esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition
## 3 Materials and Methods
### IBVS Model
Therefore, we use the inner region feature and the line feature as the visual features of the camera's translation and rotation movement, respectively. The real-time target feature can be written as \(s=\left[x_{0},y_{0},a,\theta,0,0\right]^{\mathrm{T}}\), where \(x_{0},y_{0}\) respectively correspond to the centroid coordinates of the circular feature, \(a\) is the circular feature of the inner area, and \(\theta\) corresponds to the direction angle value of any edge of the target area. The desired feature can be written as \(s^{*}=\left[x_{0}{}^{*},y_{0}{}^{*},a^{*},\theta{}^{*},0,0\right]^{\mathrm{T}}\). Attitude control based on the line feature can be denoted as  
\[\omega_{\varphi}=-\lambda_{c}{J_{c}}^{-}(\theta-\theta^{*}) \tag{10}\]  
\(J_{c}^{-}\) is the Moore-Penrose pseudo-inverse of the Jacobian matrix \(J_{c}\). \(\lambda_{c}\) is the pose controller gain. The displacement velocity vector of the camera is defined as follows  
\[v=v_{\alpha}+v_{\omega} \tag{11}\]  
where \(v_{\alpha}\) and \(v_{\omega}\) are the camera translation velocities with the compensation of the inner region feature vector and the center of mass, as in [33]. Considering that the real-time target feature is \(s\) and the desired target feature \(s^{*}\), the feature error \(e\) can be calculated as  
\[e=s^{*}-s. \tag{12}\]
## 3 State Evaluation and Fault Prediction of Protection System Based on Digital Twin Technology
### State Evaluation of Protection System Based on Matter-Element Extension
#### 3.2.4 Closeness Calculation and State Determination  
This paper uses the concept of closeness degree to calculate the closeness degree of each characteristic index to the state level [30]. The calculation formula of closeness is shown in Equation (11).  
\[K=1-\frac{1}{n(n+1)}\sum_{i=1}^{n}Dw_{i},(i=1,2,\cdots,n) \tag{11}\]  
In this paper, the formula of close degree between protection system and \"normal\", \"attention\", and \"severe\" three classical domains \(R_{1-3}\) is  
\[K_{j}(\mathbf{N})=1-\frac{1}{n(n+1)}\sum_{i=1}^{n}D_{j}(v_{i})w_{i}(\mathbf{X}),(j=1,2,3) \tag{12}\]  
\(K_{j}(\mathbf{N})\) is the closeness between the current protection system and the \(j\)-th classical domain \(R_{j}\). \(n\) is the number of characteristic quantities. The distance between the protection system to be evaluated and the classical domain \(D_{j}(v_{i})=\left|v_{i}-\frac{a_{ij}+b_{ij}}{2}\right|-\frac{1}{2}\left(b_{ij}-a _{ij}\right)\), \(a_{ij}\) and \(b_{ij}\) are the left and right boundaries of the classical domain \(R_{j}\), respectively.  
In order to better display the closeness degree and change trend with each classical domain, and to judge the state level, according to the calculated closeness degree, the relative closeness degree of the matter-element to be evaluated can be calculated by the following formula.  
\[\overline{K_{j}}(\textbf{N})=\frac{K_{j}(\textbf{N})-minK_{j}(\textbf{N})}{ maxK_{j}(\textbf{N})-minK_{j}(\textbf{N})},(j=1,2,3) \tag{13}\]  
In the formula, \(maxK_{j}(\textbf{N})\) is the maximum value of the closeness of the matter-element to each state level, and \(minK_{j}(\textbf{N})\) is the minimum value of the closeness of the matter-element to each state level.  
The state level with relative closeness degree one is the state evaluation level.
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 3 Cooperative Control of Variable-Sweep Wing UAV via MADDPG  
MARL has proven to be highly effective in the cooperative control of variable-sweep wing UAVs, particularly in achieving efficient formation control within complex environments. The primary challenge lies in managing continuous action spaces in dynamic flight states, which traditional reinforcement learning methods struggle to address. To overcome these challenges, the MADDPG-VSC algorithm has been specifically developed and optimized, taking full advantage of the unique characteristics of variable-sweep wing UAVs. This algorithm allows for dynamic adjustment of the sweep angle, thereby optimizing formation control and enhancing trajectory planning and obstacle avoidance capabilities, as illustrated in Figure 5.  
Figure 4: Aerodynamic characteristics of the L-30A variable-sweep wing UAV. (**a**) Lift and drag coefficients. (**b**) Lift-to-drag ratio.  
Figure 5: Schematic of cooperative formation control in variable-sweep wing UAV system.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 6 System Implementation
### System Components
#### 6.1.1 Vehicle Mobility Generation  
We chose a one-square-kilometer area from Istanbul's Besiktas District (Figure 5) to reflect an urban traffic and road infrastructure in our system. The data from the selected region is imported by using the OSM Web Wizard, a utility application bundled with SUMO. On each run, we retrieved location data from the SUMO Traffic Control Interface (TraCI) iteratively while tracking the progress on the GUI interface. To visualize the topology better, UAVs and the BS are inserted as point-of-interest objects on the GUI interface and updated at each step. Additionally, circles are used to highlight APs' terrestrial ranges on the interface. In our simulations, we used five different vehicle classes: two of them with the task owner role, two of them with the task processor role, and one with no network role. The last one represents private vehicles that our network does not serve. Vehicle related simulation settings are listed in Table 5.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Symbol** & **Value** & **Definition** \\ \hline \(p_{medium}\) & 0.7 & The probability of the task offloaded to the cloud if the pool is moderately occupied \\ \hline \(p_{light}\) & 0.3 & The probability of the task offloaded to the cloud if the pool is lightly occupied \\ \hline \(\Theta_{heavy}\) & 10 & The threshold value to classify the controller pool as heavily occupied \\ \hline \(\Theta_{medium}\) & 7 & The threshold value to classify the controller pool as moderately occupied \\ \hline \(\Theta_{light}\) & 3 & The threshold value to classify the controller pool as lightly occupied \\ \hline \(\Theta_{decision}\) & 10 M & The maximum number of candidate solutions that the algorithm will attempt to solve within a time-window \\ \hline \(P_{tx,U_{t}}\), \(P_{tx,V_{i}}\) & 23 dBm & Transmission power of \(U_{i}\), \(V_{i}\) \\ \hline \(G_{tx,U_{t}}\), \(G_{tx,V_{t}}\), \(G_{rx,V_{i}}\) & 3 dBm & Antenna gains \\ \hline \(\sigma^{2}\) & \(-91\) dBm & Variance of White Gaussian Noise \\ \hline \(f\) & 2.4 GHz & The system carrier frequency \\ \hline \(\rho\) & 2 & Air-to-air path loss exponent \\ \hline \(\eta_{LoS}\), \(\eta_{NLoS}\) & 1 dBm, 20 dBm & The additional LoS,NLoS attenuation factors due to the LoS,NLoS connections \\ \hline \(z\) & 60 & The extra penalty point if the task deadline is missed \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._Figure 4: System components.  
Figure 5: The visualization of topology elements in Besiktas network. The terrestrial coverage ranges of UAVs are indicated by the yellow circles around them.
## 3 Results and Discussion
### Influence of pH on the Dye Sorption Efficiency on MES and MMS
At low pH, protons from hydronium cations are bound to basic functional groups (-NH\({}_{2}\) + H\({}_{3}\)O\({}^{+}\rightarrow\)-NH\({}_{3}\)+ H\({}_{2}\)O). The solution pH is inversely proportional to the levels of hydronium ions. In contrast, at high pH, acidic functional groups release their protons, resulting in the neutralization of the hydroxide anions (-COOH + OH\({}^{-}\rightarrow\) -COO\({}^{-}\) + H\({}_{2}\)O) and lower pH. The sorbents always tend towards a system pH close to the pH\({}_{\text{PZC}}\) for the given sorbent (PZC--point of zero charges), i.e., the pH at which the number of positively charged sites on the tested sorbent is the same as the number of negatively charged ones. The point of zero charges determined for MES and MMS using Boehm titration is pH\({}_{\text{PZC}}\) = 7.26 and pH\({}_{\text{PZC}}\) = 7.02, respectively (Figure 3e,f). The higher pH\({}_{\text{PZC}}\) determined for MES compared with MMS may be due to a higher number of basic functional groups (e.g., -NH\({}_{2}\) or -NHCOCH\({}_{3}\)). Subsequent stages of the study, as described in Sections 3.3 and 3.4, were carried out at optimal sorption pH for RB5 and RY84--pH 2.
# Advanced Label-Free Laser Scanning Microscopy and Its Biological Imaging Application
## 2 Laser Scanning Coherent Raman Scattering Microscopy (CRSM)
### Laser Scanning Stimulated Raman Scattering Microscopy (SRSM)
By now, SRSM has achieved a high speed with video-rate, sensitivity down to single molecules, spatial resolution breaking the diffraction limit and volumetric imaging depth with millimeter [53]. Moreover, SRSM permits rapid imaging of polystyrene and polymethyl methacrylate beads in Brownian motion in dimethyl sulfoxide (DMSO) at 70 ms intervals without motion artifacts, and shows powerful ability to monitor real-time cancer treatment effects and in vivo transport of drug solvent and penetration of DMSO into the plant tissue in vivo [54]. Recently, SRSM rapidly developed and was widely applied in various branch of life science, such as histopathology, cell biology, neuroscience, tumor research etc. Histopathological diagnosis in vivo has been demonstrated by using multicolor images originating from CH2 and CH3 vibrations of lipids and proteins, which retrieved the subcellular resolution from fresh tissue [55]. Moreover, SRS imaging to lipid metabolism was reported to reveal an aberrant accumulation of esterified cholesterol in lipid droplets of high-grade prostate cancer and metastases [56]. Label-free SRS imaging of DNA enabled noninvasive visualization of live cell nuclei in both human and animals. Through the distribution of DNA retrieved from the strong background of proteins and lipids by linear decomposition of SRS images at three optimally selected Raman shifts, it is possible to obtain instant histological tissue examination during surgical procedures [57]. SRSM has also been proved to be able to differentiate healthy human and mouse brain tissue as well as tumors from non-neoplastic tissue based on their different Raman spectra (Figure 3b) [58]. Likewise, SRS imaging has shown its potential capability and application for differentiating misfolded amyloid-\(\beta\) on Alzheimer's disease pathology (Figure 3c) [59]. These works provided new approaches for tracing dynamics and metabolism in living cells, which can be used for diagnosing and treating diseases. SRS imaging has also been used for monitoring the breakdown of biomass to biofuels. It helps researchers to understand the conversion mechanisms of plant cell biomass to biofuels and find cost-effective industrial conversion strategy. By recognizing the Raman signal, the different chemical bonding is detected. A chemical movie recording degradation of lignin and cellulose in cell wall was used to understand which parts of plant are degraded most efficiently by the treatment process [60]. By comparing the two-colored SRS imaging before and after delignification reaction, another study revealed that hemicellulose uses were generally resistant to acid chlorite at room temperature, and digestion of the delignified walls apparently depended on wall mass concentration but not on wall types (Figure 3d) [61]. These works have greatly promoted the development of SRSM and its application in biomedicine [62; 63; 64; 65].
## 3 Methods  
### Crop Growth Model  
The CERES-Wheat model was developed and integrated into DSSAT software in an International Benchmark Sites for Agro-technology Transfer project sponsored by the United States Department of Agriculture [8]. Driven by input data on weather, soil, field management, and genetic information, the CERES-Wheat model can simulate daily phenological development, vegetative and reproductive plant developmental stages, assimilate partitioning of the growth of leaves and stems, senescence, biomassaccumulation, and root system dynamics under stressful environments, _i.e._, those involving light, temperature, water, nitrogen, carbon, and field management interventions [11]. The water and nitrogen sub-models have feedback effects on plant growth and development. This model has been widely applied to field sites to assess crop potential productivity [51,52] and the influence of climate change on grain yields [40,41], farmland water, and fertilizer management [53].  
To obtain accurate predictions, a model calibration is first performed, _i.e._, an estimation of cultivar characteristics using field winter wheat experimental data. In this study, _in situ_ data for the study area were employed to estimate the genetic coefficients in the CERES-Wheat model such that differences between the simulated and measured values were minimized to within acceptable error limits.
## 2 Related Work
### Detection of Long-Tail Imbalanced Datasets  
The dataset in remote sensing images is usually imbalanced, which is in line with the distribution patterns of different categories of objects in real life. However, if we directly use these data to train the network without any data preprocessing, it will lead to overfitting of the classifier to frequently occurring categories and underfitting to tail categories. Therefore, how to train a class-balanced classifier using imbalanced data has great research significance.  
For data with imbalanced samples, a common method is to use resampling and reweighting. However, their essence is to reverse-weight the sampling frequency of different categories based on the ground truth instances. Equalization Loss (EQL) achieves the effect of balancing the detector by alleviating gradient suppression from the head class to tail class. EQLv2 [16] uses gradient-guided methods to re-measure the weight of losses caused by different categories, which is more effective than EQL [17]. Some scholars have also explained the problem of long-tailed distribution from a non-statistical perspective and proposed Adaptive Class Suppression Loss [18].  
Inspired by Focal Loss, Equalized Focal Loss [19] achieves balanced detection by designing a hyperparameter based on changes in class imbalance to apply different weights to samples of different categories. DisAlign [20] proposed a generalized reweighting method for balancing classes before loss calculation. BACL [21] achieves balance detection by reverse-weighting the imbalance of statistical samples. However, the above research contents are all within the scope of horizontal object detection, and there are still gaps in the research scope of rotating object detection for remote sensing images.
## 4 Unique Challenges
### Considerations for Medical Sensor Networks  
Security mechanisms developed for other sensor networks or in general may not be applicable to medical sensor networks due to their unique characteristics, as we explain below.  
Communication Pattern_sensor nodes on different patients do not communicate with one another_. Instead, they communicate with one or more base stations close to the healthcare provider. Therefore, we need to protect the communication between the sensor nodes and base stations. However, most existing research focuses more on enabling secure communication among sensor nodes. Often, a simple symmetric-key mechanism is used between the sensor nodes and base stations, e.g., pre-configuring a shared secret in each sensor node and the base station. These mechanisms do not offer enough protection against _physical compromise of sensor nodes_.  
Threat Model_physical compromise of sensor nodes_ is a greater threat in medical sensor networks due to two factors: (1) _mobility_: while most other sensor networks have stationary sensors, medical sensors move with the patients and may get lost without even being noticed; (2) _accessibility_: it is relatively easy for attackers to find targets, i.e., patients in local healthcare facilities, as opposed to in remote locations such as forests or battlefields. Note that physically compromising the base stations is harder because they can be protected in secure locations. Once a sensor node is compromised, the attacker can capture any sensitive information remaining in the mote, including personal medical data and secret key material. The attacker can then use the key material to decrypt any previously recorded communication. Furthermore, the attacker can inject false data using the node. Therefore, we need to limit the amount of private information disclosed after the physical compromise as well as the damage that the attacker can cause through impersonating the patient. This has several implications for our design: (1) secret keys should be periodically updated; (2) when a node is turned off, no security material (such as a shared secret or a static public/private key) should have to be stored permanently in the node's non-volatile memory (a pre-configured shared secret obviously does not satisfy this requirement); and (3) authentication must rely on something that the attacker cannot easily capture or forge.
# Pyramid Feature Attention Network for Speech Resampling Detection
## 2 Proposed Method
### Speech Preprocessing
#### 2.1.2 The Performance of Resampling on the Spectrogram  
Resampling can be achieved by up-sampling, interpolation, and down-sampling, depending on the desired sampling multiple. For any multiple of sampling, a combination of up-sampling, interpolation, and down-sampling is required. Assuming an audio signal \(x(n)\) with a length of \(m\) sampling points, \(p\) is the interpolation factor, and \(q\) is the decimation factor. After resampling by the sampling factor \(p/q\), the number of samples of the signal can be increased or decreased. The process is as follows:  
* Up-sampling: By inserting \(p-1\) zeros between every two sample points of the signal, the sampling point of the signal \(x_{u}(n)\) becomes \(p(m-1)+1\) after the change, so that the signal \(x(n)\) is expanded by a factor of \(p\) in the time domain. When \(n=1,2,...,m\), \(x_{u}(p(n-1)+1)=x(n)\); otherwise, \(x_{u}(n)=0\).
* Interpolation: The signal \(x_{u}(n)\) is convolved by a low-pass linear interpolation filter \(h(n)\) to obtain the interpolated signal \(x_{c}(n)=x(n)*h(n)\). According to the use of different filters [35], the interpolated signal can be divided into linear interpolation, spline interpolation, and cubic interpolation.
* Down-sampling: One point is extracted every \(q-1\) sample points, so that the signal \(x_{c}(n)\) is reduced by a factor of \(q\) in the time domain. The changed signal is \(x_{d}(n)=x_{c}(1+q(n-1))\). When \(n=1,2,...,\frac{(p-1)m}{p},\frac{(p-1)m}{p+1}\), the resampled signal is \(y(n)=x_{d}(n)\).  
Figure 3 shows the spectrogram of the original speech signal that has undergone resampling by a factor of 2. Different resampling results are obtained based on different interpolation filters \(h(n)\). It is observed that the length of the original signal is doubled in the time domain, the low-frequency region has high resolution and the bandwidth is narrowed, which is affected by a certain degree of noise and distortion. After applying the linear interpolation filter, there are jagged changes in the spectrogram, and these are prominent in the high-frequency region. Spline interpolation is an interpolation method based on local polynomial approximation, which is more accurate and complex than linear interpolation. From the analysis of the spectrogram, it alleviates the jagged changes in the high-frequency region to a certain extent. Cubic interpolation based on cubic polynomial approximation is a more accurate interpolation method than the previous two. It can be seen that after cubic interpolation, a smooth spectral curve is obtained, which can better analyze and process speech signals.  
Figure 3: Spectrogram: (**a**) Original, (**b**) linear interpolation, (**c**) spline interpolation, and (**d**) cubic interpolation.
## 1 Introduction  
Recently, orthopedic implant surgeries have become increasingly routine, including artificial joint replacement, spinal instrumentation, and fracture repair. Such procedures can do much to improve the patient's quality of life, but they are accompanied by the risk of complications. One of the most serious of these is postoperative peri-implant infection, which can occur regardless of patient age. The incidence rate of peri-implant infection is reported at 2.6% to 11.9% for spinal instrumentation surgery [1, 2] and 0.2% to 8.5% for implant arthroplasty [3, 4, 5, 6, 7, 8]. Methods have also been investigated for applying coatings containing silver, gold, iodine, or antibiotics such as gentamycin or vancomycin to the metal surface of an implant, with the aim of preventing peri-implant infection [9, 10, 11, 12, 13, 14, 15, 16, 17, 18].  
Diamond-like carbon (DLC) is a general term for carbon thin films that have diamond-like properties, such as high hardness, electrical insulation, and infrared transmission [19]. F-DLC coating with added fluorine has an anticoagulant effect. It is now being used in clinical settings and is expected to have applications in artificial blood vessels and indwelling stents within blood vessels [20]. [PERSON] et al. reported the use of F-DLC coating on the most common titanium alloy in orthopedic implants, for the purpose of suppressing infection around the implant, and noted that this coating provided excellent antibacterial properties and safety levels [21].  
Effects on osteogenesis are also a concern. The osteogenesis-inducing effects of DLC coating [22; 23; 24] are attributable in part to its highly hydrophilic properties. No studies have yet been performed on the effects of the F-DLC coating, which is highly hydrophobic, on either osteogenesis or bone resorption. If the F-DLC coating inhibits bone union, its use in intraosseous implants will be problematic. Therefore, to obtain a better understanding of the risks and benefits of such use, the authors investigated the effects of F-DLC coating on osteogenesis in vivo compared to uncoated titanium alloy.  
In addition to these effects on osteogenesis, the slow release of F ions from the F-DLC coating was measured over time. This supplemental experiment was performed to address the potential negative effects of F ions in vivo during long-term use, since fluorine can elicit both direct cytotoxicity and metabolic toxicity and could potentially bond with calcium or magnesium in the body to cause hypocalcemia or hypomagnesemia.