esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Conversational System: CORES
### Workflow of CORES
#### 3.5.6 Hybrid Contextual Embedding-Based Ranking  
The final recommendation score is calculated using a weighted linear combination of feature similarity, social similarity, and QoS similarity scores.  
\[Similarity=\alpha\times Sim\_Feature+\beta\times Sim\_Social+(1-\alpha-\beta) \times QoS \tag{12}\]  
where \(\alpha\) and \(\beta\) are the weight indicating the importance of each criterion (features, social, QoS, delivery). Users visually choose their preferred features, social factors, and QoS using GUI of VRShop. This selection process aids in calculating the weights for \(\alpha\) and \(\beta\) by assigning a high weight to the most preferred feature, a low weight to the least preferred, and a neutral weight to unselected options. Preferences vary among users; some prioritize items that match their specific preferences over social feedback, while others value proximity and high social or emotional ratings. Leveraging feedback from others is essential to enhancing the success of product recommendations. Items are ranked based on their final scores, with the highest-ranking ones recommended to the user. Items from stores with the shortest delivery times and highest user attention are prioritized for the next step. The proposed item recommendation is shown in Algorithm 1.  
```
Inputs:
\begin{tabular}{} \end{tabular}
```  
**Outputs:** _Top Recommended Items_  
**Begin**  
**(1):** **Foreach** digital store \(DS_{i}\in DS\)**Do**  
**(2):** \(DomainScore\leftarrow\ Sim\_Domain(Q,DS_{i})\)
**(3):** **IF** (\(DomainScore==1\)) **Then**
**(4):** \(D\gets D\ \cup\ \left(d_{j}\ \in DS_{i}\right)\) ;
**(5):** **End**
**(7):** **Foreach** item \(d_{i}\in D\)**Do**
**(8):** \(FeatureScore\leftarrow\ Sim\_Feature(Q,d_{i})\)
**(9):** **Foreach** comment \(c_{ij}\ \in C\)**Do**
**(10):** \(EmotionalScore\gets EmotionalScore+Sim\_Emotion\left(Q,c_{ij}\right)\)
**(11):** \(SocialScore\leftarrow\ EmotionalScore/L\)
**(12):** \(QoSScore\leftarrow\ QoS(Uctx,DS_{i})\)
**(13):** \(Score\leftarrow\ \alpha\times FeatureScore+\beta\times SocialScore+(1-\alpha- \beta)\times QoSScore\)
**(14):** **End**
**(15):** **End**
**(16):** \(SortScores\leftarrow\)Sort the items in D by their Score in descending order
**(17):** \(TopItems\leftarrow\)take the first \(n\) items from \(SortScores\)
**(18):** **Return**\(TopItems\)
**End** ```
**Algorithm 1**Items Recommendation Algorithm
###### Abstract
Firstly, time domain and frequency domain features are compressed by stacked denoising autoencoder (SDAE). Through redefinition and combination of correlation, redundancy, and monotonicity, the sensitivity measurement standard \(Std_{DI}\) of DI is further proposed. The optimal features are selected as optimal DI set \(Opt_{DI}\). Then bearings DI in set \(Opt_{DI}\) and its corresponding RUL are taken as input and output variables of temporal convolution network quantile regression (TCNQR) model. The relationship between input variables and response variables under different quantiles is obtained, based on the above results, the probability density function of the predicted bearing RUL is obtained by KDE, which provides more abundant information than traditional point prediction results. The main contributions of this paper are summarized as follows:  
1. The original features are compressed and reconstructed based on the SDAE compression method to obtain low-dimensional representative features and retain sufficient information on the premise of not increasing the size of the neural network. 2. The redundancy, correlation, and monotonicity measures were incorporated into the DI selection criteria. The sensitivity standard of DI is redefined to further reduce the dimension of input feature variables on the premise of ensuring the efficiency of DI set. 3. Combining TCN with QR, a probability density prediction method based on TCNQR is proposed to obtain the predicted value of bearings RUL probability density, obtain more comprehensive and effective information of bearings degradation state, and further reflect the uncertainty of bearings RUL, so as to guide the equipment maintenance decision of actual production and manufacturing activities and avoid large errors and economic losses. The rest of the paper is organized as follows: Section 2 introduces the basic theory of SAE and QR. The detailed implementation process of the construction of bearings DI set and the TCNQR prediction model are presented in Section 3. The performance of the proposed method was verified using the motor rolling element bearings datasets from Xi'an Jiaotong University (Shaanxi, China) and compared with other methods in Section 4. Finally, conclusions are drawn in Section 5.
## 4 Discussion
### Impact of EVI and PAR on Photosynthesis  
A typical difference among the GPP models is the use of EVI. For example, EVI (a measure of canopy greenness) is selected as a proxy of fraction of photosynthetically active radiation (FPAR) in  
Figure 3: Seasonal variations of measured and estimated GPP for each site during the study period (2004–2005 for XLGL and DX; 2003–2005 for CBS, DHS, QYZ, XSBN, YC, and HB).  
the VPM and GR models for calculating photosynthetically active radiation absorbed (APAR) by the vegetation canopy. However, EVI is directly used as an indicator of greenness and canopy photosynthetic capacity [28, 29], such as in the AVM and TG models.  
We observe there is a good correlation between EVI and measured GPP; for example, the correlation coefficient (r) values are 0.82, 0.91, and 0.95 (P \(<\) 0.01) for croplands, grasslands, and MF, respectively, so EVI can be used for estimating GPP at grasslands and MF sites with high accuracy. However, r values between EVI and measured GPP at EBF and ENF are 0.41 and 0.55 (P \(<\) 0.01), respectively. Thus, more environmental factors (PAR, temperature, and water stress) should be considered as conditions for GPP estimation. On the other hand, the differences in r values for different land cover types suggest that EVI as a proxy of both LUE and FPAR is a good indicator for GPP estimation.  
There is a significant positive correlation between EVI and PAR for most land cover types except grasslands, indicating that PAR can be obtained from EVI for describing canopy photosynthetic capacity. At the same time, the variation in r value between PAR and measured GPP is higher than that between EVI and measured GPP at ENF sites, so a combination of PAR and EVI in the VPM model can produce a good estimation. As seen above, EVI can be a good indicator of GPP when there is a good correlation between EVI and GPP (r values \(>\) 0.9 for grasslands and MF), and PAR is more important when the correlation is not so good (ENF sites).
## 4 Opposite Normalized Trust-Region Reflective (ONTRR) Algorithm
### Step 3: Construct the Two-Dimensional Subspace \(S\)  
In Algorithm 3, the subspace computing function starts with the Cholesky factorization of \(\hat{M}\) in Equation (22). If \(\hat{M}\) is positive definite, the Cholesky factor \(R\) will be immediately obtained by QR decomposition. If the factorization fails, which means \(\hat{M}\) is not positive definite, a direction of negative curvature is usually available. In lines 4-7 of Algorithm 3, the modification for singular \(\hat{M}\) is repeated until the smallest diagonal eigenvalue is larger than eps\({}^{0.5}\), where \(\lambda\) is set to be 1 and speye returns a sparse identity matrix. With the aid of preconditioner \(R^{T}R\) and residual stopping tolerance stopTol, a PCG linear solver is carried out in lines 9-19 of Algorithm 3 to determine the inexact Newton direction or negative curvature direction by solving the local Newton system Equation (29) with no more than \(k_{max}=dim/2\) CG iterations. For constructing the 2-D subspace \(S\) via Gram-Schmidt orthogonality, the last two lines of Algorithm 3 are slightly different from the trdog function of MATLAB, where the second direction \(v_{2}\) of \(S\) is not always used unless its norm is larger than eps\({}^{0.5}\).
```
1 function\(S=\) SubSpace(\(C\), \(D\), \(J\), \(g\), dim)
2\(\%\) Determine the Cholesky factor of M = D*(\(J\)*)*D + C
3\(M\)s = [\(J\)*D; sqrt(\(C\))]; h = 1:dim; \(R=\) \(\mathbf{q}\)r(\(M\)s(\(\cdot\), h));
4\(R=\) R(h, h); \(\text{mid}\)diag = min(\(\mid\) diag(\(R\))\(\mid\)); \(\lambda=1\);
5 while\(\text{mid}\)\(\text{<}\) eps\({}^{0.5}\)do \(\%\) Modify for singularity
6\(M\)s = [\(J\)*D; sqrt(\(C\)) + \(\lambda\)*speye(dim)]; \(R=\) \(\mathbf{q}\)r(\(M\)s(\(\cdot\), h)); \(\text{\quad}\)\(\_\)R = R(h, h); \(\text{mid}\)\(\text{mid}\)\(\text{=}\) min(\(\mid\) diag(\(R\))\(\mid\)); \(\lambda=4\);
7\(\%\) PCG detect Newton step or negative curvature
9\(g\)_w = D*g; \(r=\) - \(g\)_w ;
10\(z=R\)(\(R\)'\(\backslash\)r); \(\text{stopTol}\) = norm(\(z\))/10;
11 for\(k=1\)to max(1, \(\text{floor}(\text{dim}/2)\))do
12\(w\) = (D*(\(J\)*))*D*z + C*z; \(\text{denom}=z\)*(\(w\);
13 if\(\text{denom}\leq 0\)then
14\(p\) = \(z\);  break;
15
16 else
17\(\alpha\) = \(r\)*z/denom; \(p\) = \(p+\alpha\)*z; \(r=r-\alpha\)*w;
18\(z=R\)\(\backslash\)(\(R\)'\(\backslash\)r); \(\%\) Precondition
19 if\(\text{norm}(z)\leq\text{stopTol}\)then \(\%\) Exit?
20\(\%\) Construct the 2-D subspace \(S\)
21\(v_{1}\) = \(p\)/norm(\(p\)); \(v_{2}\) = \(g\)_w_/norm(\(g\)*u); \(v_{2}\) = \(v_{2}\) = \(v_{1}\)*(\(v_{1}\)*\(v_{2}\));
22\(S\)(\(\cdot\), 1) = \(v_{1}\); \(S\)(\(\cdot\), 2) = \(v_{2}\);
23 return
```
**Algorithm 3**Construct the two-dimensional subspace \(S\)
## 7 Discussion and Open Questions
### Identified Gaps in Current DT-Based Validation Approaches  
Although the integration of SiL, HiL, and DT technologies offers a promising validation architecture for automotive lighting systems, several critical gaps remain. A key concern is the disconnect between theoretical requirements and their practical testability. Many requirements are defined at a conceptual level without being fully traceable by virtual or hybrid validation stages. This lack of end-to-end alignment creates ambiguity about what is being tested, how it is being validated, and with what level of confidence.  
Additionally, there are integration gaps between abstraction levels, particularly between L3 (test drives with DT) and L4 (full DT integration). In many cases, feedback loops are fragmented or manual, preventing real-time synchronization between the physical system and its digital counterpart. Without a continuous, bidirectional data exchange pipeline, predictive validation remains narrow in scope. Finally, validation often emphasizes functional coverage under standard operating conditions while neglecting edge-case scenarios or rare environmental conditions that are crucial for safety and robustness.  
Gate-based development processes (G10-G120) assume a sequential and coherent flow between design, integration, and validation phases. However, in real-world implementations, certain gates may be handled superficially or skipped entirely. For instance, if G60 (requirements traceability) is not thoroughly addressed before G80 (scenario-based testing), validation activities may be performed based on incomplete or unverified specifications. This misalignment between gates compromises the integrity of the testing process and risks passing systems that have not been fully evaluated.  
Most validation workflows focus on common operational conditions. Yet, lighting systems must also function reliably under rare or extreme conditions, such as  
* Reflective glare from wet roads;
* Dense fog that diffuses headlamp output;
* Snow occlusion on sensors;
* Interactions with vehicles having non-standard light signatures.  
Neglecting these conditions leaves the system vulnerable to failure in critical real-world situations. Digital twins offer an opportunity to simulate these edge cases with high fidelity. However, this requires a rich library of scenario models and advanced simulation capabilities that are often missing or underdeveloped.
## 4 Experiment Results and Discussion
### Confusion Matrix  
A confusion matrix was used to generalize the results of the classification method. For an unqualified number of observations in each class and more than two classes in the data sets, the correctness of the classification can be deceptive. Calculating the confusion matrix gives a clearer idea of what the classification model is doing right and where it is wrong. The two-class classification confusion matrix is shown in Table 5.  
To measure the performance of the model, we need to define a few measuring parameters, based on the multiclass classification confusion matrix. The performance evaluation was measured with the accuracy, precision, recall, \(F_{1}\) score and kappa statistical measures explained in Equations (11)-(15).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{11}\]  
Figure 8: Training and validation loss of MCMS-BCNN-Attention + Efficientnetv2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Class** & \\ \hline
**Actual Class** & **Positive** & **Negative** \\ \hline Positive & True positive (TP) & False negative (FN) \\ \hline Negative & false positive (FP) & True negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion Matrix explanation for binary classification.  
\[Precision=\frac{TP}{TP+FP} \tag{12}\]  
\[Recall=\frac{TP}{TP+FN} \tag{13}\]  
\[F_{\text{I}}Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{14}\]  
[PERSON]'s kappa coefficient (k) is [38]:  
\[k=\frac{P_{o}-P_{e}}{1-P_{e}} \tag{15}\]  
where \(P_{o}\) and \(P_{e}\) represent observed agreement and expected agreement, respectively. This measurement shows how much better the classifier's performance is compared to a balanced or unbalanced dataset.
## 2 Integration and Excitation of Plasmonic Structures
### Localized Surface Plasmons
#### 2.2.1 Coupling Efficiency  
Figure 5 shows the FDTD calculations of a TE mode in a dielectric waveguide evanescently coupled to an LSP chain. The (infinite) LSP chain is directly deposited on a SOI waveguide, and Figure 5b-d reveals the anticrossing of both dispersion curves which generates the resulting odd and even supermodes. The strong coupling between both waveguides, in addition to the low slope of the LSP chain curve, induces supermodes with very different indices and thus a very short coupling length. Depending on the wavelength, the fundamental TE mode of a SOI waveguide can be totally transferred in the 4 th or 5 th MNP of the finite chain [83], which corresponds to a \(\sim\)600 nm coupling length (Figure 5f). The coupling efficiency is near 99% in the case of a 5 MNP chain (Figure 5e). Due to a lower dielectric effective index, the optimal configuration in the case of a Si\({}_{3}\)N\({}_{4}\) waveguide at 633 nm enables a total transfer of the mode energy in an 8 MNP chain with a coupling length of \(\sim\)800 nm.  
In fact, the energy transfer efficiency is improved with respect to a single MNP case as soon as a second MNP is involved (\"dimer\" chain). In return, however, the chain length increase implies an increase in ohmic losses. In order to exploit the high electromagnetic power density at the surface of a nanoparticle, the best compromise must be thus identified between the efficient energy transfer induced by the collective resonance in the chain and the ohmic losses.
# Assessment of Several Approaches to Biofortified Products: A Literature Review
## 2 Materials and Methods
### The Complexity of Dimensions of the Biofortified Products
#### 3.2.4 Biofortification, Cultivation, Factors Influencing Agronomic Technical Choices  
The international scientific community has intensified its efforts to promote development and dissemination of biofortified crops. According to an estimate conducted by HarvestPlus in 2017 (see Figure 2), there are currently more than 150 biofortified varieties, bundled in 12 crops, that have been authorized in 30 countries [70; 71; 72; 73].  
Biofortification of staple crops can be achieved through three different approaches, transgenic, conventional, and agronomic, based on biotechnology, breeding, and the use of mineral fertilizers, respectively [2; 74]. Regardless of the method used, only with a multidisciplinary approach is it possible to \"measure the nutritional value of biofortified products such as bio-accessibility and bio-availability [75; 76].  
Figure 2: In purple, crops currently released commercially. In lilac, crops being tested [70].  
In Figure 3, as highlighted by [PERSON]; it can be seen that several biofortified crops, including wheat, rice, maize, potato, and tomato were obtained through the application of all three strategies listed above [2; 77].  
The current results in genomic biofortification are certainly useful for the development of biofortified products as a sustainable solution to the problem of \"hidden hunger,\" to solve the lack of micronutrients, thus serving for human health and as an economic saver [77; 78; 79; 80; 81; 82].  
Several biofortified crops have been developed with very encouraging results to counteract vitamin deficiency [53; 76; 83].  
In this context, the new perspective of biofortification is that of \"tailor-made plant nutrition\" to obtain products for specific population groups, so-called \"tailored foods.\" In order to achieve this objective, the most suitable cultivation systems seem to be those without soil, because they allow a more appropriate management of plant nutrition, regulating the presence of the main nutrients in the plants by regulating the presence of the main mineral elements (calcium, iodine, zinc, selenium, silicon, iron, silicon, iron, etc.,) which are beneficial to human health or which may sometimes be harmful to consumers with specific metabolic alterations [75; 84; 85; 86].  
The major problems associated with the development of biofortified transgenic crops are the long and complicated breeding cycles and/or the lack of genetic variability in the germplasm, which make the traditional breeding approach time-consuming, expensive, and sometimes impractical [26; 74]. On the other hand, the public negatively perceives them and the slow regulatory processes are required for their approval.  
To date, however, many crops produced through such approaches are subject to the same legal and social constraints as conventional GMO varieties [1; 87].
# Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO
## 2 Methods
### Parametric Dynamics Equation of Exoskeleton
The physical parameters \(I_{t}\), \(I_{s}\), \(m_{t}\), \(m_{s}\), \(L_{t}\), \(L_{s}\), \(L_{Gt}\), \(L_{Gs}\), \(h_{Gt}\), \(h_{Gs}\) in Equation (2) were parameterized into a set of inertial  
Figure 2: Simplified model of APAL’s lower extremity. parameters \(M_{xt}\), \(M_{xs}\), \(M_{yt}\), \(M_{ys}\), \(I_{t}\), \(J_{s}\) that are capable of fully expressing the dynamic characteristics and independent of each other [16; 22; 23; 40]. Equation (3) is linear for the parameters to-be-identified. \[ \begin{array}{l}T_{1}=[J_{t}+2L_{t}(M_{xs}\cos q_{2}-M_{ys}\sin q_{2})]\ddot{q }_{1}+[J_{s}+L_{t}(M_{xs}\cos q_{2}-\\ M_{ys}\sin q_{2})]\ddot{q}_{2}+L_{t}(M_{xs}\sin q_{2}+M_{ys}\cos q_{2})[\dot{q}_ {1}^{2}-(\dot{q}_{1}+\dot{q}_{2})^{2}]+\\ g[M_{xt}\sin q_{1}+M_{yt}\cos q_{1}+M_{xs}\sin(q_{1}+q_{2})+M_{ys}\cos(q_{1}+q_{ 2})]+\\ f_{c1}\text{sgn}(\dot{q}_{1})+f_{c1}\dot{q}_{1}\\ T_{2}=[J_{s}+L_{t}(M_{xs}\cos q_{2}-M_{ys}\sin q_{2})]\ddot{q}_{1}+J_{s}\ddot{q }_{2}+L_{t}(M_{xs}\sin q_{2}+\\ M_{ys}\cos q_{2})\dot{q}_{1}^{2}+g[M_{xs}\sin(q_{1}+q_{2})+M_{ys}\cos(q_{1}+q_{ 2})]+\\ f_{c2}\text{sgn}(\dot{q}_{2})+f_{v2}\ddot{q}_{2}\end{array}  \tag{3}\]  
where  
\[ \begin{array}{l}M_{xt}=m_{t}(L_{t}-L_{Gt})+m_{s}L_{t}\\ M_{yt}=m_{t}h_{Gt}\\ M_{xs}=m_{s}(L_{s}-L_{Gs})\\ M_{ys}=m_{s}h_{Gs}\\ J_{t}=I_{s}+m_{s}[(L_{s}-L_{Gs})^{2}-h_{Gs}^{2}]+I_{t}+m_{t}[(L_{t}-L_{Gt})^{2 }-h_{Gt}^{2}]+m_{s}L_{t}^{2}\\ J_{s}=I_{s}+m_{s}[(L_{s}-L_{Gs})^{2}-h_{Gs}^{2}]\end{array} \]  
Equation (3) is the linearization dynamic equation of APAL, where \(M_{xt}\), \(M_{xs}\), \(M_{yt}\), \(M_{ys}\), \(J_{t}\), \(J_{s}\), \(f_{c1}\), \(f_{c2}\), \(f_{v1}\), \(f_{v2}\) are the parameters to-be-identified.
# Cooperative Opportunistic Pressure Based Routing for Underwater Wireless Sensor Networks
## 3 Our Proposed Work
### Forwarding Set Selection  
The mechanism of a forwarder set is same for all the schemes (Improved Hydrocast, Co-Hydrocast, and Co-Improved Hydrocast). In the proposed schemes, sensor nodes are assumed static [21; 22], all nodes find their neighbors and sub neighbors, and calculate their Euclidean distance with neighbors. Suppose that a source node has a set of neighbors \(\Gamma_{k}\) in which \(k\) neighbors are sorted based on their progress towards the destination and the packet delivery probability [23]. The priority is given to nodes that are closed to the sink with at least one neighbor as shown in Figure 3 that source node \(S  
on the basis of low pressure than \(S\). Where \(d_{n_{i}}^{p}\) is the \(n_{i}\) neighbor progress towards the destination (in meters), and \(p_{n_{i}}\) is the \(n_{i}\) neighbor packet delivery probability. The neighbor with the highest priority becomes the next hop forwarder and if it fails to transmit the data packet, then a next neighbor with high priority contributes to deliver the data \(d_{n_{i+1}}^{p}p_{n_{i+1}}(1-p_{n_{i+1}})\). The average holding time (AHT) for a candidate node is calculated as given in Equation (4).  
\[AHT=\frac{W_{f}\times H_{t}}{\textit{speed}\times PL} \tag{4}\]  
\(W_{f}\) is computed according to the Equation (5).  
\[W_{f}=P_{\textit{level}}\times n_{\textit{neigh}} \tag{5}\]  
where \(H_{t}\) is a holding time, _speed_ is the acoustic channel speed (1500 m/s), \(W_{f}\) denotes the weight function, \(P_{\textit{level}}\) represents a pressure level of a node, and \(PL\) is a path loss between the source and the destination.  
When a node transmits a data packet, all neighbors those receive the data packet, set a holding time according to Equation (4). A priority is given to a neighbor node which is not a void node and has a less holding time. The selected forwarder node transmits the received data packet, if the received data packet SNR is not within a predefined threshold then nodes start cooperation that is discussed in Section 3.5. A node with the second highest priority acts as a relay node in the cooperation process which is discussed in Section 3.4. Figure 5 shows the forwarding mechanism, if a source node is itself a void node, or all lower pressure level neighbor nodes are void nodes, then the transmission shifts to recovery mode and the source node selects a higher pressure level neighbor as the next hop forwarder, as shown in Figure 5 (recovery mode is discussed in Section 3.2).  
Figure 4: Next hop forwarder selection.  
Figure 5: Forwarding set selection.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 3 Results
### Data Inputs
#### 3.5.2 Weather and Climate Data  
In addition to other data sources, 25 studies incorporated weather or climate data into their calculations. Specific data inputs included outdoor air temperature [14; 15; 18; 19; 25; 28; 46; 52], climate classifications [24], and weather trends [10; 13; 22; 31; 33; 35; 38; 40; 51; 55; 58; 59; 62; 66] relevant to the area under study. These inputs were used to predict building loads specifically in relation to outdoor temperatures. This is not surprising because higher air conditioning (A/C) demand is expected during warmer months and heating demand is higher in winter months, contributing to increased flexibility potential at these times of the year.  
Figure 2: Venn diagram of sectors analyzed across studies reviewed, including residential, commercial, industrial, and studies that focused on more than one sector.