esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Short Description of the Donor Site and Selected Halophyte Species  
The halophyte species used in the present study were collected in Ria de Aveiro coastal lagoon salt marshes (40 \({}^{\circ}\)38\({}^{\prime}\) N 08 \({}^{\circ}\)44\({}^{\prime}\) W). This coastal system is approximately 45 km long and 10 km wide with one single connection with the Atlantic Ocean. The lagoon geomorphology is characterized by four main channels, each of them with small channels and islands, large areas of intertidal sand and mudflats, seagrasses meadows, and one of the largest continuous saltmarshes in Europe [29]. The region is characterized by a temperate maritime climate with an average temperature of 14 \({}^{\circ}\)C and precipitation of 1000 mm [30]. Figure 1 features the three selected species, which can be briefly described, according to Flora Iberica ([PERSON], 1986), as follows:  
_Halimione portulacoides_--shrubby perennial up to 150 mm tall, with woody stems at the base and succulent leaves at the top. Stems can grow prostrate, ascending or erect. Leaves (spatulate or lancoleate to linear-lancoleate, exceptionally deltoid) are opposite in the lower part and alternate in the upper part of the plants, with a light petiole. The infloresances consist of inconspicuous flowers.  
_Salicornia ramsissima_--annually erect, rarely decumbent subshrub up to 400 mm tall. Stems are generally quite branched, terminating in spike-like apparently jointed infloresances, with two opposite three-flowered crimes partly hidden in the internode tissue. Each cyme holds one large central flower and two smaller lateral flowers. The central flower has its base generally covered by the scarious margin of the lower segment.  
_Sarocornia perennis_--perennial subshrub up to 700 mm tall, with woody steams at the base, prostrate to proumbent, and above with fleshy-articulated, erect-ascending, simple or sparingly branched stems. Leaves are reduced to a sharp scale with a hyaline border. It has a spiciform inflorescence, lateral or terminal, formed by opposing triflora crests at the base of each fertile core-and decussate. The central flower is slightly larger than the lateral ones.  
Figure 1: Halophytes _cultured in_ aquaponics tanks supplied by the effluent from a super intensive fish farm: (**A**) _Halimione portulacoides_; (**B**) _Salicornia ramsissima_ and (**C**) _Sarocornia perennis_.
# Elastic Modulus Prediction Model of Foamed Concrete Based on the Walsh Formula
## 5 Parameter Sensitivity Analysis
### Elastic Modulus of Foamed Concrete Matrix  
The elastic modulus of foamed concrete with the elastic modulus of matrix material in the range 1450-3450 MPa was calculated; Figure 16 shows the relationship between the  
Figure 14: Variation in elastic modulus of foamed concrete with porosity.  
Figure 15: Variation in elastic modulus of foamed concrete with Poisson’s ratio of matrix.  
elastic modulus of foamed concrete and the elastic modulus of the matrix. When the elastic modulus of the matrix increases from 1450 to 3450 MPa, the elastic modulus of foamed concrete with porosity 0.59, 0.62, 0.65, 0.68, and 0.71 increases by 118%, 116%, 113%, 111%, and 108%, respectively. This shows that there is a positive correlation between the elastic modulus of foamed concrete and that of the matrix material.  
In addition, comparison of the curve slopes under different porosity calculation conditions shows that the smaller the porosity, the higher the curve slope. That is, the elastic modulus of foamed concrete has a high correlation with the elastic properties of the matrix in the case of low porosity. The performance of foamed concrete in the case of high porosity is not only related to the matrix but is also restricted by the pore state.  
Quantitative analysis based on theory shows that for foamed concrete, a two-phase medium material composed of matrix material and bubble pores, its elastic properties are controlled by two main factors: material porosity and matrix material properties. Adjusting the level of these two factors can effectively change the elastic properties of foamed concrete.
## 4 Results and Discussion
### Driving Stability Analysis on Multi-Factor
#### 4.2.2 Correlation Analysis of Stability Indicators  
Figure 13 presents the coefficient matrix of Pearson's Correlation between indicators LA, SA, LTR, and RA. It demonstrated that the LA and SA had a strong correlation to evaluate the sideslip stability of the vehicle, with a [PERSON]'s coefficient of 0.8573. The LA and LTR, as well as the SA and LTR, both had acceptable correlations with each other, and their Pearson's coefficients were greater than 0.6. Nevertheless, LTR was generally used to evaluate the roll-over stability of the vehicle, which is not incompatible with LA and SA, as given in previous studies. Likewise, the LTR and RA were normally used to characterize the roll-over stability of the vehicle. However, the Pearson's correlation between the LTR and RA was only 0.3427, which implied the single indicator (LTR or RA) was unable to efficiently evaluate the roll-over stability of the vehicle. So, a comprehensive indicator CRG was proposed with the grey relational method to enhance this defect.  
Figure 12: Driving stability indicator based on orthogonal simulation results: (**a**) LA; (**b**) SA; (**c**) LTR; (**d**) RA.
# HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN
## 3 Method
### Mask
Q score compares the quality of a test image to that of a reference image based on the human perception. The high score indicates that the test image is close enough \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **L1** & **HDR-VDP-2-2** & **PU21-SSIM** & **PU21-PSNR** \\ \hline No mask & 0.0697 & 50.615 & 0.7073 & 9.8803 \\ Identical mask & **0.0631** & 52.3293 & 0.7493 & 15.4056 \\ Inverted mask & 0.0672 & **52.5142** & **0.7761** & **16.6479** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the network with no mask, the network with the identical mask, and the network with the inverted mask. The values in bold indicate the best scores for each metric. Figure 8: **(left)** Value mask, \(M_{u}\). **(right)** Saturation mask, \(M_{d}\). Figure 7: Saturated region: the upper highlighted area and the lower highlighted area are considered as the oversaturated region and the undersaturated region, respectively. to the reference image. PU21 is a metric for HDR images using perceptually uniform transform while overcoming the nonlinearity of PSNR and SSIM.
## 3 Results and Discussion
### Automated Column Sorption System
#### 3.2.4 Heavy Metallic Ion Concentration Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**HMI**} \\ \cline{2-11}  & \multicolumn{2}{c}{**Cu\({}^{2+}\)**} & \multicolumn{2}{c}{**Fe\({}^{3+}\)**} & \multicolumn{2}{c}{**Ni\({}^{2+}\)**} & \multicolumn{2}{c}{**Pb\({}^{2+}\)**} & \multicolumn{2}{c}{**Zn\({}^{2+}\)**} \\ \cline{2-11}  & **meq/L** & **\%R** & **meq/L** & **\%R** & **meq/L** & **\%R** & **meq/L** & **\%R** & **meq/L** & **\%R** \\ \hline \multirow{10}{*}{**Cl**} & C1-A & 0.000 & 100.00 & 0.000 & 99.98 & 0.007 & 99.67 \({}^{\rm{age}}\) & 0.0003 & 99.98 & 0.002 & 99.92 \({}^{\rm{age}}\) \\  & C1-B & 0.129 & 93.55 \({}^{\rm{age}}\) & 0.000 & 99.99 & 0.911 & 54.47 & 0.0006 & 99.97 & 0.541 & 72.96 \\  & C1-1 & 0.517 & 74.17 & 0.000 & 99.99 & 1.483 & 25.85 & 0.0004 & 99.98 & 0.974 & 51.30 \\  & C1-2 & 0.938 & 53.09 & 0.001 & 99.93 & 1.906 & 4.71 & 0.0006 & 99.97 & 1.320 & 34.02 \\  & C1-3 & 1.221 & 38.93 & 0.011 & 99.46 & 2.116 & 0.00 & 0.0003 & 99.98 & 1.507 & 24.66 \\  & C1-4 & 1.369 & 31.54 & 0.030 & 98.49 & 2.231 & 0.00 & 0.0006 & 99.97 & 1.622 & 18.91 \\  & C1-5 & 1.431 & 28.47 & 0.052 & 97.42 & 2.269 & 0.00 & 0.0004 & 99.98 & 1.671 & 16.43 \\  & C1-6 & 1.468 & 26.60 & 0.072 & 96.40 & 2.273 & 0.00 & 0.0005 & 99.97 & 1.703 & 14.83 \\  & C1-7 & 1.584 & 20.78 & 0.102 & 94.90 \({}^{\rm{age}}\) & 2.316 & 0.00 & 0.0005 & 99.98 & 1.816 & 9.20 \\  & C1-8 & 1.563 & 21.86 & 0.118 & 94.08 & 2.199 & 0.00 & 0.0005 & 99.98 & 1.751 & 12.43 \\  & C1-9 & 1.543 & 22.84 & 0.138 & 93.11 & 2.174 & 0.00 & 0.0004 & 99.98 & 1.730 & 13.51 \\  & C1-10 & 1.571 & 21.44 & 0.167 & 91.64 & 2.134 & 0.00 & 0.0004 & 99.98 & 1.739 & 13.04 \\  & C1-11 & 1.598 & 20.12 & 0.209 & 89.55 & 2.123 & 0.00 & 0.0004 & 99.98 & 1.752 & 12.38 \\  & C1-12 & 1.604 & 19.79 & 0.268 & 86.59 & 2.096 & 0.00 & 0.001 & 100.00 & 1.750 & 12.49 \\  & C1-13 & 1.638 & 18.09 & 0.349 & 82.54 & 2.130 & 0.00 & 0.0002 & 99.99 & 1.786 & 10.71 \\ \hline \multirow{10}{*}{**C2**} & C2-B & 0.00 & 100.00 & 0.0004 & 99.98 & 0.002 & 99.90 & 0.0003 & 99.99 & 0.002 & 99.88 \\  & C2-1 & 0.00 & 100.00 & 0.0003 & 99.98 & 0.002 & 99.88 & 0.0003 & 99.99 & 0.001 & 99.94 \\  & C2-2 & 0.00 & 100.
# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
## 4 Experiment
### Parameter Sensitivity Study
#### 4.5.4 Influence of Convolutional Layers  
We test the effect of a different number of layers given to the network. Observing Table 7, we can find that the number of convolutional layers increases, and the segmentation metrics mIoU and accuracy grow gradually. However, when the number of convolutional layers reaches four layers, the two metrics do not increase but rather decrease. Through analysis, we find that due to increasing the number of convolutional layers, it causes overfitting of the model, which affects the segmentation results of the model.
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 4 Agricultural Applications  
Computer vision and deep learning are applied to various stages of agricultural processes. This chapter summarizes their applications in the growth management stage. For this stage, we review four main aspects: crop identification and detection, rating and classification, disease monitoring, and weed identification.
# A Low-Cost IoT-Based System to Monitor the Location of a Whole Herd
## 2 Materials and Methods  
### System Components  
For gathering, transmitting, and processing the location data of all animals in a herd, a cost-effective solution has been developed. The system is based on the integration of two devices: GPS collars connected to a LPWA network (Sigfox, Labge, France) and low-cost Bluetooth low energy (BLE) tags connected to those collars. The system requires some animals in the herd being fitted with GPS collars and the rest with BLE tags. The collar/tag ratio defines the cost per animal of the solution for tracking the location of the entire herd. Every GPS collar gathers data about the location of the animal fitted with such a device. At the same time, collars record the identification of the BLE tags situated around them, enabling the user to know the approximate location of animals equipped with BLE tags. Data about the collar location and identification of nearby tags are transmitted to a server using Sigfox, where they are stored and analyzed.  
The GPS collar has the following hardware elements: a long-range IoT communications module based on Sigfox technology that allows the transmission of messages between the collar and the server; a short-range communications module based on BLE technology that enables the reading of the advertisement messages coming from the BLE tags (this module can record the identification of the BLE tag and the received power); a GPS unit to gather location data; a microcontroller with the low-level firmware running on it; a lithium battery pack; and an IP67 enclosure.  
BLE tags consist of a Bluetooth 4.2 communications module, a microcontroller, a coin battery and an IP67 enclosure. The module is configured to send advertisement messages using channels 37, 38, and 39. The message sent by each BLE tag consists of an advertisement that includes the device identification and a code referring to the device owner. Thus, two BLE tags could have the same identification code if their owners were different.  
The transmission of the information gathered by the solution to the cloud platform uses a MQTT connectivity protocol. The cloud platform is based on FIWARE components. Metadata are managed with Orion Context Broker through NGI data models in order to ensure data interoperability. Farmers get location data through a multiplatform app and may configure some alerts, e.g., \"animal outside the plot\".  
Figure 1 summarizes the main elements of the system architecture.
# Research on Wi-Fi Fingerprint Database Construction Method Based on Environmental Feature Awareness
## 2 Background and Related Works
In practice, Wi-Fi positioning systems can be utilized to track the activities of seniors and ensure their safety in daily life. For instance, such systems can monitor the real-time location of seniors with limited mobility, preventing them from entering hazardous areas (e.g., stairways or remote sections). Additionally, in nursing homes, a positioning system can help family members keep track of the seniors' whereabouts in real-time, fostering a sense of security and trust. Given that nursing homes tend to have similar layouts, with uniform room and corridor arrangements, Wi-Fi signals can be affected by walls and obstacles. This paper's findings demonstrate that seniors can better understand their location using Wi-Fi-based systems. Furthermore, by integrating environmental factors such as room geometry and wall thickness, the construction of Wi-Fi fingerprint databases can be made more efficient and accurate, reducing the time and cost of data collection. This ensures the system provides reliable localization services in multi-room, obstacle-rich environments like nursing homes.
## 4 Joint Decoding-Complexity and Rate Control
### Determining the Model Parameters and Trade-Off Factors
#### 4.2.2 Determining \(\lambda_{r}\) and \(\lambda_{c}\)  
Next, from (6), for the \(k\)th CTU in the \(j\)th frame  
\[\lambda_{r}(k,j,q)-\frac{\partial\mathrm{MSE}}{\partial\mathrm{ bpp}} = -a_{k,j}(q)\,b_{k,j}(q)\,\mathrm{bpp}^{b_{k,j}(q)-1} \tag{16}\] \[= \alpha_{k,j}(q)\times\mathrm{bpp}^{\tilde{\rho}_{k,j}(q)}\]  
and  
\[\lambda_{c}(k,j,q)-\frac{\partial\mathrm{MSE}}{\partial\mathrm{ cpp}} = -c_{k,j}(q)\,d_{k,j}(q)\,\mathrm{cpp}^{d_{k,j}(q)-1} \tag{17}\] \[= \rho_{k,j}(q)\times\mathrm{cpp}^{\tilde{\tau}_{k,j}(q)}.\]  
Equations (16) and (17) imply that the CTU-level model parameters, together with the CTU-level bit rate and decoding-complexity allocations described in the previous subsection, completely define the optimization function in (2) needed to determine the optimum coding structure.  
Thus, from (14)-(17) for the \(k\)th CTU in the \(j\)th frame the bit rate and decoding-complexity trade-off parameters can be expressed as  
\[\lambda_{r}(k,j,q_{0})=\alpha_{k,j}(q_{0})\times\left\{\frac{R_{\rm T}^{\rm CTU}( k,j)}{N}\right\}^{\beta_{k,j}(q_{0})} \tag{18}\]  
and  
\[\lambda_{c}(k,j,q_{0})=\rho_{k,j}(q_{0})\times\left\{\frac{C_{\rm T}^{\rm CTU}( k,j)}{N}\right\}^{\tau_{k,j}(q_{0})}, \tag{19}\]  
respectively, where \(N\) is the number of pixels in the CTU.  
It now becomes apparent that the two trade-off parameters are both content and QP-dependent via the four modeling parameters in (6), (16), and (17). However, a content-independent generic set of parameters can also be obtained (to be used as initial values in the adaptive model parameter computation process described in the following subsection (Section 4.3) from the data collected in Section 3 and [24]. In this case, MSE, bpp, and cpp values from 50 inter-coded and intra-coded frames of six different test sequences (three HD, and three CIF) [24] have been considered to derive these generic model parameters, which can be expressed as  
\[\begin{bmatrix}\alpha_{0}(q)\\ \beta_{0}(q)\\ \rho_{0}(q)\\ \tau_{0}(q)\end{bmatrix}_{\rm Intra}=\begin{bmatrix}6.8\times 10^{10}\times q ^{-8.745}\\ 0.0671\times q-7.375\\ 2.28\times 10^{-6}\times q^{7.188}\\ -4.24\times 10^{-6}\times q^{3.51}-1.275\end{bmatrix} \tag{20}\]  
and  
\[\begin{bmatrix}\alpha_{0}(q)\\ \beta_{0}(q)\\ \rho_{0}(q)\\ \tau_{0}(q)\end{bmatrix}_{\rm Inter}=\begin{bmatrix}0.000721\times q^{2.516} \\ 3.89\times 10^{-5}\times q^{2.48}-1.707\\ -39.38\times q^{4.473}+1.76\times 10^{9}\\ -0.02157\times q-3.684\end{bmatrix}, \tag{21}\]  
for the two frame types. Naturally, the bit rate and decoding-complexity achieved using (20) and (21) will be inaccurate and not adaptive to the content. Hence, a mechanism to dynamically update the model parameters is necessary for joint decoding-complexity and rate-controlled encoding.
# Broken versus Non-Broken Time Reversal Symmetry: Irreversibility and Response
## 4 The Dissipation Function
Picking \(r=0\) together with the identity \(\left\langle\mathcal{O}\right\rangle_{t+s}=\left\langle\mathcal{O}\circ S^{s }\right\rangle_{t}\) (see Appendix) gives:  
\[\frac{d}{ds}\left\langle\mathcal{O}\right\rangle_{s}=\left\langle\mathcal{O} \cdot(\Omega^{f_{0}}\circ S^{-s})\right\rangle_{s}=\left\langle(\mathcal{O} \circ S^{s})\cdot\Omega^{f_{0}}\right\rangle_{0}=\int\mathcal{O}(S^{s}\Gamma )\Omega^{f_{0}}(\Gamma)f_{0}(\Gamma)d\Gamma, \tag{23}\]  
which we can use to describe the evolution of the ensemble average \(\left\langle\mathcal{O}\right\rangle_{t}\) in terms of averages with respect to the _initial distribution_\(f_{0}\):  
\[\mu_{t}(E)=\mu_{0}(S^{-t}E)\quad\text{or, equivalently, by}\quad\int_{E}f_{t}( \Gamma)\mathrm{d}\Gamma=\int_{S^{-t}E}f_{0}(\Gamma)\mathrm{d}\Gamma \tag{15}\]0  
Note that by choosing \(r=s\) in (22), we find:  
\[\mu_{t}(E)=\mu_{0}(S^{-t}E)\quad\text{or, equivalently, by}\quad\int_{E}f_{t}( \Gamma)\mathrm{d}\Gamma=\int_{S^{-t}E}f_{0}(\Gamma)\mathrm{d}\Gamma \tag{15}\]1  
which leads to an interesting (symmetry breaking) result for \(\mathcal{O}=\Omega^{f_{t}}\):  
\[\mu_{t}(E)=\mu_{0}(S^{-t}E)\quad\text{or, equivalently, by}\quad\int_{E}f_{t}( \Gamma)\mathrm{d}\Gamma=\int_{S^{-t}E}f_{0}(\Gamma)\mathrm{d}\Gamma \tag{15}\]2  
where equality holds only if \(f_{t}\) is an equilibrium distribution. We can compare Expression (24) with the Transient-Time Correlation Function (TTCF) formalism [27]:  
\[\mu_{t}(E)=\mu_{0}(S^{-t}E)\quad\text{or, equivalently, by}\quad\int_{E}f_{t}( \Gamma)\mathrm{d}\Gamma=\int_{S^{-t}E}f_{0}(\Gamma)\mathrm{d}\Gamma \tag{15}\]3  
for a thermodynamic system with temperature T and volume V which is driven away from equilibrium by an external field \(\mathbf{F}_{\text{ext}}\) turned on at \(t=0\). From this comparison, it is easy to see that if we choose \(f_{0}\) in (24) to be the distribution for an initial equilibrium state, \(\Omega^{\prime 0}\) plays the role of the energy dissipation (product between the dissipation flux and the external force), hence the name dissipation function. The TTCF expression reduces to the Green-Kubo result in the weak-field limit. Equations (18) and (24) together constitute the so-called _Dissipation Theorem_, which leads to a general formalism that is worth further investigation in statistical physics and in dynamical systems theory. In the next section, we consider the case of time-dependent perturbations, which to the best of our knowledge has not been considered so far.
## 4 Discussion and Conclusions
In the second case study, the higher misclassification was related to the classification of a non-draw, instead of being classified as a draw. The probable reason of this misclassification could be the small dataset, which in turn suggests insufficient quantity of the training data. Based on this assumption, as a limitation, the lack of data can be mentioned, considering also the fact that they are presented on a daily rate and there is no exact information at the same time when certain football matches were played. In addition, the location factor should be noted, as the weather stations are far from the stadiums and they do not provide accurate data about the stadiums. In general, it can be concluded that integrating weather data with other features can be useful in predicting the result of a soccer match. Future work could be other types of features involvement in the analysis (for example, altitude), also the used features with more often rate can be valuable for the final output. Conceptualisation, D.I., F.R., and D.A.P. ; Funding acquisition, S.T. ; Methodology, D.I., F.R., and D.A.P. ; Supervision, S.T. and F.R. ; Writing--original draft, D.I. ; Writing--review and editing, S.T. and F.R. All authors have read and agreed to the published version of the manuscript. [PERSON] has been funded by the predoctoral programme PINV2018 - Universitat Jaume I (PREDOC/2018/61). [PERSON] has been funded by the [PERSON] - Incorporacion postdoctoral programme of the Ministry of Science and Innovation - Spanish government (IJC2018-035017-I). This work has been funded by the Generalitat Valenciana through the Subvenciones para la realizacion de proyectos de l+D+i desarrollados por grupos de investigacion emergentes program (GV/2020/035). The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The following abbreviations are used in this manuscript:  
AEMETAgencia Estatal de Meteorologia  
AFLAustralian Football League  
AUC-ROCArea under the Curve of Receiver Operating Characteristics
## 7 Results
### Scenario 1: Constant Irradiance and Temperature  
As shown in Table 14, the ANN achieved the best performance, presenting the lowest MAE and IAE values, combined with the shortest convergence time (0.1763 s). Although DQN recorded a slightly lower MSE, it required more time to converge. Conventional algorithms such as P&O and InC demonstrated higher error metrics and lower average efficiencies, confirming their limitations under stable conditions.  
To better visualize the dynamic behavior of the AI-based algorithms, Figure 15 shows the convergence performance of the ANN and DQN during the first 0.5 s of operation. The ANN agent demonstrates faster stabilization and lower initial error compared to the DQN agent.  
Additionally, Figure 16 presents the error evolution of all the tested MPPT algorithms up to 0.5 s. It is evident that conventional approaches such as P&O and InC exhibit larger and more persistent oscillations. In contrast, intelligent controllers, particularly ANN and DQN, provide smoother convergence with reduced transient errors.