esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Salinity Alters Productivity of Spirulina  
Gross primary production (GPP) showed no significant change across varying light intensities at the lowest salinity point of 10 ppt, indicating poor photophysiological responses when salt levels were low. GPP increased linearly with light intensity only in _Spirulina_ cultures maintained at salinity levels greater than or equal to 17 ppt. Under saline stress, the strain enhances its photosynthetic efficiency, which enables it to maximize oxygenic photosynthesis when using available light [28].  
The observed change in productivity patterns corresponds with [PERSON] et al. [12], who proved that _Anabaena_ together with _Anabaenopsis_ and other freshwater and marine cyanobacteria perform better photosynthetically at intermediate to high salinity ranges. The calculated photosynthetic efficiency based on GPP-light response curve slopes showed higher values at 27, 40, and 45 ppt when compared to 10 ppt. The results demonstrate this strain's ability to adapt its light-harvesting complexes and pigment composition as a photoacclimation response to salt stress to maintain photosynthetic output during osmotic challenges [29; 30].  
Cyanobacteria that experience salinity stress typically change their metabolic processes to maintain energy flow through their photosynthetic electron transport chain [11]. Cyanobacteria exposed to salty conditions create compatible solutes while turning on ion transport systems that need ATP and reducing power for operation. The enhanced photosynthetic efficiency at increased salinity levels could represent a regulatory response that provides enough energy to sustain stress adaptation mechanisms [28].  
The _Spirulina_ strain displayed declining specific growth rates across the same salinity conditions, even though photosynthetic efficiency rose at higher salt concentrations. The separation between photosynthetic capacity and growth indicates that cellular resources moved toward sustaining basic cellular operations and stress defense instead of cell division [31]. The observed growth patterns in cyanobacteria subjected to photooxidative stress or nutrient limitation show that increased photosynthetic rates enable survival instead of promoting biomass growth [6; 26].  
The effects of salinity and light interactions on primary productivity demonstrate that evaluations of algal performance need to examine multiple environmental factors simultaneously. Light conditions essential for photosynthesis require proper conditions to function effectively because their impact on photosynthesis varies based on the surrounding salinity levels [32]. The research shows that _Spirulina_ cultivation optimization requires integrating both environmental and physiological factors, especially when salinization occurs in regions affected by climate change or human activities.
# Vibration Properties of Dual-Rotor Systems under Base Excitation, Mass Unbalance and Gravity
## 2 Modeling for a Dual-Rotor System under Base Motions
### Modeling for the Disks, Unbalance Force and Linear Bearings
\[\mathbf{c}_{\text{d}}=\mathbf{c}_{\text{d1}}\omega_{by} \tag{5}\]  
\[\mathbf{k}_{\text{d}}(t)=\mathbf{k}_{\text{d1}}\dot{\omega}_{by}+\mathbf{k}_{ \text{d2}}\omega_{bx}^{2}+\mathbf{k}_{\text{d3}}\omega_{by}^{2}+\mathbf{k}_{ \text{d4}}\omega_{bz}^{2}+\mathbf{k}_{\text{d5}}\omega_{bx}\omega_{bz}+\mathbf{ k}_{\text{d6}}\Omega_{r}\omega_{by} \tag{6}\]  
\[\mathbf{f}_{\text{dtr}}(t)=-\mathbf{p}_{\text{d1}}a_{bx}-\mathbf{p}_{\text{d2} }a_{bz} \tag{7}\]  
\[\mathbf{f}_{\text{dlb}}(t)=\mathbf{p}_{\text{d1}}\Big{(}x_{lb}(\omega_{by}^{2}+ \omega_{bz}^{2})+y_{lb}(\dot{\omega}_{bz}-\omega_{bx}\omega_{by})-z_{lb}(\dot{ \omega}_{by}+\omega_{bx}\omega_{bz})\Big{)} \tag{8}\]  
\[\mathbf{f}_{\text{dlb}}(t)=\mathbf{p}_{\text{d1}}\Big{(}x_{lb}(\omega_{by}^{2 }+\omega_{bz}^{2})+y_{lb}(\dot{\omega}_{bz}-\omega_{bx}\omega_{by})-z_{lb}( \dot{\omega}_{by}+\omega_{bx}\omega_{bz})\Big{)} \tag{9}\]  
\[+\mathbf{p}_{\text{d2}}\Big{(}x_{lb}(\dot{\omega}_{by}-\omega_{bx}\omega_{bz} )-y_{lb}(\dot{\omega}_{bx}+\omega_{by}\omega_{bz})+z_{lb}(\omega_{bx}^{2}+ \omega_{by}^{2})\Big{)}\]  
\[\mathbf{f}_{\text{dan}}(t)=\mathbf{s}_{\text{d1}}\dot{\omega}_{bx}+\mathbf{s}_ {\text{d2}}\dot{\omega}_{bz}+\mathbf{s}_{\text{d3}}\omega_{bx}\omega_{by}+ \mathbf{s}_{\text{d4}}\omega_{by}\omega_{bz}+\mathbf{s}_{\text{d5}}\Omega_{r} \omega_{bx}+\mathbf{s}_{\text{d6}}\Omega_{r}\omega_{bz} \tag{10}\]  
\[\mathbf{f}_{\text{dg}}(t)=\mathbf{p}_{\text{d1}}g\cos\alpha\sin\beta-\mathbf{ p}_{\text{d2}}g\cos\alpha\cos\beta \tag{11}\]  
The final unbalancing forces are given by  
\[\mathbf{f}_{\text{du}}=m_{u}e\left[ \begin{array}{c}\Big{(}\big{(}\Omega_{r} +\omega_{by}\big{)}^{2}+\omega_{bz}^{2}\Big{)}\cos(\Omega_{r}t+\varphi_{u})+ (\dot{\omega}_{by}+\omega_{bx}\omega_{bz})\sin(\Omega_{r}t+\varphi_{u})\\ -\Big{(}\big{(}\Omega_{r}+\omega_{by}\big{)}^{2}+\omega_{bx}^{2}\Big{)}\sin( \Omega_{r}t+\varphi_{u})+(\dot{\omega}_{by}-\omega_{bx}\omega_{bz})\cos( \Omega_{r}t+\varphi_{u})\\ 0\\ 0\end{array} \right] \tag{12}\]  
where \(e\) denotes the eccentricity of the unbalanced mass, and \(\varphi_{u}\) is the unbalanced angle with respect to the axis \(O_{b}X_{b}\). The linear bearings are concerned by placing the stiffness \(k_{b}\) and damping \(c_{b}\) of the bearings in the proper position in the stiffness matrices and damping matrices.
## 7 GPU-Based Embedded Platforms for Real Time Object Detection
### Zynq Board  
For CV real-time applications in image and video processing, the Zynq-7000 family was introduced by Xilinx Company. Zynq-7000 SoC [261] is fabricated using 28 nm technology and integrates both \"processing system (PS) and programmable logic (PL) on a single chip\". A variety of tools are available to develop code for Xilinx FPGAs, and one of the best tools for configuring FPGAs is Vivado. An FPGA family such as Virtex-7, Kintex, or Artix, uses the Vivado tool, and the same was adopted for Zynq. An advanced version of the Xilinx integrated synthesis environment (ISE) design is Vivado, which is generally used for programming FPGAs. Besides, vivado also includes a high-level synthesis (HLS) tool for C-based IP generations for high-level languages, such as \"C, C++, or System C\". For sequential algorithms [262], Vivado tool is used to develop optimized codes. Table 12 shows a comparison between various Zynq and Pynq boards.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **PYNQ-Z1** & **PYNQ-Z2** & **ZCU104** \\ \hline Device & Zynq Z7020 & Zynq Z7020 & ZynqUltrascale+ XCZU7 EV \\ \hline Memory & 512 MB DDR3 & 512 MB DDR3 & 2 GB DDR4, PL DDR4 SODIMM \\ \hline Storage & \(\mu\)SD & \(\mu\)SD & \(\mu\)SD \\ \hline Video & In \& Out HDMI & In \& Out HDMI, Display Port \\ \hline Audio & \
\begin{tabular}{l} PDM integrated mic, \\ 3.5 mm PWM audio jack \\ \end{tabular} &
\begin{tabular}{l} ADAU1761 codec \\ with HP + mic \\ \end{tabular} & - \\ \hline Network & 10 \(\times\)1, \(\times\)10, \(\times\)100 Ethernet & 10 \(\times\)1, \(\times\)10, \(\times\)100 Ethernet & 10 \(\times\)1, \(\times\)10, \(\times\)100 Ethernet \\ \hline Expansion & USB host (PS) & USB host (PS) & USB2.0/3.0 host (PS) \\ \hline \multirow{4}{*}{GPIO} & 1\(\times\) Arduino Header & 1\(\times\) Arduino & LPC FMC \\  & 2\(\times\) Pmod & 2\(\times\) Pmod & 3\(\times\) Pmod (2\(\times\) PL) \\  & 16\(\times\) GPIO pins & 1\(\times\) RaspberryPi & - \\ \hline \multirow{4}{*}{Other} & 6\(\times\) user LEDs & 6\(\times\) user LEDs & 4\(\times\) user LEDs \\  & 4\(\times\) Pushbuttons & 4\(\times\) Pushbuttons & 4\(\times\) Pushbuttons \\  & 2\(\times\) Dip switches & 2\(\times\) Dip switches & 4\(\times\) Dip switches \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparisons between various versions of Pynq boards.
## 5 Experiments and Results
### Discussion  
This section elaborates on the experimental environment, baseline algorithms and experimental results in detail. Experiments in the SUMO simulator demonstrate the effectiveness of hierarchical-based multi-agent reinforcement-learning algorithms in solving traffic congestion problems. The level of cumulative average reward reflects the overall performance of the algorithm. The three indicators of average queue length, speed and waiting time measure the degree of congestion in the traffic system from different dimensions. Based on these four aspects, we make an overall comparison of different algorithms in Table 1. It can be seen from the results that the basic MADDPG algorithm performs generally in the face of specific environments. However, MA2C and FMA2C include additional considerations in multi-agent cooperation, and their effects were also improved. Finally, our proposed GMHM algorithm fully considers the key element of the goal. The GMHM algorithm generates more reasonable goals through the transformer in the upper layer, and uses the goals in the lower MADDPG algorithm to speculate on the neighbors. In this way, we achieve collaboration between layers and within layers to adequately solve the TSC problem.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{4}{c}{**Grid Network**} & \multicolumn{4}{c}{**Monaco Network**} \\ \cline{2-9}  & **GMHM** & **FMA2C** & **MA2C** & **MADDPG** & **GMHM** & **FMA2C** & **MA2C** & **MADDPG** \\ \hline Reward & \(-\)**137.6** & \(-\)692.7 & \(-\)714.5 & \(-\)1131.6 & \(-\)**16.3** & \(-\)53.7 & \(-\)158.4 & \(-\)221.9 \\ \hline Queue length & **0.27** & 0.89 & 1.73 & 1.85 & **0.47** & 1.35 & 1.98 & 2.33 \\ \hline Speed & **8.29** & 7.94 & 3.17 & 2.88 & **11.69** & 9.74 & 4.02 & 2.53 \\ \hline Waiting time & **10.9** & 39.7 & 42.6 & 105.3 & **9.6** & 40.3 & 75.7 & 79.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall performance of different algorithms in the grid network and Monaco network.  
Figure 5: Experiment results in Monaco traffic network. The top two figures are the results of our method applied to A3C. The bottom two figures are the experiment results based on PPO.
## 2 Materials and Methods
### Data Set and Analysis  
The extensive data set collected from the alfalfa sites under fully irrigated practices (site A-I) and moderate summer deficit irrigation strategies (site A-D) were used for this analysis. The data set includes three-year data (2019-2021) for the sites A through D and two-year data (2020-2020) for the sites E through I.  
Both the EC and SR techniques were individually employed to determine sensible heat flux density. The advantage of using both the SR and EC methods was that they are independent and similar results provide a high level of confidence in the data used [32; 33]. Latent heat flux density was calculated as \(\rm{LE=Rn-G-H}\). After determining LE, ETA in mm d\({}^{-1}\) was calculated by dividing the LE in MJ m\({}^{-2}\) d\({}^{-1}\) by 2.45 to obtain the ET values in mm d\({}^{-1}\). Water productivity (WP) was calculated as the ratio of alfalfa dry matter (kg ha\({}^{-1}\)) to ETA (mm). [PERSON]'s protected least significant difference (LSD) test was used to evaluate significant differences in DM yield and WP values between the experimental sites.  
Using the daily ETA determined in each monitoring site and the daily ETo from the spatial CIMIS (California Irrigation Management Information System) data [34] for the coordinates of the monitoring site, the daily actual crop coefficient (Ka) was calculated as the ratio of ETA to ETo. CIMIS uses the Penman-Monteith equation, and a version of [PERSON]'s equation modified by [PERSON] [35]. Spatial CIMIS combines remotely sensed satellite data with traditional CIMIS stations data to develop site specific ETo on a 2000 m grid.
## 4 Simulation
The convergence curve of the network approximation error is shown in Figure 7.  
Firstly, the command signal is set as the sine signal \(\dot{\theta}_{ref}=\sin(2\pi t)\). The speed-tracking simulation results are shown in Figure 8. It shows that the tracking error near the zero point is 54.7% for the NPD, 23.6% for the ADRC and 2.2% for the SRBF-MADRC. Although the typical ADRC can improve the speed control accuracy compared with the NPD algorithm, the speed still fluctuates near the speed zero point because the ability of a single ESO to track nonlinear friction is insufficient. The SRBF-MADRC utilizes the information of the auxiliary model and the adaptive supervisory ability of the SRBF to realize the rapid compensation for friction disturbance and overcome the problem of zero-speed fluctuation. Then, the command signal is set as the low-speed step signal \(\dot{\theta}_{ref}=0.1\). The simulation results of low-speed tracking are shown in Figure 9. Due to the limited control gain, the output control quantity of the NPD is lower than the friction torque, resulting in a speed stall. The low-speed response of the ADRC has overshoot and static errors. The main reason is the limited estimation and tracking capability of single ESO. The SRBF-MADRC quickly tracks low-speed commands without overshoot and steady-state error. This shows that the proposed algorithm has a more accurate estimation and compensation for disturbances. Figure 8: Simulation results of sinusoidal speed-tracking. Figure 7: Network approximation error convergence curve. Finally, the command signal is set as the high-speed step signal \(\dot{\theta}_{ref}=10\). Figure 10 shows the high-speed tracking results of the three algorithms. The response time of the NPD and ADRC is about 300 ms, while that of the SRBF-MADRC is only 80 ms. This shows that the proposed algorithm has higher response speed. In addition, a large amplitude shock is added to the system to test the anti-disturbance performance of three algorithms. The amplitude of the shock is 50 and the duration is 1 ms. The shock response curves of three algorithms are shown in Figure 11. The fluctuation peak of the NPD is 0.81, ADRC is 0.72 and the SRBF-MADRC is 0.34. Moreover, the SRBF-MADRC converges to the stable instruction fastest. This reflects the strong anti-interference performance of the SRBF-MADRC. Figure 11: Simulation results of interference pulse. Figure 10: Simulation results of high-speed tracking. Figure 6:0 Simulation results of low-speed tracking.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 5 Summary of Research Findings and Practical Implications
The laptop nodes for all the protocols proved stable on all the accounts, and all had similar throughput of packets across the board, which was surprising considering we thought there would be more of a variance in the results. * _Packet delivery ratio performance_ The graphs show how many packets are being sent and received in bytes for each routing protocol. The result indicates that each protocol is nearly identical, and there is not much gap difference in the ratios. This result is based on running a mobility scenario with two fixed hosts, five mobiles and five laptops, with a simulation time limit of 600 s (10 min), random mobility in an area 600 \(\times\) 600 m, and random nodes in every protocol. As you can see, the laptop nodes are not included in the graph because the configuration parameters are not correctly configured. Therefore, AODV has the most significant packet delivery ratio amongst the other protocols in the mobility scenario, which means AODV has the most successful rate when delivering packets to the destination.
# Learning-Assisted Rain Attenuation Prediction Models
## 4 Insights of LARA Models
### LARA Model with Three or More Factors
Low statistical data is processed through the equation:  
\[J\ =\ \sum\left[\overline{x}_{j}-\overline{c}_{i}\right]^{2}, \tag{7}\]  
where \(\overline{x}_{j}\) is the j th input-output pair; \(\overline{c}_{i}\) is the center of the i th class mean value of the vector \(\overline{x}_{j}\) belongs to the class, and the sum of the distance between each vector \(\overline{x}_{j}\) and the center of the class is minimized. **Advantages:** It describes a mechanism to eliminate low statistical data. **Disadvantages:** The structural information regarding the structure of the ANN network is not provided in the article. **Future improvement:** Low statistical data idea can be used with a well-designed ANN structure. **Application area:** To predict rain attenuation. In the above section, all the available LARA models were discussed. Figure 7 presents the timeline of the outcome of the LARA model yearly. It is noticeable from the figure that, starting in the year of 2004 with a single publication, there are few year gaps in the year with LARA model publications. However, in the year 2019, four LARA models were published. This means researchers were becoming interested in developing learning-assisted rain attenuation prediction models.
# Research on Solidification Methods and Stabilization Mechanisms of Sulfate Saline Soils
## 4 Results and Analysis
### Microstructure Analysis of Stabilized Soils  
#### 4.3.1 XRD Analysis  
Figure 6 shows the XRD test results of saline soils and stabilized saline soils cured for 7 d and 28 d using three binders.  
Figure 6a shows the XRD patterns of specimens L1, L2, and L3 after 7 days of curing. Diffraction peaks of quartz (SiO\({}_{2}\)), calcium carbonate (CaCO\({}_{3}\)), muscovite (KAl\({}_{2}\)(AlSi\({}_{3}\)O\({}_{10}\))(OH)\({}_{2}\)), and lime (Ca(OH)\({}_{2}\)) may be seen in the lime-stabilized soil, but no saltpeter peaks can be found. The composition and proportion of components differ from the XRD of saline soils. Weak diffraction peaks of CaCO\({}_{4}\), Ca(OH)\({}_{2}\), and CaCO\({}_{3}\) can be seen in L1, L2, and L3, showing the early reaction of sulfate with lime to generate CaCO\({}_{4}\) and lime with CO\({}_{2}\) to form CaCO\({}_{3}\). The higher the lime content, the more CaCO\({}_{4}\), Ca(OH)\({}_{2}\), and CaCO\({}_{3}\) are present, demonstrating a link between the strength of the stabilized soil and these three substances.  
Figure 6b shows the XRD patterns of specimens F1, F2, and F3 after 7 d of curing. FA-stabilized soil had micro-peaks of hematite (Fe\({}_{2}\)O\({}_{3}\)). The content and percentage of each component in the stabilized soils changed, although it was not possible to determine whether FA chemically reacted with sodium sulfate. Figure 6c shows the XRD patterns of  
Figure 5: Strength residual ratio after freeze–thaw cycles: (**a**) UCS DBR of stabilized soils; (**b**) splitting strength DBR of stabilized soils.  
the P3 specimens after 7 d and 28 d of curing. No new minerals were produced compared to the saline soils, which indicates that the PAM did not interact with the saline soils.  
Figure 6d shows the XRD pattern of specimens L3, F3, and P3 stabilized after 28 d of curing. PAM did not change the composition or proportions of the stabilized soil. FA and lime converted the sulfates in the saline soils into other minerals, reducing the salt content in the stabilized soil and increasing its strength.  
The reaction products generated during the stabilization processes of lime, FA, and PAM may not be detectable by XRD. The main reason for this is that these compounds are either in a low crystalline state or they are too small to be distinguished by XRD.
# LDPC Codes on Balanced Incomplete Block Designs: Construction, Girth, and Cycle Structure Analysis
## 6 Conclusions  
In this paper, we proposed a method for constructing LDPC codes based on a \(\text{BIBD}(\kappa,1;\upsilon)\). We analyzed the cycle structure of the constructed LDPC codes and determined their girth. Numerical results show that the constructed LDPC codes exhibit excellent performance under iterative decoding. Furthermore, we presented a method for counting cycles in the Tanner graphs of these codes, specifically enumerating cycles of lengths 6 and 8. This method can be extended to systematically count cycles of lengths greater than 8 (e.g., 10, 12, etc.). However, the complexity of the analysis increases significantly with the cycle length, primarily due to the need to account for the presence of existing shorter cycles (such as those of lengths 6 and 8) and their interactions within the Tanner graph.  
**Author Contributions:** Conceptualization, H.X. and X.Z.; methodology, H.X. and X.Z.; software, M.X. and H.Y.; validation, H.X. and M.X.; formal analysis, H.X. and X.Z.; investigation, H.X.; resources, H.Z.; data curation, H.X. and M.X.; writing--original draft preparation, H.X. and M.X.; writing--review and editing, H.X., X.Z., M.X., H.Y. and H.Z.; supervision, H.Z.; project administration, H.Z.; funding acquisition, H.X., X.Z., M.X., H.Y. and H.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 62172457, 11971311, and 61801527), the TianYuan Special Funds of the National Natural Science Foundation of China (Grant Nos. 12026230 and 12026231), and the Development Project of Henan Provincial Department of Science and Technology (Grant Nos. 252102210230, 252102210236, and 242102210200), and the Key Scientific Research Project in Colleges and Universities of Henan Province (Grant Nos. 22A520002 and 24A510003).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 4: An EXIT chart example for the (3,37)-regular LDPC code ensemble.  
**Data Availability Statement:** The data that support the findings of this research are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.