esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Instrument Description, Calibration, and Relevance to ERB Measurement Challenges
Their analysis highlights several critical challenges affecting the accuracy and long-term reliability of TOA radiative flux measurements. One major concern is the degradation of optical coatings in orbit--particularly in the shortwave spectral region--which leads to on-orbit gain variations that require empirical and often uncertain correction strategies. In addition, thermal fluctuations can induce drifts in instrument offsets, complicating the separation of instrumental noise from the true radiative signal. [PERSON] et al. (2018) [8] also emphasize the difficulties in achieving consistent inter-satellite calibration, especially when there are differences in sensor footprint sizes or limited opportunities for overlapping observations. The process of inverting radiance measurements to TOA fluxes introduces further complexity, particularly for shortwave radiation where angular anisotropy and scene-dependent geometry contribute substantial uncertainty. Moreover, edge effects within the instrument footprint and partial illumination--especially in limb or terminator regions--complicate radiative flux retrievals due to non-uniform scene brightness. Finally, changes in satellite altitude can bias flux measurements by altering footprint geometry and viewing angle, further affecting retrieval accuracy and comparability over time. These findings underscore the need for careful instrument design, calibration strategies, and data processing techniques when interpreting measurements from non-scanner CubeSat platforms. In light of these challenges, the Uvsq-Sat and Inspire-Sat instruments were designed to minimize thermal gradients (use of thermal insulation material), electrical noise (use of high-stability resistors), and optical degradation (use of thermo-optical coatings with low aging in space conditions). While onboard correction mechanisms are not available, the ERS architecture and the use of inert, space-proven materials provide robust radiative flux measurements within the mission's targeted uncertainty envelope. Methodology for Reconstructing TOA Radiative Flux Maps from Uvsq-Sat and Inspire-Sat Time-Series Data  
We use time-series data from Uvsq-Sat [10] and Inspire-Sat [11] to reconstruct specific-date maps of OSR and OLR at TOA with \(1^{\circ}\times 1^{\circ}\) resolution. Then, by combining these maps with incoming solar radiation data from the Spectral And Total Irradiance REconstruction in the satellite era (SATIRE-S) model (Figure 2, Top), we can analyze the Earth's radiation budget and its energy imbalance over time.
## 2 Beamsteering System Concept and PAA's Simulation Model
### Beamsteering System Hardware  
The beamsteering system hardware needed to implement the proposed approach (Figure 1) includes a controller and a beamforming circuit built on digital phase shifters connected to the linear PAA. The controller executes the ML algorithm based on a priori information and data from the received vehicle's request message and manages the phase shifters by the eight-bit sequences encoding the required phase shift at the input of the PAA emitters. Due to the accurately determined phase shifts on the PAA elements, an RP is formed with the direction of the main lobe corresponding to the predicted sector of the vehicle location.  
The goal function of the ML algorithm is to determine the vehicle location relative to the V2I transceiver at the road infrastructure object and maximize the signal power on the user receiver, as shown in Figure 2.  
To simplify the beamforming circuit and achieve 360-degree operation, it is proposed to divide the service area of the smart infrastructure object into four scanning zones. One PAA is required to maintain a connection in each zone. Thus, the foremost technical essential for the PAA is a 90-degree scanning sector. Thus, vehicle positioning becomes a classification task if we divide the scanning sector into sub-sectors with a width coinciding with the width of the main lobe. The sub-sector where the vehicle should be located was determined based on input data from road users and a priori information about the service area. The maximum range of the scanning sector is 600 m along the normal to the PAA's plane [15]. When the vehicle approaches the border of the scanning sector, the service switches to the following PAA (Figure 2). The selected sub-sector (azimuth angle of the PAA's main lobe) was converted into the phase distribution between emitters.  
Figure 1: Schematic of the beamsteering system. A—controller running the ML algorithm, B— microwave splitter, C—beamforming circuit, D—linear antenna array.
## 5 Experimental Validation  
In Section 3, the experimental results showed the effectiveness of our proposed machining modes for corresponding CNC parameters, which is relevant to the dynamic errors of the machine. According to the tuning methodology and procedures of the CNC parameters implemented, the relevant CNC parameters were estimated based on the anticipated dynamic errors in Section 4. This section proposed a single square path with the same 4000 mm/min feedrate and the length was 30 mm to verify the validation of the tuning methodology. We used the three sets of CNC parameters (see Table 2)  
Figure 18: The smoothing time constant led to the oscillation of the tool path.  
corresponding to the \"HS, HP, and HQ modes\" in the actual machine platform for validation, thereby observing the performance of the motion dynamic under various modes.  
The experimental results are listed in Table 3. We observed the time of the completion of the tool path was the fastest for HS mode. In HP mode, the minimum dynamic errors were of the corners. There was small oscillation that was caused by the tracking error after the cornering in HQ mode. According to the experimental results, the servo parameters could be ignored, and the relevant CNC parameters of the tool-path planning could be adjusted, which can affect the completion time, dynamic errors, and tracking error of the same path.  
The comparison of HS, HP, and HQ modes are listed in Table 4. In the HS mode, the completion time of the path was fastest than the other modes. However, the dynamic errors and the tracking error were increased by 66% and 85%, respectively. In the HP mode, although the completion time of the path was increased by 37% compared with the HS mode and the tracking error was increased by 28%, the HP mode was closed to the ideal path with highest accuracy. In the HQ mode, the surface quality of the workpiece was improved by focusing on reducing the machine resonance, although the dynamic errors were worse than the HP mode. The surface quality improved by 85% compared with the HS mode. However the completion time was only increased by 6% compared with the HS mode.  
The experimental results verified the tuning methodology for CNC parameters of Section 4. We found that no single set of CNC parameters was able to simultaneously satisfy all of the three machining modes: \"High speed\", \"high accuracy\", and \"high surface quality\". Actually, these three machining modes often contradict each other. For example, we may have the \"high productivity\", but with \"low accuracy and poor surface finish.\"
## 3 Establish an EFLP Model and Solution Method
### Notations and Proposed Assumptions
#### 3.1.2 Propose Assumptions  
We make the following assumptions before establishing a robust optimal location problem for RDGT emergencies under uncertain parameters:  
1. In the case of any RDGT emergency, emergency facility construction costs, transportation costs, etc., are not considered.
2. In the case of any RDGT emergency, if the emergency facilities are operating normally, the emergency facility sites must be able to provide emergency rescue services.
3. The locations of the candidate and demand points for emergency facilities are known.
4. The distance between the facility point and the demand point is expressed in terms of the abstract mileage of the railroad line.
# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 5 Experimental Evaluation and Discussion
### Discussion and Future Work  
The biggest problems in SAR segmentation are the need for labeled data, the uncertainty of how to label data, and the inherent problems of SAR images. The noise of the SAR image, geometric corrections, and backscattering errors must be minimized before beginning the labeling process. This paper explains in detail how to segment SAR images by obtaining all kinds of data free of charge, and in order to prove the success of new dataset creation, three CNN models have been applied. We compared our results with similar previous studies [43; 44; 45]. For land cover segmentation/classification with SAR images, refs [43; 44; 45] obtained results between 79-88% using machine-learning algorithms. Refs. [46; 47] attained a nearly 93% accuracy. However, in ref. [46], optical images with 5 m spatial resolution were used, and [19] utilizes fully polarimetric images with better resolution than Sentinel-1 SAR resolution. Another land cover mapping study [47] used SAR C-band Sentinel-1 (S-1) time-series data for classification with machine-learning models. The classes used in the paper were water, shrubs and scrubs, trees, bare soil, built-up areas and cropland, categories which are similar to ours. Overall accuracy obtained with the random forest classifier in [47] was 84%. The prior study most similar to ours is [31]. In ref. [31], a new Finland dataset was created, the same classes as ours were used, and the accuracy was 92.78% with FC-DenseNet. Comparing our work with [31], we obtained nearly the same accuracy, with 92.23%. Our method provides extra information about how to label datasets in a pipeline. In our proposed method, 6722 images were processed by deep learning models. However, use of a more extensive dataset for deep learning is crucial to achieving more success. Additionally, using datasets with more urban areas can increase accuracy. In order to increase the success of urban areas, [48] combined the use of Sentinel-2B and Sentinel-1A imagery. Fusing Sentinel-2 Multispectral Instrument (MSI) and Sentinel-1 SAR images, they reached an overall accuracy of 92.12%, with a support vector machine with composite kernels. Furthermore, including Sentinel-1A SAR data with Sentinel-2B MSI data improves the model's capability to differentiate between urban and water areas. Applying our pipeline over a combined dataset as proposed in [48] may be pursued as future work. Reducing speckle noise is one of the most crucial steps in order to obtain more successful results. In ref. [49], a modified U-net with temporal attenuation encoder (U-TAE) was used as a semantic segmentation method. First, the temporal median filter was used to
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 1 Introduction
### Roadmap of the Paper  
The rest of the paper is organized as follows. In Section 2, we present the most related recent works in the literature and highlight our contribution beyond the state of the art. In Section 3, we present the methods for industrial data distribution that we consider in this paper and provide a description of their building blocks. In Section 4, we present the developed simulation model and the related route computation and channel scheduling processes. In Section 5, we present the performance results of the presented methods in two different scenarios: one with a variable number of nodes and fixed percentage of producers and consumers and one with a fixed number of nodes and variable number of consumers. In Section 6, we describe the two test cases demonstrated in an industrial smart factory environment, and we show the integration with an industrial fog node which controls the shop-floor devices. We report selected results in much larger scales, obtained via simulations. Finally, in Sections 7-9, we present some open problems, future perspectives and conclusions, respectively.
## 3 Results
### Characterization of NDVI Change  
#### 3.2.1 Temporal Variation in NDVI  
The temporal trends in NDVI values for Inner Mongolia as a whole and each grassland type from 1982 to 2020 are shown in Figure 6. In the last 39 years, the mean NDVI in Inner Mongolia has been increasing at 0.013/10a (\(p<0.05\)), with annual mean values ranging from 0.4 to 0.6, with the highest value occurring in 2020 and the lowest in 1989. Among all grassland types, forest and meadow steppe showed increasing trends, both at 0.019/10a (\(p<0.05\)), with the highest mean NDVI values in forest areas fluctuating from 0.6-0.8, with the highest value (0.79) occurring in 2016 and the lowest (0.68) in 1992, and the highest value in meadow steppe occurring in 2013 and the lowest value in 1992. Typical steppe showed an increasing trend at 0.02/10a (\(p<0.05\)), with the highest and lowest values occurring in 2020 and 1989, respectively. NDVI values in desert steppe showed a non-significant decreasing trend at a rate of \(-0.0003\)/year. In addition, the desert NDVI showed a non-significant decreasing trend of 0.004/10a over the last 39 years, with the highest value occurring in 1985 and the lowest in 1989.  
The Mann-Kendall mutation test was used to diagnose the mutation scenario of NDVI in Inner Mongolia over the last 39 years (Figure 7). The results showed that the UF and UB curves of NDVI intersected in the confidence interval in 2001, indicating that a mutation in NDVI may have occurred in 2001. The overall NDVI values in Inner Mongolia exhibited a fluctuating trend, with a decreasing trend from 1982-2001 and an increasing trend after 2001. That is, 2001 was the year when NDVI values changed abruptly from decreasing to increasing.  
Figure 5: Frequency ratios of SDHI in different grassland types in Inner Mongolia from 1982 to 2020.
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
###### Abstract  
In this paper, the integration of directional antennas in a room-level received signal strength (RSS) fingerprinting-based indoor localization system (ILS) is studied. The sensor reader (SR), which is in charge of capturing the RSS to infer the tag position, can be attached to an omnidirectional or directional antenna. Unlike commonly-employed omnidirectional antennas, directional antennas can receive a stronger signal from the direction in which they are pointed, resulting in a different RSS distributions in space and, hence, more distinguishable fingerprints. A simulation tool and a system management software have been also developed to control the system and assist the initial antenna deployment, reducing time-consuming costs. A prototype was mounted in a real scenario, with a number of SRs with omnidirectional and directional antennas properly positioned. Different antenna configurations have been studied, evidencing a promising capability of directional antennas to enhance the performance of RSS fingerprinting-based ILS, reducing the number of required SRs and also increasing the localization success.  
Keywords: indoor localization system; received signal strength; fingerprinting technique; artificial neural networks; directional antennas +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Statistical Analysis
\begin{table}
\begin{tabular}{l l l l l l l} \hline  & \multicolumn{2}{c}{**Low Frequency**} & \multicolumn{2}{c}{**Medium Frequency**} & \multicolumn{2}{c}{**High Frequency**} \\
**Source of Variance** & **F-Value** & \(p\)**-Value** & **F-Value** & \(p\)**-Value** & **F-Value** & \(p\)**-Value** \\ \hline
**Rambling in x-direction** & & & & & \\ Condition & 202.97 & \textless{}0.001 & 148.08 & \textless{}0.001 & 94.63 & \textless{}0.001 \\ Trial & 22.74 & \textless{}0.001 & 9.99 & \textless{}0.001 & 14.50 & \textless{}0.001 \\ Group & 1.35 & 0.268 & 1.93 & 0.154 & 0.62 & 0.539 \\ Age & 2.06 & 0.157 & 3.28 & 0.075 & 2.37 & 0.129 \\ Condition \(\times\) Trial & 1.08 & 0.378 & 1.73 & 0.070 & 1.65 & 0.088 \\ Condition \(\times\) Group & 0.69 & 0.734 & 1.72 & 0.072 & 2.71 & 0.003 \\ Trial \(\times\) Group & 0.31 & 0.869 & 1.57 & 0.180 & 1.42 & 0.227 \\ Condition \(\times\) Trial \(\times\) Group & 1.36 & 0.131 & 1.32 & 0.156 & 1.19 & 0.250 \\ \hline \end{tabular}
\end{table} 
Table 3: Analysis of variance table for the linear mixed model examining main effects and interactions of condition, trial, and group on the low-, medium-, and high-frequency band of the x and y rambling components, with age as a covariate.
# Off-Axis Tension Behaviour of Unidirectional PEEK/AS4 Thermoplastic Composites
## 1 Introduction
Current research on the off-axis behaviours of composites is mainly focused on the pure shear response of 10\({}^{\circ}\) off-axis specimens. However, when a lamina is under off-axis loading, normal stresses would produce shear strains and shear stresses produce normal strains. This tension/shear coupled effect on the material mechanical behaviour and the relationship with different fibre misalignment angles still require thorough study. Although the damage initiation and failure of thermoset composites were well investigated, as demonstrated in the Second World-Wide Failure Exercise (WWFE-II) [28]. The development of appropriate failure theories for thermoplastic composites is insufficient due to the lack of experimental information on biaxial tests. The aim of this study is to (1) develop an oblique end-tab for off-axis tension tests, based on the mechanical properties of PEEK/AS4 thermoplastic composites, to eliminate stress concentration and in-plane bending moment; (2) conduct off-axis tension tests on unidirectional PEEK/AS4 thermoplastic laminates and, investigate the full field deformation and strain fields using the DIC method; (3) analyse the failure mechanisms and the tension/shear coupling effect under biaxial stress states based on DIC and SEM results.
# Quantum Antenna as an Open System: Strong Antenna Coupling with Photonic Reservoir
## 5 Conclusions  
In summary, we developed a model of quantum antenna in the strong coupling regime basing on the general theory of open quantum systems [1; 33]. The far-field zone of antenna radiation is considered as the thermal photonic reservoir. We formulated and solved the master equation with Lindblad-terms related to the energy losses via antenna emission. The general concept was applied to the wire antenna with Fermionic type of excitation. Spectral density of power in the far-field zone was calculated. It is shown that the strong coupling regime dramatically changes the radiation pattern compared to the macroscopic antennas and optical nano-antennas of different well-known types. The calculated radiation pattern consists out of three components, each of which corresponds to the resonance line in the Mollow triplet and turned one with respect to another at some angle. The value of this angle strongly depends on the geometric parameters of antenna, energy spectra of used materials, and the value of coupling (Rabi-frequency). It opens new ways for highly effective electric control of antenna's characteristics for using in different nano-photonic applications. On the other hand, the influence of EM field on the pattern should be accounted from the point of view of electromagnetic compatibility in nanoscale [38; 39].  
Developments of the physical models, derivation of the basis equations, interpretation of the physical results, and paper writing have been done by [PERSON] and [PERSON] jointly.  
The authors declare no conflict of interest.
# Controlled Drainage Effectiveness in Reducing Nutrient Outflow in Light of Climate Changes
## 2 Materials and Methods  
### Study Area Description  
The study site is a drainage field of arable land located in Ostrowo Szlacheckie (Figure 1) in the central-western part of Greater Poland, Poland (N 52\({}^{\circ}\)21\({}^{\prime}\)38.5\({}^{\prime\prime}\) and E 17\({}^{\circ}\)36\({}^{\prime}\)34.2\({}^{\prime\prime}\)). In late 2018, modifications were made to drainage section number 42, which is divided into four subsections. Two of these subsections were equipped with CD, while the remaining two were maintained as FD. The study area is flat (average slope of less than 1.0%), and the soils within it, based on field and laboratory studies, were classified as Gleyic Luvisols [34]. The spacing of drains was 14 m, while the tile depth was 0.9-1.0 m. The basic soil properties of the analyzed study area are shown in Table 1.  
During the CD experiment in 2019-2020, GWT was recorded hourly and separately from the CD area and the FD area using Solinst LTC Leveloggers and Barologger Edge. Also, the depths of the GWT were observed manually (approximately once a week) using the study plot. Hourly meteorological data were gathered from the experiment meteorological station at Sokolowo, next to the Ostrowo Szlacheckie field. Daily data were used for the measured precipitation (P) and minimum and maximum air temperature (T). These data from March-September 2019-2020 were used to calibrate and validate the model.  
A comprehensive description of the study area, the field measurement equipment installed, and details of the fieldwork and laboratory analyses carried out can be found in the publication by [PERSON] et al. [34].  
The main forms of biogenic compounds were determined in water samples from drainage outflows. Table 2 outlines the standard methods employed to determine various parameters, including nitrite nitrogen and orthophosphate (V).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Horizon** & **Thickness** & **Sand/Silt/Clay** & **Bulk Density** & **Organic Carbon** & **Saturated Water** \\  & **(cm)** & **(\%)** & **(g cm\({}^{-3}\))** & **Content (g kg\({}^{-1}\))** & **Content (cm\({}^{3}\) cm\({}^{-3}\))** \\ \hline Ap & 36 & 70/21/9 & 1.62 & 14.8 & 0.358 \\ Bt & 21 & 64/16/20 & 1.77 & 6.1 & 0.315 \\ Cg or Ck & 29 & 67/16/17 & 1.74 & 3.8 & 0.326 \\ Ckg & 60 & 64/19/17 & 1.84 & 1.9 & 0.298 \\ \hline
**Horizon** & **\(\alpha\) (cm\({}^{-1}\))*** & **\(n\) (-)*** & **Saturated Hydraulic** & **Water Drainage** & **Field Capacity** \\  & & & **Conductivity (cm day\({}^{-1}\))** & **Capacity (cm\({}^{3}\) cm\({}^{-3}\))** & **(cm\({}^{3}\) cm\({}^{-3}\))** \\ \hline Ap & 0.0412 & 1.2967 & 43.5 & 0.127 & 0.231 \\ Bt & 0.0511 & 1.1620 & 11.8 & 0.076 & 0.239 \\ Cg or Ck & 0.0620 & 1.1910 & 14.8 & 0.098 & 0.228 \\ Ckg & 0.0443 & 1.1522 & 7.5 & 0.062 & 0.236 \\ \hline \end{tabular}  
* \(\alpha\) and n—parameters of Van Genuchten equation.  
\end{table}
Table 1: Input model parameters for soil layers of drainage section No. 42.  
Figure 1: The drainage experimental field in Ostrowo Szlacheckie.
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 3 Results
### Delay  
Delay results from the OLSR and AODV protocols are shown in Figure 6. The results show that OLSR has a better delay than AODV. The default AODV protocol delay has an average value of 0.1722 ms, while that of the OLSR protocol is 0.022 ms. Meanwhile, reducing the value of RREQ_RETRIES to 2 (AODV 2) and increasing it to 4 (AODV 3) reduce delay. When the value of RREQ_RETRIES is 2, the delay value is reduced by 3.4%, while when the value of RREQ_RETRIES is 4, the delay value is reduced by 0.2%. Regarding the decrease and increase in the value, MAX_RREQ_TIMEOUT does not always reduce the delay value. For the value of MAX_RREQ_TIMEOUT, 7 s (AODV 6) causes an increase in delay of 0.7%, while increasing the MAX_RREQ_TIMEOUT value to 12 s (AODV 10) decreases the delay by 0.96%. The lowest delay value is obtained with the MAX_RREQ_TIMEOUT value of 5 s (AODV 4).