esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling of the Power Stage
### Circuital Interface  
The electrical scheme of the proposed hybrid storage system, formed by a battery and a supercapacitor, is illustrated in Figure 2. This power stage was designed using the Sepic topology, which was selected to enable the energy transmission for any voltage relation between the battery and the supercapacitor. In the electrical scheme, the supercapacitor is modeled with the capacitance (\(C_{sc}\)) and the parasitic resistance (\(R_{sc}\)), while the battery is modeled by the series connection of the voltage source (\(v_{boc}\)) and the battery resistance (\(R_{b}\)). The load connected to the hybrid source is modeled by the current source (\(i_{dc}\)) and the voltage imposed to the load is named \(v_{dc}\). The elements of the Sepic converter are the inductors (\(L_{1}\) and \(L_{2}\)), the capacitor (\(C_{i}\)), and the MOSFETs (\(M_{1}\) and \(M_{2}\)). Moreover, the electrical scheme also considers the parasitic resistances of both the MOSFETs (\(R_{on}\)) and inductors (\(R_{L1}\), \(R_{L2}\)).  
The load modeled by a current source (\(i_{dc}\)) makes possible to take into account discharge (positive \(i_{dc}\)), charge (negative \(i_{dc}\)), and stand-by (null \(i_{dc}\)) conditions. In addition, the battery current (\(i_{b}\)) is the result of the load current (\(i_{dc}\)) and the Sepic current (\(i_{sz}\)).  
The main objective of this power interface is to control, in an indirect way, the battery current (\(i_{b}\)) to avoid high-frequency transients. This process requires the measurement of both the load current and the Sepic output current (\(i_{sz}\)), where the Sepic converter provides the high-frequency transients required by the load.  
Figure 1: Topology for semi-active HESS.
## 3 ICN Architecture
### MAP-Me enhancements (MM)  
An unintended consequence of the ICN architecture is that, when the producer is mobile, Interest PDUs may end up being forwarded toward sectors of the network where the producer is no longer attached. As only consumers can retransmit Interest PDUs, depending on how these are handled, significant delay is expected [25].  
To overcome this disadvantage, the authors in [26] create a protocol that handles producer mobility events by dynamically updating the FIB. When a producer changes point of attachment, it sends a modified Interest PDU, named MAP-Me-IU that acts as an update protocol. The MAP-Me-IU includes the ICN name prefixes that the producer is offering, along with a sequence number. This method ensures that the newest MAP-Me-IU is utilized to update the FIB. Since the MAP-Me-IU leverages an Interest PDU, when the PDU is received by a CR, it can be forwarded upstream in the same way as a normal Interest PDU. This permits the MAP-Me-IU to be forwarded via all interfaces from which Data PDUs related to the ICN name have been received. After forwarding the MAP-Me-IU, the interface on which the MAP-Me-IU was received is used to update the CR's FIB. The ingress interface for the ICN name is enabled if the sequence number on the MAP-Me-IU is newer than previously seen MAP-Me-IU at the CR. A significant drawback of this protocol is that it relies on the existence of a routing protocol responsible for creating and updating the FIBs of CR. When MAP-Me-IU PDUs are not being transmitted, the network relies on standard ICN methods to update the FIB.
## 2 Experiments
### Measurements
#### Simulation Procedures of All-Atom Molecular Dynamics (AAMD)  
The EDTA molecule as a center molecule formed four ester bonds with the 1,4-BDO, to investigate the rigidity of EDTA as a node, and then connected with three AA and one IA (named EDTA-BDO-BA/BI), for our all-atom simulation model. This model could be described as the chain movement to affect the geometric deviation of the cross-linking point, through torsional angle distribution analysis. The artificial model was built in a coplanar and tetrahedral geometrical structure, and then the torsional angle of N-C-C-N, within EDTA and N-C-C-O, between EDTA and 1,4-BDO, were analyzed to investigate the structural deviation [56]. All-atom molecular dynamics (AAMD) with a COMPASS force-field, was performed for a simulation model, to calculate the torsional angle distribution and to investigate the dynamic structure behavior. COMPASS force-field has shown robust performance in predicting structures and dynamics of organic molecules in AAMD simulations [57]. Thus, the probability distribution results obtained through the AAMD could illustrate the molecular chain behavior. The EDTA-BDO-BA/BI model was centered within the cubic box, with a side length of 100 A, which was large enough to ensure an isolated molecular chain state. All simulations were adopted in the canonical ensemble (NVT), with a 1 fs time-step. The thermostat chosen was the Nose-Hoover method, with a Q ratio of 0.01 [58; 59], and 15.5 A was selected as the cutoff. The simulation temperatures were chosen based on the \(T_{\rm m}\)--those below \(T_{\rm m}\) with 298 K, near \(T_{\rm m}\) with 333 K, and above \(T_{\rm m}\) with 373 K. First, the conjugate gradient geometry minimization method was used to relax the chain conformation and equilibrate the artificial structure. After the equilibration process, the dynamic simulations were performed in 1 ns, with a time-step of 1 fs at 298, 333, and 373 K, respectively. Finally, the molecular dynamics (MD) simulation was taken, and then the trajectory was analyzed through the averaged distribution functions of torsional angles in the N-C-C-N in EDTA and the N-C-C-O, between EDTA and 1,4-BDO, under various temperatures.
# Phubber's Emotional Activations: The Association between PANAS and Phubbing Behavior
## 5 Discussion
Overall, our work provided preliminary evidence concerning phubbers' emotional activation, which was completely absent in the literature up to now. Indeed, a few studies already assessed the emotional repercussions of being phubbed [35; 40; 41], but no work prior to ours addressed the emotional activation of the individual that engages in phubbing. According to the theory of attachment [68; 69], emotions serve as adaptive mechanisms for human survival and sociality. Positive affects bring people closer, while negative affects impair relationships [70]. We already knew from the literature that the phubbee usually experiences negative emotions during phubbing [35], but we now know that even the phubber has a negative activation. Clearly, our results are correlational and no causation can be inferred. Therefore, we do not know if the phubbers have a negative emotional activation because they do phubbing or, on the contrary, if they do phubbing because they experience those emotions, or if both possibilities are simultaneously true. Moreover, our exploratory results are based on a biased sample owing to a non-random sampling technique and self-selection bias. Another limitation associated with our study is that the closeness of phubbers and people being phubbed was not controlled for, which it should be in future works. Finally, our measure of emotional activations relied on trait rather than state emotional reactivity and self-reported answers that could be prone to overestimation or underestimation of people's moods. Despite these limitations, the need to reduce phubbing occurrence seems evident, for example, through technological solutions capable of making it less frequent (e.g., batching smartphone notifications) [71; 72] and interventions aimed at raising awareness about what happens at a relational level during phubbing [36; 39; 73].
# An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers
## 4 Experiments and Results  
In this part, several independent experiments will be carried out to test the performance of the CLAMBDA-search method with real-world data. First, we introduce a static experiment and its results. Then, we will show a dynamic one. The antenna coordinates in the body frame are obtained from the GNSS static results in the paper.
## 3 Materials and Methods
### Feature Selection Algorithm  
The aim of feature selection is to reduce the number of predictors while training an ML model to speed up computations and enhance prediction accuracy. The filter method, wrapper method, and embedding method are the three primary divisions of the feature selection procedure. The nature-based feature selection methods used in this study follow the wrapper feature selection method. Therefore, before going into the details of these feature selection methods, a brief summary of the wrapper feature selection method is presented below.
# Log Volume Measurement and Counting Based on Improved Cascade Mask R-CNN and Deep SORT
## 5 Discussion
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Models** & _AP_ & _AP\({}_{s}\)_ & _AP\({}_{m}\)_ & _AP\({}_{l}\)_ & _IOU\({}_{mask}\)_ \\ \hline SOLOV2 [44] & 74.9 & 66.5 & 53.2 & 82.3 & 0.829 \\ CondInst [43] & 78.9 & 70.3 & 60.0 & 84.9 & 0.851 \\ BoxInst [45] & 73.2 & 64.2 & 57.8 & 78.3 & 0.803 \\ Yolov8-seg [11] & 59.8 & 47.1 & 45.8 & 64.4 & 0.716 \\ WM R-CNN [28] & 79.1 & 72.3 & 61.2 & 85.0 & 0.878 \\ Ours & **82.3** & **75.3** & **70.9** & **86.2** & **0.958** \\ \hline \hline \end{tabular} Bold values represent the highest performance for each metric.  
\end{table} 
Table 6: Comparison with other segmentation models. (WM) R-CNN [28], achieving an error rate of 1.6833% in final log volume measurements. Despite these notable achievements, the lack of standardized benchmark datasets presents a challenge for directly comparing our approach with previous methods, even when using the same evaluation metrics. In contrast to our prior work, we focus on localizing log detection in more complex scenarios. The three cascade heads of the Cascade Mask R-CNN, combined with a backbone network utilizing an attention mechanism, enable us to achieve improved detection results, even in intricate log environments. Additionally, we have integrated the Deep SORT object tracking model to address the task of counting large-scale and complex log stacks. In future work, we intend to refine the detection of log diameters in our methodology, aiming to achieve even smaller error margins. The proposed improved Cascade Mask R-CNN operates at approximately 3 FPS. In our test images, each of which contains an average of 110 logs, the model achieved a detection accuracy of 98.6%. As an instance segmentation model, Cascade Mask R-CNN can accurately output the contours of the logs, which is critical for subsequent log end size measurements. We also compared our model with one-stage object detection models such as SOLOv2 and YOLOv8-seg. While these models offer faster detection speeds and can achieve real-time processing, the comparison results in Table 6 demonstrate that their detection accuracy is significantly lower than that of our proposed model. Given that log measurement is still primarily performed manually and that this task requires high precision, improving detection accuracy remains the most critical priority. In future work, we will continue to optimize the model by techniques such as model pruning to further improve processing speed while ensuring that detection accuracy remains high.
# Innovative Vaporization of Wood Panel Waste into Activated Biochar for Efficient Phenol Adsorption
## 3 Results and Discussion
### Characterization of Reconstituted Wood Panels, Biochar, and Activated Biochar
Figure 2 shows that carbonization increases with increasing temperature while volatile elements are eliminated. The carbon content increases from 69.2% for BWP to 81.2% for BWP950, reflecting increased condensation of carbon structures and aromatization of the graphite-like network [53, 54]. Concurrently, the oxygen content decreases substantially from 23.4% to approximately 13.4% for BWP950, indicating loss of oxygenated functional groups under the influence of pyrolysis [55]. This transformation enhances the activated biochar's hydrophobicity and chemical stability, which are critical parameters for effective adsorption [56, 57]. Moreover, the hydrogen and nitrogen content are reduced due to the volatilization of non-refractory organic compounds and heteroatoms, which typically occurs during high-temperature activation of carbonaceous materials [58]. These chemical changes directly influence the adsorption performance of biochar by modifying its interactions with organic pollutants in aqueous systems. To further investigate the thermal behavior and volatile emissions from the reconstituted panels, thermogravimetric analysis coupled with gas chromatography-mass spectrometry (TGA-GC/MS) was performed [46]. The results revealed the release of various nitrogen-containing and potentially toxic gases such as ammonia (NH\({}_{3}\)), nitric oxide (NO), and trimethylamine (TMA). Notably, highly hazardous species like isocyanic acid (HNCO) and hydrogen cyanide (HCN), often reported in slow pyrolysis of wood-based composites,  
Figure 1: Textural properties of biochar and activated biochar. Error bars represent standard deviation (SD) calculated from three independent replicates. were not detected under the fast pyrolysis conditions applied in this study. This can be attributed to the short residence time and rapid heating rates associated with fast pyrolysis, which limit secondary reactions responsible for generating these toxic compounds. Thus, the two-step thermochemical approach (fast pyrolysis followed by activation) not only produced biochars with favorable surface properties for gas adsorption but also mitigated the formation of highly toxic nitrogenous volatiles, thereby enhancing the environmental benefits of using fast pyrolysis to valorize reconstituted wood waste.
## 5 Conclusions  
A lot of work has been carried out to save farms from pathogen attacks, although some attacks have been really devastating, as in the citric industry in Florida. Research is being done locally in each country or through grants with developed countries where sophisticated equipment is available for doing the tests. Results have saved million of hectares from its premature damage, and research will continue if we need to provide food for the world inhabitants in the future.  
Disease warning systems are being used all around the world for all the existing crops requiring only a net of meteorological stations. Conventional stations require the addition of the wetness sensor which has the ability to determine the humidity in the canopies and fruits, being the essential component of the risk system. Although there are a lot of sensor designs its trend is to find a non-contact sensor which can provide the information quickly and accurately. Programs and algorithms are not sophisticated and software properly designed for tomatoes is being also used in apple orchards (TOMCAST). This software has the advantage that it predicts the disease but it cannot be used in a general approach to detect all type of diseases on an orchard. These warning systems provide huge savings on fungicide application reducing fruit contamination for the consumers. Its maintenance is high, loosing accuracy with time so research is being carried out to find other risk systems.  
Remote sensing technology is advancing quickly and soon will be able to predict farm diseases if the temporal resolution can be reduced to a maximum of one day. Its spatial accuracy should be reduced to 10 cm increasing the quantity of data collected. A hyperspectral imaging, thermographic and chlorophyll fluorescence setup is the optimal system required to predict diseases from the sky, but environmental variables and canopy structures affect measurements and require special algorithms for proper operation.  
Disease detection of fruits on vegetable in sorters is in a more advanced stage. Defect products can be separated from healthy ones with high accuracies avoiding higher losses during transport and user consumption. In some cases NMR has been used and its application on real-time systems is not economically feasible at the moment, as well as the electronic nose. X-ray systems are getting cheaper and can be used to detect weevils inside fruits in a continuous sorter system. Throughout all this review advances on sensor technologies and their algorithms have been analyzed, although it was impossible to include all fruits and vegetables. In the future NMR high speed on-line systems will be available and all the internal properties of the produce will be known, while remote sensing will have to work in order to avoid farm damaging by diseases.  
This paper has reviewed important applications carried out during this century and important changes will come with nanotechnology sensors, biosensors and MEMS. Algorithms will be simpler and always iterative, changing as fruits evolve naturally. Even intelligent ants, spiders, wasps and birds can be used to assess crop status in the field.
## 4 Actual Project Study
### Establishment of Finite Element Model  
The finite element method is utilized to assess the dynamic response of the existing structure due to the construction of the new tunnel crossing, with the results being compared to theoretical calculations. As depicted in Figure 9, the MIDAS GTS was used to create a three-dimensional finite element model for the cut-and-cover tunnel project of the Jinan Silk Belt Park--Ligeng Road interval underneath the existing structure. In accordance with the survey results, the formations primarily impacted by tunnel excavation encompass  
Figure 8: Equivalent schematic diagram of foundation beam of existing structure.  
silty clay, consolidated conglomerate, fully weathered marl, and moderately weathered marl. In this paper, with reference to these soil properties and the applicability of various constitutive models, the modified Mohr-Coulomb model, combining the nonlinear elastic and elastoplastic models, is adopted.  
Figure 9b depicts the model of the existing structure. The roof and floor of the underpass, along with the tunnel's initial support, are simulated using slab elements. The envelope piles, columns, and grid steel frames are simulated using beam elements. The lock-foot bolt and lead conduit are simulated using implantable trusses. The interface unit emulates the relative movement or separation between the envelope pile, steel grid frame, and solid unit. For beam, plate, column, pile, and lock anchor, the linear elastic model is adopted.  
To mitigate the influence of boundary conditions, the model's length and height are extended outward by 3R (where \(R\) is the tunnel span) from the tunnel edge and arch bottom, respectively. The modeling dimensions are 62.4 m \(\times\) 40 m \(\times\) 36 m, as shown in Figure 9a. In this model, the Z-axis represents the vertical direction, the Y-axis aligns with the tunnel axis with the positive direction being the tunnel excavation direction, and the X-axis is perpendicular to the tunnel axis. The top surface is a free surface, while normal constraints are applied to the other four sides and the bottom.
## 3 Methodology
### Geophysical Corrections  
The geophysical corrections considered for inland waters are provided by L2 products, and following the recommendations of [PERSON] et al. [21], these include the following: the modeled wet and dry troposphere correction (WTC and DTC), the ionosphere correction (IC) based on Global Ionospheric Maps (GIMs), the solid earth tide correction (SETC), the pole tide correction (PTC), and the load tide correction (LTC) from the FES2014 ocean tide model. Thus,  
\[\Delta range_{g\alpha}=\text{WTC}+\text{DTC}+\text{IC}+\text{SETC}+\text{PTC}+ \text{LTC} \tag{3}\]  
To apply these corrections to the range values derived from the FFSAR processor, they are linearly interpolated using the time variables present in L1A and L2 products.  
Figure 4: Left panel: Example of the Gaussian retracker applied to a waveform obtained from a Sentinel-6 pass over the Itoiz reservoir on 25 July 2022. The power of the waveform has been normalized with respect to the maximum power within the waveform and then converted to dB. Right panel: Graph representing the Gaussian fit over the maximum values of the waveform. The vertical dashed line indicates the position of the maximum of the fitted Gaussian function. Note: 1 mW is equal to \(1\times 10^{-18}\,\text{W}\).
# A Review of Research on Secure Aggregation for Federated Learning
## 5 Challenges and Future Directions  
As federated learning secure aggregation schemes are deployed and further developed, numerous new challenges emerge that warrant deeper investigation. Future research should focus on improving the model quality, enhancing the security, and increasing the adaptability of secure aggregation schemes.
## 2 Materials and Methods
### Skyrme-Hartree-Fock Model
These sets define the strengths of the effective NN interactions via adjustable parameters: \(t_{0}\), \(t_{1}\), \(t_{2}\), \(t_{3}\), \(t_{4}\) are coefficients representing different components of the effective interaction, including central, momentum-dependent, and density-dependent terms, while \(x_{0}\), \(x_{1}\), \(x_{2}\), \(x_{3}\) are dimensionless parameters that modify the strength of each corresponding t-term, allowing for isospin dependence in the interaction, and \(\alpha\) is the exponent of the density-dependent term (\(t_{3}\)), controlling the nonlinearity of the density dependence. Together, these parameters allow the Skyrme interaction to simulate a wide range of nuclear phenomena by effectively capturing the bulk and shell properties of nuclei.
## 4 Numerical Examples
### Thermal Fracturing Due to Rock Heterogeneity
When thermal stress exceeds the strength of mineral crystals, numerous thermal cracks emerge (Figure 19d). Figure 21 presents the distribution of internal stress, elastic modulus, thermal conductivity, and thermal cracks in specimens subjected to different temperatures. As the treatment temperature rises, thermal stress inside the sandstone shows a gradual increase. However, due to the varying thermal expansion coefficients of mineral crystals, high thermal stress is generated at the mineral boundaries, which in turn causes thermal damage to sandstone (Figure 21a,b). Figure 21e shows the thermal damage distribution inside sandstone at different temperatures. It can be found that from when temperature is below 400 \({}^{\circ}\)C, there are almost no thermal cracks generated inside the sandstone; When temperature reaches 400 \({}^{\circ}\)C, due to the thermal stress exceeding the tensile strength of some mineral crystals, thermal cracks begin to appear in the sandstone, resulting in a rapid decrease in the elastic modulus and thermal conductivity of the sandstone, as shown in Figure 21c,d. When the temperature exceeds 400 \({}^{\circ}\)C, the cracks generated by heat treatment further propagate and connect, causing irreversible damage. Figure 19: Cloud diagram of sandstone heated to 600 \({}^{\circ}\)C: (**a**) temperature; (**b**) Mises stress; (**c**) maximum principal stress; (**d**) damage. Figure 20: Local temperature distribution in the sandstone when the heating temperature is 600 \({}^{\circ}\)C.  
Figure 21: _Cont._  
Figure 22 compares the thermal cracks from the TM-PFM with the FEM results for sandstone specimens at different heat treatment temperatures. They show good consistency, proving the coupled model's accuracy in simulating thermal fractures from non-uniform mineral crystal expansion. For quantitative analysis of sandstone damage at different heat treatment temperatures, the average elastic modulus of sandstone specimens after various heat treatments was obtained through post-processing (Figure 23). The elastic modulus trend matches experimental results. Few thermal damage units exist in sandstone specimens when the heat treatment temperature is below 400 \({}^{\circ}\)C. However, when it exceeds 400 \({}^{\circ}\)C, numerous thermal cracks form. Figure 21: Cloud diagram of sandstone at different temperatures.