esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Description of the Medical Rescue Operations after Mass Casually Incident
### Characteristics of the Health Condition of People Injured in a Mass Casualty Incident
#### 3.3.3 Degree of Basic Life Dysfunctions  
The degree \(c_{l,3}\) of basic life dysfunctions of the \(l\)-th injured person is determined in accordance with the _Triage Revised Trauma Score_ (TRTS) used during mass casualty incidents and given in [46]. The assessment of basic life dysfunctions is characterized by the following couple:  
\[c_{l,3}=<c_{l,3,1},c_{l,3,2}>, \tag{25}\]  
where \(c_{l,3,1}\) is the value representing the respiratory rate per minute and \(c_{l,3,2}\) is the value determining the systolic blood pressure of the \(l\)-th injured person:  
\[c_{l,3,1}=\left\{\begin{array}{ll}4,\;\text{if respiratory rate is 10-29 per minute},\\ 3,\;\text{if respiratory rate is larger than 29 per minute},\\ 2,\;\text{if respiratory rate is 6-9 per minute},\\ 1,\;\text{if respiratory rate is 1-5 per minute},\\ 0,\;\text{if doesn't breathe}.\end{array}\right. \tag{26}\]  
\[c_{l,3,2}=\left\{\begin{array}{ll}4,\;\text{if systolic blood pressure is larger than 89 mm Hg},\\ 3,\;\text{if systolic blood pressure is 76-89 mm Hg},\\ 2,\;\text{if systolic blood pressure is 50-75 mm Hg},\\ 1,\;\text{if systolic blood pressure is 1-49 mm Hg},\\ 0,\;\text{if systolic blood pressure is 0 mm Hg}.\end{array}\right. \tag{27}\]  
When determining the degree of basic life dysfunctions \(c_{l,3}\) of the \(l\)-th injured person according to the TRTS, the level of his/her state of consciousness should be additionally taken into account [46]. For this purpose, quantity \(c_{l}\) is introduced, defined as:  
\[e_{l}=\left\{\begin{array}{ll}4,\;\text{if}\;13\leq c_{l,2}\leq 15,\\ 3,\;\text{if}\;9\leq c_{l,2}\leq 12,\\ 2,\;\text{if}\;6\leq c_{l,2}\leq 8,\\ 1,\;\text{if}\;4\leq c_{l,2}\leq 5,\\ 0,\;\text{if}\;c_{l,2}=3.\end{array}\right. \tag{28}\]  
After determining the value of \(e_{l}\), the resultant assessment of the degree of basic life dysfunctions of the \(l\)-th injured person can be determined as [46]:  
\[c_{l,3}=e_{l}+c_{l,3,1}+c_{l,3,2}, \tag{29}\]  
whereby the lower the value of the resultant score \(c_{l,3}\), the higher the degree of basic life functions dysfunctions of the \(l\)-th casualty.
## 4 Results and Analysis  
### The Current Situation and Characteristic of Wetland  
The wetland monitoring results of 2012 (Table 2) show that the total wetland area is 22,481 km\({}^{2}\), accounting for 6.3% of the total land. Swamps, streams & rivers, and lakes are the dominant wetland types. Swamps cover an area of 7791 km\({}^{2}\), occupying 34.7% of the total wetlands, followed by streams & rivers and lakes, of which the area was 7511.4 km\({}^{2}\), occupying 33.4% and 7151.2 km\({}^{2}\), occupying 31.8%, respectively. On the other hand, reservoirs & ponds covered an area of 27.3 km\({}^{2}\), accounting for 0.1% of the total wetlands in this region.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Wetland category**} & \multicolumn{2}{c}{**Early 1990s**} & \multicolumn{2}{c}{**2004**} & \multicolumn{2}{c}{**2012**} \\ \cline{2-7}  & **Area (km\({}^{2}\))** & **Percent (\%)** & **Area (km\({}^{2}\))** & **Percent (\%)** & **Area (km\({}^{2}\))** & **Percent (\%)** \\ \hline Streams and rivers & 7,549.7 & 34.0 & 7,541.2 & 34.2 & 7,511.4 & 33.4 \\ Lakes & 6,595.6 & 29.7 & 6,664.5 & 30.2 & 7,151.2 & 31.8 \\ Reservoirs and ponds & 15.4 & 0.1 & 26.9 & 0.1 & 27.3 & 0.1 \\ Swamps & 8059.8 & 36.3 & 7,817.7 & 35.5 & 7,791.1 & 34.7 \\ \hline Total wetlands & 22,220.4 & 6.2 & 22,050.3 & 6.2 & 22,481.0 & 6.3 \\ \hline \hline \end{tabular} Note: The percent of each wetland category is the proportion of its area in total wetland. The percent of total wetland is the proportion of total wetland in the total land.  
\end{table}
Table 2: Wetland area changes since early 1990s in the TRH region.  
Figure 3 shows the spatial distribution of different wetland types in the TRH region. Swamps were concentrated at the Yellow River source region and Yangtze River source region (including the Tuotuo River, Chumaer River and Dangqu River) and Lancang River source region, which were the dominant one in Yangtze River basin, covering an area of 5368.3 km\({}^{2}\), occupying 49.8%. With a dense river network, developed river system and numerous tributaries, streams & rivers were extensively distributed in the TRH region. In the Yangtze River basin, the area of streams & rivers was 3442.8 km\({}^{2}\), occupying 31.9%. Lakes were concentrated in the northwestern parts of Maduo County in the Yellow River source region and the northwestern parts of Tanggulashan County in the Yangtze River source region and northwestern parts of Zhiduo County.
## 3 Results
### Firing Rate Analysis  
SNNs have the potential to be more efficient than traditional ANNs [45], and the way they can achieve this is through the sparsity of (virtual) neurons that are firing. Fewer neurons firing means a lower number of calculations are required to train and run the models.  
In non-spiking ANNs, each neuron's weight calculation requires a Multiply and Accumulate (MAC) calculation operation per input. In LSTM layers, four MAC calculations are required [46]. In SNNs, however, the output is either a 0 or 1 instead of a floating-point representation. This means only one addition operation [27] is required. In SLSTM layers, again, this is reduced to four addition (ADD) operations. However, SNNs must also update their membrane potential with each step, which, as a floating-point variable, requires one MAC operation per neuron. Each SLSTM layer also has a synaptic membrane potential, which requires two MAC calculations. Using a conservative estimate of three ADD to one MAC operation according to the ratio of energy in [47], we can estimate the cost of operations for a forward pass.  
The Unscented Kalman Filter's complexity is outlined in [48] and each measurement update requires \(8/3\text{L}^{3}+12\text{NL}^{2}+2\text{N}^{2}\text{L}+6\text{L}^{2}+10\text{ NL}+\text{N}^{2}+19/3\text{L}+\text{N}\) floating point operations (FLOP) per measurement update, where L is the number of parameters and N is the sigma points. This leads to 3.48 \(\times\) 10\({}^{7}\) FLOP. Since two FLOPs equal about one MAC, this is 1.74 \(\times\) 10\({}^{7}\) MAC operations.  
For an input of 872 timesteps, Table 2 indicates the number of operations required.  
In Figure 5A, we can clearly see that all SNN and SLSTM models require fewer total operations than the LSTM model and the membrane-based models require significantly less operations, mainly due to the fact they do not require the output 600 spike layer. We can also see that the forward pass of the UKF5 requires more calculations and takes a lot more time to run; however, there is practically no training time for the UKF5.  
Figure 5B shows the firing rate of neurons in layers 1-3, the layers directly preceding the output. It shows that the majority of neurons fire very sparsely.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**LIF Spikes**} & **SLSTM** & \multirow{2}{*}{**MAC**} & **ADD** & **Total** \\  & & **Spikes** & & & **Operations** \\ \hline UKF5 & 0 & 0 & \(1.74\times 10^{7}\) & 0 & \(1.74\times 10^{7}\) \\ \hline LSTM & 0 & 0 & \(5.25\times 10^{5}\) & 0 & \(5.25\times 10^{5}\) \\ \hline swSNN & \(1.89\times 10^{5}\) & 0 & 900 & \(1.89\times 10^{5}\) & \(6.4\times 10^{4}\) \\ \hline memSNN & \(6.7\times 10^{4}\) & 0 & 300 & \(6.7\times 10^{4}\) & \(2.7\times 10^{4}\) \\ \hline swSNN-SLSTM & \(2.4\times 10^{5}\) & \(1.4\times 10^{4}\) & 1000 & \(2.96\times 10^{5}\) & \(1.0\times 10^{5}\) \\ \hline memSNN-SLSTM & \(5.3\times 10^{4}\) & \(1.4\times 10^{4}\) & 400 & \(1.09\times 10^{5}\) & \(3.7\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of forward pass operations.
# An Approach for Detecting Feasible Paths Based on Minimal SSA Representation and Symbolic Execution
###### Abstract  
Static analysis is one of the techniques used today to analyze source codes and minimize the issue of software vulnerability. Static analysis has the ability to observe all possible software paths in an application through the scrutiny of a web application's source code. Among those paths, some may be considered feasible paths, which refer to any paths that the test cases can execute. The detection of feasible paths in the results of a static analysis helps to minimize the false positive rate. However, the detection of feasible paths can be challenging, especially for programs that have multiple conditions in the same branch. The aim is to ensure that each feasible path is detected only once (not duplicated). This paper proposes an approach based on minimal static single assignment (MSSA) form and symbolic execution to detect feasible paths. The proposed approach starts by converting the source code into an abstract syntax tree (AST), followed by converting the AST to minimal SSA representation, which helps to decrease the number of instructions in the SSA form. An algorithm was built to examine all of the instructions of the SSA form, identify whole paths in the source code, and extract constraints along each path. A path weight method (PWM) is proposed in this work to avoid detecting duplicated feasible paths. The satisfiability modulo theory (SMT) solver was used to check the satisfiability of each path condition. The proposed approach was tested on seven well-known test programs that have been used in related studies and 10 large scale programs. The experimental results indicate that the proposed method (PWM) can avoid detecting duplicated feasible paths, and the proposed approach reduced the time required for generating the paths compared to that in related studies.  
detection; feasible paths; SSA; static analysis; symbolic execution +
Footnote †: journal: Applied Sciences  
15 April 2021  
31 May 2021  
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**Copyright:** 2021 by the authors. License MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 4 Deep Learning Models  
### Introduction to Deep Learning Technology  
Deep learning technology, an extension of machine learning, involves numerous processing layers, which enable the learning of features at high levels of abstraction. In recent years, various sensor devices have been utilized to capture urban traffic parameters, generating large volumes of data with diverse types and characteristics. At the same time, deep learning techniques have demonstrated their capacity for high-dimensional data mining, extracting complex spatiotemporal dependencies from intricate traffic datasets, thus improving the accuracy of traffic predictions.  
Essentially, deep learning bypasses the need for human involvement in the feature selection process. As a widely researched area in recent years, deep learning has achieved notable success in fields such as image recognition, object detection, and natural language processing. Compared to traditional machine learning, deep learning offers a broader range of applications, significantly expanding the field of artificial intelligence. The core of deep learning lies in constructing machine learning models with multiple hidden layers, which are trained on vast amounts of data to derive more meaningful features, thereby enhancing the accuracy of prediction and classification tasks. Prominent and effective deep learning models currently include convolutional neural networks (CNNs), autoencoders, recurrent neural networks (RNNs), graph convolutional networks (GCNs), attention mechanisms with Transformers, and hybrid neural networks in deep learning.
## 4 Results and Discussion
### Identification and Categorization of Factors  
Derived from the 10 selected studies, we have identified a total of 19 significant factors that contribute to the decision to adopt DATs. As indicated in Table 4, these factors can be distributed into five major categories, which are socioeconomic, agroecological, technological, institutional, psychological, and behavioral. The role of each of these categories is discussed in the subsequent subsections. ### Identification and Categorization of Factors  
Derived from the 10 selected studies, we have identified a total of 19 significant factors that contribute to the decision to adopt DATs. As indicated in Table 4, these factors can be distributed into five major categories, which are socioeconomic, agroecological, technological, institutional, institutional, psychological, and behavioral. The role of each of these categories is discussed in the subsequent subsections. \begin{table}
\begin{tabular}{c c c c} \hline
**Categories of Factors** & **Significant Variables** & **Effects** & **References** \\ \hline Socioeconomic factors & Age & Positive & [PERSON] et al. [30]; [PERSON] et al. [32] \\  & Gender-female & Negative & [PERSON] et al. [34]; \\  & & & [PERSON] et al. [36] \\  & Education & Positive & [PERSON] et al. [30]; [PERSON] et al. [32]; [PERSON] et al. [35] \\  & Health & Positive & [PERSON] et al. [30]; [PERSON] et al. [32] \\  & Total income & Inconclusive & [PERSON] et al. [32]; [PERSON] et al. [35]; [PERSON] et al. [36] \\  & Agricultural income & Positive & [PERSON] et al. [34]; \\  & & & [PERSON] et al. [36] \\  & Farming experience & Positive & [PERSON] et al. [30]; [PERSON] et al. [31] \\  & Member of cooperatives & Positive & [PERSON] et al. [31]; [PERSON] et al. [32] \\ \hline Agroecological factors & Farm size & Positive & [PERSON] et al. [31]; [PERSON] et al. [35]; [PERSON] et al. [36] \\ \hline Technological factors & Access to digital information & Positive & [PERSON] et al. [31]; [PERSON] et al. [32]; [PERSON] et al. [38] \\  & Cost of technology & Negative & [PERSON] et al. [35] \\ \hline Institutional factors & Government subsidy & Positive & [PERSON] et al. [30]; \\  & Availability of financial services & Positive & [PERSON] et al. [35]; [PERSON] et al. [38]; [PERSON] et al. [37] \\  & Environmental regulation & Positive & [PERSON] et al. [31]; \\  & Extension services & Positive & [PERSON] et al. [31]; [PERSON] et al. [35] \\  & Contract farming & Positive & [PERSON] and [PERSON] [39] \\ \hline Psychological and behavioral & Perceived profitability of using & & [PERSON] et al. [31]; \\ factors & technology & Positive & [PERSON] et al. [34]; \\  & Perceived ease of using & Positive & [PERSON] et al. [37] \\  & technology & Positive & [PERSON] et al. [33]; \\  & Risk perception & Negative & [PERSON] et al. [33]; [PERSON] et al. [35]; [PERSON] et al. [38] \\ \hline \end{tabular}
\end{table} 
Table 4: Significant factors influencing the adoption DATs.
## 4 Results and Discussion
### Daily ET\({}_{\text{o}}\) Evaluated Using the Predicted Environmental Parameters  
For two solar greenhouses, the maximum and minimum air temperature in the solar greenhouse were predicted using the optimized temperature prediction model, respectively. Net radiation in two greenhouses was predicted by the sliding mean filtering method. By incorporating the above data into the PM formula, the hourly ET\({}_{\text{o}}\) is calculated using the predictive maximum, minimum, and average air temperature, net radiation and RH, at the same time, the actual value of hourly ET\({}_{\text{o}}\) was calculated using the monitored environmental parameters. Daily ET\({}_{\text{o}}\) is obtained by accumulating hourly ET\({}_{\text{o}}\).  
For the three models, the RMSE and MAE of the predicted daily ET\({}_{\text{o}}\) and the actual daily ET\({}_{\text{o}}\) for the Greenhouse A under four forecast time points are shown in Figure 21. It can be seen from the results, for the test set of T+3h, T+12h and T+24h, the RMSE and MAE of the best LSTM and CNN-LSTM models increase with the training set time horizon, and for the test set of T+6h, the RMSE and MAE of the best LSTM and CNN-LSTM models are decrease with the training set time horizon. On the contrary, for four forecast time points, the RMSE and MAE of the best DNNR models decrease with the training set time horizon. In general, the performance of DNNR was best among the three models.  
The RMSE and MAE of the predicted daily ET\({}_{\text{o}}\) and the actual daily ET\({}_{\text{o}}\) for Greenhouse B under four forecast time points are shown in Figure 22. It can be seen from the results, for the test set of T+6h and T+24h, the RMSE and MAE of the best LSTM and CNN-LSTM models increase with the training set time horizon, and for the test set of T+6h and T+12h, the RMSE and MAE of the best LSTM and CNN-LSTM models are decrease with the training set time horizon. On the contrary, for four forecast time points, the RMSE and MAE of the best DNNR models decrease with the training set time horizon. In general, the performance of DNNR was best among the three models.  
Figure 21: The RMSE and MAE of predictive daily ET\({}_{\text{o}}\) and actual ET\({}_{\text{o}}\) for Greenhouse A under four forecast time points.
## 5 Conclusions  
In conclusion, an 800 Gbps silicon photonic transmitter with surface-normal optical interfaces is designed and numerically demonstrated, which is comprised of eight Mach-Zehnder optical modulators and an interleaved AMMI WDM device. The interleaved AMMI is built up by a Mach-Zehnder optical interleaver and two 1 \(\times\) 4 AMMIs. With apodized bidirectional grating coupling technology, perfectly vertical optical coupling with about \(-0.5\) dB coupling loss can be achieved by simulation. Mach-Zehnder E-O modulators and optical interleaver can be built up by using the 3-dB power splitting behavior of the bidirectional grating couplers. To achieve a high performance optical transmitter, the optical devices are fully optimized with simulations. By building up the circuit-level model of the optical transmitter, both the static and dynamic performance characterization were carried out. The overall optical loss of the optical transmitter is (4.86-6.72) dB and (5.38-7.27) dB for eight wavelength channels with NRZ and PAM4 modulation format, respectively. Eye diagram test results show that all the wavelength channels can support a 100 Gb/s NRZ data rate or 50 GBaud/s PAM4 symbol rate and the BER results for two modulation formats are acceptable for optical communication standards.  
Conceptualization, Z.Z. and M.L.; methodology, Z.Z., M.L. and K.Z.; software, Z.Z., M.L., K.Z. and T.L.; validation, Z.Z., M.L. and H.J.; formal analysis, Z.Z., M.L. and J.X.; investigation, Z.Z., M.L. and Q.W.; resources, Z.Z., M.L. and B.Y.; data curation, Z.Z., M.L. and Y.L.; writing--original draft preparation, Z.Z., M.L.; writing--review and editing, Z.Z., M.L. and H.L.; visualization, Z.Z., M.L. and B.H.; supervision, Z.Z., M.L. and P.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (Grant No. 2018 YFA0209000), the Natural Science Foundation of China (Grant Nos. 61675191, 61974099, 61634006, 61675154, 61704009), the Youth Innovation Promotion Association of Chinese Academy of Sciences under Grant No. 2018146.  
We acknowledge the useful discussion with [PERSON] from Chang'an University of China and [PERSON] from University of Southampton of UK.  
The authors declare no conflict of interest.
## 4 Results and Analysis
### Evaluation of Hybrid Encryption Algorithm Strength
#### 4.1.2 Pattern Analysis of Encryption Algorithm
This inadequacy in randomness manifests in some key segments, causing sparse intervals in the ciphertext distribution. The problem can be mitigated by introducing dynamic mutation rates based on key entropy, providing greater randomness in key evolution. Additionally, introducing a post-processing randomization layer prior to RSA or improving current entropy-based selection criteria during key generation may also help. The non-uniformity in distribution suggests areas where certain plaintext structures have a more significant influence on the encrypted output. This fact is evidently shown in Figure 9.  
Figure 9 highlights that some byte values appear more frequently than others in the encrypted messages. The byte value with the highest density is around 0, and it progressively tapers off toward the edges of the plot. The various visibly clustered formations and high-density areas, as shown in Figures 8 and 9, provide evidence of some level of predictability in the encryption algorithm's data processing. These predictable patterns could be related to fixed initialization vectors, repetitive key scheduling, or deterministic features inherent to the algorithm [51]. In conclusion, based on the observed trends and clusters shown in Figures 8 and 9, important implications for cryptographic security arise. Predictability conveyed by such patterns alludes to possible vulnerabilities that attackers could exploit. These vulnerabilities may include those techniques identified through frequency analysis or pattern recognition that might help to pinpoint weaknesses in the encryption process. The way in which the number of the positions of each byte was contemplated, along with their scatter, possibly suggests that enhanced randomness in the encryption algorithm, followed by less predictability, is needed.
## 4 Results and Discussion
### Effect of Feed Rate of Sand on the Flow Pattern
#### 4.3.3 Velocity Distribution  
The flow passage of the lower part of the primary impeller is mainly occupied with water, so the velocity distribution in that part is not presented here. In each blade channel, the velocity distribution is not uniform, as shown in Figure 14. Low velocity tends to arise near the outlet part of the blade suction side. The expansion of the low-velocity zone is prominent when the feed rate of sand is relatively low. At the impeller outlet, high-velocity elements are recognized in the wake of each blade, similar to that shown in Figure 8b. Consequently, in the circumferential direction, high- and low-velocity zones emerge alternately, promoting the mixing in the volute chamber.  
Although the patterns of the velocity distribution differ with the blade passage, the overall tendency of the increase in velocity with the feed rate of sand is explicit, as indicated in Figure 14. Since the rotational speed of the impeller and the profile of the blade cross-section remain invariant, the total flow rate increases with the participation of sand particles. Therefore, the number of high-velocity elements in the blade channels increases with \(q_{\mathrm{s}}\), as shown in Figure 14. In the volute chamber, no clear variation in the overall velocity magnitude is identified as \(q_{\mathrm{s}}\) varies. Moreover, velocity distributions are not uniform in the circumferential direction, which is shared by the three cases.