esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 2 Theoretical Review
### Ad-Hoc Networks Routing Protocols  
The purpose of a routing algorithm is to find good paths between a source and a destination node. Usually, the best path is one that has the lowest cost [47]. There are several routing algorithms, some of them aim to find the lowest cost path according to a defined metric. Then, for these protocols, the most common metric is the hop count where the cost of a path is the sum of the number of hops between source and destination.  
Ad-hoc network routing protocols must be able to handle a dynamic topology. This feature brings several challenges in their development. In general, these routing protocols can be divided into three subclasses:  
* Reactive protocols [48] which exchange topology information on demand. In this type of protocol, the exchange of information about the topology occurs only when a node wants to send a message. In the reactive protocol, the redundancy in the transmission of service messages is lower in relation to other type of protocols.  
* Proactive protocols [49] which continuously update the route information by sending control messages. In this type of protocol, the exchange of information between nodes about the network topology occurs even before any node sends any packets. Proactive protocols generally provide greater flexibility in route selection compared to reactive ones. However, it produces a greater number of control messages that increases the overhead.
* Hybrid protocols [50] that combine proactive and reactive characteristics. In hybrid protocols, some routes are created using a proactive approach and later the protocol works reactively.  
As previously stated, for performance validation, the proposed algorithm is compared to BATMAN, OSPF and RLRP routing protocols, which are described in the following lines.
## 5 Design, Manufacturing and O&M Integration Key Technology of Complex Product Based on Digital Twin
### LSTM Neural Network Fault Prediction Technology  
LSTM belongs to one of the recurrent neural networks. Such a network can shorten the prediction time when improving prediction precision to guarantee the research of the relevant field. The logic processing unit of the LSTM neural network is shown in Figure 8.  
In the figure, \(f_{t}\), \(i_{t}\) and \(O_{t}\) represent the door control signal of the forgetting gate, in-gate and out-gate, respectively. \(x_{t}\) indicates the grid input data at \(t\). \(C_{t}\) indicates the output value of a hidden layer at the current moment and the input value of the output layer. \(C_{t-1}\) indicates the output value of the hidden layer at the last moment. \(\sigma\), activation function, is used to finish the nonlinear transformation of data and indicates the nonlinear mapping relationship between the in-gate and out-gate. The top line represents the state of the neuron. The logic processing unit of LSTM has the following mathematical expression:  
\[\begin{array}{l}f_{t}=\sigma(W_{if}x(t)+W_{hf}h_{t-1}+b_{f})\\ i_{t}=\sigma(W_{ii}x(t)+W_{hi}h_{t-1}+b_{i})\\ O_{t}=\sigma(W_{iO}x(t)+W_{ho}h_{t-1}+b_{o})\\ g_{t}=\tanh(W_{ig}x_{t}+W_{hc}h_{t-1}+b_{g})\\ h_{t}=O_{t}\cdot tanh(C_{t})\\ C_{t}=f_{t}\cdot C_{t-1}+i_{t}\cdot g_{t}\end{array}\]  
wherein \(W_{if}\), \(W_{ii}\) and \(W_{iO}\) indicate the weight matrix of input with the forgetting gate, in-gate and out-gate, respectively; \(W_{hf}\), \(W_{hi}\) and \(W_{ho}\) indicate the weight matrix of hidden layer input with forgetting gate, in-gate and out-gate, respectively, at the last moment; and \(b_{f}\), \(b_{i}\) and \(b_{O}\) indicate the bias matrix.  
During input, the LSTM network enters the nerve cell status through the sigmoid function control information in the forgetting gate so that the output at the last moment passes or partially passes. Second, the information needing updating is generated and is composed of two parts, including the input door layer, which determines the update value through the sigmoid function, and the tanh layer, which is used to generate new candidate values in addition to obtain new candidate values. Those two parts are combined to throw away unnecessary information and add new information. Finally, the model output is carried out. An initial value is worked out through the sigmoid layer, then is zoomed between \(-1\)-\(1\) based on the tanh function, and multiplies with the output value obtained through sigmoid function one by one.  
Figure 8: The logical processing unit of the LSTM neural network.
## 2 Results and Discussion
### Antioxidant Activity
#### 2.4.2 Lipid Peroxidation Inhibition Assay  
The oxidative process is complicated in food and biological systems and embroiled in multifarious reactions for the propagation of lipid radicals hydroperoxides [41; 47]. As a consequence, we used the lipid peroxidation inhibition assay in a linoleic acid model system to determine the antioxidant abilities of five antioxidant peptides (STP1-STP5). As presented in Figure 6, the absorbance values at 500 nm of the STP2 and STP5 solutions were significantly lower than that of the blank control (without antioxidant) and the other three peptides (STP1, STP3, and STP4). Furthermore, the absorbance values at 500 nm of the STP2 and STP5 solutions were a little higher than that of the positive control of GSH. The data indicated that the abilities of STP2 and STP5 on lipid oxidation inhibition were similar to that of GSH in the tested system during 7 days incubation.  
4.3 Relationship among the Molecular Size, Amino Acid Composition, and Antioxidant Activity of STP1-STP5  
Molecular size and amino acid composition and sequence are thought of as the key roles in the antioxidant capacities of peptides [41]. In the study, the five antioxidant peptides (STP1-STP5) from the gelatin hydrolysate of skipjack tuna bone are tetrapeptide to octapeptide with MWs ranging from 374.39 Da to 756.84 Da (Table 3). The data indicated that the five isolated peptides have a higher possibility of interacting with free radicals to prevent lipid peroxidation [42; 43].  
Hydrophobic amino acids, such as Pro, Leu, Met, Ala, Val, and Ile, have a high reactivity to hydrophobic PUFAs and exert their significant effects on radical scavenging in lipid-rich foods [41; 59]. Therefore, the hydrophobic amino acid resides of Ala, Ile, and Val in STP2, and Ala and Ile in STP5 will help antioxidant peptides more easily contact with target free radicals. Moreover, aromatic groups of Phe, Trp, and Tyr can keep radicals stable during the scavenging process by contributing protons to electron deficient radicals [60]. Therefore, Phe residues in the sequences of STP5 should contribute to its antioxidant activity.  
Polar amino acids are reported to play a critical role in hydroxyl radical scavenging and metal ion chelating activities because of their carboxyl and amino groups in the side chains [61; 62]. [PERSON] et al. reported that peptides consisting of Glu, Lys, and Asp have strong abilities to chelate metal ions as well as scavenge hydroxyl radicals [62]. [PERSON] et al. reported that Arg had a greater capacity to scavenge hydroxyl radicals [63]. A single hydrogen atom of Gly residue can provide high flexibility to the peptide backbone and serve as a proton-donor to neutralize active free radicals [46; 64]. Therefore, polar amino acids, including Gly, Asp, and Arg residues in STP2, and Gly and Glu residues in STP5, could play a critical role in their radical scavenging and lipid peroxidation inhibition activities.
# Integrity Monitoring of PPP-RTK Positioning: Part II: LEO Augmentation
## 4 LEO Augmentation
### Horizontal Protection Level  
The HPL is supposed to overbound the horizontal positioning errors (HPEs) under a pre-defined \(\mathrm{PMI}_{\mathrm{H}}\). The method to calculate the HPL was described in detail in the first part of this series of papers [14]. As mentioned in [14], the HPLs, while they depend on the observation quality and geometry, the nominal error model and the threat model, they are also affected by multipath environments, ionospheric activities, network size and \(\mathrm{PMI}_{\mathrm{H}}\). In this section, the same settings of the overbounding standard deviations and biases as in Table 6 of [14] will be used to evaluate the influences of the LEO augmentation on the HPLs. Recall that the multipath environments were distinguished into four categories (A, B, C and D) with the overbounding standard deviation for phase noise/multipath \(\delta_{u0,\varphi}\) varying from 0.005 to 0.02 m, and with the corresponding overbounding bias \(\bar{b}_{u0,\varphi}\) varying from 0.01 to 0.04 cycles. Their counterparts for code noise/multipath vary from 0.5 to 2 m for the overbounding standard deviation \(\bar{\
u}_{u0,\varphi}\) and from 0.2 to 0.8 m for the overbounding bias \(\bar{b}_{u0,\varphi}\). Similarly, the ionospheric environments were distinguished into categories A, B, C and D, with the \(\bar{\
u}_{i_{1},\mathrm{lim}}\) varying from 5 to 20 mm. Details are described in Table 7 of [14]. In this contribution, the \(\mathrm{PMI}_{\mathrm{H}}\) is set to \(2\times 10^{-6}\), the smallest value among the test values in the first part of this series of papers. Four different constellation combinations are tested for the HPLs without, and with, the LEO augmentation under the  
Figure 13: (a) Convergence time of the ambiguity-float formal horizontal precision (1\(\sigma\)) to 0.02 m; (b) Ambiguity-fixed formal horizontal precision at 1 h. Scenario 1 and 2 denotes the network corrections from Network 1 with a \(\sigma_{i_{1},\mathrm{lim}}\) of 1.5 mm and from Network 3 with a \(\sigma_{i_{6},\mathrm{lim}}\) of 5 mm, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scenario** & **GEC(L)** & **GE(L)** & **GC(L)** & **EC(L)** & **G(L)** & **E(L)** & **C(L)** \\ \hline
1 & 54.2\% & 69.9\% & 65.8\% & 64.9\% & 86.2\% & 83.8\% & 79.4\% \\ \hline
2 & 68.8\% & 78.9\% & 77.3\% & 75.9\% & 93.2\% & 92.1\% & 89.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Improvement of the convergence time to 0.02 m for the ambiguity-float formal horizontal precision, when adding the LEO constellation.  
L1/L5 scenario, i.e., GPS-only (G), GPS/Galileo-combined (GE), GPS/BDS-combined (GC) and GPS/Galileo/BDS-combined (GEC).
# A Fuzzy-Logic-Based Load Balancing Scheme for a Satellite-Terrestrial Integrated Network
## 2 Materials and Methods
### Problem Description  
The process of load balancing in STIN with sparsely deployed TN cells is shown in Figure 1. Some users are on the edge of the area, having poor signal reception and requiringmore bandwidth. Some users cannot find a better cell to handover. These make TN cells likely to overload in this network. An example is shown in Figure 1. The solid arrows point to cells that users are currently accessing. The dashed arrows point to cells that users previously accessed. User \(a\) and user \(b\) are users on the edge of the area that initially accesses the NTN cell. When the TN cell \(A\) suffers overloading, user \(c\) is selected to be offloaded to the NTN cell, and user \(d\) is selected to be offloaded to the adjacent TN cell \(B\) for relatively better QoS. Thus, the problem is split into two sub-problems: the access algorithm decides which cell users initially access, and the offloading algorithm decides which users are offloaded and how to offload them.  
For the access algorithm, according to the link budget, in a scenario where cells are sparsely deployed, such as the rural scenario in 3 GPP [24], the average SNR is 9.21 dB. In the coverage range of the central beam of a GEO satellite, the average SNR is only 2.95 dB. If the received signal quality is used to determine initial access, only a few users will access the NTN cell. As a result, NTN resources are not effectively utilized and TN cells are more likely to overload. To limit access to the NTN to suitable users, we aim to consider not only received signal, but also the impact of users on cell overloading. Therefore, fuzzy logic is utilized to propose an overload evaluation metric. Then, to deal with dynamic changes in the environment and determine the appropriate number of users allowed to access the NTN, the FLRL-AC is proposed.  
For the offloading algorithm, even if the initial access is optimized, overloading may still occur after a certain period of time due to users' movements, especially when the user density in the environment is large. Additionally, if the FLRL-AC is utilized to make a global re-access decision at this time, the QoS of users in the cells which are not overload is affected. Therefore, the FL-OL is proposed for those overloaded cells. The FL-OL offloads the most suitable users to the most suitable cells by utilizing FEM.
# Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits
## 1 Introduction
At present, the use of spectroscopy imaging technologies, hyper- and multispectral imaging (HSI and MSI), is more favored than the manual technique. The reason is due to their ease of use while being rapid and nondestructive. Nondestructive refers to the ability to reuse samples without damaging them during analysis. In contrast, chromatography tools, namely gas chromatography [33] and liquid chromatography [33; 34], require a lot of sample preparation and are time-consuming [35]. In such a way, destructive steps need to be performed, including drying, grinding, and extracting before injecting into the instrument [36]. In agriculture, research on HSI and MSI has been extensively conducted. Some of the publications related to the application of HSI and MSI are listed in Table 1.  
Besides the above cited publications, HSI and MSI are also applicative for monitoring plant growth status. Plant phenotyping is still believed to be time-consuming and labor-intensive work [49]. The use of HSI and MSI can provide the physical and chemical features of plants [50] based on their reflectance/absorbance profiles. In this study, we present a systematic literature review of the application of HSI and MSI to monitor plant growth status according to the natural problems encountered by plants. We also provide (i) general information on plant traits; (ii) the interaction of light and plants; (iii) data analyses; and (iv) recent trends of HSI and MSI for monitoring plant growth status. For the review, we collected published papers from Google Scholar with the keywords, \"plant abiotic/biotic stress detection using hyperspectral and multispectral imaging\", in 2010-2023.
## 4 Methodology and Modeling
### Results for Bioclimate Zone I/ASHRAE 3A (Curitiba, PR)
#### 5.1.3 Roof (Plenum)  
In contrast, the absence of insulating material makes heat transfer across the surface much more sensitive to temperature gradients. Comparing the envelope in question with  
Figure 11: Hourly behavior of the PCM’s physical state applied to the north facade during a typical winter week in Curitiba, PR (ZB1/3A).  
Figure 12: Monthly net heat balance across the south facade in Curitiba, PR (ZB1/3A).  
those that have a material applied, it is visible that the plenum exchanges heat with the outdoor environment intensely, particularly at the height of summer and winter: for the typical year, the envelopes with PCM and insulation present, respectively, annual heat gains of \(2.12\,\mathrm{kW\,h\,m^{-2}}\) and \(4.57\,\mathrm{kW\,h\,m^{-2}}\) and annual heat losses of \(-3.07\,\mathrm{kW\,h\,m^{-2}}\) and \(-5.11\,\mathrm{kW\,h\,m^{-2}}\), whereas the roof without any material applied records annual gains of \(47.53\,\mathrm{kW\,h\,m^{-2}}\) and losses of \(-34.82\,\mathrm{kW\,h\,m^{-2}}\).  
Between May and August of the typical year, the building with an uninsulated roof records the highest heat losses, with a minimum of \(-3.68\,\mathrm{kW\,h\,m^{-2}}\) in June and the highest EUI values with the HVAC system in the same month. The low plenum temperature directly affects the supply air--a mixture of the return air in the plenum and outdoor air--and demands more thermal energy from the heating coils to reach the thermal zone's setpoint. Still, in the non-insulated roof case, in certain months of autumn (April and May) and spring (September and October), during the TMY, and also June and July of the climate 2050 climate, net heat balance through the surface is close to zero, justified by the thermal balance between the surface faces (internal and external), solar radiation intensity, and return air temperature in the plenum. The significant heat gains in the summer period stand out for both climate projections, exceeding \(5\,\mathrm{kW\,h\,m^{-2}}\).  
Conceptualization, K.B. and V.K.; Methodology, K.B. and V.K.; Formal analysis, V.K.; Writing--original draft K.B.; Writing--review & editing, V.K.; Supervision, V.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Concealed Conduit Routing in Building Slabs
## 2 Materials and Methods
### Pipe Path Routing
#### 2.2.2 Pipe Ordering  
As revealed in Figure 6, the pipe scheduling is performed after all the abstract paths have been constructed. Since a building usually possesses multiple concealed pipes, pipe scheduling is a necessary job in the piping process. A good schedule can greatly reduce the  
Figure 6: Flowchart of the pipe-routing computation. The key computations include abstract path routing, pipe scheduling, pipe path finding, pipe surface modeling, and workspace updating steps.  
total costs of the resultant pipes. Unfortunately, the optimum scheduling problem of pipe routing is NP-hard [1], and pursuing the best schedule is impractical.  
In this research, we order the pipes based on a principle published in [1]. This principle suggests that long and thick pipes should be routed first such that the utilization of the workspace will be higher, and the quality of the final pipe paths could be better. This principle was widely used in pipe-routing computations and has been proven to be effective [3; 4; 5; 6]. Nonetheless, it is logically contradictory for this research since the actual lengths of the concealed pipes are unknown before the routing process starts. To resolve this conflict, we sort the pipes according to the lengths of their abstract paths.  
The rationale supporting this approach can be explained as follows: The abstract path of an individual concealed pipe records the slabs that will be penetrated by this pipe. The sizes of these slabs and the coplanar relationships between these slabs are reflected by the length of the abstract path. If the length of the abstract path is longer, then at least one of the following conditions is true: (1) This pipe would go through larger slabs (longer length), (2) the pipe would penetrate slabs that are not coplanar (more bends), (3) the pipe would cover more slabs (longer length), or (4) the pipe has many terminals (more branches). Based on these conditions, this pipe is likely to be long and consume more spatial resources. It should be given a higher priority over other pipes.  
Other widely used criteria for ordering pipes include the sizes, functionalities, and safety regulations of the pipes. Large pipes need more space. It is difficult to arrange optimal paths for them if the available spaces are occupied by other pipes. Therefore, they should be built earlier. However, concealed conduits are buried inside slabs. Their sizes will be approximately equal. Pipe lengths are more important for scheduling pipes. It is not uncommon that some pipes are more essential than others, for example, the electricity harnesses in a manufacturing factory. In other cases, safety regulations are the key factors, for example, natural gas pipes in a building with cooking or heating facilities. Under these conditions, the functionalities and safety regulations of pipes play key roles in the pipe scheduling process. However, in this research, we regard merely the pipe lengths as the only factor when scheduling the pipes. Other considerations would be included in the future work.
# Hybrid Optimization Method for Correcting Synchronization Errors in Tapping Center Machines
###### Abstract
At present, additive manufacturing can be used to produce energy, aerospace, or biomedical parts, providing high-strength and lightweight products. The machine tools manufacturer MAZAK's hybrid multi-objective machine tool type can perform laser additive manufacturing and 5-axis machining processes on machine tools, demonstrating the possibilities and characteristics of future advanced techniques [13]. The learning control modifies the \(z\)-axis and the spindle commands to optimize synchronicity between \(z\)-axis output responses and the spindle. Their experiments revealed that 10 runs of the ILC algorithm reduced synchronization error from 0.26 mm to 2.6 \(\times\) 10\({}^{-13}\). That is, the use of ILC learning control can substantially reduce synchronization error. [PERSON] et al. [14] proposed intelligent computer-aided process planning (i-CAPP). When the machining process's complexity increases, i-CAPP's Integration intelligence function works together with the domain expert's procedure. [PERSON] et al. [15] accord the coupling between the radial and axial vibrations and the dynamic cutting forces modeled along the tapping path. The radial and axial chatter stability is separately predicted in the frequency domain for the tapping process's stability. In the past, when the machining process was executed in the factory, the teeth or the tapping tools occasionally broke when the tapping speed was different. Usually, it was necessary to wait for the equipment manufacturer to inspect the equipment to obtain the best parameters. The current study developed a novel method of adjusting rigid tapping electrical control parameters and explored the effects of the adjusted parameters on thread quality obtained by tapping in internally threaded parts. The proposed method combines uniform design (UD) [16; 17; 18; 19], regression analysis [20; 21; 22; 23], fractional-order particle swarm optimizer (FPSO) [24; 25; 26; 27; 28], and the Taguchi method [29; 30; 31; 32; 33; 34; 35; 36] to analyze the effects of rigid tapping parameters on thread quality and to determine the key parameters and the optimal parameter values. Application of the proposed method in an actual factory process verified its effectiveness for improving thread tapping quality. Through the method provided in this article, the processing industry can get the minimum error parameter combination at different tapping speeds through experiments to avoid breaking the tool when rigid tapping is performed at the end of the processing process and causing damage to the workpiece. This paper is organized as follows. The problem considered in this study is briefly described in Section 2. Section 3 briefly discusses relevant methods, including UD, regression analysis, FPSO, and the Taguchi method. Section 4 presents and discusses the experimental and simulation results. Finally, Section 5 concludes the study.