esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Modeling Languages for Applications Hosted on Cloud-Edge Computing Environments
## 2 Review Methodology
### Research Questions  
This survey paper seeks to answer the research question regarding the portrayal of an application component by CMLs. Given the absence of a uniform syntax, scope, and dictionary among CMLs, the paper aims to explore the various approaches they employ in describing application components. Additionally, the survey examines how CMLs address the depiction of deployment phases and runtime adaptation, alongside their descriptions of application components. The research questions are the following:  
1. How modeling languages can model application components along with their requirements?
2. How modeling languages are describing the deployment phase of an application?
3. Are there any CMLs that can model the runtime adaptation of application components? If there are any, how are they able to provide this description?
## 8 Technical Challenges and Potential Solutions
### Strength-to-Weight Issues of Industrial Modular Units  
IMUs are made of varying materials depending on the building's height, size, fire protection, sound and thermal insulation, strength, and connection techniques. In addition to the advantages, demerits are also associated with each of them. For example, besides being prone to fire, timber IMUs cannot exceed four stories in height unless supported by additional structures, making them uneconomical [182]. Concrete IMUs have a high bearing capacity, easy availability, and good fire, sound, and thermal insulation, but the hoisting difficulty, size limitation, uneasy transport, and challenging joining make them unsuitable [19]. Aluminum IMU avoids welding and has a high strength-to-weight ratio and lower construction tolerances than steel, but it is brittle and costly [19]. The steel IMU is 10-15 tones lighter, the construction is faster, and the connectivity is simpler. However, a poorer thermal and acoustic insulation, fire protection, larger cross-sectional needs, bracing, and column instability necessitate a complex design and construction [38]. In addition, with the reduction in the self-weight, the FRP/GFRP industrial panel systems showed a good strength, fast handling, reusability, high strength-to-weight ratio, ability to withstand loads, and excellent post-fire performance [125].  
Given the merits and demerits referred to above, it is recommended that further research be carried out on developing an IMU that combines the materials listed with the particular merits of a high strength-to-weight ratio, durability, stability, and sustainability. The authors attempted to integrate several approaches to make recommendations for future work. It includes the development of composite IMU columns and beams using high-strength, high-performance, lightweight concrete, and lightweight steel-composite or FRP/GFRP-composite walls, floors, and ceiling panels. This can increase the IMUs' strength/weight ratio, increasing the IMUs' size while its number is lowered. The composite columns and beams technique was applied in SOHO Tower, in Australia, to achieve the 29-story height of a purely 3D PFISB without a concrete core for the lateral stabilization. Moreover, using SHS with infilled concrete sheathing, multi-column wall systems have increased the in-plane and out-of-plane strength while achieving an 18-story 3D PFISB [45]. Therefore, these systems can also be adapted to achieve lightweight modules for high-rise steel facilities. However, more research is required to manufacture lightweight, sustainable, stable IMUs.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
###### Abstract  
This paper proposes a new communication protocol for output-feedback control through multi-hop Wireless Sensor Network (WSN). The protocol is based on a Hop-by-Hop transport scheme and is especially devised to simultaneously fulfill two conflicting criteria: the network energy consumption and the stability/performance (in terms of \(\mathcal{H}_{\infty}\) norm) of the closed-loop system. The proposed protocol can be implemented by means of three heuristics, basically using distinct rules to control the maximum number of retransmissions allowed in terms of the voltage level of the batteries of the network nodes. As another contribution, a Markov jump based representation is proposed to model the packet loss in the communication channel, giving rise to a systematic procedure to determine the transition probability matrix and the Markov chain operation modes of a network with multiple information sources. The synthesis of the output-feedback controller is made in two steps (observer filter plus a state-feedback controller) for the Markov model assuming partial availability of the operation modes. The efficiency and applicability of the communication protocol is illustrated by means of a numerical experiment, based on a physical model of a coupled tanks plant. The features of each heuristic of implementation of the proposed protocol are presented in the numerical comparisons.  
networkedcontrol systems; Wireless Sensor Networks; Hop-by-Hop transport scheme; semi-reliable communication protocol; packet loss
# An Aeromagnetic Compensation Algorithm Based on a Temporal Convolutional Network
## 2 Materials and Methods
### MagTCN
#### 2.2.3 Reversible Instance Normalization Module  
In time-domain aeromagnetic compensation tasks, the difference in the mean or variance between the training and testing datasets leads to a decrease in compensation accuracy. To address the issue of distribution differences between different survey data, we introduced a reversible instance normalization module (RevIN) [29], on both sides of MagTCN, consisting of a normalization module and a denormalization module.  
The input of the MagTCN backbone is represented as \(X=\left\{x^{(i)}\right\}_{i=1}^{N}\), and the corresponding output is represented as \(Y=\left\{y^{(i)}\right\}_{i=1}^{N}\). If \(K\) is the number of channels, \(T_{x}\) is the length of the input sequence, and \(T_{y}\) is the length of the output sequence, then there exists an input \(x^{(i)}\in R^{K\times T_{x}}\) and an output \(y^{(i)}\in R^{K\times T_{y}}\).  
Figure 3: The convolution process of point-wise convolutions. (*) indicates the operation of convolution on the input.  
Figure 2: The convolution process of depth-wise convolutions. (*) indicates the operation of convolution on the input.  
For the normalization part of the MagTCN backbone input, the mean and variance are used for normalization. The mean can be expressed as follows:  
\[\mathbb{E}_{t}\Big{[}x_{kt}^{(i)}\Big{]}=\frac{1}{T_{x}}\sum_{j=1}^{T_{x}}x_{kj}^ {(i)} \tag{30}\]  
The variance can be expressed as follows:  
\[\text{Var}\Big{[}x_{kt}^{(i)}\Big{]}=\frac{1}{T_{x}}\sum_{j=1}^{T_{x}}\Big{(}x_ {kj}^{(i)}-\mathbb{E}_{t}\Big{[}x_{kt}^{(i)}\Big{]}\Big{)}^{2} \tag{31}\]  
The normalization process can be expressed as follows:  
\[\hat{s}_{kt}^{(i)}=\gamma_{k}\Bigg{(}\frac{x_{kt}^{(i)}-\mathbb{E}_{t}\Big{[}x _{kt}^{(i)}\Big{]}}{\sqrt{\text{Var}\Big{[}x_{kt}^{(i)}\Big{]}+\epsilon}} \Bigg{)}+\beta_{k} \tag{32}\]  
where \(\gamma,\ \beta\in R^{K}\) represent learnable affine transformation parameters.  
The denormalization process can be expressed as follows:  
\[\hat{y}_{kt}^{(i)}=\sqrt{\text{Var}\Big{[}x_{kt}^{(i)}\Big{]}+\epsilon}\cdot \Bigg{(}\frac{\hat{y}_{kt}^{(i)}-\beta_{k}}{\gamma_{k}}\Bigg{)}+\mathbb{E}_{ t}\Big{[}x_{kt}^{(i)}\Big{]} \tag{33}\]
###### Abstract  
Rolling element bearings (REB) are widely used in all walks of life, and they play an important role in the health operation of all kinds of rotating machinery. Therefore, the fault diagnosis of REB has attracted substantial attention. Fault diagnosis methods based on time-frequency signal analysis and intelligent classification are widely used for REB because of their effectiveness. However, there still exist two shortcomings in these fault diagnosis methods: (1) A large amount of redundant information is difficult to identify and delete. (2) Aliasing patterns decrease the methods' classification accuracy. To overcome these problems, this paper puts forward an improved fault diagnosis method based on tree heuristic feature selection (THFS) and the dependent feature vector combined with rough sets (RS-DFV). In the RS-DFV method, the feature set was optimized through the dependent feature vector (DFV). Furthermore, the DFV revealed the essential difference among different REB faults and improved the accuracy of fault description. Moreover, the rough set was utilized to reasonably describe the aliasing patterns and overcome the problem of abnormal termination in DFV extraction. In addition, a tree heuristic feature selection method (THFS) was devised to delete the redundant information and construct the structure of RS-DFV. Finally, a simulation, four other feature vectors, three other feature selection methods and four other fault diagnosis methods were utilized for the REB fault diagnosis to demonstrate the effectiveness of the RS-DFV method. RS-DFV obtained an effective subset of five features from 100 features, and acquired a very good diagnostic accuracy (100%, 100%, 99.51%, 100%, 99.47%, 100%), which is much higher than all comparative tests. The results indicate that the RS-DFV method could select an appropriate feature set, deeply dig the effectiveness of the features and more exactly describe the aliasing patterns. Consequently, this method performs better in REB fault diagnosis than the original intelligent methods.  
fault diagnosis; feature selection; dependent feature vector; rough set; rolling element bearing; empirical mode decomposition (EMD) +
Footnote †: journal: Applied Sciences
# Semi-Supervised Drivable Road Segmentation with Expanded Feature Cross-Consistency
## 3 Methodology
### Network Structure  
* **Main Encoder** The main-encoder is based on ResNet-50 [44], with dilated convolutions, followed by one PSP module [45] for additional enhancements in extracting features. It is a widely used general backbone that has been proven to have good performance in different segmentation tasks. The input of the main-encoder is unperturbed images and it outputs feature maps as the input for the main-decoder. The feature maps concatenate both high-dimensional and low-dimensional features, extracted by residual layers composed of a series of bottleneck blocks, shown in Figure 3.
* Main Decoder: Feature maps generated from the main-encoder are fed into the main-decoder to predict the semantic segmentation results of the drivable road area. To maximize the robustness of decoding both original and perturbed features from different encoders, after one Conv2d layer, we only employ the simple \(1\times 1\) 2d-convolution and three pixel shuffle modules as the main-decoder, where a pixel shuffle module consists of three layers: Conv2d, ReLU, and PixelShuffle, shown in Figure 4.  
Figure 3: The detailed structures of the main-encoder.  
* Auxiliary Encoders: Auxiliary encoders consist of several aux-encoders with different perturbations, including VAT, dropout, feature noise, salt noise, color jittering, and lighting perturbations. And there is more than one aux-encoder for each kind of perturbation. It is denoted as \(\mathbf{En}_{aux}=\{\mathrm{En}^{1}_{aux},\dots,\mathrm{En}^{i}_{aux},\dots, \mathrm{En}^{K}_{aux}\}\), where \(K\) is the total number.
* Auxiliary Decoders: Auxiliary Decoders \(\mathbf{De}_{aux}\) are composed of aux-decoders with perturbations including VAT, dropout, feature noise, feature drop, cutout, and masking, which are not exactly consistent with those used in the aux-encoder because of differences in properties during training in different modules. In the same way as aux-encoders, it can be formulated as follows: \(\mathbf{De}_{aux}=\{\mathrm{De}^{1}_{aux},\dots,\mathrm{De}^{i}_{aux},\dots, \mathrm{De}^{K}_{aux}\}\).
# Integrated Design of Hydraulic Coupling Bolts for Large Shaft Systems
## 5 Operational Experiment of Hydraulic Coupling Bolts  
To verify the results of the design and structural analysis of coupling bolts performed in this study, prototypes of coupling bolts were fabricated, and tightening and untightening experiments were conducted. Figure 14 shows all the components of the coupling bolt with the M72 specification and the hydraulic pump manufactured for the experiment. The material of the bolt and sleeve was SNCM439. Before conducting the experiment, a thorough examination of the bolt and sleeve was carried out to identify any potential flaws using liquid penetrant testing by the Korea Testing and Research Institute. The entire test procedure, equipment calibration, and repeatability were meticulously overseen and validated by the Research Institute of Medium and Small Shipbuilding.  
The procedures and results for coupling bolt operation are displayed in Figure 15. First, oil was applied to the hydraulic tensioner at a pressure of 400 bar, as shown in Figure 15a, which is the first tightening pressure determined from the FE analysis results. This induced the tensile force to the bolt through the adapter in the direction of the red arrow, as indicated in Figure 15a. While being inserted in the interference fit state, the bolt expanded the sleeve in the radial direction, resulting in the first tightening force by a contact pressure. Following this, a pressure of 730 bar was employed for the second tightening process, as shown in Figure 15b. With one end fixed by the nut, the bolt was tensioned, which was subsequently clamped by the nut in the distance ring, as shown in the inset of Figure 15b. When the hydraulic oil was released, the elastic behavior of the bolt generated a compressive axial force, i.e., second tightening force, on the contact surfaces between nuts and the coupling. The untightening process was performed in the reverse order of the tightening process. The clamping force in the axial direction was unloaded by tensioning the bolt with the same pressure as that of the second tightening process and loosening the nut in the distance ring. To release the radial fastening force, oil was supplied into the passage through an oil injector, as shown in Figure 15c, and the oil pressure was gradually increased within the range of the second tightening pressure. Consequently, the bolt was effectively disengaged from the sleeve in the direction of the blue arrow, as indicated in Figure 15c. The results of the experiment show that the proposed design method and FE analysis model were effective in designing the hydraulic coupling bolt, leading to sound tightening and untightening processes under the operation conditions determined through theoretical and FE analyses. Therefore, we believe that the proposed design method could be widely used to design hydraulic coupling bolts for large shaft systems.  
Figure 14: Components of hydraulic coupling bolt and hydraulic pump.
# Phosphorus Mobilizing Enzymes of _Alnus_-Associated Etomycorrhizal Fungi in an Alaskan
## 3 Results
### Fungal Community Identification  
After removal of potential fungal pathogens and non-EMF taxa, we identified 86 unique fungal phylotypes, comprised of 45 basidiomycetes and 41 ascomycetes on 181 roots tips across all plant hosts (Appendix C). The overall fungal community was dominated by basidiomycetes (65%) over ascomycetes (35%). This trend was similar on the 102 _Alhus_ roots tips (69% basidiomycetes versus 31% ascomycetes) as well as for _Salix_ sp. tips (73% basidiomycetes versus 27% ascomycetes). However, on _Picea_ root tips, there tended to be more ascomycetes (61%) than basidiomycetes (39%). No ascomycetes were identified on _Populus_ root tips.  
Figure 2: Comparison of mean \(\pm\) S.E. activities of acid phosphatase, phosphodiesterase and phytase of alder root tips with Salicaceae root tips in early succession (**a**), and with white spruce root tips in late succession (**b**). Significant differences between hosts are indicated above bars at \(p<0.10\) (*), 0.05 (**), and 0.01 (***). Marginal (\(R^{2}\)\({}_{m}\)) and conditional (\(R^{2}\)\({}_{c}\)) values generated for linear mixed effects models evaluate variance explained by fixed and fixed + random effects, respectively (see Statistics section above).  
Across all hosts, root tips with basidiomycete fungi had significantly higher activities of all enzymes compared with root tips with ascomycete fungi (Figure 3). In general, there was little overlap of dominant fungal species among the EMF communities on different plant hosts (Figure 4). Analyses of fungal communities indicated that EMF fungal community composition varied significantly among hosts (\(p<0.05\)), with significant differences between alder and Salicaceae root tips (\(p<0.01\)), and between alder and white spruce (\(p<0.10\)), but not between Salicaceae and white spruce (\(p=0.45\)) (Figure 4). Horizon did not explain any of the variation in fungal community structure, and comparisons among stages, while significant (\(p<0.05\)), are difficult to interpret due to unequal representation of host species across stages.
# ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles
## 2 Related Work
However, EPBP still is suitable for the straight road scenarios. In order to solve the problem, a complete EPBP-based curved road relay-node selection method is proposed in [25]. It implements relay-node selection in curved road scenarios through three modes: the normal selection, the reverse selection, and the double-direction selection. When a vacant appears in the normal selection, it will enter the double-direction selection. At this time, the reverse selection and the normal selection are performed simultaneously, and the farthest point from the sender in a vacant as the end point in the reverse selection. Through the three modes, it achieves a high broadcast coverage. However, it has a disadvantage in that it does not consider the influences of obstacles. Thus, an EPBP-based junction relay-node selection method is proposed in [26]. Improving on EPBP, it implements relay-node selection in junction scenarios with obstacles through two phases: the junction phase and the branch phase. It selects the node close to the center of the junction as the relay-node in the junction phase, and selects the farthest node on each branch as the relay-node in the branch phase. Compared to BPAB, it achieves a lower delay. However, it does not consider the situation where the branches are not a straight road. Though these black-burst-based methods [21; 22; 23; 24; 25; 26], including our EPBP-based work [24; 25; 26], show better performance compared to the beacon-based methods [19; 20], they are only suitable for a certain road structure, e.g., the methods in [24; 27] are only for straight roads, that in [26] are only for junctions, and in [25] are only for curved roads. However, in the real world, varied road structures may exist in RoI and multiple road structures in the communication range of the sender. Moreover, the distribution of the obstacles can affect the relay-node selection. Therefore, in this paper, we have designed ARNS by fully considering the above situations to achieve better robustness. In the next section, we will describe the scenarios and state the problems.
# Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
## 3 Experimental Results and Discussion  
### Model Parameter Selection  
When MA-LSTM is used for TEC modeling, the optimal parameters of the model need to be determined first. In this paper, the grid search method is adopted to find the optimal super parameters. Result of grid search is as shown in Table 2.
# A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks
## 3 State Estimation Model Based on Sample Migration
### Algorithm Process of Proposed Method  
This paper proposes a data-driven state estimation method based on sample transfer to tackle the issue of low-observable distribution networks being unable to perform state calculations normally. The specific process is demonstrated in Figure 2.  
During offline training, the state estimation model is trained using a historical database (source domain). The model's input features include measurements of a distribution network described in Section 3.1. In the online estimation stage, real-time measurements of the distribution networks are collected, and the integrity of these measurements is evaluated. If the measurement data are sufficient and the original model meets the estimation accuracy requirements, the input features are directly input into the trained model. Otherwise, the distribution networks serve as the target domain for feature extraction and transfer learning.  
Figure 2: Algorithm flowchart of this article.
## 5 Sub-Areas of Traffic Prediction Applications Using RNNs
### Traffic Flow Prediction
[88] proposed a sequential model called an LSTM-Light Gradient-Boosting Machine (LSTM-LightGBM) for hourly traffic flow prediction, taking into account the temporal, periodic, and spatial features of traffic flow. The model leverages LSTM to capture temporal characteristics and LightGBM to capture spatial and periodic features. Testing on the Chicago traffic dataset demonstrated that the LSTM-LightGBM model outperforms other baseline models, with a potential reduction in the RMSE of up to 50%, considering that weather factors are crucial for traffic flow prediction. However, existing models typically utilize shallow prediction techniques, resulting in suboptimal accuracy when accounting for external factors, such as weather. [PERSON] et al. [89] introduced an enhanced LSTM model with an attention mechanism for short-term traffic flow prediction that considers weather factors. Experiments conducted on the Caltrans PeMS traffic dataset demonstrate that the proposed model outperforms the original LSTM model, reducing its Mean Absolute Percentage Error (MAPE) by 23.36%. [PERSON] et al. [90] introduced a novel short-term traffic flow and speed prediction approach. The model effectively combines attention mechanisms, CNNs, and GRUs within a multi-task learning framework. This innovative method demonstrates enhanced accuracy and robustness in predicting traffic conditions in diverse urban and highway scenarios, although it requires substantial computational resources and complex data processing. The literature presents a diverse array of approaches for traffic flow prediction, encompassing advanced deep learning architectures and incorporating multiple dynamic factors and spatiotemporal data fusion. These developments underscore the ongoing progress and innovation in the field as researchers strive to enhance the accuracy and reliability of traffic flow prediction.
# On the Spectral Efficiency for Distributed Massive MIMO Systems
###### Abstract  
Distributed MIMO (D-MIMO) systems are expected to play a key role in deployments for future mobile communications. Together with massive MIMO technology, D-MIMO aims to maximize the spectral efficiency and data rate in mobile networks. This paper proposes a deep study on the spectral efficiency of D-MIMO systems for essential channel parameters, such as the channel power balance or the correlation between propagation channels. For that purpose, several propagation channels were acquired in both anechoic and reverberation chambers and were emulated using channel simulators. In addition, several frequency bands were studied, both the sub-6 GHz band and mmWave band. The results of this study revealed the high influence of channel correlation and power balance on the physical channel performance. Low-correlated and high-power balance propagation channels show better performances than high correlated and power unbalance channels in terms of spectral efficiency. Given these results, it will be fundamental to take into account the spectral efficiency of D-MIMO systems when designing criteria to establish multi-connectivity in future mobile network deployments.  
distributed MIMO systems; anechoic chamber; reverberation chamber; spectral efficiency; power balance +
Footnote †: journal:  
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