esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Discussion of Adsorption Mechanisms
#### 3.3.2 Particle Diffusion Model  
A particle diffusion model was used to fit the diffusion patterns of the four metal ions adsorbed by the GO/P(AA-co-AM) hydrogels shown in Figure 7. For the intraparticle diffusion model, if the C value of the fitted curve is 0 and the fitted curve passes through the origin, then intraparticle diffusion is the only limiting factor for the adsorption of the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Ions**} & \multirow{2}{*}{\(Q_{e}\)/mg g\({}^{-1}\)} & \multicolumn{3}{c}{**Pseudo-First Order**} & \multicolumn{3}{c}{**Pseudo-Second Order**} \\ \cline{3-7}  & & \(Q_{e}\)/mg g\({}^{-1}\) & \(k_{1}\) & \(R_{1}\) & \(Q_{e}\)/mg g\({}^{-1}\) & \(k_{2}\) & \(R_{2}\) \\ \hline Cu\({}^{2+}\) & 785.72 & 661.41 & 0.03073 & 0.907 & 787.4 & 0.000661 & 0.997 \\ Pb\({}^{2+}\) & 970.03 & 950.24 & 0.06489 & 0.876 & 990.09 & 0.000843 & 0.999 \\ Zn\({}^{2+}\) & 764.96 & 695.31 & 0.03993 & 0.960 & 763.36 & 0.001361 & 0.999 \\ Cd\({}^{2+}\) & 729.89 & 687.83 & 0.04752 & 0.667 & 729.93 & 0.000885 & 0.998 \\ \hline \end{tabular}
\end{table}
Table 1: Model coefficients for the reaction kinetics of GO/P(AA-co-AM) for the adsorption of Cu(II), Pb(II), Zn(II), and Cd(II) ions.  
Figure 6: Kinetic model for quasi-mono and quasi-directional reactions of **(a)** Cu(II), **(b)** Pb(II), (c) Zn(II), and **(d)** Cd(II) ions adsorbed by GO/P(AA-co-AM) (\(C_{0}\) = 1000 mg\(\cdot\)g\({}^{-1}\), pH = 4, T = 25 \({}^{\circ}\)C).  
GO/P(AA-co-AM) hydrogels. Conversely, if it does not pass through the origin, then there are other adsorption mechanisms working together in the adsorption process of the hydrogels, and the larger the C value, the more pronounced the boundary layer effect, which means that the adsorption effect of the boundary layer of the adsorbent is more important for the overall adsorption process. The larger the C value, the more pronounced the boundary layer effect, and the greater the role of the adsorber boundary layer in the overall adsorption process.  
As shown in the figure, the fitted curves of the intraparticle diffusion model for the adsorption of the four metal ions by the GO/P(AA-co-AM) hydrogels do not pass the origin, which indicates that the adsorption mechanism of the hydrogels for the four metal ions is not only the intraparticle diffusion mechanism, but also other mechanisms acting together between the hydrogels and the metal ions. From the fitted results, it can be seen that the boundary effect C values are relatively large, indicating that the surface adsorption of the GO/P(AA-co-AM) hydrogels is very important for the whole adsorption process, which is consistent with the results from the quasi-secondary kinetic model, and chemisorption is also an important mechanism in the adsorption of metal ions by the GO/P(AA-co-AM) hydrogels.
## 5 Experiemental Result and Analysis  
### Main Experiment Result and Analysis  
The main results of our proposed MVK model and other compared baselines are presented in Table 3. The key evaluation indicator, F1, for the model proposed in this article, is 83.92%, with an accuracy (Acc) of 86.68%. Additionally, the precision (Pre) and recall (rec) have also improved. In the compared baselines, using only text modality resulted in better results than using only image modality, indicating that text contains more information than images. However, they cannot utilize the complementarity between multimodal data sufficiently. Current research focuses on building multimodal fusion networks using  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Non-Sarcasm** & **Sarcasm** & **Total** & **Percentage** \\ \hline Training set & 8642 & 11,174 & 19,816 & 80.44\% \\ Val set & 959 & 1451 & 2410 & 9.78\% \\ Test set & 959 & 1450 & 2409 & 9.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistics of the multimodal dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameters** & **Value** \\ \hline Batch size & 256 \\ Number of knowledge words & 5 \\ Max length of text & 75 \\ Dropout rate & 0.25 \\ Learning rate & 0.00025 \\ Number of multi-view sublayers & 2 \\ Epoch & 15 \\ BERT embedding dimension & 768 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyper-parameter setting.  
multimodal data as input. For example, HFN and InCrossMGs use attention mechanisms and cross-modal graph convolutional networks to fuse text and image features directly. Attr-BERT, on the other hand, directly concatenates text and image data, using the BERT structure to extract multimodal features. However, these approaches ignore the interactivity between modalities and do not leverage visual commonsense knowledge to enrich multimodal representations. We draw visual commonsense knowledge into the model to consciously enhance the representation vectors of each modality. Simultaneously, our model uses multi-view to interact with inter-modal information dynamically in multiple turns. Finally, we fuse the multimodal representation through attention fusion. The experimental results manifest that our multi-view interaction based on the knowledge enhancement model can effectively improve the performance of multimodal sarcasm detection.  
In this section, we also quantified the total number of parameters of both the proposed model and the comparative baselines, as demonstrated in Table 3. The comparative baselines employ multimodal data as input. In contrast to the HKE model, our model, as proposed in this article, exhibits comparable performance, but it contains significantly fewer parameters. This observation suggests that our approach, when deployed in mobile devices, can effectively mitigate memory constraints on devices. Furthermore, the reduced parameter count results in a decreased computational overhead.
## 1 Introduction  
Many methods have been proposed to evaluate the stability of bank slope, such as limit equilibrium method (LEM) and limit difference method (LDM). A stability analysis based on LEM cannot constrain the evolution characteristics of the slope stress and strain during slope failure [1] ([PERSON], 2014). A time-varying analysis method was proposed to analyze the temporal and spatial changes of the mechanical properties of engineer objects [2]. This method focuses on the change of pore water pressure over time, but pays little attention to the change of safety factors. [PERSON] et al. [3] established a time-varying analysis model for slope stability on the basis of Geostuido, and replaced the traditional independent safety factors with the safety factor curve, but using the safety factor alone can hardly assess the accuracy of the results. [PERSON] et al. [4] improved the FISH function in the FLAC\({}^{\text{3D}}\) software, and studied the three-dimensional evolution features of the seepage field of the unsaturated slope under rainfall infiltration, but little on the slope mechanism. At present, many secondary developments have been added to FLAC\({}^{\text{3D}}\) software, and they concentrate on establishing constitutive models of rock-soil mass [5; 6; 7], but rarely consider the time series analysis of slope safety factors. These related studies either use LEM to explore the time-varying characteristics of slopes, or use the finite difference method to calculate the safety factor of the slope at a single time. However, the finite difference method is rarely used to analyze the stability in a long time span and with a small time interval, especially for tailing pond bank slopes.  
In this study, a time-varying analysis method (TSFDM) is proposed for analyzing slope stability based on the finite difference method. We use Python to improve the FISH function in the TSFDM. The improved FISH function can analyze the temporal change features of the unsaturated slope seepage and numerically simulate the slope under rainfall, water level changes and a combination of the two conditions.
## 6 Conclusions  
Based on FY-3 remote sensing satellite data, seven methods were applied to extract the surface water of the Poyang Lake, China. A confusion matrix analysis on the verification samples selected by Landsat 8 OLI was conducted to compare with the seven classification methods. Compared with other methods, SMDPO+LFE is considered to be the best method for extracting the water of Poyang Lakebecause of its high accuracy on water extraction, especially on small tributary waters. It also shows good performance in terms of sensitivity and durability.  
Among the surface water extraction methods in the Poyang Lake region, the classification accuracy and Kappa coefficient of the seven methods were all higher than 93% and 0.84 respectively. SMDPSO+LFE classification accuracy reached 97.56% and Kappa coefficient reached 0.95. These results show that the SMDPSO+LFE method represents the highest accuracy among the seven methods in this case study. The algorithms of SMDPSO+ LFE determined three parameters of standard water spectrum, block size, and iteration times in the early stage of the surface water body extraction model and have a good extraction effect.  
In terms of the adaptability of different regions, eight qualitative regions of different terrestrial surface water types, landscapes, and topographic features are selected globally. SMDPSO+LFE algorithm was used to extract surface water from FY-3 images of eight regions. The results show that water extraction is effective and can be divided into ice, shoal, silt, and small tributaries. Sensitivity analysis was carried out through the standard water spectrum, block size, and iteration times. The results show that the selection of Lake Superior as the standard water spectrum, block size of 4, and iteration times of 2500 have high classification accuracy and Kappa coefficient. The algorithm is also more efficient.  
However, compared with other methods, the SMDPSO+LFE method is slightly more complicated in the implementation process. Other conventional methods can be completed with many software. SMDPSO+LFE does not support by any software by now, which means it needs to be programmed. In a follow-up study, intelligent optimization algorithms will be introduced. The surface water body extraction stability of SMDPSO+LFE method under different programming languages will also be explored.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON] and [PERSON] contributed to the data and material collection; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Program of Hebei, grant number 19255901D, and the National Defense Science and Technology Key Laboratory of Remote Sensing Information and Image Analysis Technology of China, grant number 6142A010301.  
The authors declare no conflict of interest.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 5 Conclusions and Outlook  
As the 6G communication network evolves towards ultra-high speed and global coverage, VLC has become a key technology for building an integrated space-air-ground-sea communication system due to its ultra-high bandwidth, low power consumption, and resistance to electromagnetic interference. MFR as a core component of VLC adaptive transmission directly affects the dynamic optimization capability and scenario adaptability of the communication system. This paper systematically reviewed the research status of MFI technology, revealing the process of technological evolution and the challenges faced, from traditional likelihood ratio methods and feature-driven models to deep learning frameworks (as shown in Table 5).
## 5 Simulation and Results Analysis
However, in the scenario where 300 sensors are deployed, the average number of active nodes of EDCA-H decreases sharply to 58, which is much less than the 17.5% of LEACH-C and 45.2% of SEP. Obviously, the greater the distance of the nodes' sensing, the superiority of EDCA-H algorithm is more obvious. Figure 6: The number of initial sensors _vs._ the number of active sensors. Figure 7: The sensing range _vs._ number of active sensors.
# Revolutionizing Palm Dates Harvesting with Multirotor Flying Vehicles
## 5 Conclusions  
A novel system has been developed to revolutionize the process of date palm harvesting by leveraging the advanced capabilities of multirotor flying vehicles (MRFVs). This  
Figure 14: Quadrotor speed in the lateral direction.  
Figure 15: Quadrotor speed in the vertical direction.  
innovative system offers a remote, aerial solution, eliminating traditional challenges such as the need for manual tree climbing or reliance on ground-based machinery. By incorporating cutting-edge technology, this system represents a significant leap forward in agricultural automation, enhancing both the safety and efficiency of harvesting operations.  
The system's design and engineering have been carefully executed, with extensive manufacturing and testing conducted under controlled laboratory conditions. The results from these tests demonstrate the system's ability to perform precision cuts on date branches with minimal disruptions. The quadrotor maintains a stable attitude and altitude control, showing only minor perturbations in its angles and velocity. These small movements do not interfere with the cutting process, as the robotic arm securely grips and stabilizes the branches during cutting operations. This stability highlights the potential of MRFVs to perform intricate tasks, even in complex environments like date palm plantations.  
In its current configuration, the system is primarily focused on the accurate and efficient cutting of date branches. However, this is only the beginning of what can be achieved. The next logical step involves expanding the system's capabilities to include the transport of harvested branches. Given that date branches can weigh upwards of 40 kg, this will require the development of a more powerful and robust heavy-load MRFV that can handle such payloads while maintaining the same precision and control demonstrated in the cutting process.
## 2 Electroluminescence Technique  
### EL Principles Applied to PV Panels  
The working principle of a PV solar cell is based on a photoreceiver diode which captures the photons doing a photoelectric conversion. According to this fact, the classic electric model of a PV panel is shown in Figure 1a [12; 13].  
Therefore, the current provided by the PV cell is defined by expression (1),  
\[I=I_{L}-I_{D}-I_{sh} \tag{1}\]  
where \(I_{L}\) is the current generated by the captured light, \(I_{sh}\) is the dissipated current by the shunt PV panel resistor and \(I_{D}\) is the lost current due to the recombination. In particular, \(I_{D}\) is defined by the following expression,  
\[I_{D}=I_{o}\left(e^{\frac{V+I_{R}}{nV_{T}}}-1\right) \tag{2}\]  
where \(I_{0}\) is the inverse saturation current of the diode, \(V_{T}\) is the thermal voltage, \(n\) is the diode ideality factor and \(R_{s}\) is the series resistance.  
Consequently, the characteristic I-V curve of a PV panel is described by the dashed curve in Figure 1b [14], known as the LIV curve. It has a behaviour determined by the electric characteristics of the diode, and the vertical position is determined by the irradiation value. Besides, the dashed curve at Figure 1b has been calculated for an irradiance value of 800 W/m\({}^{2}\). On the other hand, when the irradiation is equal to zero it reaches the solid curve, known as the DIV curve. Due to the physical characteristics of PV cells, they start to work as photo-emitters (4 th quadrant), instead of phosphorescent (1 st quadrant), when a current is supplied to them, in the same way as the light-emitting diode (LED) technology [14].  
However, the light emitted in the 4 th quadrant is relatively low, due to the fact that in the PV silicon panels this process is done through the carriers recombination in a forbidden indirect band of the semiconductor, by means of the recombination by defects or Auger [15]. Nevertheless, there is a small radiative recombination, which is enough to be detected by an external sensor. This phenomenon means that the EL technique could be useful as a non-destructive and fast evaluation method to get the health state of PV cells.  
Figure 1: Classic electric model of photovoltaic (PV) panel (**a**) and light I–V (LIV) and dark I–V (DIV) curves of a standard 36 PV cell module according to the work in [14] (**b**).
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 2 Micromegas Fabrication Techniques  
Micromegas detectors are built using different types of meshes depending on the fabrication technique and the application. We can distinguish three main categories: flat meshes made of thin (4-10 \(\upmu\)m) metallic sheets, where holes are produced by micromachining procedures (e.g., electroforming, chemical etching, and vaporisation,...) with a typical pitch of 500 Lines Per Inch (LPI).  
In the second category, the mesh is made of mechanically woven stainless-steel wires. The typical wire thickness is of 18 \(\upmu\)m, and a maximum mesh thickness of about 30 \(\upmu\)m is achieved by flattening the cross-points of the wires in the mesh. Figure 2 shows a microscopic image of a woven and an electroformed mesh. The third category concerns the cases where the mesh is formed at the same time with the spacers with some specific technique, as is the Microbulk [11] or the InGrid [12] technologies, described later in this section.  
In the traditional way, Micromegas is obtained by suspending a mesh over the anode strips or pads. Since 2004, several technologies have been developed to attach the mesh structure to the anode plane using higher precision methods and envisaging the transfer of the production to industry. A large step in the direction of the industrial manufacturing of large-size detectors was the development of the \"Bulk\" Micromegas technology [13].  
In the direction of high precision and a low material budget, the Microbulk technology was developed in collaboration between CEA and CERN [11]. To improve the robustness of the Micromegas technologies against sparks and the durability at high detection rates, different resistive readout technologies have been developed [14]. A short description of those technologies and their main advantages and drawbacks is presented in the following.
## 4 Evaluation
### Results and Discussion
#### 4.2.6 The Impact of the Number of Anchors on Consumer QoS  
To quantify the impact of the number of Anchors on our proposed strategy's performance, we particularly focus on two metrics impacted by the Anchor-based approach: the path stretches and Data packet retrieval delay. We tested these metrics across varying the number of Anchors from one to five using the K-Medoids-based Anchor placement algorithm. Figure 13 depicts the path stretch and the average Data packet retrieval delay of  
Figure 12: Handoff latency vs. producer speed.  
the DMMS as a function of the MP speed using a number of Anchors varying from one to five. As the main observations, we notice that the number of Anchors significantly affects both path stretch and Data packet retrieval delay. Increasing the number of Anchors from \(N\) = 1 to \(N\) = 4 resulted in a 10% reduction in path stretch and a 16% reduction in delay. This is because an increasing number of Anchors leads to smaller clusters formed in the network, which decreases the distance between the consumer, producer, and Anchor. Consequently, it mitigates path stretch, especially in the worst scenarios where the consumer and producer are located in the same cluster and the path between them is stretched to the Anchor. We also observed that adding more than four Anchors in our scenario did not result in any additional reduction in either path stretch or Data packet retrieval delay. This is expected because increasing the number of Anchors results in shorter distances between Anchors. However, the clusters formed will become increasingly small, leading to the creation of clusters with few routers with no additional impact.
# An Overview on Push-Based Communication Models for Information-Centric Networking
## 4 ICN Migration Towards IoT Scenarios
### Periodic Data Communication Support  
A wide range of control and monitoring IoT applications require consumers to periodically collect information from a large set of IoT devices. An example are heat monitoring systems which require frequent updates on the temperature in different buildings and rooms. Since the standard pull operation mode of ICN/NDN is not suitable, as discussed, IoT gateways can be leveraged to implement Long-lived Interests.  
To keep backward compatibility, IoT producers should keep on sending data as stipulated by their applications. As illustrated in Figure 4, it is expected that data consumers, such as IoT gateways (C), are able to adjust the frequency of Interest packets in order to gather all data produced by IoT devices (A, B), while saving network resources. This is achievable, for instance, by considering a Long-lived Interest approach. As mentioned before, IoT consumers may adjust the time period of Long-lived Interests based the analysis of the rate at which data is received.  
Long-lived Interests need to be backed up by adequate forwarding strategies and/or routing protocols. Most adaptive forwarding strategies [20] are not suitable for Long-lived Interests, since metrics such as the Interest satisfaction ratio cannot be used to assess the quality of selected paths. Furthermore, intermittent connectivity is expected to occur in IoT scenarios, given that such scenarios are connected via wireless and/or cellular infrastructures. Under such conditions, a routing protocol such as DABBER can assist transmission even in the verge of intermittent connectivity. DABBER was developed to extend the reach of ICN to opportunistic wireless networks, while: i) avoid flooding the network with Interest packets, which is a major requirement of opportunistic networks; ii) selective forwarding of Interest packets based on data reachability information, and context awareness (neighborhood and node itself). This unique combination makes it highly appealing for ICN environments [21].