esa-sceva/satcom-chunk-collection huggingface dataset:
# Failure Length and Primary Stability: An In Vitro Study on Polyurethane Foam
###### Abstract  
(1) Background: Recently, novel dental implants that are characterized by different levels of surface roughness in the distinct parts of the fixture's body have been introduced in the market. These surface characteristics could affect the primary stability of the implants. The aim of this in vitro study was to compare the primary stability of short and long implants, characterized by multiscale surface roughness, inserted on polyurethane blocks. The secondary aim was to understand if the implant length could be a crucial factor in the decision-making in immediate or rather than delayed loading protocol in the different bone densities. (2) Methods: A total of 20 cylindrical dental implants with a diameter of 5.0 mm were tested for the lengths 6.0 mm (short implants) versus 13.0 mm (long implants) on two different solid rigid polyurethane blocks (20 and 30 PCF). The primary stability was evaluated by measuring the insertion torque value (ITV), the removal torque (RTV), and the resonance frequency analysis RFA. (3) Results: The values of ITV, RTV, and RFA showed the same trend in all measurements. Long implants showed a significantly higher primary stability on 30 PCF blocks that present mechanical properties similar to high-density bone. On the contrary, no relevant differences were found on 20 PCF blocks, which mimic trabecular bone density. (4) Conclusions: The impact of fixture length on the primary stability of implants with multiscale surface roughness is significant in 30 PCF polyurethane corresponding to higher bone density, but not in lower ones.  
primary stability; resonance frequency; insertion torque; polyurethane foam Vol. 2022, 12, 2683. [[https://doi.org/10.3390/app12052683](https://doi.org/10.3390/app12052683)]([https://doi.org/10.3390/app12052683](https://doi.org/10.3390/app12052683))
## 4 Discussion and Conclusions
This result can have useful repercussions, i.e., in case of need, tiltmeters could be used to integrate seismic networks. Although the outlined procedure needs to be further tested on a wider dataset, the obtained results are promising and show that it could be easily applied in other sites. Future improvements are likely to be achieved by using more recent and realistic approaches which take into account topographic effects, heterogeneities, and the behavior of the medium. These approaches could include Seismic Moment Tensor inversion, to estimate the source mechanisms, and/or the Boundary Element Method, to quantify the static displacement. Conceptualization, P.C. and C.R. ; methodology, P.C., C.R., I.A. and S.P. ; software, P.C., C.R., I.A. and S.P. ; validation, P.C., C.R., I.A. and S.P. ; formal analysis, P.C., C.R., I.A. and S.P. ; investigation, P.C., C.R., I.A. and S.P. ; resources, P.C., R.A. and S.P. ; data curation, P.C., C.R., I.A. and S.P. ; writing--original draft preparation, P.C., C.R., I.A. and S.P; writing--review and editing, P.C., C.R., I.A. and S.P. ; visualization, P.C., C.R., I.A. and S.P. ; supervision, P.C. and C.R. ; project administration, S.P. ; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This work is part of the INGV research project LAST: Linked Analysis of Seismic and Tiltmeter observables (Ricerca Libera 2019--Del. n. 53/2020). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are available upon request. **Acknowledgments:** We are grateful to the three referees that revised the manuscript, whose suggestions contributed to the improvement of this paper. **Conflicts of Interest:** The authors declare no conflict of interest.
# Leveraging Adversarial Samples for Enhanced Classification of Malicious and Evasive PDF Files
## 3 Materials and Methods
### Methodology
#### 3.1.2 Approach 2  
The system consists of three separately trained models operating hierarchically. At the first level, we want to detect whether a PDF is evasive or not, and at the second level, we want to detect whether it is malicious or not. The models are as follows:  
1. [noitemsep]
2. Evasion Classifier: This model is trained on a dataset containing evasive and non-evasive samples. The goal is to detect whether a PDF is evasive or not by analyzing its features.
3. Evasive Malware Classifier: This model is trained on the evasive dataset solely, and, therefore, it expects to receive an evasive sample to classify whether it carries malware or not.
4. Non-evasive Malware Classifier: This model is trained using only the non-evasive dataset and it expects to receive a non-evasive sample to classify whether it carries malware or not.  
We hierarchically combine these three models to form a system that receives a PDF file as input, extracts its corresponding features, and then feeds them to the Evasion Classifier. If the PDF is evasive, features are passed to the Evasive Malware Classifier to check for malware. If the PDF is not evasive, features are passed to the Non-evasive Malware Classifier to check for malware. The advantage of this system over the first one is that we can detect evasion in addition to detecting maliciousness. Even though in this approach the training happens separately for each model, during inference, the models depend on each other as the prediction of maliciousness is depending on whether the file was predicted to be evasive or not. For example, if an evasive PDF sample is wrongly classified as non-evasive, then it would be forwarded to the Non-evasive Malware Classifier, which might not be able to guess if it contains malware or not.  
Figure 3: Training in the three approaches.
The acidification process is particularly important for stretched cheeses because a pH drop in the range 5.2-5.4 is necessary for stretching [21; 22], since dicalcium paracasienate is converted into monocalcium paracasienate determining the formation of fibres that can be reorganized to give shape and sheen to the cheese [23]. A pH value > 5.4 does not allow stretching of the curd, while at pH below 5.2 the acidified curd is too tough due to the excessive fat loss [24]. After draining, the curd for PDO Provola dei Nebrodi cheese production is traditionally left to acidify at room temperature for about 24 h on a wooden open-topped table, namely \"mastredda\" [3] (Figure 1). Figure 1: “Mastredda”, a wooden table used for curd acidification during PDO Provola dei Nebrodi cheese production. So far, no study on the microbiological characterization of \"mastredda\" is available in the literature. The purpose of the present research was to fill the gap in knowledge on the microbial biofilms of an important wooden tool used for traditional dairy productions. In particular, wooden tables of different dairy facilities producing PDO Provola dei Nebrodi cheese were investigated for their microbial biofilms through several approaches: scanning electron microscopy (SEM) inspection; culture-independent strategy applied to the bacterial community; plate counts of the main dairy-involved microbial groups; and isolation, characterization and identification of LAB.
###### Abstract  
An omni-directional optical system can be used as a surveillance camera owing to its wide field angle. In cases in which a system is designed with a central screen obscuring structure to increase the resolution of the off-axis field, however, the conventional methods cannot be used to measure the effective focal length (EFL). We assumed the actual and theoretical distortion values of the fabricated optical system to be the same and determined the system's EFL by finding the minimum deviation point of the measured and theoretical distortions. The feasibility of the determined EFL was verified through a tolerance analysis of the system. For these precise measurements we also analyzed the sources of error. To verify our proposed measurement method, we measured the focal length of a center-obstructed omni-directional reflective optical system with an 80-135\({}^{\circ}\) field of view (FOV). The EFL from the measurement was 0.3739 mm and was only approximately 11 \(\upmu\)m different from the EFL calculated using the design software. Thus, the reliability of focal length measurements in omni-directional optical systems was improved.  
omni-directional reflective optical system; focal length measurement; geometrical optics; optical design; tolerance analysis +
Footnote †: journal: Applied Physics Letters
## 3 The Effect of Biochar on the Environmental Fate of Pesticides
### The Effect of Biochar on Degradation of Pesticides  
Sorption and degradation are the major mechanisms that regulate pesticide effectiveness and the danger of runoff contamination [93]. Due to its high adsorption capacity, biochar has been extensively studied for its influence on pesticide sorption, desorption, and degradation in agricultural soils [94]. Dissipation study is critical because it will aid in the correct and safe application of pesticides [95]. Because of its high specific surface area and abundance of oxygen-containing functional groups, biochar is considered an effective pesticide amendment that reduces the biodegradation of pesticides in soils. This helps pesticides remain stable in the soil and is likely to play an important role in this process [96].  
Figure 3: Mechanism of biochar remediation of soil pollution.  
Pesticide dissipation is reduced in biochar-amended soil due to enhanced adsorption and decreased desorption from the biochar surface, limiting pesticide bioavailability. However, adding biochar may increase microbial stimulation and hence pesticide breakdown. Application of biochar to agricultural soils can change pesticide persistence and break down products. When applied to agriculture, biochar's sorption nature can affect pesticide absorption and breakdown. Biochar can affect pesticide environmental destiny through sorption, desorption, and degradation in soils [97]. Soil biodegradation of pesticides is altered by biochar depending on which activity is prominent. The persistence of pesticides in soil is closely related to their degradation kinetics. The influence of pesticide properties and environmental conditions plays an important role in environmental fate. Pesticide residues and half-life increase with the dose and number of applications. Its degradation grade in soil is divided according to the guidelines for an environmental safety evaluation test of chemical pesticides. Biochar has a certain degradation effect on pesticides, and the amount of biochar has a significant impact on the degradation rate of pesticides. The more biochar added to the soil, the slower the degradation rate of pesticides in the soil [27,98]. Biochar improved the soil and delays the digestion process of insecticides chlorpyrifos and fipronil [99]. After pesticide application, pesticide residues in soil gradually decrease with time, and the degradation trend is according to the first-order kinetic equation.
# Power Allocation and User Grouping for NOMA Downlink Systems
## 1 Introduction
### Related Work
Secondly, FTPA is applied to allocate power to the multiplexed users. However, the first step does not consider the influence of channel conditions on the system sum rate when allocating power among subcarriers, which limits system performance improvement. The sum rate maximization of multi-user NOMA wireless systems with perfect channel state information (CSI) was studied in [18]. FTPA is applied to power allocation among subchannels and the closed form solution of power allocation for multiple users is obtained by establishing the Lagrange function under constraint conditions. However, the FTPA algorithm cannot obtain the global optimal solution. Power allocation requires two steps, which increases complexity. In [13], the authors used the simulated annealing algorithm to optimize user power to maximize the throughput. In [19], the authors studies the power allocation problem of NOMA downlink systems. In order to ensure the quality of service and maximize the energy efficiency of the NOMA system, the authors applied a modified particle swarm optimization algorithm to allocate power. However, heuristic algorithms may stay at the local optimal solution and cannot effectively deal with constraints. Deep learning (DL) and reinforcement learning (RL) in machine learning have also been applied in power allocation of NOMA systems. The work in [20] proposed a DL power allocation scheme. Firstly, the optimal power with maximum energy efficiency under constraint conditions is obtained by the exhaustive search method. Secondly, the results of the power allocation are used as the dataset to train a designed neural network. Finally, the neural network predicts power allocation. In [21], the authors proposed an RL method to perform the dynamic power allocation. An RL framework of the Actor-Critic algorithm is used, and the energy efficiency of the system is defined as a reward function. However, the machine learning methods need huge datasets and a lot of training in the early stage, which is extremely time consuming. The performance of these models depends on the training results of the networks. There has been some research on wireless resource management in NOMA; most of it has focused on the sum rate and energy efficiency of resource allocation. However, future research should make efforts to improve the efficiency of resource allocation and ensure the quality of service.
# Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks
## 2 Materials and Methods
### Q-Matrix Constraint-Based Neural Network
This algorithm details the construction of the Dual Q-Net model, including the calculation of the interactive Q-matrix from the original Q-matrix, and describes both the forward computation and training processes. Notably, it specifies how the network parameters are constrained by the Q-matrix and the interactive Q-matrix. Additionally, Equation (13) presents the calculation of the network parameter count of the Dual Q-Net model. \[N_{\text{params}}=(K+K^{*})(K+2)+\sum_{j=1}^{J}\sum_{k=1}^{K}q_{jk}+\sum_{j=1}^{J }\sum_{k^{*}=1}^{K^{*}}q_{jk^{*}}^{*} \tag{13}\]  
where \(N_{\text{params}}\) denotes the total parameter count of the Dual Q-Net model, with \(q_{jk}\), \(q_{jk^{*}}^{*}\), \(J\), \(K\), and \(K^{*}\) retaining the same definitions as provided in Equations (1)-(3). ```
Input: Training data \((\mathbf{X},\mathbf{A})\), i.e., the response data \(\mathbf{A}\) and the real skill mastery patterns \(\mathbf{X}\); learning rate \(\eta\); number of epochs \(E\); loss function \(\mathcal{L}\) as defined in Equation (12); model \(f\) with parameters \(\mathbf{\theta}\), i.e., the parameters included in Equations (9)-(11); Q-matrix \(\mathbf{Q}\) as defined in Equation (1); the skill interaction matrix \(\mathbf{Q}^{{}^{\prime}}\) as defined in Equation (3) Output: Trained model parameters \(\mathbf{\theta}\)
1\(\mathbf{Q}^{*}\gets f_{5}(\mathbf{Q},\mathbf{Q}^{*})\) ; // Calculate the interactive Q-matrix \(\mathbf{Q}^{*}\) using Equation (5)
2 Initialize the model \(f\) and its parameters \(\mathbf{\theta}\) using \(\mathbf{Q}\), \(\mathbf{Q}^{*}\) and Equations (9)-(11);
3 forepoch = 1 to Edo
4 foreach batch \((\mathbf{X}_{b},\mathbf{A}_{b})\) in \((\mathbf{X},\mathbf{A})\)do
5\(\hat{\mathbf{A}}_{b}\gets f(\mathbf{X}_{b};\mathbf{\theta})\) ; // Forward pass to get predictions \(L_{b}\leftarrow\mathcal{L}(\mathbf{A}_{b},\hat{\mathbf{A}}_{b})\) ; // Compute loss using Equation (12)
6 Compute \(\
abla_{\theta}L_{b}\) via BP ; // Calculate gradients w.r.t. \(\mathbf{\theta}\)
7 forach parameter \(\theta_{i}\) in \(\mathbf{\theta}\)do
8 ifthe linkage of \(\theta_{i}\) falls under the constraints of either \(\mathbf{Q}\) or \(\mathbf{Q}^{*}\)then
9\(\theta_{i}\leftarrow\theta_{i}\times 0\); // Constrain parameters using both \(\mathbf{Q}\) and \(\mathbf{Q}^{*}\)
10 else
11\(\theta_{i}\leftarrow\theta_{i}-\eta\cdot\
abla_{\theta_{i}}L\) ; // Update parameters by gradient descent
12
13 end if
14
15 end for
16
17 end for
```  
**Algorithm 1**Algorithm description for our proposed Dual Q-Net
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 4 FoMs Analysis
### SSC Analysis Results
 } & \multicolumn{4}{c}{**SSC (dB/Hz)**} & \multicolumn{4}{c}{**SSC (dB/Hz)**} \\ \cline{2-9}  & & **BPSK(K1)** & **BOC\({}_{\rm s}\)(S-RS)** & **Globalstar** **(S-RS)** & **Self SSC** & **(MHz)** & **BPSK(I)** & **BOC\({}_{\rm s}\)(S-RS)** & **Globalstar** **(for 1 Ch. )** & **Self SSC** \\ \hline BPSK(1) & \(-\)61.85 & \(-\)77.80 & \(-\)73.04 & \(-\)61.85 & 2.046 & \(-\)61.86 & \(-\)77.51 & \(-\)73.04 & \(-\)61.86 \\ BPSK(4) & \(-\)66.47 & \(-\)78.27 & \(-\)77.45 & \(-\)67.85 & 8.184 & \(-\)66.48 & \(-\)79.80 & \(-\)77.45 & \(-\)67.86 \\ BPSK(5) & \(-\)67.36 & \(-\)78.76 & \(-\)78.37 & \(-\)68.81 & 10.23 & \(-\)67.36 & \(-\)78.95 & \(-\)78.37 & \(-\)68.82 \\ BPSK(5) & \(-\)69.27 & \(-\)74.86 & \(-\)80.34 & \(-\)70.83 & 16.368 & \(-\)69.27 & \(-\)74.86 & \(-\)80.34 & \(-\)70.83 \\ BOC\({}_{\rm c}\)(1,1) & \(-\)67.86 & \(-\)80.04 & \(-\)75.80 & \(-\)64.84 & 4.092 & \(-\)67.87 & \(-\)83.33 & \(-\)75.80 & \(-\)64.88 \\ BOC\({}_{\rm c}\)(2,1) & \(-\)73.87 & \(-\)76.66 & \(-\)76.83 & \(-\)66.06 & 6.138 & \(-\)73.88 & \(-\)77.90 & \(-\)76.83 & \(-\)66.11 \\ BOC\({}_{\rm c}\)(2,2) & \(-\)73.87 & \(-\)77.81 & \(-\)77.48 & \(-\)67.83 & 8.184 & \(-\)73.88 & \(-\)79.67 & \(-\)77.48 & \(-\)67.86 \\ BOC\({}_{\rm s}\)(3,1) & \(-\)77.38 & \(-\)80.34 & \(-\)76.87 & \(-\)66.35 & 8.184 & \(-\)77.39 & \(-\)80.88 & \(-\)76.87 & \(-\)66.35 \\ BOC\({}_{\rm s}\)(3,2) & \(-\)73.40 & \(-\)77.33 & \(-\)78.01 & \(-\)68.72 & 10.23 & \(-\)73.40 & \(-\)77.34 & \(-\)78.01 & \(-\)68.73 \\ BOC\({}_{\rm s}\)(3,3) & \(-\)77.39 & \(-\)76.83 & \(-\)79.03 & \(-\)69.59 & 12.776 & \(-\)77.39 & \(-\)76.83 & \(-\)79.03 & \(-\)69.59 \\ BOC\({}_{\rm c}\)(4,4) & \(-\)79.87 & \(-\)72.97 & \(-\)80.28 & \(-\)70.82 & 16.368 & \(-\)79.87 & \(-\)72.97 & \(-\)80.28 & \(-\)70.82 \\ BOC\({}_{\rm c}\)(5,2) & \(-\)77.80 & \(-\)69.20 & \(-\)78.45 & \(-\)69.20 & 14.322 & \(-\)77.80 & \(-\)69.20 & \(-\)78.45 & \(-\)69.20 \\ BOC\({}_{\rm c}\)(1,1) & \(-\)73.38 & \(-\)79.37 & \(-\)76.52 & \(-\)66.08 & 4.092 & \(-\)74.03 & \(-\)84.27 & \(-\)76.52 & \(-\)66.28 \\ BOC\({}_{\rm c}\)(2,1) & \(-\)79.89 & \(-\)75.29 & \(-\)76.37 & \(-\)66.43 & 6.138 & \(-\)80.09 & \(-\)78.02 & \(-\)76.37 & \(-\)66.53 \\ BOC\({}_{\rm c}\)(2,2) & \(-\)79.88 & \(-\)74.87 & \(-\)78.27 & \(-\)69.06 & 8.184 & \(-\)80.00 & \(-\)79.40 & \(-\)78.27 & \(-\)69.24 \\ BOC\({}_{\rm c}\)(3,1) & \(-\)83.44 & \(-\)77.85 & \(-\)77.18 & \(-\)66.53 & 8.184 & \(-\)83.62 & \(-\)80.68 & \(-\)77.18 &Each FDM channel of the Globalstar signal has a narrow bandwidth. Therefore, the wider the main lobe of candidates and the greater the center frequency of the main lobe differs significantly from the center frequency of each FDM channel, the lower the SSC value. Self SSC for evaluating intra-system interference has a lower value as the signal power spreads widely. Among the candidates, BOC\({}_{\rm c}\)(4,4) had the lowest SSC at \(-\)72.16 for both front-end bandwidths.
Appl. Sci._**2014**, \(4\), 207-239; doi:10.3390/app4020207  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci.  
_Review_  
**Intra- and Interlayer Electron-Phonon Interactions in \({}^{12/12}\)C**  
**and \({}^{12/13}\)C BiLayer Graphene**  
**[PERSON] \({}^{1}\) and [PERSON] \({}^{2,*}\)**  
\({}^{1}\) Department of Electrical Engineering and Computer Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Physics and Astronomy, The University of Alabama, Tuscaloosa, AL 35487, USA  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-205-348-2878; Fax: +1-205-348-5051.  
_Received: 12 March 2014; in revised form: 8 April 2014 / Accepted: 8 April 2014 / Published: 29 April 2014_  
**Abstract:** This review focuses on intra- and interlayer (IL) electron-phonon interactions and phonon self-energy renormalizations in twisted and AB-stacked bilayer graphene (2 LG) composed either only of \({}^{12}\)C or a mixing of \({}^{12}\)C and \({}^{13}\)C isotopes. A simple way to imagine a 2 LG is by placing one monolayer graphene (1 LG) on top of another 1 LG. The orientation of one of the layers with relation to the other may originate a twisted 2 LG system (known as turbostratic) as well as a AB-stacked system, also known as Bernal stacking. By rotating the layers of a 2 LG one can departure from a fully misoriented system to achieve the AB-stacked configuration and their IL interactions can be dramatically different being close to zero in a fully misoriented system and maximum in an AB-stacked system. Interlayer interactions are expected to slightly perturb the intralayer phonons and they also govern the low-energy electronic and vibrational properties, which are of primary importance to phenomena such as transport, infrared (IR) optics and telecommunication bands in the IR range. Therefore, a comprehensive discussion combining intra- and interlayer phenomena is necessary and addressed throughout the text.  
**Keywords:** bilayer graphene; interlayer interaction; electron-phonon coupling
# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
## 3 Congestion-Aware Routing Algorithm
### Route Evaluation Parameters
#### 3.1.4 The Forward Average Cache Remaining Rate  
The cache remaining rate of the node itself can directly reflect its level of congestion. However, it is not enough to consider the node itself. When the node located in the congested region receives packets, the next-hop choice becomes particularly difficult. If the optional next hop has a lower cache remaining rate, it can easily cause congestion resulting in many packets being lost. To solve these problems, we use the forward average cache remaining rate to analyze the congestion of a node's forward area.  
Suppose node \(j\) is the next hop candidate node of node \(i\), according to equation (10) the cache remaining rate of the node \(j\) is \(r_{fc}(j)\). When \(num[F(j)]\) represents the number of forwarding nodes of node \(j\), the forward average cache remaining rate \(r_{fave}(j)\) of node \(j\) is  
\[r_{fave}(j)=\frac{1}{num[F(j)]}\bullet\sum_{t\in F(j)}r_{fc}(t) \tag{11}\]  
The forward average cache remaining rate of a node reflects the load of its forward transmission area. Thus, during packet transmission, the routing protocol should select as far as possible the node with a large forward average cache remaining rate, which is conducive to the further forwarding of the packet.