esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Functional foods to prevent aging, particularly skin aging, have grown in popularity as society ages and science uncovers the pathological mechanisms of aging. In this study, we used processed ginseng, one of the famous functional foods, along with fermentation utilizing the novel _Aspergillus niger_ strain KHT-1 to enhance its value and efficacy. We successfully produced fermented black ginseng (FBG) and investigated its anti-skin aging properties, such as anti-melanogenic, anti-wrinkle, and antioxidant activities. The results showed that FBG at 100 \(\mu\)g/mL inhibited tyrosinase and melanin production in IBMX-stimulated B16F10 cells. FBG significantly reduced tyrosinase, TYRP-1, and MITF mRNA expression. Moreover, FBG stimulated anti-wrinkle effects in UVB-irradiated human dermal fibroblasts (FBG) cells by inhibiting elastase activity, and decreasing MMP-1 and MMP-9 while increasing COL-1 mRNA expression. Furthermore, FBG showed antioxidant activity by reducing ROS levels in H\({}_{2}\)O\({}_{2}\)-induced HaCat cells. All tests showed that FBG had better anti-melanogenic, anti-wrinkle, and antioxidant activities than black ginseng (BG) and white ginseng (WG). Interestingly, FBG had lower toxicity in B16F10, HDF, and HaCat cells compared with BG. Thus, this study discovers the underlying mechanism of FBG as a functional material with anti-skin-aging properties.  
Keywords:anti-melanogenic; anti-wrinkle; antioxidant; _Aspergillus niger_; fermented black ginseng; skin aging +
Footnote †: journal: Applied Sciences
# Recommender Systems in the Real Estate Market--A Survey
## 3 Survey Strategy  
In this section we explain the selection procedure of relevant research papers. As opposed to narrative and traditional literature reviews, in this paper we used a systematic literature review (SLR) [14] procedure to systematically and comprehensively review relevant studies. An SLR should provide precise and understandable protocols to reduce the bias and systematic faults and, therefore, to guarantee the repeatability and trustworthiness of the review conclusions. Hence, SLR is more comprehensive, has lower risk of bias, has more formal and systematic protocols, but is relatively slower compared to narrative and traditional literature reviews. Recently, several studies used this type of review in the field of recommender systems [15, 16, 17].  
To find the papers related to real estate RSs, we checked four main bibliography databases: ACM Digital Library, IEEE Xplore, Scopus, and Web of Science. We also used Google Scholar, which is one of the most popular scholarly search engines, to verify that we had covered all the relevant papers. We queried these sources to retrieve papers related to recommendation systems in real estate with the following query in February 2021:  
_((\"recommender system\"OR\"recommendation system\")AND(\"real estate\"OR\"housing\"))_  
The initial list contained 150 papers. For each paper, we reviewed the abstracts based on the exclusion criteria described in Table 1 and identified 27 papers that passed the filtering. After reading the full text of these papers, we further excluded seven papers based on the exclusion criteria. Additionally, we reviewed the _related work_ section of the remaining 20 papers and added six more papers to our corpus. As a result, we end up with 26 papers constituting of 13 conference papers and 13 journal papers. The summary tables of these selected papers are reported in Appendix A.  
The distribution of the publication year of selected papers is depicted in Figure 1. The first paper related to real estate recommendation emerged in 1996 [18], indicating that the need for an online tool to recommend real estate items to users is not something new. However, the number of papers grows substantially in the course of time. As can be seen in Figure 1, 14 papers have been published very recently (between 2018 and 2021) which implies the recent trend of RSs in the real estate domain.
## 4 Challenges and Limitations of Nanosensor Deployment
### Environmental Factors and Durability  
#### 4.3.1 Protecting Nanosensors from Environmental Nano-Particles  
Nanosensors, due to their size and sensitivity, can potentially be influenced by airborne nanoparticles or molecular-scale environmental variables, thereby demanding enhanced protective measures, such as specialized nanocoatings, that do not inhibit their sensing capabilities [95,96].
# A Fast Point Clouds Registration Algorithm for Laser Scanners
## 3 Experimental Results  
The experiment dataset contains \"Bed\", \"Motorbike\", and \"Piano\" from the basic geometry library and \"Dragon1\", \"Dragon2\", and \"Bunny\" from Stanford University Graphics Lab. The point numbers of the point clouds are Bed (110372, 108615), Motorbike (41214, 40023), and Piano (54139, 54139), Dragon1 (126546, 132000), Dragon2 (184982, 155208), Bunny (40256, 40097). Our proposed algorithm is implemented in Microsoft Visual Studio 2015 and PCL (Point Cloud Library), while all the experiments are conducted on a 3.6 GHz Intel(R) Pentium(R) G4600 processor with 8 GB RAM. In order to verify the rationality and efficiency of the algorithm in this paper, it is compared with ICP, and sample consensus initial alignment ICP (SAC-IA+ICP), and 3D histograms of point distributions ICP (3 DHoPD+ICP) [21] algorithms under the same environment.
## 6 Computational Implementation  
A MATLAB program was developed to calculate the thermoelastic effective coefficients by using square or hexagonal cell distributions. Since the representative cell has symmetric geometry, calculations are performed by using only \(1/4\) of the cell. This allows to reduce the number of operations and CPU time.  
The program receives as input the type of cell that constitutes the composite material as well as the elastic (Young's modulus, Poisson's ratio) and thermal properties (conductivity and thermal expansion) of the materials that correspond to the fiber and the matrix. The fiber volume fraction values \(\gamma\), for which the calculations of the effective properties will be performed, are selected. For each value of \(\gamma\) a mesh that discretizes the quarter of the cell is generated. The method described in ref. [30] was used to construct an 8-node quadrilateral mesh. The 4-node quadrilateral meshes are created by using the obtained 8-node quadrilateral meshes. The effective thermoelastic properties, such as the elastic coefficients \(\overline{C}\), the thermal expansion \(\overline{\mathbf{\pi}}\) and thermal conductivity \(\overline{\kappa}\) coefficients, are obtained for each value of \(\gamma\). Linear and quadratic FEM formulations are used to solve the local problems obtained from the AHM. The generated meshes are used to perform calculations through the linear and quadratic FEM formulations, respectively. Graphics are created and tables are estimated for each value of \(\gamma\), where the numerical values of the thermoelastic effective coefficients are shown.  
As a result of applying the AHM and in order to obtain the thermoelastic effective coefficients, the local problems must be solved for each value of \(\gamma\). The values of \(\gamma\) vary from 0 to 1 with a step size of 0.05 as a default value. For each local problem, every element within the mesh is analyzed. The local matrices of each element are calculated and the global matrices are assembled. The dimension of the matrices is twice the number of nodes in the mesh, for the elastic plane local problems and the \({}_{pp}T\) local problem. The dimension of the matrices is the same as the number of nodes in the mesh, for the elastic anti-plane local problems and the \({}_{q}I\) local problems. The matrices have several zero values, and due to the large volume of calculations that must be performed with large matrices where a large number of zeros is present, the MATLAB's _sparse_ data structure was used. The _sparse_ data structure stores only the positions and the values of the elements that are not zero, occupying a significantly less amount of memory. Additionally, MATLAB redefines the _sparse_ array operations so that only nonzero elements are considered. Finally, approximately 30% of the execution time can be saved through this strategy.  
Figure 13 shows a flowchart that describes the computational implementation steps.
# In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods
## 4 Results
### Results with TRIAD Method  
The TRIAD method (Section 3.1) provides the CubeSat attitude matrix. The Sun LOS in the body frame is computed from the model and the attitude matrix. The nadir direction in the body frame is obtained from the transformation of the nadir vector in the orbital frame (defined as the third axis of the reference frame basis). Those two vectors are represented in Figure 8.
## 2 About the Chosen Digital Signature Algorithms
### Dilithium
#### 2.1.2 Advantages and Disadvantages According to the Authors  
The authors of [20] listed the following advantages of Dilithium:  
* Significantly higher speed and significantly smaller size compared to hash-based schemes (e.g., the Sphincs+ scheme).
* Simple implementation, as Gaussian sampling is not required.  
They focused on the comparison between Dilithium and Falcon, as these are the only two lattice-based schemes in the third round of NIST's call, and NIST announced that it will standardize only one such scheme.  
The authors emphasized that Falcon's signature and key sizes are about 2.3 times smaller than those of Dilithium (Figure 2) but pointed out its disadvantages:  
* It uses high-precision Gaussian sampling (64 bit precision), which makes it difficult to notice subtle implementation errors (the distribution would still look Gaussian even if it is not satisfactory), possibly leading to the leakage of the secret key.
* It is complex to mask, and there have been no serious attempts to improve this, so far. However, masking may not be required for signing a small number of messages (in the order of 100 messages).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Security Level** & **2** & **3** & **5** \\ \hline Public Key Size (bytes) & 1312 & 1952 & 2592 \\ Signature Size (bytes) & 2420 & 3293 & 4595 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of Dilithium [19].  
* Dilithium has the advantage of using only uniform sampling within a power-of-two range, making it much easier to detect implementation errors.  
Figure 1: Execution speed of Dilithium (cycle counts) [19].  
Figure 2: Comparison of Dilithium and Falcon by public key and signature lengths (Falcon’s values for Level 3 are not available) [20].  
Further, the authors compared the two algorithms by their execution speed (according to [9; 21]), concluding that Falcon's only advantage is in much faster verification (Table 2).  
While both algorithms are competitive, the authors of [20] stressed the applicability of Dilithium and Falcon for specific uses. In the end, both schemes were standardized, since Dilithium is easier to implement, and Falcon provides significantly shorter signatures. The authors suggested that Dilithium may serve as a \"general-purpose\" algorithm, while Falcon could be used in applications requiring multiple signatures (in the order of 100-1000 signatures).
## 4 Measurement Results  
The measurements of BAM were performed with calibrated geophones (velocity transducers, Geospace, Houston, TX, USA) and a measurement system (SCADAS, DIFA, Breda, The Netherlands) with 72 channels of sample and hold amplifiers and adjustable analogue  
Figure 3: Calculated spectra of the axle impulses from the passage of a bogie due to the distribution of the static axle loads by a ballast track with (**a**) rail pads of \(k_{R}=\times\) 20, + 40, \(\triangle\) 80, \(\Circ\) 160 kN/mm, \(\square\) no rail pads, (**b**) under-sleeper pads of \(k_{ll}=\times\) 25, + 50, \(\triangle\) 100, \(\Circ\) 200 kN/mm, \(\square\) no under-sleeper pads, (**c**) under ballast mats of \(k_{M}\arcsec\)\(\approx\) 20, + 40, \(\triangle\) 80, \(\Circ\) 160 kN/mm/m\({}^{2}\), \(\square\) no under ballast mat, (**d**,**e**) under-ballast plates \(h_{p}=\square\) 0, \(\Circ\) 0.15, \(\triangle\) 0.3, + 0.45 m on (**d**) stiff soil (\(r_{S}\) = 300 m/s), (**e**) soft soil (\(r_{S}\) = 150 m/s), and (**f**) \(\Circ\) mat (\(k_{M}\arcsec\) = 100 kN/mm/m\({}^{2}\)), \(\triangle\) plate (\(h_{p}\) = 0.3 m), + mat and plate, \(\square\) no plate and mat.  
filters. Similar measurements have been performed with similar results at the Regensburg site (Heiland&Mistler, Bochum, Germany), at the Lengnau site (Ziegler Consultants, Zurich, Switzerland), and at the Altheim site (Muller BBM, Planegg, Germany).
## 4 Experimental Results
### Experimental Study 2
#### 4.2.3 Results Obtained Using the MobileNet Model  
The MobileNet model is typically designed to operate on input images with a resolution of 224 \(\times\) 224 \(\times\) 3. For this reason, the dimensions of all images in the dataset were resized from 50 \(\times\) 50 \(\times\) 3 to 224 \(\times\) 224 \(\times\) 3. The classification layer of the model was replaced by a two-output sigmoid activation function. In addition, the weights of the model were initialized randomly, and the model was then retrained on the dataset. The model parameters were set as follows: Adam optimizer, BCE loss function, a learning rate of 0.001, 50 epochs, and a batch size of 32. The model trained for 50 epochs yielded an accuracy rate of 95.54%, precision of 90.90%, recall of 71.43%, and an F1 score of 80%. The accuracy graph and loss function graph of the MobileNet model developed for experimental study 2 are depicted in Figure 11.
## 2 Related Work
In particular, and leveraged by the impressive evolution in the quality of the cameras, processing power, and memory, smartphone-based solutions are being explored to implement cost-effective platforms for microscopic inspection of samples [21]. A wide range of applications has also been used to test the feasibility of affordable approaches based on smartphones, including the screening of blood smears [22] or the detection of parasites [23; 24; 25] and viruses [26]. Regarding the cervical cytology use case, a device called uSmartScope [27; 28] was adapted for the digitalization of cervical cytology samples [29; 30], (Figure 1). This device is a fully automated, 3D-printed smartphone microscope tailored to support microscopy-based diagnosis in areas with limited access. The device aims to decrease the burden of manual microscopy examination by being fully powered and controlled by a smartphone, in addition to the motorized stage. Supported by such devices for cervical cytology, a recent work [6] proposed a region-based approach for the mobile detection of cervical lesions. This work used the public dataset SIPAKMED and a new private dataset acquired with the uSmartScope (hereinafter referred as Region-Based Cervical Lesion Dataset), depicted in Figure 1. Promising results for cervical cancer screening have been achieved using a Faster R-CNN model with a ResNet50 backbone while also focusing on being a cost-effective Internet of Things (IoT)-based solution. Nevertheless, the authors identified the low data volume and high structure variability of the region-based dataset as the major bottlenecks of the study. Thus, this work follows this research stream and explores the development of a nucleus-based approach for automated adequacy assessment and cervical lesion detection using LBC samples digitalized with the uSmartScope device that strictly follows the TBS guidelines on both tasks.
## 2 Methods
### Analog to Photon-Counting Signal Gluing  
In the context of pBRL data analysis, signal gluing is performed to match the LOTR AN and PC signals, taking into account their signal and background variances, dead-time corrections, and signal saturation.  
We implemented two methods: one based on a \(\chi^{2}\)-minimization between the two signal channels, with sliding windows and different window sizes, extending the methods described by Whiteman et al. [46], Lange et al. [47], Li et al. [48], and a second algorithm based on maximization of a likelihood function that combines probability density functions from both channels, building on the method of Veberic [49].
# Deep Learning-Based Detection Technology for SQL Injection Research and Implementation
## 5 Experiment and Result Analysis
### Testing Index  
There are various evaluation indicators for classification models, including accuracy rate, precision rate, recall rate, AUC, etc. In the field of SQLIA detection, according to actual needs, the focus of selecting indicators will also be different. When detecting SQL injection attacks, it not only pays attention to the accuracy rate but also has strict requirements on the false negative rate and high requirements on the discrimination speed and generalization performance. This paper mainly uses the deep learning model based on the improved TextCNN and LSTM. The cross-entropy loss serves as the loss function employed during the training procedure in this study. In the domain of SQL injection attack detection, the prevalence of imbalanced positive and negative samples renders the use of accuracy as an evaluation metric unreasonable. Therefore, in addition to the detection accuracy (Accuracy), recall (Recall), and precision (Precision), the evaluation indicators used for the detection of SQL injection attack statements also use F1-Score as a comprehensive evaluation standard; its calculation formula is shown in (4)-(7). Meanwhile, this paper presents a comparative analysis of the model proposed herein with other existing models. Discrimination speed is also very important in the era of big data, so the experiment also uses discrimination speed as an auxiliary evaluation index.  
\[\text{Acc}=\left(1-\frac{errors\_sum}{sum}\right)\times 100\% \tag{4}\]  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100\% \tag{5}\]  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100\% \tag{6}\]  
\[\text{F1}=\frac{2\times\text{P}\times\text{R}}{\text{P}+\text{R}}\times 100\% \tag{7}\]  
where \(errors\_sum\) quantifies the quantity of misclassified samples, and \(sum\) denotes the aggregate quantity of samples.  
FP is a false positive example, representing the number of samples in which normal SQL statements are predicted to be SQL injection statements. FN is a false negative example, indicating the number of samples of SQL injection that are predicted to be normal.  
The F1 score is an evaluation metric used in machine learning for classification models. It is a statistical metric employed to assess the accuracy of a binary classification model. It considers both the precision and recall of the classification model simultaneously, representing a harmonic mean of the model's precision and recall.
## 3 Implementation
### Feasibility of the Motion  
Given an execution time \(t_{f}\) fixed a priori, the trajectory resulting from an optimization from certain value \(\alpha\) may result in a non-feasible trajectory. Therefore, in order to find a feasible trajectory in terms of robot limits and mechanical safety, it may have to be scaled in order to have a feasible design. However, since the arc-length does not change after a re-parametrization, we can state that for any \(\alpha>0\), the solutions of (29) may be linearly scaled without losing their property of being minimum jerk trajectories.  
If the planned motion violates robot limits or mechanical safety standards, we apply a linear dilation \(\sigma:C^{0}(\mathbb{R},\mathbb{R}^{n})\longrightarrow C^{0}(\mathbb{R}, \mathbb{R}^{n})\) to the computed trajectory given by:  
\[\sigma(\mathbf{q}(t))=\mathbf{q}(\sigma_{0}t)\text{ with }\sigma_{0}\in \mathbb{R}^{+}, \tag{33}\]  
where \(\sigma\) is a function that takes the continuous vector-valued trajectory \(\mathbf{q}\) and returns the same trajectory scaled by the scaling factor \(\sigma_{0}\).  
The mechanical safety is considered by using the concept of a danger field [31], which is a map \(DF:\mathbb{R}^{3}\times\mathbb{R}^{n}\times\mathbb{R}^{n}\longrightarrow \mathbb{R}\) such that \(DF(\mathbf{x},\mathbf{q},\dot{\mathbf{q}})\) represents a measure of the hazard at the position induced by a robot, which has its joints at positions \(\mathbf{q}\) moving with speeds \(\dot{\mathbf{q}}\). A robotic application is considered safe if the value of the danger field is below a given threshold \(DF_{\text{max}}\) determined during the application risk assessment. In particular, we consider danger fields that are homogeneous with respect to \(\dot{\mathbf{q}}\):  
\[DF(\mathbf{x},\mathbf{q},\sigma_{0}\dot{\mathbf{q}})=\sigma_{0}DF(\mathbf{x}, \mathbf{q},\dot{\mathbf{q}}). \tag{34}\]  
The limits of the robots are considered by taking into account the maximum values of velocity \(V_{\text{max}}\) and acceleration \(A_{\text{max}}\) achievable by the robot and retrieved by the data sheets and manuals provided by the robot manufacturer. If follows that, to make a motion feasible, we have to compare the given feasibility thresholds with respect to the following values:  
\[\begin{split}& V=\max_{t}\|\dot{\mathbf{q}}(t)\|_{\infty}\\ & A=\max_{t}\|\ddot{\mathbf{q}}(t)\|_{\infty}\\ & D=\max_{t}DF(\mathbf{x}_{0},\mathbf{q}(t),\dot{\mathbf{q}}(t)),\end{split} \tag{35}\]  
Then, if the planned movement violates the limits of the robot or mechanical safety, the linear scaling to assure the feasibility of the motions is:  
\[\sigma_{0}=\max\left\{\frac{V}{V_{\text{max}}},\sqrt{\frac{A}{A_{\text{max}}} },\frac{D}{D_{\text{max}}}\right\}. \tag{36}\]
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 7 Simulation Results
### Link Utilization and Throughput  
Figure 6 shows the link utilization in the aforementioned scenario. Proper resource allocation makes all the links highly utilized while improper ones overload some links and under-utilize others. Therefore, the network throughput decreases sufficiently. The effect of comprehensive resource allocation on the network throughput is depicted in Figure 7.  
Considering flow 1 = 100 MB/s, flow 2 = 110 MB/s, and flow 3 = 90 MB/s, the network throughput in both approaches is the same. Increasing the size of flow 1 to 110 MB/s does not have a negative impact on the approaches, since, in both approaches, the resources are assigned to the flows in a way that they can tolerate this amount of traffic rate increment. However, when the size of flow 1 increases more than 110 MB/s, in the configuration which is done by the conventional approaches, the link between S1 to S3 becomes a hot-spot and could not handle this much traffic load. On the other hand, the proposed scheme can handle up to 200 MB/s flow rate increment. Therefore, the throughput increases up to 400 MB/s in our scheme, while it cannot exceed 310 MB/s in conventional approaches.