esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental and Numerical Predictions of Cryogenic Leakages in Welded Steel Plates
## 4 Results and Discussion
### Thermal Stress within the Whole Welding Steel Plate  
Figure 12 shows the strain curves in the longitudinal and transverse directions at two different strain measurement points in the area that was subjected to direct cryogenic liquid exposure for 1600 s. The present study assumed that the load by weight of LN\({}_{2}\) could be neglected. Hence, the welded plate was considered to experience only the strain generated by the cryogenic temperature. As shown in the figures, the strain in both directions varied in a similar way and fluctuated considerably during the first 300 s of cryogenic liquid application. This phenomenon occurred due to a sudden thermal shock, given the significant difference between the temperatures of the steel plate surface and the liquid nitrogen. As shown in Figure 12a for example, the longitudinal and transverse strains suddenly rose between 50 and 100 s and reached about 500 \(\upmu\varepsilon\). Afterward, the strain values fell sharply to about 200 \(\upmu\varepsilon\) and \(-200\)\(\upmu\varepsilon\) (respectively); the temperature at this point reached \(-196\)\({}^{\circ}\)C, and this value remained relatively stable. Due to the difference in thermal expansion coefficients between the welded and the base materials, and due to the boundary constraint, the tensile or compression behavior was different for each measurement point. In particular, at measurement points that were directly exposed to cryogenic temperatures (1 and 3), the strain in the horizontal direction converged to a negative value, as shown in Figure 12a,c. By comparison, Measurement Point 2 underwent less contraction, and little deformation occurred in the horizontal direction, as shown in Figure 12b. The strain in the longitudinal direction converged to a positive value when the constraint was not considered. This occurred because there was no discontinuous section of material in the longitudinal direction, so the resulting expansion-shrinkage behavior was typical. In particular, the tensile strain converged to 200 \(\upmu\varepsilon\). Figure 12d, which shows the strain time history curves at Measurement Point 4, illustrates a remarkable difference from the other measurement points presented above. Firstly, the strain tendencies in the two directions contrasted with each other. Specifically, the vertical strain presented a compression trend, whereas the horizontal strain showed an expansion process. Secondly, the lowest vertical strain value was at approximately \(-800\)\(\upmu\varepsilon\), which is about twice the magnitude of the peak horizontal strain value.  
Figure 11: (**a**) Image of LN\({}_{2}\) exposure on the welded steel plate and (**b**) the typical cooldown curves of test points during the LN\({}_{2}\) experiment.
## 4 Materials and Methods
### Protein Purification, Quantification and Spectrometric Analysis  
_R. dairenensis_ was cultivated to stationary phase in MMM (1 L) and extracellular \(\beta\)-tructofuranosidase RdINV was purified as described [26]. In brief, yeast was grown for ~60 h (~4 ODU600; ~1.9 U/mL), culture filtrate was concentrated through 50000 MWCO PES and applied to a DEAE-Sephacel column equilibrated with 20 mM Tris-HCl pH 7. Samples eluting at 0.15 M salt were pooled and concentrated using Amicon Ultra-15 ultracold-100K filters to ~41 U/mL (~146 U/mg). A single band corresponding to an apparently pure protein of ~170 kDa was obtained by ProtoBlue Safe Colloidal Coomassieie staining (National Scientific, Atlanta, GA, USA)-SDS-PAGE (8%). Precision Plus Protein Standards Unstained 10-250 kDa (Bio-Rad, Hercules, CA, USA) were used as markers. Protein concentration was determined by using Nano Drop-1000 Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA) at 280 nm.  
For spectrometric analysis, proteins (~2 ug) were excised from SDS-PAGE gels, digested in situ with trypsin or chymotrypsin and analyzed by Reverse Phase Liquid Chromatography coupled to MS (RP-LC-MS/MS) in an Agilent 1100 system coupled to a LTQ-Velos mass spectrometer (Thermo Scientific, Waltham, MA, USA). Peptides were separated using a 0.18 mm \(\times\) 150 mm Bio-Basic C18 RP column (Thermo Scientific, Waltham, MA, USA) at 1.8 uL/min, using a 35 min gradient from 5-40% solvent A: 0.1% formic acid-solvent B: 0.1% formic acid, 80% acetonitrile. ESI ionization was performed using a microspray \"metal needle kit\" (Thermo Scientific, Waltham, MA, USA) interface. De Novo sequencing was carried out using PEAKS Studio 6 [48] (Bioinformatics Solutions, Waterloo, ON, Canada) at the Proteomic Service of Centro de Biologia Molecular (CBMSO, Madrid, Spain).  
For purification of the protein expressed in _P. pastoris_, transformants carrying the pIB4-derivative construction were cultivated in BMG, expression of proteins induced in BMM and heterologous activity evaluated in culture filtrates as referenced [34]. Empty pIB4 transformants were used as control. The extracellular fraction was concentrated and fractionated through 50,000 MWCO PES membranes and Amicon Ultra-15 ultracold-100K filters (if required). About 70-80% of the initial activity was recovered. PNGase F (Sigma-Aldrich, St. Louis, MO, USA), O-glycosidase+neuroaminidase and \(\alpha\)-(1-2,3,6) mannosidase (both from NEB, Ipswich, MA, USA) treatments were performed according to manufacturer's protocols.
## 4 Results and Discussion
### Four-Sensor Configurations
#### 4.4.1 Four-Sensors: Model Performance with Non-Overlapping Data
The deep learning models GRU and LSTM offer consistent results, with F1 Scores of 0.9738 and 0.9752, respectively, on the test set, highlighting their effectiveness in sequential data processing, even without overlapping segmentation [37; 38]. Despite its strengths, the Transformer model performs slightly lower than TCN in this configuration, with an F1 Score of 0.9760 on the test set, suggesting that while its self-attention mechanism is beneficial for long-range dependencies, TCN may be more effective in handling structured, sequential data in non-overlapping setups [29]. In conclusion, the **TCN** model emerges as the best performer in this configuration, demonstrating high accuracy, precision, recall, and F1 Score across all sets. Its superior generalization capabilities highlight its robustness for human activity recognition in non-overlapping data settings. The ensemble models such as RF and XGBoost show commendable results, reinforcing the utility of ensemble learning approaches in enhancing classification performance. Meanwhile, GRU and Transformer also contribute valuable insights, particularly in capturing temporal dependencies, although they slightly trail behind TCN in this context.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 4 Key Capabilities for Resilience in IoMT
### Route Optimization  
Route optimization is critical for resilient IoMT networks, as it is required to avoid congestion in the channel, during which packet drops and other transmission delays  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Technology** & **Anomaly Detection** & **Authorization, Accessibility** & **Scalability** & **Distributed Functionality** & **Traffic Classification** & **Route Optimization** & **Global View** \\ \hline ML & ✓ & \(\times\) & \(\times\) & \(\times\) & ✓ & ✓ & \(\times\) \\ \hline SDN & \(\times\) & \(\times\) & ✓ & \(\times\) & ✓ & ✓ & ✓ \\ \hline MSA & \(\times\) & ✓ & ✓ & ✓ & \(\times\) & ✓ & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key capabilities for resilience in IoMT.  
occur [38; 39]. In complex and dynamic IoMT network scenarios, route optimization takes place to avoid the congested traffic routes [40]. The route optimization characteristic makes the IoMT networks capable of rerouting data traffic when it detects the anomalies [41]. It also provides the facility to create a dynamic topology in the IoMT networks [42].
## 2 Results and Discussion
### Effect of Germination on the Volatile Profile of IWG
#### 2.2.3 MHE Optimization
Conversely, reports on the odor or incidence of 3-methylpentadecane are scarce, but few studies report its incidence in vegetables [48; 49]. The concentrations of these compounds significantly reduced after germination, from 708.56 to 18.71 mg/kg for styrene and from 48.02 to 2.73 mg/kg for 3-methylpentadecane. It was also observed for esters, organic acids, and terpen. It might be associated with the oxidation to generate energy during germination in a process similar to the \(\beta\)-oxidation of lipids [50]. The same behavior was also observed in germinated faba beans [19]. A principal component analysis (Figure 6A,B) was performed using the volatile compound concentrations of the IWG samples. The two principal components (PC1 and PC2) explained more than 71% of the data. The impact of hexanal (loading 1, \(-\)0.14568) and nonanal (loading 1, \(-\)0.20006) was observed by separating the ungerminated samples from the germinated ones. Additionally, esters and organic acids such as ethyl cis,cis-9,12-octadecadiene (loading 2, \(-\)0.27537) and ethyl palmitate (loading 2, 0.27298) discriminated the samples germinated at 20 \({}^{\circ}\)C after 6 days. Volatile compounds and FAs were evaluated using Pearson correlation (Table S3--Supplementary Material). The results showed a positive and significant correlation between the unsaturated FAs and some volatile compounds. Correlation values higher than 0.65 were found for C18:1 cis and hexanal (\(p\) < 0.005) and C18:2 n-6 and 2-nonanal (\(p\) < 0.005). Overall, germination can be used to improve the volatile profile of seeds [9]. In this study, many volatiles responsible for off-odors decreased as germination progressed. At 10 \({}^{\circ}\)C and 15 \({}^{\circ}\)C, germination time had a more significant effect on the reduction of these compounds than temperature. However, at 20 \({}^{\circ}\)C, the concentration of some esters and organic acids, such as butyl butanoate, n-decanoic acid, and ethyl palmitate, significantly increased with the germination time, suggesting that a fermentation process could have occurred.
## 3 Method
### Initialization Process  
QPIO presents the best convergence rate among all PIO algorithms, but it is still suffering from a major disadvantage that is vulnerable to fall into local optimum [37]. Specifically, when the initialization is away from the global optimum, this error will guide other pigeons to search in wrong spaces and even leading, eventually, to failure. Since most initialization of pigeon population is achieved by random distribution, their performance quality could not be ensured in conventional methods. Therefore, the chaotic strategy is introduced in our algorithm to generate some initial solutions that can obtain wider solution space coverage.  
Chaos is a kind of seemingly random motion appearing in the decisive dynamic system. Chaotic motion has three distinct characteristics: (1) high sensitivity to initial values; (2) ergodicity of motion trajectories; (3) randomness. These properties of chaos can be used to initialize the pigeon with chaotic variables. Logistic mapping is a common method used in this paper to produce chaotic variables, and avoid premature convergence [38]. In the initialization process, the logistic mapping strategy is used to make the pigeon position random and traversable, so that it has the opportunity to jump out of the local optimum and find the global optimum, thus effectively avoiding premature convergence. The \(D\) dimensions vector \(u_{1}=(u_{1,1},u_{1,2}\ldots u_{1,D})\in[0,1]^{D}\) is generated randomly. Then, a chaotic sequence \(U\) with \(N\) vectors \(u_{1},u_{2},\ldots u_{N}\) is obtained according to the recurrence Equation (16):  
\[u_{n+1}=\mu*u_{n}(1-u_{n}),\ n=0,1,2,\cdots N-1\ 0<u_{1}<1. \tag{16}\]After the chaos process, the chaos sequence \(U\) is converted into the problem solution space \([X_{min},X_{max}]^{D}\). The corresponding individual initialization formula can be converted as  
\[X_{i,j}=X_{min,j}+u_{n}*(X_{max,j}-X_{min,j}),\;j=1,2\cdots D. \tag{17}\]  
Initialization of pigeon by chaotic sequence is based on the generation of initial values, from which the initial population can be determined by selecting the best one. Although it does not change the stochastic nature of AOQPIO initialization, the chaotic process improves the diversity of population and the ergodicity of pigeon search by applying chaotic theory.
# 5G/B5G mmWave Cellular Networks with MEC Prefetching Based on User Context Information +
## 2 System Model
### mmWave Heterogeneous Network  
In this research, we consider HetNet based on the centralized radio access network (C-RAN) architecture [33] where mmWave small cells are overlaid on conventional LTE macro cells and they can tightly interwork. mmWave communication is assumed to be 5G NR in the 28 GHz band and NR-U [4] in the 60 GHz band, which includes coordination with IEEE802.11 ad/ay standards [34], etc. The system performance is evaluated at the center macro cell surrounded by six interfering macro cells. Each small-cell BS is installed at the center of hotspot area and has three-sector mmWave access, a wired backhaul line, and Edge Cloud storage. This paper assumes only one sub-channel among the four sub-channels of 802.11 ad to be used commonly at all the three sectors of each small-cell BS. In each sector, transmit signal is steered to the desired user by beam forming protocol using massive antennas equipped in mmWave small-cell BSs.  
In addition, the mmWave Edge Cloud system has a novel C-plane, which can gather abundant user context information, such as mobility, traffic generation time, traffic quantity, and traffic type. This paper assumes that the network (macro/small-cell BSs) can acquire the context information of UE perfectly and that the UE handsets have dual connectivity to both small and macro cells. Based on the context information, if a UE's demand traffic is small or of a type that cannot be stored in the edge storage, a macro cell BS will take charge of the UE's U-plane.  
Otherwise, if demand traffic is large and of a type that can be stored at the edge, the data can be gradually transmitted and stored in advance at the Edge Cloud storage, which is installed at the site of mmWave small-cell BS. This procedure is defined as 'prefetching'. Prefetching can reduce or zero out the user-experienced backhaul latency and enables communication through high-speed mmWave access even under the constraint of low-capacity backhaul.
# Grouping Bilinear Pooling for Fine-Grained Image Classification
## 3 Grouping Bilinear Pooling
### Intra-Group Bilinear Pooling  
Define \(F\) is the high-order feature, \(F^{T}=[f_{1},f_{2},\ldots,f_{C}],F_{k}\in\mathbb{R}^{\frac{C}{d}}\), \(F\) contains \(C\) different feature layers. Then, dividing \(F\) into \(g\) groups. Noting that the maximum of \(g\) is \(C\) while carrying out Intra-GBP. \(G=\left[G_{1},G_{2},\ldots,G_{C/g}\right]\in\mathbb{R}^{\frac{C}{d}}\), for the \(k\)th group \(G_{k}^{T}=\left[f_{g(k-1)+1},f_{g(k-1)+2},\ldots,f_{g(k-1)+C/g}\right]\), \(G_{k}\in\mathbb{R}^{\frac{C}{d}}\), Intra-GBP is as follows:  
\[Z_{k}=\frac{1}{HW}G_{k}G_{k}^{T} \tag{6}\]  
\[Z_{G}=Concat(Z_{1},Z_{2},\ldots,Z_{k}) \tag{7}\]  
\[Output=Z_{G}W_{G}+b_{G} \tag{8}\]  
\[rank(W_{G})\leq\min\left(\frac{C^{2}}{g},N\right) \tag{9}\]  
where \(vector(Z_{k})\in\mathbb{R}^{\left(\frac{C}{d}\right)^{2}},k\in[1,2,\ldots,g],Z _{G}\in\mathbb{R}^{\frac{C^{2}}{d}},W_{G}\in\mathbb{R}^{\frac{C^{2}}{d}\times N },b_{G}\in\mathbb{R}^{N}\).  
For FBP, when the full bilinear representation \(Z\) is used for classification, the parameters that the full-connection layer needs to learn are as high as \(C^{2}N\). While for the intra-group bilinear pooling (Intra-GBP), the parameters that the full-connection layer needs to learn reduce to \(C^{2}N/g\).  
Figure 2: (**a**) Full bilinear pooling (FBP), bilinear pooling is performed in pairs in all feature layers. (**b**) Compact bilinear pooling (CBP), the feature layer is mapped and the Hadamard product is used to simplify bilinear pooling. (**c**) Low-rank bilinear pooling (LRBP), which obtains statistics without performing bilinear pooling, instead using the Frobenius norm as the classification score. (**d**) The intra-group bilinear pooling (Intra-GBP), bilinear pooling performs in each grouped feature layer group. (**e**) The inter-group bilinear pooling (Inter-GBP), bilinear pooling performs between two different grouped feature layer groups.  
When \(g\) is small, this bilinear operation after grouping still requires large computational resources (Intra-GBP at \(g=1\) is equivalent to FBP), and the dimension of Intra-GBP is still a larger number. As \(g\) gets bigger and bigger, it will bring huge benefits. The influence of changing \(g\) will be explained in the experiment section.  
The representation \(Z_{k}\) in Equation (6) has similar properties to the representation \(Z\) obtained by FBP, that is, \(Z_{k}\) is also a symmetric matrix and there is still computational redundancy. Thus, we propose inter-group bilinear pooling for further improvement.
# Enhancing QoS in Opportunistic Networks Through Direct Communication for Dynamic Routing Challenges
## 2 Related Work  
In this section, we review existing contributions to the development of routing protocols for Opportunistic Networks (OppNets).  
In [13; 14; 15], routing protocols were designed based on geographic location knowledge, which has proven to be a suitable approach, particularly for network scalability. In this method, nodes are equipped with information about their own geographic location and forward data to neighboring nodes that are closer to the destination. For such routing algorithms to function, a node must be aware of its own location and the location of its neighbors and the source must have knowledge of the destination node's location.  
Epidemic routing (ER) is a widely recognized protocol in Opportunistic Networks (OppNets), known for making minimal assumptions about network topology and connectivity, as it primarily depends on opportunistic encounters between nodes. ER forwards the message to the destination by maintaining information known as a summary vector, which is based on the information of the data available within the node's cache. The nodes in the network are tagged with unique identifiers, which are also used to initiate a session. This summary vector is also exchanged with other nodes upon some encounter. ER provides an efficient mechanism of dissemination of the message to the destination; however it also experiences a redundancy issue of the message at multiple nodes within the network.  
The Probabilistic Routing Protocol (PRoPHET) [16] relies on encounter history and transition information to forward the data to the destination. The PRoPHET routing algorithm was mainly designed for the node's mobility and also incorporates the delivery probability between two nodes \(P(a,b)\), which is directly related to the likelihood of successful data delivery. In PRoPHET routing, the node with the highest delivery probability receives higher transmission chances. The history-based routing protocol (HBRP) [17] uses the node's current condition to forward the data. The node's current condition is determined by incorporating two tables, i.e, the identity table (IT) and the history table (HT). The HBRP combines the aforementioned tables and forwards the message to the destination based on that information. Context-aware routing (CAR) [18] protocols leverage contextual prediction information to enhance the effectiveness of data delivery.  
Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and, more recently, Physics-Informed Deep Learning (PIDL) have demonstrated remarkable potential in addressing complex and dynamic problems, such as path trajectory planning--particularly in virtual rail train scenarios. Among these, PIDL has shown a distinct advantage by preserving physical constraints while accurately modeling complex vehicle dynamics [19].
## 3 Experiment
### Model Evaluation Indicators
#### 3.3.2 Classification Task Evaluation Metrics  
Accuracy measures the overall proportion of correct predictions among all samples. It is well-suited for balanced classification tasks such as alarm type identification in IDC environments. The formula is given by  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{23}\]  
where TP, TN, FP, and FN represent true positives, true negatives, false positives, and false negatives, respectively.  
Precision evaluates the proportion of correctly identified positive instances among all predicted positives. It is crucial in fault detection systems where minimizing false alarms is essential. Precision is defined as follows:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{24}\]  
Recall (also known as sensitivity) measures the proportion of actual positive samples that are correctly identified. It is particularly important when detecting rare events or anomalies. Recall is computed as follows:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{25}\]  
The F1-score is the harmonic mean of precision and recall, offering a balanced view of both metrics. It is especially valuable when both false positives and false negatives need to be controlled simultaneously. The formula is as follows:  
\[F1=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{26}\]  
The area under the curve (AUC) quantifies the model's overall discriminative ability across varying classification thresholds. It is particularly effective in imbalanced datasets commonly found in IDC equipment monitoring. A higher AUC value indicates better classification performance. It is defined as  
\[\text{AUC}=\int_{0}^{1}\text{TPR}(\text{FPR}^{-1}(x))dx \tag{27}\]  
where TPR is the true positive rate (TPR \(=\frac{\text{TP}}{\text{TP}+\text{FN}}\)), FPR is the false positive rate (FPR \(=\frac{\text{FP}}{\text{FP}+\text{TN}}\)), and FPR\({}^{-1}(x)\) is the inverse function of FPR used in the integral expression.
# Textile Flexible Job-Shop Scheduling Based on a Modified Ant Colony Optimization Algorithm
## 3 Product Ant Colony Optimization Algorithm
### Heuristic Function  
In the original ACO algorithm, the heuristic function is the inverse of the distance. In research on the T-FJSP, with the goal of minimizing the total workshop completion time, the machine operating time is a key component in the calculation of the total workshop completion time, which is determined by ants when they access a machine. When an ant moves from one operation to the next operation, it selects a machine from among all machines corresponding to the next operation, with the machine's current operating time being the primary consideration. To meet optimization requirements, the reciprocal of the machine's current operating time is chosen as the heuristic function. In this case, the greater the current operating time of a machine, the smaller the probability of its being selected, which not only ensures ants in the product ant colony are more evenly distributed among the machines of the same operation, but also achieves algorithmic goal optimization. During machine selection, priority is given to unused machines to ensure the full utilization of all machines in the workshop and improve overall production efficiency. Thus, the heuristic value of unused machines is set to a relatively large value, making the next ant more inclined to choose an unused machine when selecting a machine in an operation. The formula is shown in Equation (19).  
\[\eta_{ij}=\left\{\begin{array}{ll}100&m.chosed=0\\ \frac{1}{T_{m}^{j}}&m.chosed\geq 1\end{array}\right., \tag{19}\]  
where \(\eta_{ij}\) is the heuristic function value for the ant to choose machine \(j\) from machine \(i\), \(m.chosed\) is the number of times machine \(j\) has been selected when the ant chooses, and \(T_{m}^{j}\) is the current total operating time of machine \(j\).