esa-sceva/satcom-chunk-collection huggingface dataset:
# Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
## 3 Proposed Method
### Local Domain Relational Convolution  
We construct a spherical neighborhood for the points after edge convolution and take the sampling point \(x_{i}\) as the center point and \(x_{j}\in N(x_{i})\) as the other points in the  
Figure 4: Extraction of feature center point in edge convolution.  
neighborhood. Our aim is to learn the potential relational information between points in this neighborhood. It can be learned by the following convolutional operation formula:  
\[f_{p}=\sigma(\varphi(\{T(fx_{i})\})), \tag{4}\]  
where \(f\) is the feature vector and \(x\) is the 3D point.  
We first transform the features of all \(x\) points by the function T, and then aggregate them by the function \(\varphi\) to obtain \(f_{p}\). We choose max pooling as the aggregation function, and use the nonlinear activation factor \(\sigma\). The nonlinear activation factor \(\sigma\) is chosen as the ReLU function.  
In a 3D spatial neighborhood, the geometric relation between \(x_{i}\) and its neighbors is an explicit expression about the spatial layout of points, which further discriminatively reflects the underlying shape. To capture the relationship, learnable weight \(w_{i}\) is replaced by \(w_{ij}\), which can be defined as \(M(h_{ij})\). The effect of \(M\) is to abstract the expression of the high-level relationship between two points so as to encode their spatial layout. \(M\) uses a Multi-Layer Perceptron; \(r_{ij}\) is the relationship between two points, which can be defined as the Euclidean distance; and finally, \(f_{p}\) can be expressed as  
\[f_{p}=\sigma\big{(}\varphi\big{(}\{M(r_{ij})\cdot f_{x_{i}}\}\big{)}\big{)}, \tag{5}\]  
According to Equation (5), the convolution aggregates the relationships between all points within the neighborhood and the central point, thereby enabling inferential understanding of the spatial layout of points and generating effective shape perception.
## 4 Experiments and Results
### Ablation Study  
To systematically validate the contributions of individual modules in the proposed framework, an ablation study was performed on the Kvasir dataset. The quantitative results are shown in Table 3, where the symbol \"\(\times\)\" indicates the cancellation of the module and \"\(\surd\)\" indicates the activation of the module. The visualization results of the ablation study are shown in Figure 6. From Table 3, it can be seen that the integration of the BMFM improved the baseline model's mIoU from 0.8392 to 0.8455 (+0.63%), mDice from 0.8955 to 0.9004 (+0.49%), and recall from 0.9061 to 0.9207 (+1.84%). These enhancements demonstrate the ability of BMFM to aggregate multi-scale spatial features, which is particularly important for maintaining fine-grained object boundaries in complex segmentation scenes. For example, the hierarchical feature fusion mechanism of this module improves the accuracy of boundary localization by adaptively weighting low-level texture details and high-level semantic context.  
The BFRM further improved segmentation performance, with an mIoU of 0.8623 (an increase of 2.31% compared to baseline) and F2 score of 0.9127 (an increase of 2.93%). This improvement can be attributed to the dual channel attention mechanism of BFRM, which dynamically prioritizes channel dependence and spatial feature distribu  
Figure 5: Qualitative results of different methods.  
tion. Specifically, the module amplifies discriminative features through learnable spatial channel recalibration, thereby reducing false positives in low contrast areas such as subtle lesion edges.  
The FNRM achieved the most significant performance improvement, with a peak mDice of 0.9204 (an increase of 2.49% compared to baseline) and a recall rate of 0.9323 (an increase of 2.62%). This indicates that the module effectively reduces interference such as bubble noise, enhances and adjusts various aspects of feature processing, expands the perception range, and thus improves the performance of the model in segmentation tasks.  
The complete model integrating BMFM, BFRM, and FNRM achieves state-of-the-art performance, with an mIoU of 0.8655 and an F2 score of 0.9251, which are 3.2% and 3.5% higher than some configurations, respectively. The hierarchical integration of BMFM, BFRM, and FNRM establishes a balanced framework while optimizing local texture preservation and global semantic consistency.
## 6 Application of the Optimization Techniques: Routing Protocols
### Geographical Routing Protocols  
These algorithms take advantage of the location information to make routing techniques more efficient. Specifically, neighbors exchange information about their location so when a node needs to forward a packet, it sends it to the neighbor which is assumed to be closest to the final destination. To operate, the source inserts the destination's coordinates in the packets. The location information used in geographical algorithms can be derived from specific devices such as GPS or it can be modeled by virtual coordinates [14].  
Concerning geographical protocols, geocasting is the process by which a packet is delivered to the nodes placed in an area. This primitive is especially suitable in wireless sensor networks since the sink usually demands information from the nodes that are in a zone. The zone can be statically determined by the source node or it can be constructed dynamically by the relaying nodes in order to avoid some nodes that may cause a detour.  
On the other hand, in geographic-based rendezvous mechanisms, geographical locations are used as a rendezvous place for providers and seekers of information. Geographic-based rendezvous mechanisms can be used as an efficient means for service location and resource discovery, in addition to data dissemination and access in wireless sensor networks [15]. The most popular forwarding techniques in geographical routing protocols are:
# Passive Sampling and Analysis of Naphthalene
## Experiments
### Verification of TWA-SPME Method for Naphthalene Quantification in Vehicle Exhaust Gas  
A sampling of naphthalene in the diesel exhaust gases was carried out in Iowa on a 2005 Ford F-250 XL Super Duty pickup truck with \(\sim\)139,200 km mileage and a John Deere 5830 forage harvester, \(\sim\)30 years old. Exhaust gas sampling setup consisted of a 102 mm o.d. and 1 m length galvanized steel pipe with 7 gas sampling ports equipped with Thermogreen septa, three of them for simultaneous TWA SPME sampling with three fibers, two of them for controlling the contribution of metallic surfaces, one for gas collecting gas into Tedlar gas sampling bags, and the last one for measuring temperature of exhaust gases with a digital thermometer. Gas TWA SPME sampling time was set to 8 h and 100 \(\upmu\)m PDMS fibers were used. Retraction depth was \(Z\) = 10 mm (Figure S1).  
Tedlar bags, pumps, and direct injection were used for side-by-side comparisons with retracted SPME approach. Exhaust gases were sampled into 5-L Tedlar bags (Fisher Scientific, Tustin, CA, USA) using air sampling pump (Universal PCXR8, SKC Inc., Pittsburgh, PA, USA) with a set flow rate of 100 mL-min\({}^{-1}\). Model M-30 Mini Buck calibrator (A.P. Buck, Inc., Orlando, FL, USA) was used for controlling and confirming the gas sampling flow rate. A 10 \(\upmu\)L gastight microsyringe was used for direct injections of sampled gas. One \(\upmu\)L of gas from Tedlar bags was injected into GC at the same parameters as described in Section 3.3 for comparison with SPME-based measurements.
## 2 Notations and Previous Results
### Basic definitions and notations  
All graphs in this work are undirected and finite. A graph is denoted \(G=(V,E)\), with \(n=|V|\), and \(m=|E|\). The subgraph of \(G\) induced by the subset \(A\) of vertices is denoted \(G(A)\). The _neighborhood_ of a vertex \(x\) in \(G\) is denoted \(N_{G}(x)\). The _closed neighborhood_ is \(N_{G}[x]=\{x\}\cup N_{G}(x)\). The neighborhood of a set of vertices \(A\) is \(N_{G}(A)=\cup_{x\in A}N_{G}(x)-A\), and \(N_{G}[A]=A\cup N_{G}(A)\) (we will omit subscript \(G\) when the graph we work on is clear from the context.)  
A _clique_ is a set of pairwise adjacent vertices. A _module_ is a subset \(X\) of vertices which share the same external neighborhood: \(\forall x,y\in X,N(x)-X=N(y)-X\).  
A graph is _connected_ if for any pair \(\{x,y\}\) of vertices, there is a path from \(x\) to \(y\). When a graph is not connected, the maximal connected subgraphs are called the _connected components_ of the graph.  
An ordering \(\alpha\) on the set \(V\) of vertices is a one-to-one mapping from \(\{1,2,...,n\}\) to \(V\). In every figure of this paper showing an ordering \(\alpha\) on \(V\), a vertex \(x\) will be named by its number \(\alpha^{-1}(x)\). To simplify notations, we will sometimes refer to vertex \(\alpha(1)\) as simply \(1\).  
Throughout, for any integers \(i<j\), \([i,j]\) will denote the set of integers \(\{i,i+1,..,j\}\).
# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 2 Related Work
### HSVCM Features  
Moments are well known for their application in image analysis since they can be used to derive invariants concerning specific transformation classes. Color moments are scaling and rotation invariant. It is usually the case that only the first three-color moments are used as features in image retrieval applications, as most of the color distribution information is contained in the lower-order moments [38]. The color moment that symbolizes the global color details can be derived from any color space. The \((p+q)\) order moments of a given image \(f(x,y)\) with the size of \(M\times N\) can be defined by:  
\[m_{pq}=\sum_{x=1}^{M}\sum_{y=1}^{N}x^{p}y^{d}f(x,y) \tag{7}\]  
Based on the zeroth-order moment of the image, the average value, standard deviation, and skewness from each HSV component are calculated. The color moment generates a nine-dimensional feature vector.  
\[Mean_{cm}=\frac{1}{M\times N}\sum_{\alpha-1}^{M}\sum_{y-1}^{N}F_{cc}(x,y),cc= \{H,S,V\} \tag{8}\]  
\[Standard\ Deviation_{cm}=\left[\frac{1}{M\times N}\sum_{x-1}^{M}\sum_{y-1}^{N} F_{cc}(x,y)^{2}\right]^{\frac{1}{2}},cc=\{H,S,V\} \tag{9}\]  
\[Skewness_{cm}=\left[\frac{1}{M\times N}\sum_{\alpha-1}^{M}\sum_{y-1}^{N}F_{ cc}(x,y)^{3}\right]^{\frac{1}{3}},cc=\{H,S,V\} \tag{10}\]  
where cm holds the moments of each color channel of an image. \(M\) and \(N\) are the rows and column sizes of an image. \(Fcc(i,j)\) specifies the pixel intensity of the \(i\)th row and \(j\)th column of the fastidious color channel.
## 4 Experimental Analysis and Results
### Implementation of Metaheuristic GA-Based Feature Selection
#### 4.6.2 Results of the Arabic Sentiment  
The first scenario is studying the sentiment of users' opinions about the COVID-19 topic in which two cases (positive or negative) are only considered, as explored in Table 8. In addition, we have extended the result section with experimentation by augmenting the data using back translation with a transfer learning model. Furthermore, we have applied the proposed model for the sentiment class, as seen in Table 9. We can notice that the classifiers of the proposed approach have better results with augmented data.  
Here, a different number of models are trained to serve as sentiment classifiers. The evaluation outcome for the trained models is summarized in Table 8 for original data and Table 9 for augmented data. The results with bold font represent the highest achieved F1 scores. With and without using the genetic algorithm for feature selection and two feature vectors, we implemented the LR, RFC, and ETC. Additionally, we used the unigram features to improve the outcome. According to the obtained results for original data, the ETC is the superlative successful model, but RFC was the best with augmented data. The best F1-score result of the sentiment scenario using the ETC with feature selection is 77.38% and 77.12% without feature selection. In addition, the RFC was 86.51%. The F1-score of the  
Figure 8: Density distribution of the word counts and unique word counts of the stance data texts: (**a,c**) are the density distribution of the word counts and unique word counts of the training set regarding the stance classes; (**b,d**) are the density distribution of the word counts and unique word counts of the training and test sets.  
proposed GA-based ensemble ETC model for original data is better than the F1-score of the LRC and RFC by 2.28% and 0.29%, respectively.
## 3 Results  
### Surface Quality  
In Figure 2, the number of particles counted on the samples cleaned with A-CP1 to B-CP4 is a logarithmic scale. The highest particle count occurred for standard automatic cleaning using only 40 kHz ultrasonic (B-CP1). For example, a dramatically high number of about 227 particles with a size between 1 and 5 \(\upmu\)m were counted and even 576 particles were detected with a size between 100 and 200 nm. Applied additional 80 and 120 kHz (B-CP2) instead of only 40 kHz brings down the particle count to 48 and 109 for particle classes 1 to 5 \(\upmu\)m and 100 to 200 nm, respectively. In both cases, particles greater than 5 \(\upmu\)m were detected with an amount of 6 and 4, respectively. For high-end optical applications, these numbers are still unacceptably high. A drastic reduction in remaining particles down to roughly 10 particles on the surface is obtained for longer dwell time (B-CP3), an additional application of 500 kHz ultrasonic during standard rinsing (B-CP4), or quick dump rinse (A-CP1). In these cases, no particles greater than 5 \(\upmu\)m were detected. In all cases, no prediction can be made on particles smaller than 83 nm, but it is intuitively clear that such particles exist.  
Whereas the particle density strongly depends on the applied cleaning strategy, the RMS surface roughness seems to be independent of the cleaning process. Figure 3 shows the white light interferometer scans (scan size: 0.53 mm \(\times\) 0.71 mm) of the surface prior to cleaning and after cleaning (B-CP1) as well as after coating (B-CP2). As can be seen, the as-polished sample's surface provides a high quality and random polishing structure with an RMS roughness of about 2.3 A. No scratches or polishing traces are visible in the interferogram. The mean value of the RMS roughness of the as-polished samples is 2.7 A. After cleaning and coating, the random structure and RMS roughness remain almost unchanged with 3.2 A. Table 2 shows the RMS roughness measurements for the cleaning of set A and set B.  
Figure 2: Particle counts for cleaning processes A-CP1 to B-CP4 in a logarithmic scale.
## 7 Overall Impacts of the Decision Support Workflow  
The impact of the decision support mechanism presented above was also assessed by measurements both in the mini-factory and also in the broader context of our whole manufacturing facility. The main findings, related to the operation of the mini-factory, are the following:  
* Daily inventories of parts and semi-finished products at the lines--the so-called Work-in-Process--decreased from an average level of 3000 pieces to 1000 pieces. Note that this did not render the increase of some in-line buffers unnecessary, since these served to accommodate extra demand not rare in HMLV production. Along with lower inventories, the lead time on these lines was reduced from 2.5-3 days to 1.6-2 days (see Section 4.2).
* In the meantime, one of the machining centers was replaced with a newer, more reliable machine. The rough-cut sensitivity analysis already suggested that such a change could have a substantial impact on delivery performance (see the tornado diagram in Figure 9), and this was what actually happened: according to our measurements, the delivery performance improved by 10%.  
Our systematic efforts to find the root causes behind delivery performance issues and implementing changes to improve its reliability had beneficial factory-wide effects as well:  
* Initiating a survey of key factors influencing CDD performance (see Section 5.1 and Figure 7) alone came with an overall improvement of the main KPIs in the range of 3-5%. This is because the survey made our decision-making mechanism more transparent, opened new channels of communication, and everyone in its place became more aware of the other colleagues' ends and means when responding to challenges of HMLV production.
* Strikingly, the reliability of planning improved: in particular, the deviation between the planned and the realized lead times decreased by ca. 30%. This was partly due to assessing the key impact factors and making the planners conscious of them. Moreover, lessons learned in the mini-factory about the use of specific sequencing and lot-sizing policies could also be transferred to other segments of production (which were also managed by the same personnel).  
For the time being, the biggest threats to stable delivery performance are unexpected machine breakdowns and, in the recent era of the COVID-19 pandemic, unprecedented material shortages. In these cases, re-balancing the production can only be achieved with overtime. Anticipating and preventing such situations would call for some novel forecasting, inventory handling, and supplier managing policies whose impact on delivery performance could also be assessed in the proposed framework.
## 3 Probabilistic Forecasting of ALSC
### The Basic Theories of Lhs
#### 3.3.2 The Permutation  
For a probabilistic problem, the random input variables often have certain correlations. However, the correlations between random variables after sampling are uncontrollable, which can negatively impact the results. Therefore, it is necessary to reorder the sampling values to adjust the correlations to the expected level. There are several permutation methods, such as ranked Gram-Schmidt orthogonalization [37], simulated annealing [38], the genetic algorithm [39], and Cholesky decomposition [40]. Among them, Cholesky decomposition is the most popular method due to its efficiency and accuracy.  
The detailed computational steps of Cholesky decomposition method for permutation are as follows.  
1. According to the procedure of sampling, obtain the initial sampling matrix \(S\);
2. Randomly permute 1 to \(N\) in every row to obtain an \(M\times N\) ranking matrix \(R\);
3. Calculate the Spearman rank correlation coefficient matrix \(\rho_{R}\) of \(R\) as shown in (35) and (36) \[\rho_{R}=\begin{bmatrix}1&\rho_{12}&\dots&\rho_{1M}\\ \rho_{21}&1&\dots&\rho_{2M}\\ \vdots&\vdots&\ddots&\vdots\\ \rho_{M1}&\rho_{M2}&\dots&1\end{bmatrix}\] (35) \[\rho_{ij}=\frac{\text{cov}(R_{i},R_{j})}{\sigma_{i}\sigma_{j}}=1-\frac{6 \sum\limits_{n=1}^{N}\left(r_{in}-r_{jn}\right)^{2}}{N(N-1)(N+1)}\] (36) where \(\rho_{ij}\) denotes the Spearman rank correlation coefficient between the \(i\)th row and the \(j\)th row of \(R\); \(\text{cov}(R_{i},R_{j})\) denotes the covariance between the \(i\)th row and the \(j\)th row of \(R\); \(\sigma_{i}\) and \(\sigma_{j}\) are the standard deviations of the \(i\)th row and the \(j\)th row of \(R\), respectively; \(r_{in}\) represents the \(n\)th element in the \(i\)th row of \(R\); and \(r_{jn}\) represents the \(n\)th element in the \(j\)th row of \(R\);
4. Perform Cholesky decomposition on \(\rho_{R}\) as shown in (37) \[\rho_{R}=DD^{T}\] (37) where \(D\) is the lower triangular matrix from the decomposition.
5. Multiply the inverse of \(D\) with \(R\), and then obtain an identity correlation matrix \(G\) as follows: \[G=D^{-1}R\] (38) (6) Conduct Cholesky decomposition on the expected Spearman rank correlation coefficient matrix \(\rho_{\text{exp}}\) of random input variables as follows: \[\rho_{\text{exp}}=D_{\text{exp}}D_{\text{exp}}^{T}\] (39) (7) Multiply \(G\) to \(D_{\text{exp}}\), then obtain a matrix \(G_{\text{new}}\) whose Spearman rank correlation coefficient is approximately equal to the \(\rho_{\text{exp}}\) as follows:  
Figure 3: Schematic diagram of sampling process of LHS.  
\[G_{\text{new}}=D_{\text{exp}}G \tag{40}\]
8. According to the amplitude order of elements in each row of matrix \(G\), reorder the corresponding rows of \(R\) to obtain a new permutation matrix \(R_{\text{new}}\);
9. According to the order in each row of \(R_{\text{new}}\), reorder the rows of the initial sampling matrix \(S\) to obtain the final sampling matrix \(S_{\text{new}}\).
## 3 Results and Discussion
### Linear Ordering Analysis for Building the Quality Ranking of Table Grapes
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**Ranking No. 1**} & \multicolumn{3}{c}{**Ranking No. 2**} \\  & \multicolumn{2}{c}{**T, Q, M, R and TPC Values**} & \multicolumn{3}{c}{**L*a*h Values**} \\ \hline \multirow{2}{*}{**Grape Variety**} & \multirow{2}{*}{**Rank**} & \multirow{2}{*}{**Rank***} & \multicolumn{2}{c}{**AM**} \\  & & & **(Mean Value)** & & **Rank*** & **(Mean Value)** \\ \hline Autumn Royal & 4 & 1 & 0.595 & 23 & 3 & 0.811 \\ Autumn Royal & 9 & 1 & 0.559 & 10 & 1 & 0.852 \\ Autumn Royal & 3 & 1 & 0.597 & 6 & 1 & 0.862 \\ Autumn Royal & 6 & 1 & 0.571 & 18 & 2 & 0.837 \\ Autumn Royal & 2 & 1 & 0.606 & 12 & 2 & 0.847 \\ Autumn Royal & 5 & 1 & 0.578 & 17 & 2 & 0.838 \\ Autumn Royal & 10 & 1 & 0.530 & 13 & 2 & 0.846 \\ Autumn Royal & 7 & 1 & 0.569 & 5 & 1 & 0.863 \\ Autumn Royal & 8 & 1 & 0.563 & 20 & 2 & 0.822 \\ Autumn Royal & 1 & 1 & 0.627 & 2 & 1 & 0.880 \\ Black Magic & 29 & 3 & 0.372 & 4 & 1 & 0.864 \\ Black Magic & 17 & 3 & 0.399 & 7 & 1 & 0.858 \\ Black Magic & 28 & 3 & 0.390 & 15 & 2 & 0.841 \\ Black Magic & 30 & 4 & 0.368 & 1 & 1 & 0.886 \\ Black Magic & 26 & 3 & 0.377 & 8 & 1 & 0.858 \\ Black Magic & 25 & 3 & 0.371 & 3 & 1 & 0.875 \\ Black Magic & 27 & 3 & 0.376 & 14 & 2 & 0.845 \\ Black Magic & 23 & 3 & 0.398 & 11 & 2 & 0.847 \\ Black Magic & 24 & 3 & 0.391 & 16 & 2 & 0.841 \\ Black Magic & 11 & 3 & 0.421 & 9 & 1 & 0.854 \\ Palieri & 21 & 2 & 0.453 & 22 & 3 & 0.813 \\ Palieri & 16 & 2 & 0.458 & 29 & 3 & 0.785 \\ Palieri & 15 & 2 & 0.458 & 30 & 3 & 0.778 \\ Palieri & 12 & 2 & 0.469 & 24 & 3 & 0.804 \\ Palieri & 14 & 2 & 0.455 & 27 & 3 & 0.794 \\ Palieri & 13 & 2 & 0.460 & 25 & 3 & 0.800 \\ Palieri & 20 & 2 & 0.453 & 26 & 3 & 0.799 \\ Palieri & 22 & 2 & 0.452 & 28 & 3 & 0.789 \\ Palieri & 18 & 2 & 0.451 & 21 & 3 & 0.821 \\ Palieri & 19 & 2 & 0.452 & 19 & 2 & 0.822 \\ \hline \end{tabular}
\end{table} 
Table 4: Results of linear ordering analysis for the quality assessment of table grapes, based on two set of variables: Taxifolin (T), quercetin (Q), myricetin (M), rutin (R) and Total polyphenol content (TPC) as well as Lightness (L*), red (a*) and hue angle (h). AM—Aggregation measure. for scoring the table grape quality. The method could be used for food product promotion such as grapes on the market. The ranking built on color coordinates will show what position of table grapes is in the set of other grape varieties. This could eliminate the extraction and hydrolysis methods used to test phenolic content of fruit and could replace the cost and time-consuming analytical techniques of phenolic determination with simple and quick color measurements to get the knowledge on the phenolic content.