esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix C
### Error Detection. Pre-Computation Stage  
To detect an error, we have to restore the number \(Xt\) in the WNS and compare it with the dynamic range \(M_{K}\), or calculate the weighted characteristic \(F(Xt)\) and compare it with the corresponding weighted characteristic \(F(MK)\) of the dynamic range. To reduce calculations, we detect errors using weighted characteristics yielded by the CRT. We calculate all necessary constants that can be precomputed, saving them in memory (See, Table A4). In the memory of the computing device, the constants are stored in the binary representation. In this example, we use the decimal representation for clarity.  
_We calculate the dynamic and full ranges of this RRNS:_  
\[M_{K}=5\cdot 7=35_{(10)}=100011_{(2)},\]  
\[M_{N}=5\cdot 7\cdot 8\cdot 9\cdot 11\cdot 13=360360_{(10)}=1010111111110101000_{(2)}.\]  
_The constants for error detection (Equation (5)) are_  
\[N=\log_{2}\left(M_{K}\sum\limits_{i=1}^{6}(m_{i}-1)\right)=\log_{2}\left(35 \cdot(4+6+7+8+10+12)\right)=25,\]  
\[k_{1}^{*}=\frac{\left|m_{1}/M_{N}\right|_{m_{1}}}{m_{1}}\cdot 2^{N}=\frac{ \left|5/360360\right|_{5}}{5}\cdot 2^{25}=\frac{3}{5}\cdot 2^{25}=20132660_{(10)}= = 1001100110011001100_{(2)},\]  
\[k_{2}^{*}=\frac{\left|m_{2}/M_{N}\right|_{m_{2}}}{m_{2}}\cdot 2^{N}=\frac{ \left|7/360360\right|_{7}}{7}\cdot 2^{25}=\frac{4}{5}\cdot 2^{25}=19173962_{(10)}= = 10010010010010010010010_{(2)},\]  
\[k_{3}^{*}=\frac{\left|m_{3}/M_{N}\right|_{m_{3}}}{m_{3}}\cdot 2^{N}=\frac{ \left|8/360360\right|_{8}}{8}\cdot 2^{25}=\frac{5}{8}\cdot 2^{25}=20971520_{(10)}= = 101000000000000000000000_{(2)},\]  
\[k_{4}^{*}=\frac{\left|m_{4}/M_{N}\right|_{m_{4}}}{m_{4}}\cdot 2^{N}=\frac{ \left|9/360360\right|_{9}}{9}\cdot 2^{25}=\frac{8}{9}\cdot 2^{25}=29826162_{(10)}= = 111000111000111001100_{(2)},\]  
\[k_{5}^{*}=\frac{\left|m_{5}/M_{N}\right|_{m_{5}}}{m_{5}}\cdot 2^{N}=\frac{ \left|11/360360\right|_{11}}{11}\cdot 2^{25}=\frac{6}{11}\cdot 2^{25}=18302418_{(10)}= = 10001011101000111001_{(2)},\]  
\[k_{6}^{*}=\frac{\left|m_{6}/M_{N}\right|_{m_{6}}}{m_{6}}\cdot 2^{N}=\frac{ \left|13/360360\right|_{13}}{13}\cdot 2^{25}=\frac{10}{13}\cdot 2^{25}=25811102_{(10)}= = 11000100111100010011110_{(2)}.\]  
The weighted characteristic of the dynamic range \(M_{K}\) is obtained by Equation (6):  
\[F(M_{K})=\frac{M_{K}}{M_{N}}\cdot 2^{N}=\frac{35}{360360}\cdot 2^{25}=3259_{(10) }=11001011101_{(2)}.\]
## 2 Experimental Setup and Details  
The present investigation was performed in a closed-circuit wind tunnel (SMC-WTI) belonging to the Joint Laboratory of Wind Tunnel and Wave Flume, Harbin Institute of Technology, P. R. China. The closed-loop wind tunnel test section, which has a geometric scale of 505 \(\times\) 505 mm\({}^{2}\) in cross-section and 1050 mm in the airflow direction, possesses optically transparent walls to observe the experimental phenomenon conveniently. The honeycombs, mesh structure, and contraction section were installed upstream of the test channel to produce a high-quality uniform incoming flow entering the square test channel. The incoming flow velocity can be successively regulated at the range of 0-25 m/s. The turbulence intensity level of the free incoming flow was evaluated as less than 0.4%.
## 3 Results and Discussion
### Dye Removal Rate by K\({}_{2}\)FeO\({}_{4}\) and NaOCl  
Dye removal rates by K\({}_{2}\)FeO\({}_{4}\) and NaOCl treatment were examined by obtaining the slope of In(C\({}_{\rm t}\)) as a function of reaction time (t). First, the dye oxidation behavior of K\({}_{2}\)FeO\({}_{4}\) was compared with that of NaOCl. Figure 2 shows the remaining concentrations of OD and LGB after oxidation as a function of time at pH 3, 6, 9, and 12. As most of the dyes were removed in 5 min, the removal efficiency was compared based on the removal rates at 5 min using the slope (In(C\({}_{\rm t}\))/t) of In(C\({}_{\rm t}\)) as a function of time. Table 1 lists the reaction rates for all conditions. While K\({}_{2}\)FeO\({}_{4}\) achieved the highest OD removal efficiency (80%) at pH 3, its removal efficiency dropped to 60% as the pH increased to 12, corresponding to a decreasing reaction coefficient (\(-\)0.8489). The fastest OD removal rate (\(-\)1.1894) was at pH 3. The single oxidation process with NaOCl showed the lowest removal efficiency (\(-\)30%) at pH 3 with the slowest reaction rate (-0.6330); however, dye oxidation increased marginally with an increase in pH (to alkali). At pH 12, OD removal using NaOCl was higher than that by K\({}_{2}\)FeO\({}_{4}\). This is because the removal efficiency of K\({}_{2}\)FeO\({}_{4}\) decreases under alkaline conditions (pH 12) [21].  
Meanwhile, at pH 3, LGB was almost completely removed by NaOCl oxidation; thus, NaOCl performed better than K\({}_{2}\)FeO\({}_{4}\) at pH 3. However, the removal efficiencies of the NaOCl oxidation decreased to 60% at pH 3 with minimal differences between them. There were distinctive differences between OD and LGB, except for the molecular weight. It may be assumed that the decomposition of LGB by hydrochloric acid was proceeded by the injection of hydrochloric acid into the jar tester to adjust the pH to acidic conditions (pH 3). The removal efficiency by oxidation-coagulation using K\({}_{2}\)FeO\({}_{4}\) was slightly higher than  
Figure 1: Effect of pH on the removal efficiency of (**a**) orange II azo dye (**b**) and lissamine green B dye by potassium ferrate.  
that by single oxidation using NaOCl, except at pH 3. Moreover, the marginal decrease in the removal of LGB may be due to the larger molecular weight of LGB in comparison to OD. This indicates that LGB needs additional reaction time for complete removal with K\({}_{2}\)FeO\({}_{4}\), compared to OD.  
Figure 2: Reaction rates of potassium ferrite and NaOCl with orange II azo dye and lissamine green B dye under different pH conditions (3, 6, 9, and 12). Dye concentration: 50 mg/L; K\({}_{2}\)FeO\({}_{4}\): 56.40 mg/L as Fe; NaOCl: 56.40 mg/L as Cl\({}_{2}\).
# Optimizing Replaced Nozzle Diameter of Abrasive Blasting Systems Using Experiment Technique Design
## 4 Results and Discussions
### Analysis of Variance (ANOVA)  
The analysis of variance is essential for the quantitative estimations of the impacting degree of each input parameter and their interactions on the response. Table 4 shows the \(p\)-values of some parameters. It is seen that all parameters and some interactions also have statistical significance when their \(p\)-values are lower than 0.05, such as \(d_{N0}\) (A), WR (B), t\({}_{\mathrm{nc}}\) (C), P\({}_{\mathrm{c}}\) (D), C\({}_{\mathrm{m,h}}\) (E), C\({}_{\mathrm{n,p}}\) (F), C\({}_{\mathrm{s}}\) (G), \(d_{N0}\) * P\({}_{\mathrm{c}}\) (AD), \(d_{N0}\) * C\({}_{\mathrm{n,p}}\) (AF), \(d_{N0}\) * C\({}_{\mathrm{s}}\) (AG), WR * P\({}_{\mathrm{c}}\) (BD), WR * C\({}_{\mathrm{n,p}}\) (BF), WR * C\({}_{\mathrm{s}}\) (BG), Pc * C\({}_{\mathrm{n,p}}\) (DF), P * C\({}_{\mathrm{m,h}}\) (DE), P\({}_{\mathrm{c}}\) * C\({}_{\mathrm{s}}\) (DG), C\({}_{\mathrm{m,h}}\) * C\({}_{\mathrm{n,p}}\) (EF), C\({}_{\mathrm{m,h}}\) * C\({}_{\mathrm{s}}\) (EG), C\({}_{\mathrm{n,p}}\) * C\({}_{\mathrm{s}}\) (FG).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Term** & **Effect** & **Coef** & **SE Coef** & **T-Value** & \(p\)**-Value** & **VIF** \\ \hline Constant & & 8.81664 & 0.00165 & 5336.88 & 0.000 & \\ \(d_{N0}\) & 7.52984 & 3.76492 & 0.00165 & 2278.98 & 0.000 & 1.00 \\ WR & 0.02297 & 0.01148 & 0.00165 & 6
# Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems
## 2 Second-Order Systems  
A model whose input \(R(s)\)-output \(C(s)\) formulation is a second-order closed-loop transfer function is known as a second-order system. One of the main elements that make a second-order model important is its ability to present very different behaviors depending on the configuration of its parameters. A generic second-order model can be formulated under the following expression [29],  
\[\frac{C(s)}{R(s)}=\frac{\omega_{n}^{2}}{s^{2}+2\zeta_{b}\omega_{n}s+\omega_{n }^{2}}, \tag{1}\]  
where \(\zeta\) and \(\omega_{n}\) represent the damping ratio and \(\omega_{n}\) the natural frequency, respectively, while \(s\) symbolizes the Laplace domain.  
The dynamic behavior of a system is evaluated in terms of the temporal response obtained through a unitary step signal as input \(R(s)\). The dynamic behavior is defined as the way in which the system reacts, trying to reach the value of one as time evolves. The dynamic behavior of the second-order system is described in terms of \(\zeta\) and \(\omega_{n}\)[30]. Assuming such parameters, the second-order system presents three different behaviors: Underdamped (\(0<\zeta<1\)), critically damped (\(\zeta=1\)), and overdamped (\(\zeta>1\)).
# Evaluation of Deep Learning-Based Automatic Floor Plan Analysis Technology: An AHP-Based Assessment
## 5 Discussion and Conclusions  
The main contribution of this study is to evaluate the list of criteria and factors for assessing the value of the floor plan analysis technology. Using the AHP method, the relative weights of qualitative criteria and factors could be quantified, and with this value, an assessment of the technology value of both manual and automatic approaches could be derived. This study developed a technology hierarchy assessment model and identified important criteria and their weights to evaluate the relative importance of the factors. Furthermore, this study contributes to extending the research on technology value evaluation by applying the AHP method, which is one of the most structured techniques for industrial and technical decision making. The results suggest that distinctiveness is the predominant criterion of the floor plan analysis technology, followed by substitutivity, completeness, and supply and demand. Particularly, efficiency, possibility of existence of similar technologies, and customer demand have distinctively higher importance, indicating that they are key factors for the technology value of floor plan analysis. Based on the weights of the factors derived from AHP, this study performed a scoring checklist to provide comparative evaluation results between manual and automatic methods. Consequently, automatic extraction floor plan analysis technology rated 1.3 times higher than the manual editing approach. In addition, this study highlights the practical efficiency of automating floor plan analysis. Beyond internal comparisons using detection rates in automatic floor plan analysis, this study enables quantitative identification of the strengths and weaknesses of automatic floor plan technology compared with the traditional method of manual editing technology. As a result, this study determined the effectiveness and usefulness of automatic floor plan analysis as a reasonable technology for acquiring indoor spatial information. However, the limitation of this study is that it relies on weights and technical evaluation scores based on responses from only 61 experts in the field of spatial information. Additionally, this study focused on the EAIS floor plan, and therefore, it might not be directly applicable to other floor plans. However, by utilizing the derived criteria and factors from this study, it is possible to apply it universally in the field of floor plan analysis evaluation in future studies. The result herein suggests that overall, the automatic approach is more efficient technology than the manual approach. However, technology can be valuable or required regardless of efficiency. For example, there is a demand for more information or qualitative interpretation on the floor plan, which can be obtained from skilled technicians by manual digitization. The result showed that the technical development phase factor of completeness criteria had higher technical value in the manual approach
# Valorization of Cassava By-Products: Cyanide Content and Quality Characteristics of Leaves and Peel
## 2 Materials and Methods
### Analysis of Dry Matter, Organic Matter, Crude Protein, and Fiber  
The dry matter of cassava juice, press cake, and leaves was measured by drying the samples at 105 \({}^{\circ}\)C for 12 h [33]. Ash and organic matter were determined by heating the oven-dried samples in a muffle furnace according to AOAC Official method 923.03 [33]. To measure the crude protein of the cassava leaves, the Kjeldahl method and the Kjeldahl analysis system (Vapodest 500, C. Gerhardt GmbH & Co. KG., Konigswinter, Germany) were used. This process involves three main steps: digestion, distillation and titration. First, the sample was digested in the presence of concentrated sulfuric acid, which converts organic nitrogen to ammonium sulfate. Then, the digested sample was distilled in the presence of an alkali, which released the ammonia gas from the ammonium sulfate. Finally, the ammonia gas was titrated with a standardized acid solution, and the nitrogen content of the sample was calculated based on the amount of acid used in the titration. The total nitrogen content was converted to crude protein using a conversion factor of 6.25. The neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and crude fiber (CF) of the cassava peel were examined according to official AOAC method 973.18 (FiberBag Analysis System FBS6, C. Gerhardt GmbH & Co. KG., Konigswinter, Germany) [33]. In this method, the cassava peel was first ground and then placed in a FiberBag, which is made of a porous material that allows the solutions to penetrate the sample. The FiberBag was then placed in the FBS6 system, which performed the entire analysis automatically. Neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and crude fiber (CF) were measured separately. NDF is the fraction of fiber that is insoluble in neutral detergents, ADF is the fraction of fiber that is insoluble in acid detergents, ADL is the fraction of ADF that consists of acid-insoluble lignin, and CF is the residual material after extraction of the fibers with acid and alkali. All measurements were performed at least in triplicate.
## 5 Concluding Remarks  
This article develops a dimensionality reduction method, which is named the SGHW method. This method can be regarded as an extension of the GHW method by treating frequencies and time translation factors as random variables. In the GHW method, it is still difficult to obtain accurate target EPSD even though hundreds of series of harmonic components are used in the simulation. Excessive components also lead to a high demand of calculation. However, the proposed method in this paper can overcome these shortcomings of the GHW method. Using the SGHW method, random processes with accurate EPSD can be synthesized using a few random harmonic components (less than ten components), which greatly reduces the number of random variables and difficulty of random process analysis. The conclusions of this paper include the following:  
1. The expression of the SGHW method for generating random process samples is given.
2. The amplitude of components can be deduced from the joint probability density function (JPDF) of frequency and time translation factors by the proposed SGHW method.
3. Numerical examples show the advantages of the proposed methods. (1) A random process sample with accurate probability information can be obtained by only retaining a few harmonic components. This is demonstrated by comparing the EPSD model with the reconstructed EPSD. (2) The proposed method has high computational efficiency. The comparisons of relative errors and computational times shown in Table 1 between SGHW method and classical SRM prove the superiority and efficiency of the SGHW method.  
The future developments of this study include extending the proposed method to simulate wind speed field, wave motion, and so on, or extending it to simulate multi-dimensional stochastic processes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SRM**} & \multicolumn{3}{c}{**SGHW**} \\ \hline
**Relative** & **Number of** & **Computational** & **Relative** & **Number of** & **Computational** \\
**Errors** & **Components** & **Time (s)** & **Errors** & **Components** & **Time (s)** \\ \hline
25.74\% & 50 & 7283.067 & 0.39\% & 5 & 2440.754 \\
13.85\% & 100 & 5821.299 & 0.21\% & 10 & 609.601 \\
3.79\% & 250 & 2520.253 & 0.051\% & 25 & 243.847 \\
1.74\% & 500 & 1258.778 & 0.012\% & 50 & 122.108 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparations of relative errors, number of components, and computational times between the SRM and SGHW method.  
**Funding:** This research was funded by the Natural Science Foundation of Hebei Province (E2020203103 and E2020203054) and Science and Technology Project of the Hebei Education Department (BJ2021043).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Method  
The proposed model, based on the ABM and non-homogeneous CA approaches researched in [12; 41], has been exploited to provide a multi-layered decision support system in cases of accident congestions, and it develops a new simulation method to understand the movement of vehicular traffic in case of accidents and/or congestion.  
In this model, the ABM approach is used where the network conducts data acquisition as well as data processing within individual vehicles. It is quite different from the traditional centralized approach in which the service recommends that the driver follows an alternative route regardless of other vehicles. This alternative route is recommended by the ABM approach to the specific vehicle, as opposed to all the vehicles on the road. The individual vehicles are able to make decisions and proceed. Since the alternative route is dependent on the decision variables of the drivers, they are provided with the most practical routes available, therefore making it an effective solution to the dynamic routing problem.  
It is assumed that all agents are in a position to cooperate and participate within this multi-agent system. They are doing so by providing data to their neighbors. It is also believed that the drivers are behaving cognitively and rationally.  
This work represents an approach for modeling and simulating complex and dynamic vehicular systems at both the microscopic and macroscopic levels. The highest layer represents the macroscopic phenomena of the traffic network that would be difficult to model in CA frames. This layer represents the connections between intelligent guide agents that enhance the decisions for the whole system. The base layer is composed of a high determination CA framework for every open space, which shows how the agents' neighborhood moves, as well as how the development of decision-making at the microscopic level of the system is created. Figure 2 shows the two-layered structure of the proposed system.  
The MLMS-ABM simulation model consists of multiple sub-models starting with the model of how the agent selects its intermediate goal destination. The act of avoiding obstacles and collisions with nearby agents is then modeled. The hybrid version of the agent-based model includes simulating the leading and following behaviors of agents after dynamically upgrading certain agents to the \"intelligent\" level and enabling them to perform some sort of guidance behavior, as detailed below. Besides avoiding collisions with neighboring vehicles, the framework includes a model of avoiding high density areas in order to reduce overall travel time.
# Physical and Respiratory Rehabilitation in Spinal Muscular Atrophy: A Critical Narrative Review
## 3 Results
### Motor Rehabilitation
#### 3.1.4 Whole Body Vibration  
[PERSON] et al., in their multi-case study, evaluated the safety and effectiveness of strength and motor performance of whole-body vibration training in ambulatory children with Duchenne muscular dystrophy (DMD) and SMA [60]. After a learning phase in the hospital with the help of experienced physiotherapists, 4 children with DMD and 8 with SMA underwent an 8-week vibration training programme on a Galileo MedM at home (\(3\times 3\) min twice a day, 5 days a week). For the first training, a low vibration frequency of 10 Hz was chosen, gradually increasing to 15-18 Hz as the patients felt comfortable with the training. The target frequency of 15-18 Hz was achieved on the first day for all patients. The vibration amplitude was 4 mm. Each training session consisted of three units, each lasting three minutes, with a three-minute rest between each unit. During each unit, the children performed a physical exercise on the vibrating platform: 1) light squatting, 2) stretching of the gastrocnemius muscle, and 3) light alternating weight shift from the right to the left leg for two consecutive days with two and three training sessions per day in the hospital. After the hospital training phase, the patients continued to practice WBVT at home, \(3\times 3\) min twice a day, with a minimum break of 4 h between the two sessions, on five freely selected weekly days. The intervention was well tolerated clinically. In boys with DMD, creatine kinase levels initially increased by 56% after the first day of training but returned to baseline after eight weeks of continuous whole-body vibration therapy. No significant changes in laboratory parameters were observed in children with SMA. Regarding training effectiveness, secondary outcomes such as muscle strength and ankle dorsiflexion showed mild, non-significant improvements. However, performance on the 6 min walk test (6 MWT) significantly increased from 371.3 m to 402.8 m (\(p<0.01\)).
# Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture
## 1 Introduction
### Limitations of UAVs in Precision Agriculture Applications  
Despite their great advantages, there are also some recognized technical difficulties related to drones and UAVs, like the engine power, the short duration of the flights, the maintenance of flying altitude, the stability in turbulence and conditions of wind, the self-shadowing depending on the orientation of the sun and the current altitude, the existence of particles in the surrounding area, and the variations in lighting because of the clouds [3; 22]. Reliability is yet another concern for UAV applications [22]. Payload weight is another important matter which limits the selection of the cameras for UAVs. It is generally around 20-30% of the global weight of the vehicle [23], thus influencing the camera that can be installed.  
Accordingly, different low-cost cameras have been analyzed for agricultural applications due to their low weight [24]. However, there are other identified problems with this kinds of cameras, such as the small optical quality, the zoom and the focus [23]. Issues arising from aviation regulations [22; 25] can also be serious obstacles to the use of UAVs in agriculture. For example, as a part of the flying permission, in order to cover the risks of causing damages to buildings, livestock or even humans, an insurance is necessary. This kind of requisites are considered as the major impediments for their use in practice [26]. Besides, the UAVs must always be in the view of the operator, who also needs to have a pilot license. Thus, a flying team is necessary to operate the UAV, thus increasing the total cost.