esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
**Symbol** & **Meaning** \\ \(IoT_{R}\) & IoT relay node \\ \(P_{s}\) & Power of source node transmit signal \\ RF & Radio Frequency \\ EH & Energy Harvesting \\ SNR & Signal-to-noise ratio \\ NOMA & Non-orthogonal multiple access \\ PS & Power splitting \\ DF & Decode and Forward \\ \(\epsilon\) & Power splitting factor \\ \(x_{s}\) & Source node information data \\ \(T\) & Time period \\ \(y_{IoT_{R}},g_{IoT_{R}}\) & Information signal received at \(IoT_{R}\) \\ \(n_{IoT_{R}}\) & Additive White Gaussian Nosie at \(IoT_{R}\) \\ \(\sigma_{IoT_{R}}^{2}\) & Noise variance at \(IoT_{R}\) \\ \(h_{IoT_{R}}\) & Channel co-efficient between source node and \(IoT_{R}\) node \\ \(\lambda_{h}\) & Mean variance of \(h_{IoT_{R}}\) \\ \(h_{s_{mc}}\) & Channel co-efficient between \(IoT_{R}\) and source user \\ \(\lambda_{g}\) & Mean variance of \(h_{s_{mc}}\) \\ \(h_{IoT_{mc}}\) & Channel co-efficient between \(IoT_{R}\) and \(IoT\) user \\ \(\lambda_{z}\) & Mean variance of \(h_{IoT_{mc}}\) \\ \(\eta\) & Energy conversion efficiency \\ \end{tabular}  
\(E_{h_{\omega T_{R}}},\hat{E}_{h_{\omega T_{R}}}\) Energy harvested at \(IoT_{R}\) node  
\(P_{IoT_{R}},\hat{P}_{IoT_{R}}\) Transmit power of \(IoT_{R}\) node  
\(Z_{{}_{1C1}}\mathcal{Z}_{{}_{1C1}}\) Superimposed composite signal for NOMA protocol  
\(\phi_{1}\phi_{2}\) Power allocation factors for NOMA protocol  
\(x_{10T_{R}}\) \(IoT_{R}\) node information data  
\(y_{s_{rec}},\hat{g}_{s_{rec}}\) Received signal at destination source user  
\(y_{10T_{rec}},\hat{y}_{10T_{rec}}\) Received signal at destination \(IoT\) user  
\(n_{s_{rec}},\hat{g}_{10T_{rec}}\) Additive White Gaussian Nosie at destination source user  
\(n_{IoT_{rec}}\) Additive White Gaussian Nosie at destination \(IoT\) user  
\(\gamma_{IoT_{R}},\hat{\gamma}_{IoT_{R}}\) Received SNR at \(IoT_{R}\) node  
\(\delta,\delta\) Transmit SNR  
\(\gamma_{s_{rec}}\rightarrow\gamma_{s_{rec}},\gamma_{s_{rec}}^{x_{IoT_{R}} \rightarrow\gamma_{s}}\)  
SNR required at the destination source user to decode and cancel \(IoT_{R}\) information data  
\(\gamma_{s_{rec}}\) Received SNR at destination source user node  
\(T_{10T_{rec}},\hat{\gamma}_{IoT_{rec}}\) Received SNR at destination \(IoT_{R}\) user node  
\(\sigma_{s_{rec}}^{2}\) Noise variance at destination source user node  
Noise variance at destination \(IoT_{R}\) user node  
\(\psi,\hat{\psi}\) Outage probability  
\(P_{OutS},\hat{P}_{OutS}\) Outage probability of source node  
\(P_{Out_{1R_{R}}},\hat{P}_{Out_{1R_{R}}}\) Outage probability of \(IoT_{R}\) node  
Rate in bits per second per hertz  
\(Thr_{S},Thr_{S}\) Throughput of source node  
\(Thr_{10T_{R}},Thr_{10T_{R}}\) Throughput of \(IoT_{R}\) node  
\(Thr\), \(Thr\) Sum-throughput of whole system  
\(\alpha^{*}\) Optimal time switching factor  
\(\varepsilon^{*}\) Optimal power splitting factor  
\(K_{1}(.)\) First-order modified Bessel function of the second kind  
\(E_{n}(a)\) Exponential integral of order \(n\)
## 4 Applications  
This section mainly introduces the intelligent batching system and its practical application in a steel plant in China. The environmental protection department of the steel plant uses a trial-and-error method to make the production plan of the raw material ratio of the rotary hearth furnace. There are two problems: the first problem is that different methods need to be formulated for different materials, and the the thinking process is complex. The second problem is that this method is time-consuming. The intelligent batching system solves these two problems.
# Interdiffusion in Zr-Mo/W Intermetallics
###### Abstract  
Intermetallic compounds or solid solutions can form between Zr and Mo/W, in which the multiphase of the diffusion may be influenced by each other. Interdiffusion kinetic data in such intermetallic systems are highly demanded for material design. In this work, solid-solid diffusion couples of Zr-Mo and Zr-W were prepared by the fixture method, and the interdiffusion behaviors of Zr-Mo and Zr-W at 1300-1500 \({}^{\circ}\)C were systematically investigated. The results showed that the intermetallic compounds Mo\({}_{2}\)Zr/W\({}_{2}\)Zr formed in the Zr-Mo/W diffusion systems. The growth constants of the Mo\({}_{2}\)Zr and W\({}_{2}\)Zr phases varied with temperature in accordance with the Arrhenius relationship, and the activation energies of growth were 109 kJ/mol and 285 kJ/mol, respectively. In addition, (Zr, Mo) solid solution formed between Mo\({}_{2}\)Zr and pure Zr as diffusion proceeded, resulting in a lower chemical potential for the formation of Mo\({}_{2}\)Zr. The [PERSON] method was used in calculating the interdiffusion coefficients of the solid solution. The results showed that the interdiffusion coefficient in the (Zr, Mo) solid solution decreased with the increase of Mo concentration. Moreover, the diffusion activation energy of the solid solution was evaluated based on the Arrhenius relationship, and the activation energy was 145-170 kJ/mol when the Mo content was in the range of 2-10 at. %. These diffusion kinetic data provide a reference for the composition design and preparation technic of Zr-based alloys.  
diffusion couple; Zr-based alloys; interdiffusion coefficients; activation energy +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Model Architecture and Training
It is a fast and effective off-the-shelf model and is not sensitive to the scale of its input data, in contrast to ANNs and linear regression [26]. Since there is an individual SPU for each feature \(j\), there is the possibility to define a separate set of parameters for each unit. Also, due to the setup of the sensor grid, the units at each sensor position have unique prerequisites regarding the information contained in their input data. To give an example, the prediction of strain values at sensor 5 is based on strain values provided by four close-by sensors. These values additionally describe the strain state around sensor 5. In contrast, the prediction of strain values of an edge sensor, e.g., sensor 1, relies on only two close-by sensors. In this context, an attempt has been made to compensate the unequal spatial prediction prerequisites by training each SPU with an individual parameter set. Initially, the parameters of each SPU were determined via a 5-fold cross-validated grid search over the training data, using the _GridSearchCV_ method of _scikit-learn 1.4.1_. The parameter ranges for the grid search were chosen to promote high sensitivity to variations and support the detection of anomalies in the experimental data during the damage identification step. It was found that the SHM application is more robust when the same parameter set is used for all SPUs. Therefore, the most frequently selected parameter values from the individual grid searches were adopted uniformly for all SPUs. The non-default histogram-based GBRT hyperparameter values are listed in Table 1.  
In a final step, the available transformation results are processed for damage detection and localization. Figure 6: Architecture of the SHM application, illustrated for an arbitrary feature \(j\). The spatially discrete strain sensor values are illustrated as circles in a grid layout. The fitted regression model is emphasized through a green box. The regression models are implemented such that they accept two-dimensional as well as one-dimensional arrays (i.e., data matrices and vectors) as input [25].
# Digital Twin-Driven Virtual Network Architecture for Enhanced Extended Reality Capabilities
## 3 Architecture's Proposed Approach
### System-Supporting Architecture
#### 3.2.1 Original Network Manager (ONM)  
The ONM is the entity responsible for acquiring the ON information and forwarding it towards the Digital Twin Manager with the aim of creating/updating the DTN. The modules that compose the ONM are described next:  
* Edge Router: This is designed to serve as a communication interface, enabling the transmission of ON information to the DTM and the reception of DTN status information from the DTM. It is fundamental that the Edge Router is designed with the intelligent ability to determine the optimal transport channel to guarantee a successful packet delivery to the DTM.
* Network Twinning: This is responsible for receiving a request generated in the DTM for the collection of complete network information (network entities, topologies and configurations) from the ON. The collected data are forwarded to the Edge Router for the space transmission process.
* Event Controller: This is in charge of the continuous ON network status monitoring. In the event of network modifications, the Event Controller transmits the \"event\" data (information regarding the ON modification) to the Edge Router for the DTN synchronization process.
* DTN Synchronization Status: This is responsible for receiving the current DTN status in order to keep the ONM aware of the DTN synchronization state. In addition to synchronization status, this module is capable of receiving information from the DTN related to extended functions with the aim to provide the extended service to the ON.
* Content Delivery Agent: This has the function of forwarding incoming DTN data packets to the ON device and vice versa.  
Regarding the Network Twinning and Event Controller modules, both need to use different mechanisms to compile the ON data, i.e., sketches or templates. In addition, such modules need a network model (adjacency matrix, knowledge graph, etc); while it is not mandatory that the network model owned by these ONM modules is the same as the network model owned by the corresponding DTM modules, both network models must be compatible. Both aforementioned modules must permanently maintain the ON configuration and topology information as network models; the Network Twinning is involved in the DTN deployment phase, while the Event Controller transmits information when an update is performed in the ON and it must be updated in the DTN. Finally, it is important to highlight that the Network Twinning, Event Controller and DTN Sync.Status deal with control packets, while the Content Delivery Agent deals with data packets.
## The Impact of Different Aerosol Models
### DFS of CO\({}_{2}\)  
Based on the theory and equations set out in Section 2, the averaging kernel matrix **A** of CO\({}_{2}\) retrieval is calculated. The diagonal elements of **A** correspond to the DFS of each parameter in the state vector; then, those elements related to the CO\({}_{2}\) profile are summarized as CO\({}_{2}\) DFS. The dependence of CO\({}_{2}\) DFS on the number of aerosol parameters to be retrieved is shown in Figure 7 for the different aerosol types. When the number of aerosol parameters to be retrieved is fixed, various combination groups of parameters are available. Each whisker-box in Figure 7 represents the statistical distribution of DFS for all groups, with a constant number of retrieved aerosol parameters.  
Figure 7 shows that the DFS of CO\({}_{2}\) ranges from 1.4 to 1.9 when different aerosol parameters are retrieved, suggesting that fewer than two CO\({}_{2}\) profiles could be retrieved. A comparison of the four aerosol types reveals that the CO\({}_{2}\) DFS for DD was the largest, at 1.6-1.9 DFS, while slightly less CO\({}_{2}\) DFS for UI and BB shows the scramble for more aerosol DFS due to the larger AOD. As the number of aerosol parameters involved in the CO\({}_{2}\) retrieval changes from 1 to 16, the mean CO\({}_{2}\) DFS decreases, especially in the case of DD with a reduction of approximately 0.3 DFS. In contrast, the CO\({}_{2}\) DFS for MA is only slightly reduced, by approximately 0.1. Because the range of the CO\({}_{2}\) DFS expands when there are more aerosol parameter combination groups with the same number of parameters, careful consideration must be given to the selection of parameters. In conclusion, if more aerosol parameters are added during the retrieval, the CO\({}_{2}\) information will decrease to the detriment of the XCO\({}_{2}\) retrieval. Therefore, in combination with the aerosol information analysis presented in Section 4.2, it is more appropriate for the CO\({}_{2}\) retrieval to be limited to four to seven simultaneous aerosol parameters. The question of which aerosol parameters are most appropriate for retrieval will be discussed in the following section.
# Deformation Behaviour of Cold-Rolled Ni/CNT Nanocomposites
## 3 Results and Discussion
### Microstructural Characterization of Cold-Rolled Samples
Figure 7 shows the average density values of the GNDs, and the respective maps observed for Ni and Ni/CNTs samples after sintering and cold-rolling up to different strains. In this figure, it is possible to observe a decrease in dislocation density for a strain value of 0.11. For strains of 0.36 and 0.69, the dislocation density increases similarly. As predicted by [PERSON]'s model, the slight growth of GNDs is observed for these strains [36]. This can be attributed to the fact that for high strains, the statistically stored dislocations (SSDs) become more significant, resulting in the stabilization of the density of GNDs. The decrease in dislocation density observed for a strain of 0.11 can explain the relatively low hardness values observed for the samples when subjected to small strains by  
Figure 7: Estimated average geometric necessary dislocation (GNDs) density evolution and maps of Ni and Ni/CNT as sintered and cold-rolled with strains of 0.11, 0.36, and 0.69. (scale bar = 20 \(\upmu\)m). rolling (0.11 and 0.22). This can be related to the Bauschinger effect due to the annihilation and reorganization of dislocations by reversing their path during the initial rolling phase. The Bauschinger effect can be influenced by the presence of the phase in the matrix [37]. For nanocomposites, due to the presence of the CNTs and the few particles of Ni\({}_{3}\)C, this effect can be pronounced and can result in a decrease in the strain hardening of the nanocomposites. Another aspect that needs to be pointed out is that, for the nanocomposites, the initial density of GNDs is higher than that of the Ni matrix, which will also affect dislocation motion during the cold-rolling and, consequently, the Bauschinger effect. The Bauschinger effect was also already observed by [PERSON] et al. [38] in aluminium nanocomposites reinforced by CNTs through loading-unloading tests, where the authors observed an accentuated Bauschinger effect on the nanocomposites rather than in the aluminium matrix, showing that the CNTs can in fact influence this behaviour. Similar conclusions were also drawn by [PERSON] et al. [39] regarding a 6061-aluminium alloy, and [PERSON] et al. [40] with Al, both reinforced by CNTs, and by some other authors studying similar nanocomposites [17]. This Bauschinger effect can also explain that the hardness values of the nanocomposites are lower than those of the nickel matrix. However, the texture evolution may also affect this variation in hardness values since the samples show different crystallographic orientations after sintering, which can affect the active slip planes during the rolling deformation process.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 6 Conclusions  
In this survey, we reviewed the research literature on congestion control and load balancing in RPL. To understand the trends, we categorized the proposals based on several criteria including target traffic patterns, routing metrics, cross-layer design approaches, and whether to exploit path diversity. In particular, the trends regarding routing metrics and cross-layer design approaches were summarized in more detail. It is notable that many schemes actively exploit path diversity by forwarding packets via multiple parents on a per-packet basis.  
We also proposed future research directions which include RPL design considering more capable hardware platforms, investigation through real implementation and experiments, application of multiple RPL instances, interoperability with TCP, and cross-layer based design. We expect that the paradigm shifts mentioned in [55] will motivate researchers to revisit more issues. As diverse IoT application scenarios evolve, congestion control mechanisms for RPL are expected to continue to adapt to new requirements as well.  
**Funding:** This research was supported by 2018 Hongik University Research Fund.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Work
### Unsupervised Feature Selection Methods on Incomplete Data Sets  
Since obtaining class label information is difficult in real-world applications, UFS is one of the dimensionality reduction techniques used to handle high-dimensional data.  
UFS methods are usually divided into three categories: filter models, wrapper models, and embedded models. The filter model [24] first selects the features of the data set and then trains the classifier, and the feature selection process is independent of the data mining task. Hence, they usually have a lower computational cost. Lapscore [25] is one of the classical filtering methods, which independently calculates the score of each feature according to its ability to retain the internal structure of the original data, and then select the feature with the highest-ranking score.  
The wrapper model [26] does not consider the difference of a specific classifier and directly takes the performance of the classifier to be used as the evaluation criterion of the feature subset. In general, the wrapper method can achieve better performance than the filter method but with high computational cost. LVW (Las Vegas wrapper) is a typical encapsulated feature selection method. It uses a random strategy to search subsets within the framework of the Las Vegas method and evaluates feature subsets based on the error of the final classifier.  
The embedded model [27] integrates feature selection into the learning model. Since there is no need to evaluate feature subsets, they are more efficient than the wrapper approach. Therefore, many representative embedded methods have emerged continuously. For example, the general framework for sparsity regularization (GSR) [28] is a general sparse embedding model, which can simultaneously perform feature selection and reduce outliers through parameter adjustment. Robust feature selection (RFS) [29] is another typical embedded model of feature selection. The method has proven its effectiveness in reducing the influence of outliers. Regularized self-representation (RSR) [8] is a framework in which every feature is reconstructed from all features using self-representation, and features are selected using L\({}_{2,1}\)-norm regularization.  
The authors of [30] present a method of multicriteria-based feature selection in cost-sensitive data with missing values, using a rough set theory to deal with unobserved information. [PERSON] proposed the HQ-UFS method, whichcan be directly applied to incomplete data sets [23]. The index matrix is used to filter the unobserved information, and the half-quadratic minimization technique is used to make the weight of outliers negligible or even zero, whereas the weight of essential samples is more prominent, thereby reducing the influence of outliers. In [31], an L\({}_{2,1}\)-norm minimization method for UFS from incomplete data was proposed, with the authors showing that the proposed algorithm can select more accurate features from a large data set and showing an improved clustering effect.
# Tuning of a Viscous Inerter Damper: How to Achieve Resonant Damping Without a Damper Resonance
## 1 Introduction
### Hybrid Mass and Inerter Dampers  
Because of the working similarity of TMDs and inerter-based TIDs, the merger of the two damper concepts has been considered extensively in the research literature, especially in connection with the damping and protection of building-type structures. The fundamental extension of the TMD by simply connecting an inerter between the auxiliary TMD mass and another building floor is introduced in [33] and referred to as the tuned mass damper inerter (TMDI). In [12], the TMDI is investigated in detail for various applications: vibration suppression, damper mass reduction, and the harvesting of energy. Subsequently, the TMDI has been considered in [34; 35; 36] for installation in building structures, focusing on harmonic excitation, earthquake loading, and wind-induced vibrations. As for the basic inerter dampers, the TMDI has been shown to possess superior base-isolation abilities with limited damper stroke in [37; 38]. Furthermore, the TMDI also allows for the bridging of stories, as considered in [35; 39] for wind-induced vibrations. In the detailed study in [39], it was shown that for a realistic bridging of stories, corresponding to the space occupied by a pendulum absorber, the classic TMD outperforms the TMDI.  
More advanced inerter augmentations of the classic TMD have been proposed in the literature. The basic TMDI in [39] was improved by the extending the inerter by a spring and the TMD dashpot [14]. This more advanced TMD inerter device was shown in [14] to give better performance when tuned precisely. The inclusion of various inerter-type devices in the classic TMD is considered [16], whereby double-resonance absorbers are created. It is shown that these augmented absorbers create a very wide and flat plateau in the frequency response curves at the targeted structural frequency. Closed-form solutions for the tuning of such double-resonant TMD inerter absorbers were derived in [40] for the most common combinations of inerter, spring, and dashpot. Reviews of inerter-based dampers for structural vibration damping are given by [41; 42].
###### Abstract  
The effectiveness of shot peening is mainly determined by the peening coverage. The peening coverage is required to be 100% for current technical standards of shot peening. With the increase of material strength, higher peening coverage is required in shot peening process. However, the influence of high peening coverage on Almen intensity and residual compressive stress is unclear, the difficulty mainly lies in the lack of quantitative description of peening coverage in finite element analysis. To analyze the influence of high peening coverage on Almen intensity and residual compressive stress, firstly an approximate quantitative description of peening coverage based on dent size, the distance of shots and shot numbers is proposed in this study. Based on this quantitative description of peening coverage, the arc height and residual stress of the Almen test are simulated with the finite element method. The simulation results of arc height and saturation curve agree well with that of the Almen test, by which the effectiveness of the quantitative description and FE simulation are proved. The further study indicates that in shot peening processes, the excessive peening coverage doesn't improve Almen intensity and residual compressive stress.  
Keywords:shot peening; quantitative description of peening coverage; high peening coverage; Almen intensity; residual compressive stress +
Footnote †: journal: Applied Sciences