GRU) network.
# Review: A Survey on Configurations and Performance of Flow-Mode MR Valves
## 4 Analysis
### Shear Stress Metric Evaluation  
In this section, we evaluated the shear stress metric for several configurations for which the geometry information and testing conditions can be reconstructed. The presented information in Table 2 was collected based on the available references from force-velocity plots or pressure-flow rate characteristics if available. The information which we acquired was spread over a quarter of the century, and the earliest reference included in the comparison was by [PERSON] et al. [92]. The data set in Table 2 includes the performance data of several automotive MR flow-mode single-tube dampers for which the geometry and performance characteristics were acquired. The information in the table can be considered only as ballpark figures. The lack of the unified testing procedure, various approaches to presenting the performance information did not make the comparison easy. Therefore, only the data concerning MR valves sharing similar features in the flow path can be reliably compared against each other. Moreover, the configurations used in commercial MR actuators have been the subject of engineering efforts and optimization over the years to enhance their performance. This is not the case for research or academic prototypes. We deemed the inclusion of commercial MR actuator data necessary as they offer excellent performance which is usually achieved with relatively simple means. However, despite the differences and discrepancies, the following statements can be drawn:  
1. The top performers of the analyzed set are the concept valve with radial coils analyzed in [55] (numerical study only, no experimental verification), and the benchmark valve of the train damper [93]. 2. All commercial MR valves dominate in the upper range of the examined set of MR valves. 3. The enhancement in the performance of the valves with the radial coils is due to their active area ratio of nearly 100%. 4. The performance of nearly all analyzed valves having one coil assembly, one single flow channel is within the range from 500-1000 kPa for the velocity range \(0.1,\ldots,1.0\) m/s (\(\Delta\tau\)). 5. The proposed metric can be a convenient way of characterizing the actuator's force capacity (see Equation (10)). 6. Several claims on superior dynamic range of the proposed configuration cannot be substantiated based on their evaluated \(\tau\) (\(\Delta\tau\)) metrics.
# AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain
## 2 Overview of Agriculture 4.0  
Agriculture has gone through many revolutionary movements, all of which have increased the agriculture industry's performance and effectiveness.  
Plant cultivation \"in 10,000 BC\" resulted in the birth of the world's first societies and civilizations. Machines were used in the farming sector to execute work, which increased production from 1900 to 1930. Farmers were able to use newly discovered crops and agrochemicals during the Green Revolution (from around the 1960s). Biotechnology facilitated the production of plants with previously selected attributes, with increased yield and pest resistance, desertification, and herbicide, from 1990 to 2000. Now, in the 21 st century, digitalization has the potential to help humanity survive and flourish for a long time [4].  
Figure 1 is used to explain the features of agriculture development to readers (from Agriculture 1.0 to Agriculture 4.0).  
Figure 1: The uprising of Agriculture 1.0 to agriculture 4.0.  
Agriculture 1.0: During the conventional agricultural age (1784-1870), which was dominated by animal and human resources, the fundamental problem with agriculture was its lack of operational efficiency.  
Agriculture 2.0: During the automated agricultural period of the 20 th century, resource waste was the major problem.  
Agriculture 3.0: During the period of rapid advancement of automated agriculture (1992-2017), the key problem was the lack of intelligence.  
Agriculture 4.0: the age of smart agriculture (which began in 2017 and is defined by unmanned operations) is primarily characterized by the application of contemporary information technology to both support and intelligently improve agriculture.  
The term \"smart agriculture\" [15] refers to a new approach to agriculture that personalized service through the integration of contemporary information technologies such as the internet, big data, IoT, and many more [16]. In a nutshell, the new mode is an intelligent agricultural solution that fuses current information technology with agriculture. Although recent information technology opens up new possibilities for the progress of agricultural output, it also places heavy demands on security and privacy in the context of smart agriculture and poses significant obstacles therein [17].  
More than a movement, agriculture 4.0 represents the next development in the sector's transition to a more intelligent, effective, and environmentally responsible one. The supply chain's daily operations produce a tremendous amount of data [18]. Most of the time, this information went unused, but thanks to big data and new technology, it can now be used to boost any crop's performance and productivity.
## 5 Experimental Analysis
### Why Proposed Architecture Performs Better?
Furthermore, our architecture outperformed MobileNet architecture which used 4.2 million parameters and other studies that worked with low-cost mechanisms [40; 42; 43] in terms of accuracy and number of parameters. Additionally, the dropout technique was used for bypassing overfitting so that the output cannot rely on some specific neurons only. However, selecting the values of dropouts was an experimental tuning procedure. Moreover, we chose the 'Adam' optimizer rather than the momentum function or RMSprop algorithm as the 'Adam' optimizer bears both properties of momentum and RMSprop. Furthermore, the learning rate was not kept consistent. The first epoch started with a 0.001 learning rate and ended with a 0.000001 learning rate after the completion of 200 epochs. Because of all these reasons, the proposed BengaliNet performed better despite being a low-cost architecture. From this discussion, some major differences between the proposed model and the baseline models can be noticed. Unlike the baseline models, the proposed model does not repeat the same convolution block multiple times. Rather, the proposed model introduces a three-way architecture with filters of three sizes with different convolution blocks and branches in each way. Instead of using a voting method, the proposed model uses the SoftMax averaging technique. The input of the proposed model is a \(28\times 28\) grayscale image which is much smaller in size than the baseline approaches. Moreover, dropouts and batch normalization have been used here for better learning.
# Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm
## 5 Gas Cyclone Design
### Particle Cut-Off Size \(D_{pc}\)  
According to [PERSON] et al. [20; 21; 22], cyclone performance depends on the geometry and operating parameters of the cyclone, as well as the particle size distribution of the entrained particulate matter. Several mathematical models have been developed to predict cyclone performance. [PERSON] [23] developed a semi-empirical relationship to predict the cut point of cyclones designed according to the classical cyclone design method, where cyclone cut point is defined as the particle diameter corresponding to a 50% collection efficiency. [PERSON] et al. showed that [PERSON]'s approach did not discuss the effects of particle size distribution on cyclone performance. The [PERSON] model was based on the terminal velocity of particles in a cyclone [23]. From the theoretical analysis, Equation (7) was derived to determine the smallest particle that will be collected by a cyclone if it enters at the inside edge of the inlet duct:  
\[d_{pc}=\sqrt{\frac{9\mu b}{2\Pi N_{e}V_{i}(\rho p-\rho g)}} \tag{7}\]  
where:  
\(d_{pc}\) (cut-point) = diameter of the smallest particle that will be collected by the cyclone if it enters on the inside edge of the inlet duct (\(\mu\)m);  
\(\mu\) = gas viscosity (kg/m/s);  
\(b\) = width of inlet duct (m);  
\(Ne\) = number of turns of the air stream in the cyclone;  
\(V_{i}\) = gas inlet velocity (m/s);  
\(\rho p\) = particle density (kg/m\({}^{3}\));  
\(\rho g\) = gas density (kg/m\({}^{3}\)).
## 2 Background
### Threat Model  
Abusing CPLS as C&C Channel  
Understanding the life cycle of a botnet that abuses the CPLS as C&C is crucial for the successful analysis of botnet detection systems. By comprehending each phase of this cycle, it becomes possible to enhance and develop more efficient detection systems. Figure 2 illustrates how threat actors abuse CPLS as centralized C&C servers to controlbots. After compromising a victim's machine, the bot requires a remote control mechanism to communicate with the C&C server for further malicious instructions. To establish this remote C&C channel, bot connection requests are routed through CPLS instead of direct connections [12, 13].  
The life cycle of abusing the CPLS as a C&C channel involves the following steps:  
1. The botmaster issues commands to the bots in the botnet through the CPLS.
2. Bots continuously monitor the designated CPLS for new commands from the botmaster.
3. Bots then execute the malicious commands.
4. Bots report the results of the commands back to the botmaster through the CPLS.  
This structure is stealthy and difficult to detect, as the bot retrieves commands and exfiltrates data through legitimate cloud-based services, making it challenging for network defenders and security solutions to differentiate between malicious and benign network traffic.