esa-sceva/satcom-chunk-collection huggingface dataset:
# An Effective Hybrid Routing Algorithm in WSN:
## 5 Experimental Results and Analysis
### Comparison of the Network Lifetime  
As we mentioned before, the network lifetime is one of the most important indicators of WSNs. The energy of a node in a WSN is mostly spent on data transmission. Especially, the nodes who are responsible for data forwarding in their region. If these nodes are out of power, data will not be able to be normally forwarded to the sink node. Therefore, energy exhausting of a node in WSNs may affect the normal operation of the whole network. In this paper, the network lifetime is defined as the duration beginning when the network is activated until when one node's battery goes flat.  
Therefore we simulate and compare the network lifetime of the classic ACO, LTAWSN, ACO-GA, EALD, and ACOHCM algorithm in the experiments as follows. The networks which consist of 50, 60, 70, 80, 90, and 100 sensor nodes are generated respectively in the simulation area, and The nodes' routing information is produced by running five algorithms respectively. Figure 3 is the comparison of the network lifetime of our proposed one against other four algorithms. It is clearly to see that the network lifetime of ACOHCM is the longest. The main reason why ACOHCM algorithm can extend the network lifetime at a maximum extent is that it uses the dynamic energy threshold strategy to take a strict control of the node energy consumption, trying to avoid the low energy nodes in the process of path selection. On another hand, the proposed algorithm finds the best path with minimum total energy consumption in the energy-abundant nodes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of the Algorithms** & **Time per an Iteration** & **Success Rate** \\ \hline ACOHCM & 3.46 ms & 99\% \\ ACO-GA & 5.79 ms & 92\% \\ LTAWSN & 8.71 ms & 88\% \\ Classic ACO & 15.63 ms & 55\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time consumption for iteration and the success rate of searching for the optimal solution of the algorithms.
## 2 Vehicle Modeling for State Estimation
### Planar Vehicle Model  
According to [PERSON]'s second law, the 3-DOFs vehicle body motion equations can be expressed as follows. Longitudinal and lateral motions along the \(x\) and \(y\)-axis:  
\[m\_{x}=\sum F_{x\_{,}ij}-\frac{1}{2}C_{d}A\rho u^{2} \tag{3}\]  
\[m\_{y}=\sum F_{y\_{,}ij}. \tag{4}\]  
Rotational motions of yaw about \(z\)-axis:  
\[I_{z}\cdot\dot{r}=a\Big{(}F_{y\_fl}+F_{y\_{,}fr}\Big{)}-b\Big{(}F_{y\_{,}rl}+ F_{y\_{,}rr}\Big{)}-T/2\Big{(}F_{x\_{,}fl}+F_{x\_{,}rl}\Big{)}+T/2\Big{(}F_{x\_{,} fr}+F_{x\_{,}rr}\Big{)}, \tag{5}\]where \(a_{x}\) is the longitudinal acceleration; \(a_{y}\) is the lateral acceleration, \(r\) is the yaw rate and \(T\) is the vehicle wheel track; \(C_{d}\), \(A\), and \(\rho\) denote the air resistance coefficient, the frontal projected area, and the air density, respectively. Moreover, the acceleration terms are defined as  
\[a_{x}=\dot{u}-vr \tag{6}\]  
\[a_{y}=\dot{v}+ur. \tag{7}\]  
In Equations (3)-(5), \(F_{x\_{-}ij}\) and \(F_{y\_{-}ij}\) represent the resultant force of the longitudinal and lateral tire forces along the \(x\) and \(y\) axis in the vehicle coordinate system, which could be expressed by the following equations:  
\[F_{x\_{-}ij}=F_{t\_{-}ij}\cdot cos\ \delta_{T\_{-}ij}-F_{s\_{-}ij}\cdot sin\ \delta_{T\_{-}ij} \tag{8}\]  
\[F_{y\_{-}ij}=F_{t\_{-}ij}\cdot sin\ \delta_{T\_{-}ij}+F_{s\_{-}ij}\cdot cos\ \delta_{T\_{-}ij}. \tag{9}\]  
As seen in Figure 1, \(F_{t\_{-}ij}\) and \(F_{s\_{-}ij}\) are the longitudinal and lateral tire force in the wheel coordinate system. Without regard to the roll motion, the steering angle of each wheel is simplified as  
\[\left\{\begin{array}{c}\delta_{T\_{-}fl}=\delta_{T\_{-}fr}=\delta_{sw}/i_{s }\\ \delta_{T\_{-}rl}=\delta_{T\_{-}rr}=0\end{array}\right., \tag{10}\]  
where \(i_{s}\) is the transmission ratio from the hand wheel to front wheels.
## 3 Calculation Methodology of the PVCS Carbon Impact
### Presentation of the Study Perimeter
#### 3.2.5 Carbon Impact of Electricity Provided by the Power Grid  
The carbon emission coefficient of the power grid depends on the electricity production method. It represents the average emissions emitted during a year depending on the composition of the energy mix of primary energy. The entire electricity production process is considered. The main primary energies used are nuclear, hydraulic, coal, gas, etc. [32].  
Each production mode has an associated carbon emission coefficient. The carbon emission coefficient of the power grid is calculated in proportion to the amount of electricity used for each type multiplied by its own emission coefficient. In France, the carbon emission coefficient \(CO_{2,PG}\) of the French energy mix is 0.0599 kgCO\({}_{2,eq}\)/kWh. It should be noted that the carbon emission coefficient of the French power grid is particularly low, as most electricity is nuclear power with a carbon emission coefficient of only 0.006 kgCO\({}_{2,eq}\)/kWh.  
Therefore, the carbon impact \(Imp_{PG}\) (kgCO\({}_{2,\text{eq}}\)) of the electrical energy provided by the power grid is expressed in the equation below:  
\[Imp_{PG}=E_{PG}\cdot CO_{2,PG} \tag{43}\]  
where \(E_{PG}\) is the energy provided by the power grid in kWh.  
Now, it is possible to assess the global carbon impact of PVCS, given as the sum of the carbon impacts of the different sub-systems constituting them, as expressed in the following equation:  
\[Imp_{PVCS}=Imp_{PV}+Imp_{Li-battery}+Imp_{Suspended\,CT}+Imp_{ Grounded\,CT}+Imp_{infra}+Imp_{PG} \tag{44}\]
## 4 Discussion
### Comparison and Analysis of Short-Circuit Currents  
In the IEC 61660-1 (1997) standard [31], when calculating, the short-circuit current of the rectifier output is simplified and approximated. Figure 8 shows the equivalent circuit that adopts, in which there is no capacitor for stabilizing voltage on the DC-link; therefore, a large deviation may arise. Figure 9 shows the approximated waveform of the short-circuit current used in this standard.  
For the system shown in Figure 5, Table 2 shows the results of calculating the short-circuit currents according to the method of the IEC 61660-1 (1997) standard, according to Equations (2) and (3) in this research, and according to the simulation.  
It can be seen that the results from the method of the IEC 61660-1 (1997) standard had a large deviation. According to the analysis of the calculation Formulas (13)-(15) and the equivalent circuit in the IEC 61660-1 (1997) standard, the quasi-steady-state current \(\mathrm{I_{k}}\) and peak current \(\mathrm{t_{p}}\) were mainly affected by the impedance of the AC-link.  
According to Formulas (2) and (3) in this research, the short-circuit peak current was mainly influenced by the components on the DC-link. In order to output a stable DC voltage and reduce the ripple in it, the capacitance for stabilizing voltage C is usually relatively large, and the capacitor discharges at the moment of a short circuit to form an impulse current, so the peak current \(\mathrm{i_{p}}\) is greatly affected by the capacitor C.  
According to the analysis of Equations (4)-(7) in this research, the short-circuit steady-state current depends on the total impedance \(Z\) of the fault circuit. The filter inductance on the AC-link of the converter is usually relatively large, so the steady-state current is greatly influenced by the filter inductance \(\mathrm{L_{g}}\).
## 4 Experimental Results
### Classification Results  
This section presents the classification results of the proposed method using the M-FD technique for extracting features and the ANN classifier for classifying breast cancer mammogram images as benign or malignant. The multilayer perceptron, which was used as the ANN classifier to identify malignant cases from benign ones, is based on the generalized least mean square (LMS) rule that uses the gradient search method to minimize the average difference between the output and the target value of the neural network. Three layers, namely input, hidden, and output layers, were used to build the ANN. The input and hidden layers include the same number of nodes (125 nodes). ANN works to change weights during the training stage, and we reach a minimum error close to 0.1.  
We extract 25 features from each ROI block and 125 features from all ROI blocks using the proposed M-FD technique. This finding indicated that all extracted features are not significant for classification. The set of extracted features may present some correlated features because features were extracted from each block and blocks were classified separately. More so, all extracted features do not contribute to feeding the ANN classifier to the overall performance evaluation of the proposed model. Therefore, we initially omit correlated features, use GA as the feature selection technique, and select relevant features from each block. The ANN classifier was trained for each block to classify mammogram cases. Parameter settings of the ANN classifier present a value learning rate, momentum, and number of hidden nodes of 0.05, 0.5, and 8, respectively. We obtained five results for the entire ROI based on the five trained ANN classifiers in this stage. Finally, the fusion process was performed on the obtained results to achieve the final decision.
# Evolution and Testing of NB-IoT Solutions
## 5 End-to-End NB-IoT Testing
### Design of NB-IoT Network Scenarios for Testing  
As commented previously, repetitions are used to improve the coverage, providing an increase of 20 dB compared to GPRS. Specifically, the testing environment introduced in this paper enables the configuration of repetitions for NPDCCH (Physical Downlink Control Channel), NPDSCH (Narrowband Physical Downlink Shared Channel), and NPUSCH (Narrowband Physical Uplink Shared Channel).  
NPRACH is the channel used for uplink preamble transmission for the RACH (Random Access Channel) procedure. A repetition scheme is applied in this channel to increase the reliability of the procedure. The number of repetitions of the preamble is also configurable, in particular, for the robust scenario, four repetitions have been configured.  
NPRACH periodicity is also a key parameter in NB-IoT, and it specified the amount of time between consecutive random access transmission attempts. For FDD, NPRACH periodicity can be configured to 40 ms, 80 ms, 160 ms, 240 ms, 320 ms, 640 ms, 1280 ms, and 2560 ms. For the scenarios defined in this paper, 40 ms has been applied.  
The NPDCCH (Narrowband Physical Downlink Control Channel) conveys control information on where and how the data transmissions will happen between the eNB and the UE. The number of repetitions is increased as needed to protect this control information, as the UE would miss the actual data if it does not receive the control information in advance.  
NPDSCH and NPUSCH repetitions increase significantly the overall signal energy to improve the probability correct reception in for robust (\(\times 2\)) and extreme (\(\times 16\)) coverage for data transmissions, and are significantly more redundant for initial messages to securely establish the connections. The number of repetitions for HARQ ACK/NACK is also modified to preserve the integrity of the feedback information.  
In addition to the repetitions, additional protection is added to the transmissions in robust and extreme scenarios by reducing the Modulation and Coding Scheme (MCS) from 10 to 1 and 0, respectively.  
Finally, the power transmission of the base station has been configured to 23 dBm.
## 4 Simulation Results and Discussions
### PCC of Biomimetic Modulated Signal  
In this subsection, the PCC of the biomimetic modulated signal generated by the proposed BC-UAC method is evaluated.  
The impact of the symbol length (\(T_{\text{sym}}\)) on the PCC is given in Table 1, where \(M=4\), \(\Delta f=200\) Hz in BC-UAC-TD and \(\Delta f_{0}=200\) Hz in BC-UAC-FS. From Table 1, we observe that the PCCs of BC-UAC-TD and BC-UAC-FS are greater than 0.98. Moreover, as \(T_{\text{sym}}\) decreases, the PCC of BC-UAC-TD remains unchanged, while the PCC of BC-UAC-FS increases gradually. The reason for this phenomenon is that the division of symbol length has an influence on BC-UAC-FS.  
The impact of the frequency offset on the PCC is given in Table 2, where frequency-shift (\(\Delta f\)) is used in BC-UAC-TD, unit frequency-shift (\(\Delta f_{0}\)) is used in BC-UAC-FS, \(M=4\), and \(T_{\text{sym}}=0.066\) s. From Table 2, we observe that as \(\Delta f\) increases, the PCC of BC-UAC-TD changes a little and is greater than 0.99, which is very similar to the original whistle signal. For BC-UAC-FS, as \(\Delta f_{0}\) increases, the PCC obviously decreases.  
The impact of the modulation order (\(M\)) on the PCC is given in Table 3, where \(T_{\text{sym}}=0.066\) s, \(\Delta f=200\) Hz in BC-UAC-TD and \(\Delta f_{0}=200\) Hz in BC-UAC-FS. From Table 3, we observe that as \(M\) increases, the PCC of BC-UAC-TD increases a little, while the PCC of BC-UAC-FS decreases gradually, and the change is relatively large. Moreover, with the increase in \(M\), the number of information bits in each symbol increases and the corresponding communication rate increases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Segment** & **Duration of a** & **Transmission** & \multicolumn{2}{c}{**PCC**} \\ \cline{3-5}
**Number** & **Symbol (s)** & **Rate (bps)** & **BC-UAC-TD** & **BC-UAC-FS** \\ \hline
4 & 0.132 & 15.09 & 0.99745 & 0.98919 \\
8 & 0.066 & 30.19 & 0.99744 & 0.99019 \\
16 & 0.033 & 60.38 & 0.99744 & 0.99232 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The impact of \(T_{\text{sym}}\) on the PCC.  
Hence, under the same conditions, the concealment of the biomimetic modulated signal in BC-UAC-TD is better than that in BC-UAC-FS.
# High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks
## 3 Design of the HECRA
### Network Optimization Phase  
Owing to the limited communication distance between CHs, there may be situations where some CHs cannot communicate directly. For example, in Figure 3, \(CH_{3}\) and \(CH_{1}\) cannot communicate directly owing to the long distance, and this situation will greatly reduce the network transmission efficiency. To solve this problem, this paper introduces AUV assistance, which allows the AUV to act as a dynamic relay node to relay messages from CHNs, thus improving transmission efficiency and network performance.  
The AUV follows a predefined path and determines clusterhead locations and distances in the underwater sensor network using existing localiz  
Figure 3: AUV dynamic relay schematic.  
Figure 2: CH pathfinding schematic.  
the detected network topology information, the \(CH_{3}\) that cannot communicate directly with other CHs is identified, and the AUV calculates the closest \(CH_{1}\) that can communicate with the sink to \(CH_{3}\) and then moves to the midpoint position of \(CH_{1}\) and \(CH_{3}\) to act as a dynamic relay node and forward the message from \(CH_{3}\) to \(CH_{1}\). After completing the transmission, the AUV continues to the next relay position to perform the task. The pseudocode for the specific steps of HECRA is as follows (Algorithm 1).  
```
Input: node ID, initial energy, depth Output: Cluster results State 1: Network Initialization For each node \(i\)  Broadcast HELLO = {node ID} If receive HELLO \(Ne_{i}=Ne_{i}+1\) End If End For State 2: Cluster head selection and cluster formation For each node \(i\)  Calculate weight \(T^{{}^{\prime}}_{i}\) as in (9)  Generate a random number \(0\leq rand\leq 1\) If \(rand<T^{{}^{\prime}}_{i}\)  Elect node \(i\) to become CH Else if receive Cluster formation message  Calculate weight \(H_{j}\) as in (10)  Join the cluster corresponding to the CH \(H_{j}\) Else  Node goes to sleep End If End For State 3: Data transmission For all CHs  Collecting and fusing information from nodes in the cluster  Broadcast wayfinding message If receive wayfinding message Determines whether to reply to the sender of wayfinding message End If If receive messages of confirmation  Calculate weight \(\omega_{i}\) as in (11)  Find the node that forwarded the message End If End For State 4: Optimization of the network  AUV seeks CHs that are unable to communicate with other nodes  Find the location of the relay transmission  Assisted in completing data forwarding and continued cruising
```  
**Algorithm 1**HECRA
# Dynamic Response and Geogrid Strain Analysis of GRS Retaining Wall
## 4 Geogrid Strain Calculation Method  
### Basic Assumptions  
The analysis method of geogrid tension established in this paper is based on the following assumptions:  
1. geogrids are laid horizontally and remain horizontal under the action of soil self-weight;
2. the additional stress acting on the pressurized zone of the geogrid distributes uniformly along the horizontal direction;
3. the tension caused by compaction of the geogrid during filling is ignored;
4. under the action of self-weight and external load, the deformation of the geogrid is mainly elastic deformation, and the plastic deformation is small and can be ignored.  
Assumption (1) above is consistent with the reality for most reinforced earth retaining walls. A large number of large-scale tests and field monitoring results in China and abroad show that the geogrid strain is not large under normal working load. Under the condition of guaranteed construction quality, the geogrid strain caused by the self-weight of filling soil can be neglected. Assumption (2) is based on assumption (1), that the vertical additional stress generated by the external load in the soil decreases gradually with the increase in depth. The calculation of the vertical additional stress of layer \(i\) of the geogrid can be carried out by the simplified method of 45\({}^{\circ}\) diffusion angle (as shown in Figure 13). The larger the \(z_{i}\) is, the larger the diffusion length \(l_{i}\) of the vertical additional stress is, and it is evenly distributed along the tensile direction of the geogrid. Regarding assumption (3), it is mentioned above that the geogrid will be deformed during filling and compacting, and this deformation is very small and can recover by itself some time after the filling is completed, which has no effect on the calculation results. From the strain of each layer of the geogrid in the test, the maximum value of strain does not exceed 5%, and the tensile strength of the geogrid is not fully exerted. It is reasonable to assume that the deformation of the geogrid conforms to [PERSON]'s law (4) [50].
## 3 Results and Discussion
### Bacterial Community Profiles  
The bacterial community profiles varied among the soil samples of 13-year-old WSG cultivation sites. The relative abundance of bacterial community at phylum levels is shown in Figure 2. The soil bacterial communities were grouped based on the cultivation sites. _Acidobacteria_ (33.6%) was the most dominant phylum in all soil samples, followed by _Proteobacteria_ (23.9%), _Verrucomicrobia_ (11.2%), _Chloroflexi_ (5.9%), _Actinobacteria_ (4.4%) and _Planctomycetes_ (3.9%). The relative abundance of _Acidobacteria_ and _Chloroflexi_ was significantly higher in cultivation site I compared to the other cultivation sites, whereas that of _Proteobacteria_ was significantly higher in cultivation site F. On the other hand, _Verrucomicrobia_, _Actinobacteria_ and _Plantomycetes_ were significantly more abundant in cultivation site C. This observation corroborates to previous studies where _Acidobacteria_, _Proteobacteria_, _Verrucomicrobia_ and _Actinobacteria_ were the major bacterial communities at the phylum level present in soils used for cultivation of _Panax ginseng_[3, 35, 36]. _Acidobacteria_ are acidophilic bacteria mainly present in acidic soils [37]; hence, soil pH is one of the major factors determining _Acidobacteria's_ community composition [38, 39, 40]. Bacteria belonging to _Acidobacteria_ have evolved mechanisms that prefer acidic pH by stabilization of intracellular enzymes [41]. In this study, the relative abundance of _Acidobacteria_ was shown to be significantly higher in the cultivation site I, which had the lowest soil pH compared to other studied groups. _Acidobacteria_ are shown to be negatively correlated with soil pH in WSG cultivation sites [3], and it was also reported that the _Acidobacteria_ population is higher in the cultivated soil of _Panax ginseng_[42, 43].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Cultivation Sites** & **Sosl Texture** & **pH** & **EC\({}^{*}\)** & **OM\({}^{b}\)** & **TN\({}^{c}\)** & **Avail. PrQ\({}_{0}\)\({}^{d}\)** & \multicolumn{4}{c}{**Exhausplable Cation**} & \multicolumn{1}{c}{**CEC\({}^{*}\)**} \\ \cline{2-13}  & & **(L9)** & **d5\(\,\)m\({}^{-1}\)** & **\%** & **\%** & **mg\(\,\)kg\(
# Forest Volatile Organic Compounds and Their Effects on Human Health: A State-of-the-Art Review
## 3 Results
### Evidence about the General Effects of Forest VOCs on Health
#### Nervous System and Psychological Behavior  
A systematic review on the general effects of forest bathing found that this meditative practice can be associated with positive health benefits, like a reduction in heart rate and blood pressure, increased relaxation, ameliorated general wellbeing, and improvement of depression scores [86], although available evidence is still limited and observed effects are likely to be multifactorial, thus probably depending not only on the presence of BVOCs in the atmosphere but also on the whole natural, green context [87]. These results are in line with preclinical studies on animal behavioral models investigating BVOCs' neurological role in central nervous system (CNS) depression. In fact, BVOCs have been able to reduce locomotor activity and increase muscle relaxation, with consequent improvement of sleep, pain, and anxiety in mice [88; 89; 90]. Recently, it has been reported that substances derived from pine trees, such as \(\alpha\)-pinene and 3-carene, can enhance sleep by acting as a positive modulator for GABA-A-BZD receptors [91]. Similar effects are reported for borneol, verbenol, isopulegol, and pinocarveol [92; 93]. Moreover, inhaled linalool and \(\beta\)-pinene have shown anxiolytic [94; 95] and antidepressive [96; 97] properties in different animal models.