esa-sceva/satcom-chunk-collection huggingface dataset:
# BioShare: An Open Framework for Trusted Biometric Authentication under User Control
## 4 Sample Implementation
### Secured Communication Module (SCM)  
The BioShare Secured Communication Module is implemented as trusted functions running in trusted environments on both the terminal and server sides to provide cryptographic services. In our implementation, we choose Advanced Encryption Standard (AES) as the symmetric encryption algorithm and specify 128 or 192 bytes as the key length to ensure a sufficient encryption intensity. We select RSA or ECC as the asymmetric encryption algorithm, specify 3072 or 7680 bytes as the key length for RSA, and 256 or 384 bytes as the key length for ECC; and we specify the SHA-256 algorithm in the hash value calculation.  
During the entire lifecycle of each transaction, all biometric data are transmitted in encrypted format in untrusted environments, and decrypted and processed only in trusted environments; and the result is returned with a digital signature to ensure non-repudiation.
Furthermore, [PERSON] and co-workers proposed an interesting flower-like CZTS/Cu2-xS structure for their nanoparticles, which degraded 99.8% of the RhB in 100 min, with degradation being carried out using a metal-halide lamp as the light source [37]. So, thanks to the promising results reported regarding dye degradation, as well as its low cost and high stability, CZTS is gaining recognition as an excellent choice in visible-light-driven catalysis. Moreover, its possible application in the photocatalytic degradation of micropollutants, as well as in photo/electro-catalytic hydrogen production reactions [38; 39; 40], has promoted CZTS as a material of interest and prompted the research community to focus their efforts in this direction. However, a direct photocatalytic test on a hazardous water micropollutant to test CZTS in a system emulating environmental wastewater has not been reported so far. In this work, we report a direct study on the photocatalytic degradation of Diclofenac (DCF) mediated by CZTS Nanoparticles (NPs). DCF was chosen as a benchmark molecule due to its persistence in ecosystems and its natural photolytic degradation to even more toxic and hazardous carbazoles, assessing it as one of the most dangerous emerging water micropollutants conventionally encountered in primary and secondary waters all over the world. The successful photocatalytic degradation of DCF has been studied and reported, with studies featuring catalysts different from CZTS, such as TiO2, C3N4/ZnO/TiO2 halosite nanotubes, g-C3N4 nanosheets, MOF/MgAl-hydrolcalcite, Nitrogen-Doped Carbon Quantum Dot-Graphitic Carbon Nitride, BaFeyTi1-yO3, etc. [9; 11; 12; 41; 42; 43]. However, these photocatalysts' complex and expensive nature and their need for UV irradiation hinder their potential for widespread use. Here, we not only demonstrate that CZTS NPs are a cheap and active photocatalyst suitable for DCF degradation but also reveal how they are superior to photolysis kinetics when the reaction is performed under UV-vis light irradiation, leading to an outstanding result, with 90% of the DCF being degraded. Moreover, the same degradation rates were recorded even with only-visible-light irradiation.
# Smooth Fractional Order Sliding Mode Controller for Spherical Robots with Input Saturation
###### Abstract  
This study considers the control of spherical robot linear motion under input saturation. A fractional sliding mode controller that combines fractional order calculus and the hierarchical sliding mode control method is proposed for the spherical robot. Employing this controller, an auxiliary system in which a filter was used to gain smooth control performance was designed to overcome the input saturation. Based on the Lyapunov stability theorem, the closed-loop system was globally stable and the desired state was achieved using the fractional sliding mode controller. The advantages of the proposed controller are illustrated by comparing the simulation results from the fractional order sliding mode controllers and the integer order controller.  
fractional sliding mode control; spherical robot; linear motion; input saturation +
Footnote †: journal: Applied Sciences
# Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
## 2 Materials and Methods
### Artificial Neural Network
In order to observe the ANN performance, the root-mean-square errors (RMSE), correlation coefficient (R), and bias were calculated using the following expressions:  
\[Bias=\frac{1}{n}\sum_{i=1}^{n}\Bigl{(}y_{i(Predict)}-y_{i(Target)}\Bigr{)} \tag{4}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\Bigl{(}y_{i(Predict)}-y_{i(Target)}\Bigr{)}^{2 }}{n}} \tag{5}\]  
\[R=\frac{\sum_{i=1}^{n}\Bigl{(}y_{i(Target)}-\overline{y}_{(Target)}\Bigr{)} \Bigl{(}y_{i(Predict)}-\overline{y}_{(Predict)}\Bigr{)}}{\sqrt{\sum_{i=1}^{n} \left(y_{i(Target)}-\overline{y}_{(Target)}\right)^{2}\sum_{i=1}^{n}\left(y_{i( Predict)}-\overline{y}_{(Predict)}\right)^{2}}} \tag{6}\]  
where \(y_{i(Target)}\) is the target value at the \(i\)th step, \(y_{i(Predict)}\) is the predicted value at the same step, \(n\) is the number of increments, \(\overline{y}_{(Target)}\) is the average target, and \(\overline{y}_{(Predict)}\) is the average of the predicted values. The time history data that contain 3000 s responses (i.e., surge, heave, pitch, and top tension) of the spar platform obtained from FEM were used as the target in the ANN. The data were randomly divided into three groups: 70% for training, 15% for validation, and 15% for testing. Before training the ANN, the input and target data were normalized within the range of \(-1\) to \(1\) using the following equation:  
\[a\frac{x_{i}-x_{min}}{x_{max}-x_{min}}+b \tag{7}\]  
where \(x_{min}\) and \(x_{max}\) are the minimum and maximum values of input and target, and \(a\) and \(b\) are the scaling factors. The network was trained by the iterative process, and it stopped when the error variation was too small.
# A Clustering WSN Routing Protocol Based on k-d Tree Algorithm
## 5 Simulation and Results Analysis
### Simulation Metrics
#### 5.2.3 Packet Delivery Ratio (PDR)  
This is the ratio between the number of data packets received by the Sink/BS node and the number of data packets sent by the network nodes [56]. The PDR value can be obtained by the following equation:  
\[PDR(\%)=\frac{\sum No\_packet_{received}}{\sum No\_packet_{sent}}\times 100. \tag{7}\]
## 4 Results and Discussion
### Relationship Analysis
#### 4.6.3 Interaction Profiles  
Interaction profiles, as shown in Figures 13 and 14, provided a triple-index-like formation where each strength parameter could be analysed in terms of visualised combinations of two variables. Furthermore, the behaviour could be studied in terms of changing patterns along with minimum and maximum values.  
Figure 12: Prediction profiles for compressive and flexural strength with respect to factors.  
Figure 13: Interaction profile ranges for compressive strength with respect to factors.
# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 4 Experiments and Analysis
### Ablation Study
#### 4.6.3 Ablation of Global Feature Extraction Module  
To compare the global feature extraction capabilities of Mamba and Transformer, a comparative experiment was conducted on the RealDOF dataset. Specifically, the Mamba branch in the global feature extraction module of the parallel block was replaced with a window attention module to observe the differences between the two approaches. Compared to traditional attention, window attention is more efficient and can maintain a similar parameter count to the Mamba branch, facilitating subsequent analysis. Additionally, due to computational resource limitations, it was challenging to test the high-resolution (\(2320\times 1520\)) images from the original RealDOF dataset in the window attention-based structure. Therefore, the original images were uniformly cropped to a size of \(640\times 640\), and the test metrics for both methods at this resolution were recorded. The final results are presented in Figure 6. As shown in the table, Mamba, as the global feature extraction module, outperforms the window attention, indicating that although the modified attention module reduces computational resource consumption to some extent, it also impacts the effectiveness of global feature extraction, weakening the ability to capture global information. In contrast, Mamba demonstrates superior performance with much lower resource consumption. Furthermore, in terms of memory usage for inference on a single image, Mamba significantly reduces the required GPU memory, needing only 4.97 GB, while the window attention-based method requires 16.38 GB. This shows that although window attention reduces the parameter count of traditional Transformer models to some extent, the proposed model offers a significant advantage in terms of memory consumption during actual inference.
## 6 Future Directions
Geospatial and big data integration will be critical to the development of state-of-the-art decision support tools [24; 121]. Development of crowd-sourced apps, such as LandPKS [22; 23; 122] and iNaturalist [123], provides citizen scientists a way to collaborate and provide geotagged field data to inform soil, water, grazing, and wildlife management research and objectives.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 2 Load Period Partitioning Method Based on Improved Fuzzy C-Means Clustering
### Improved FCM Algorithm with Adaptive Clustering and Temporal Feature Fusion
To preserve the time-dependent nature of load characteristics, this paper incorporates time-series information into the clustering framework, thereby improving the consistency, realism, and reliability of the partitioning results. Overall, the enhancements made to the FCM algorithm address key limitations of traditional methods and enable more accurate and adaptive time period segmentation for dynamic network reconfiguration. The final objective function of the improved FCM is given as Equation (10):  
\[J=\sum\limits_{i=1}^{N}{\sum\limits_{j=1}^{C}{u_{ij}^{m}.S_{ij}^{f}}} \tag{10}\]  
where \(u_{ij}\) is the membership degree of time period \(I\) to cluster \(j\), \(m\) is the fuzziness coefficient (typically set to 2), and \(S_{ij}^{f}\) represents the time-fused similarity as defined in Equation (8).
# Removal of Phosphate from Aqueous Solution by
## 3 Results and Discussion
### Desorption Studies  
As shown in Figure 12, the desorption rates of zeolite, biochar, and PZC 7:3 for phosphate were 76.03%, 44.85%, and 20.82%, respectively. The reason was that the adsorption of zeolite on phosphate was mainly physical adsorption with weak intermolecular force bonding, which led to the phosphate being easily desorbed. In contrast, the adsorption of phosphate by PZC 7:3 was mainly chemisorption with strong chemical bonding. Therefore, it was not easy to desorb, indicating that the zeolite-biochar composite was more stable for phosphate adsorption than zeolite and biochar alone. In addition, the adsorption of phosphorus by zeolite-biochar composites could enhance its availability as a slow-release fertilizer in the soil, allowing phosphate to be retained in the soil for a long period of time to supply nutrients to the plants [80; 81]. This requires further research.
## 2 Materials and Methods
### Inspection Procedure
Durability is assessed on the basis of the impulse stiffness modulus (ISM) at the edges or corners, which is defined as follows [28]: \[ISM_{i}=\frac{F_{center}/D_{0-center}}{F_{i}/D_{0-i}}\] (1) where: \[\bullet\quad F_{center},D_{0-center}\text{: load and maximum deflection at the slab center;}\] \[\bullet\quad i\text{: location referring to either a slab edge or corner;}\] \[\bullet\quad F_{i},D_{0-i}\text{: load and maximum deflection at the $i$-location of a slab.}\] The last part of the FWD measures was devoted to the assessment of the ability of adjacent slabs to distribute load, which was performed with the LTE ratio, defined as: \[LTE\,(\%)=\frac{D_{unloaded}}{D_{loaded}}\] (2) where: Figure 2:0 \[\bullet\quad D_{unloaded}\text{: deflection (Figure 2:1m) recorded in the adjacent unloaded slab, usually at a radial distance of 300 mm from the FWD plate center (i.e., DFigure 2:2). For the assessment of LTE at joints, the FWD configuration in Figure 5b was used, as well as for the measurement of slab edges. In this case, it is clarified that the distance between the sensor/geophone measuring the loaded slab and the sensor/geophone measuring the unloaded slab is 30 cm, and the FWD system is placed so that the joint is exactly in the middle. Similar to the LTE calculation for joints, LTE assessment is useful for assessing the ability of two individual slab sections separated by a crack to cooperate with each other and transfer loads efficiently. Therefore, additional FWD measurements were performed on randomly selected cracks with small and large widths. The FWD was positioned so that the crack was at the midpoint between the loading plate and the third sensor/geophone measuring the deflection D\({}_{300}\) (Figure 5d). For the destructive part of the test campaign, a limited number of cores were taken to obtain an overview of the concrete thickness and to check the crack propagation depth of random cracks, regardless of their width. A total of sixteen cores were taken, with a mean thickness of 27.2 cm and a coefficient of variation (CV) of 6%. With this value, the upper limit of the thickness range shown in Figure 2 was satisfactorily achieved at all stations investigated.
## 3 Methodology
### Sub-Stack Data Processing  
In the sub-stack data processing phase, we initiated our analysis by carefully examining the first set of 28 images. This meticulous review aimed to extract all PSCs with sufficient amplitude stability. These PS were then geocoded using their SAR coordinates, ensuring each was accurately mapped within our study framework.  
For each sub-stack, we conducted an APS estimation to correct atmospheric phase delays that could affect the accuracy of our estimates. We consistently applied the same parameters as in the full stack processing--specifically, a linear velocity of \(\pm\)50 mm and a height variation of \(\pm\)200 m. This uniformity in parameter settings across all stacks ensured the coherence and comparability of our results.  
After setting these parameters, we continued with the selected reference point from the full stack processing. We assessed the quality of the APS estimation by analyzing the temporal coherence of PS points selected within each sub-stack using an AS-Index threshold \(>\) 0.8. This ensured reliable points were used for estimating atmospheric delays specific to that sub-stack. Based on these findings and following the recommendations [42], we adapted our selection criteria for the final set of PS points used for height estimation within each sub-stack to be less strict for multi-image sparse processing, specifically setting the AS-Index threshold above 0.75 to ensure adequate data quality and spatial coverage. Thisadjustment allowed for a denser collection of PS points, facilitating more comprehensive coverage across the study area.  
Ultimately, all 13 image stacks were processed using these standardized parameters, referencing the established common point for consistency. The extracted PS points were geocoded and integrated into a Geographic Information System (GIS) for further analysis and visualization. For generating vertical height maps, we utilized only those PS points demonstrating a high AS-Index (higher than 0.75). This selective criterion ensured that the height data derived from these points were accurate and reflective of stable surface features, providing a robust basis for detailed analysis of vertical urban growth.
# User Association Performance Trade-Offs in Integrated RF/mmWave/THz Communications
## 3 System Model
### Beamforming Model
#### 3.2.2 Beam Scanning:  
The beam scanning mechanism models the periodic beam scanning performed by the base station to enable user discovery and handovers between beams/BSs. During the scanning process, the BS sequentially surveys each of the \(S\) sectors in its coverage area. For each sector, there is a time \(t_{train}\) required to train the beam in that direction. Once the beam training is complete, there is also a detection time \(t_{det}\) needed to detect the pilot signal from any user equipment currently in that sector. The total time spent scanning each individual sector is therefore given by \(t_{train}+t_{det}\). As the BS must sequentially scan through all S sectors, the overall latency experienced by a UE to associate with the network through the beam scanning process is \(S*(t_{train}+t_{det})\). This model captures the overhead incurred during the initial network access and handovers between beams or cells.
## 6 Discussion
In future work, time-to-progression and survival time will be a better endpoint to classify treatment response. Another limitation comes from the 3D ROI mapping tool. This tool is more computationally efficient compared to the co-registration techniques, but it cannot correct for patient motion. Therefore, in our study, a board-certified radiologist's visually checked and edited all segmentation results as needed. In the future, a more sophisticated registration method with an image similarity measure may improve the accuracy of the tumor contours on ADC maps, and consequently improve the accuracy of the extracted features and the classifier performance. ADC values obtained on pre-operative MRI scans are reported to be of prognostic value in patients with glioblastoma [25,42]. The term \"prognosis\" refers to predicting the likely outcome of treatment. ADC, reported to be inversely proportional to tumor cellularity, is gaining interest in predicting GBM tumor prognosis. Our proposed framework now uses changes in DW-MRI for early prediction of treatment response; however, the framework with feature extraction and machine learning technique could be generalized to pre-treatment DW-MRI for prognosis prediction. In this study, we developed a CADrx framework with machine learning techniques to automatically predict tumor treatment response before the size change using DW-MRI. In our preliminary study, our major contributions are extracting statistical ADC histogram features, applying GMM to model the ADC histogram to interpret the competing effects of cellular density and edema, and applying machine learning techniques using all the extracted features. Cell density and edema may be reflected in ADC values before size changes are apparent on standard MRI sequences. Therefore, ADC holds promise as a biomarker, in determining both which tumors are more likely to respond to treatment and which tumors are actually responding. In conclusion, this work shows that a CADrx system using quantitative ADC histogram features and a machine-learned classifier has better performance in treatment response assessment over conventional analysis using only a mean ADC value. This will have major implications for clinical trials. This work has potential clinical significance for early treatment response assessment in GBM.
## 5 Numerical Results  
In the simulation, the Gamma-Gamma fading channel for FSO link is assume to follow weak (\(\alpha~{}=~{}2.902\) and \(\beta=2.51\)), moderate (\(\alpha=2.296\) and \(\beta=1.822\)), and strong (\(\alpha=2.064\) and \(\beta=1.342\)) turbulent FSO channel conditions [20]. We also assume that \(\eta=0.8\) and \(\Phi=1\) at \(r=0\) for FSO link. For the Nakagami-\(m\) faded RF link, \(m=1.1\) and \(\Omega=1\) are assumed. The average SNRs for the FSO and the RF links are assumed to be the same and denoted by \(\bar{\gamma}=\bar{\gamma}_{FSO}=\bar{\gamma}_{RF}\). For the outage threshold, \(\gamma_{th}=1\) is assumed. When numerically evaluating the infinite series contained in the exact CDF obtained in (16), we use an upper bound on sequence indices \(j,l,k\) such that \(j,l,k\leq 50\).  
Figures 2 and 3 verify the analytical result on the CDF of \(\gamma_{e}\) (equivalently, OP performance) by comparing it with the simulation result. Different pointing error assumptions \(\delta=1\) and \(6.7\) are used in Figures 2 and 3, respectively. In both of the Figures, it is seen that the analytical result is exactly matched with the simulation. In the Figures, the gap between \"Exact\" and \"Approx\" result reveals the effect of noise amplification. It becomes large when the test environment changes from high to low SNR, from IM/DD to HD, from strong to weak turbulence and from high to low pointing error assumption, respectively. At SNR 10 dB, the degradation in OP due to the noise propagation is about 10.71% at HD receiver in weak turbulence environments in Figure 2. It diminishes to 7.98% in strong turbulence but increases to 18.5% in Figure 3 where lower pointing error is assumed. If IM/DD receiver is considered, the above three percentages reduce to 2.43%, 1.69% and 6.58%, respectively. If the SNR is greater than 30 dB, the gap in every tested point becomes less than 1%.  
Figure 2: Comparison of CDFs (equivalently, OP) from \(\gamma_{e}\) and \(\gamma_{a}\); \(\delta=1\) under weak or strong turbulence, respectively.  
In Table 1, the performance degradation percentage is shown for the average BER (letting \(p=q=1\)) and the ergodic capacity (\(\tilde{C}\)). The trend in changing the degradation volume for the average BER is similar to the result for OP. However, for the ergodic capacity, the trend seems different (actually the opposite) according to the detection type, the pointing error assumption, and the turbulent parameter especially if HD detection is assumed. For any case, the gap is from 2.44% to 12.17% at 10-dB SNR but it diminishes to less than 1% at 30-dB SNR.
## 1 Introduction
In this paper, the linewidth increased by a factor of 1.6 when the front facet reflectivity was reduced from a value of 32% (for the uncoated facet) to about 5%. This result is not in agreement with the findings of References [14]. Therefore, further studies on the influence of the facet reflectivity on the spectral linewidth are required. In this paper, we will present the new results of a spectral linewidth study with high power DFB diode lasers emitting in the 780 nm region. Theoretical simulations of external differential efficiency and linewidth power product vs. front facet reflectivity have been conducted. Furthermore, DFB diode lasers with the front facet reflectivity varying from R < 0.01% to 30% have been characterized experimentally. The laser linewidths were derived from measurements of the frequency noise power spectral density (PSD). Linewidth-power dependence of DFB diode lasers will be presented.