esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
## 2 Framework
### Emissivity Data  
The operational MODIS Level-2 LST product (MOD11_L2 and MYD11_L2) uses a classification-based approach for a number of defined land cover types when determining the pixel-level emissivities. This classification approach works well for surfaces where the emissivity can be accurately assigned, such as full vegetated canopies, lakes and snow. It is less optimum for surfaces with large variability in emissivity, such as arid and semi-arid regions [39]. To overcome this weakness, the MOD21_L2/MYD21_L2 product utilises the Temperature-Emissivity Separation (TES) approach, fits applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument [40], in order to simultaneously retrieve LST and emissivity using information from three thermal channels of MODIS [39].  
Here we use a different approach and utilise an external emissivity dataset as input to the GSW algorithm. The most appropriate dataset is the well-established global database of land surface emissivities produced by the University of Wisconsin (UWIREMIS), also known as Cooperative Institute for Meteorological Satellite Studies (CIMSS) [41]. This is a monthly dataset, starting in 2002, derived using the standard land surface emissivity product from the MOD11B1 product. The dataset is available at ten wavelengths between 3.6 \(\upmu\)m and 14.3 \(\upmu\)m at a spatial resolution of 0.05\({}^{\circ}\). For the split-window channels used in the GSW algorithm, UWIREMIS emissivities at 10.8 \(\upmu\)m and 12.1 \(\upmu\)m can be used. The ten wavelengths were chosen as hinge points to represent the shape of the higher resolution emissivity spectra. The spectral gap between the input MOD11B1 channels is filled in by applying a baseline fit method [41].  
In the implementation, the monthly 0.05\({}^{\circ}\) emissivity data is spatially and temporally interpolated onto the 1-km instantaneous observation native swath grid to provide pixel-level emissivities. Spatially, a bilinear interpolation is applied and temporally, a linear interpolation is applied to the day of the MODIS acquisition. Where no data exists for a month--for example before the start of the UWIREMIS database--a climatology of the monthly database is used instead.
## 4 Model-Based Calibration
### Calibration Constants  
The procedures described in the preceding sections have provided range frequency and propagation direction dependent corrections. The last step in the external calibration is then to introduce calibration constants. These account for systematic offsets in gain, delay and phase that are inherent to the sensor and are not related to the physical characteristics of the imaged scene.  
Indeed, such constants have already been estimated: gain offsets \(\Delta\text{RCS}_{pq}\), range offsets \(\Delta r_{pq}\) and phase offsets \(\Delta\phi_{pq,uv}\) are byproducts of the minimization of error measures (29), (30) and (39), respectively. As corrections are introduced sequentially, however, gain and delay offsets need to be re-estimated to ensure consistency. Robust re-estimation is accomplished by solving minimization problems of the form (21) for simplified versions of the error measures (29) and (30). Simplification entails symbolically substituting \(\vec{\delta}_{e}^{q,p,q}=0\) and \(\Delta\vec{L}_{e}=0\), such that the model parameter sets \(\mathcal{O}\) are reduced to the calibration constants of interest.  
While the calibration constants \(\Delta\text{RCS}_{pq}\) and \(\Delta r_{pq}\) can be easily and conveniently applied to the raw data of each channel \(pq\) before SAR focusing, the phase calibration constants \(\Delta\phi_{pq,uv}\) would have to be applied each time an interferogram is formed. In practice, it is therefore desirable to derive phase calibration information for individual channels, such that  
\[\Delta\phi_{pq}+\Delta\phi_{uv}=\Delta\phi_{pq,uv}. \tag{43}\]  
Solving this system of equations is not straight-forward due the fact that the interferometric phase is wrapped. Constants for individual channels can, however, be obtained by constructing a graph with nodes \(\Delta\phi_{pq},\Delta\phi_{uv},\dots\) connected by directed edges associated with phase differences \(\Delta\phi_{pq,uv}\). A phase unwrapping algorithm for arbitrary grids, such as [27; 28], can then be used to obtain the desired individual channel calibration constants.
## 4 Model Predictive Control for Power Optimization and DDPG-Based Optimization Scheduling
### Model Predictive Control for Power Optimization
#### 4.1.3 Optimize Control Inputs  
The objective is to minimize electrical power consumption while ensuring compliance with the requirements of the flight mission.  
\[f(t)=\min_{u(t)}\sum_{t=k}^{k+N}\left[P(t)+\lambda\cdot J(x(t),u(t))\right] \tag{33}\]  
where \(P(t)\) is the electrical power consumption, \(J(x(t),u(t))\) is the cost function of the control objective, and \(\lambda\) is the weighting parameter.
## 2 UGV System Configuration  
As shown in Figure 1, the developed UGV was equipped with a landing pad (UAV interface system) for takeoff and landing of UAVs, while a 2D LiDAR sensor was installed in the front part for recognizing obstacles. The top of the UGV was equipped with GPS/INS sensors and a mapping system [20] for cooperative missions with UAVs.  
The configuration of the system is shown in Figure 2. The mission control system (MCS) was developed using a TI micro controller (TMS320F28335) and equipped with a modem to communicate with the control station, as well as transmit/receive data through RS232. It receives the UGV operation mode and target points from the control station, and it sends the vehicle's location, direction, and battery status. In addition, it transmitscommands and information through CAN communication with the internal driving control system (DCS) and UAV landing pad controller.  
Adventech's industrial PC UNO-3284G was used as the DCS; it receives and processes data from the navigation sensors and LiDAR, and it generates a local path to avoid obstacles by receiving target points from the MCS. The generated local path is transmitted to the MCS capable of real-time control, and the steering angle and pedal control amount are calculated.  
Figure 1: UGV for cooperative mission.  
Figure 2: Block diagram of UGV system.  
The actuator controller receives the steering angle and acceleration/brake pedal input from the MCS to control the actuators mounted on the UGV. The steering angle is received in the form of an angle, and the pedal input is received as a percentage; it is controlled to generate a constant torque until it reaches the target value within a certain range.  
Trimble's Applanix AP-20E GPS/INS was used as a navigation sensor, and Sick's LMS511 was used for LiDAR. The LiDAR is connected with the DCS via Ethernet, while the GPS/INS is connected with the DCS via RS232.  
The UAV landing pad is equipped with an IMU, actuators, and controllers to keep the UAV level when landing, as well as a mechanism for fixing the UAV so that it can travel in the landed state.
# Experimental Study on the Straw Flexible Space Assembly Flood Control System
## 2 Design of the Flood Control System
### Material Selections for Straw Flexible Space Flood Control System  
Water baffles are one of the main component materials of the flood control system, and aluminum alloy is used in the water baffle panels. Aluminum alloy has good corrosion resistance and does not rust easily even after prolonged contact with water; thus, it increases the service life of the water baffles. As one of the main force members, columns must have high resistance to overturning, good stability, and other characteristics. Moreover, they must have good welding performance. Hence, Q235 steel is selected as the column material so that the columns are easier to process. In addition, it can ensure that the flood control system can withstand larger waves and maintain the stability of the overall structure. Straw has a strong deformation capacity as well as good water absorption and expansion characteristics. Hence, straw is used as a filling material for flexible spaces. The mechanical property parameters of the component materials of the straw flexible space assembly flood control system are summarized in Table 1.
## 5 Simulation Results and Discussion
### Climbing Maneuver  
This section presents the climbing performance of the aircraft with and without the presence of wind. The climbing maneuver was performed at the trim airspeed of 20.8 m/s, without the presence of wind. The simulation results are shown in Figure 15. The initial altitude is 150 m and the commanded altitude is 158 m. During the aircraft climbing, the airspeed is maintained within the variation of \(\pm\)0.2 m/s from trim airspeed. The settling time for the climbing maneuver is around 40 s. Moreover, the change of pitch rate, \(\Delta q\), is regulated within \(\pm\)0.01 rad/s, which is the same as in level flight. On the other hand, \(\dot{L}_{\mathrm{s}}\) is regulated around the neighborhood of 0.01. Note that there is a slight decrease of \(\dot{L}_{\mathrm{s}}\) at 18 th second, which corresponds to the increase in flight path angle of the aircraft.  
Figure 14: Simulation results of level flight with the presence of wind.  
Figure 16 shows the simulation results with the presence of wind and the largest magnitude of wind is 8 m/s throughout the climbing maneuver. During the aircraft climbing, the change of pitch rate, \(\Delta q\), is regulated within \(\pm\) 0.15 rad/s and \(\dot{L}_{\mathrm{s}}\) is regulated within the variation of \(\pm\)0.1. Besides that, the airspeed is maintained at trim airspeed within the variation of \(\pm\)1 m/s. The settling time of altitude tracking is around 65 s.  
Figure 15: Simulation results of climbing without the presence of wind.
# Efficient Fine-Tuning of Large Language Models via a Low-Rank Gradient Estimator
## 3 Background  
**Naive Full-Rank Training.** For a given weight \(\mathbf{W_{0}}\in\mathbb{R}^{m\times n}\), the conventional update can be expressed as follows:  
\[\mathbf{W_{T}}=\mathbf{W_{0}}+\eta\sum_{0}^{\mathbf{T}-1}\mathbf{G_{t}} \tag{1}\]  
where \(\mathbf{G_{t}}\) represents the gradient added to the weight matrix at step \(t\). In training, the backward pass is both memory-intensive and computationally demanding. For example, with Adam, gradients \(\mathbf{G_{t}}\) and additional optimizer states \(\mathbf{M},\mathbf{V}\in\mathbb{R}^{m\times n}\) are used to regularize the gradient, increasing memory requirements by \(3 mn\). Gradient calculations involve two full-rank operations:  
\[\partial\mathbf{O}/\partial\mathbf{x}=\delta_{\mathbf{O}}\mathbf{W}\qquad\partial \mathbf{O}/\partial\mathbf{W}=\delta_{\mathbf{O}}^{\mathbf{T}}\mathbf{x} \tag{2}\]  
Here, \(\mathbf{x}\in\mathbb{R}^{b\times n}\) is the input, and \(\delta_{\mathbf{O}}\in\mathbb{R}^{b\times m}\) represents the output error. This naive approach incurs \(4 bm\) FLOPs during the backward pass.  
**Low-Rank Adaptation.** For a linear layer \(\mathbf{W}\in\mathbb{R}^{m\times n}\), LoRA and its variants model the incremental update of the pre-trained weight with the product of two low-rank matrices \(\mathbf{AB}\):  
\[\mathbf{W}=\mathbf{W_{0}}+\mathbf{BA}, \tag{3}\]  
where \(\mathbf{W,W_{0}}\in\mathbb{R}^{m\times n}\), \(\mathbf{A}\in\mathbb{R}^{r\times n}\), and \(\mathbf{B}\in\mathbb{R}^{m\times r}\), with \(r\ll min(n,m)\). With Adam, LoRA needs \(3 mr+3 nr\) memory to store optimization states and gradients. The gradient calculation for weight involves two low-rank operations:  
\[\partial\mathbf{O}/\partial\mathbf{A}=(\delta_{\mathbf{O}}\mathbf{B})^{T} \boldsymbol{x}\qquad\partial\mathbf{O}/\partial\mathbf{B}=\delta_{\mathbf{O}} ^{T}(\boldsymbol{x}\mathbf{A}) \tag{4}\]  
During backpropagation, LoRA simplifies FLOPs to \(2 bmn+6 bmr+6 bmr\), thereby reducing the calculation.  
**Quantization.** Quantization is the process of mapping continuous or high bit-width data types into discrete and often lower bit-width values. For instance, converting a 16-bit floating point (FP16) tensor into a 4-bit integer (INT4) tensor with a range of [-7, 8] can be expressed as follows:  
\[\mathbf{X^{INT4}}=\text{round}(\frac{15}{\text{absmax}(\mathbf{X^{FP16}})} \mathbf{X^{FP16}})=\text{round}(\frac{\mathbf{X^{FP16}}}{\mathbf{s^{FP16}}}) \tag{5}\]  
where \(\boldsymbol{s}\) is the quantization scale. Dequantization is the inverse:  
\[\text{dequant}(\boldsymbol{s^{FP16}},\mathbf{X^{INT4}})=\boldsymbol{s^{FP16}} \mathbf{X^{INT4}})=\widehat{\mathbf{X}}^{FP16} \tag{6}\]  
Here, \(\widehat{\mathbf{X}}\) is an estimated value, as quantization will introduce quantization error for original values. Quantization is introduced to reduce the weight memory usage for LLMs, as PEFT reduces the learnable parameters, thereby reducing memory usage for weight gradients.
## 1 Introduction
This implementation could also be used in military and other government applications. A similar private network with one, two, or more portable base stations could also be usable for forest firefighting forces, acting in variable area fields, or when public base stations are damaged. The advantage of the proposed private 4G/LTE network is that in addition to VoIP calls and sensor data transmitting (like GPS coordinates), photos, video, and other data files may be sent as well. Moreover, using an appropriate application, it can also make real-time video calls. Lastly, it is based on mobile network security standards, providing secure telecommunications to isolated network users. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Simulation Scenarios** & **Experimental Scenario** \\ \hline LTE Base Stations & 1 - 2 & 1 \\ Base Station TX Power & +16 dBm & +16 dBm (max) \\ Mobile Devices & 10 & 2 \\ Mobile Device TX Power & +24 dBm & +24 dBm (max) \\ Voice Codec & G.711 & G.711 \\ Coverage Area & 2 km\({}^{2}\) & 100 m\({}^{2}\) \\ Tested Services & VoLTE & VoLTE, data transmission \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation and experimental scenario parameters.
## 3 Experimental Results
### Results and Discussion
#### 3.2.2 Multitemporal Analysis of the Vegetation Activity  
The NDVI graphs are the other option with which the INSPECTORMAP tool allows to know the evolution of the vegetation. Unlike static NDVI maps, NDVI graphs allow for knowing the activity and growth of vegetation over a period of time. It is a very interesting alternative to know the behavior of the vegetation over time and plan the possible maintenance tasks. As can be seen in Figure 11, for each of the photovoltaic plants analyzed, a moment of maximum growth and vigor of the vegetation is always appreciated. Depending on the type of plant, this behavior will vary throughout the year. It is worth mentioning that the value of NDVI on cloudy days decreases sharply, hence all the sharp drops in the charts. All those graphs that represent the vegetative activity of the existing grass between the solar panels (Figure 11a,c,e) show a similar behavior, since during the months of June, July, and August there is either no vegetation (Figure 11e) or it is dry (Figure 11a,c). Although Figure 11b shows a totally different behavior, the vegetation on the plot is also grass. On the contrary, in the case of Figure 11d, the behavior of a vegetation characterized by being watered is represented where the NDVI presents a high value due to the vigor and humidity of the crop. Similarly, the plot to the left of the plant 6 exhibits high NDVI from April to October due to the vigor of the vegetation and its humidity.  
Figure 9: Graphic representation of the state of the vegetation through the NDVI for the different case studies in the month of May: (**a**) plant 1, (**b**) plant 2, (**c**) plant 3, (**d**) plant 4, (**e**) plant 5, and (**f**) plant 6.  
Figure 11: Vegetative activity through time in the vicinity of the photovoltaic plants analyzed by means of multitemporal NDVI graphs: (**a**) of the vegetation between the solar panels, (**b**) of the vegetation present in the adjacent right plot, (**c**) of the area without the presence of solar panels within the installation plot, (**d**) of the vegetation of the lower triangular plot, \(\mathbf{\xi}\) of the vegetation surrounding the solar installation, (**f**) of the vegetation of the plot located to the left of the installation.  
Figure 10: Areas detected and quantified by the INSPECTORMAP alert system for the dates that reveal greater vegetal vigor: (**a**) plant 1, (**b**) plant 3, (**c**) plant 4, and (**d**) plant 6.
# Excavation and Construction Technology of Diversion Tunnel under Complex Geological Conditions
## 2 Project Overview and Construction Difficulties Analysis
### Analysis of Construction Difficulties  
Based on the geological conditions of the project, the difficulties in construction are analyzed, and the results are as follows:  
1. Due to insufficient geological exploration during the feasibility study phase of the project, the potential risks during the construction phase are high.
2. The excavation volume of the sedimentation tank and water inlet is large, with high strength and poor geological conditions. Local rock mass is broken, especially at the top of the tunnel, which belongs to Class IV 1 surrounding rock. Moreover, the tunnel body is cut by large geological faults, which can easily cause rock mass detachment and falling off during blasting construction and disturbance, affecting the quality of tunnel cross-section formation. In addition, the slope is steep after excavation, and the problem of slope stability is prominent.
* The excavation section of the diversion tunnel by a conventional drilling and blasting method is characterized by complex geological conditions, small tunnel excavation section, high crustal stress, long line, large quantities, tight construction period, etc. The underground water seepage (gushing), rockburst, surrounding rock stability, etc. in the tunnel are extremely harmful to safety during the construction period.
* The TBM excavation section of the headrace tunnel is characterized by large burial depth, high crustal stress, complex geological conditions, long route, and tight construction period. Underground water seepage, rockburst, and surrounding rock convergence deformation in the tunnel are unfavorable to TBM construction.
* The single head excavation of the diversion tunnel has a long distance, making ventilation and smoke exhaust extremely difficult. At the same time, due to the problem of high ground temperature, it has a significant impact on work efficiency.
* The water diversion and pressure-regulating shaft is deep, has a large diameter, and has poor geological conditions. During the excavation process, it is easy to cause of the shaft, which poses a high safety risk during the construction period. Therefore, high requirements are placed on the excavation accuracy of the guide shaft and the performance of the excavation support equipment.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 2 Underwater Communication Technology Overview
### Optical Communication  
The medium behavior is the main difference between optical and RF propagation in seawater. It is dielectric for optical propagation and conductor for RF. The description for this phenomenon is based upon the plasma frequency, which behaves as either a conductor or dielectric, according to the frequency range. At around 250 GHz, seawater changes from conductor to dielectric. EM waves have lower attenuation in dielectric media than in conductor ones. For a propagation range restricted to 10s of meters, higher data rates can be provided using optical technology. The effect and Doppler spread are minor in communications of optical wireless systems, as the speed of light is larger than the acoustic wave propagation speed in fluids, i.e., by around 4 to 5 orders of magnitude.
## 2 Literature Review
### Hydrogen Storage and Delivery Challenges  
The literature review revealed that one of the most significant barriers to hydrogen aviation is fuel storage and delivery [33]. Unlike conventional jet fuel, hydrogen must be stored either as a compressed gas or a cryogenic liquid. Compressing hydrogen requires tanks that can support pressures of up to 700 bars. This requirement adds structural weight to aircraft and increases their design complexity. As a cryogenic liquid, hydrogen achieves greater energy density. However, keeping it at \(-253\)\({}^{\circ}\)C requires advanced insulation and boil-off mitigation strategies [5]. Hydrogen storage systems must be lightweight, safe, and space-efficient. These features necessitate research into novel cryogenic tanks, composite materials, and heat exchanger technologies.  
Additionally, hydrogen delivery systems must maintain stable fuel flow while mitigating risks such as leakage, embrittlement, and pressure fluctuations [4]. Furthermore, refueling at airports must be within the loading and unloading turnaround time to maximize aircraft utilization efficiency and minimize parking costs [34]. To address these challenges, researchers have proposed several delivery system innovations tailored to aerospace conditions. One common solution involves thermally integrated piping systems that use engine waste heat to preheat cryogenic hydrogen, ensuring consistent gas-phase delivery at optimal pressures [5]. Advanced regulator valves with embedded sensors can dynamically adjust flow rates based on altitude and engine demand, reducing the risk of pressure drops or surges [33]. To combat hydrogen embrittlement, composite pipelines with non-metallic liners and corrosion-resistant alloys have been developed for improved durability and weight savings [4]. Additionally, companies are exploring modular refueling interfaces (some robotic or self-aligning) to streamline turnaround times while minimizing leakage risk [34]. Together, these solutions aim to create a resilient hydrogen delivery infrastructure that can support safe and efficient aircraft operations.