esa-sceva/satcom-chunk-collection huggingface dataset:
# Agile Stage-Gate Approach for Design, Integration, and Testing of a 1U CubeSat
## 2 Materials and Methods
### Integration Development Process  
The integration procedure introduced the principles of Agile project management into the Stage-Gate approach to handle challenges with change. The hybrid model provides a framework to respond to uncertainties and accelerate the process through different iterations. Ultimately, integration and user iteration feedback product processes were released. This method provided incremental success in the development of the project. The idea of interaction among team members and experts in the early stages yielded  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Unit** & **Description** & **Quantity** \\ \hline FlatSat & This unit is for testing the subsystems and payload independently at UPAEP. All software builds are tested with this unit. & 1 \\ \hline Engineering Model & This unit is for integration testing for both the subsystems and the payload at UPAEP. All integration software builds are tested with this unit. & 1 \\ \hline Flight & There are two flight units, and each one has a payload and a bus. One is the flight-ready unit and the other is for backup. The first unit is tested under real space conditions and the other is intended to be used in case of any failure. & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: AztechSat-1 builds.  
Figure 8: Testing and integration flow diagram.  
improvements for the new product. Although the integration stage was the first to deploy a working method, the new hybrid method can be used in all stages from conceptualization to launch.  
The benefits of combining Agile and Stage-Gate approaches for integration planning can be summarized as follows:  
* Verification of integration conditions;
* Visual inspection of the components;
* Interface verification;
* Mechanical-electrical integration;
* Integration and functionality tests;
* External integration elements;
* Full system test.
# ML-Based JIT1 Optimization for Throughput Maximization in Cluster Tool Automation
## 1 Introduction
To date, various strategies have been studied to improve semiconductor tools' efficiency [12]. In other words, there have been studies to improve throughput using simulation and compare strategies' performance with changed schedules [11]. This study is based on the schedule change of the current initial transition period. We propose a strategy to respond to the time fluctuation of the optimal initial transition in the stable state so that it can be connected to the schedule of the ideal stable state. In addition, simulations are performed to verify these strategies and collect data. The real-time collected data allowed optimization modeling of cluster tools suitable for a specific process through ML analysis, and simulations were performed to verify these strategies. The main contents of this paper are as follows. * To understand the structural characteristics of cluster tools, ML-based analysis of JIT1 is proposed to maximize throughput, and an automation system is used to predict and apply optimal JIT1 to facilities. Lam Research's cluster facility simulation was used to obtain cluster facility data, and Lam Research's host simulation was used for automation modeling. The simulation configuration cited the SECS/GEM procedure of semiconductor manufacturing fabs. * Various data analyses were performed to maximize throughput in the semiconductor equipment field, but the efficiency was lowered because the analysis was not automated. In order to solve this problem, an automated technique was used to obtain the optimal JIT1 with an ML-based analysis method. This process shortened the time-consuming data analysis process and, at the same time, guaranteed maximum throughput while sensitively responding to changes in the production environment in real time by applying an automation system. The structure of this paper is as follows. First, in Section 2, cluster equipment is introduced. After discussing the operation method of cluster equipment, various factors affecting the throughput of cluster equipment are explained. Moreover, the research related to throughput maximization that has been conducted so far is addressed. Moreover, in Section 3, JIT1 optimization and real-time cluster facility optimization process methods through ML-based analysis for maximizing throughput based on real-time data of cluster tools are described. Section 4 compares and evaluates the strategies proposed through the automated simulation proposed in Section 3. Section 5 discusses the results, implications, and future tasks of this research.
# A Reversible Data-Hiding Method with Prediction-Error Expansion in Compressible Encrypted Images
## 3 Proposed Method
### Procedure of Encryption and Data hiding  
Here, we explain data-hiding and encryption processes in reference to Figure 5. We assumed that the size of an original image \(I\) was \(M\times N\) pixels.
## 4 Experimental Preparation
### Data Description  
In this model for forecasting slope stability, the parameters influenced by geological and topographical conditions were soil weight (\(\gamma\)), angle of internal friction (\(\varphi\)), height (\(H\)), cohesion (\(c\)), slope angle (\(\alpha\)), and pore water pressure (\(ru\)). The sample consisted of 352 sets of slope data based on the literature and a field survey [48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58].  
The goal of this study was to assess the accuracy of the safety factor prediction using three intelligent methods: BP neural network, PSO-BP, and ISSA-BP. The database was randomly divided into two portions, similar to prior studies [59; 60]: the training set and test set had ratios of 80% and 20%, correspondingly. The training set randomly selected 80% of the data, and the remaining 20% was for the test set. Some of the experimental data are listed in Table 1.  
Figure 5: ISSA–BP algorithm.  
Due to the possibility of equipment failure and other reasons, some of the original data have abnormal values, which will affect the model's prediction effect if directly entered into the model, and these data were processed considering the small impact of a small part of the data on the overall data. To avoid the inconsistency of each data parameter producing bias in the results, the sample data were normalized, and the calculation formula is as follows:  
\[X_{norm}=2\times\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}}-1 \tag{35}\]  
where (35) \(X\) is the initial vector without sample normalization, \(X_{norm}\) is the normalized vector of the \(X\) vector, and \(X_{\text{min}}\) and \(X_{\text{min}}\) represent the minimum and maximum values of the sample, respectively.  
Violin plots were used to show the data structure, combined with density estimation and box line plots, and to visualize the distribution of the input parameters, the parameters were normalized and plotted in Figure 6.  
It can be seen from Figure 6 that the cohesion values are more concentrated, the internal friction angle and slope angle are more widely distributed, and there is no large correlation among the six parameters, which means that each sampled parameter had an independent role in assessing the slope safety factor. To evaluate the most suitable model, we compared the different models with each other.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**No.** & \(\gamma\) & \(c\) & \(\varphi\) & \(\alpha\) & \(H\) & _ru_ & **Safety Factor** \\ \hline
1 & 22.4 & 10 & 35 & 45 & 10 & 0.4 & 0.9 \\
2 & 20 & 20 & 36 & 45 & 50 & 0.5 & 0.83 \\
3 & 20.6 & 16.3 & 26.5 & 30 & 40 & 0 & 1.25 \\
4 & 20 & 0.1 & 36 & 45 & 50 & 0.5 & 0.67 \\
5 & 18 & 24 & 30.15 & 45 & 20 & 0.12 & 1.12 \\
6 & 23.4 & 15 & 38.5 & 30.3 & 45.2 & 0 & 1.587 \\
7 & 20 & 0 & 24.5 & 20 & 8 & 0.35 & 1.37 \\
9 & 27.3 & 14 & 31 & 41 & 110 & 0.25 & 1.249 \\
10 & 27.3 & 31.5 & 29.7 & 41 & 135 & 0.25 & 1.245 \\
11
## 2 5G/NR Network Architecture Overview  
The 5G system, along with its predecessors, comprises three main components: user equipment, the 5G core network (5 GC), and next generation radio access (NG-RA) [4, 5]. User equipment connects to the 5G NR network, while the 5 GC manages authentication, configuration loading, and mobility. NG-RA handles radio connections in the telecommunications system [4, 5].  
In the physical-layer architecture of 5G, there are significant changes compared to 4G. The focus is on data and control channels managed by NG-RA. The transmitter and receiver involve processes like power control, cyclic redundancy check (CRC), 5G polar coding/encoding, modulation/demodulation, and multiple-input multiple-output (MIMO) techniques [4, 5].  
A channel is the transmission medium through which information travels from a transmitter to a receiver. In 5G/NR there are logical, transport, and physical channels (upstream and downstream) [4, 5]. Also, there are channels for user data and control information. The physical layer controls the mapping of transport channels to physical channels [4, 5]. One of the most important channels for the control of information is the physical downlink control channel (PDCCH).  
The PDCCH channel was designed in Release 15 [4]. The PDCCH finds applications in scheduling both uplink (UL) and downlink (DL) data channels. It has a variety of specialized functions such as uplink power control, slot format indication, and preemptive scheduling [6, 7]. Information carried on the PDCCH is called downlink control information (DCI) [6, 7]. To receive DCI via the PDCCH, the UE observes a collection of PDCCH candidates during one or more configured monitoring instances within designated control resource sets (COREST) according to specified search space set (SS-Set) configurations [6].  
The PDCCH employs polar coding with a predefined payload size, rate matching, and QPSK modulation [4, 5]. In the context of PDCCH monitoring, NR employs a similar blind decoding approach to the PDCCH, where the UE performs cyclic redundancy checks (CRC). In this process, the CRC bits are concealed using an identifier (RNTI) recognized by the user [4, 5, 6].
## 3 Results and Discussion
### Stage 2
#### 3.2.2 pH  
The pH of the AGS was 7.82 \(\pm\) 0.1 in V1. The disintegration of the AGS using SCO\({}_{2}\) caused its pH to decrease to 6.95 \(\pm\) 0.1 (V4) (Figure 4). An increase in the SCO\({}_{2}\) dose decreased the pH of the AGS from 6.43 \(\pm\) 0.1 (V5) to 6.33 \(\pm\) 0.1 (V6). The pH measured in the respirometers after AD decreased from 7.5 \(\pm\) 0.1 to 7.08 \(\pm\) 0.1 (V1) (Figure 4). The AGS pH ranged from 7.39 \(\pm\) 0.1 (V2) to 7.16 \(\pm\) 0.1 (V4) and decreased after AD to 6.89 \(\pm\) 0.1 (V2) and 6.76 \(\pm\) 0.1 (V4) (Figure 4). In turn, a sharp decrease in the pH values was observed after AD in the variants V5 and V6, i.e., from 6.96 \(\pm\) 0.1 to 6.46 \(\pm\) 0.1 in V5 and from 6.87 \(\pm\) 0.1 to 6.31 \(\pm\) 0.1 in V6 (Figure 4). As proved in other authors' investigations, pH decline leads to the reduction of the methanogen population and CH\({}_{4}\) production [21]. [PERSON] (2019) [33], who also pre-treated SAS with the use of SCO\({}_{2}\), reported a pH decrease that could be associated with an AD yield reduction.  
CO\({}_{2}\) is well soluble in aqueous solutions, and its solubility reaches 2900 mg/dm\({}^{3}\) at a temperature of 25 \({}^{\circ}\)C [37]. During CO\({}_{2}\) sublimation, its part dissolves in the supernatant, resulting in the formation of hydrogen ions, H\({}^{+}\), carbonate ions, CO\({}_{3}\)\({}^{2-}\), and bicarbonate ions, HCO\({}_{3}\)\({}^{-}\), which contribute to a pH decrease [38]. This pH decrease negatively influences the AD effectiveness, including gaseous metabolite production [39]. Hence, the pH values measured in the anaerobic chambers were a result of the initial conditions after pre-treatment with SCO\({}_{2}\). The pH value is an important factor of the anaerobic process because it determines its stability and may contribute to its inhibition [40].
## 1 Introduction
### Configuration Optimization  
The configuration of a selected framework has a large impact on the stability of the ultimate system. As the scope of surgical management guidelines is to provide broader information on the complete healthcare process, they have limited information relating to fixator selection and pin placement (Figure 1) [24]. The AO guideline for fracture management provides extensive information, but does not provide a tool to compare different configurations [28].  
[PERSON] et al. completed one of the first studies in the area of stability comparison by cataloging the stiffness of 6 external fixator frames [29]. Many studies have looked at existing literature and patient data to analyze and validate types of intervention [21; 30; 31; 32]. [PERSON] et al. presented an algorithm for management of bone loss based on fracture properties and soft tissue damage, which helped identify best fixation method [33]. A major shift in fracture fixation biomechanics studies occurred with the implementation of finite element analysis (FEA). Studies in this area ranged from device optimization [34] to performance analysis [35] to comparison between different fixators [36; 37] and different configurations [34; 38; 39]. [PERSON] et al. created a complete workflow, for the optimization  
Figure 1: **Left-**Several types of linear fixation configurations used in tibial fractures. **Right-**Safe corridor for pin placement on the Tibia [24]. The image is from the Standards for the management of open fractures of the lower limb (2009) by the British Association of Plastic Reconstructive and Aesthetic surgeons and the British Orthopaedic Association and is reproduced with permission from the lead author and publisher.  
of a cancellous bone transplant [40] which was automated [41]. [PERSON] et al. developed an optimization model using 1D finite element analysis [22].  
Studies have been conducted in trying to understand the biological response of mechanical stimuli and thereby model healing [42], and has been used to predict fracture healing in conjunction with FEA, with studies looking at fracture properties [43] and fixation properties [44]. Even though a large number of studies have been conducted to understand the healing process, not all mechanisms and parameters of interest have been identified [18]. In addition to these methods, analytical models have also been used [45].  
The overall scope of this study is to develop a framework for configuration optimization for external fixators, especially to be used in developing countries. In this paper the proposed framework is presented and a simplified workflow of the framework is analyzed under a pilot study. The objective of the pilot study is to understand the feasibility of implementation and validate the proposed method. During the study 4 analytical and computational models were compared to identify the best method for the use case.
# Improvement of Speech/Music Classification for 3 GPP EVS Based on LSTM
## 4 Experiments and Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{1}{c}{**dB**} & **EVS** & **RNN** & **Proposed** \\ \hline clean & - & 0.9980 & 0.9981 & 0.9985 \\ \hline \multirow{4}{*}{car} & 5 dB & 0.8830 & 0.8911 & 0.9135 \\  & 10 dB & 0.9131 & 0.9414 & 0.9517 \\  & 15 dB & 0.9933 & 0.9940 & 0.9951 \\  & 20 dB & 0.9927 & 0.9941 & 0.9943 \\ \hline \multirow{4}{*}{speeech} & 5 dB & 0.5757 & 0.6157 & 0.6524 \\  & 10 dB & 0.7214 & 0.7561 & 0.7752 \\  & 15 dB & 0.9649 & 0.9710 & 0.9760 \\  & 20 dB & 0.9905 & 0.9914 & 0.9920 \\ \hline \multirow{4}{*}{speech} & 5 dB & 0.6633 & 0.6917 & 0.7017 \\  & 10 dB & 0.8362 & 0.8581 & 0.8710 \\  & 15 dB & 0.8792 & 0.9012 & 0.9104 \\  & 20 dB & 0.9642 & 0.9775 & 0.9867 \\ \hline \multirow{4}{*}{pink} & 5 dB & 0.3752 & 0.6016 & 0.6312 \\  & 10 dB & 0.6900 & 0.6925 & 0.8110 \\  & 15 dB & 0.9312 & 0.9450 & 0.9757 \\  & 20 dB & 0.9853 & 0.9937 & 0.9948 \\ \hline \multirow{4}{*}{factory1} & 5 dB & 0.3289 & 0.5948 & 0.6391 \\  & 10 dB & 0.6702 & 0.7491 & 0.8471 \\  & 15 dB & 0.9265 & 0.9481 & 0.9513 \\  & 20 dB & 0.9850 & 0.9871 & 0.9906 \\ \hline \multirow{4}{*}{factory2} & 5 dB & 0.7251 & 0.8362 & 0.8974 \\  & 10 dB & 0.9119 & 0.9341 & 0.9591 \\  & 15 dB & 0.9711 & 0.9721 & 0.9812 \\  & 20 dB & 0.9855 & 0.9856 & 0.9878 \\ \hline \multirow{2}{*}{music} & classical & - & 0.9868 & 0.9912 & 0.9931 \\  & others & - & 0.9311 & 0.9435 & 0.9514 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of speech/music classification accuracy. Figure 4: Speech with white noise (5 dB SNR) and music waveform, and the decision result of the speech/music classification algorithms (manual markings-silence = 0, speech = 1, music = 2). (**a**) Test waveform, (**b**) decision of EVS codec, (**c**) decision of RNN based algorithm, and (**d**) decision of LSTM algorithm.
## 2 Materials and Methods  
### Plant Materials and Treatments  
The two oilseed rape cultivars used in this study were previously characterized as having high (Xiangyou 15, denoted \"15\") or low (814) N efficiency [16]. Seeds of 15 and 814 were provided by the National Center of Oilseed Crops Improvement, Hunan Branch, Hunan, China. Mature seeds were sown in trays with growth medium containing a mixture of peat and vermiculite (1:1, _v/v_). After producing -3-4 leaves (35 d after sowing), we transplanted uniform seedlings into different pots for physiological and transcriptomic experiments. To prevent NH4\({}^{+}\) from being converted into NO3\({}^{-}\) by microorganisms, this study used pot culture (diameter: 30 cm; height: 30 cm) with quartz sand containing no nutrients. The nutrient solution used was based on the improved Hoagland nutrient solution [17].  
The physiological experiment was conducted in 2016. All pots with different N treatments contained the same levels of N (15 mmol/L) and dicyandiamide (DCD) (10 \(\upmu\)mol/L) to prevent NH4\({}^{+}\) transformation. There were six treatments, i.e., 2 cultivars \(\times\) 3 N treatments. Ten seedlings were used for each treatment,with only one seedling per pot, for a total of 60 seedlings. Table 1 shows the macronutrient composition of the hydroponic solution of the three N treatments. The micronutrient composition was as follows: B, 1.0 mg/L; Mn, 0.5 mg/L; Zn, 0.05 mg/L; Cu, 0.02 mg/L; and Mo, 0.01 mg/L. The NT + AT (75% NO3\({}^{-}\) + 25% NH4\({}^{+}\)) treatment (Table 1) received a conventional oilseed rape fertilizer and was set as a control in the physiological experiment. The hydroponic solution was refreshed every five days. To prevent reaction between Ca2\({}^{+}\) and CO3\({}^{-}\)/SO4\({}^{-}\), CaCl2 was applied separately. The total N input of each treatment was 2.8 g/pot, and all N was added to pots before 60 d after transplanting.  
The transcriptome experiment was conducted in 2017. Most of the methods were similar to those used in the physiological experiment. The NT (100% NO3\({}^{-}\)) and AT (100% NH4\({}^{+}\)) treatments were the same as those in the physiological experiment. We used a nitrogen-free treatment (control, CK) instead of the NT + AT treatment to further distinguish the transcriptomic characteristics of oilseed rape roots in the NT and AT treatments. The 11.25 mmol/L KNO3, 1.875 mmol/L (NH4\({}_{4}\))2 SO4 and 1.875 mmol/L K2 CO3 (Table 1, NT + AT) treatments were replaced by 7.5 mmol/L K2 CO3 (CK), and the other nutrients were the same. Twenty seedlings were included in each treatment, with only one seedling per pot, for a total of 120 seedlings. At 48 h after exposure to different N treatments, root tissues from five seedlings in each treatment group (three biological replicates) were collected, mixed, washed with double-distilled water, immediately frozen in liquid nitrogen, and stored at \(-80\)\({}^{\circ}\)C for use in RNA extraction.
# The SF12 Well in LoRaWAN: Problem and End-Device-Based Solutions
## 3 Related Work
In fact, the literature highlights that this mechanism was designed for coverage problems, and it fails in the presence of congestion [17]. However, the authors of these works do not investigate this area further. A modified ADR scheme, intended to be congestion-aware, has been proposed in [34]. However, this proposal exhibits the following drawbacks: (i) It requires all network traffic to be confirmed as ACKs are used for congestion estimation; and (ii) it is based on too simple an analytical model, where the DC restrictions are not considered, and the number of losses due to congestion is assumed to have a constant value, for which, in addition, no motivation is given. The works reviewed above are based on simulation (with one exception [22]). There also exists a wide body of LoRaWAN studies focused on experimental or real-application scenarios [35; 36; 37; 38; 39]. Some of these works investigate LoRaWAN features that are relevant regarding network performance. The authors in [35] study the use of different DR settings at the edge of link quality and as a result propose using higher DR settings, even when packet delivery probability is lower, for the sake of a greater effective bit rate. Another work reports the performance of a public LoRaWAN deployment; the authors of this work collect the Signal-to-Noise Ratio (SNR) and Received Signal Strength Indication (RSSI) values to determine maximum network coverage and the SF distribution of the EDs [36]. Other works focus rather on the feasibility of specific applications or deployments. For example, the authors in [37] explore the viability of a sailing monitor system based on LoRa, while another study measures the performance a LoRa link can achieve in underground to aboveground communications for different types of soil [38]. Another study evaluates methods for LoRaWAN coverage survey in the context of a smart campus [39]. However, no empirical work investigates the congestion collapse issues in LoRaWAN. Based on the literature review, we conclude that, to the best of our knowledge, the present paper is the first one that shows and characterizes the SF12 Well problem. In addition, the paper presents and compares several ED-based solutions, showing how this problem can be mitigated without involving the NS.