esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Spatial Modeling of Forest Productivity Using APEX Data  
FP was approximated with forest GPP as suggested in [78]. GPP was obtained using a simple global calibration equation as introduced by [PERSON] et al. [79] that relates GPP to the Enhanced Vegetation Index (EVI). The EVI was calculated according to [PERSON] et al. [80] as:  
\[\text{EVI}=\text{G}\frac{\rho_{NIR}-\rho_{red}}{\rho_{NIR}+\text{ }\text{C}_{1}\times\rho_{red}\text{ }-\text{ }\text{C}_{2}\,\times\,\rho_{blue}\,+L} \tag{1}\]  
where \(\rho\) indicates the surface HCRF for the near infrared (NIR), red and blue band, and \(L\) (1) is a canopy background adjustment. \(G\) (2.5) is the gain factor and \(C_{1}\) (6) and \(C_{2}\) (7.5) are band-specific atmospheric resistance correction coefficients. All values and the coefficients have been optimized for the MODIS EVI product [80]. We therefore spectrally convolved APEX HCRF data to match the spectral resolution of respective MODIS bands and assumed validity of both gain factors (i.e., \(C_{1}\) and \(C_{2}\)) for the higher spatial resolution of our data as demonstrated in [81; 82; 83]. However, the differences in spatial resolution (i.e., 2 m of APEX vs. 500 m of MODIS) require further consideration. The high spatial resolution of APEX yields detailed measurements of forest ecosystems, including shaded and illuminated parts of tree crowns. These illumination patterns are primarily caused by geometric optical scattering and complicate the description of radiative transfer, particularly in shaded canopy areas, eventually impacting the accuracy of retrieved vegetation indices [84] and estimated GPP. Geometric-optical scattering scales with spatial resolution and its impact decreases with increasing ground-sampling distances. We consequently masked shaded pixels using an empirical threshold approach to be in better correspondence with the differently resolved MODIS and APEX measurements. FP, the carbon uptake by all trees of a specific forested area, was eventually obtained by averaging resulting GPP values of tree crowns for a 30 m grid cell and normalizing for tree fractional cover in the respective area. Fractional cover of the 30 m grid cell was estimated from the ALS based CHM (Figure 2).  
We are aware of the limitations in using EVI to estimate GPP, since it is representative for potential, rather than actual, photosynthetic activity only. However, the study area is highly complex in terms of topography and we lack reliable data to facilitate new approaches (i.e., SIF-based GPP estimates [49]). We consequently applied the greenness-based approach using the EVI to obtain first limited insights into GPP distribution in alpine environments.
## 3 Results  
### Path-Tracking Test Design  
To verify the effectiveness of the method in path-tracking quality improvement, a real vehicle test was conducted in Guan-Tang Greenhouse of Zhejiang Agricultural and Forestry University located in Zhejiang province, south east of P.R. China from April to May 2022. The UWB anchors were placed at four vertices of a 2 m wide and 10 m long rectangle for receiving and transmitting position signals. The frequency of data and signal sampling was 5 Hz, the vehicle speed \(v\) was set at 0.6 m/s, and the length of the straight line for the path-tracking test was 10 m, as shown in Figure 7.  
Considering the frequent occurrence of the right angle steering of agricultural machinery in actual greenhouse operation, three common deviation scenarios (1 m, \(-90^{\circ}\); 1 m, \(0^{\circ}\); and 0 m, \(90^{\circ}\)) were designed as the initial states of the prototype. The former data in brackets comprise the lateral deviation d, and the latter comprise the heading deviation \(\theta\). The pure tracking model with a fixed look-ahead distance was contrast-tested, and the fixed look-ahead distance used in the comparison test was set to 1.5 m.  
The average deviation, maximum deviation, stability distance, stability time, and steady-state deviation were used as the evaluation indicators of path-tracking quality. The average deviation refers to the average of the lateral deviation of all sampling points throughout the whole path tracking. Stability distance refers to the distance that the prototype vehicle travelled from the initial state to the stable state, i.e., the distance from converging to the forward direction when the lateral deviation was less than 0.1 m. The maximum deviation refers to the maximum lateral deviation of the actual path relative to the desired path. Stability time refers to the time elapsed from the initial state to the steady state when lateral deviation was less than 0.1 m. Steady state deviation refers to the mean of the lateral deviation of all data points after the prototype vehicle reached the steady state.
## 3 Results and Discussion
### Mechanical Properties-Uniaxial Compression  
Figure 7 shows the stress-strain curves for both 3D scaffolds (300; 400; 500 \(\upmu\)m). The mechanical properties were analyzed based on uniaxial compression tests. The 3D scaffolds showed a compressive strength of 3.9 \(\pm\) 0.1, 3.4 \(\pm\) 0.3, and 3.3 \(\pm\) 0.9 MPa; an elastics modulus of: 2.0 \(\pm\) 0.3, 1.5 \(\pm\) 0.2, 0.9 \(\pm\) 0.1 MPa; and a maximum strain before crush of 82.8 \(\pm\) 0.7, 84.9 \(\pm\) 3.9, and 94.5 \(\pm\) 3.7%. As shown in the following curves, [PERSON]'s modulus decreases with increasing mesh size. Thus, the higher the pore value, the less resistance to damage for the interior walls of the pieces in the yield. This becomes evident in the variability of the replicas corresponding to 500 \(\upmu\)m. On the contrary, stiffness is higher, and repeatability of the replicas is very high in the other two sizes. This characteristic reveals  
Figure 6: SEM analyses of 300, 400 and 500 \(\upmu\)m. The figure contains images to measure the pore size, the molten layers deposited in the top view and the thickness of the 3D scaffolds in the cross-section view.  
that small pore sizes better support viscous stresses applied in the center, where buckling stresses are most critical. The lower Young's modulus of all the 3D scaffolds suggests that the sample has a similar behavior under load, a higher elastic character and withstands greater deformation. The stress-strain curve shows a first elastic region until reaching 25-30% deformation, when the material behaves like a foam material (with a certain viscoelasticity). Subsequently, the curve presents an elastic zone where the densification remains practically constant up to the yield point. From there, a hardening takes place due to the deformation up to the crush of the structure without rupture. The mechanical properties obtained by the 3D scaffolds are similar to structures of hydroxyapatite, silica, and gelatine that have maximum strain stress of 3.8 \(\pm\) 1.1 MPa or compositions of gelatine, carbox-ymethyl chitosan, and \(\beta\)-TCP with maximum values of 2.5 \(\pm\) 0.2 MPa [43].
# Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov-Arnold Networks
## 4 Results
### Dataset B--California Housing
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **MAE** & **RMSE** & **SMAPE (\%)** \\ \hline XGBoost & 30,625 & 42,477 & 15.81 \\ LGB & 30,792 & 42,709 & 15.93 \\ KAN & 31,961 & 44,191 & 16.45 \\ Random forest & 32,774 & 45,458 & 16.87 \\ GRU–LSTM & 33,174 & 46,012 & 16.98 \\ MLP & 35,723 & 49,548 & 17.14 \\ CatBoost & 36,345 & 50,411 & 18.67 \\ Linear regression & 40,452 & 56,107 & 20.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results in Dataset B—California Housing. training and testing. Both KAN and XGB models were fine-tuned and evaluated on the test set using the augmented dataset. The results, presented in Table 4 below, indicate that KAN outperformed XGB in MAE, RMSE, and SMAPE.
## 8 Challenges and Future Directions
### Future Directions
#### 8.2.2 Massive and Diverse Datasets  
In order to develop more rounded models, there is a need to create massive and well-annotated datasets, which would cover a wide range of activities and also involve a wide demography of people. A diverse dataset would ensure that models trained on them are less likely to exhibit bias. Ensuring proper representation of the data of people from diverse demography ensures that the resultant HAR systems are unbiased. A massive dataset ensures that the model can handle variability especially in HAR where individuals perform activities differently. Annotations of such massive datasets can be expensive; however, with LLMs and other non-supervised methods, the effort needed is greatly reduced.
## 2 Materials and Methods
### Lightweight Design of the Water Body Segmentation Network
#### 2.4.2 Decoder with CATM and LKF Module  
Due to the inherent limitations of shallow architectures, lightweight models often struggle to capture long-range semantic dependencies across regions, resulting in insufficient global context modeling. To address this issue, we introduce the CATM mechanism into the decoder part of the model to enhance the feature representation ability, as shown in Figure 7.  
Figure 7: Structure of the Convolutional Additive Token Mixer Module (CATM).  
This mechanism combines multi-head self-attention with spatial-channel operations. It generates queries (\(Q\)), keys (\(K\)), and values (\(V\)) through independent linear transformations, thus capturing the global dependency information in the feature map, as shown in Equation (8).  
\[Q=W_{q}x,K=W_{k}x,V=W_{v}x \tag{8}\]  
Different from the traditional self-attention module, CATM adopts additive similarity calculation in the Query and Key branches and replaces the complex softmax normalization with the Sigmoid activation function. This effectively preserves the original feature dimensions, avoids the loss of information in the two-dimensional score vector, and significantly reduces the computational complexity and the number of parameters at the same time. This design improves the parallel processing ability and deployment efficiency of the network. Specifically, CATM defines the similarity function as the sum of the context scores of \(Q\in\mathbb{R}^{N\times d}\) and \(K\in\mathbb{R}^{N\times d}\), as shown in Equation (9).  
\[Sim(Q,K)=\Phi(Q)+\Phi(K)\;s.t.\;\Phi(Q)=\mathcal{C}(\mathcal{S}(Q)) \tag{9}\]  
where \(\Phi(\cdot)\) represents the context mapping function, which is embodied as the Sigmoid-based channel attention \(C(\cdot)\in\mathbb{R}^{N\times d}\) and spatial attention \(S(\cdot)\in\mathbb{R}^{N\times d}\). Here, \(s.t.\) stands for \"subject to\", indicating that the integration process is constrained by both channel and spatial attention weights. Then, \(\Gamma(\cdot)\in\mathbb{R}^{N\times d}\) is used to integrate the context information.  
Finally, the optimized output features are obtained:  
\[O=\Gamma(\Phi(Q)+\Phi(K))\cdot V \tag{10}\]  
In the decoder stage, both the CATM and the LKF modules are integrated into the final upsampling layer of the U-shaped architecture. After upsampling and feature fusion, the CATM module employs an additive self-attention mechanism to enhance interactions between global contextual information and local spatial details, thereby improving segmentation accuracy. Simultaneously, the LKF module performs feature enhancement to suppress noise and stabilize spatial representations. This design significantly improves the decoder's capability to recover fine-grained structures and delineate precise boundaries while maintaining the overall lightweight nature of the network.
# Estimation of Synaptic Activity during Neuronal Oscillations
## 5 Conclusions  
Estimation of synaptic conductances from the time course of a neuron's membrane potential is a challenging inverse mathematical problem, with strong implications in the study of the brain's functional activity. Few methods are known that address the problem when the neuron is spiking. The aim of our research is building up a general method to estimate synaptic conductances based on the knowledge of the frequency versus synaptic conductance curve (\(f-g_{syn}\)). This strategy is complemented with the choice of a mathematical model for which the \(f-g_{syn}\) curve can be accurately computed and, simultaneously, can fit a variety of data sources. We have shown that the exponential integrate-and-fire model (EIF) fulfills these requirements and can be a good reference model for future applications.  
We have successfully applied the method to data generated from mathematical neuron models driven by different synaptic input profiles. We have tested different levels of knowledge of the \(f-g_{syn}\) curve, namely, directly obtained from the neuron model, using the \(f-g_{syn}\) curve corresponding to the base model and assuming no model behind. Of course, the latter gives the worst results, but still within high accuracy. The method has also proven to be robust under normal levels of measurement noise and alternation of spiking and subthreshold activity.  
These results allow us to be optimistic about the application of the proposed methodology to obtain trustable estimations of the time course of synaptic conductance from real membrane potential traces presenting heterogeneous activity (combining subthreshold activity with intervals of spiking). Therefore, the next challenge is to discern between the excitatory (\(g_{exc}\)) and inhibitory (\(g_{inh}\)) conductances, which is relevant to understand how brain activity results from the interaction of different types of neurotransmission. For this purpose, one needs additional (uncorrelated) measurable quantities to build up a one-to-one relationship between pairs of measurable elements and the two parameters \(g_{exc}\) and \(g_{inh}\). Following the ideas in [25], we think that the amplitude of the spikes is a potential candidate as it is easily measurable and typically highly uncorrelated with the frequency.  
All the authors have contributed in a similar way to this paper in methodology, software, validation, formal analysis, investigation, resources, data curation, writing original draft preparation, writing review and editing, visualization, and supervision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** R.P., A.E.T., and C.V. are supported by MCYT/FEDER grant number MTM2014-54275-P, and AEI/FEDER grant number MTM2017-83568-P. A.G. is supported by the Government of Catalonia under grant 2017-SGR-1049 and by the Spanish grants AEI/FEDER/UE PGC-2018-098676-B-100 and RTI2018-093860-B-C21.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 System Description
As a result, many types of broadcasting services, such as HDTV, HDTV/SDTV, and multi-channel SDTV are supported by ISDB-T. For this purpose, the entire bandwidth is evenly split into 13 segments, such that each segment can support different services using different MCS. The bandwidth of ISDB-T is classified into two types: wideband ISDB-T composed of 13 segments, and narrowband ISDB-T using one, or up to three segments. The broadband ISDB-T is used to simultaneously provide a range of services, such as HDTV and other high-speed multimedia services. Figure 1 shows the ISDB-T service examples and transmission signals. Some kinds of pilot and control symbols, such as SP, CP, AC, and TMCC, are specified to enable reliable synchronization between the transmitter and receiver, whose configuration varies according to the ISDB-T segment format. The CP is mainly utilized to perform time and frequency synchronization [20; 21], whereas the SP is provided only in coherent modulated segments for the purposes of channel estimation [22; 23]. The AC2 is present only in differential modulated segments. On the other hand, the TMCC signal is used to carry some sort of basic transmission information, like hierarchical configuration and OFDM-segment transmission parameters. The multiple uses of the TMCC signal can also be explored for receiver synchronization. Since the TMCC is the information-bearing signal, the existing pilot-aided IFO estimation schemes are not applied directly to ISDB-T when the TMCC information is used as a pilot symbol.
## 5 Simulation Results and Analysis
### Simulation Results of the Sphere-to-Plate Electrode  
The motion trajectories and release characteristics of particles in pure oil under a sphere plate electrode configuration were obtained, as depicted in Figure 18. From the figure, it can be observed that the motion of charged particles in the oil was influenced by the variation gradient of electric field strength between the two electrodes. The motion state of the charged particles underwent significant changes in this region with the highest field strength variation. At positions further away from the electrode, where the field strength was weaker, the acceleration effect on the charged particles was noticeably reduced. Overall, the charged particles in the oil underwent irregular accelerations under the influence of the non-uniform electric field. Moreover, it can be observed from the figure that, due to the unaffected motion of charged particles by external substances, the density of motion trajectories was higher. This provides a microscopic explanation for oil gap breakdown.  
Similarly, by incorporating nanoparticles into the sphere plate electrode simulation model, the obtained simulation results are shown in Figure 19. From the figure, it can be observed that the motion velocity of charged particles significantly decreased after being adsorbed by nanoparticles. Unlike the ball-covered electrode, the potential variation near the sphere plate electrode was larger, resulting in a more non-uniform electric field surrounding it. This led to a higher acceleration of particle motion compared to the ball-covered electrode. However, the simulation results indicate that, under the sphere plate  
Figure 17: Motion trajectories of charged particles after the adsorption of nanoparticles between a pair of spherical cap electrodes.  
electrode configuration, nanoparticles also played a certain obstructive role in the motion of charged particles, thereby effectively reducing their migration rate.
###### Abstract
[63], plant leaf area index and biomass are more sensitive to the red band at roughly 680 nm, while plant moisture status is more sensitive to the NIR near 950 nm. The correlation among the plant pathology has been employed by the remote sensing technique in contemplation to discover the discrete plant characteristics from their spectra reflectance. The application of hyperspectral remote sensing is comprehensively used for different weed detection analysis studies, for example, weed discrimination in maize [64], discrimination of grassweeds in winter cereal crops [65], in early detection of spotted knappeved (_Centaurea maculosa_) and babysbreath (_Gypsophila paniculate_) with hyperspectral sensor [66], herbicide-resistant weeds classification [67], identification of (_Rannuculus acris_ [giant buttercup] and _Cirsium arvense_ [Californian thistle]) by [PERSON] et al. [53] and spectral features extraction from hyperspectral images to differentiate weed rice and barnyard  
Figure 3: Spectral reflectance for healthy and stressed leaf in visible and NIR wavelength [59]. grass [68]. [PERSON] et al. [57] stated that hyperspectral imaging application has also been used to identify between crop types, for instance, the utilization of satellite-based hyperspectral sensors to distinguish mustard, potato, sugarcane, sorghum, and wheat in the range of 700-750 nm. The efficacy of hyperspectral sensors for plant species characterization has been documented in a number of different studies, which is included in Table 6 below.
## 2 System Model
### Case Study  
There are three identical control loops in the WCS, i.e. \(N=3\). Each of the processes under control is an independent DC motor [27] modelled in continuous-time form:  
\[G(s)=\frac{2029.826}{(s+26.29)(s+2.296)} \tag{1}\]  
The controllers use the PID (Proportional-Integral-Derivative) control law with a continuous-time form \(G_{PID}(s)=K_{p}+\frac{K_{L}}{s}+K_{D}s\). The controller parameters are: \(K_{P}=0.1701\), \(K_{I}=0.378\), and \(K_{D}=0\). Digital controllers are designed by discretizing continuous-time controllers. As sampling periods are changed, the controller parameters of digital controllers are updated accordingly.  
Figure 1: Architecture of a wireless control system.  
Due to node movement, the communication distance between the controller and the process (where the sensor and the actuator are attached) may change during runtime. According to the properties of wireless signal transmission, the received signal strengths drop with increasing communication distances. When the signal-to-noise ratio of the received signals is below a certain level, IEEE 802.11b will make the trade-off between speed and communication reliability by reducing the transmission rate, for example, from the usual maximum value of 11 to 5.5, 2, or even 1 Mb/s [10]. This inherent feature of 802.11b gives rise to variability of channel capacity, a crucial issue that should be taken into account when designing control systems closed over WLAN.  
Apart from the changes in channel capacity, another problem that needs to be addressed is the effect of noise interference on QoC. In the subsequent sections, a general solution for these problems will be proposed and validated, while using this case as an illustrative example.
# On Multi-Hop Decode-and-Forward Cooperative Relaying for Industrial Wireless Sensor Networks
###### Abstract  
Wireless sensor networks (WSNs) will play a fundamental role in the realization of Internet of Things and Industry 4.0. Arising from the presence of spatially distributed sensor nodes in a sensor network, cooperative diversity can be achieved by using the sensor nodes between a given source-destination pair as intermediate relay stations. In this paper, we investigate the end-to-end average bit error rate (BER) and the channel capacity of a multi-hop relay network in the presence of impulsive noise modeled by the well-known [PERSON]'s class-A model. Specifically, we consider a multi-hop decode-and-forward (DF) relay network over Nakagami-\(m\) fading channel due to its generality, but also due to the absence of reported works in this area. Closed-form analytical expressions for the end-to-end average BER and the statistical properties of the end-to-end channel capacity are obtained. The impacts of the channel parameters on these performance quantities are evaluated and discussed.  
wireless sensor network; cooperative relay; decode-and-forward; fading channel; harsh environment; impulsive noise; system performance; channel capacity; bit error rate +
Footnote †: journal: Sensors  
_Article_  
On Multi-Hop Decode-and-Forward Cooperative Relaying for Industrial Wireless Sensor Networks