esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
The classification performance of DNNs trained on data affected by different levels of label noise is shown in Figure 6. Each image database is presented in a separate graph, and different noise models are color-coded. Faded regions indicate the range of classification accuracies across the different DNN architectures.  
The results of noisy label detection are presented in the form of precision-recall curves in Figures 7 and 8. Precision and recall are defined using true positive (TP), false positive (FP), and false negative (FN) detections as follows:  
\[\text{precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{3}\]  
\[\text{recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}. \tag{4}\]  
Each curve was created by stepping a \(q\)-quantile value from 0 to 0.99. This corresponds with moving from the right side to the left side of each plot. The red line indicates the results of the MultiNET algorithm, while the results of a variety of algorithms which combine a single DNN with a \(q\)-quantile approach are indicated by the additional lines. The results of each algorithm when using mean probability instead of a \(q\)-quantile--corresponding to the classic CL algorithm--are presented as larger dots. The red dot shows the results of the CL algorithm when using a combination of DNNs, as in our approach. The graphs are grouped according to the image database used and the type and level of label noise.  
An area under the precision-recall curve (AUC) measure was used to compare the efficiency of noisy label detection. The results are summarized in Table 3, wherein we highlight in bold the best results for each noise type and level combination.  
Figure 6: Dependence of DNN classification accuracy on label noise type and level.  
Figure 7: Precision–recall curves for the CIFAR10 and CIFAR100 datasets. The red line denotes the results of the MultiNET algorithm, while the other curves show the outcomes of single-DNN CL methods. The original concept utilizing the mean confidence level is denoted by a dot on each curve.  
Figure 8: Precision–recall curves for the GTSRB and MNIST datasets. The red line denotes the results of the MultiNET algorithm, while the other curves show the outcomes of single-DNN CL methods. The original concept utilizing the mean confidence level is denoted by a dot on each curve.
# A Brief Review on Challenges in Design and Development of Nanorobots for Medical Applications
## 2 Challenges in the Design and Development of Nanorobots
### Actuators
For instance, a magnetic microrobot can be constructed of a thermoresponsive hydrogel charged with magnetic nanoparticles and a molecular payload released on demand by magnetic induction heating [52]. Instead, the payload may be activated by the unfolding of tubular microrobots with a graphene oxide hydrogel sheet centred on PNIPAM [53]. Microrobots may often release molecules naturally in reaction to an external cue, such as a shift in pH that is frequently correlated with inflamed tissue or tumours. A magnetic microrobot, for example, can be coated with a chitosan gel layer that shrinks under acidic conditions [54]. Self-oscillating gels are active soft materials that absorb energy and can be used in microrobots to implement various behaviours. The reaction-diffusion coupling induces wave propagation in a hydrogel greater than the chemical wavelength, resulting in per-static shape changes and autonomous locomotion of centimetre-sized gel strips [50; 51]. Self-oscillating hydrogels may theoretically be used to build amieboid imitating microrobots capable of spontaneous movement; however, the long duration and low amplitude of the mechanical oscillations remain challenges to be overcome [55]. Amoeboid locomotion refers to the various types of cellular locomotion. Cells such as leukocytes or cancer cells utilize amoeboid locomotion to move on 2D surfaces (for example, along blood vessel walls) and manoeuvre across 3D porous structures like the dynamic extracellular matrix polymeric network [56]. Implementing amoeboid locomotion in microrobots will be especially important for medical applications such as allowing the robot to enter biological tissues without the use of the vasculature [30]. The cell's cytoskeleton, which possesses unique out-of-equilibrium properties and can thus be represented as a \"working gel\" plays a crucial role in the locomotion of amoeboid [57].
# A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks
## 7 Simulation and Results
### Simulation Setup
Under the condition of querying friends' locations, ten locations are randomly selected as the initial locations of an issuing user. The total number of users \(n\) is set to be 1000 and the users are randomly distributed throughout the grid structure. The query distance \(l\) of the issuing user and the number of friends for each user \(f\) are set as 5 km and 50, respectively. The number of pseudo-IDs for each user \(k\) changes from 10 to 30. We execute 100 times and then calculate the average number of effective users and the average time cost under different \(k\). The above settings are named scenario-3, which is used to study the effect caused by \(k\). 4. Under the condition of querying friends' locations, ten locations are randomly selected as the initial locations of an issuing user. The total number of users \(n\) changes from 1000 to 2500 and the users are randomly distributed throughout the grid structure. The query distance of the issuing user \(l\), the number of pseudo-IDs for each user \(k\), and the number of friends for each user \(f\) are set as 5 km, 10, and 50, respectively. We execute 100 times and then calculate the average number of effective users and the average time cost under different \(k\). The above settings are named scenario-4, which is used to research the effect caused by \(n\).
## 2 Non Destructive Sensing
### Non Destructive Measurements
Fluorescence results from excitation of a molecule by a high energy light (short wavelength) and its subsequent instantaneous relaxation with the emission of lower energy light (longer wavelength). Fluorescence measurements of chlorophyll containing tissue are routinely used for investigations of photosynthetic activity in plant leaves [72]. Chlorophyll content and its photosynthetic capacity are often related to maturity of plant organs and can detect defects or injuries. Physiological stresses that affect chloroplast or photosynthesis, such as temperature, salinity, moisture [73], atmospheric pollutants [74] and mechanical damage can also be detected [75]. Fluorescence or delayed light emission (DLE) imaging is used on physiological studies of chilling injury and similar stress responses on fruits and vegetables [76, 77]. Fluorescence and DLE have been studied as possible methods for evaluating maturity in fruits and vegetables that lose chlorophyll as they ripen or mature [78]. Fluorescence detected citrus rind based on fluorescence of oils that leaked from damaged cells.
## 1 Introduction
In most of the related studies, the converter geometry, lance height, and nozzle inclination angles (NIAs) are assumed to remain unchanged and numerical and experimental analyses are carried out for different NTAs. However, the lance height is often adjusted in BOF steelmaking, which makes the optimum NTA difficult to determine. Addressing this issue requires exploration of the influence of the slagging reaction and the stirring effect from the oxygen lance height, NIA, and NTA. To accelerate the slagging reaction and improve energy efficiency under a developmental lance height, fuzzy control methods have been used to realize an oxygen lance self-adjustment strategy. In fuzzy control methods, an effective algorithm is used to deal with highly nonlinear models [15]; these methods have been widely applied in various control systems, including those in the metallurgy, machine, artificial intelligence, automation, and other fields [16]. Fuzzy control methods are similar to the human brain control mechanism and are not restricted by a controlled model [17]. The BOF process involves a series of strong nonlinear problems such as chemical reactions, supersonic flow, and multiphysics coupling, among [18]. The fuzzy control method may be adaptable to the adjustment of a multi-nozzle oxygen lance, and it is also the most feasible scheme for use with small-scale data sets [19]. In summary, a self-adjustment control strategy of the oxygen lance may be applicable for accelerating the slagging reaction and saving smelting energy. A solution to the stirring effect during the slagging reaction and optimization of design scheme for the multi-nozzle oxygen lance are key scientific objectives of this paper. To advance these goals, this research was conducted as follows. First, on the basis of computational fluid dynamics (CFD) and a fuzzy control method, a reactor model of a molten bath in MASTEEL is configured. Accordingly, the blow behavior and flow field characteristics during the slagging process are analyzed [20]. Then, the evolution of the gas-liquid interface is evaluated, and the stirring effect under different nozzles is discussed. Finally, a method for adjusting multi-nozzle oxygen lances is proposed to achieve better slagging effects [21]. This research can provide technical support for BOF steelmaking and also provide beneficial reference information for engineering applications in the metallurgy [22], chemical industry [23], machine [24], astronautics[25] and other fields [26]. This paper is organized as follows. In Section 2, the fluid model of the BOF process is set up and the boundary conditions are described. In Section 3, the jet dynamics behavior and stirring effect during the slagging process are discussed. In Section 4, an optimized design scheme for a multi-nozzle oxygen lance is proposed to save smelting energy. The conclusions are presented in Section 5.
# Continuous-Variable Quantum Key Distribution Robust Against Polarization-Dependent Loss
###### Abstract  
Polarization is one of the physical characteristics of optical waves, and the polarization-division-multiplexing (PDM) scheme has gained much attraction thanks to its capability of achieving high transmission rate. In the PDM-based quantum key distribution (QKD), the key information could be encoded independently by the optical fields \(E_{x}\) and \(E_{y}\), where the 2-dimensional modulation and orthogonal polarization multiplexing usually result in two-fold channel capacity. Unfortunately, the non-negligible polarization-dependent loss (PDL) caused by the crystal dichroism in optical devices may result in the signal distortion, leading to an imbalanced optical signal-to-noise ratio. Here, we present a polarization-pairwise coding (PPC) scheme for the PDM-based continuous-variable (CV) QKD systems to overcome the PDL problem. Numerical simulation results indicate that the PDL-induced performance degradation can be mitigated. In addition, the PPC scheme, tailored to be robust against a high level of PDL, offers a suitable solution to improve the performance of the PDM-based CVQKD in terms of the secret key rate and maximal transmission distance.  
Keywords:continuous variable quantum key distribution; polarization-division-multiplexing; polarization-dependent loss; polarization-pairwise coding +
Footnote †: journal: Applied Physics Letters
## 4 Results and Discussion
### Semi-Empirical Models
#### 4.4.1 Longitudinal Modulus  
The starting point for this effort is to find a model that properly predicts the longitudinal modulus of the gyroid at various relative densities. By looking at a plot of the FEA results, it is clear that the gyroid's longitudinal modulus (\(E_{g}\)), when normalized against the neat material modulus (\(E_{m}\)), is nearly independent of all parameters except relative density. A variety of models have been found in the literature relating to cellular structures such as foams and honeycombs. For example, a power law is used to represent the modulus of closed-cell foams in [46]. In addition, a variety of polynomial ratios have been used for various foam and honeycomb predictions in [47; 48; 49; 50; 51]. None of these, however, fit the data satisfactorily. The model found to best fit the data was a modified power law,  
\[E_{n}(D)=\frac{E}{E_{m}}=D-(1-D)^{C_{1}}\cdot D^{C_{2}} \tag{12}\]  
where \(E_{n}\) is the normalized longitudinal modulus, \(E_{m}\) is the neat-material elastic modulus, \(C_{i}\) are curve fitting coefficients, and D is the relative density of infill (\(0\leq D\leq 1\) which corresponds to 0% \(\leq D\%\leq\) 100%). The best value of fitting parameters \(C_{1}\) and \(C_{2}\) were determined to be 0.77 and 1.2, respectively. This results in an average error of 0.44% (maximum 1.13%). Figure 13 shows the FEA and semi-emperical predictions.
# Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM
## 2 Basic Principles
### Convolutional Neural Network
#### 2.2.1 Convolutional Layer  
The convolutional layer performs convolutional operations on local regions of the input image by means of convolutional kernels to generate new feature maps. The mathematical expression for the convolution operation is shown below:  
\[x_{j}^{l}=f(\sum_{i\in m_{j}}x_{i}^{l-1}*k_{ij}^{l}+b_{j}^{l}) \tag{7}\]  
where: \(l\) is the convolutional layer of layer \(l\), \(x_{i}^{l}\) is the output of layer \(l\), \(x_{i}^{l-1}\) is the input of layer \(l\), \(k_{ij}^{l}\) is the weight matrix, \(b_{j}^{l}\) is the bias and \(f\) is the activation function.
# Road Extraction Based on Improved Convolutional Neural Networks with Satellite Images
## 3 Experiments
### Implementation Details  
The method from [40] is selected as our optimizer. During the training period, the learning rate is set as 0.0001, which would be decreased 10 times after the loss value, decreasing slowly. In addition, the batch size in the training phase is set as 2.
# LDPC Codes on Balanced Incomplete Block Designs: Construction, Girth, and Cycle Structure Analysis
## 4 Short Cycles of Specific BIBD-LDPC Codes  
In this section, we provide specific examples of BIBD-LDPC codes and determine their short-cycle distributions. By applying the cycle-counting method developed in the previous sections, we calculate the number of cycles of lengths \(6\) and \(8\) for these codes. This analysis highlights the relationship between the parameters of the BIBD (e.g., \(\kappa,v\)) and the cycle structure of the resulting LDPC codes.  
First, we provide some important parameters of BIBD-LDPC codes. Based on a BIBD(\(\kappa,1;v\)), we can construct a BIBD-LDPC code of length \(v(v-1)/\kappa(\kappa-1)\), the parity-check matrix of which has a column weight of \(\kappa\) and a row weight of \((v-1)/(\kappa-1)\). The code rate is about \((1-\kappa(\kappa-1)/(v-1))\). The detailed parameters of the BIBD-LDPC codesare presented in Table 1. Second, we provide some existing BIBDs. For convenience, we do not introduce the construction of the existing BIBDs and only provide their existence conditions. We summarize some existing BIBDs in Theorem 1.  
**Theorem 1**.: _A \(BIBD(\kappa,1;v)\) exists for \(\kappa=3\), 4, 5, 6, and 7 if one of the following conditions is satisfied._  
1. _If_ \(v\equiv 1\mod 6\) _or_ \(v\equiv 3\mod 6\)_, there exists a_ \(BIBD(3,1;v)\)_._
2. _If_ \(v\equiv 1\mod 12\) _or_ \(v\equiv 4\mod 12\)_, there exists a_ \(BIBD(4,1;v)\)_._
3. _If_ \(v\equiv 1\mod 20\) _or_ \(v\equiv 5\mod 20\)_, there exists a_ \(BIBD(5,1;v)\)_._
4. _If_ \(v\equiv 1\mod 15\) _or_ \(v\equiv 6\mod 15\) _for_ \(v\
eq 16\)_, 21, 36, 46, 51, 61, 81, 166, 226, 231, 256, 261, 286, 316, 321, 346, 351, 376, 406, 411, 436, 441, 471, 501, 561, 591, 616, 646, 651, 676, 771, 796, and 801, there exists a_ \(BIBD(6,1;v)\)_._
5. _If_ \(v=42i+1\) _for_ \(i\
eq 1\)_,_ 2,_ 3,_ 5,_ 6,_ 12, 14, 17, 19, 22, 27, 33, 37, 39, 42, 47, 59, and 62 or \(v=42t+7\) _for_ \(i\
eq 3\)_,_ 19, 34, and 39, there exists a_ \(BIBD(7,1;v)\)_._  
Based on the cycle-number equations derived in Section 3.3, we can directly determine the number of short cycles in BIBD-LDPC codes constructed from a \(BIBD(\kappa,1;v)\). By applying Theorem 1, we can obtain a BIBD and subsequently construct a BIBD-LDPC code. Using this approach, the number of short cycles in the Tanner graphs of the constructed BIBD-LDPC codes can be accurately counted. These results are recorded in Table 2.  
Notably, the proposed cycle-counting method achieves \(O(1)\) complexity, as the enumeration of cycles of lengths 6 and 8 reduces to closed-form expressions parameterized by \(v\). This contrasts sharply with existing approaches that require greater computational complexity. For instance, counting cycles with a length of 6 in a BIBD-LDPC code constructed from \(BIBD(\kappa,1;v)\) using the competitive message-passing algorithm proposed in [10] incurs complexity of \(O(gv(v-1)/\kappa(\kappa-1))\), whereas our method requires only two subtractions, three multiplications, and one division operation.
## 2 Results and Discussion  
### Purification of APs from Collagen Hydrolysate Fraction of F7  
#### 2.1.1 Gel Filtration Chromatography of F7 Using Superdex(r) Peptide 10/300 GL Column  
Superdex(r) Peptide 10/300 GL is designed for high-efficiency and lab-scale isolation of biomolecules including peptides and proteins with molecular weights (MWs) between 0.1 and 7.0 kDa. Therefore, hydrolysate fraction of F7 was further separated using the column and divided into four components, defined as FSP-I, FSP-II, FSP-III, and FSP-IV, respectively (Figure 1A). Their DPPH\(\bullet\) and HO\(\bullet\) scavenging activities are shown in Figure 1B. These data indicated that EC\({}_{50}\) values of FSP-III on DPPH\(\bullet\) and HO\(\bullet\) were \(0.72\pm 0.09\) and \(0.41\pm 0.02\) mg/mL, which were significantly lower than those of F7 (DPPH\(\bullet\): \(1.57\pm 0.12\) mg/mL; HO\(\bullet\): \(1.20\pm 0.09\) mg/mL), FSP-I (DPPH\(\bullet\): \(2.65\pm 0.23\) mg/mL; HO\(\bullet\): \(1.89\pm 0.14\) mg/mL), FSP-II (DPPH\(\bullet\): \(1.03\pm 0.15\) mg/mL; HO\(\bullet\): \(0.84\pm 0.06\) mg/mL), and FSP-IV (DPPH\(\bullet\): \(3.52\pm 0.29\) mg/mL; HO\(\bullet\): \(3.17\pm 0.28\) mg/mL), respectively (\(p<0.05\)). Therefore, FSP-III should contain more APs amount and was suitable for the following separation process.
However, in situ measurement of Si anode poses extra problems when the anodes are within the electrolyte when they are in service. In addition, it is difficult to deposit any speckles or other types of optical marks onto the anode surface. In the current study, the above-mentioned issues in the experimental aspect have been discussed in detail. The novelty of this work includes the following. A real time/full field 3D measurement method, combining the traditional DIC technology with the laser scanning method, is developed to realize the characterization of anode deformation in 3D space. A parameter defined as mean strain gradient (MSG) is proposed to describe the uniformity extent of electrochemical reactions based on full field strain distribution data. Besides, a button cell with an optical window opened, matched with a laser confocal scanning microscopy (LCSM) system, is designed and self-refitted, by which the anode surface can be observed and measured in situ. The remainder of the paper is divided into the following sections. In the Methodology Section, the basic principles of the in-suit measurement method are presented. In the Experimental Section, the sample preparation, the testing procedures and other experimental details are described. In the Results and Discussion Section, the experimental results are discussed and analyzed. In the Conclusion Section, we provide a summary of the study.
## 2 Existing Routing Methods for the DTN
### Covered Area  
Traveling route information-based routing uses information about the route of a traveling node to define the geographical area in which the traveling node is supposed to be able to relay bundles asynchronously. This geographical area is referred to as a \"covered area\". Specifically, we consider two types of covered area: circular covered area and vectorial covered area.  
As shown in Figure 4, a circular covered area is a circle that contains the traveling route. A covered area is associated with its location information. An advantage of using a circular covered area is that it can be identified by extremely simple location information. The data size required for this information is fixed and small. A downside is that its oversimplification of a complex shape into a circle means that a covered area can contain pockets of area to which bundles cannot actually be relayed.  
Figure 4: Example of generating a circular covered area.  
Figure 3: Two examples of destination area.  
A vectorial covered area is a set of points where the direction of the traveling route changes, as shown in Figure 5. A vectorial covered area represents the actual area more accurately than a circular covered area, but the data size of information needed to represent its shape is variable and much larger.
## 3 Related Work and Methodology
### Categorization of Concentrated Activity Areas
#### 3.4.1 Getis-Ord \(Gi^{\star}\)  
The Getis-Ord \(Gi^{\star}\) statistic is a measure used to assess the degree of spatial clustering of values within a geospatial seismicity dataset. It is valuable for identifying areas with heightened or diminished seismic activity [46] of high or low earthquake magnitude values. The formula for calculating the Getis-Ord \(Gi^{\star}\) statistic is as follows [45]:  
\[Gi^{\star}(d)=\frac{\sum_{j=1}^{n}w_{ij}x_{j}-\overline{x}\sum_{j=1}^{n}w_{ij}} {\sqrt{\frac{\sum_{j=1}^{n}x_{j}^{2}-\left(\sum_{j=1}^{n}x_{j}\right)^{2}}{n-1 }}} \tag{9}\]  
where \(Gi^{\star}(d)\) represents the Getis-Ord \(Gi^{\star}\) statistic at a given distance threshold \(d\). \(n\) is the total number of spatial units or observations. \(x_{j}\) represents the value of the variable of interest at spatial unit \(j\). \(\overline{x}\) represents the mean of the variable across all spatial units. \(w_{ij}\) represents the spatial weight between spatial units \(i\) and \(j\), typically based on distance.  
The Getis-Ord \(Gi^{\star}\) statistic evaluates whether the observed spatial clustering of high or low values is significantly different from what would be expected under spatial randomness. Positive values of \(Gi^{\star}\) denote spatial clustering of high values, indicative of concentrated areas with heightened seismic activity. Conversely, negative values signify clustering of low values, highlighting regions with diminished seismic activity [46].