esa-sceva/satcom-chunk-collection huggingface dataset:
# A Tolerance Type Screening Method Based on Similarity Evaluation
## 3 Similarity Assessment Strategies
### Calculation of Similarity Between Resource Cases and Target Cases  
The overall similarity \(S(\mathbf{T}_{\rm c},\mathbf{T}_{\rm o})\) between the resource case and the target case is shown in Equation (12):  
\[S(\mathbf{T}_{\rm c},\mathbf{T}_{\rm o})=\mathbf{\omega}_{1}S(\mathbf{E}_{\rm tc},\mathbf{E}_{\mathbf{ t}0})+\mathbf{\omega}_{2}S(\mathbf{R}_{\rm tc},\mathbf{R}_{\rm to})+\mathbf{\omega}_{3}S(\mathbf{A}_{ \rm cc},\mathbf{A}_{\rm co})+\mathbf{\omega}_{4}S(\mathbf{F}_{\rm tc},\mathbf{F}_{\mathbf{to}}) \tag{12}\]  
Among them \(0\leq\mathbf{\omega}_{1}\), \(\mathbf{\omega}_{2}\), \(\mathbf{\omega}_{3}\), \(\mathbf{\omega}_{4}\leq 1\) and \(\mathbf{\omega}_{1}+\mathbf{\omega}_{2}+\mathbf{\omega}_{3}+\mathbf{\omega}_{4}=1\), \(\mathbf{\omega}_{1}\), \(\mathbf{\omega}_{2}\), \(\mathbf{\omega}_{3}\), and \(\mathbf{\omega}_{4}\) are the weight coefficients of tolerance element similarity \(S(\mathbf{E}_{\rm tc},\mathbf{E}_{\rm tc})\), spatial relationship similarity \(S(\mathbf{R}_{\rm tc},\mathbf{R}_{\rm to})\), part component element similarity \(S(\mathbf{A}_{\rm cc},\mathbf{A}_{\rm co})\), and part functional feature similarity \(S(\mathbf{F}_{\rm tc},\mathbf{F}_{\rm tc})\), respectively. According to Equation (14), the overall similarity value of the case is determined by the values of four weight coefficients \(\mathbf{\omega}_{1}\), \(\mathbf{\omega}_{2}\), \(\mathbf{\omega}_{3}\), and \(\mathbf{\omega}_{4}\), as well as the values of four local similarities. Usually, the impact of local similarity on the overall similarity of a case is not equal. In order to determine the importance of local similarity in case similarity, similar elements in the case are selected as evaluation factors, and an evaluation factor set is established: \(\zeta=\{\mathbf{E}_{\rm t},\mathbf{R}_{\rm t},\mathbf{A}_{\rm t},\mathbf{F}_{\rm t}\}\). On this basis, determine \(\mathbf{E_{\text{t}}}\). The relative importance between various evaluation factors such as \(\mathbf{R_{\text{t}}}\), \(\mathbf{A_{\text{t}}}\) and \(\mathbf{F_{\text{t}}}\) are determined using the data processing method of Analytic Hierarchy Process [31] to determine the values of weight coefficients \(\mathbf{\omega}_{1}\), \(\mathbf{\omega}_{2}\), \(\mathbf{\omega}_{3}\), and \(\mathbf{\omega}_{4}\).  
According to the Analytic Hierarchy Process [26], the final set of ordered weight coefficients is \(\mathbf{\omega}\) = {0.3994, 0.3599, 0.0812, 0.1595}. The overall similarity calculation formula for geometric tolerance cases is shown in Equation (13):  
\[\mathbf{S(T_{c},T_{o})}=0.3994\times\mathbf{S(\mathbf{E_{tc},E_{to}})}+0.3599\times\mathbf{S( \mathbf{R_{tc},R_{to}})}+0.0812\times\mathbf{S(\mathbf{A_{cc},A_{co}})}+0.1595\times\mathbf{S( \mathbf{F_{tc},F_{to}})} \tag{13}\]
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 2 Methods
### Platforms for Clinical Decision-Making and Personalized Medicine
#### 2.4.2 OmicsAnalyst: A User-Friendly Web Platform for Clinical and Personalized Medicine Research  
OmicsAnalyst is a user-friendly, web-based platform designed for the integration and analysis of multi-omics data, with a strong focus on applications in clinical research and personalized medicine. The platform allows the combination of genomics, transcriptomics, proteomics, metabolomics, and other omics data types to gain insights into biomarkers, pathway analysis, and disease prediction [30]. One of the main advantages of OmicsAnalyst is its intuitive web interface, which makes it accessible to users who may not have extensive computing experience. The platform offers a variety of data analysis tools tailored for clinical applications, including statistical analysis, pathway enrichment and biomarker discovery. In addition, machine learning algorithms are built into the platform, supporting predictive modeling for disease classification and biomarker identification, which are essential to personalized treatment strategies.  
OmicsAnalyst's integration of machine learning algorithms enables predictive biomarker discovery, demonstrated in its application to classify subtypes of breast cancer. Its focus on user-friendly workflows makes it accessible to clinical researchers, though its lack of support for spatial omics limits its use in advanced tissue-specific studies. The platform's capacity to combine different omics types enables the exploration of molecular pathways and identify connections between various biological layers, such as gene expression, protein levels, and metabolite concentrations. This makes it especially useful for disease research where understanding the molecular interactions and drivers of dis ease is crucial for personalized therapeutic approaches. A feature of OmicsAnalyst is its interactivity, allowing the exploration of data in depth through customizable visualizations such as heatmaps, PCA plots and network analysis. These visual tools assist the quick identification of relevant molecular patterns and the gain of actionable insights for clinical decision making. While OmicsAnalyst offers broad multi-omics integration capabilities, its primary focus on clinical research might limit its use in certain basic research fields or in areas where highly specialized omics types, such as epigenomics or spatial omics, are required.
## 3 Results
### Adsorption of Cd(II) on ZnO, PDA/ZnO, and DMSA/PDA/ZnO  
Figure 5 illustrates a change in Cd(II) concentration with contact time. The bulk concentration of Cd(II) decreased sharply in the first 30 min. and then gradually decreased over the next 150 min. when DMSA/PDA/ZnO was used as adsorbent (Figure 5a). When compared with the high adsorption ratio (\(R\), 100%) on DMSA/PDA/ZnO, the adsorption ratio on ZnO and PDA/ZnO was much lower (inset Figure 5a). The results demonstrated that the adsorption capacity of DMSA/PDA/ZnO mainly resulted from the DMSA. The adsorption reached equilibrium within 180 min. (3 h). Moreover, the change of \(c_{\text{t}}\) with adsorption time \(t\) could be well-fitted with the pseudo-second-order model relative to the pseudo-first-order model, which was supported by the higher adjusted correlation coefficient of 0.993 (Table 2). The pseudo-second-order reaction rate constant was 0.22 g/(mg\(\bullet\)min.).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Element** & **C** & **N** & **H** & **S** \\ \hline ZnO & 0.03 & 0 & 0.041 & 0.010 \\ PDA/ZnO & 8.63 & 1.24 & 0.941 & 0.012 \\ DMSA/PDA/ZnO & 8.08 & 1.18 & 1.076 & 0.834 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Element (C, H, N, and S) contents (%) in ZnO, PDA/ZnO, and DMSA/PDA/ZnO.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Kinetics**} & \multicolumn{4}{c}{**Isotherm**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Pseudo-First-Order**} & \multicolumn{2}{c}{**Pseudo-Second-Order**} & \multicolumn{2}{c}{**Langmuir**} & \multicolumn{2}{c}{**Freundlich**} \\ \hline parameters & \(k_{1}\) & \(c_{\text{e}}\) & \(k_{2}\) & \(c_{\text{e}}\) & \(K_{L}\) & \(q_{\text{max}}\) & \(K_{\text{F}}\) & \(1/n\) \\ value & 0.11 & 0.88 & 0.22 & 0.94 & 0.14 & 28.5 & 4.55 & 0.55 \\ correlation coefficient & 0.989 & & 0.993 & & 0.997 & 0.9 The adsorption of Cd(II) to DMSA/PDA/ZnO could be better fitted by the Langmuir isotherm model relative to Freundlich isotherm model, as listed in Table 2. The Langmuir isotherm is an empirical model that assumes monolayer adsorption, namely, that each adsorbate exhibits constant enthalpies and sorption activation energy [32]. The adsorption of Cd(II) on DMSA/PDA/ZnO followed the Langmuir adsorption isotherm model, as shown in Figure 6. The adsorption capacity (\(q_{\rm max}\)) and adsorption constant (\(K_{\rm L}\)) were 28.5 mg/g and 0.14 L/mg, respectively. When comparing \(q_{\rm max}\) reported in this study with the reported values for other adsorbents (4-137 mg/g) [22], \(q_{\rm max}\) reported in this study is in the medium rank. However, other adsorbents, like meso silica (MCM-48, SBA-15 etc.), could not be directly dissolved in acid solution.
## 2 Literature Review
### Scheduling of Bottling Operations
#### 2.1.3 Methods for Quantifying Clogging Potential  
To quantify the fouling potential of wine, experimental methods are used to evaluate its hydraulic behavior under controlled conditions. These tests do not aim to replicate the industrial filtration process exactly--where both flow rate and pressure may vary dynamically--but rather employ a constant-pressure system to isolate the effect of filter clogging on flow capacity.  
In the wine industry, the most commonly used indicators for this purpose are the Laurenty Index and Vmax [7; 39]. Both indicators are calculated under the same operational conditions: a temperature of 20 \({}^{\circ}\)C, a constant pressure of 30 psi, and filter discs of 25 mm in diameter with a pore size of 0.65 \(\upmu\)m, using a constant-pressure setup [40; 41]. The test involves filtering a total volume of 400 mL using a sealed, pressurized system that maintains steady pressure throughout the procedure.  
The Laurenty Indexis calculated by recording the time required to filter the first 200 mL and comparing it with the total time taken to filter all 400 mL. The difference between these two times reflects the rate at which the flow decreases due to progressive clogging of the filter. A larger time gap indicates higher fouling potential. In industrial practice, wines with a Laurenty Index below 35 are typically considered suitable for bottling, as values above this threshold are associated with significant filtration deviations and potential operational disruptions.  
Figure 1: The hydraulic performance of the filtration system considering the impact of fouling potential on flow rate and volume; Stage I: Filtration proceeds under normal conditions without noticeable clogging; Stage II: Partial clogging reduces the effective filtration area, resulting in increased pressure differential and declining flow performance; Stage III: Severe clogging halts operation, requiring filter replacement or cleaning.  
On the other hand, Vmax is defined as the maximum volume that can be filtered before reaching a critical operating threshold--such as a minimum acceptable flow rate or a critical pressure drop [8]. This metric is directly linked to the effective filtration area, providing a more direct and operationally useful interpretation of hydraulic performance. In this context, Vmax offers a clearer interpretation, as it allows the filtered volume to be associated with a known surface area [40; 41]. The industry generally requires wines to exceed a minimum Vmax value of 4000 mL to ensure process efficiency and avoid excessive filter replacements.  
These evaluations are carried out systematically once the wine has completed its stabilization and is ready for bottling, as part of quality-control procedures aimed at anticipating hydraulic behavior, optimizing lot allocation, and minimizing unplanned downtimes in bottling operations [42].
## 3 System Model  
### Transmission Power Control Algorithms  
We assume the WSNs consist of many common sensor nodes and some sink nodes. Each sink node is aware of its own location by using GPS or some other localization mechanism. A sink node broadcasts power control assistant messages (PCAM) to the other sink nodes by one-hop communication. PCAM contains the sender's location information. We also make some assumptions about the sensor nodes and the underlying network model. For the sink nodes, we assume that all sink nodes can transmit with enough power to reach each other, and that the sink nodes can use a power control scheme to vary the amount of transmission power. Otherwise, each node has the computational power to support different MAC protocols and perform signal processing functions.  
In order to obtain satisfactory network connectivity, sink nodes need to negotiate the broadcast radius. A sink node working outside its radio range employs an energy dissipation algorithm to adjust the actual broadcast range.  
In [9], the total energy dissipation of node \(i\) described as:  
\[e_{i}^{r}(d)=\alpha d^{*}+\beta \tag{1}\]  
where \(\alpha\),\(\beta\in\mathcal{R}\) are real numbers. \(\beta\) is the overhead energy, representing the sum of the receiver, sensing and computation energy which is a constant value with varying distance \(d\).  
A more acknowledged model is proposed in [25]. If node \(X\) send a packet with power \(P_{t}\), which is heard by node \(Y\) with power \(P_{r}\), the following equation holds:  
\[P_{x}=P_{y}\,(\frac{\lambda}{4\pi d})^{*}\,g_{i}\,g_{r} \tag{2}\]  
where \(\lambda\) is the carrier wavelength, \(d\) is the distance between the transmitter and the receiver, \(n\) is the path loss coefficient, and \(gt\) and gr are the antenna gains at the transmitter and receiver. The typical value of n is 2. Usually, \(\lambda=Light Velocity/Frequency=0.1\) m or so in 2.4 GHz WSNs applications.
# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
## 1 Introduction
Therefore, there is a pressing need for monitoring approaches that can continuously capture early warning signals preceding slope collapses. In this context, acoustic emission (AE) technology emerges as a promising monitoring method. AE captures elastic wave signals generated by stress-induced deformation within materials, offering a novel perspective for landslide monitoring. It can detect micro-damage within rock and soil masses during the early stages of landslide incubation, addressing the limitations of traditional monitoring methods. AE involves the generation of stress wave emissions resulting from irreversible changes in material microstructures under stress, such as friction, cracking, fracturing and melting/crystallization of rocks, resulting in the emission of stress waves [27; 28]. Unlike active ultrasonic techniques, AE employs a passive monitoring approach by detecting naturally emitted elastic waves from deforming structures [29]. This enables the faster monitoring of landslides compared to traditional methods. Figure 2 illustrates the schematic diagram of the landslide process and AE monitoring. When integrated with transient signal recording and analysis capabilities, AE monitoring shows potential as an early warning indicator for impending failures in geomaterials [30]. Recent field deployments of AE systems have demonstrated a clear linear relationship between cumulative displacement and AE data [31]. However, challenges remain in capturing signals in highly attenuating soil conditions and establishing precise correlations between AE parameters and deformation states [32]. Figure 2: Schematic diagram of landslide process. This paper aims to offer a thorough review of the latest advancements in AE-based landslide monitoring techniques. After introducing the topic, the paper compares different methods used for monitoring landslides. It then delves into the basic principles and equipment used in the AE technique. The paper also examines how AE testing is applied in studying the stability of granular and rock slopes in both laboratory and field settings. Lastly, it discusses ongoing challenges and recent progress, and provides insights into potential future developments in the field.
# Sustainable Cross-Laminated Timber Structures in a Seismic Area: Overview and Future Trends
## 3 Traditional CLT Buildings
### Role of the Mechanical Connections  
Mechanical connections in CLT structures can be divided in two groups. The first refers to connections selected to prevent rocking (HD) and translation (AB) of the vertical panels. The second includes small diameter metal fasteners used for vertical joints in segmented walls, or for panel to panel connections of the floors, or for floor to wall connections.  
To date, only HD and AB placed in panel to panel and in panel to foundation connection zones--and vertical joints in segmented walls--are considered as potential dissipative fuses against seismic actions. Experimental tests highlighted that in the majority of cases ductile failure modes defined by the [PERSON]'s theory [39] have occurred for HD and AB loaded in tension and in shear, respectively. These mechanisms are characterized by a combination of progressive embedding of the wood and cyclic yielding of the steel fasteners (nails or screws) with a good hysteretic behaviour.  
Figure 6: Seismic action paths for a CLT shear-wall.  
Extensive programs aimed at investigating the cyclic behaviour of HD and AB have been conducted, for instance within the SOFIE [21] and SERIES [40] projects. [PERSON] et al. [21] investigated cyclic behaviour of HD and AB under different loads configuration: HD subjected to tension, HD subjected to shear, AB subjected to shear and AB subjected to tension. Test results demonstrated that HD tested in tension show high strength and stiffness in their primary direction (tension), while in shear direction they do not achieve high strength and stiffness due to local buckling of steel plates. AB, instead, have significant strength and stiffness in both directions. Cyclic response of HD is characterized by wide loops in tension and null in compression, while AB have a hysteretic response with pinching due to the slip of the fasteners in the embedded holes of the wood. In Figure 7 are reported the force-displacement diagrams relative to HD and AB loaded in tension and in shear, respectively. Other similar experimental campaigns finalized to evaluate the mechanical behaviour of connections used in low and mid-rise CLT buildings have been conducted in [41; 42; 43], in which the same results have been achieved.  
Although in design practice it is commonly assumed that AB carry-out shear forces and HD tension forces only, a simultaneous presence of shear-axial interaction occurs during an earthquake. The coupled shear-to-tension behaviour for HD [44] and AB [45] have been also investigated. Results proved that when HD and AB connections are subjected to significant combined shear and axial loads, the global ductility and dissipative capacity of the entire wall panels assembly can be significantly deteriorated.
# Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method
## 3 Theoretical Method
### Principle of SVD Denoising  
Given an original signal \(X=\left(\text{x}_{1},\text{x}_{2},\text{x}_{3},\ldots,\text{x}_{N}\right)\), a noise signal \(S=\left(\text{s}_{1},\text{s}_{2},\text{s}_{3},\ldots,\text{s}_{N}\right)\), and a noisy signal \(Y=\left(\text{y}_{1},\text{y}_{2},\text{y}_{3},\ldots,\text{y}_{N}\right)\), where \(N\) represents the length of the signal, the relationship among the three signals can be expressed as follows:  
\[Y=X+S \tag{1}\]  
To reconstruct the noisy signal \(Y\) in phase space, it is transformed into a Hankel matrix \(H_{m\times n}\left(\text{m}\leq\text{n}\right)\):  
\[H_{m\times n}=\begin{bmatrix}y_{1}&y_{2}&\cdots&y_{n}\\ y_{2}&y_{3}&\cdots&y_{n+1}\\ \vdots&\vdots&\cdots&\vdots\\ y_{m}&y_{m+1}&\cdots&y_{N}\end{bmatrix} \tag{2}\]  
where \(1<n<N\), m is the embedding dimension and satisfies \(m+n-1=N\).  
SVD of the Hankel matrix \(H\):  
\[H=U\sum V^{T} \tag{3}\]  
where the orthogonal matrix \(U\in R^{m\times m}\) and the orthogonal matrix \(V\in R^{n\times n}\) are the left and right singular matrices of the \(H\) matrix, respectively, and \(\sum\) is the singular value matrix of the following form  
\[\sum_{m\times n}=\begin{bmatrix}\Delta&0\\ 0&0\end{bmatrix} \tag{4}\]  
where \(\Delta=diag(\lambda_{1},\lambda_{2},\cdots,\lambda_{r})\), and \(\lambda_{1}\geq\lambda_{2}\geq\cdots\geq\lambda_{r}\geq 0\), \(\lambda_{r}\) are the singular values of the Hankel matrix, r is the rank of this matrix, and 0 is the zero matrix.  
Normally, the vital attributes of the genuine signal are encapsulated by the first k (k \(<\) r) significant singular values, with the noise signal being contained in the remaining (r \(-\) k) singular values. Therefore, through the proper selection of the SVD order, the subsequent setting of the remaining singular values to zero, and the subsequent reconstruction of the Hankel matrix using the inverse of the SVD, the denoising goal can be accomplished. The resultant Hankel matrix can then be simplified into a one-dimensional signal, effectively eliminating the noise.
## 3 Results and Discussion
### Flow Cytometry Studies  
To further determine the effect of TauRP (1-14) peptide pre-associated with fibrillar Tau on the LRP1-mediated uptake of tau, we performed cellular internalization studies using flow cytometry. The results are shown in Figure 11. As shown in the figure, the highest uptake by the LRP1-expressing cells was seen for neat Tau fibrils. As seen from previous studies, these results further confirm that LRP1 receptor mediates the internalization of fibrillar tau [115]. In comparison, samples containing the TauRP (1-14) peptide showed a clear shift in fluorescence to the left, suggesting that internalization was reduced. A significantly lower uptake was observed for cells treated with TauRP (1-14) pre-associated with Tau fibrils, as revealed by the blue peak, which showed a lower intensity. To ensure that the uptake was LRP1-mediated, we also examined the effect of Tau uptake in the presence of a peptide, angiopep-2, which is known for its high affinity, internalization and interactions with LRP1 receptor and has shown LRP1-mediated cellular uptake in previous studies. Furthermore, angiopep-2 (TFFYGGSRGRNNFKTEY) is known to effectively cross the BBB through LRP1-mediated transcytosis [116]. Thus, the LRP1-expressing HEK293 cells were also treated with fibrillar tau pre-associated with TauRP-14 and angiopep-2. The results showed that in the presence of angiopep-2, the uptake of Tau fibrils was lower than that compared to neat fibrils, but slightly higher compared to those seen for TauRP1-14 pre-associated with Tau. This confirms that even in the presence of angiopep-2, slightly less internalization is observed when Tau fibrils are pre-associated with TauRP 1-14. This suggests that LRP1 may be potentially saturated by angiopep-2 [117], which reduced the transport efficiency of fibrillar tau in the presence of TauRP1-14. The peptide angiopep-2 has been shown to selectively target LRP1 [118]; therefore, the goal was to determine if the presence of this competitive ligand would reduce the uptake of tau. Untreated non-transfected cells were used as a control and showed the lowest fluorescence due to background cellular autofluorescence.  
Figure 10: Comparison of change in percent reflectivity upon interaction of LRP1-expressed immobilized HEK-293 cells with neat Tau fibrils and Tau fibrils pre-associated with different concentrations of TauRP1-14 peptide.  
Overall, the results showed that in the absence of the TauRP (1-14) peptide, internalization of fibrillar tau was the highest, whereas in the presence of the TauRP (1-14) peptide, LRP1-mediated internalization of fibrillar tau was substantially reduced. This confirms that the peptide that the designed peptide (DNSDEENCES) effectively binds to fibrillar tau and inhibits its LRP1-mediated internalization.