esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### ZnO-NPs Characterization  
X-ray diffraction (XRD) patterns were recorded with a Bruker D8 HRXRD X-ray diffractometer using Ni-filtered Cu K\(\alpha\) radiation (\(\lambda\) = 0.154606 nm, 40 kV, 40 mV). XRD patterns of the samples were collected in the range of 20-65\({}^{\circ}\) (20) with a step size of 0.02\({}^{\circ}\) and step time of 2 s. Transmission electron microscope (TEM) observations were conducted with JEOL-2010F microscope operated at 200 kV. The samples for TEM measurements were suspended in ethanol and dropped onto holey carbon films that were supported on Cu grids for imaging. The specific surface areas of the samples were determined via gas physisorption at \(-\)196 \({}^{\circ}\)C using a Micromeritics ASAP 2020M analyzer and assessed from nitrogen adsorption data in the partial relative pressure from 0.05 to 0.25 using the Brunauer-Emmett-Teller (BET) method [34; 35]. The zeta potential and particle size of _n_ZnO with the presence/absence of SRHA (1 and 5 mg L\({}^{-1}\) as TOC concentration) in the NaCl (0.1, 1, and 10 mM) solution were measured using ELS-Z (Otsuka, Osaka, Japan); the measurements were conducted 10-15 times repeatedly and immediately prior to the column test at room temperature (25 \({}^{\circ}\)C). The zeta potential was converted from measured electrophoretic mobility values using the Smoluchowski equation, and the particle size was determined using a dynamic light scattering (DLS) method [36; 37]. The zeta potential and particle size of the two types of \(n\)ZnO, which were measured in NaCl in the presence/absence of SRHA and according to the ionic strength, are shown in Figure 1.
## 3 Materials and Methods
### Indoor Rn Concentration Assessment  
To implement the monitoring campaign, an Rn probe developed in the RnMonitor project [30] was used for in situ investigation, which was in line with approaches presented in previous works [48; 49; 50; 51; 52; 53; 54]. This probe works in continuous mode as a data collection unit. It consists of an IoT edge device with an architecture divided into three main blocks:sensing, processing, and communication. The probe is equipped with different sensors, which allow for the acquisition of information on various parameters, as shown in Figure 1.  
According to [PERSON] et al. [30], for radon measurements, the RD200M sensor from Radon FTLab was selected for its accuracy of \(\pm\)10% and measurement range of up to 3700 Bq\(\cdot\)m\({}^{-3}\). This sensor utilizes a Universal Asynchronous Receiver/Transmitter (UART) for receiving commands and transmitting data, which facilitates communication with the microcontroller. The measurement of relative humidity and the temperature was performed using the DHT11 from Aosong. This low-cost sensor uses a single-wire, bi-directional communication with 16 bits to communicate with the microcontroller and boasts a repeatability of 1% for relative humidity and 2 \({}^{\circ}\)C for temperature measurements. The MPL3115A2 from NXP was chosen to provide data on pressure levels. The sensor operates within a range of 20 kPa to 110 kPa and uses an I2C digital output interface to transmit its measurements to the microcontroller. To facilitate LoRa communication, the RN2483 from Microchip was used. This radio transceiver offers a transmission power of 14 dBm and boasts a receiver sensitivity of \(-\)14 dBm. The device communicates with the microcontroller using the Universal Asynchronous Receiver/Transmitter (UART) protocol. In terms of the processing unit, a microcontroller with various communication modes was selected to receive data from the multiple sensors, featuring options for low power consumption and, for the sake of simplicity, an integrated Wi-Fi radio. The ESP8266 from Espressif was deemed the appropriate device for this purpose.  
The monitoring campaign of the indoor Rn concentration and the remaining parameters took place in continuous mode from 18.09.2020 to 16.03.2022, totaling 546 days. Data were collected with a sampling rate of 10 min and later converted into daily averages. The probe was placed in the server room, approximately 1.5 m from the ground, with the power source connected.
## 3 Data and Methods
### Earthquake Relocation  
The relocated small earthquakes were used to analyse the deep seismic environment in the Linfen Basin. This dataset was based on seismic phase observations from January 1981 to June 2018 in Shanxi Province and adjacent regions. Initially, the absolute positioning method (Hypo2000) was used for absolute localisation. Hypo2000 is a tool used for earthquake location that is based primarily on the least squares method for determining the position of an earthquake source. It utilises observed arrival times of seismic waves, combined with the geometric arrangement of seismic networks, and optimises the positioning through an iterative process to improve accuracy. Subsequently, the double-difference relative positioning method (HypoDD) was used to optimise the absolute positioning resultsfurther. The HypoDD is a tool for earthquake source relocation that improves accuracy by refining the source spacing. It is mainly used for earthquake sequence analysis and employs a complex inversion algorithm to enhance source location results. This process yielded a dataset of relocated small earthquakes (\(M\leq 3.0\)) with an average error of less than 1.2 km [53].  
Figure 3: Seismic structure and profile distribution map of the Linfen Basin. Red lines represent the Holocene active faults, and black lines represent the Pre-Holocene fault. F1: Huoshan Fault, F2: Fushan Fault, F3: Luoyunshan Fault, F4: Hongtong–Subao Fault, F5: Guojiazhuang Fault. The A–A is the deep seismic reflection profile; B–B’, C–C’, and D–D’ are velocity tomography imaging profiles. The pink dots represent the relocation data of small earthquakes from 1981 to 2018.
# Design of a Polarization-Insensitive and Wide-Angle Triple-Band Metamaterial Absorber
###### Abstract  
This paper proposes a tri-band wide-angle polarization-insensitive absorber operating in the C-band and Ku-band, based on the design concept of metal-dielectric-metal. The absorber achieves absorption efficiencies of 99.05%, 99.3%, and 97.9% at 4.23 GHz, 7.403 GHz, and 14.813 GHz, respectively. The first two absorption frequencies are in the C-band, while the third absorption frequency is in the Ku-band, both of which are commonly used in satellite communication. The designed absorber consists of three differently sized regular hexagonal rings. To analyze the interaction mechanism between the electromagnetic wave and the absorber, we applied the theory of impedance matching and equivalent media to analyze the metamaterial properties of the absorber. In addition, the equivalent circuit model of the absorber has been analyzed. We then determined the existence of coupled electromagnetic resonances between the top and bottom surfaces by analyzing the distribution of the electric field, magnetic field, and surface currents on the absorber. By varying the polarization angle and incident angle of the incoming wave, we found that the absorber exhibits polarization insensitivity and wide-angle absorption characteristics. The TE and TM waves maintain more than 90% absorption efficiency up to incident angles of 50\({}^{\circ}\) and 60\({}^{\circ}\), respectively. The absorber's thickness is 1.07 mm, which is 0.0154 times the wavelength corresponding to the lowest resonant frequency \((\lambda_{0})\), and the edge length of the subunit's regular hexagon is 7.5 mm (0.108\(\lambda_{0}\)), making the absorber sub-wavelength in scale while maintaining its compactness. The proposed absorber operates in the C-band and Ku-band, and can be applied in the field of satellite communications, achieving functions such as electromagnetic shielding and stealth.  
p _photonics_2025, 12, 386
# Instantaneous Networking Service Availability for Disaster Recovery
## 3 System Architecture  
### Conventional Restoration Process  
A network or cloud service such as Facebook or Twitter basically relies on the Internet. Theses global services confront with breakdown in emergencies. The restoration process of global services is a procedure that allows servers and routers to work again or to be replaced. Hence, the restoration process may take a few days or even a few months when there is large-scale damage in a disaster.  
In the conventional restoration method, both connections to the local infrastructure nodes (such as BSs) and global network are required to exist or to be recovered for communication between users.
# The Potential of Focusing Acoustic Retroreflectors for Architectural Surface Treatment
## 2 Materials and Methods
### Ideal Retroreflection from a Ceiling
#### 2.1.3 Calculating Retroflected Energy Levels from Impulse Responses  
Retroreflected energy levels (Equation (10)) were calculated from octave-band-filtered impulse responses (125 Hz-4 kHz or 125 Hz-8 kHz).  
\[L_{\text{retro,oct}}=10\log\left(\frac{\int p_{t,\text{retro,oct}}^{2}dt}{\int p _{t,\text{Im,oct}}^{2}dt}\right) \tag{10}\]  
To provide a single-number result, the octave bands were then weighted using a combination of the ISO 3382-3:2022 omnidirectional speech spectrum and A-weighting (Table 1) [65]. The concept of this weighting is to provide results relevant to long term speech power levels and simplified hearing sensitivity, without any assumptions about the talker's orientation relative to the reflector. The weighting, \(w_{\text{oct}}\), over the six or seven octave bands, was applied as per Equation (11). This is referred to as speech-A-weighting in this paper.  
\[L_{\text{retro,AS}}=10\log\left(\frac{\sum_{\text{oct}=1}^{N}10^{(L_{\text{ retro,oct}}+w_{\text{oct}})/10}}{\sum_{\text{oct}=1}^{N}10^{w_{\text{oct}}/10}}\right) \tag{11}\]  
Similar calculations were done for receivers that were not collocated with the source.
# Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images
## 2 Materials and Methods  
Figure 2 shows the overall architecture of our medium transmission map guided underwater image restoration network (MTUR-Net). This network takes underwater images as input and predicts the corresponding MT map and underwater enhanced images as output in an end-to-end manner. In general, the network first uses CNN to extract semantics and generate feature maps and share weights. Then two decoding branches are generated: (i) the MT prediction subnet, which is generated by the encoding and decoding network, to regress a medium transmission map from the input and (ii) the underwater image enhancement network, guided by the predicted MT map, which predicts the enhanced image from the input underwater image.  
Figure 2: Schematic diagram of MTUR-Net. It consists of an encoder–decoder network for predicting the MT map (green), a set of dilated residual blocks (yellow) to generate local features, a convolutional layer (purple) for processing MT features before fusion, and the convolutional layer (blue part) to upsample the feature map and generate the underwater enhanced images. \(\oplus\) pixel-wise addition.
# A Method for Energy Balance and Data Transmission Optimal Routing in Wireless Sensor Networks
## 4 Power Line Connection Scheme for Wireless Sensor Networks  
### Shortest Path of Connecting Nodes  
The fundamental reason the energy in wireless sensor networks cannot be fully utilized is that the energy consumption of each node is different. We propose to connect all nodes with power lines, so that the energy between nodes can be transmitted to each other, so as to achieve energy balance.  
After the sensor nodes are deployed in a certain location, a shortest path can be found to connect all the nodes. A sensor network can be regarded as a weighted undirected graph, a node can be regarded as a vertex in the weighted undirected graph, and a connection between any two nodes can be regarded as an edge connecting two vertices, and its weight value is the length of the power line. The minimum weight spanning tree T of the weighted undirected graph can be found, and the sum of its weights is the smallest. Connecting power cables according to the topology of tree T is the shortest way to use cables. Figure 5 depicts a simple sensor network with 20 nodes randomly distributed. According to the distance information between two nodes, we can construct a minimum spanning tree based on the base station, and then connect the power line. The actual power line connection scheme is shown in the figure.  
In the connection method in which the minimum spanning tree is applied, when a certain number of nodes are evenly distributed in a mesh shape, the total length of the cable required is the longest. In this case, the length of the cable connecting each node to the others is equal to the node spacing L (meter), so the average cable cost per node is W  
\[\mathrm{W}=\mathrm{L}\times P_{\mathit{cable}} \tag{7}\]  
In the formula, \(P_{\mathit{cable}}\) is the price per meter of the power cable. According to this formula, the system cable cost can be roughly estimated.
# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 5 Discussion
### Classification and Accuracy Assessment
The opposite happens for herbaceous vegetation, which, during the spring, is in the midst of its photosynthesis activity, while it tends to dry out during the summer; this implies that herbaceous vegetation is classified with lower accuracy in the spring, probably as a result of a lower NDVI contrasting with other photosynthetically active vegetation. Moreover, analysis of the spatial distribution of the classes reveals that the difference between the two classification algorithmsfor the same season leads to almost the same results, with small differences. Differences between the seasons with the same classification algorithm can be also considered small, and the classifier does not appear to be affected by the cyclical and biological behavior of the species. In fact, with the use of the NDVI information layer, the algorithms similarly distinguished herbaceous vegetation from shrubs, even if they were clearly affected by drought. The larger problem to be faced is related to the large area of shadows in the summer mosaic.
# Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security
## 3 Proposed Method
### Image Description Process  
The pseudocode for the image decryption algorithm is Algorithm 2, and it goes like this. The opposite of encryption is image decryption.  
Step 1: Private key \(d\), or \(y_{oi}=c_{i}^{d}mod\ n,i=1,\ 2,\ 3,\ 4\), is used to decrypt public ciphertext data \(c_{i},i=1,\ 2,\ 3,\ 4\). The Arnold map's parameter pairs \(a\) and \(b\) are then constructed using (3) and (4).  
Step 2: The chaotic sequences \(s^{\prime}\) and \(r^{\prime}\) are produced when the two pairs of parameters are replaced into the generalized Arnold map equation, and the values produced are translated into the range from 0 to 255:  
\[S^{\prime}=mod\Big{(}floor\Big{(}\big{(}s^{\prime}+100\big{)}\times 10^{14} \Big{)},256\Big{)}, \tag{11}\]  
\[R^{\prime}=mod\Big{(}floor\Big{(}\big{(}r^{\prime}+100\big{)}\times 10^{14} \Big{)},256\Big{)} \tag{12}\]  
Step 3: The process of inverse diffusion and modulus addition is used to combine the cypher image \(E\) and chaotic sequence \(R^{\prime}\) to create the image \(C^{\prime}\).  
\[C_{i}^{\prime}=2\times 256+E_{i}-E_{i-1}-R^{\prime}_{i}mod\ 256 \tag{13}\]  
where \(C_{i}^{\prime}\), \(E_{i}^{\prime}\), and \(R_{i}^{\prime}\) represent the elements of \(C^{\prime},E^{\prime}\), and \(R^{\prime}\).  
Step 4: A matrix is created from \(S^{\prime}\), Then, using a confusing of \(C^{\prime}\) to create image \(B^{\prime}\).  
Step 5: to obtain image \(A^{\prime}\), conduct the XOR diffusion procedure with images \(B^{\prime}\) and \(S^{\prime}\), as given by  
\[A_{i}^{\prime}=B_{i-1}^{\prime}\oplus B_{i}^{\prime}\oplus S_{i}^{\prime} \tag{14}\]  
where \(A_{i}^{\prime}\), \(B_{i}^{\prime}\) and \(S_{i}^{\prime}\) signify the essentials of \(A^{\prime},B^{\prime}\), and \(S^{\prime}\).  
Step 6: After completing two rounds of decryption, plain picture \(P^{\prime}\) is acquired.  
The suggested method makes use of the best private key to increase medical image security. In order to significantly improve the picture security process, we use obstruction bloom breeding optimization approach in this case.
## 3 Results and Discussion  
### Hygrothermal Monitoring Assessment and Model Validation  
As explained before, the reference building monitoring results are key to calibrate the numerical model. Figure 8 shows the detailed air temperature, monitored and simulated, of the main thermal zones as well as the exterior air temperature during the monitoring period. The spaces were monitored from 1 August through 31 August 2019, according to the household's convenience. The exterior weather data were collected from a local weather station.  
The model was validated by comparing the differences between the monitored and simulated indoor air temperatures. The weather file used in simulations was modified to contain the real site data collected from the local weather station. Figure 9 shows the results of the output correlation factor, \(\mathrm{r}^{2}\), between the real and simulated data for the building. Overall, for temperatures between 22 \({}^{\circ}\)C and 24 \({}^{\circ}\)C, the deviation between the simulated results and the monitored data is small, with the cloud of points tending to a symmetric relation with a maximum deviation of 1 \({}^{\circ}\)C. The good of fitness (GOF) index depicted in Figure 9 is 2.04 which, compared with the ASHRAE Guideline [45] that recommends a GOF value below \(11\%\) for trials acceptance, means that the model can be validated.
# Estimating Carrot Gross Primary Production Using UAV-Based Multispectral Imagery
## 5 Conclusions  
This work shows that it is possible to estimate the GPP of a carrot crop using high-spatial resolution UAV-based multispectral imagery and simple linear regression models, which can be used to monitor GPP on small or medium size farms that cannot be monitored using satellite imagery due to coarse resolution and high cloud cover present in humid areas, as is the case in Colombia. The integration of cloud-free high-resolution UAV imagery and the EC method can be used to obtain accurate models to estimate carbon uptake from VIs in several agroecosystems. In the context of precision agriculture, estimating GPP from VIs computed at high spatial resolution while avoiding mixing soil and vegetation is an important step to estimate crop yield, and to ensure the efficient use of resources (water, carbon, and nutrients).  
**Author Contributions:** Conceptualization, A.M.C.-M. and G.A.G.-V.; methodology, J.M.D.-C., D.F.S.-V. and G.A.G.-V.; software, J.M.D.-C. and G.A.G.-V.; validation, A.M.C.-M., G.A.G.-V., G.A.A.-C., J.M.D.-C. and D.F.S.-V.; formal analysis, A.M.C.-M., G.A.G.-V., G.A.A.-C. and J.M.D.-C.; investigation, A.M.C.-M., G.A.G.-V., G.A.A.-C., J.M.D.-C. and D.F.S.-V.; resources, A.M.C.-M. and D.F.S.-V.; data curation, A.M.C.-M., G.A.G.-V., J.M.D.-C. and D.F.S.-V.; writing--original draft preparation, A.M.C.-M., G.A.G.-V., J.M.D.-C. and D.F.S.-V.; writing--review and editing, A.M.C.-M., G.A.G.-V., J.M.D.-C., D.F.S.-V. and G.A.A.-C.; supervision, A.M.C.-M.; project administration, A.M.C.-M.; funding acquisition, A.M.C.-M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Ministerio de Ciencia, Tecnologia e Innovacion (MINCIENCIAS), and Corporacion Colombiana de Investigacion Agropecuaria (AGROSAVIA), funding number 1002035.  
**Data Availability Statement:** Data presented in this study are available upon request from the Agrosavia Intellectual Property Department. The data are not publicly available due to Agrosavia's copyright. Your request can be sent to [EMAIL_ADDRESS].  
**Acknowledgments:** This work is part of a larger project in the Corporacion Colombiana de Investigacion Agropecuaria (AGROSAVIA) named \"Sistema de Informacion Agroclimatica del cultivo de la papa en la region de Cundinamarca, Colombia (SIAP)\". We thank [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for their contribution to the equipment installation process, and farmers [PERSON] and [PERSON] for providing a suitable lot for the study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Probability Density Estimation through Nonparametric Adaptive Partitioning and Stitching
## 3 Results and Discussions
### Mean Percent Error Comparison between NAPS and NMEM
For more challenging distributions, typically those containing sharp peaks, heavy tails, or divergences, as shown in Figure 8c-h, NAPS outperforms NMEM, particularly  
Figure 8: Mean percent error comparisons: The MPE for the NAPS and NMEM estimators are compared using box and whisker plots for eight distributions and for sample sizes in the range \(2^{9}\)–\(2^{22}\) for 100 estimates per sample size for distributions: (**a**) normal(5, 1) (**b**) uniform(4, 8) (**c**) trimodal normal (**d**) Beta(2, 0.5) (**e**) Beta(0.5, 1.5) (**f**) Beta(0.5, 0.5) (**g**) generalized-Pareto(2, 1, 0) (**h**) and stable(0.5, 0.5, 1, 4). To avoid overlaps in data, NAPS and NMEM sample sizes are slightly displaced about the size being plotted. as the sample size increases. The advantage of breaking the large task into many smaller ones pays off in terms of accuracy in all of these cases. Moreover, NAPS generally shows an MPE of less than 35% at the smallest size of \(N=2^{9}\), except for the stable distribution, which starts with an MPE of 70%. However, there is a rapid improvement in accuracy in the form of a power-law as the sample size increases, based on visual inspection of the roughly straight lines for NAPS in all log-log graphs in Figure 8. In contrast, NMEM errors typically saturate or, in some cases, increase. It is worth noting that these levels of error in terms of MPE are also quite good compared to popular KDE methods since NMEM compares favorably to KDE (see Section 3.5 for direct comparisons involving KDE). While the MPE plots in Figure 8 provide an overview of the average errors across the entire PDF estimate, we also consider where the greatest contribution to the MPE occurs over localized regions of a PDF. The plots in Figure 9 break down the MPE according to quantiles of the CDF for the stable distribution, as demonstrated in Figure 9a. Note that the only modification to the way MPE is calculated is to include all data points in the averaging procedure shown in Equation (7) that are members of the specified quantile range, which we consider in 2% increments. The remaining plots in this figure compare NAPS and NMEM for increasing sample sizes, with several key observations. In general, errors decrease for larger sample sizes with NAPS but remain roughly constant for NMEM, consistent with Figure 8h. Figure 9b-d show that the improvement in MPE with sample size for NAPS is primarily driven by the low-density tails. This point will be further highlighted with examples in the next section.
## 3 Methodology
### Data Preprocessing
#### 3.4.2 Dummy Variables  
Using OneHotEncoder, categorical variables such as player workload, player usage, age category, etc., were converted into numeric dummy variables. Dummy variables are binary (0 or 1) indicators that represent categorical data in numerical form. Each category within a variable is transformed into a separate column, with a value of 1 indicating the presence of that category and 0 indicating its absence. This conversion is necessary because many of the ML techniques used in this study require numerical inputs to produce predictions for the performance metrics and injuries of football players.