esa-sceva/satcom-chunk-collection huggingface dataset:
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 6 Conclusions  
Using LoRa as the main infrastructure for mangrove monitoring offers longer range connections, reduced costs (as the technology is free to use and the longer range of communication reduces the number of gateways required), and low power consumption, in line with the environmental goals of the mangrove monitoring project. In this paper, we demonstrated the concept of point-to-point data transfer by transmitting images over the LoRa physical layer. This transfer was achieved despite the limited bandwidth of LoRa, using a novel image encryption technique. Our demonstration highlights LoRa as an ideal technology for implementing a WSN for monitoring mangrove forests, which will aid authorities, researchers, and farmers in their efforts to restore dwindling mangrove plantations.  
Data Curation, A.H.J.; Formal Analysis, A.H.J.; Investigation, A.H.J.; Methodology, A.H.J.; Resources, A.H.J.; Software, A.H.J.; Visualization, A.H.J.; Writing-Original Draft, A.H.J.; Conceptualization, A.S.; Funding Acquisition, A.S.; Project Administration, A.S.; Supervision, A.S., A.I., M.F.A.R.; Validation, A.S., A.I., M.F.A.R.; Writing-Review & Editing, A.S., A.I., M.F.A.R.  
Funding: This research was funded by UPM Putra Grant, grant number IPS/2017/9557100.  
The authors would like to thank the technical support team comprised of [PERSON], [PERSON], and [PERSON], who gave a significant help in establishing the project for conducting measurements in the target area.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distance (km)** & **Fresnel Zone Radius (m)** & **Fresnel Zone Diameter** & **Percentage of Clear** \\  & & **(\(r\times 2\))** & **Fresnel Zone** \\ \hline
1 & 9.01 & 18.02 & 72.2\% \\
2 & 12.75 & 25.50 & 65.69\% \\
3 & 15.61 & 31.22 & 62.82\% \\
4 & 18.03 & 36.06 & 61.4\% \\
5 & 20.16 & 40.32 & 59.3\% \\
6 & 22.08 & 44.16 & 59.06\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Fresnel zone calculations for the measured distances.  
Figure A4: LoRa command preparing the receiver node to listen for incoming data.
# Growth and Photosynthetic Response of _Capsicum annum_ L. in Biochar Amended Soil
###### Abstract  
The present study investigated the growth of _Capsicum annuum_ L. (pepper) in an outdoor pot experiment. Changes in the plants' aboveground and root biomass, leaf area, plant height, stem thickness, and yield, as a response to different doses of biochar amendments were observed. During the 12.5-week-long study, four treatments with biochar amounts of 0, 0.5%, 2.5%, and 5.0% (by weight) were added to silt loam soil. Photochemical responses of plants, the plants photochemical reflectance index (PRI) modified by the different doses of biochar were continuously monitored. Plant height and fruit yield were initially the highest for BC5.0; however, by the end of the experiment, both parameters showed higher values for BC2.5, e.g., 15.9 and 9.1% higher plant height and 32.5 and 22.6% higher fruit yield for BC2.5 and BC5.0 compared to control, respectively. By the end of the experiment the BC2.5 treatments had significantly higher stem thickness (\(p<0.001\)) compared to all other amendments. Root dry matter in biochar treatments increased relative to controls with the highest values (54.9% increase) observed in the BC2.5 treatment. Biochar treatment increased leaf area index (LAI) values for the higher doses (1.58, 1.59, 2.03, and 1.89 for C, BC0.5, BC2.5, and BC5.0, respectively). Significant differences between control and biochar amended soils' PRI measurements were observed (\(p<0.001\)), showing less plant sensitivity to environmental changes when biochar was applied to the soil. While biochar amendment could greatly enhance plant growth and development, there is an optimal amount of biochar after which additional amount might not result in substantial differences, or even can result in lower fruit yield as found in the present study.  
pepper; biomass; fAPAR; PRI; LAI; biochar Article
## 3 Results and Discussion
### Analysis of the Binding Sites Based on Molecular Docking (MD)  
In mandarin fish MPs, myosin heavy chain-7 was the major protein, and homologized 5 TBY (PDB ID) was used as a receptor for MD. A total of 84 active sites were found in 5 TBY. The best docking sites were identified by the highest Libdock scores. The 3D and 2D structures of these myosin-fermentation-stinky complexes are shown in Figure 7. The result showed that in the myosin-indole complex, indole bound to Tyr 287, Leu 290, Met 357, and Ile 313 residues by the conjugate system (pi-sulfur and pi-sigma), and Tyr 310, Phe 312, etc. residues by van der Waals force (Figure 7A). In the myosin-isovaleric acid complex, isovaleric acid, Asp 309, and Tyr 310 residues had conventional hydrogen bond interactions, and Pro 307, Ile313, Tyr 308, and Leu 290 residues had conjugate system interactions (Figure 7B). Figure 7C displayed that Tyr 287 residue was the main contributor to the formation of hydrogen bond interactions with dimethyl disulfide, and the corresponding distance was 2.73 A. Dibutyl phthalate was bound to MPs by various interactions, especially hydrogen bonds interacted with Tyr 310 and Asp 309 residues (Figure 7D).  
Figure 6: Heatmap of the correlation analysis among the percentage of \(\alpha\)-helix, \(\beta\)-sheet, \(\beta\)-turns, and random coil and the fluorescence intensity at 280 nm, \(\Delta\lambda\) = 15 nm, and \(\Delta\lambda\) = 60 nm, and the adsorption capacity of (**A**) indole, (**B**) isovaleric acid, (**C**) dimethyl disulfide, and (**D**) dibutyl phthalate. The adsorption capacity was calculated by 100% minus the free stinky compounds (%).  
Gaussian contact maps (Figure 8) observed that hydrogen bonds on the surface of the complexes had either positive or negative effects on the binding system of MPs. The  
Figure 7: The best docking model, 3D, and 2D structure of (**A**) myosin-indole complexes, (**B**) myosin-isovaleric acid complexes, (**C**) myosin-dimethyl disulfide complexes, and (**D**) myosin-dibutyl phthalate complexes.  
stronger the surface hydrophobicity the complexes presented, the lower the internal hydrophobicity they had. Moreover, the low interpolated charge played a part in the interactions. The MD results could support the above interactions analysis of GC-MS, fluorescence spectra, and CD spectra. To sum up, these four fermentation-stinky compounds presented similar interactions to MPs, which included stronger hydrogen bond, van der Waals force, ionic bone, conjugate system, and lower hydrophobicity interactions.  
Figure 8: The binding bond distribution of (**A**) myosin-indole complexes, (**B**) myosin-isovaleric acid complexes, (**C**) myosin-dimethyl disulfide complexes, and (**D**) myosin-dibutyl phthalate complexes.
## 2 Definition of System Requirements and Assessment of Fitting Communication Technologies
### Privacy  
The intended transport route and instantaneous position of the EV should not be publicly disclosed. In the proposed approach, a risk of misusing this information is minimized by replacing broadcasting by communicating the potentially sensitive messages to the relevant vehicles only. By design, it is the EV's responsibility to decide for which vehicle the message is relevant and to disseminate the information about the expected time and transport infrastructure to be used (e.g., emergency lane).  
A communication route to the relevant vehicle is found on a network-layer level as presented in Section 3.
## 4 Methodology
### Entity-Specific Trajectory Analysis
\begin{cases}0&\text{if }x<\text{LB}\\ 1&\text{if LB }\leq x<Q_{1}\\ 2&\text{if }Q_{1}\leq x<Q_{2}\\ 3&\text{if }Q_{2}\leq x<Q_{3}\\ 4&\text{if }Q_{3}\leq x<\text{UB}\\ 5&\text{if }x\geq\text{UB}\end{cases} \] (4)
6: Step 2: Categorical Assignment
7:for\(i=1\) to \(n\)do
8:for\(t=1\) to \(m\)do
9:for\(f=1\) to \(k\)do
10:\(c_{i,t,f}\leftarrow\text{CategoryMap}(x_{i,t,f})\)
11:endfor
12:endfor
13:endfor
14:Step 3: Trajectory Construction
15:for\(i=1\) to \(n\)do
16:if\(k=1\)then
17:\(T_{i}\leftarrow(c_{i,1},c_{i,2},\ldots,c_{i,m})\)
18:else
19:\(T_{i}\leftarrow\text{FeatureAggregation}(\{c_{i,t,f}\}_{f=1}^{k})_{t=1}^{m}\) {e.g., weighted average}
20:endif
21:endfor
22:Step 4: Distance Computation
23: Initialize distance matrix \(D\in\mathbb{R}^{n\times n}\)
24:for\(i=1\) to \(n\)do
25:for\(j=1\) to \(n\)do
26: Standard Hamming: \(d_{H}(T_{i},T_{j})=\sum_{i=1}^{m}\mathbf{1}[c_{i,t}\
eq c_{j,t}]\)
27: Modified Hamming: \(d_{MH}(T_{i},T_{j})=\sum_{t=1}^{m}|c_{i,t}-c_{j,t}|\)
28:\(D_{i,j}\gets d_{MH}(T_{i},T_{j})\)
29:endfor
30:endfor
31:Step 5: Diversification Index Calculation
32:\(\text{HDI}=\frac{1}{n(n-1)}\sum_{i=1}^{n}\sum_{j\
eq i}d_{MH}(T_{i},T_{j})\)
33:Complexity: \(\mathcal{O}(n^{2}m+nmk)\) where \(n\) = entities, \(m\) = time periods, \(k\) = features
```  
**Algorithm 1** Hamming Diversification Index \begin{table}
\begin{tabular}{c c} \hline \hline
**Category Value** & **Category Quartile** \\ \hline
0 & Below LB \\
1 & Low Bound \(<\) value \(<Q_{1}\) \\
2 & \(Q_{1}\)\(<\) value \(<Q_{2}\) \\
3 & \(Q_{2}\)\(<\) val \(<Q_{3}\) \\
4 & \(Q_{3}\)\(<\) val \(<\) UB \\
5 & Above UB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ordinal mapping for categorical transformation. clustered using feature \(f_{1}\), we observe an increasing trend for \(X\), a decreasing trend for \(Z\), and a more arbitrary trend for Y. When clustered using \(f_{2}\), we observe a significant shift in the cluster position for \(X\), and a vacillating trend between extremities for Y. For our calculated feature, we see object \(X\) not showing major deviations from its trends across \(f_{1}\) and \(f_{2}\) while \(Y\) and \(Z\) have seen a sharp rise in volatility in their oscillations.
# Autonomous Wireless Sensor Networks in
## 3 Results
### Testing the WSN
#### 3.1.1 Testing the SatCom Module  
Software in C# was developed to test the SatCom module so that the it could be easily configured. The developed software allowed us to test the following parameters of the RockBlock SBD module: (a) Signal quality with use of embedded antenna; (b) Data loss and reliability; (c) Bandwidth; (d) Use of course grain localization by means of trilateration without use of GNSS.  
1. _Signal strength_: The signal strength is examined on a 0-5 level scale, 0 meaning no connection and 5 signifying maximum signal strength. Signal strength was 0 for cases where the module did not have a clear view of the sky. When placed outdoors, the test demonstrated signal attenuation as a function of cloud cover and precipitation. Signal strength varied between 1 and 5. A reliable connection (\(\geq\)3) was reached when cloud cover was less than 5/10. Concerning precipitation, the extent of signal attenuation depends on the rate as well as on the raindrop size. The interference during the test was significant, reducing the signal to 0.
2. _Data Loss_: Since the RockBlock SBD module does not store information for tracking the number of lost data packages, for the purpose of this test we developed a set with sequenced, numbered and time-stamped data packets with the maximum payload (i.e., 340 bytes). The test was carried out on three segments of the communication chain: (i) The number of transmissions requested by the sink; (ii) number of transmissions billed by the RockBlock data-operator; and (iii) number of packets received at the sink. Two configuration bugs were discovered: a discrepancy in the number of sent and number of billed packets which can be attributed to the module itself, and another between the number of billed and the number of packets received at the sink which is related to the signal strength. Namely, when the signal strength is below 3 some packets that were billed were not received at the sink.
3. _Bandwidth_: The RockBlock 9603 SBD module operates with RF between 1616-1626.5 MHz. The bandwidth depends heavily on weather conditions such as cloud cover, precipitation and in a lesser manner temperature and relative humidity. The attenuation effect of rain depends on the ratio of the corresponding uplink wavelength and raindrop diameter. We were able to send up to 20 data-packets with full payload per minute without data loss under ideal signal conditions (level 5).
4. _Course Grain Localization_: The communication module provides very basic course grain localization without the need for an extra GPS module, reducing the overall cost of the system. The localization precision exhibited significant variation with the radius between 2 and 30 km depending on the strength of the SatCom signal.
# Noninvasive Suspicious Liquid Detection Using Wireless Signals
## 3 Method Design  
### Band Selection  
The C-band is a frequency band from 4.0 to 8.0 GHz, which is used as the frequency band for downlink transmission of communication satellite signals. In the application of satellite television broadcasting and various small satellite ground stations, the band was first adopted and has been widely used. The Ministry of Industry and Information Technology (MITT) has announced the frequency band division of China's fifth generation of mobile communications in its latest official document [24]. The 4.8-5 GHz frequency band in China's 5G is located in the C-band, which is an important part of 5G communication in China.  
The S-band refers to the electromagnetic wave band with a frequency range of 2-4 GHz, which is mainly used in relaying, satellite communication, radar, and so on. Now widely used in Bluetooth, ZigBee, wireless routing, and wireless mouse devices also use S-band electromagnetic waves.  
Many researches have indicated that the longer the wavelength of the electromagnetic wave, the stronger its ability to diffract. For example, radio waves can be transmitted around tall buildings, and red light can travel far in fog to remind drivers, which is more effective than green light and yellow light. The shorter the wavelength of the electromagnetic wave, the greater the energy of the wave, and the stronger the penetrating capacity. As such, X-ray can penetrate through skin and bones, ultraviolet rays can kill bacteria, and strong ultraviolet rays can cause skin cancer.  
Therefore, we selected electromagnetic waves of two frequency bands to study the influence of electromagnetic waves of different bands on liquid detection. We chose a 2.4 GHz signal located in the S-Band and a 4.8 GHz signal located in the C-band (5G frequency band) for comparison.
# Research on Improving Optical Field Distribution of TOSA Based on 3D Defocusing Coupling
## 4 Verification of 3D Defocusing Coupling Model
### Designing of Model
Besides, the accuracy of the TO-can package between the laser and the ball lens affects the Gaussian diameter also. The large Gaussian diameter eventually makes coupling efficiency lower. The theoretical coupling efficiency is 14% with PC fiber coupling the TO, while the coupling efficiency is 12.5% when the fiber is APC (Angled Physical Contact). IEEE Std 802.3 CA defines \(-\)8.2\(-\)0.5 dBm (0.15-1.13 mW) as the output power of the transmitter for a 10 G 10 km optical module, so the output power of the LD TO is >3.6 mW @1 th +20 mA, and 5% is enough for the coupling efficiency. Therefore, an appropriate coupling mode is very important for the performance of a TOSA. Based on the two coupling models in this paper, 50 pcs samples are made by using IEC61755-3-1 Class B fiber jumper. First, as shown in Figure 3a, the end-face of the receptacle is at the focal point of the TO, and only two-dimensional coupling is performed in the X/Y plane. When the optical power is within \(-\)8.2\(-\)0.5 dBm, the receptacle is weld on the body using laser welding. Second, as shown in Figure 3b, 3D defocusing coupling is used. The maximum power is found in the X/Y plane, then the power is adjusted by moving up the receptacle along the Z-axis step by step. When the optical power is within \(-\)8.2\(-\)0.5 dBm, the receptacle is welded on the ring. The receptacle with the ring moves up on the body to couple maximum power, then is welded on the body.
# Class Activation Map Guided Backpropagation for Discriminative Explanations
## 5 Potential Applications  
In this section, we present two potential applications: model debugging and medical image diagnosis. We use the IS-DS as a new metric to serve as a reference for saliency map fidelity.  
In model debugging, CGBP helps developers analyze the causes of misclassification. As shown in Figure 6, the true category of the image is \"tailed frog\", but its younger stage closely resembles a tadpole. Since \"tadpole\" is not a category in ImageNet, the VGG16 model misclassifies it as a common newt and a California newt (top-2 categories). CGBP reveals that the model mistakenly identifies the tadpole's head as the head of a common newt and its tail as that of a California newt. This inference is validated by the highest Ins-Del score.  
In medical image diagnosis, CGBP enhances user trust. We trained a ResNet50 model on the Kaggle retinal image dataset for glaucoma diagnosis. Glaucoma patients typically exhibit an enlarged optic cup in the optic nerve head, making this region a critical focus for clinical diagnosis. We compared the performance of several methods, as shown in Figure 7. The results demonstrate that CGBP, guided by class activation maps, accurately localizes the optic nerve head, indicating that the model relies on this region for diagnosis, consistent with clinical experience.  
Figure 5: The influence of the cascading randomization test on the saliency maps generated by GBP, RectGrad, and CGBP.  
Figure 6: Saliency map visualizations for model debugging.
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 4 Hybrid-Precision Circuit Simulation Validation and Stress Analysis
### Development of Behavioral Simulation Models for Integrated Devices Using Verilog
#### 4.1.1 Development of Simulation Models for DS26C31 and DS26C32  
Before constructing the complete circuit model of the communication module, it is necessary to build simulation models for key integrated devices individually. DS26C31 is a CMOS differential line driver, and DS26C32 is a CMOS differential line receiver. These components enable the conversion between the FPGA and RS422 differential signals transmitted by single-ended signals within the communication module.  
A simulation test circuit for DS26C31 and DS26C32 is constructed, as shown in Figure 4. The circuit's input is a digital clock signal with a period of 1 \(\upmu\)s, which is converted into a differential signal by DS26C31 and then restored to a single-ended clock signal by DS26C32. Ideally, this simulation test circuit's input and output signals should be nearly identical.  
Figure 5 shows the simulation results. The output signal is nearly identical to the input signal, demonstrating that DS26C31 and DS26C32 can successfully convert differential and single-ended signals.
# Human Factors Analysis of Air Traffic Safety Based on HFACS-BN Model
## 2 Methodology and Materials
### Noisy MAX Model  
In general, the conditional probability table can be obtained from a database or the judgment of relevant experts [29]. However, it is challenging work to directly get all the conditional probabilities for a large-scale network since the number of parameters grows exponentially with the number of parents [30]. At present, the most widely used algorithm is the Noise-OR model proposed by [PERSON] [31]. [PERSON] further extends the model to multivariate variables [32]. Based on [PERSON]'s model and formalizing it, [PERSON] proposed a kind of evidence propagation algorithm \"MAX gate\" [18]. To focus on the ternary variable BN model, this paper, therefore, adopts the \"noisy MAX\" dealing with conditional distribution of multiple variables.  
In noisy MAX, the child node Y, taken sequentially from 0 to \(y_{\text{max}}-1\), has a total of \(y_{\text{max}}\) states, and N parents, \(Pa(Y)=\{X_{1},\ldots,X_{n}\}\), representing the causes of Y. The following are two basic axioms of noisy MAX [18]:  
\[P(Y=0|X_{i}=0,\forall i)=1 \tag{1}\] \[P(Y\leq y|x_{1},x_{2},\ldots,x_{n})=\prod_{i}P(Y\leq y|X_{i}=x_{ i},X_{j}=0,\forall j\
eq i) \tag{2}\]  
where the \(X_{i}\) are independent of each other.  
The parameters for link \(X_{i}\to Y\) are \(c_{y}^{X_{i}}\), representing the probability of \(Y=y\) when parent \(X_{i}\) takes on the value \(x_{i}\) and all other \(X_{j}\) values are 0.  
\[c_{y}^{x_{i}}=P(Y=y|X_{i}=x_{i},X_{j}=0,\forall j\
eq i) \tag{3}\]  
Figure 3: Three basic structures of Bayesian networks. (**a**) Head to head; that is, given the condition of \(Z\), the communication between X and Y is blocked, also known as head-to-head condition independence. It is obvious that variables X and Y have a common result Z. The joint probability can be expressed as P (X, Y, Z) = P(X) P(Y) P (Z|X, Y). (**b**) Head to tail. For a given condition of Y, the communication between X and Z is blocked, which is called head-to-tail condition independent. X, Y, and Z are connected serially, and the corresponding joint probability distribution is P (X, Y, Z) = P(X) P (Y|X) P(Z|Y). (**c**) Tail to tail. For a given condition of \(Z\), the communication between X and Y is blocked, which is called tail-to-tail condition independence. Obviously, X and Y have a common cause. The joint probability is therefore calculated as P (X, Y, Z) = P (Z) P (X|Z) P (Y|Z).  
Figure 2: (**a**) Directed acyclic graphs for Bayesian network; (**b**) simple Bayesian network model.  
Define a new parameter:  
\[C_{y}^{x_{i}}=P(Y\leq y|X_{i}=x_{i},X_{j}=0,\forall j,j\
eq i)=\sum_{k=0}^{y}c_{k }^{x_{i}}. \tag{4}\]  
Then, Equation (2) can be rewritten as:  
\[P(Y\leq y|x_{1},x_{2},\ldots,x_{n})=\prod_{i}C_{y}^{x_{i}}. \tag{5}\]  
Finally, the CPT is obtained using the following formulas:  
\[P(y|X)=\left\{\begin{array}{ccc}P(Y\leq 0|X)&if&y=0\\ P(Y\leq y|X)-P(Y\leq y-1|X)&if&y>0\end{array}\right. \tag{6}\]
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 4 Simulation Results
### Impact of AP Number
#### 4.1.2 Performance of the Metric of Double Measurements  
Figures 5 and 6 illustrate the Fisher information provided by different metrics of double measurements and the corresponding error bounds. In the simulation, we set \(\sigma_{1}=3\) dB, \(\sigma_{\alpha}~{}=~{}2.25\) deg and \(\sigma_{\tau}~{}=~{}3\) ns. From these figures, the metric of TOF/AOA can be verified to perform best in providing the most Fisher information. Similarly, the larger number of APs corresponds to the higher Fisher information and lower error bound. In addition, we can find that the increase of the absolute value of correlation coefficient generally increases the Fisher information, as well as decreases the error bound.  
Figure 4: Error bounds achieved by the metrics of Receive (**a**) RSS, (**b**) TOF and (**c**) AOA.  
Figure 3: Sum of diagonal elements in the Fisher Information Matrix (FIM) with respect to the metrics of (**a**) Signal Strength (RSS), (**b**) Time-of-flight (TOF) and (**c**) Angle-of-arrival (AOA).