esa-sceva/satcom-chunk-collection huggingface dataset:
# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## Materials and Methods
### Datasets
#### 2.1.4 NUCAPS-Forecast  
In 2019, NUCAPS-Forecast was introduced (Figure 2e). Soundings are advected forward in time assuming adiabatic parcel theory [27] throughout the NUCAPS swath using HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) [28] trajectories driven by numerical weather prediction (NWP) model forecast wind fields. The NWP winds are obtained from the Global Forecast System (GFS)or the High-Resolution Rapid Refresh (HRRR) forecast fields; the former was used for the first version of NUCAPS-Forecast within AWIPS, while the latter is currently being tested for future operations. The data are quality controlled, and only observations with good retrievals are advected. The rebuilt soundings are generated in one-hour increments for up to 6 h in the future and are regridded to a 0.25 degree spatial resolution. Stability indices (e.g., Convective Available Potential Energy (CAPE), Convective Inhibition, Equilibrium Level, Lifted Condensation Level, and Level of Free Convection) were calculated with SHARPpy and made available at one-hour increments for a total of 6 h following the 1:30 a.m. and 1:30 p.m. overpasses.  
While gridded NUCAPS was developed in response to forecasters requests to see horizontal gradients in moisture, temperature and stability indices, NUCAPS-Forecast was created in response to their desire to have increased temporal coverage. The \"first look\" of this product at HWT allowed us to gather feedback on a blended model-observation hybrid product, and several cases of interest were documented by forecasters.
## 2 Effective Diffusivity
### Effective Diffusivity on a 2D Lattice
#### 2.2.1 Lattice Representation of Mean Squared Displacement  
The mean squared displacement after \(N\) time steps and for \(K\) realizations is  
\[\langle r_{K}^{2}(N)\rangle=\frac{1}{K}\sum_{i=1}^{K}x_{i}^{2}(N)+y_{i}^{2}(N), \tag{3}\]  
where \(x_{i}(N)\) and \(y_{i}(N)\) are the horizontal and vertical particle locations, respectively. The mean squared displacement is then \(\langle r^{2}\rangle=\lim_{K\to\infty}\langle r_{K}^{2}(N)\rangle\).  
For brevity, let \(x_{i}^{N}\) and \(y_{i}^{N}\) denote the horizontal and vertical positions of the \(i^{th}\) particle at time step \(N\), respectively, (the full notation is utilized in the Appendices). We write these positions as  
\[x_{i}^{N}=x_{i}^{N-1}+h_{xi}^{N},\ \ y_{i}^{N}=y_{i}^{N-1}+h_{yi}^{N},\ \ i=1, \ldots,K, \tag{4}\]  
where \(h_{xi}^{N}\) and \(h_{yi}^{N}\) are the possible displacements with associated step sizes of \(\Delta x\) and \(\Delta y\), respectively. In any one time step \(\Delta t\), the five possible transitions are  
\[\text{(i)}\ \ h_{xi}^{N}=\pm\Delta x,h_{yi}^{N}=0,\ \ \text{(ii)}\ \ h_{xi}^{N}=0,h_{yi}^{N}=\pm\Delta y,\ \text{(iii)}\ \ h_{xi}^{N}=h_{yi}^{N}=0, \tag{5}\]  
where cases (i) (horizontal transition) and (ii) (vertical transition) occur with equal probability, and case (iii) represents no transition.  
Substituting (4) into (3), \(\langle r^{2}\rangle\) can be written in recursive form as  
\[\langle r^{2}\rangle =\lim_{K\to\infty}\langle r_{K}^{2}(N)\rangle=\lim_{K\to\infty} \frac{1}{K}\sum_{i=1}^{K}\Bigl{(}x_{i}^{N-1}+h_{xi}^{N}\Bigr{)}^{2}+\Bigl{(}y_ {i}^{N-1}+h_{yi}^{N}\Bigr{)}^{2},\] \[=\lim_{K\to\infty}\frac{1}{K}\sum_{i=1}^{K}\Bigl{[}(x_{i}^{N-1})^ {2}+x_{i}^{N-1}h_{xi}^{N}+(h_{xi}^{N})^{2}\] \[\qquad\qquad\qquad+\ (y_{i}^{N-1})^{2}+y_{i}^{N-1}h_{yi}^{N}+(h_{yi}^{N})^{2} \Bigr{]},\] \[=\lim_{K\to\infty}\langle r_{K-1}^{2}(N)\rangle\] \[\qquad\qquad+\lim_{K\to\infty}\frac{1}{K}\sum_{i=1}^{K}\Bigl{[}x_ {i}^{N-1}h_{xi}^{N}+(h_{xi}^{N})^{2}+y_{i}^{N-1}h_{yi}^{N}+(h_{yi}^{N})^{2} \Bigr{]}. \tag{6}\]  
In the next subsection, we briefly discuss limits used to simplify (6).
## 3 Simulation Modeling and Verification
### Creation of a Surface-Fitting Contact Model
#### 3.1.3 Error Analysis  
During the simulation process, although we employed precise modeling and meticulous mesh division, some potential sources of error still exist. Firstly, the material properties set in the Abaqus software are approximate values based on experimental data, such as [PERSON]'s modulus and [PERSON]'s ratio. These parameters may exhibit a certain range of variation in practice, thereby affecting the accuracy of the simulation results. Secondly, although the mesh division is fine, there may still be some errors in the extremely fine structures of the contact area, especially in regions with high curvature. Additionally, environmental factors such as temperature and humidity were not considered in the simulation, which may significantly impact material properties and contact behavior during the actual polishing process.  
To quantify these errors, we conducted the following analyses: Firstly, by comparing simulation results under different mesh densities, we found that when the mesh size is smaller than 1 mm, the difference in simulation results is less than 5%. Therefore, the errors caused by mesh division are considered to be within an acceptable range. Secondly, considering the influence of environmental factors, we recommend incorporating these factors in future studies to further enhance the accuracy of the simulations.  
Figure 7: Simulation of the relationship between the polishing force and different curvatures of the SG flexible tool.
# Multi-Line Prefetch Covert Channel with Huge Pages
## 4 The Design of a Multi-Line Prefetch Covert Channel with Huge Pages
### Synchronization and Timing Optimizations  
Lightweight Flag-Based Coordination:  
The sender and the receiver synchronize using a shared memory flag (e.g., receiver_done_flag) to coordinate the encoding and decoding of each message. This approach avoids race conditions while minimizing busy waiting. To improve timing precision, both parties poll this flag while adaptively adjusting their polling intervals based on a locally maintained timestamp obtained via rdtscp(). This hybrid approach balances responsiveness and CPU efficiency, using short delays (e.g., usleep(0.1)) to avoid excessive spinning.  
Limitations in Secure Environments:  
In our current setting, both the sender and the receiver operate outside of secure enclaves, allowing for unrestricted access to high-resolution timers such as rdtscp(). However, in trusted execution environments like Intel SGX and AMD SEV, access to precise timers is either restricted or unavailable. This makes rdtscp-based synchronization infeasible for enclave-resident senders wishing to transmit sensitive data covertly. In these scenarios, we consider using clock_gettime(CLOCK_MONOTONIC) as a viable alternative for cross-core timing measurement. It provides stable wall-clock timestamps and is accessible even in virtualized environments. We adopt this approach in our AMD SEV evaluation. Although clock_gettime() has lower precision than that in hardware cycle counters, it avoids the issues associated with virtualized rdtscp() and delivers consistent results.  
Figure 1: Covert channel with labeled sequence steps.  
We also explored loop-based counters [28; 29], which estimate the timing by counting instruction iterations; however, they are highly susceptible to noise from interrupts, context switches, and scheduling variability in multi-tenant or virtualized environments. These interrupts are asynchronous and unpredictable. This uncertainty in the location and timing of interrupts results in significant variance in the loop counter timing. These significant timing fluctuations over multiple runs make such methods unreliable for the precise latency discrimination required in covert channel decoding.  
Semaphore-Based Alternatives and Trade-Offs:  
In such restricted environments, semaphores or barriers provide viable alternatives for synchronization. These primitives block the receiver until signaled by the sender, thus avoiding the need for polling and enabling more efficient CPU usage. However, these mechanisms typically rely on atomic operations or memory fences, which introduce additional microarchitectural side effects such as cache line invalidation and memory ordering constraints. Such effects may interfere with the prefetch timing behavior and degrade the performance and accuracy of timing-based covert channels. As a result, while semaphores offer an enclave-compatible solution, their influence on the cache state must be carefully considered when designing prefetch-based transmission mechanisms.
# Auto-Encoder Learning-Based UAV Communications for Livestock Management
### Related Works
A dual-stream deep learning (DL) architecture that combined exploration strategies learned from previous experiences with instantaneous sensory inputs was proposed to capture the movement of the cattle [26]. Nonetheless, accurate livestock counting in a multi-path crossing by the same animal is still an open problem. While many works of the literature mainly focus on combining ML algorithms with UAVs for efficient smart farming and livestock management, little or no attention is paid to the part that involves data transfer within the UAV communication network. ML techniques, specifically DL, have been used to solve many physical layer communication problems [27, 28, 29]. Therefore, this paper proposes a learning framework for an efficient and reliable communications system for UAV-based livestock management. Our contributions are summarized below:  
* We built an auto-encoder for end-to-end wireless communications for UAV-assisted livestock management systems. We showed that learning the entire transmitter (UAV) and receiver (GCS or UAV) implementations for a given communication channel link optimized for a chosen loss function (e.g. minimizing BER) is possible. The basic idea is to describe the transmitter, channel, and receiver as a single deep CNN that can be trained as an auto-encoder. Interestingly, this technique can be used as a model approximator to approximate optimal solutions for systems with unknown channel models and loss functions. * We simulated the communication links with a different set of communication rates to learn various communication schemes, such as QPSK, 8 PSK and 16 QAM. * For a (7, 4) communication rate, the proposed auto-encoder performance matched the optimal Hamming code maximum likelihood decoding scheme. The remainder of the paper is structured as follows: The system model and problem are presented in Section 2. The proposed methodology is described in Section 3. Section 4 presents the simulation results and discussions. Finally, Section 5 summarizes and concludes the paper. **Notations:** We use bold uppercase symbols for matrices, bold lowercase symbols for vectors and lowercase symbols for scalars. Finally, notation \(\mathscr{L}(\cdot)\) is reserved for the loss function.
# Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment
## 5 Conclusions  
The review article explored durability issues and corrosion of structural materials and systems in the farm environment. Following the review of different publications relating to the corrosion of concrete and steel structures, the following observations were made. First, all materials (wood, metal, and concrete) used in the construction of farm structures are at risk of corrosion, which in turn compromises the durability of the structures and their service life. Metallic structures are easily corroded by chemicals found in animal manure products, organic and inorganic acids, leading to the formation of ferric and ferrous oxides and mass losses. Prolonged exposure of concrete structures to animal manure, agricultural effluents, and other chemicals results in the hydration of the cement, the formation of calcite crystals, and the disintegration of the structure. The threat to the durability of the structure is dependent on the composition of manure, and the nature of use--structures which are submerged in lagoons, or floor areas that come into contact with manure have a higher risk of corrosion compared to roofs and walls. In addition, poultry manure was associated with a pronounced risk of corrosion compared to swine or cattle manure, possibly due to the high concentration of H\({}_{2}\)S and NH\({}_{3}\) and other corrosive agents. The durability of agricultural structures can be reinforced by the selection of corrosion-resistant materials such as chromium steel in place of BS, FRP concrete, and sulfur concrete in place of standard Portland concrete and concrete, which contains adequate ratios of fly ash and silica and nano additives. In brief, the degradation of agricultural structures cannot be avoided, but it can be partly mitigated in the material selection process.  
Figure 8: Geopolymer resistance to acid attacks [17]. “Reproduced with permission from [PERSON]; [PERSON]; [PERSON], _Int. Biodetorior. Biodegradation_; published by Elsevier, 2018.”  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Changes in Meteorological Elements and Its Impacts on Yunnan Plateau Lakes
## 2 Methodology
### Data Source  
As a source of data for the analysis of climate change in the Yunnan Plateau lakes, we used an ERA5 re-analysis dataset, compiled by the European Centre for Medium-range Weather Forecasts (ECMWF), which was downloaded from the Copernicus Climate Change Service (C3S) Climatic Data Centre ([[https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)]([https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)), accessed on 19 January 2023), and is the most qualitative re-analysis dataset worldwide. For analytical  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Lake** & **Watershed** & **Surface** & **Water Surface** & **Average** & **Maximum** & **Water Change** \\  & **Area/km2** & **Area/km2** & **Elevation/m** & **Depth/m** & **Depth/m** & **Cycle/Day** \\ \hline Chenghai & 318 & 77.20 & 1501.00 & 24.98 & 35.87 & 5861 \\ Dianchi & 2920 & 297.90 & 1887.40 & 5.01 & 11.35 & 1540 \\ Erhai & 2565 & 249.00 & 1974.31 & 8.80 & 19.50 & 3500 \\ Fuxian & 675 & 211.00 & 1722.50 & 95.20 & 158.90 & 11,584 \\ Lugu & 248 & 48.50 & 2690.80 & 38.40 & 105.30 & 11,727 \\ Yangzonghai & 292 & 31.70 & 1770.00 & 21.50 & 28.59 & 4602 \\ Yilong & 360 & 38.00 & 1414.20 & 3.90 & 5.70 & 1295 \\ Xingyun & 378 & 34.70 & 1722.50 & 6.01 & 10.81 & 1642 \\ Qilu & 354 & 36.90 & 1797.65 & 4.20 & 6.84 & 316 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Morphological parameters of the nine major lakes on the Yunnan Plateau.  
Figure 1: A map showing the location of lakes in the study area.  
purposes, we downloaded and extracted data for the hourly near-surface air temperature, short-wave radiation, long-wave radiation, relative humidity, wind speed, rainfall, and snowfall of the ERA5 grid units nearest to each lake from January 1980 to December 2019. Due to the close location of the three lakes, Yilong Lake, Xingyun Lake and Qilu Lake, the data was extracted as the same grid, and Yilong Lake was used as a proxy for Qilu Lake and Xingyun Lake.
## 6 Simulation Results
### Network Performance Parameters: Definitions
#### 6.2.3 End-to-End Delay  
Figure 12a-c shows the end-to-end delay of the compared schemes (_i.e._, AURP, AEERP, AEDG and AEDG without GN rotation) subject to node density and network area increase. In AEDG, nodes remain alive for a long duration and transmit data packets through the SPT. End-to-end delay depends on the distance between communicating nodes and the speed of the acoustic signal. As the speed of the acoustic signal is almost constant (1450 m/s), end-to-end delay only depends on the transmission distance. In AEDG, the maximum number of nodes are alive for a longer duration, and each node transmits over a certain distance. Therefore, the end-to-end delay of the AEDG is greater than the AURP and the AEERP. In our protocol, there is a trade-off between end-to-end delay and network throughput. Figure 12a-b shows the effect that node density has on end-to-end delay; a high node density means a relatively greater number of communication links (distant communication), which leads to a high end-to-end delay. On the other hand, Figure 11c shows the direct impact of the network area on the end-to-end delay. Moreover, these figures show that the impact of the node density is far greater than the impact of the network area.  
Figure 11: Path loss. (**a**) 10 nodes deployed in 100 m \(\times\) 100 m; (**b**) 100 nodes deployed in 100 m \(\times\) 100 m; (**c**) 100 nodes deployed in 10 km \(\times\) 10 km.
###### Abstract  
Localization algorithms based on global navigation satellite systems (GNSS) play an important role in automotive positioning. Due to the advent of autonomously driving cars, their importance is expected to grow even further in the next years. Simultaneously, the performance requirements for these localization algorithms will increase because they are no longer used exclusively for navigation, but also for control of the vehicle's movement. These requirements cannot be met with GNSS alone. Instead, algorithms for sensor data fusion are needed. While the combination of GNSS receivers with inertial measurements units (IMUs) is a common approach, it is traditionally executed in a single-frequency/single-constellation architecture, usually with the Global Positioning System's (GPS) L1 C/A signal. With the advent of new GNSS constellations and civil signals on multiple frequencies, GNSS/IMU integration algorithm performance can be improved by utilizing these new data sources. To achieve this, we upgraded a tightly coupled GNSS/IMU integration algorithm to process measurements from GPS (L1 C/A, L2C, L5) and Galileo (E1, E5a, E5b). After investigating various combination strategies, we chose to preferably work with ionosphere-free combinations of L5-L1 C/A and E5a-E1 pseudo-ranges. L2C-L1 C/A and E5b-E1 combinations as well as single-frequency pseudo-ranges on L1 and E1 serve as backup when no L5/E5a measurements are available. To be able to process these six types of pseudo-range observations simultaneously, the differential code biases (DCBs) of the employed receiver need to be calibrated. Time-differenced carrier-phase measurements on L1 and E1 provide the algorithm with pseudo-range-rate observations. To provide additional aiding, information about the vehicle's velocity obtained by an odometry model fed with angular velocities from all four wheels as well as the steering wheel angle is incorporated into the algorithm. To evaluate the performance improvement provided by these new data sources, two sets of measurement data are collected and the resulting navigation solutions are compared to a higher-grade reference system, consisting of a geodetic GNSS receiver for real-time kinematic positioning (RTK) and a navigation grade IMU. The multi-frequency/multi-constellation algorithm with odometry aiding achieves a 3-D root mean square (RMS) position error of 3.6 m/2.1 m in these data sets, compared to 5.2 m/2.9 m for the single-frequency GPS algorithm without odometry aiding. Odometry is most beneficial to positioning accuracy when GNSS measurement quality is poor. This is demonstrated in data set 1, resulting in a reduction of the horizontal position error's 95% quantile from 6.2 m without odometry aiding to 4.2 m with odometry aiding.  
Global navigation satellite systems; GNSS; inertial measurement unit; IMU; odometry; tightly coupled; multi-frequency; multi-constellation
# Performance Analysis of a Zero-Energy Building Using Photovoltaics and Hydrogen Storage
## 4 Conclusions  
Hydrogen storage is an alternative and promising method for storing significant electricity quantities and helping the development of stand-alone buildings without grid connection. The present work investigates a building of 400 m\({}^{2}\) with photovoltaics, electrolyzer, hydrogen storage, and fuel cell in Athens, Greece. The most critical conclusions of this analysis are the following:  
* The specific heating thermal loads of the building per floor area are found at 24.44 kWh/m\({}^{2}\), while for cooling at 18.09 kWh/m\({}^{2}\). The thermal load of the domestic hot water was found at 8.63 kWh/m\({}^{2}\), while the electrical demand for appliances & lighting was at 39.42 kWh/m\({}^{2}\).
* The global electrical demand for satisfying all the building's needs was estimated at 59.14 kWh/m\({}^{2}\). This quantity is separated into 10.3% for heating, 7.6% for cooling, 15.4% for DHW, and 66.7% for appliances and lighting. So, it was found that the appliances and lighting demand are the major contributors to the total electricity demand.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Energy Parameter** & **Value (kWh)** \\ \hline Heating thermal energy demand & 9777 \\ Cooling thermal energy demand & 7237 \\ DHW thermal energy demand & 3452 \\ Electricity demand for heating & 2444 \\ Electricity demand for cooling & 1809 \\ Electricity demand for DHW & 3634 \\ Electricity demand for appliances and lighting & 15,769 \\ Total electricity demand & 23,656 \\ Electricity production by the PV & 63,637 \\ Available solar energy & 352,971 \\ Directly absorbed electricity from the PV & 11,062 \\ Electricity input in the electrolyzer & 52,575 \\ Electricity production by the fuel cell & 12,594 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Final data of the simulation analysis.  
The PV area was determined at 203 m\({}^{2}\) which is about half of the roof area. The storage capacity for the hydrogen was determined at 258 kg which means a tank of about 34 m\({}^{3}\). Regarding the building loads, the PV covers directly 46.8% of the building loads, while 53.2% is covered by the fuel cell.  
The PV performance is found at 18.03% and it corresponds to a produced electricity of 63,637 kWh or 313.5 kWh\({}_{\mathrm{el}}\)/m\({}^{2}\) of the PV field. From the produced quantity, only the 11,062 kWh are directly absorbed by the building, which is about 17.4% of the produced energy, while the remaining quantity feeds the electrolyzer device.  
The minimum stored quantity was observed on 27 March and it corresponds to 2.6% of the maximum stored quantity, while the maximum charge (100%) was found on 21 October.  
Conceptualization, E.B., P.L, C.T.; methodology, E.B., P.L.; software, E.B., P.L.; formal analysis, E.B.; investigation, E.B., P.L.; writing--original draft preparation, E.B., P.L, C.T.; writing--review and editing, E.B., P.L, C.T.; supervision, C.T. All authors have read and agreed to the published version of the