esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Specified Adaptive Robust Control Framework for Managing Nonlinear System Uncertainties
## 2 Background and Related Work
### Fixed-Time Control  
Fixed-time control is an emerging method that ensures the system reaches the desired state within a predetermined time, independent of the system's initial conditions or external disturbances. Unlike finite-time control, which ensures convergence within a finite time but where the convergence time depends on the initial state, fixed-time control guarantees that the time to reach the target is fixed. This control approach is designed through a control law that guarantees the system will achieve the desired state in a fixed time. It utilizes appropriate Lyapunov functions and mathematical techniques to ensure that the convergence time remains constant. The main benefit of fixed-time control is that it provides a reliable and fixed time to reach the target state, regardless of the initial conditions.  
Fixed-time control offers a significant advantage by ensuring convergence to the target state within a predetermined time, regardless of the system's initial conditions. This characteristic distinguishes it from finite-time control, where the settling time is influenced by the initial state. However, the implementation of fixed-time control involves a more complex design process, requiring a thorough understanding of the system's dynamics and parameters to achieve accurate controller tuning and effectively manage uncertainties. Due to its ability to provide rapid and precise control, fixed-time control is particularly well-suited for high-performance applications. These include spacecraft attitude regulation, unmanned vehicle operations, and real-time control systems that necessitate strict response times and robustness within a fixed time frame.
# Instance-Based Learning Following Physician Reasoning for Assistance during Medical Consultation
## 4 Clinical Knowledge Model to Follow Physician Reasoning
### Patient Representation
#### 4.2.3 New Medical Record  
Let \(CKB^{M}_{t}=\) {\(CT^{M}_{1}\), \(CT^{M}_{2}\),..., \(CT^{M}_{n}\)} be the composition of the clinical knowledge base of physician \(M\) at time \(t\). A medical record \(m^{P}_{t}\) is generated as a result of the interaction of physician \(M\) and patient \(P\), during a consultation at time \(t\).  
Since a physician usually takes a case type \(CT^{M}_{x}\) as basis to record a specific consultation, a transformation \(T^{*}\) can be applied to generate a new medical record. Consequently, a record \(m^{P}_{t}=T^{*}(CT^{M}_{x})\) is created, taking into account the active information of a selected case type. The active information of a case type is defined by the units of thought with _inuse_ attribute in true. Transformation \(T^{*}\): \(CKB^{M}\to MR^{P}\) is defined as \(T^{*}(CT)=mr\), where \(mr\) is generated by applying Algorithm 1.  
```
1:units \(\leftarrow\) getAllUnitsIncludedIn(CT)
2:content \(\leftarrow\emptyset\)
3:for unit in units do
4:if unit.inuse then
5:if not (unit.uapt or unitexph) then
6:itemCont \(\leftarrow\) [copyCurrentText(unit.ptext), unit]
7:content \(\leftarrow\) content \(\bigcup\) [itemCont]
8:endif
9:endif
10:endfor
11:mr\({}_{t}^{P}\leftarrow\) \(<\)content,P,t\(>\)
```  
**Algorithm 1** New medical record of patient \(P\)  
Algorithm 1 starts by getting all units of thought referenced in a case type \(CT\) (line 1). The algorithm iterates over all referenced units to identify units of thought marked with _inuse_ attribute (lines 3-4). Further, units marked with _uapt_ or _exph_ attributes are not taken into account for creating a new medical record (line 5). A new data pair is created for each identified unit (line 6), each pair includes the identified unit of thought, and a copy of its current text presentation. All new pairs are joined to build the full content of the consultation record (line 7). Finally, \(mr_{t}^{P}\) is created as a new medical record, containing the full description of the consultation of patient \(P\) at time \(t\).
## 2 Materials and Methods
### Dependence of Radiation Efficiency on Moisture Content in Grain  
The optimal matching between the radiation source and the waveguide is an essential requirement that ensures the full transfer of the energy generated by a magnetron ([PERSON], 2010). In our case, when the waveguide performs the function of a radiator directly applied to a grain layer, it is important to find out whether the energy transfer efficiency depends on the moisture content in grain during processing. Therefore, the change in radiation efficiency indicators was defined for grains of various moisture content.  
Antenna radiation efficiency (or radiation efficiency) can be defined as the following relationship between the input power and that emitted by the antenna:  
\[Rad\ Eff=\frac{P_{rad}}{P_{inp}}, \tag{1}\]  
where \(Rad\ Eff\) is the antenna's radiation efficiency that may be expressed in either arbitrary units or in dB, \(P_{rad}\) is the power dissipated in the antenna's output (dB), \(P_{inp}\) is the power supplied to the antenna's input (dB).  
One more indicator, called the antenna's total efficiency (\(Tot\ Eff\)), is used for evaluating its efficiency. Total efficiency can be defined using the radiation efficiency indicator and the power loss in the antenna:  
\[Tot\ Eff=Rad\ Eff\cdot P_{los}, \tag{2}\]  
Figure 3: Directional diagrams obtained by modelling, for microwave field emitted from the waveguide’s slots: (**a**) directional diagram of radiation, for Phi = 0\({}^{\circ}\); (**b**) directional diagram of radiation, for Theta = 90\({}^{\circ}\).  
where \(P_{los}\) is the power loss in the antenna due to impedance mismatching.  
In the course of modeling, the dependence of these two indicators on the moisture content in grain during processing in the convective-microwave zone was evaluated.
# SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition
## 3 Stacking of Distilled Vision Transformers (SDViT)
### Knowledge Distillation
To incorporate this knowledge transfer, the same temperature parameter \(T\) is employed when applying the SoftMax function to the student's logits during the computation of the loss function with respect to the teacher's soft targets. This loss function is commonly known as the distillation loss. In addition to the distillation loss, it is advantageous to ensure that the distilled model can accurately predict labels based on the true data. Consequently, the student loss is computed, which reflects the typical loss between the student's forecasted class probabilities and the actual ground truth labels. The comprehensive loss function, which combines both the distillation loss and the student loss, is formulated as follows:  
\[ \begin{split}\mathcal{L}(x;W)=\alpha*\mathcal{H}(y,\sigma(z_{s};T =1))+\\ \beta*\mathcal{H}(\sigma(z_{t};T=\tau),\sigma(z_{s};T=\tau))\end{split}  \tag{6}\]  
Figure 4: Network architecture of knowledge distillation of pretrained vision transformer model. In the given equation, \(x\) represents the input, \(W\) denotes the student model's parameters, \(y\) represents the actual ground truth label, and \(\mathcal{H}\) represents the cross-entropy loss function. The SoftMax function, which depends on the temperature parameter \(T\), is denoted by \(\sigma\). The coefficients \(\alpha\) and \(\beta\) are utilized to balance the contributions of the distillation loss and the student loss, where \(\beta=1-\alpha\). The logits of the student and teacher models are denoted as \(z_{s}\) and \(z_{t}\), respectively, with the subscripts \(s\) and \(t\) indicating the student and teacher models. From the loss function, it is observed that the values of \(\tau,\alpha\), and \(\beta\) are hyperparameters. The temperature parameter, \(\tau\), controls the richness of information in the soft labels distribution. Increasing the temperature leads to a more diverse distribution. However, it is important to note that using higher temperatures may not always be beneficial for the student model, as excessively high temperatures can make it difficult for the student model to capture all of the rich information. It is generally recommended to use lower temperatures, although the most suitable temperature value could differ depending on the unique attributes of the student model. On the other hand, \(\alpha\) and \(\beta\) dictate the weighted mean of both the distillation loss and the student loss, with \(\beta\) being defined as \(1-\alpha\). The authors have observed that setting \(\alpha\) to be much smaller than \(\beta\) leads to the best results. This implies that the distillation loss has a higher weight in the overall loss function, emphasizing the transfer of knowledge from the teacher to the student. It should be noted that the values of \(\tau\), \(\alpha\), and \(\beta\) are determined through experimentation and fine-tuning.
## 3 Model Concepts and Dynamic Routing Setup
### Base Score of a Waypoint  
Each waypoint receives a \"Base Score\" that follows a basic decision tree (the following decision tree is graphically represented in Figure 3 to aid the reader) based on the following attributes: whether the UAV has visited the waypoint or not (unvisited), whether the waypoint is initially in an SBW or not, and whether there is damage in the waypoint area. If a waypoint is not in the search area and not visited, the base score is 0.0. If a waypoint is in the search area and not visited, the base score is 0.5. If the waypoint is visited and not damaged, the waypoint base score is 0.0. If the waypoint is visited and damaged, the waypoint is given a base score of 5.0 (This value was initially set to 1.0; however, with  
Figure 2: Example of waypoints in the vicinity of the tornado, Green dots represent waypoints within the damage, blue dots represent waypoints within the search area that do not correspond to damage, and orange dots represent waypoints outside the damage area and outside the search area. Note: the size of the waypoints are not to the scale of \(r_{scan}\).  
initial testing, we quickly realized the score of 1.0 would not be sufficient). The base score is dynamic, but it only changes upon visitation with the UAV. Waypoints outside the SBW can be visited if the dynamic program decides to visit the waypoint, but will never receive the base score of 0.5: either the visitation will affirm the waypoint's base score of 0.0 (no damage), or it will receive the damaged score of 5.0. A decision tree of this process can be seen in Figure 3.
# Electrospinning-Based Carbon Nanofibers for Energy and Sensor Applications
## 2 Electrospinning of Carbon Precursor Polymer  
### Setup and Conditions for Electrospinning  
The electrospinninging machine consists of three main components: a syringe pump, a high-voltage power supplier, and a collector (Figure 1). Depending on the used material, the collector can be a simple planar-type metal plate or rump-type equipment. The setup of basic electrospinninging is so simple that it has become available to almost everyone [30]. It has been found that both a direct-current and an alternating-current power supply can be used in the electrospinninging system. During the operation of the device, the solution at the needle outlet was pulled towards the collector by an electrohydrodynamic process [31]. The polymer solution is then electrified, in which the solution vaporized while the polymer was stretching and generating fibers. The difference in the surface area of the needle outlet compared to the collector can cause the fiber to be split into many smaller fibers. There are many known factors that can affect the shape of the collected fibers, such as the viscosity of the polymer solution, needle size, distance between the needle and the collector, used voltage, flow rate, and the environment's humidity [32]. To improve the shape of the fibers, a lamp can be installed into the system. The lamp can provide heat to speed up the vaporization of the solvent, as well as the stabilization of the fibers before a new layer of fibers was put over the top. It is noted that the vaporized solvent will be released into the surrounding environment. Though it may cause air pollution, the spinning speed is so slow that it became an inconsiderable factor. However, the electrospinninging system can be installed in a fume hood, which can further reduce the impact of electrospinninging on the environment. Nowadays, the voltage used for an electrospinninging machine can vary from 8 kV to 35 kV and the current is maintained at a constant 0 A. The used voltage is known to be the most important factor that can significantly affect the quality of the collected fibers [33; 34]. Electrospinning can create a large number of precursor fibers for the fabrication of CNFs with high uniformity. There exist advanced modifications of the electrospinninging technique that allows the user the manually create a pore, hole, or channel, or decorate the metal/metal oxide in the nanostructure of the CNFs.  
In addition, the electrospinning system's working temperature is not high, hence it does not require a cooling system.
# LEO-Based Satellite Constellation for Moving Target Detection
## 5 Conclusions  
The existing theoretical research on multi-source fusion detection mainly focuses on the incoherent energy accumulation in image domain, which does not deeply explore the difference between the target cell and other background cells caused by the high-speed movement of LEO satellite platform. Therefore, it is necessary to explore and construct a novel fusion detection theory based on LEO-based satellite constellation. In this paper, the proposed multi-source fusion CFAR detection method is carried out from two aspects, i.e., SCR and SNR. On one hand, the distribution characteristic difference between the target and the scene clutter in DFR domain is applied to realize the clutter suppression effectively. On the other hand, the precise focusing of multi-source echo is completed by introducing the pre-compensation function. On this basis, the exponential weightedlikelihood ratio model is deduced in terms of the given multi-source fusion CFAR detection theory to further improve the target SNR. Simulation results indicate that the proposed method provides an efficient way for moving target detection with regard of LEO-based satellite constellation. Our future work will concentrate on exploring the fusion method among different types of satellite echoes, including communication, navigation and radar, and thus to expand the universality of multi-source fusion detection theory.  
Conceptualization, C.D., Y.L. and W.W.; methodology, C.D. and Y.L.; software, C.D., Y.L. and W.W.; validation, W.W. and J.L.; formal analysis, C.D.; investigation, C.D. and Y.L.; resources, C.D. and Y.L.; data curation, C.D. and Y.L.; writing--original draft preparation, C.D.; writing--review and editing, Y.L. and W.W.; visualization, Y.L. and J.L.; supervision, J.L.; project administration, Y.L. and J.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 62107033.  
The authors are grateful to University of Electronic Science and Technology of China for supporting this research. We also thank the associate editor and anonymous reviewers for their insightful comments and suggestions.  
The authors declare no conflict of interest.
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
###### Abstract  
Abandoned cropland may lead to a series of issues regarding the environment, ecology, and food security. In hilly areas, cropland is prone to be abandoned due to scattered planting, relatively fewer sunlight hours, and a lower agricultural input-output ratio. Furthermore, the impact of abandoned rainfed cropland differs from abandoned irrigated cropland; thus, the corresponding land strategies vary accordingly. Unfortunately, monitoring abandoned cropland is still an enormous challenge in hilly areas. In this study, a new approach was proposed by (1) improving the availability of Sentinel-1 and Sentinel-2 images by a series of processes, (2) obtaining training samples from multisource data overlay analysis and timeseries viewer tool, (3) mapping annual land cover from all available Sentinel-1 and Sentinel-2 images, training samples, and the random forest classifier, and (4) mapping the spatiotemporal distribution of abandoned rainfed cropland and irrigated cropland in hilly areas by assessing land-cover trajectories along with time. The result showed that rainfed cropland had lower F1 scores (0.759 to 0.8) compared to that irrigated cropland (0.836 to 0.879). High overall accuracies of around 0.90 were achieved, with the kappa values ranging from 0.851 to 0.862, which outperformed the existing products in accuracy and spatial detail. Our study provides a reference for extracting the spatiotemporal distribution of abandoned rainfed cropland and irrigated cropland in hilly areas.  
abandoned cropland; hilly areas; annual land-cover maps; Sentinel-1 and Sentinel-2 images; Google Earth Engine Article  
Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries  
1
## 2 Results and Discussion
### Characterization of Materials
#### 2.1.5 Determination of pH\({}_{\text{PZC}}\)  
Figure S4 reports the pH-drift titration of the AO-APEI sorbent. The pH\({}_{\text{PZC}}\) is close to 6.15. In acidic solutions, the sorbent is protonated: highest proton binding is observed at pH\({}_{0}\) 3-4 (initial pH), followed by a pH increase of about 1 unit. The equilibrium pH varies, under the optimal conditions, between 4 and 5.5. Under neutral or alkaline conditions, the equilibrium pH decreases by less than 1 unit. At higher pH (i.e., above 9), the pH decrease may reach up to 1.5 units. These properties directly control the ability of the sorbent to bind metal species through chelation/ion-exchange properties and the attraction/repulsion mechanism. At pH below pH\({}_{\text{PZC}}\), amine moieties in either amidoxime groups or PEI are protonated while the hydroxyl moieties are not deprotonated: the global charge of the amidoxime group is cationic. On the other hand, the carboxylic groups on alginate have pK\({}_{\text{a}}\) values close to 3.38 and 3.65 for mannuronic and guluronic acid groups, respectively [60], and those of primary, secondary, and tertiary amine groups in branched PEI are 4.5, 6.7, and 11.6, respectively [61]. [PERSON] et al. [62] reported pK\({}_{\text{a}}\) values for amidoxime higher than 11. The overall pH\({}_{\text{PZC}}\) is controlled by the relative influences of these different reactive groups and their respective deprotonations. In the case of amidoximated MCM-41, [PERSON] et al. [63] reported pH\({}_{\text{PZC}}\) value close to 5.35. [PERSON] et al. [47] reported a pH\({}_{\text{PZC}}\) value close to 6.84 for SiO\({}_{2}\)/Styrene/DVB functionalized with amidoxime groups. In the case of bis-amidoxime polymers, [PERSON] et al. [64] reported that the protonated-neutral proton transition happens around pH 5.5, while the neutral-anionic step occurs at pH close to 1. Both the environment and support of reactive groups strongly influence the acid-base properties of the sorbent.
## 3 Results and Discussion
### Removal of TSS, NH\({}_{3}\)-N and COD
#### 3.2.2 Reduction Rate of NH\({}_{3}\)-N  
Figure 4 shows the average reading value of NH\({}_{3}\)-N in the treatment sample by the four plants. The average of initial reading for NH\({}_{3}\)-N in the sewage sample was approximately 10.20 mg/L. On day 2 of treatment, the graph shows a slightly decreased NH\({}_{3}\)-N reading when treated with _Lemma minor_ (9.70 mg/L), _Salvinia minima_ (8.53 mg/L) and _Centella asiatica_ (6.45 mg/L). While the average reading of NH\({}_{3}\)-N reduction by _Ipomoea aquatic_ was significant, with a value of approximately 5.8 mg/L. On day 8, the phytoremediation of sewage wastewater by _Ipomoa aquatic_ shows the highest reduction in NH\({}_{3}\)-N content, which was about 0.28 mg/L, followed by _Salvinia minima_, _Lemma minor_, and _Centella asiatica_, with average values of 1.03 mg/L, 2 mg/L and 2.13 mg/L, respectively. Plants need nitrogen as a nutrient requirement for growth. The decreased NH\({}_{3}\)-N value observed when the ammonium ions and nitrogen were absorbed by plants through the root system. The selection of plants to reduce the NH\({}_{3}\)-N content in wastewater is important because the effectiveness of the treatment through phytoremediation can be enhanced by selecting aquatic macrophytes with a high tolerance towards ammonia level in wastewater [32]. The present of NH\({}_{3}\)-N content in wastewater was due to the processes of organic waste matter decomposition and nitrogen fixation [33].  
Figure 3: Graph of the average of TSS reading by the phytoremediation using _Lemma minor_, _Salvinia minima_, _Ipomoea aquatic_ and _Centella asiatica_.
# Long-Term Dynamics of Water Droplet Impact on Rotating Hydrophilic Disk
## 2 Experimental Description
The static contact angle of a deionized water droplet on the disk surface was measured to be 80 \(\pm\) 2\({}^{\circ}\), determined using side-view images processed with the public domain software ImageJ2 and its contact-angle plug-in (2018). During the experiments, droplets were released from heights ranging from 3.0 cm to 30.0 cm and impacted the center of the disk in stable rotation. The corresponding freefall speeds can be used to approximate the impact velocities within this range of heights ([PERSON], 2018). The resulting impact velocities span from 0.77 to 2.43 m/s, with Weber numbers varying between 20 and 200. The disk's rotating speeds in the experiments were set at 0, 500, 1000, 1500, 2000, 2500, and 3000 rpm, chosen with reference to the fan speeds of commercial turbofan engines ([PERSON], 2018). These correspond to rotational bond numbers of 0, 0.3, 1.2, 2.6, 4.7, 7.3, and 10.5, respectively. The disk rotates counter-clockwise during experiments.
## 6 Discussion and Future Work
### Trade-Off Analysis Between Four-Port and Two-Port Designs  
To validate the transition from the initial four-port configuration to the final two-port design, both antenna systems were fabricated and evaluated under identical free-space conditions. The four-port configuration exhibited a higher estimated MIMO channel capacity (approximately 15-18% increase) due to the additional spatial diversity and increased number of ports. The capacity was estimated using the well-known [PERSON]'s theorem applied to the measured S-parameters and simulated radiation efficiencies to account for correlation and per-port losses. MIMO channel capacity inherently depends on the number of antenna ports and the channel conditions, and it was calculated using the standard formula derived from [PERSON]'s capacity theorem. However, the measured isolation was limited to \(-\)4.6 dB, and the ECC exceeded 0.3 across significant portions of the n78 band, failing to meet the system-level performance requirements. Moreover, the four-port design required more complex impedance matching networks and precise dimensional control of the coupling gaps, which increased tuning complexity and reduced manufacturing yield. Mechanical integration studies further indicated that the battery, side buttons, and structural ribs restricted the available space along the metal frame, preventing consistent implementation of the four-port layout in production devices. In contrast, the two-port configuration provided more stable impedance matching, consistent isolation and reliable integration within the existing chassis envelope. For these reasons, the two-port design was selected as a more practical solution offering a balanced trade-off among capacity, complexity, and manufacturability.