esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
It substantiates the effectiveness of GSRPnet, emphasizing that its accuracy improvement is not solely reliant on blindly expanding the number of network parameters. In general, this paper thoroughly demonstrates the potential of extracting UGS at a high spatial resolution using GSRPnet and provides a feasible scheme for UGS detection. However, GSRPnet still has room for further improvement. In future work, we will aim to delve deeper into enhancing the precision of the UGS segmentation task.
# Rethinking the Operation Pattern for Anomaly Detection in Industrial Cyber-Physical Systems
## 6 Experiments and Results
### Evaluation Results
#### 6.3.3 Performance of the Last Defence Line  
As shown in Table 2, the deviations in the operation execution time of this coal mine system were small; almost all of them are less than 2 ms. Therefore, the execution time defence line can accurately identify the anomaly operations whose execution time varies from the normal. As expected, and as shown in Table 5, this defence line achieved an average of 98.67% in F1-Score, with a 0.90% false-positive rate. Abnormal operations are easier to detect when the deviations become more severe.
## 5 Mitigation of Pharmaceutical Pollutants by Nanomaterials
### Biochar-Based Adsorbents  
Magnetic biochar-based adsorbents, which are manufactured for quicker separation, are another new adsorbent. Biochar is a low-cost and commonly accessible adsorbent that has been frequently used for pharmaceutical adsorption. However, the inconvenient and arduous separation has reduced its use [100]. Thus, magnetic biochar adsorbent was prepared for the adsorption of ibuprofen, sulfamethoxazole [101], carbamazepine, tetracycline [102], ibuprofen, and acetylsalicylic acid [103]. In a study by [104], biochar was magnetized and used for the adsorption of ibuprofen (IBP). Adsorption was observed to be a slightly lengthy process for a contact time of 1 h and the adsorption reached equilibrium after 1440 min (24 h), after which the sorption sites were completely utilized and the sorption kinetics appeared to follow the pseudo-second-order, indicating the formation of a chemical bond. The Langmuir adsorption capacity of biochar-based adsorbents yielded34.3% P-BC at 600 \({}^{\circ}\)C, P-BC being the name given to the product, which was most likely due to the fact that most of the cellulose and hemicellulose decomposed at higher temperatures, resulting in a lower yield, as was suggested by the authors. According to Figure 5a,b, the surface of P-BC was rough with an irregular stacking structure and an irregularly arranged irregular pore structure, which increased the surface adsorption active sites. As shown in Figure 5c,d, some irregular substances were added to the surface of P-BC, demonstrating that IBP molecules were adsorbed on this specific surface.
# A Hybrid JADE-Sine Cosine Approach for Advanced Metaheuristic Optimization
## 2 Literature Review
### Overview of Sine Cosine Algorithm  
The sine cosine algorithm (SCA) is a population-based optimization technique that utilizes the mathematical properties of sine and cosine functions to guide the search process for optimal solutions. The core idea of SCA is to update the positions of candidate solutions based on sine and cosine functions, which ensure diverse and efficient exploration and exploitation of the search space. The position update mechanism in SCA is governed by the following equations:  
\[\mathbf{X}_{i}(t+1) =\mathbf{X}_{i}(t)+r_{1}\cdot\sin(r_{2})\cdot|r_{3}\cdot\mathbf{ X}_{\text{best}}(t)-\mathbf{X}_{i}(t)|,\] \[\mathbf{X}_{i}(t+1) =\mathbf{X}_{i}(t)+r_{1}\cdot\cos(r_{2})\cdot|r_{3}\cdot\mathbf{ X}_{\text{best}}(t)-\mathbf{X}_{i}(t)|.\]  
In these equations, \(\mathbf{X}_{i}(t)\) represents the position of the \(i\)-th candidate solution at iteration \(t\), and \(\mathbf{X}_{\text{best}}(t)\) denotes the best solution found so far. The parameters \(r_{1}\), \(r_{2}\), and \(r_{3}\) are random numbers that influence the direction and magnitude of the position updates. Specifically, \(r_{1}\) controls the step size, \(r_{2}\) determines whether the update uses a sine or cosine function, and \(r_{3}\) scales the influence of the best solution. The use of sine and cosine functions allows SCA to alternate between exploration and exploitation phases effectively. During the exploration phase, candidate solutions are spread out across the search space, increasing the likelihood of discovering global optima. In the exploitation phase, the algorithm fine-tunes the positions of solutions, converging towards the best solution.  
The SCA's simplicity and ability to balance exploration and exploitation make it a versatile optimization tool applicable to various domains, including engineering design, machine learning, and energy management. Its mathematical foundation in trigonometric functions ensures that candidate solutions can move in diverse and unpredictable ways, avoiding local optima and enhancing the algorithm's overall robustness and convergence speed.
# Freeform Wide Field-of-View Spaceborne Imaging Telescope: From Design to Demonstrator
## 2 Methods: Optical Design
### Performance Evaluation  
The simulated image quality is evaluated using the root-mean-square (RMS) spot size and MTF. Considering the RMS spot diagram, the spot sizes for each of the fields approximate the Airy disk radius that equals 15 \(\upmu\)m, indicating a diffraction-limited performance (Figure 3). All presented spots are located next to each other on the image sensor. The relative positioning of the spots with respect to each other on the detector plane is visualized in Figure 4, indicating the spots are focused on a line, showing only minor spatial distortion induced by the present barrel distortion. The barrel distortion is the main impacting aberration on the performance, increasing with the field angle and reaching a maximum value of 23% at 60\({}^{\circ}\), but can be corrected using post-processing. As a second evaluation, the performance is confirmed by the MTF, showing a close to diffraction-limited design for all fields and both tangential and sagittal rays (Figure 5). MTF values between 0.5 and 0.7 are obtained for all fields at 17 cycles/mm, while the diffraction limit at this frequency gives a value of about 0.75.  
Figure 3: Spot diagram indicating all RMS spot sizes are approximately the size of the Airy disk (indicated by the black circle). Symmetry is properly achieved with respect to 0\({}^{\circ}\). OBJ (in degrees) defines the object field and IMA (in mm) defines the image height of the centroid on the detector. The different colors correspond to the different fields.  
Figure 2: Optical design of the telescope proof-of-concept demonstrator, including a fold mirror enabling the use of a Xenics Bobcat 320 GigE 100 camera at the detector plane.
## 3 Markov Chain Monte Carlo Algorithms
### Delayed Rejection Adaptive Metropolis (DRAM)
#### 3.1.1 Delayed Rejection (DR)  
To address those issues in MH, [PERSON] et al. [18] developed an advanced MCMC that combines the Delayed Rejection (DR) [45] with Adaptive Metropolis (AM) [46] to further improve the sampling performance. DR modifies the MH algorithm to improve the acceptance rate of samples and sampling efficiency by deploying a multi-stage strategy. The basic idea in DR is that when a candidate sample is evaluated and rejected, rather than retaining the same sample, another stage is proposed [18].  
1. _First Stage Acceptance Rate_ Suppose the current sample is \(y^{k}\), a candidate sample \(x_{1}\) is generated from a proposal \(x_{1}\sim q_{1}\Big{(}\big{|}y^{k}\Big{)}\), and the acceptance rate of the candidate sample is calculated as \[\pi_{1}\Big{(}y^{k},x_{1}\Big{)}=\min\Bigg{\{}1,\frac{p(x_{1})q_{1}\Big{(}x_{ 1},y^{k}\Big{)}}{p\big{(}y^{k})q_{1}\big{(}y^{k},x_{1}\Big{)}}\Bigg{\}},\] (13)where \(p(\cdot)\) denotes the posterior calculated at the sample. \(q_{1}(\cdot)\) denotes the proposal distribution that is usually decided by a normal or uniform distribution, making \(q_{1}(\cdot)\) symmetric and rigorously positive over the entire parameter space [47].  
2. _Second Stage Acceptance Rate_  
If the candidate sample \(x_{1}\) is rejected, a second stage is proposed. A new candidate sample \(x_{2}\) is generated from an updated proposal \(x_{2}\sim q_{2}\Big{(}\cdot\big{|}y^{k},x_{1}\Big{)}\). The acceptance rate at the second stage is expressed as [18]  
\[\pi_{2}\Big{(}y^{k},x_{1},x_{2}\Big{)}=\min\left\{1,\frac{p(x_{2})q_{1}(x_{2}, x_{1})q_{2}\Big{(}x_{2},x_{1},y^{k}\Big{)}[1-\pi_{1}(x_{2},x_{1})]}{p\big{(}y^{k} \big{)}q_{1}\big{(}y^{k},x_{1})q_{2}\big{(}y^{k},x_{1},x_{2}\big{)}\big{[}1- \pi_{1}\big{(}y^{k},x_{1})\big{]}}\right\}, \tag{14}\]  
The process of DR can continue and be iterated for many stages as long as desired due to its time-reversibility. The detailed deviation for the high stage of delaying rejection can be found in [18].
# Clustering-Based Channel Allocation Method for Mitigating Inter-WBAN Interference
## 2 Related Works  
To resolve the inter-WBAN interference problem, IEEE 802.15.6 standard specifies three coexistence strategies: a beacon shifting, an active superframe interleaving, and a channel hopping [6]. For the beacon shifting, a WBAN schedules the beacon frame transmission during an idle period. However, since the idle period approaches to zero as the number of coexisting WBANs increases, a beacon shifting is not appropriate when the density of the coexisting WBANs is high [10]. For the active superframe interleaving, each WBAN adjusts its superframe length and inactive duration so that the coexisting WBANs take turns in sending data in an active period. However, the transmission opportunity of a WBAN decreases with the number of coexisting WBANs. Therefore, the active superframe interleaving increases the transmission delay as the number of coexisting WBANs increases [21]. In case of the channel hopping, a coordinator in each WBAN determines the channel hopping sequence. However, since WBANs can move around, an optimal channel hopping sequence must be selected adaptively according to the changes in the set of coexisting WBANs. In addition, a specific mechanism to implement these strategies is left as an open issue in the standard. Thus, a proper algorithm must be devised to overcome the shortcomings of these methods.
# DRAM Retention Behavior with Accelerated Aging in Commercial Chips
## 4 Experimental Results and Analysis
### Experimental Framework  
A DRAM testing framework based on Xilinx ML605 FPGA, and a DDR3 memory controller called SoftMC [29] is used to test the DDR3 modules in our experiments. Retentionerror data are collected from nine commercially available off-the-shelf single rank DDR3 SODIMMs modules (small outline dual in-line memory module) covering all three major DRAM vendors. We used a Micron 1 GB DDR3 module, Hynix 2 GB module, and Samsung 2 GB modules in our tests. Each memory module contains four or eight ICs with eight banks spreading across the whole module, and each bank consists of 16 k or 32 k rows (1 GB or 2 GB).  
SoftMC is an FPGA-based open-source experimental memory testing infrastructure that implements all low-level DRAM commands in a programmable memory controller. This programmable memory controller is introduced to the user with an easy-to-use and flexible interface. This interface can be programmed in C++ with customized library files to generate bit files. The user can generate a sequence of DDR3 operations by calling the SoftMC API functions [29]. DRAM cell data can be written or read from each memory bank using the testing platform implemented on a Xilinx Virtex ML605 evaluation board with SoftMC.  
The experimental framework provides the ability to:  
1. Generate different timing parameters and test patterns for various latency-based tests of the DRAM module.
2. Facilitate with an interface to connect the host machine with the Xilinx FPGA board.
3. Enable precise and accurate control of the temperature of the DRAM module during the test.  
In order to sustain a stable operating temperature for the DIMMs during the DRAM aging process, the modules are placed on the FPGA board under an ATS Series Thermostream device [30]. The Thermostream device allows for fast and precise thermal conditioning with a temperature range of \(-\)20 to +225 \({}^{\circ}\)C.
## 3 Materials and Methods
### Study Area and Data
#### 3.1.3 Hydrological Data  
Hydrological data include water level (stage height) measurements obtained from a monitoring station located at 40\({}^{\circ}\)41\({}^{\prime}\)46.1\({}^{\prime\prime}\) N, 21\({}^{\circ}\)39\({}^{\prime}\)48.2\({}^{\prime\prime}\) E. The station is equipped with an ultrasound sensor capable of recording water level data at a 10-min interval temporal resolution that were aggregated to hourly resolution. Figure 5 depicts the two monitoring stations used in this study.  
Since direct discharge measurements were not available; streamflow was estimated using an empirical stage-discharge relationship:  
\[\text{Q}=\text{a}\ \text{h}^{\text{b}}\]  
where Q is the discharge (in m\({}^{3}\)/s), h is the stage height (in meters), and the parameters a = 0.00009 and b = 2.2203 were fitted based on multi-year field measurements provided by the regional water management authority.  
The resulting discharge time series spanned the period from 1 January 2018 to 1 September 2024 (Greek local time) capturing both high- and low-flow conditions. Standard preprocessing steps were applied to remove outliers and fill any missing values through linear interpolation. These data points were used for both training and evaluation of the proposed hybrid forecasting framework.  
Figure 6 visualizes the standard deviation of the hourly discharge on daily, weekly, and monthly bases. These metrics show how variability evolves over time at different resolutions.  
Figure 4: Daily time series of evapotranspiration (mm) from 2018 to 2024 in the study area.  
Figure 5: (**Left**) Hydrological monitoring station located under a bridge in the study area. (**Right**) Meteorological station used for collecting weather data including temperature, precipitation, wind speed, and solar radiation.
## 7 Fate of Soil Carbon Transported by Aeolian Processes  
Aeolian processes are dominant in arid regions, but both hydric and aeolian processes are causes of accelerated erosion in semi-arid regions. Globally, wind erosion may affect 0.6 B ha of land [50]. Wind erosion also involves different stages: detachment, saltation, wind-blown dust, and deposition. Similar to hydric erosion, wind erosion is also a selective process which leads to a significant lateral transport of C and fine clay fraction. Wind-blown dust leads to a long-range transport on an inter-continental scale. Examples include North African dust plumes over the Atlantic Ocean, summer dust plumes from the Arabian Peninsula over the Arabian Sea and the Indian Ocean, and spring dust plumes from East Asia over the Pacific Ocean [51]. These dust plumes strongly affect the radiation budget, the hydrologic cycle, and the global C budget. Depending on the magnitude of C being transported, distributed, and deposited/buried, a significant amount may be emitted as CO\({}_{2}\) (and other GHGs) into the atmosphere.  
SOC stocks are strongly reduced in soils prone to wind erosion. In Western Australia, [PERSON] and [PERSON] [52] estimated that the mean net SOC erosion was up to 0.2 Mg C/ha. y for the period of 1990 to 2013. Moreover, in southwestern Australia, wind erosion resulted in the loss of up to 3% of the total stock of C to 1-m depth or 3.6 Mg C/ha for the eroded soils. In the Texas High Plains, [PERSON] et al. [53] observed that a greater amount of CO\({}_{2}\)-C was being assimilated by the wheat cover crop from the atmosphere than was lost from the soil, which reduced the net C losses from the soil. However, the erosion-related loss of SOC from wind erosion could be more severe during the fallow phase of the rotation cycle. Thus, farmers must be especially judicious in protecting the soil against SOC loss by wind erosion during the fallow phase in the Pacific Northwest region of the U.S. [54]. Similar to the high risks of wind erosion from croplands on plowed fields during the fallow phase, overgrazing can also reduce SOC content in the long term.  
The loss of nutrients (N, P, K, and C) by wind erosion is mostly attributed to losses by suspension, because suspension preferentially removes the finest--and thus the lightestfractions. However, the greatest mass of the sediment is removed by saltation. Thus, the majority of the nutrients and C may also be moved by saltation. [PERSON] and [PERSON] [55] observed that nutrient losses from one field during one event in the Sahel realm of Africa can be as much as 73% of the N and 100% of the P requirements of crop production. A study conducted in a desert grassland of Southern New Mexico, USA, by [PERSON] et al. [56] showed the dramatic increase in wind erosion between 75% grass cover reduction and 100% grass cover reduction, suggesting that sparsely distributed mesquites are rather ineffective at reducing wind erosion and nutrient loss compared to grasses.
## 4 Description of Selected Classifiers Implemented in the Python Scikit-Learn Library
### Nearest Neighbor Classifier  
The nearest neighbor algorithm [78] categorizes a sample based on a simple majority vote taken from a subset of adjacent training samples located in the multidimensional feature space. Subset selection can be performed based on the number of samples (KNN: K-nearest neighbor method) or the radius around the analyzed sample. Moreover, majority voting may take into account various distance metrics to privilege samples located closer to the analyzed sample. The main drawback of the nearest neighbor classifier is related to the handling of the entire training dataset in order to classify a single sample and to the computational overhead of the extensive search. On the positive side, the classifier is robust and accurate for relatively small data samples.
## 4 Results and Discussion
### Two-Sensor Configurations
#### 4.3.2 Confusion Matrix Analysis for Two-Sensor Non-Overlapping Configuration
Although its self-attention mechanism aids in long-range dependency handling, it may not fully leverage the sequential structure needed in non-overlapping setups [29]. In summary, both the table and confusion matrix analyses consistently highlight the **TCN** model as the best performer for this two-sensor, non-overlapping configuration. Its ability to handle non-overlapping temporal data without sacrificing accuracy or interpretability makes it ideal for human activity recognition tasks where data continuity is limited. TCN outperforms other models in classifying a range of activities accurately, proving its robustness and adaptability in this setup.
## 2 Problem Statement  
Let us consider a nonlinear parameter varying system:  
\[S:\left\{\begin{array}{l}\dot{x}=f(x,\theta,u,t)\\ y=Cx+d(t),\end{array}\right. \tag{1}\]  
where: \(x\in R^{l}\), \(\theta\in R^{p}\), \(u\in R^{m}\) and \(y\in R^{n}\) denote the system state, parameter, input and output vector respectively. \(d(t)\) is a independent and identically distributed Gaussian noises with covariance matrices Q. The \(\theta\) is unknown time varying parameter. The term \(f(x,\theta,u,t)\) is known as nonlinear functions with appropriate dimensions.  
**Assumption 1**.: _The function \(f(x,\theta,u,t)\) is globally Lipschitz in \(x\) and \(u\)._  
**Assumption 2**.: _The uncertain time varying parameter \(\theta(t)\) satisfy_  
\[|\theta(t)-\theta_{o}|\leq\Gamma \tag{2}\]  
_where \(\theta_{0}\) is known parameter nominal values, \(\Gamma\) is known constant vector. Here, the assumed nonlinear system \(S\) is linear on parameters. If the process parameter nominal value \(\theta_{o}\) is known, the nominal model of the system is_  
\[S_{o}:\left\{\begin{array}{l}\dot{x}=f(x,\theta_{o},u,t)\\ y=Cx.\end{array}\right. \tag{3}\]  
The goal of this paper is twofold:  
* Design a dynamic observer that estimate the state and parameter of the noiseless system (1) in a finite time or in a fixed time, under the assumption that the initial state is unknown.
* The observer must be robust (in an input-to-state sense) with respect to measurement noise \(d(t)\).
## 4 The QACMOR Routing Method
### The QEAs in QACMOR
#### 4.2.2 Updating the Pheromone with Quantum Rotation Gate  
In QACMOR, similarly to Formulas (10) and (11), the quantum rotation gate G acting on the \(i^{th}\) bit of the \(j^{th}\) individual \(q_{j}\) of solution \(Q\) is described as follows:  
\[\left[\begin{array}{c}\alpha^{\prime}_{ji}\\ \beta^{\prime}_{ji}\end{array}\right]=G\left[\begin{array}{c}\alpha_{ji}\\ \beta_{ji}\end{array}\right] \tag{16}\]  
\[G=\left(\begin{array}{cc}\cos\theta_{ji}&-\sin\theta_{ji}\\ \sin\theta_{ji}&\cos\theta_{ji}\end{array}\right) \tag{17}\]  
\[\theta_{ji}=\Delta\theta_{ji}\times s(\alpha_{ji},\beta_{ji}) \tag{18}\]  
where \(i=1,2,\ldots,n\), \(\left(\alpha^{\prime}_{ji},\beta^{\prime}_{ji}\right)^{T}\) represents the updated bit, \(\theta_{ji}\) is the rotation angle, \(\Delta\theta_{ji}\) signifies the magnitude of the rotation angle, and \(s(\alpha_{ji},\beta_{ji})\) is a function of \(\alpha_{ji}\) and \(\beta_{ji}\), and controls the direction of rotation. For the computation speed, the look-up table was applied to compute the rotation angle as shown in Table 1, which includes all feasible solutions. \(f(\cdot)\) denotes the fitness function as Formula (20); \(x_{ji}\) and \(b_{i}\) represent the \(i^{th}\) bit of the \(j^{th}\) individual of the current solution x and the best solution \(b\), respectively. The schematic diagram in Figure 3 shows the rotation gate polar plot for a qubit individual.  
Additionally, a conventional binary solution is significantly important for performance evaluation, and can be obtained by observing the qubits. For example, it is assumed that \(x_{i}\) (\(i=1\), \(2\),..., \(n\)) is a certain bit of the binary individual \(x\), then \(\alpha_{i}\) of the qubit individual is compared with a random number \(w\) (\(0<w<1\)). If \(\left|\alpha_{i}\right|^{2}>w\), then set the value of \(x_{i}\) to be \"0\", otherwise set the value of \(x_{i}\) to be \"1\". Therefore, for \(Q=\left(q_{1},q_{2},\ldots,q_{j},\ldots q_{m}\right)\), \(j=1,2,\ldots,m\), its binary solution is \(P=\left(p_{1},p_{2},\ldots,p_{j},\ldots p_{m}\right)\), while \(p_{j}(j=1,2,\ldots,m)\) is a n-length binary individual, and then every element of \(p_{j}\) (for example, \(p_{ji}\)) is determined by comparing \(\alpha_{ji}\) of \(q_{j}\) with \(w,0<w<1\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(x_{i}\)} & \multirow{2}{*}{\(b_{i}\)} & \multirow{2}{*}{_f(x)\(>\)/(b)_} & \multirow{2}{*}{\(\Delta\theta_{i}\)} & \multicolumn{6}{c}{\(s(\alpha_{ii}\beta_{i})\)} \\ \cline{5-8}  & & & & \(\alpha_{i}\beta_{i}\)\(>\)0 & \(\alpha_{i}\beta_{i}\)\(<\)0 & \(\alpha_{i}\)\(=\)0 & \(\beta_{i}\)\(=\)0 \\ \hline
0 & 0 & False & 0 & 0 & 0 & 0 & 0 \\
0
## 4 Results and Discussion
### Performance Evaluation
#### 4.2.5 Bjontegaard Delta Performance Evaluation  
Table 10 presents the Bjontegaard metric performance results of DIVCOM, PDIVCOM, and DISCOVER. The codecs' performance results are presented in terms of BD-Rate and BD-PSNR. As reported in Table 10, DIVCOM and PDIVCOM give an improvement in bit rate ranging from 2.46% to 6.13% and 3.47% to 11.37%, respectively, compared to DIS for different video sequences. However, for the Foreman video sequence, the performance of PDIVCOM in terms of bit rate saving is degraded up to 1.33%. The BD-PSNR analysis shows that DIVCOM and PDICOM gain 0.05 dB to 0.18 dB and 0.14 to 0.35 dB, respectively, for different video sequences compared to DIS. However, PDIVCOM performance for Foreman is degraded by 0.026 dB compared to DIS.