esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
We can also report that older adults with HoF and FoF adopted significantly reduced ipsi ankle angles at TO, with the latter also exhibiting reduced contralateral ankle angles at TO, in accordance with [PERSON] et al., who reported reduced plantarflexion at TO in older adults [47]. Several authors have focused on studying older adults during stair descent [8; 11; 12; 14; 15; 16; 18; 19; 21; 30; 32; 48]; however, to the best of our knowledge, there is no previous literature that has evaluated the association between HoF and FoF. There are also a few authors who have analyzed both stair descent and gait transition [8; 11; 14; 19]. Most of the existing literature only analyzes the influence of age in a sample of older and younger adults, ensuring different characteristics at baseline [12; 14; 15; 16; 19; 32]. Some authors studied different moments and strategies in older adults during stair descent and their step negotiation with irregular staircase characteristics [16; 30; 32; 35]. Thus, this study holds its pertinence in evaluating not only the association between HoF and FoF in older adults during stair descent but also during the gait transition. It also differentiates itself from the previous literature by including not only a spatiotemporal analysis but also a kinematic one. One limitation of the current study was related to the simulation of external conditions in the laboratory, with the assumption that it would be representative of daily life conditions. Although participants wore usual fitting clothes and shoes to ensure the data collection resembled daily life conditions, controlled laboratory conditions do not include environmental, individual, and/or other factors such as dual-tasking while descending stairs [48]. Also, the analysis of the medication type was not the object of this study; therefore, future studies should address this relationship, particularly given that older adults take more medication than their counterparts and are highly influenced by their medication intake [7]. Future research may also incorporate participants with a history of recurrent falls to analyze the association with higher incapacity levels, causing more changes in spatiotemporal and kinematic parameters during stair descent and gait transition. Future research should also consider differentiating the degrees of fear of falling to analyze if higher levels lead to more changes in the aforementioned parameters. It is also important to identify other changes in the spatiotemporal and kinematic parameters of older adults during stair descent and gait transition that are associated with a higher risk of fall to implement prevention strategies and, ultimately, reduce fall occurrence.
# A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal
###### Abstract
[PERSON] proposes a CNN-based framework to perform preamble detection for underwater acoustic communications applications [28], which can learn features from the TF spectrum and can give an efficient solution for preamble detection under complicated underwater acoustic communications. For signal classification, [PERSON] uses WVD instead of the spectrogram as basic input into CNN to classify one-dimensional non-stationary signals and has achieved good performance [29]. Inspired by the above ideas, in this paper, we propose a local search maximum likelihood parameter estimator based on the deep learning denoising approach for chirp parameter estimation in low SNR conditions. As shown in Figure 1, the proposed approach consists of a DnCNN, which is trained to perform denoising for noisy observation chirp signals, and a local search estimator applied on the denoised signals and the observations. We explain how DnCNN is composed and the details of the local search estimator. Numerical results show that the proposed approach can yield a substantial improvement in estimation accuracy and efficiency over the RWT and FrFT. The benefit is attributed to the DnCNN denoiser, which can reshape the signal of observation, leading to a great improvement of SNRs.
# Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers
## 1 Introduction
### Contributions  
This paper investigates SDN controllers' energy consumption and how to improve their energy efficiency therefore improving CNEE metric (2) of any DC that employs SDN multicore controllers. It controls the number of available cores and the processor core frequency to distribute the load in a multicore environment. Many experiments were executed to demonstrate the approach's feasibility with different scenarios, reducing energy consumption while keeping latency and throughput. In addition, the results point to future works on load balancing capabilities of a distributed multicore controller scenario.  
The main contributions of this paper are:  
* The evaluation of processor power on controller energy consumption.
* A parallel implementation of the Ryu controller that improves multicore processors performance.
* A novel energy-reducing solution for SDN controllers not tied to any topology or knowledge of the current topology and that may be applied to any number of controllers in a hierarchical or centralized organization, with no special hardware.  
Subsequent sections are organized as follows. Section 2 shows a brief overview of the literature on SDN energy efficiency. Section 3 describes some fundamental concepts and the approach proposed to save energy on the control plane. Section 4 presents the experiments executed to validate our solution. Finally, Section 5 provides a further discussion on the results and points to next steps on the energy efficiency for SDN control plane.
###### Abstract  
In this paper, a generation method procedure based on a reflective metasurface is proposed to generate multiple pseudo Bessel beams with accurately controllable propagation directions and high efficiency. Firstly, by adjusting the miniaturized unit cell of the reflective metasurface to modulate the electromagnetic waves using the proposed method, some off-axis pseudo Bessel beams with different propagation directions are generated. Then, by achieving the large-angle deflection and comparing the results with previous existing methods, the superiority of the proposed method is demonstrated. Based on the generated single off-axis pseudo Bessel beam and the superposition principle of the electromagnetic wave, a reflective metasurface with \(47\times 47\) elements is designed and fabricated at 10 GHz to generate dual pseudo Bessel beams. Full-wave simulation and experimental measurement results validate that the dual pseudo Bessel beams were generated successfully. The propagation directions of the dual pseudo Bessel beams can be controlled accurately by the reflective metasurface, and the efficiency of the beams is 59.2% at a propagation distance of 400 mm. The energy of the beams keeps concentrating along the propagation axes, which provides a new choice for wireless power transfer and wireless communication with one source to multiple receiving targets.  
Keywords: off-axis pseudo Bessel beam; multiple pseudo Bessel beams; reflective metasurface; accurately controllable propagation direction; high efficiency +
Footnote †: journal: Applied Sciences
## 2 Proposed FFT-DCNN Architecture  
Figure 1 illustrates the architecture of the designed FFT-DCNN. It has five layers: input layer, FFT layer, convolutional neural network (Conv1, Conv2, MP1, Conv3, Conv4, and MP2), fully connected network (FC1, FC2), and output layer. In the CNN algorithm, Conv1 represents the first convolutional operation, and MP1 represents the first pooling operation. In this frame, raw vibration data are transformed into frequency information via FFT method. Considering that the CNN can be powerful in capturing the spatial features, frequency information with one dimension is transformed into a feature map with two dimensions via dimension transformation, improving the ability of features extraction for DCNN. Then, convolutional and max-pooling layers are utilized to capture spatial features from frequency information. Finally, the extracted features are fed into a fully connected network with a \"softmax\" activation function to recognize the damaged conditions of the structure. In addition, DCNN is powerful in handling two-dimension data. However, the frequency information data that are transformed by the FFT method is one-dimension. DCNN cannot effectively extract the feature from the frequency information. Thus, the dimension transformation concept is referenced to transform one dimension into two dimensions.
## 2 _Salmonella_ Efficiently Targets and Invades Tumor  
An interesting fact regarding _Salmonella_ is their efficiency in selectively colonizing tumor tissue by being able to sense tumor-specific microenvironment. Solid tumors are characterized by depleted oxygen concentration ranging between 10 to 30 mmHg--otherwise known as hypoxia within the tumor tissue [11; 12]--that is beneficial for _Salmonella_ due to their facultative nature. To leverage this capability in targeting tumor tissue, an obligate anaerobic _S. typhimurium_ strain (YB1) was generated by placing _asd_ (aspartate-semialdehyde dehydrogenase) gene under the control of a hypoxia-conditioned promoter, that will be essential for survival only in a hypoxic condition [13]. Moreover, certain chemicals present in the tumor microenvironment, mostly in the necrotic region, could act as chemotactic agents that attract _Salmonella_ to invade tumor tissue [6]. _Salmonella_ have the ability to exploit ethanolamine as a signal to target and recognize niches including tumors [14; 15]. The ability of _Salmonella_ to efficiently reach and colonize tumor is facilitated by its motility, which is essential for effective dispersal in tumor [16]. Furthermore, _Salmonella_ migration in the tumor is directed towards the core of the tumor and away from the vasculature [17]. Most _Salmonella_ strains used for tumor therapy experiments and in clinical trials were attenuated, such as in the case of VNP20009 strain having partial deletion of the _msbB_ gene for lipid A modification; in addition to that, the strain used bears _CheY_ gene non-synonymous single nucleotide polymorphism, resulting in a reduced chemotactic ability [18; 19]. Lipid A modification by _msbB_ gene mutation reduced the lipopolysaccharide-associated toxicity or septic shock induction [20]. _CheY_ gene is part of the two-component system CheA/CheY that is necessary or chemotactic response or motility and bacterial distribution in tumor tissue [6; 18]; however, some reports showed that CheY protein deficiency does not compromise colonization in tumor [15]. In VNP20009, the efficiency of chemotactic response was only recovered after the replacement of the mutated _CheY_ with the wild-type copy and restoring _msbB_ gene [18; 20] and further developed to enhance its hypoxia-targeting capacity [21]. Although several ariulent _Salmonella_ strains generated via mutation demonstrated a reduced targeting and fitness ability, a variety of mutants categorized as class 1 such as _htrA_, _SPI_-2, and _STM3120_ mutations did not exhibit significant change in fitness and exclusive targeting of tumors [22]. In vitro targeting using tumor-on-a-chip device and in vivo targeting in tumor-bearing mice was achieved by Trg-deficient _Salmonella_ through penetration of, and dispersal in, the quiescent tumor region [23].
# Photonic-Assisted Scanning Receivers for Microwave Frequency Measurement
## 2 Principle of Operation
To reconstruct \(X[k]\), deconvolution based on matrix pencil method is used, and \(X[k]\) is modelled as a sum of exponential sinusoids in the presence of noise, which has the following form  
\[X[k]=\sum\limits_{t=1}^{M}|b_{t}|e^{j\
u_{t}k}+n_{k}, \tag{3}\]  
where \(n_{k}\) is the noise and \(M\) is the number of sinusoids. For double sideband modulation where the input microwave signal has \(P\) tones, \(M\) is equal to \(2P+1\). \(w_{t}\) and \(\mid b_{t}\mid\) are the frequencies and amplitudes of the sinusoids, respectively. Specifically, to solve \(w_{t}\) and \(\mid b_{t}\mid\), which correspond to the frequency and amplitudes of the modulated signal, the Hankel matrices based on \(Y[k]\) are first constructed as  
\[[\Omega_{0}]=\left[ \begin{array}{cccc}Y(1)&Y(2)&\cdots&Y(L)\\ Y(2)&Y(3)&\cdots&Y(L+1)\\ \vdots&\vdots&&\vdots\\ Y(N-L)&Y(N-L+1)&\cdots&Y(N-1)\end{array} \right]_{(N-L)\times L}, \tag{4a}\] \[[\Omega_{1}]=\left[ \begin{array}{cccc}Y(2)&Y(3)&\cdots&Y(L+1)\\ Y(3)&Y(4)&\cdots&Y(L+2)\\ \vdots&\vdots&&\vdots\\ Y(N-L+1)&Y(N-L+2)&\cdots&Y(N)\end{array} \right]_{(N-L)\times L}, \tag{5b}\]  
where \(L\) is called the pencil parameter. Then the singular value decomposition (SVD) of the matrix is carried out as  
\[[\Omega_{z}]=[U_{z}][\Sigma_{z}][V_{z}]^{H}, \tag{5}\]  
where \([U_{z}]\) and \([V_{z}]\) are unitary matrices and \([\Sigma_{z}]\) is the diagonal matrix for \([\Omega_{z}]\) (\(z\)=0,1), respectively. \(H\) denotes a complex conjugate. Eventually, by solving the generalized eigenvalues of \([\Omega_{0}]-\lambda[\Omega_{1}]\), the amplitude \((\mid b_{t}\mid)\) and the frequency (\(w_{t}\)) of the frequency components for the modulated optical signal (\(x[n]\)) can be obtained. It can be noted that with the recent advent of analog-to-digital converters developed in complementary metal-oxide-semiconductor platforms that are available up to 65 Gsamples/s [26], such digital signal processing can be performed at extremely high speeds.
# A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure
## 4 Analysis of Authentication Factors
### Weakness of Single-Factor Authentication  
In a recently conducted study by CyberNews, the investigation team [86] in 2021 analyzed the 15 billion passwords exposed in various database breaches. The study found that only around 2 billion of those passwords were unique. The ten most commonly used passwords are 123456, 123456789, qwerty, password, 12345, qwerty123, 1q2w3e, 12345678, 111111, and 1234567890. It signifies that even after knowing the threats associated with password security, people still prefer to use these easy-to-crack or easy-to-guess passwords. Even if people use difficult-to-guess or secure passwords, there are still chances of the passwords being compromised through phishing and spoofing attacks.  
Due to the dependency on telecommunication and data networks, associated factors like OTP, CAPTCHA, PIN, and modern methods like RFID have definite vulnerabilities while functioning as an SFA. User characteristic factors like biometrics are potentially more stable due to their uniqueness associated with the corresponding user.
# Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
## 2 Materials and Methods  
### Wind Power-Related Dataset  
The wind power-related dataset is defined as sequence \(X=\left[X_{1},X_{2},\ldots,X_{t}\right]^{\top}\), where \(X_{t}\) represents sequential data at different time points, which can be represented as \(X_{t}=\left[x_{1},x_{2},\ldots,x_{i}\right]\), where \(x_{i}\) represents various meteorological factors related to wind power. The dataset used in this article comes from the actual operation data of the Hami Wind Farm in Xinjiang, China. It includes wind speed and wind direction information at 10 m, 30 m, and 50 m on the measurement tower, as well as meteorological information such as temperature, humidity, pressure, and historical wind power. The data were collected between 1 January 2022 and 30 January 2022, resulting in a total of 2880 data observations. The sampling frequency was 15 min.
# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
### Social Recommendation
#### 3.4.2 Deep Social Recommendation Based on Graph Embedding  
Traditional social recommender systems only integrate explicit trust relationships between users and ignore implicit trust relationships. Graph embedding models represent all entities as nodes and their relationships as edges in a graph. These models transform nodes into vector representations, allowing them to retain their approximate relationships from the graph structure in a Euclidean space. Afterwards, vector distances are computed to predict the scores of candidate items for the target user.  
Using the application of graph embedding techniques, it becomes possible to acquire low-dimensional feature representations of users within a social network. These low-dimensional representations enable the inference of detailed trust relationships between users. Consequently, by incorporating the weighted ratings from trusted and similar users for a specific item, predictions regarding the user's rating for that item can be generated. Figure 8 illustrates the framework of a recommendation algorithm employing graph embedding models.  
Prominent graph embedding techniques, such as Deep Walk [133], graph factorization [44], and LINE [134], possess distinct characteristics and limitations. For instance, graph factorization [45] is suited for performing embedding learning in large-scale networks but fails to preserve the network's global structure and is solely applicable to  
Figure 8: Recommendation framework based on graph embedding.  
undirected information networks. In contrast, while Deep Walk reveals certain attributes of an information network using a random walk algorithm, it lacks a comprehensive depiction of the preserved network properties and overlooks the connections between feature vectors. In contrast, LINE effectively maintains both local and global structural aspects of information networks and has found widespread application in recommender systems. Additionally, the process of graph embedding techniques in terms of extracting the semantic relationships within structures is evident in applications such as the work performed by [PERSON] et al. [135] on POI recommendations and the research conducted by [PERSON] et al. [136] on low-dimensional vector representations for users and items. Graph embedding representation learning, with its powerful representational capabilities, low memory consumption, and efficient computations, has been widely researched and applied for semantic relationship extraction in network structures [137].  
Overall, given the advantages of graph embedding techniques in modelling and optimization tasks, they present broad prospects for various applications and merit further exploration.
# Optimization of the Position and Stiffness of Passive Walking Assistance Devices
## 3 Results
### Optimal Stiffness of Passive Walking Systems at Different Walking Speeds  
Walking speed affects joint angles and moments. Therefore, it was expected that walking speed would affect the optimal parameters of the walking assistance system. For this purpose, the stiffness and free length of the spring system were optimized at walking  
Figure 4: (**a**) Optimal position of the walking assistance system for a given stiffness. The effect of the walking assistance system on (**b**) hip joint moment and (**c**) hip joint power.  
speeds of 0.9, 1.1, 1.3, and 1.5 m/s. In addition to the optimal systems designed specific for a certain walking speed, the stiffness and free length were optimized by considering all walking speeds as given in Equation (5). \(f_{0}\) is the value of the objective function for the walking speed \(v\).  
\[\text{min}f_{general}=\frac{f_{0.9}+f_{1.1}+f_{1.3}+f_{1.5}}{4} \tag{5}\]  
The optimal stiffness and free length of the assistive system were found for each walking speed and single design that considered all walking speeds. Hip joint moments and powers with/without walking assistance systems are shown in Figure 5. The optimal parameters specific to a certain walking speed reduced the joint power more than the optimal parameters that considered all walking speeds.  
The main objective of the device was to reduce positive joint energy. The total positive joint energy at different walking speeds for the speed-specific design and general-design  
Figure 5: The effect of walking assistance devices on hip joint moment and power at different walking speeds.  
cases was calculated by using Equation (3), and the results are shown in Figure 6. The objective function value that considered the total positive power of all joints increased when walking speed increased. The maximum reduction of power was achieved at 1.3 m/s, followed by at 1.1, 1.5, and 0.9 m/s. According to Figures 5 and 6, the speed-specific design had a performance similar to that of the optimal design that considered all speeds at walking speeds of 1.1, 1.3, and 1.5 m/s. However, the difference from the performance of the optimal design was larger at a walking speed of 0.9 m/s.  
The force-displacement characteristics of the speed-specific designs and general design are shown in Figure 7. The optimal stiffness was found to be higher at higher walking speeds. The free length of the spring system was almost the same for all walking speeds. Compared with the optimal force-displacement characteristics of the speed-specific designs, the general design had similar characteristics to the speed-specific design at walking speeds of 1.1 and 1.3 m/s.  
Figure 6: The effect of walking assistance devices on total positive joint energy at different walking speeds.  
Figure 7: Force–displacement characteristics of speed-specific optimal walking assistance system and walking assistance system that considers different walking speeds.
## 3 Results
### Changing the Setting of Buffer Input Parameters
#### 3.2.1 Change in Lead Time Factor  
The aim of this experimental part was to evaluate the impact of a change in the lead time factor (LTF) value on the inventory management efficiency and output parameter value. In this set of simulation experiments, LTF = {0.1, 0.3, 0.5, 0.7, 0.9} were sequentially simulated. The simulation results are shown in Table 4 and Figure 11.  
The simulation results showed that the best inventory management parameters were achieved by the strategic sample item buffer when setting the LTF = 0.5 value. Although a slightly lower real service level was achieved at this LTF value (average = 99.49%), this value still far exceeded the baseline requirement (service level = minimum 95%).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Lead Time Factor**} & \multicolumn{3}{c}{**Service Level (\%)**} & \multicolumn{3}{c}{**Average Inventory (pcs)**} & \multicolumn{3}{c}{**Total Inventory Cost (EUR)**} \\ \cline{2-10}  & **Min.** & **Average** & **Max.** & **Min.** & **Average** & **Max.** & **Min.** & **Average** & **Max.** \\ \hline
0.1 & 99.48\% & 99.92\% & 100.00\% & 235.86 & 243.95 & 248.28 & 152,488.0 & 155,821.8 & 158,138.0 \\ \hline
0.3 & 99.40\% & 99.84\% & 100.00\% & 228.08 & 238.22 & 253.34 & 146,522.0 & 152,885.0 & 160,670.0 \\ \hline
0.5 & 97.13\% & 99.49\% & 100.00\% & 213.77 & 222.89 & 234.46 & 138,926.0 & 145,175.0 & 151,498.0 \\ \hline
0.7 & 99.69\% & 99.97\% & 100.00\% & 243.36 & 253.34 & 260.30 & 155,682.0 & 159,762.6 & 162,152.0 \\ \hline
0.9 & 100.00\% & 100.00\% & 100.00\% & 279.06 & 287.30 & 297.84 & 172,532.0 & 175,750.2 & 179,922.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary results of simulation experiments (change in LTF).  
Figure 10: Box plots of simulation outputs (default buffer settings).