esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Layer and Energy-Aware AODV Routing Protocol for Flying Ad-Hoc Networks
## 5 Performance Evaluations
### Results and Discussion  
#### 5.2.1 Packet Success Ratio (PSR)  
To analyze the performance of the CLEA-AODV protocol in FANET, we used Network Simulator version 2 (NS2). NS2 is a discrete event simulator that is freely available and open-source to facilitate networking research. FANET routing protocols can be easily implemented in NS2. Furthermore, it is represented as a verified simulation tool for different types of network environments. The proposed CLEA-AODV protocol was constructed based on the combined CH selection and a cooperative MAC model. CH selection is mainly used to reduce the energy consumption in the network whilst the cooperative MAC approach is utilized to reduce the congestion in the network. By reducing the delay, and congestion, the PSR achieved by the proposed CLEA-AODV protocol is high compared to earlier works such as the AODV, SOC-GSO, and EENFC methods.  
Figure 6 shows the PSR calculation concerning time (ms). It shows the results of the proposed CLEA-AODV protocol with earlier protocols such as AODV, SOC-GSO, and EENFC. From the graph, it was proven that our work reached a high PSR rate when compared with the earlier works. This was achieved mainly by using the cross-layer approach mechanism in our proposed model. Through the interconnection of two layers such as the network layer and MAC layer, the efficiency of the proposed work was highly increased, leading to improving the PSR rate of the network. The performance analysis  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulator & NS-2.34 \\ Simulation time & 100 ms \\ Area & 1300 \(\times\) 1300 m\({}^{2}\) \\ Transmission range & 300 m \\ No. of UAV & 30-180:30 \\ Simulation time & 0-100:10 \\ Propagation model & Two ray propagation model \\ Position exchange interval & 3 s \\ Antenna & Omni-direction Antenna \\ Traffic type & CBR \\ Traffic rate & 0.01 to 0.50 s \\ Packet size & 1024 bytes \\ Initial power & 100 J \\ Idle power & 0.05 J \\ Queue type & Drop-Tail \\ \hline \hline \end{tabular}
\end{table}
Table 1: The simulation parameters.  
results achieved by our CLEA-AODV protocol was 98%, whereas the other AODV, SOC-GSO, and EENFC-MRP methods reached 79%, 85%, and 91%, respectively.  
Figure 7 shows the PSR calculation with the number of UAVs used in the network. The number of UAVs used in the network was 180. The comparative results of the proposed and earlier work PSR were measured in terms of the number of UAVs. The packet success rate of the proposed work was higher in all scenarios such as the 30, 60, 90, 120, 150, and 180 numbers of UAVs. By analyzing the results, it is understood that by using the cross-layer approach, the network becomes more stable than the earlier models.
# A Magnetic-Balanced Inductive Link for the Simultaneous Uplink Data and Power Telemetry
## 5 Optimization of the Isolation Resistance \(R_{\text{isolate}}\)
The transmitted power of the uplink thus can be derived as Equation (13), where \(V_{\text{ul}}=3.3\) V is the output voltage root-mean-square (RMS) of the uplink transmitter for our device. When \(R_{\text{isolate}}=50\) k\(\Omega\), the uplink transmitted power is only \(\sim\)0.2 mW, which is acceptable for most implantable biomedical sensor devices. \[P_{\text{TX\_ul}}\approx\frac{{V_{\text{ul}}}^{2}}{R_{\text{isolate}}}=0.2\, \text{mW} \tag{13}\]
## 3 Discussion of Results
### Parametric Study in Flow Insensitive Region
The exponential relationship between corrosion rates and temperature given by [PERSON] is widely used in the corrosion prediction. [PERSON] gave the equation in static conditions as K = A exp(-E\({}_{\text{a}}\)/RT), where K = rate constant, R = gas constant (8.314 ) mole), T = temperature in degree Kelvin, E\({}_{\text{a}}\) = activation energy (\(\int\)) mole.K), and A = Modified frequency factor (pre-exponential factor). An attempt was made to fit the Arrhenius equation to the experimental data, but the fit was not good. The analysis, shown in Figure 13, reveals that the corrosion rate can be expressed as a power equation of temperature, showing a much better fit than the Arrhenius equation. In all the cases studied, the power constant varies when the NaCl concentration changes from low (1%) to high (3.5%). For both pH 5 and 7 at 1%NaCl concentration, the power constant is found to be about 2.7, but when the NaCl concentration is increased to 3.5%, the power constant is found to be about 0.9. The corrosion rates can be expressed as AT\({}^{0.9}\) for high NaCl concentration. Therefore, the following relation between the corrosion rate and the temperature is proposed: Corrosion Rate = A (Temperature)\({}^{0.9}\) for high salinity. The value of the coefficient A was found to be varying with both NaCl concentration and pH. The correlation for temperature concludes that more data points need to be obtained in order to be able to generalize an empirical formula. Figure 12: Corrosion rates for flow insensitive region are fitted with varying NaCl Concentration. The parametric study was conducted by maintaining constant oxygen concentration in the system. The oxygen concentration affects the corrosion rate of mild steel in NaCl, and the solubility of the oxygen will be an important factor in the predictive equation. In the work presented in this paper, individual equations (as shown in Figures 12 and 13) relating the corrosion rate in low flow velocities are developed. In future studies, the work will look at the synergistic effects of changing two or more parameters at the same time at different oxygen concentrations. This eventually leads towards a predictive model of the corrosion in pipelines and identifying the flow regions where corrosion rates can be minimized.
# Evaluation and Economics of Shale Gas Reserves in the Flysch-Eocene Formation of the Jaca Basin
## 4 Discussion
### Environmental Challenges
#### 4.3.3 Emissions of CH\({}_{4}\) and CO\({}_{2}\)  
The main component (90%) of shale gas, methane (CH\({}_{4}\)), can contaminate both water [47,49] and air [54], as well as active drilling sites [47].  
The general consensus based on IPCC reports [55] is that natural gas emits about half as much carbon as coal used in efficient power plants. The official IEA [56,57] and USEPA [58] reports on carbon emissions from fossil fuel combustion are also based on this consensus. Methane is a very potent greenhouse gas, although it remains only one-tenth of the time in the atmosphere compared to carbon dioxide.  
The carbon footprint is an indicator of the effect and life cycle of greenhouse gases [59]. Determining the influence of shale gas on climate change is complex and controversial, in part because of uncertainty about the extent of methane leakage [60; 61; 62; 63].  
When comparing the emissions between the use of coal, conventional gas, and shale gas in power generation (MJe) [64], the greenhouse impact of natural gas is only as bad as that of coal when taking into account a methane leakage rate of 7.9% and a short global warming period of 20 years.  
For leakage rates of less than 2%, the impact of shale gas is close to one-third of that produced by coal, and methane leakage is a negligible part of the greenhouse forcing compared to the CO\({}_{2}\) released during the combustion process [62].
# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 4 Main Findings
### Respiratory Complications
#### 4.4.2 Mental Condition Monitoring  
A. Emotional Well-Being and Disorders  
Regarding emotional disorders, some studies analyzed the effects in mouth sound recordings of psycho-emotional and posttraumatic stress disorders, either using ML algorithms or statistical analysis [103; 126].  
One study suggested using voice recordings to detect human psycho-emotional disorder through dormant analysis and an improved complete ensemble empirical mode decomposition with an adaptive noise algorithm [126]. First- and second-type errors were calculated for recording sets of databases, including syllables, words, and sentences, to evaluate the effectiveness of pathology and normal voice determination. The results showed that the proposed method in the study achieved the most precise identification of the disorder when analyzing sentences, as this pathology seriously affects vocalized characteristics. The first-type error (\(\alpha\)) was a false assignment of a normal speech signal produced by a person with an emotional disorder, and the second-type error (\(\beta\)) was a false assignment of the pathology to the speech signal produced by a healthy person. The results of the most precise study were \(\alpha=8.16\%\) and \(\beta=5.66\%\)[126].  
Another application of acoustic analysis of mouth sound recordings is in the research field of posttraumatic stress disorder [103]. It is suggested that the vocal analysis of the acoustic startle response could be a way to identify changes in vocal features with changing stimuli parameters, such as intensity, duration, rise time, and spectral type. The results were obtained from statistical analyses applied to f0, voice energy, response peaks, peak times, fall times, and duration [103]. Therefore, the analysis of acoustic features in speech recordings could be a useful way to assess the psycho-emotional states of patients, and it could possibly help to diagnose other pathologies related to emotional states.
# Sum Rate Optimization Scheme of UAV-Assisted NOMA under Hardware Impairments
## 2 System Model and Problem Formulation
#### Problem Formulation  
As stated above, the system's sum rate optimization problem can be modeled as:P1 :  
\[\max_{P_{s},P_{r},x,y,A,\mathbf{P}} \sum\limits_{n=1}^{N}\frac{1}{2}\log_{2}\left(1+\frac{p_{n}{A_{n}}^ {\prime}}{{A_{n}}^{\prime}\sum\limits_{k\
eq n}^{N}\alpha_{k,n}p_{k}+A_{n}B_{n }+C_{n}}\right)\] \[s.t. 0\leq p_{n},\forall n,\] (9a) \[\sum\limits_{n=1}^{N}p_{n}\leq P_{s},\] (9b) \[0\leq P_{r}\leq P_{r}^{\max},\] (9c) \[0\leq P_{s}\leq P_{s}^{\max},\] (9d) \[p_{n}-\sum\limits_{k\
eq n}^{N}\alpha_{k,n}p_{k}\geq 0,\forall n,\] (9e) \[R_{n}^{\min}\leq R_{n},\forall n,\] (9f) (7a)-(7d).  
where (9a) indicates the non-negative power constraint, (9b) indicates the constraint of the total transmitted power, (9c) and (9d) represent the maximum transmitted power constraint for UAV and BS, respectively, (9e) indicates the user fairness as [31], and (9f) shows the minimum rate constraint for each user.  
Since the objective optimization problem P1 increases monotonically with respect to \(P_{s}\) and \(P_{r}\), we can solve the sum rate maximization problem at \(P_{s}^{\max}\) and \(P_{r}^{\max}\).
# Research on a Fast Image-Matching Algorithm Based on Nonlinear Filtering
## 2 Theoretical Analysis  
### Nonlinear Diffusion Filtering  
When constructing the scale space, the traditional construction method used is Gaussian filtering to smooth the image, which loses edge information. Nonlinear diffusion filtering [12] provides a superior solution, and is implemented by using the scale parameters of the image as thermal diffusion function scattering factors. This filtering method is generally solved by partial differential equations, while the differential equations are nonlinear in nature. Its scale space consists of multiple groups, and in each group, it contains several layers of the same size, which are obtained by nonlinear diffusion, layer by layer, and the next group is obtained by downsampling. Its mathematical expression is given below:  
\[\frac{\partial L}{\partial t}=div(c(x,y,t),\
abla L) \tag{1}\]where \(L\) is the image luminance matrix, \(div\) represents the dispersion function, and \(\
abla\) represents the image gradient operator. The conduction function \(c\) is carried over to the local structural properties for diffusion, where the conduction function is  
\[c(x,y,t)=g(|\
abla L_{\sigma}(x,y,t)|) \tag{2}\]  
Usually, the function \(g\) is taken as the following equation:  
\[g=\frac{1}{1+\frac{|\
abla L_{\sigma}|^{2}}{\lambda^{2}}} \tag{3}\]  
\(\Delta\) in Equation (2) is the gradient image of the image \(L\) after Gaussian filtering, and the parameter \(\lambda\) in Equation (3) is the range over which the contrast factor determines the spread of the function, and L is taken to be the value corresponding to 70% of the histogram in the literature [12].  
In response to the issues caused by Gaussian filtering, including the blurring of image details and edges, reduced accuracy in feature point detection, and increased algorithm complexity and computational time when handling multi-scale pyramids, the use of non-linear diffusion filtering offers the following advantages: firstly, it can remove noise more effectively without losing the target boundary and key detail information; secondly, it retains the edge information of the image, making it superior to Gaussian filtering in avoiding blurring and loss of information; and thirdly, it is able to reduce the scale while maintaining image clarity, quantize the featuresmore effectively, and improve the accuracy of the algorithm as well as increase the efficiency.
# AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain
## 3 Agriculture 4.0 Security Threats in Multi-Layered Paradigm
### Security Risks at Application Layer  
This layer provides end-user assistance and data to process and make system decisions. This layer's security concerns are application-specific, focusing on preventing data theft and ensuring privacy. Some of the attacks that might affect cloud services and applications are listed below in terms of security.  
Malicious scripts: Malicious scripts can deceive clients, infuse malicious data, access confidential material, and compromise security features. This type of attack is frequently carried out by cybercriminals for specific, economic, or political reasons. They can damage or destabilize operational processes by using malicious scripts, presenting unnecessary ad campaigns, and stealing money [41].  
Phishing: It is an attack that attempts to obtain highly classified personal information, such as an Identity and password, fraudulently. Phishing typically targets end-users through fraud and sites [42]. The attacker also affects decision-making and internal operations in many cases. The most effective protection would be for users to remain vigilant while surfing the web [43].  
Denial of Service (DoS): This threat disrupts service by overburdening traffic on the network or flooding the provider with repeated attempts [44]. Because of flaws in security setup, an attacker can launch this attack from the Web or a sub-system.
## 5 Results and Discussion
### Energy Consumption Analysis  
Table 7 compares the energy usage of the four existing methods with the proposed approach.  
Figure 8 represents the energy consumption with respect to the different numbers of nodes. The proposed method shows a minimal consumption of energy of 0.0102 mJ, while the existing Sectrust-RPL, HBEER, SEHR, and SEAMHR approaches show a consumption of 0.0326 mJ, 0.0252 mJ, 0.0190 mJ, and 0.0154 mJ, respectively. Because of the smart and risk-tolerant routing approach used by the proposed protocol, the routing pathways were stable for a considerable amount of time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. of Nodes** & **Sectrust-RPL** & **HBEER** & **SEHR** & **SEAMHR** & **Proposed** \\ \hline
50 & 0.0276 & 0.0211 & 0.013 & 0.0131 & 0.0122 \\ \hline
100 & 0.0301 & 0.0212 & 0.0156 & 0.0143 & 0.0130 \\ \hline
150 & 0.0313 & 0.0223 & 0.0179 & 0.0150 & 0.0141 \\ \hline
200 & 0.0326 & 0.0252 & 0.0190 & 0.0154 & 0.0142 \\ \hline
250 & 0.0355 & 0.0271 & 0.0223 & 0.0162 & 0.0155 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative analysis of proposed energy consumption (mJ) with state-of-art approaches.  
Figure 7: End-to-end delay.
## 5 Results
### DEA Model
#### 5.1.1 Bus-DEA Layer (Business-DEA)  
The influence of business management data in the DEA assessment has been analyzed in steps 1 and 2. The efficiency of the various airline companies is shown in Table 4, which gives a descriptive analysis of the efficiency coefficients.  
Step 1 analyses only the economic and human resources of these companies, understood as businesses where the special features of the air transport sector are not considered. The average efficiency is 0.4973 and 0.6477, depending on the DEA model orientation (CCR and BCC, respectively). This suggests that companies in this sector could improve their levels of activity in terms of revenue with the same levels of input use by 51% based on constant returns, and by 36% based on variable returns. Returns may seem low in this step, but one must bear in mind that this step lacks contextual variables linked to the air transport sector, such as passengers and destinations.  
These contextual variables are incorporated into Step 2 where 14 airlines are globally efficient, all of them working at an optimal scale. There are 22 efficient firms in the BCC model, which means that eight of them are technically efficient but could scale their activity to achieve global efficiency. A high value (0.93) of scale efficiency versus pure technical efficiency is also noted. The standard deviation of efficiency scores in the two steps (both in CCR and BCC orientation) exceeds 0.20, except in scale step 2 (0.1108). This means that the global inefficiencies can be assigned to managerial or operational inefficiencies. In the light of the results and the analysis of previous works, step 2 appears to be the step that best fits a basic step based on variable returns and output orientation.  
As a result of the deterministic nature of the DEA method, the analysis of efficiency estimates in the presence of sample variations through the bootstrapping DEA approach is the suitable next  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CCR**} & \multicolumn{2}{c}{**BBC**} & \multicolumn{2}{c}{**Scale**} \\ \hline  & **Step 1** & **Step 2** & **Step 1** & **Step 2** & **Step 1** & **Step 2** \\ \hline Number efficient DMUs & 4 & 14 & 9 & 22 & 4 & 14 \\ \% efficient DMUs & 9.30 & 32.55 & 20.93 & 51.16 & 9.30 & 32.55 \\ Average efficiency & 0.4973 & 0.7413 & 0.6477 & 0.7893 & 0.7679 & 0.9345 \\ Standard deviation & 0.2674 & 0.2636 & 0.3043 & 0.2475 & 0.2657 & 0.1108 \\ Maximum & 1 & 1 & 1 & 1 & 1 & 1 \\ Minimum & 0.0004 & 0.2930 & 0.0072 & 0.3008 & 0.04 & 0.4482 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Original DEA efficiency coefficients (steps 1 and 2). DMU: decision-making unit.  
step in an efficiency analysis [53] for the following step. For each step, we present a table showing the bootstrapping DEA results, including the average of the bias-corrected efficiency scores, bias, standard deviation, and 95% confidence intervals (lower bound and upper bound) for the bias-corrected efficiency scores.
# A Kazakh-Chinese Cross-Lingual Joint Modeling Method for Question Understanding
## 2 Related Work  
### Joint Modeling of Intent Recognition and Slot Filling  
Joint modeling of intent recognition and slot filling is divided into two parts: implicit joint modeling and explicit joint modeling. Implicit joint modeling means that the model only uses a shared encoder to capture common features, without any obvious interaction between the intent recognition model and the slot filling model. Explicit joint modeling means that the model uses a shared encoder to obtain common features, while also adding an additional explicit interaction module to enhance the relationship between the intent recognition and slot filling subtasks, enabling direct transfer between the intent vector and the slot vector. Recently, an increasing number of studies have proposed using explicit interaction modules to enhance the direct connection between intent recognition and slot filling tasks. [PERSON] et al. [14] proposed a stack propagation framework that directly uses intent information as input for slot filling, enhancing the slot filling effect through intent semantic information, while using a token-level intent recognition method to avoid error propagation caused by utterance-level intent information. [PERSON] et al. [15] proposed an iterative interaction model (Slot Filling-Intent Detection model, SF-ID) to directly establish the connection between intent recognition and slot filling. The intent recognition sub-network and the slot filling sub-network exchange semantic information and use a novel iterative mechanism to enhance this interaction. To effectively model using semantic hierarchies, [PERSON] et al. [16] proposed a capsule neural network model that utilizes dynamic routing to complete the joint task of intent recognition and slot filling. [PERSON] et al. [17] introduced a novel Collaborative Memory Network (CM-Net), which captures intent and slot features through collaborative memory blocks to enhance local context representation. [PERSON] et al. [18] proposed a collaborative interaction Transformer framework, introducing BERT as a shared encoder and adding a collaborative interaction attention mechanism between the two tasks to achieve bidirectional interaction between intent vectors and slot vectors. Simple implicit joint modeling only considers the mutual connection between the two subtasks through shared latent representations, which may not achieve optimal results. Compared to implicit joint modeling, explicit joint modeling allows the model to fully capture the shared knowledge between the two subtasks, thereby improving performance on both sides. Additionally, explicitly controlling the knowledge transfer between the two subtasks helps enhance the model's interpretability and allows for effective analysis of the mutual influence between intent recognition and slot filling.
## 3 Results and Discussions  
### Setup of Experiments and Evaluation Metrics  
In all of the tests, the model was trained for 80 epochs after loading the pre-trained weights from yolov5m6. The stochastic gradient descent (SGD) method was applied to optimize the loss function, with the initial learning rate set to 0.01. The configurations used for our tests were Tesla K80 GPUs with torch 1.7.1. Each model was trained with two GPUs. The results are mainly measured by the metric of mean average precision, \(mAP=[EMAIL_ADDRESS]:0.95\). Precision (_Pr_), recall (_Re_), and the F1 score will also be presented in several of the tests for reference. These metrics are formulated as follows:  
\[Pr=\frac{TP}{FP+TP} \tag{7}\]  
\[Re=\frac{TP}{FN+TP} \tag{8}\]  
\[F1=\frac{2\times Pr\times Re}{Pr+Re} \tag{9}\]  
\[mAP=\frac{1}{C}\sum_{i=1}^{C}AP_{i} \tag{10}\]  
In the equations above, true positives (_TP_) stand for targets correctly recognized by the model. False positives (_FP_) stand for targets that are incorrectly detected. False negatives (_FN_) represent cases where targets are not detected. Average precision (_AP_) is the area under the precision-recall curve. Mean average precision (_mAP_) is the _AP_ score averaged among all categories.