esa-sceva/satcom-chunk-collection huggingface dataset:
# Minimizing Maximum Feature Space Deviation for Visible-Infrared Person Re-Identification
## 3 Proposed Method
### Feature Space Deviation Modeling  
Let \(\{x_{k,i}|x_{k,i}\in\mathbb{R}^{d\times 1},k\in 1,2,\cdots,K,i=1,2,\cdots,N\}\) and \(\{y_{k,i}|y_{k,i}\in\mathbb{R}^{d\times 1},\)\(k\in 1,2,\cdots,K,i=1,2,\cdots,N\}\) denote features extracted from visible and infrared images, respectively, where \(K\) is the number of classes in the mini-batch, and \(N\) is the number of images in each class, and \(d\) represents the number of feature dimensions. The feature space deviation modeling progress is described as follows.  
(1) Calculating visible and infrared centers of each class to construct the visible feature space \(\mathcal{X}\) and the infrared feature space \(\mathcal{Y}\) as follows.  
\[\mathcal{X}=[\overline{x}_{1},\overline{x}_{2},\ldots,\overline{x}_{k},\ldots,\overline{x}_{K}]^{\mathrm{T}}\in\mathbb{R}^{K\times d}, \tag{1}\]  
\[\mathcal{Y}=[\overline{y}_{1},\overline{y}_{2},\ldots,\overline{y}_{k},\ldots,\overline{y}_{K}]^{\mathrm{T}}\in\mathbb{R}^{K\times d}, \tag{2}\]  
where  
\[\overline{x}_{k}=\frac{1}{N}\sum\limits_{i=1}^{N}x_{k,i}\in\mathbb{R}^{1\times d}, \tag{3}\]  
\[\overline{y}_{k}=\frac{1}{N}\sum\limits_{i=1}^{N}y_{k,i}\in\mathbb{R}^{1\times d}. \tag{4}\]  
(2) Calculating distances of visible and infrared feature spaces on each single dimension as deviations. Let us re-symbolize the visible feature space \(\mathcal{X}\) and the infrared feature space \(\mathcal{Y}\) as follows.  
\[\mathcal{X}=[p_{1},p_{2},\ldots,p_{j},\ldots,p_{d}]\in\mathbb{R}^{K\times d}, \tag{5}\]  
\[\mathcal{Y}=[q_{1},q_{2},\ldots,q_{j},\ldots,q_{d}]\in\mathbb{R}^{K\times d}, \tag{6}\]where \(p_{j}\in\mathbb{R}^{K\times 1}\) and \(q_{j}\in\mathbb{R}^{K\times 1}\) represent the column vectors of \(\mathcal{X}\) and \(\mathcal{Y}\), respectively. Then, the distance of visible and infrared feature spaces on each single dimension defined as a feature space deviation as follows.  
\[d_{j}=\big{\|}\hat{p}_{j}-\hat{q}_{j}\big{\|}_{2}=\sqrt{\sum_{k=1}^{K}{(\hat{p} _{j,k}-\hat{q}_{j,k})^{2}}}, \tag{7}\]  
(3) Designing a minimizing maximum feature space deviation (MMFSD) loss function. The MMFSD loss function is calculated as follows:  
\[\mathcal{L}_{MMFSD}=\max_{k\in[1,2,\ldots,d]}\|\hat{p}_{j}-\hat{q}_{j}\|_{2}, \tag{8}\]  
where \(\hat{p}_{j}=\frac{\hat{p}_{j}}{\sqrt{\sum\limits_{k=1}^{K}{\hat{p}_{j,k}^{2}} }}\) and \(\hat{q}_{j}=\frac{\hat{q}_{j}}{\sqrt{\sum\limits_{k=1}^{K}{\hat{q}_{j,k}^{2}} }}\) are L2 normalized features of \(p_{j}\) and \(q_{j}\), respectively. Based on the MMFSD loss function, distance of the corresponding column vectors in the cross-modal feature spaces is narrowed, so that the maximum feature space deviation is optimized.
# MFHE: Multi-View Fusion-Based Heterogeneous Information Network Embedding
## 5 Experiment
### Comparative Experiment
#### 5.3.2 Node Clustering Experiment  
In the task, we evaluate the node clustering effect with NMI and ARI. KMeans algorithm is used for node clustering experiments. The results of node clustering are shown in Table 5. All experimental results are the average values obtained after five experiments on each model. Figure 7 more intuitively show the advantages of MFHE in the node clustering task.  
By observing the experimental results, we have the following findings:  
1. MFHE performs best on ACM and DBLP datasets for node clustering, with 1.34% and 1.97% higher clustering performance, respectively.
2. On the ACM dataset, MFHE clusters best, followed by GAT algorithm, and GraphSAGE is the least effective. Both GraphSAGE and GAT convert HIN into multiple homogeneous networks experiments. However, GraphSAGE cannot capture the  
\begin{table}
\begin{tabular}{c|c|c|c|c|c|c|c} \hline
**Datasets** & **Metrics** & **DeepWalk** & **HERec** & **GraphSAGE** & **GAT** & **Mp2 Vec** & **MFHE** \\ \hline \multirow{2}{*}{ACM} & NMI & 39.85 & 45.61 & 28.91 & 55.88 & 40.96 & **57.31** \\  & ARI & 36.04 & 42.03 & 27.23 & 58.37 & 42.12 & **60.02** \\ \hline \multirow{2}{*}{DBLP} & NMI & 75.3 & 70.57 & 50.7 & 69.89 & 71.41 & **77.52** \\  & ARI & 79.96 & 73.64 & 40.25 & 75.42 & 73.65 & **81.67** \\ \hline \end{tabular}
\end{table}
Table 5: Results of the node clustering task (%).  
Figure 7: Node clustering results on the ACM and DBLP datasets. (**a**) ACM. (**b**) DBLP.  
different weights between nodes and nodes in the aggregation process. GAT solves this problem based on a self-attention mechanism. Therefore, although both experiments are conducted on homogeneous networks, GAT algorithm outperforms GraphSAGE. MFHE, similar to GAT, introduces the multi-head attention mechanism in the extraction of subview information and also captures the inter-node weights. Thus, it apparently surpasses GraphSAGE. In addition, due to the introduction of the multi-view fusion module, node clustering is improved over GAT.
3. On the DBLP dataset, MFHE shows the best performance, followed by the DeepWalk algorithm, with smaller differences in the effectiveness of GAT, Metapath2 vec, and HERec clustering, and GraphSAGE works the worst. As with the node classification task, meta-path A-P-C-P-A in subviews has the greatest weight in DBLP. This contributes to most baselines with results above 70%. In spite of this, MFHE still outperforms other algorithms, which also demonstrates the importance of considering the correlation between subviews.
## 3 Elastic and Inertial Constant Identifications  
### Identification Algorithm for the Elastic Constants  
The identification of the elastic constants follows the lines of [25], according to which a suitable energy equivalence between the (fine) 3D grid beam model and the (coarse) 1D Timoshenko beam model is enforced (time is understood in what follows). The analysis here presented extends to the 3D case, the results of [25], where a planar grid beam was considered. The first step of the identification consists in defining a discrete map, which links the displacements of the fine model to that of the coarse one, at the selected abscissas \(x_{i}:=i\,h\quad(i=0,1,\ldots,n)\), \(h\) being the period of the grid beam. At each \(x_{i}\), the fine model possesses a cross-section \(\mathcal{D}_{i}\) occupying a domain of the \((y,z)\)-plane, made of the nodal points of the \((y,z)\)-fibers; two adjacent cross-sections \(\mathcal{D}_{i}\), \(\mathcal{D}_{i+1}\) bound a cell. In order to define the displacement map, a kinematic constraint is enforced--the sampled cross-section \(\mathcal{D}_{i}\) remains plane, while the displacements inside the cells are unconstrained. The rigid displacement of \(\mathcal{D}_{i}\) (the tilde denoting, from now on, a quantity relevant to the 3D body), can be written as:  
\[\left( \begin{array}{c}\tilde{u}\left(x_{i},y,z\right)\\ \tilde{v}\left(x_{i},y,z\right)\\ \tilde{w}\left(x_{i},y,z\right)\end{array} \right)=\left( \begin{array}{cc}u \left(x_{i}\right)\\ v\left(x_{i}\right)\\ w\left(x_{i}\right)\end{array} \right)+\left[ \begin{array}{cc}0&-\theta_{z} \left(x_{i}\right)&\theta_{y}\left(x_{i}\right)\\ 0&-\theta_{x}\left(x_{i}\right)\\ \mathrm{SKW}&0\end{array} \right]\left( \begin{array}{c}0\\ y\\ z\end{array} \right),\qquad\mathrm{in}\,\mathcal{D}_{i},\ i=0,1,\ldots,n. \tag{9}\]  
The second step requires enforcement of the equivalence of the energies stored by a 3D cell and a segment of equal length of the 1D beam, when the two models undergo the same displacements at the ends. This (so-called) cell analysis leads to the evaluation of the averaged energy density of the 3D cell, namely \(\tilde{\phi}:=\tilde{U}/h\), with \(\tilde{U}\) being the elastic energy of the 3D cell, as a function of the configuration variables of the 1D beam (according to Equation (9)). The energy density of the cell reads:  
\[\tilde{\phi}=\frac{1}{h}\tilde{U}\left(u\left(x_{i}\right),v\left(x_{i}\right),w\left(x_{i}\right),\theta_{v}\left(x_{i}\right),u\left(x_{i+1}\right),v\left( x_{i+1}\right),w\left(x_{i+1}\right),\theta_{v}\left(x_{i+1}\right)\right), \quad v=x,y,z. \tag{10}\]  
The last step of the identification consists in a comparison between the energy densities \(\tilde{\phi}\) and \(\phi\), in Equations (3) and (10), respectively. To this end, the strain-displacement relationships (1) must be integrated in the \((x_{i},x_{i+1})\) interval, thus giving a suitable strain test field.
###### Abstract  
Background: Monocyte-derived macrophages are key regulators and producers of reactive oxygen and nitrogen species (ROS/RNS). Pre-clinical and clinical studies suggest that cold physical plasma may be beneficial in the treatment of inflammatory conditions via the release of ROS/RNS. However, it is unknown how plasma treatment affects monocytes and their differentiation profile. Methods: Naive or phorbol-12-myristate-13-acetate (PMA)-pulsed THP-1 monocytes were exposed to cold physical plasma. The cells were analyzed regarding their metabolic activity as well as flow cytometry (analysis of viability, oxidation, surface marker expression and cytokine secretion) and high content imaging (quantitative analysis of morphology. Results: The plasma treatment affected THP-1 metabolisms, viability, and morphology. Furthermore, a significant modulation CD55, CD69, CD271 surface-expression and increase of inflammatory IL18, IL6, IL8, and MCP1 secretion was observed upon plasma treatment. Distinct phenotypical changes in THP-1 cells arguing for a differentiation profile were validated in primary monocytes from donor blood. As a functional outcome, plasma-treated monocytes decreased the viability of co-cultured melanoma cells to a greater extent than their non-treated counterparts. Conclusions: Our results suggest plasma-derived ROS/RNS shaped a differentiation profile in human monocytes as evidenced by their increased inflammatory profile (surface marker and cytokines) as well as functional outcome (tumor toxicity).  
cold physical plasma; kINPen; macrophages; RNS; ROS +
Footnote †: journal: Applied
## 2 Architectures of 5G and 6G Networks
### Feature Engineering and Preprocessing  
The performance of ML models in intrusion detection is heavily influenced by the quality of input features. Effective feature engineering and preprocessing are crucial for reducing noise and improving model accuracy:  
\(\bullet\)**Feature Selection:** Identifying and selecting relevant features is essential to enhance model performance. Techniques such as Mutual Information and Recursive Feature Elimination are employed to select optimal features that influence classification results. A study on network intrusion detection utilized feature selection methods like SpiderMonkey, Principal Component Analysis, Information Gain, and Correlation Attribute Evaluation to select optimal features, resulting in improved accuracy.  
\(\bullet\)**Data Normalization:** Standardizing data ensures that features contribute equally to the model, preventing bias toward variables with larger scales. Normalization techniques, such as Min-Max scaling, are commonly applied to prepare data for ML models.  
\(\bullet\)**Dimensionality Reduction:** Reducing the number of features through methods like Principal Component Analysis (PCA) helps in mitigating the curse of dimensionality, thereby enhancing model efficiency and reducing overfitting. A study on network intrusion detection demonstrated the use of PCA for dimension reduction, which improved detection accuracy [40].  
Emerging research is focusing on automating feature extraction using deep learning techniques, thereby reducing the reliance on expert-driven feature engineering. The deep learning models, including but not limited to Autoencoders, have this extraordinary capability of learning hierarchical feature representations directly from the raw input data. This ability significantly helps in detecting the complicated and sophisticated patterns of attacks without any manual feature selection process, which is normally quite laborious and time-consuming. Moreover, this new approach has been experimented with in various studies and research works, and that too with great success, to show its huge potential in improving and enhancing the intrusion detection systems, which are very important for cybersecurity.
# A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem
## 6 Computational Experiments
### Comparison of 4.5-Approximation Algorithm and CPLEX  
Here, a CPLEX algorithm is used to solve the MILP model given in Section 3, and we compare the CPLEX algorithm with the results of the 4.5-approximation algorithm. CPLEX always gives the optimal solution, but it cannot give the optimal solution for all instances even after operating several hours. Therefore, we set an upper execution time 1800s for CPLEX, and the best-known solution was compared. The job size and machine capacity distribution as shown in Figure 2.  
Regarding the 4.5-approximation algorithm, \(LB\) and \(UB\) are initialized as follows:  
\[\begin{array}{l}LB=\max(P_{j})\\ UB=\sum_{j=1}^{n}P_{j}.\end{array} \tag{10}\]  
Figure 3 shows the result of test suite J1M1S1P1K1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Factors** & **Levels** \\ \hline Number of jobs (n) & 10, 20, 50, 100, 200, 300 \\ Number of machines (m) & 2, 4 \\ Size of jobs (s) & [1,10], \([11,\max(K_{i})]\) \\ Processing time of jobs (P) & [1,10] \\ Capacity of machines (K) & [10,40] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Factors setting of the experiment.  
Figure 2: Job size and machine capacity distribution.  
Figure 3: Cont.  
\(\text{C}^{\prime}_{\text{max}}\) is the makespan of the 4.5-approximation algorithm with one-job-overfull batches and \(C_{\text{max}}\) is the makespan of 4.5-approximation algorithm with a feasible schedule. Figure 3a shows the makespan of the 4.5-approximation algorithm with one-job-overfull batches and the algorithm with a feasible schedule. Figure 3b,c shows that the 4.5-approximation algorithm substantiates the feasibility of this research method:  
\[\begin{array}{l}\text{C}^{\prime}_{\text{max}}\leq 9T/4\\ \text{C}_{\text{max}}\leq 4.5T.\end{array}\]  
Figure 3d shows that the run time of the 4.5-approximation algorithm is clearly better than CPLEX. Table 5 shows the results of all test suites. Though CPLEX is the best solver for linear programming problems, it cannot give an optimal solution for a long time, so we terminated CPLEX after running for 1800 s and used the best integer for comparison.  
The results illustrate that the 4.5 approximation algorithm is more effective than CPLEX in any scale test-suite. For the small-scale test-suite (10 jobs and two machines), the best solution obtained by the 4.5-approximation algorithm is closest to the CPLEX best solution. For the medium-scale and large-scale test-suites, the average result of the 4.5-approximation algorithm is no bigger than 4.5\(T\).
## 1 Introduction  
Hybrid structures involving carbon nanotubes (CNTs) and metal nanoparticles (NM) combine the unique properties of both [1]. These have been used in gas and liquid phase catalysis as well as electro catalysis [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. Several CNT-NM hybrids have shown excellent reactivity [15; 16; 17; 18; 19; 20; 21; 22; 23], and different synthetic procedures have been used to immobilize Pt, Pd, Ru, and Rh NPs and a Rh/Pd alloy on CNTs. Various approaches [8; 9; 10; 11; 12; 13; 14] have been used to attach noble metals to CNTs [24; 25; 26; 27]. These include in situ reduction [10], covalent bonding, electrostatic deposition, and physisorption [28]. Direct chemical reduction has been shown to increase metal loadings by as much as 50-fold [29] and microwave reactions have been reported as a fast method [30; 31; 32; 33].  
The pd-catalyzed reaction between aryl or alkenyl halides and olefins to deliver substituted olefins with high E-selectivity--known as the Heck reaction--is one of the most fundamental reactions in cross-coupling chemistry [34; 35]. Initially, soluble Pd/Pt containing organometallic compounds were used as catalysts [34; 35]. Later on, metal nanoparticle coated CNTs which represents the next generation of homogeneous catalyst were used to carry out these cross-coupling reactions. These have an advantage of easy separation of catalyst through centrifugation or precipitation [34; 35; 36].  
In these typical reactions, the particle size, surface area, dispersion, and chemical nature of the support are important parameters [37; 38; 39]. In the case of CNT-NM, the presence of functional groups can alter many of these properties, but its role is not well understood. An added advantage of CNTs is that covalently introducing functional groups such as carboxylic carboxylic (-COOH), and (-CONH\({}_{2}\)) can make them dispersible in different solvents and can be made solvent dispersible for different liquid phase reactions [40; 41; 42]. The synthesis of highly dispersed CNT-NM hybrids has been a challenge and is highly desirable for liquid phase reactions. We have reported the microwave induced synthesis CNT-Pt for catalytic hydrogenation of m-nitrochlorobenzene [19]. The objective of this work is to study the effect of CNT functionalization in CNT-Pd hybrids as applied to the Heck reaction and to study the effect of parameters such as dispersibility and polarity.
## 3 Results and Discussion  
### Absorbance as a Function of Temperature  
Figure 2 reports the infrared absorption spectrum of EMI-TfO measured on cooling from 330 K down to 120 K and subsequent heating from 120 K up to 330 K, in the frequency range between 500 and 1800 cm\({}^{-1}\). In all spectra, the most intense lines centered around 518, 574, 638, 1031, 1167, 1226, 1266 cm\({}^{-1}\) are known to be due to the vibrations of theTfO anions [49]. The less intense lines between 680 and 1000 cm\({}^{-1}\), as well as the absorption bands above 1300 cm\({}^{-1}\), are due to the vibration modes of the EMI cation [32].  
On cooling, small modifications of the absorption spectrum are detected between 330 and 220 K; however, between 220 and 200 K an abrupt change takes place, consisting in the splitting of the lines between 730 and 820 cm\({}^{-1}\), and between 1150 and 1600 cm\({}^{-1}\). On further cooling, the spectral features remain constant. On subsequent heating (see Figure 2 right panel), minor modifications occur up to 260 K; at this temperature, the absorption lines start to become less defined: the band around 1590 cm\({}^{-1}\) stops being a well defined doublet and resembles more the broad band observed on cooling in the liquid phase; but, on the other hand, one can still observe the three bands between 740 and 820 cm\({}^{-1}\), that are, instead, a broad feature in the liquid. The strong absorption bands between 1000 and 1300 cm\({}^{-1}\) become more intense than in the solid phase at low temperature and in the liquid one at high temperature. On further increasing the temperature, around 280 K all absorption bands return to the shape they had in the liquid phase at room temperature before the thermal cycle. The splitting of absorption bands or the abrupt disappearance or appearance of spectral lines has been largely attributed to the occurrence of solid-liquid or solid-solid transformations [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 49]. In this framework, the overall behavior as a function of temperature of EMI-TfO can be attributed to the occurrence of crystallization on cooling between 220 and 200 K and the occurrence of a solid-solid phase transition on heating around 260 K and finally to melting around 280 K. Indeed, this phase changes are in agreement with differential scanning calorimetry measurements reported in Ref. [51], which evidenced the occurrence of crystallization on cooling around 220 K, a subsequent solid-solid phase transition on heating around 230 K and melting around 260 K.
# A Simple Model of Ballistic Conduction in Multi-Lead Molecular Devices
## 5 Internal Conduction Channels
### Symmetry Considerations  
The presence of molecular symmetry has significant consequences for the transmission. We investigate this using the three-lead symmetrical device in the tight-binding approximation. Using the cluster expansion of \(\text{D}_{\text{L}}\) in Equation (53), we can explore the consequences of symmetry by constraining the connection vertices for leads 2 and 3 to be equivalent. This can be achieved by insisting that the molecular GF has elements  
\[(\text{g}_{\text{M}})_{22}=(\text{g}_{\text{M}})_{33},\text{ and }(\text{g}_{\text{M}})_{12}=(\text{g}_{\text{M}})_{13}. \tag{96}\]  
With these restrictions, Equation (54) becomes  
\[\text{D}_{\text{L}}=\Big{(}\beta_{\text{L}}e^{-iq}-\hat{k}_{22}-\hat{k}_{23} \Big{)}\Big{(}\beta_{\text{L}}^{2}e^{-2 iq}-\beta_{\text{L}}e^{-iq}(\hat{k}_{1 1}+\hat{k}_{22}+\hat{k}_{23})+(\hat{k}_{12,12}+\hat{k}_{12,13})\Big{)}, \tag{97}\]  
and Equation (56) becomes  
\[(\text{D}_{\text{L}})_{21}=\hat{k}_{12}\Big{(}\beta_{\text{L}}e^{-iq}-\hat{k}_ {22}-\hat{k}_{23}\Big{)}, \tag{98}\]so that the formula for the transmission, \(T_{\rm M\to 2}\), necessarily involves cancellation of \(\left(\beta_{\rm L}e^{-iq}-\hat{k}_{22}-\hat{k}_{23}\right)\), and effectively,  
\[|(\rm D_{L})_{21}|^{2}\to\hat{k}_{12}^{2} \tag{99}\]  
in the transmission formula, Equation (39). This is the direct term in Equation (60), so that the mixed and pure interference terms in the numerator have vanished. The denominator, \(|\rm D_{L}|^{2}\), on the other hand, is a modified version of the two-lead analogue, with three-lead terms giving shifts in pole positions that alter the positions of the features. Symmetric three-lead devices, nevertheless, have sink-lead transmissions that are similar to those of the corresponding two-lead device. Furthermore, leads 2 and 3 in this particular example have identical transmission spectra, with each carrying exactly half the transmission at any energy, \(E\).  
We now consider what happens when all three connection vertices in a three lead device are equivalent. Then,  
\[(\rm g_{M})_{11}=(\rm g_{M})_{22}=(\rm g_{M})_{33},\text{ and }(\rm g_{M})_{12}=( \rm g_{M})_{13}=(\rm g_{M})_{23}. \tag{100}\]  
Hence,  
\[(\rm D_{L})_{21} =\hat{k}_{12}\left(\beta_{\rm L}e^{-iq}-\hat{k}_{11}-\hat{k}_{12 }\right),\] \[\rm D_{L} =\left(\beta_{\rm L}e^{-iq}-\hat{k}_{11}-\hat{k}_{12}\right)^{2} \left(\beta_{\rm L}e^{-iq}-\hat{k}_{11}-2\hat{k}_{12}\right), \tag{101}\]  
and \((\rm D_{L})_{\rm p1}\) factors in the identical manner to Equation (98), because its underlying properties reflect the permutational symmetry of the sink vertices. Once again, this ensures vanishing of interference terms. The cluster expansion form of \(\rm D_{L}\), on the other hand, does not distinguish between source and sink vertices and reflects higher permutational symmetry. Devices with more leads exhibit even richer symmetry effects.