esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics
## 1 Introduction
And compared the predictive performance of three models(GRNN, BPNN and ANFIS), in forecasting the soluble solids content of Korla pear. [PERSON] et al. [20] measured the bioimpedance data of strawberries at both mature and immature stages, between frequencies of 20-300 kHz. They utilized six of the most commonly used supervised machine learning classification techniques to evaluate their effectiveness in predicting strawberry fruit ripeness. Extensive research indicates that machine learning algorithms have been widely applied in predicting and classifying fruit and vegetable quality. Appropriate postharvest treatment methods for fruit and vegetables of different maturity can ensure quality during the shelf life and increase economic value [21]. This paper focuses on \"Bale Huangjinguo\" passion fruit [22] as the research subject, analyzing the trends in dielectric properties under different frequencies and growth degree days. The relative dielectric constant and dielectric loss factor at 34 frequencies within the low-frequency range of 0.05-100 kHz are employed as feature representations. Anomaly values are filtered out using the LOF algorithm, followed by the application of four diverse feature selection methods: RFECV, PI-RF, PI-LR and GA. Ultimately, XGBoost and CatBoost models were established to predict the quality indicators of passion fruit, such as _SSC_, _Acidity_ and _PP_, and we compared the predictive performance under various feature selection methods and different models. This study demonstrates the feasibility of combining low-frequency dielectric properties and machine learning for non-destructive testing of passion fruit quality.
## 4 Test and Results
### Multistage TNEP Problem
#### 4.2.3 South Brazilian 46-Bus System  
For this system, the generation capacity for each stage is equal to 10,845 MW. The total demand for stages 1, 2 and 3 are 7743.52 MW, 8976.83 MW, and 10,406.2 MW, respectively. For all tests carried out, the HEGA parameters are: \(k_{p}=100\), \(k_{s}=7\%\), \(k_{m}=10\%\) and \(k_{dr}=1\%\) (population size, selection rate, mutation rate, and diversification rate, respectively). The number of linear blocks was Y = 5. Table 7 presents a summary of the results.  
Test 1: The best solution found by the HEGA for the multistage TNEP problem without SCC and active power losses has a total investment cost of US$220.286 \(\times\) 10\({}^{6}\) and no load shedding. This solution is found after solving 1585 LPs with a computational time of 0.48 min.  
Test 2: The solution found by the proposed approach for the static TNEP problem with SCC and without active power losses is equal to the one of test 1 (no lines compensation).  
Test 3: The best solution found by the HEGA for the multistage TNEP problem considering power losses presents a total investment cost of US$238.35 \(\times\) 10\({}^{6}\), and no load shedding. This solution is found after solving 15,395 LPs with a computational time of 5.82 min.  
Test 4: The best solution found by the HEGA for the multistage TNEP with SCC and power losses has a total investment of US$227.264 \(\times\) 10\({}^{6}\), and no load shedding. This solution is found after solving 179,863 LPs with a computational time of 60.48 min.
## 3 Description of the New Control Approach
### Pitch Control Approach  
In Region III, the control objective is to keep the power output constant. This is achieved by using a regulator (the collective pitch control, or CPC) that maintains the rotational speed constant for all wind speeds over the rated value. The control variable is the pitch angle, e.g., for a given wind speed \(v_{w1}\), the pitch angle has to be \(\beta_{1}\) in order to obtain \(P_{1}\), as can be seen in Figure 5. In this case, the power reference is the rated value.  
Figure 4: Torque control system configuration with power reference.  
Figure 3: Torque control system configuration with power feedback.  
Figure 2: Torque control system configuration for optimal torque control (OTC).  
In order to introduce up/down-regulation in wind turbine operation, a power reference is introduced. This causes the locus of pitch angles to move accordingly (see Figure 6). Thus, in order to reach the new power point \(P_{1}\), the pitch angle has to be increased to the new value \(\beta_{1}\). The idea is illustrated in Figure 6.  
In order to avoid modifications in the topology of the collective pitch control loop, the power tracking control is introduced as an additional control loop in a cascade configuration, as illustrated in Figure 7. A switch is used for a manual change between the classic CPC and the CPC in cascade configuration with a power tracking controller. Lowpass as well as notch filters are included in the feedback way in order to eliminate resonance and vibrating frequencies from the feedback variables, avoiding their amplification in the controllers. The blue lines are used to identify the new control loop.  
Following the classic cascade control design [25; 26], the inner loop has to be faster but does not need to have much accuracy, so that it can be a simpler controller. The outer loop may be slower but requires a higher level of accuracy, which leads to a more sophisticated controller. Since the intention is not to modify the CPC, which is normally implemented by a PI controller [25; 27], a nonlinear PI controller [28; 29] is chosen for the outer loop. The nonlinear PI control (NPI) law is given by  
\[\beta(t)=f_{p}[e(t)]e(t)+\int_{0}^{t}f_{i}[e(t)]e(t)dt, \tag{4}\]  
Figure 5: Typical power curve of a wind turbine and locus of pitch angles.  
Figure 6: Power curve of a wind turbine and locus of pitch angles for a given power reference.  
where the control error \(e\) is (\(\omega_{gsp}-\omega_{g}(t)\)) for the CPC and (\(P_{r\!rf}-P(t)\)) for the power tracking control loop. \(f_{p}\) and \(f_{i}\) are nonlinear functions defined as  
\[f_{x}[e(t)]=K_{x1}+K_{x2}[1-sech(K_{x3}e(t))]. \tag{5}\]  
\(K_{x1}\), \(K_{x2}\), and \(K_{x3}\) are the controller gains that have to be determined by the designer. The subscript \(x\) represents either \(p\) or \(i\). Note that by setting \(K_{x2}=0\), the classic PI controller is obtained.
# Distributed Online Multi-Label Learning with Privacy Protection in Internet of Things
## 4 Distributed OMLC Algorithm
### Distributed Discrete-Time Update
For easy of derivation, \(k_{i}\) will be used in subsequent content to replace \(\frac{1}{\hat{d}_{i}}\). We give the result of the discrete-time update as  
\[ \begin{split} P_{i}(t+1)&=P_{i}(t)-k_{i}[X_{i}^{ \prime}X_{i}P_{i}(t+1)-X_{i}^{\prime}Y_{i}]\\ &\quad-k_{i}\sum_{j\in\mathcal{N}_{i}}w_{ij}(\beta_{i}(t+1)-\beta _{j}(t))\\ \beta_{i}(t+1)&=\beta_{i}(t)+k_{i}\sum_{j\in\mathcal{ N}_{i}}w_{ij}(P_{i}(t+1)-P_{j}(t))\end{split}  \tag{9}\]  
Rewrite Equation (9) in the state form, moving all terms with \((t+1)\) moment to the left-hand side and all terms with \((t)\) moment to the right-hand side,  
\[\left[ \begin{array}{c}P_{i}(t+1)\\ \beta_{i}(t+1)\end{array} \right]=E_{i}^{-1}F_{i} \tag{10}\]where  
\[E_{i}=\left[ \begin{array}{cc}I_{d}+k_{i}X_{i}^{\prime}X_{i}&I_{d}\\ -I_{d}&I_{d}\end{array} \right] \tag{11}\]  
\[F_{i}=\left[ \begin{array}{cc}P_{i}(t)+k_{i}\sum_{j\in\mathcal{N}_{i}}w_{ij} \beta_{j}(t)+k_{i}X_{i}^{\prime}Y_{i}\\ \beta_{i}(t)-k_{i}\sum_{j\in\mathcal{N}_{i}}w_{ij}P_{j}(t)\end{array} \right] \tag{12}\]  
**Lemma 1**. : _The term \(E_{i}\) in Equations (10) and (11) is always invertible._  
**Remark 1**. : _In Equation (10), at each node \(i\), the \(X_{i}\) and \(Y_{i}\) terms originate only from node \(i\) itself. Therefore, this update method satisfies the distributed requirements. As this method performs the update in discrete-time, it is therefore capable of being used in an online manner._  
We provide a fully distributed OMLC algorithm based on the mathematical model discussed before that avoids the transfer of private data between individual nodes.
# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
## 3 Materials and Methods
### Site Description and Field Experiment  
The experiment was carried out at university research farm Koont Chakwal during 2020-2021. The wheat seed was sown on 27 October 2020 with the help of a Rabi drill; a row-to-row distance of 23 cm was maintained under rainfed conditions. The temperature at sowing time was 24 \({}^{\circ}\)C/75.2 \({}^{\circ}\)F in October 2020-2021. The recommended dose of NPK was applied uniformly to all the fields at the time of sowing @ 52:46:25 kg/ha\({}^{-1}\). The total plot size was 3 kanal. In the current study, a number of different YOLO (You Only Look Once) variants, i.e., YOLOv3-Tiny, YOLOv4-Tiny and different versions of YOLOv5, are employed to assess how well the model performed after various adjustments. Our objective was to assess the precision and effectiveness of several YOLO iterations in order to ascertain which version would be most suited to our particular application.
###### Abstract  
Line of sight (LoS) and security are the fundamental requirement for any secure visual MIMO communication based vehicular networks. Uneven speed, irregular terrains, and uncertain appearance of obstacles disagree LoS between the visual multiple-input multiple-output (MIMO) transmitter and receiver. This paper proposes optimal tracking techniques to maintain LoS using a novel quick response (QR) inspired light emitting array (LEA) pattern. Automatic geometric transformation of the QR code in conjunction with the particle filter is used to track the dynamically varying LEA position. In addition to that, an adaptive threshold technique (ATT) has been proposed to minimize error caused by ambient light variations and interferences. A novel key generation algorithm is also proposed to improve the security of the visual MIMO communication system. It utilizes a customized linear feedback shift register (LFSR) and synthetic color image to generate a stronger key. The randomness test demonstrates the robustness of the proposed key generation technique with a minimal probability value of 0.1223 (>0.01). Simulation and real-time studies have been carried out in the presence of geometric distortions, and the bit error rate (BER) performance is evaluated. Results illustrate that the proposed techniques make the visual MIMO system more secure and reliable for vehicular networks.  
Keywords:visual MIMO; LFSR based random key generation; QR-Code; particle filter; adaptive threshold technique +
Footnote †: journal:  
_Article_
# _Forentation_
## 4 Discussion
### Malic Acid Consumption  
The ability of _P. kudriuzzevii_ to consume malic acid aligns with its classification as a Krebs (+) yeast. This group includes species such as _Candida sphareica_, _C. utilis_, _Hansenula anomala_, _P. kudriuzzevii_, and _Kluyveromyces marxianus_, which are capable of metabolizing malic acid and other Krebs cycle intermediates as carbon and energy sources [40; 41]. This capability was previously demonstrated for the _P. kudriuzzevii_ NPCC1651 strain by our research group. In fact, this strain was selected over other _P. kudriuzzevii_ isolates from various substrates in the Patagonian region due to its higher malic acid consumption [31]. In that previous study, conducted in the same acidic must variety, malic acid consumption was higher than that observed in the current study, with _S. cerevisiae_ NIF8 and _P. kudriuzzevii_ NPCC1651 consuming 4.7 g L\({}^{-1}\) and 3.7 g L\({}^{-1}\) of malic acid, respectively. This different behavior could be attributed to variations in must composition, given the variability in apple harvests from year to year. Specifically, the must used in the present study contained lower malic acid levels compared to those used in the previous work (7.6 vs 13.3 g/L). Nevertheless, the malic acid consumption levels observed in the current study remain higher than those reported in studies conducted with wine. For instance, [PERSON] et al. [23] found that a _P. kudriuzzevii_ strain isolated from Korean grape wine consumed only 0.33 g L\({}^{-1}\) of malic acid when co-fermented with _S. cerevisiae_ at a 1:1 ratio. Similarly, [PERSON] et al. [22] reported that a _P. kudriuzzevii_ wine strain consumed 1.14 g L\({}^{-1}\) of malic acid in synthetic wine must. One study performed in cider reportedmalic acid reduction during fermentations involving _P. kudriavzevii_; however, it remains unclear whether the reduction was attributable to this strain or to the lactic acid bacteria inoculated alongside it in the mixed culture [14].  
Regarding _S. cerevisiae_ NIF8, it belongs to the Krebs (-) group, which can only metabolize malic acid in the presence of glucose or another assimilable carbon source. Despite this limitation, species within the Saccharomyces genus exhibit high intraspecies variability, with strains differing in their capacities for malic acid degradation and, in some cases, even producing malic acid during alcoholic fermentation [10; 42; 43; 44]. The _S. cerevisiae_ strain used in this study consumed 29% of the malic acid, equivalent to 2.2 g L\({}^{-1}\), classifying among strains capable of degrading this acid.  
The results showed that the highest percentage of malic acid consumption (35.4%, equivalent to 2.6 g L\({}^{-1}\)) was observed in the Seq S-P inoculation. Based on the Krebs (+) condition of _P. kudriavzevii_, greater malic acid consumption was expected in this strategy; this is likely due to the reduced nutrient availability encountered by _P. kudriavzevii_ upon inoculation. Nonetheless, variations among the fermentation strategies were slight, making this factor less critical in determining the best inoculation strategy.
## 3 Results and Discussion
### Structural Characterization
Further, XRD results also confirmed the presence of the phases Mg\({}_{3}\)RE and Mg\({}_{41}\)RE\({}_{5}\), which correspond to Mg\({}_{3}\)Nd/Gd and Mg\({}_{41}\)Nd\({}_{5}\)/Gd\({}_{5}\), respectively. Zn and Zr were not detected by XRD, as Zn is thought to be dissolved in magnesium, while Zr's percentage in Elektron 21 is too little and, hence, it is difficult for the filtered X-ray to detect the phase when the volume percentage of the phase present in the alloys is less than 2% [19]. Thus, from the microstructural analysis, a few distinct observations can be made: (i) Zn is present as part of the solid solution of magnesium matrix; (ii) distinct secondary phases of Mg-Nd and Mg-Gd are seen in the matrix and the different secondary phases seem to co-exist; (iii) Zr particles are seen in the Mg matrix; (iv) Mg-Nd phase had the highest concentration amongst all the other phases owing to the higher concentration of Nd in the E21 alloy [20]; (v) B\({}_{4}\)C nanoparticles are present in the matrix and the nanoparticles aided the secondary phases in the grain refinement of the material due to the observation of grain refinement in the nanocomposites with the aid of nanoparticles. Figure 2: Optical micrographs of (**a**) E21 alloy; (**b**) E21-1.5B\({}_{4}\)C nanocomposite; and (**c**) E21-2.5B\({}_{4}\)C nanocomposites. The average grain sizes are given in the insets in the images. Figure 3: Cont. From the microstructural analysis of secondary phases, nanoparticles, and grain sizes, the results confirm that the nanoparticles contributed to the further grain refining of the material. This is in contrary to the previously reported alloy nanocomposites, where marginal grain coarsening was observed when nanoparticles were added to magnesium-based alloys [14; 21]. In previous works, it was reported that the nanoparticles, in certain magnesium matrices, alter the mechanism of dynamic recrystallization, from Zener pinning to localized particle stimulated nucleation, causing an inhomogeneity and; therefore, a bimodal grain size across the nanocomposite [14]. However, in this case, the nanoparticles aided the secondary phases in pinning the grain boundaries and refine the grains. It is proposed that this could be due to the segregation of the secondary phases at the liquid-B\({}_{4}\)C nanoparticle interface [22], thereby leading to the secondary phase and nanoparticles complementing each other in the process of grain refinement.
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 3 Supersingular Isogeny Key Encapsulation Mechanism
### Key Encapsulation Mechanism
#### 3.2.3 Isogeny Algorithms  
Let \(l,m\in\{2,3\}\) with \(l\
eq m\). The two core isogeny algorithms used in SIKE are \(\mathtt{isogen}_{l}\) and \(\mathtt{isosex}_{l}\). On the one hand, the algorithm \(\mathtt{isogen}_{l}\) takes in the public parameters and a private key \(\mathtt{sk}_{l}\) as inputs and returns the corresponding public key \(\mathtt{pk}_{l}\). Specifically, it computes \(S=(x_{S},y_{S})=P_{l}+[\mathtt{sk}_{l}]Q_{l}\) and then computes the \(l^{\mathtt{e_{l}}}\)-degree isogeny \(\Phi:E_{0}\to E_{a^{\prime},b^{\prime}}\) via the composition of \(\mathtt{e_{l}}\) individual \(l\)-degree isogenies, where \(E_{a^{\prime},b^{\prime}}\cong E_{0}/\langle S\rangle\), and returns the points \(\Phi(P_{m})\) and \(\Phi(Q_{m})\) encoded as a byte array from three \(x\)-coordinates as the public key, i.e., \(\mathtt{pk}_{l}=\left(x_{\Phi(P_{m})},x_{\Phi(Q_{m})},x_{R_{m}}\right)\), where \(R_{m}=\Phi(P_{m})-\Phi(Q_{m})\).  
On the other hand, the algorithm \(\mathtt{isosex}_{l}\) receives a secret key \(\mathtt{sk}_{l}\) and a public key \(\mathtt{pk}_{m}\) as inputs, and then outputs the corresponding shared key. Specifically, on receiving a private key \(\mathtt{sk}_{l}\) and an encoded public key \(\mathtt{pk}_{m}=\left(x^{\prime}_{P_{l}},x^{\prime}_{Q_{l}},x^{\prime}_{R_{l}}\right)\), it then decodes \(\mathtt{pk}_{m}\) to get the corresponding curve \(E_{a^{\prime},b^{\prime}}\) and the points \(P^{\prime}_{l}\) and \(Q^{\prime}_{l}\), then computes \(S=(x_{S},y_{S})=P^{\prime}_{l}+[\mathtt{sk}_{l}]Q^{\prime}_{l}\) and the \(l^{\mathtt{e_{l}}}\)-degree isogeny \(\Phi:E_{a^{\prime},b^{\prime}}\to E_{a,b}\) via the composition of \(\mathtt{e_{l}}\) individual \(l\)-degree isogenies, where \(E_{a,b}\cong E_{a^{\prime},b^{\prime}}/\langle S\rangle\), and finally returns \(j(E_{a,b})\).
## 3 Experiments and Analysis
### Computational Complexity Analysis  
The proposed RCCD framework is designed not only to achieve state-of-the-art (SOTA) accuracy in CD but also to maintain a favorable efficiency-accuracy trade-off, as shown  
Figure 9: Comparison of feature visualizations for the representational consistency deep supervision method. “w/” indicates that the module is included, while “w/o” denotes its exclusion. The symbol definitions correspond to the formulas presented in Section 2.  
in Table 8. Specifically, the design of RCCD incorporates a representation consistency deep supervision strategy during training to enforce robust bi-temporal feature constraints. However, once trained, the representation consistency supervisor (comprising the local spatial and global style modules) is not required during inference. This design choice directly results in a substantial reduction in computational burden during the testing phase. Concretely, when the supervisor module is removed, experimental evaluations show that the effective parameter count decreases by approximately 24.5% and the FLOPs are reduced by 14.3%. This ensures that RCCD remains highly efficient in practical applications without sacrificing its strong predictive performance. Moreover, RCCD achieves the highest detection accuracy under these optimized conditions. On the S2 Looking and LEVIR-CD+, RCCD outperforms the CDNeXt by improving 0.89% and 0.54% F1, respectively. In addition, compared with other methods such as UNet, FC-Diff, IFNet, SNUNet, and L-UNet, RCCD not only maintains competitive (or even superior) segmentation performance but does so with a significantly lower computational cost. For example, although IFNet exhibits strong detection performance, its large model size (35.73M parameters) and high FLOPs (82.26G) undermine its practical applicability. In contrast, RCCD achieves a well-balanced trade-off by leveraging the shared encoder-decoder structure and excluding additional inference overhead from the representation consistency modules.
### Appendix A.1  
**Corollary A1 (Corollary of Zero-Potential Generalization for One-Dimensional Periodic Potentials).**_This corollary expresses the particular case in which the potential coupled to the arbitrary \(N\) potentials is zero. The equations corresponding to this approximation range from Equation (A4) to Equation (A9)._  
\[\cos\left(kT\right) =\sin\left(\alpha a\right)\left[\frac{H_{1}^{2}(E)+\alpha^{2}H_{4} ^{2}(E)}{2\alpha H_{0}^{0}(E)}\right]+\cos\left(\alpha a\right)\left[\frac{H_{ 2}^{2}(E)-H_{4}^{1}(E)}{2H_{0}^{0}(E)}\right]\] (A4) \[f_{0}(E) =\frac{H_{1}^{2}(E)+\alpha^{2}H_{4}^{2}(E)}{2\alpha H_{0}^{0}(E)}\] (A5) \[g_{0}(E) =\frac{H_{2}^{2}(E)-H_{4}^{1}(E)}{2H_{0}^{0}(E)}\] (A6) \[\Gamma_{0}(E) =\sin\left(\alpha a\right)\left[\frac{H_{1}^{2}(E)+\alpha^{2}H_{4 }^{2}(E)}{2\alpha H_{0}^{0}(E)}\right]+\cos\left(\alpha a\right)\left[\frac{H_ {2}^{2}(E)-H_{4}^{1}(E)}{2H_{0}^{0}(E)}\right]\] (A7) \[\frac{d\Gamma_{0}(E)}{dE} =\left(\frac{df_{0}(E)}{dE}-ag_{0}(E)\frac{d\alpha(E)}{dE}\right) \sin\left(\alpha(E)a\right)+\left(af_{0}(E)\frac{d\alpha(E)}{dE}+\frac{dg_{0}( E)}{dE}\right)\cos\left(\alpha(E)a\right)\] (A8) \[\frac{d^{2}\Gamma_{0}(E)}{dE^{2}} =\left[\frac{d^{2}f_{0}(E)}{dE^{2}}-a^{2}f_{0}(E)\left(\frac{d \alpha(E)}{dE}\right)^{2}\right]\sin\left(\alpha(E)a\right)-\left[ag_{0}(E) \frac{d^{2}\alpha(E)}{dE^{2}}+2a\frac{dg_{0}(E)}{dE}\frac{d\alpha(E)}{dE} \right]\sin\left(\alpha(E)a\right)\] \[+\left[af_{0}(E)\frac{d^{2}\alpha(E)}{dE^{2}}+2a\frac{df_{0}(E)} {dE}\frac{d\alpha(E)}{dE}\right]\cos\left(\alpha(E)a\right)+\left[\frac{d^{2} g_{0}(E)}{dE^{2}}+a^{2}g_{0}(E)\left(\frac{d\alpha(E)}{dE}\right)^{2}\right]\cos \left(\alpha(E)a\right)\] (A9)  
Proof of Corollary A1.: The proof of this corollary is shown in Section S7 of the Supplementary Materials.  
The energy functions \(f_{0}(E)\) and \(g_{0}(E)\) are generalized energy functions, which help to simplify the calculation of the group velocity and effective mass at the generalized zero potential.
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 2 Materials and Methods
### R50A3-LWBENet Model Components  
#### 2.2.1 ResNet  
ResNet is a deep convolutional neural network that has achieved remarkable results in several computer vision tasks such as image classification, target detection and semantic segmentation. The introduction of residual blocks in this network solves the problems of gradient vanishing and gradient explosion during the training of deep neural networks, allowing deeper networks to be trained efficiently. By stacking residual blocks, the details of features and contextual information can be captured more fully, improving the expressive power of the network. The core idea of residual blocks is to connect the original inputs directly to the outputs, forming a cross-layer connection. Each residual block contains a main path and a shortcut connection. The main path performs a series of convolutional operations and nonlinear activation functions, while the shortcut connection adds the original input directly to the output of the main path. The advantage of this is that when the main path has difficulty in learning effective features, the network can avoid feature loss by passing information from the original input directly through the shortcut connection. ResNet mainly consists of five backbone networks including ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152. We can choose ResNet for different backbone networks based on task requirements and computational resource constraints [25; 26].