esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Analysis of DS in Horizontal Directional Drilling
## 2 Model Description
### DS system Dynamics Model  
In the S-M-C system mode, the DS system is simplified into a multi-degree of freedom system for analysis. Based on the basic principle of non-linear dynamics, the dynamic balance equation of the whole DS system is as follows:  
Figure 1: The failure of DS. (**a**) Partial enlargement. (**b**) Panorama.  
\[M_{e}\ddot{u}_{e}(t)+C_{e}\dot{u}_{e}(t)+K_{e}u_{e}(t)=P_{e}(t) \tag{1}\]  
\(M_{e}\) is the mass matrix of element \(e\); \(C_{e}\) is the damping matrix of element \(e\); \(P_{e}(t)\) is the disturbance vector of the forced oscillation with time in element \(e\); \(u_{e}\) is the nodal displacement vector of element \(e\); \(\dot{u}_{e}\) is the nodal velocity vector of element \(e\); \(\ddot{u}_{e}\) is the nodal acceleration vector of element \(e\).  
The dynamic balance equation of all the elements on the DS is set, and finally, the dynamic equilibrium equation of the whole DS system is obtained:  
\[M\ddot{U}+C\dot{U}+KU=F \tag{2}\]  
where \(M\), \(C\), and \(K\) are the mass matrix, damping matrix, and stiffness matrix of the DS system, respectively, and \(U\), and \(\ddot{U}\) are the generalized displacement matrix, generalized velocity matrix, generalized acceleration matrix of the DS system, respectively; \(F\) is the load matrix.  
By introducing the boundary constraint and solving the equation of Equation (2) with the Wilson-\(\theta\) method in step-by-step integration, the dynamic characteristics of the whole DS system can be analyzed [29].
## 3 Results  
### Descriptive Statistics and Correlation Analysis of IMU-Based Parameters  
Table 2 presents descriptive statistics of movement characteristics extracted from IMU data collected from sensors on participants' lower back, torso, and head. The mean Z-acceleration for the head (\(-\)9.19 m/s\({}^{2}\), SD = 0.66) was close to the Earth's gravitational value (\(-\)9.81 m/s\({}^{2}\)), showing small vertical movement. In contrast, the standard deviation of Y-acceleration (2.53 m/s\({}^{2}\)) indicates greater variability in lateral head movements.  
The acceleration area under the curve exhibited high variability, with significant standard deviations (e.g., 88,354,176.31 m/s for the lower back). These high values likely reflected the composite nature of this metric, integrating movement across all directions. The trapezoidal method used for calculation tends to produce larger values for repeated or oscillating movements. Standard deviations for bounding box and ellipse areas varied across body locations. These values indicated considerable variation in movement distribution across body regions.  
Jerk values were small, indicating subtle rather than abrupt movement changes. The RMS angular velocity for the head showed a significant mean (25,390.09 rad/s) and high standard deviation (94,164.83 rad/s). This variability suggested considerable differences in movement strategies and postural stability among individuals.  
The correlation matrix (Figure 4) further revealed significant relationships between these variables. The 2D-related characteristics (acceleration area under the curve, ellipse area, bounding box area) showed strong inter-correlations (coefficients 0.8-1.0). These associations indicated a close link between spatial dimensions and movement variability. Larger movement areas corresponded to greater variability across different surface measures, reflecting the sensitivity of area calculations to acceleration changes over time.  
The RMS acceleration strongly correlated with the bounding box area (0.91), as greater acceleration variability led to wider dispersion of acceleration values in the XY plane. The total path length and the ellipse area had a strong correlation (0.83), which might have been due to the integration method used in the path calculation.  
The volume metric correlates with both bounding box area (0.91) and ellipse area (0.83), suggesting that increased movement volume relates to larger movement areas in the horizontal plane and greater X-Y directional variability.
# Assessing Fatality Risks in Maritime Accidents: The Influence of Key Contributing Factors
## 4 Materials and Methods
### Methodology
#### 4.2.1 Logistic Regression  
Logistic regression is a statistical method used to analyse the relationship between a binary or multinomial dependent outcome variable and one or more independent input variables. This technique models the outcome variable as a function of the input variables through the logistic function, which converts the linear combination of predictors into a probability value ranging from 0 to 1. If the outcome variable \(Y\) is binary and holds the values \(Y\in\{0,1\}\), the probability is calculated for the hit category, which is usually denoted by the value \(Y=1\)[38; 39].  
Let \(Y\) be a binary outcome variable, which takes the value \(Y=1\) with probability \(p\) and \(Y=0\) with probability \(1-p\). Let \(X_{1},\ X_{2},\ \ldots,\ X_{m}\) be the input explanatory variables.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted \(Y\)**} \\ \cline{3-4}  & & **0** & **1** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Source: [28]. \\ \end{tabular} } & \multirow{2}{*}{0} & True Negative & False Positive \\  & & (TN) & (FP) \\ \cline{1-1} \cline{3-4}  & & False Negative & True Positive \\ \cline{1-1} \cline{3-4}  & & (FN) & (TP) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification table.  
The logistic regression model relates the log odds of \(Y\) being 1 to a linear combination of the predictor variables:  
\[log\frac{p}{1-p}=\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+\ldots+\beta_{m}X_{m} \tag{1}\]  
Here, \(\beta_{0},\ \beta_{1},\ \beta_{2},\ \ldots,\ \beta_{m}\) are the parameters to be estimated, denoted by \(b_{0},\ b_{1},\ b_{2},\ \ldots,\ b_{m}\) upon estimation. The logistic function is used to convert the linear combination of input variables into a probability \(p\) of the hit value \(Y=1\):  
\[p=\frac{1}{(1+exp(-\beta_{0}-\beta_{1}X_{1}-\beta_{2}X_{2}-\ldots-\beta_{m}X_{ m}))} \tag{2}\]  
where the product is over all observations \(i\); \(y_{i}\) is the observed value of the outcome variable (0 for non-fatal accident or 1 for fatal accident) for the \(i\)-th observation; and \(p_{i}\) is the predicted probability of \(Y=1\), i.e., the probability that the \(i\)-th accident is fatal.  
The parameters of the logistic regression are estimated by the maximum likelihood method, i.e., by maximising the log-likelihood function, given by:  
\[\ln L(\beta_{0},\ \beta_{1},\ \beta_{2},\ \ldots,\ \beta_{m})=\sum(y_{i}\ln p_{ i}+(1-y_{i})\ln(1-pi)) \tag{3}\]  
The estimations of the parameters are often obtained using iterative methods, such as the Newton-Raphson or Fisher scoring algorithms [31].
# Review of Substrate Integrated Waveguide Circuits for Beam-Forming Networks Working in X-Band
## 3 Components for the Beam-Forming Network
### Four-Port Directional Coupler
The two modes were, respectively, the even and odd modes of the half structure, obtained by cutting the coupler along the symmetry plane A-A\({}^{\prime}\) (Figure 4a). Following the Riblet theory, in order to design a 3 dB coupler, the even and odd circuits must be matched, and the phase difference between the transmission coefficients in even and odd cases must be equal to 90\({}^{\circ}\). Differently from the coupler shown in Figure 2, the connection between the device and the SMA connectors was made using the transition proposed in [33]. In this way, the coupler was completely closed, thus avoiding any radiation loss due to the transitions. The final size of the device was 52 \(\times\) 44 mm\({}^{2}\), whereas the core, without the transitions to SMA connectors, was 30 \(\times\) 44 mm\({}^{2}\). Looking at Figure 4, we assume that port 1 is the input, port 2 is the direct port, port 3 is the coupled port, and port 4 is the isolated one. Figure 5 shows the comparison between the simulated and experimental scattering parameters for this coupler. Figure 6 shows the measured phase difference between the direct and the coupled port, which should be nominally 90\({}^{\circ}\). Although the designed device was quite simple, the results showed good performances, with a good return loss over the band 10.5-12.7 GHz and a very good balance of the transmission to the direct and the coupled ports. Performances can be further improved by either adding some matching structures into the line connecting the SMA ports to the device core or by using a multi-hole coupler. For both improvements, sizes, losses, and costs will be higher than the proposed solution. Figure 4: Four-port directional coupler. **(a)** Sketch of the coupler; **(b)** Photograph of the coupler. Figures adapted from [32]. Figure 5: Comparison between simulated and experimental responses of the coupler in Figure 4. **(a)** Reflection coefficient and isolation; **(b)** Transmission coefficients. Figures adapted from [32].
## 2 Results and Discussion
### Discovery of Potential Markers of HT and MT Strains
In particular, 1-octen-3-one was a possible precursor of 1-octen-3-ol being produced via reduction or autoxidation [49; 50]. Regarding the hydrocarbons, styrene was identified as the important metabolite for the MT strains and the result was in agreement with a previous study [51]. From the pathway marked in green (Figure 6), it can be assumed that less phenylalanine was used to produce the ochratoxins, and the surplus was used to synthesize more styrene than the HT strains. Limonene was firstly selected as a potential marker for MT strains, though the reason for this is not clear and needs to further research. For HT strains, 2 identified sesquiterpenes, namely cuparene and (_Z_)-thujopene, and 1 ester, methyl octanoate, were selected as potential markers. There is an unknown compound identified as a potential marker for HT strains, which has ion information of \(m/z\) 69, 84, 55. The sesquiterpenes have been considered as a main difference between different toxigenic strains, such as _Aspergillus flavus_[7]. Results from previous study showed the _Aspergillus_ strains which could synthesize OTA produced  
Figure 6: The pathways of MVOCs involved in the production of different secondary metabolites. more sesquiterpenenes [5]. These two sesquiterpenes were firstly identified as potential markers for high toxigenic _A. carbonarius_ strains. As for methyl octanoate, it has been showed that it may be play an important role in the OTA biosynthesis [37]. For comparison, VIP values were also calculated based on quantitative analysis and similar but not integrated results were obtained that three metabolites including 1-octen-3-one, 2-octen-1-ol and styrene (VIP value beyond 1.5) were selected as potential markers (Figure 5b). This result showed the robustness of untargeted profiling for analyzing the MVOCs to discover differences between HT and MT strains.
## 2 Introduction to IPv6
### Transition Methods
#### 2.3.4 Translation  
Translation mechanisms try to translate IPv6 packets into IPv4 packets and vise versa. This can be done on different layers as shown in Figure 2. Translation techniques are discouraged as transition approach because they can impede hierarchical routing and do not take advantage of the new header and extended address space [3]. The most important techniques are explained in the following.  
Stateless IP/ICMP Translation (SIIT) is used on the network layer and defined in RFC 6145 [56]. It makes use of IPv4-converted and IPv4-translatable addresses, which are a subset of IPv4-embedded addresses explained in Section 2.3.1. While still called stateless, this technique offers a stateless and a stateful mode, compliant with the rules defined in RFC 6052 [45]. SIIT can handle ICMP as well as IP packets in both directions. For ICMP only the vital messages such as echo are translated, most others are dropped. Generally, only information that is 1:1 translatable is in fact translated. Other information, such as option fields, are ignored or can cause the packets to be silently dropped. While not introducing new security issues, SIIT is not able--and not supposed--to translate all traffic. Network Address Translation-Protocol Translation (NAT-PT) is the combination of SIIT (old specification) and IPv4 NAT. It is specified in RFC 2766 [57] and is still valid but moved to historic status by RFC 4966 [58]. NAT-PT should not be used because it is vulnerable for DOS attacks, does not support DNSSEC, and IPsec cannot be translated. Furthermore, it hinders the complete deployment of IPv6 and IPv6 applications. NAT-PT is replaced by SIIT as in RFC 6145.  
Transport Relay Translation (TRT) aims to solve the translation problem on the transport layer and defines a method for Transmission Control Protocol (TCP) and UDP traffic. TRT is a stateful translation technique and uses DNS mapping between AAAA and A records and is defined in RFC 3142 [59]. It is vulnerable to DoS attacks and does not support IPsec. ALT techniques were developed to handle legacy applications that use IPv4 but cannot be upgraded to IPv6 or be replaced. Most of them present an artificial pool of IPv4 addresses to the application and then translate requests [3].
## 4 Experimental Analysis and Results
### Experimental Setup
#### 4.3.1 Comparison with Basic Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Dataset** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline AraBERT + CRF & ANERcorp & 89.2 & 87.4 & 88.3 \\ XLM-R + CRF & ANERcorp & 91.5 & 89.8 & 90.6 \\ LUKE & ANERcorp & 91.9 & 90.1 & 91 \\ Seq2 Seq NER & ANERcorp & 91.6 & 89.9 & 90.7 \\
**Proposed Model** & ANERcorp & **93.7** & **92.3** & **93** \\ AraBERT + CRF & ACE 2005 & 85.6 & 82.9 & 84.2 \\ XLM-R + CRF & ACE 2005 & 88.1 & 86.7 & 87.4 \\ LUKE & ACE 2005 & 87.5 & 85.9 & 86.7 \\ Seq2 Seq NER & ACE 2005 & 87.7 & 86.1 & 86.9 \\
**Proposed Model** & ACE 2005 & **90.2** & **89.1** & **89.6** \\ AraBERT + CRF & Biomedical & 84.7 & 82.5 & 83.6 \\ XLM-R + CRF & Biomedical & 86.3 & 84.1 & 85.2 \\ LUKE & Biomedical & 86.9 & 84.7 & 85.8 \\ Seq2 Seq NER & Biomedical & 87.1 & 85 & 86 \\
**Proposed Model** & Biomedical & **89.5** & **87.3** & **88.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative performance of the proposed model and baselines across three datasets. To provide further insights, we visualize the model's behavior using a heatmap and a precision-recall (PR) curve as follows:  
**Entity Classification Heatmap**: The heatmap in Figure 4 illustrates the alignment scores between spans and entity types for a sample sentence. High confidence scores are observed for correct span-label assignments, while misaligned predictions show lower confidence. **Precision-Recall Curve**: Figure 5 presents the Precision-Recall Curve for the AN-ERcorp dataset, illustrating the trade-off between precision and recall for each evaluated model. The curve is derived from the final validated prediction outputs and directly reflects the precision and recall values reported in Table 4. The proposed model achieves the best balance between precision and recall, reinforcing its robustness in recognizing both flat and nested entities in Arabic text. \begin{table}
\begin{tabular}{c c c c} \hline
**Model Variant** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline Full Model & **93.7** & **92.3** & **93.0** \\ \hline Without Hybrid Feature Fusion & 91.5 & 90.4 & 90.9 \\ \hline Without Compound Span Representation & 90.8 & 89.3 & 90.0 \\ \hline Without Enhanced Multi-Label Classification & 92.1 & 91.0 & 91.5 \\ \hline \end{tabular}
\end{table} 
Table 5: Results of the ablation study, showing the contribution of each component to overall performance. Removing key layers significantly reduces the F1-score. Figure 4: Entity classification heatmap illustrating the alignment scores between spans and entity types. High confidence scores indicate correctly assigned spans, while lower confidence scores highlight misclassified entities.
# Impact of Cryogenic Condition and Drill Diameter on Drilling Performance of CFRP
## 1 Introduction
They reported that the use of minimum-quantity lubrication and liquid nitrogen coolants increased the machining forces; however, they both decreased the surface roughness, adhesions and occurrence of a built-up edge on the cutting tool when compared with dry drilling. In another article by the authors [19], hole quality was evaluated by investigating the occurrence of burrs on the edges of the holes in the upper and lower aluminum sheets, and the accuracy and circularity of hole size. The results indicated that cryogenic and minimum-quantity lubrication coolants could remarkably decrease exit burr formation. The research with regard to the drilling of polymer composites with various diameter drill bits has been summarized above. New studies on the drilling of such composites under cryogenic conditions have been carried out, especially in recent years, but these works are very limited. Furthermore, there are no other studies investigating the effects of different diameter drill bits combined with dipped cryogenic conditions on the drilling performance of polymer composites in the literature. In this paper, the drilling performance of CFRP material was experimentally investigated using a constant cutting speed and different feed rates and drill bits (4 and 6 mm diameter) under dry and cryogenic conditions.
Molecules_**2013**, _18_, 3927-3947; doi:10.3390/molecules18043927  
_moleculars_  
**ISSN 1420-3049**  
www.mdpi.com/journal/molecules  
_Article_  
**Volatile and Amino Acid Profiling of Dry Cured Hams from Different Swine Breeds and Processing Methods**  
**[PERSON] *, [PERSON] and [PERSON]**  
Instituto de la Grasa (CSIC), [PERSON], 4, Sevilla E-41012, Spain;  
E-Mails: [EMAIL_ADDRESS] (R.A.); [EMAIL_ADDRESS] (R.A.-R.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-954-611-550; Fax: +34-954-616-790.  
_Received: 27 February 2013; in revised form: 28 March 2013 / Accepted: 28 March 2013 / Published: 3 April 2013_  
**Abstract:** The flavor of dry cured ham explains the high appreciation of this product and it determines consumer acceptance. Volatile compounds provide valuable information about the odor and sensory quality of dry cured hams. Since amino acids are the origin of some volatile compounds of dry cured ham, the volatile and amino acid compositions of forty-one dry cured hams from Spain and France were determined to establish associations between them. The samples included different pig breeds (non Iberian _vs._ Iberian), which were additionally affected by different maturation times and feeding types (acorn _vs._ fodder). Results showed that 20 volatile compounds were able to distinguish Iberian and non Iberian hams, and 16 of those had relevant sensory impact according to their odor activity values. 3-Methylbutanol, 2-heptanol and hexanal were among the most concentrated volatile compounds. In the case of non-volatile compounds, the concentrations of amino acids were generally higher in Iberian hams, and all the amino acids were able to distinguish Iberian from non Iberian hams with the exception of tryptophan and asparagine. A strong correlation of some amino acids with volatile compounds was found in the particular case of alcohols and aldehydes when only Iberian hams were considered. The high correlation values found in some cases proved that proteolysis plays an important role in aroma generation.  
**Keywords:** dry-cured ham; volatile compounds; amino acids; aroma; HS-GCMS; HPLC
## 3 Results and Discussion
### Correction Functions for LISST-VSF
The root mean squared deviation, _RMSD_, was calculated as \(\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(Y_{i}-X_{i}\right)^{2}}\), and the median absolute percent difference, _MAPD_, was calculated as the median value of \(\left|\frac{Y_{i}-X_{i}}{X_{i}}\right|\times\)100. \(N\) is the number of data points used in the analysis. Figure 8: (**a**) Scatter plot comparing reference values of the particulate scattering coefficient computed over the angular range 0.09–150\({}^{\circ}\), \(b_{p,150}^{REF}\), with values determined from the LISST-VSF, \(b_{p,150}^{LIST}\), before (asterisks) and after (circles) correction with \(CF_{f}\). A type II linear regression model fit to the data is indicated by the dotted lines. (**b**) Similar to (**a**), but for the particulate backscattering coefficient computed over the range 90–150\({}^{\circ}\). Figure 10a is a scatter plot of \(DoLP_{p}^{LIST}(\psi)\) vs. \(DoLP_{p}^{REF}(\psi)\) which includes all data for the six bead samples presented in Figure 9. In the region of negative values which correspond to the minima in the angular pattern of the degree of linear polarization, the \(DoLP_{p}^{LIST}(\psi)\) exhibits a positive bias relative to \(DoLP_{p}^{REF}(\psi)\). This bias is seen in the form of peaks in the angular pattern of the difference between \(DoLP_{p}^{LIST}(\psi)\) and \(DoLP_{p}^{REF}(\psi)\) for larger bead sizes (Figure 10b). The peak amplitudes generally range from 0.05 to 0.3. Importantly, however, aside from these features the data of \(DoLP_{p}^{LIST}(\psi)\) vs. \(DoLP_{p}^{REF}(\psi)\) are distributed close to the 1:1 line within the major part of the region of positive values (Figure 10a). This includes the region of maximum values of the degree of linear polarization of scattered light from natural seawater samples, which are observed at scattering angles near 90\({}^{\circ}\) or greater. The overall good agreement between \(BF_{f}\)-corrected measured \(DoLP_{p}^{LIST}(\psi)\) and \(DoLP_{p}^{REF}(\psi)\) is supported by the statistical parameters shown in Table 2 which are calculated on the basis of the entire dataset presented in Figure 10. For example, the _RMSD_ and _MAPD_ values are small, 0.065 and 5%, respectively. Also, despite some negative bias for data with negative values of the degree of linear polarization, the _MR_ for the ratio of \(DoLP_{p}^{LIST}(\psi)\) to \(DoLP_{p}^{REF}(\psi)\) for the entire dataset is 0.993, indicating  
Figure 9: Similar to Figure 6, but for measured and reference values of particulate degree of linear polarization \(DoLP_{p}\). Measurements obtained with the LISST-VSF were corrected with \(BF_{f}\). essentially no bias. These statistics would improve if the data within the minima in the angular pattern of the degree of linear polarization were removed from the analysis.
# Salt-Specific Suppression of the Cold Denaturation of Thermophilic Multidomain Initiation Factor 2
###### Abstract  
Thermophilic proteins and enzymes are attractive for use in industrial applications due to their resistance against heat and denaturants. Here, we report on a thermophilic protein that is stable at high temperatures (\(T_{trs,\,tot}\) 67 \({}^{\circ}\)C) but undergoes significant unfolding at room temperature due to cold denaturation. Little is known about the cold denaturation of thermophilic proteins, although it can significantly limit their applications. We investigated the cold denaturation of thermophilic multidomain protein translation initiation factor 2 (IF2) from _Thermus thermophilus_. IF2 is a GTPase that binds to ribosomal subunits and initiator fMet-tRNAMet during the initiation of protein biosynthesis. In the presence of 9 M urea, measurements in the far-UV region by circular dichroism were used to capture details about the secondary structure of full-length IF2 protein and its domains during cold and hot denaturation. Cold denaturation can be suppressed by salt, depending on the type, due to the decreased heat capacity. Thermodynamic analysis and mathematical modeling of the denaturation process showed that salts reduce the cooperativity of denaturation of the IF2 domains, which might be associated with the high frustration between domains. This characteristic of high interdomain frustration may be the key to satisfying numerous diverse contacts with ribosomal subunits, translation factors, and tRNA.  
Keywords:initiation factor 2; thermodynamic stability; cold denaturation; domain cooperativity; electrostatic interactions +
Footnote †: journal: Molecular Sciences