esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Tensile Properties
It is indicated that although polypropylene has better thermal stability than thermoplastic  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Post-2 nd-Recycling** & **Tensile Stress at Break (MPa)** & **Tensile Strain at Break (\%)** & **Tensile Stress at Yield Point (MPa)** & **Tensile Strain at Yield Point (\%)** \\ \hline P100 & 29.3 \(\pm\) 0.2 & 4.3 \(\pm\) 0.9 & 31.1 \(\pm\) 0.2 & 3.0 \(\pm\) 0.2 \\ T100 & 13.7 \(\pm\) 0.9 & 280.3 \(\pm\) 27.3 & 14.0 \(\pm\) 1.0 & 279.2 \(\pm\) 28.4 \\ T90 & 4.2 \(\pm\) 0.5 & 144.1 \(\pm\) 25.4 & 4.4 \(\pm\) 0.5 & 139.8 \(\pm\) 29.9 \\ T90/MA & 4.1 \(\pm\) 0.3 & 212.4 \(\pm\) 25.1 & 4.3 \(\pm\) 0.3 & 210.7 \(\pm\) 26.0 \\ T70 & 2.2 \(\pm\) 0.3 & 21.4 \(\pm\) 10.2 & - & - \\ T70/MA & 9.0 \(\pm\) 1.3 & 72.7 \(\pm\) 7.7 & 9.3 \(\pm\) 1.0 & 70.4 \(\pm\) 6.2 \\ T50 & 1.0 \(\pm\) 0.3 & 8.9 \(\pm\) 4.6 & - & - \\ T50/MA & 8.0 \(\pm\) 0.9 & 39.9 \(\pm\) 5.3 & 8.4 \(\pm\) 0.9 & 31.2 \(\pm\) 3.7 \\ \hline
**Post-3 rd-Recycling** & **Tensile Stress at Break (MPa)** & **Tensile Strain at Break (\%)** & **Tensile Stress at Yield Point (MPa)** & **Tensile Strain at Yield Point (\%)** \\ \hline P100 & 23.6 \(\pm\) 1.4 & 3.9 \(\pm\) 0.1 & 25.1 \(\pm\) 1.5 & 3.1 \(\pm\) 0.2 \\ T100 & 10.8 \(\pm\) 0.5 & 252.1 \(\pm\) 24.7 & 11.1 \(\pm\) 0.6 & 250.6 \(\pm\) 26.2 \\ T90 & 0.9 \(\pm\) 0.2 & 7.0 \(\pm\) 0.04 & -polyurethane, less polypropylene content remains than thermally unstable thermoplastic polyurethane. Hence, the tensile performance of pure material groups become poor after multiple recycling cycles. At the same time, T/P/MA blends also demonstrate similar improvement. As MA improves the high interfacial tension and low compatibility between thermoplastic polyurethane and polypropylene, which are caused by a polarity difference, T/P/MA blends exhibit better tensile properties.
## 3 Results and Discussion
### Study of the Degree of Grinding and Screw Rotation Speed on the Cake Density of Seed Cake  
In accordance with Figure 16, the change of cake density depending on the degree of grinding and screw rotation speed was investigated. The density of the cake obtained after pressing was measured as the primary outcome. The data obtained at different screw rotation speeds and grinding degrees were analyzed to identify the optimal parameters for the oil pressing process. The results indicated that increasing the screw rotation speed during the pressing of sunflower seeds led to an increase in the density of the cake (\(p<0.05\)). For instance, at a screw rotation speed of 30 rpm, the density of the cake varied depending on the grinding degree. Without pre-grinding, the density was 367 kg/m\({}^{3}\), while grinding to 2 mm resulted in a density of 1467 kg/m\({}^{3}\). At higher screw rotation speeds, such as 60 rpm, 120 rpm, and 250 rpm, similar trends were observed, with denser cakes produced as the grinding degree decreased.  
Additionally, the installation of grinding sieves with smaller hole diameters also contributed to an increase in cake density compared to using the oil press without grinding mechanisms. This suggests that the presence of grinding components enhances the compactness of the cake during the pressing process.  
A significant increase in cake density is observed both when increasing the screw speed and when grinding the cake with different hole diameters. The increase in density is due to more intensive compression during grinding in the \"knife-plate\" system, and more intensive compression in the pressing chamber due to increased screw speed and pressure in the chamber, as well as greater squeezing of oil from the cake.  
Figure 15: Dependence of power on the degree of sunflower seed grinding and screw rotation speed (different letters above the lines indicate significant differences between samples, \(p<0.05\)).
# Enhancing Methyl Orange Degradation with Laser-Generated ZnO and Ce-Doped ZnO Nanoparticles
## 2 Materials and Methods
### Kinetics Studies of Photocatalysis  
To investigate the adsorption kinetics, the data from the photocatalytic activities were further used to analyze the rate of reactions. The nature of photocatalytic activity in the decolorization of MO dye may be depicted using the pseudo-first-order reaction kinetics, given by the equations as follows:  
\[C_{t}=C_{o}-e^{k}t \tag{2}\]  
\[-\ln\ (\frac{C_{t}}{C_{o}})\ =\ kt \tag{3}\]  
in which \(k\) (min\({}^{-1}\)) refers to degradation's rate constant. The linearity obtained in the ln (\(C_{t}/C_{o}\)) plot versus irradiation time results in pseudo-first-order kinetics [18].
capacity of adsorbent (photocatalyst) at equilibrium in mg\(\cdot\)g\({}^{-1}\) was determined from the following relation:  
\[q_{e}=(C_{o}-C_{e})\times\frac{V}{M} \tag{4}\]  
where \(C_{e}\) refers to the dye's concentration at equilibrium (mg\(\cdot\)L\({}^{-1}\)), \(q_{e}\) denotes the adsorption capacity at equilibrium, \(V\) refers to the dye solution volume in L, whereas \(M\) resembles the photocatalyst mass in grams [19].  
Consequently, \(q_{t}\) (mg\(\cdot\)g\({}^{-1}\)), which refers to the amount of MO dye adsorbed per gram with respect to adsorbent (photocatalyst) at any time \(t\) (min), can be discovered using the following equation:  
\[q_{t}=(C_{o}-C_{t})\times\frac{V}{M} \tag{5}\]  
where \(C_{t}\) is the concentration of MO dye with photocatalyst through the reaction under light exposure for any time \(t\) (min) [17].
## 4 Propagation Model Evaluation
### Evaluation Results
 \tag{11}\]  
The results shown in Figure 9 reveal almost the same delay for all propagation models until the network enters in a saturation state, where MMPE and Bellhop seem to produce better results. At first sight this may suggest a lack of coherence, but if we take a close look at the distribution of packets received in the gateway from the different source nodes, we will appreciate that with MMPE and Bellhop, the gateway receives less packets from the farther nodes as they are more affected by the attenuation variability introduced by these propagation models, as shown before in Figure 6, so, this is the main cause of the lower overall packet delay with the use of more accurate acoustic propagation models, since the average packet delay decreases. Figure 9: Average packet delay with different acoustic propagation models. In the early first tests, it is clear that the propagation model is an important issue to take into account but now we go a bit further by changing the environmental parameters of the network scenario in order to assess their influence. For that purpose we will use one of the scenarios used before, fixing the network load at 2 packets/s to the point just before network saturation, and introducing two different months of the year (January and August) with different ocean average temperatures plus six different levels of wave heights (varying surface conditions) from 1 to 11 m height, the rest of network and environmental parameters remain the same as in Table 1.  
In Figure 10(a) the acoustic attenuation found between two network nodes, sensor 1 and gateway, is shown. As expected, Thorp's results remain constant since its equation does not include the effect of the physical parameters. Meanwhile, MMPE and Bellhop propagation models significantly reduce the obtained goodput as the wave height increases, _i.e._, they suffer from the wave motion effect. Also, neither Thorp nor MMPE are affected by the change of season whereas Bellhop shows different results for the months selected, we can appreciate worse performance in January than in August due to the different propagation conditions derived from the average ocean temperature. The average delay results including variable physical parameters are not included here as they are almost the same behavior than in Figure 9.  
So, summarizing this section, we can observe that in addition to having a detailed propagation model, different environmental conditions have a great impact in network performance. This leads us to seriously consider both: (a) an accurate acoustic propagation model, and (b) environmental and scenario parameters to obtain reliable simulation results that efficiently predict the real behavior of the sound propagation in a particular network scenario.
# Pathogenic Roles of RNA-Binding Proteins in Sarcomas
## 2 Regulations of RNA Metabolism by RBPs
### Alternative Splicing  
Alternative splicing (AS) plays an important role in gene regulation, through which pre-mRNA transcripts are processed to produce multiple mRNA variants with different stability and protein-coding potential. AS perturbations are frequently observed across cancers. RBP is one of the molecular determinants of AS, and the disturbance of RBP network activity has a causal relationship with cancer development [15; 16]. Functionally aberrant RBPs induce splice isomer conversions, which are widely involved in the regulation of cancer phenotypes including proliferation, apoptosis, cell-cycle progression, invasion and metastasis, angiogenesis, abnormal energy metabolism, and immune escape [17]. The main types of abnormal splicing in tumors are constituent splicing, exon skipping or inclusion, substitution of the 5\({}^{\prime}\) or 3\({}^{\prime}\) splice site, intron retention, and mutually exclusive exon [18]. Classical splicing regulators include serine-arginine (SR) proteins (such as SRSF2) and heterogeneous nuclear ribonucleoproteins (hnRNPs), which bind to exon or intron regulatory elements to promote or prevent the recognition of the 5\({}^{\prime}\) splicing site by the spliceosomal U1 small nuclear ribonucleoprotein (snRNP). These proteins can also promote or block SF1, U2 AF2, U2 AF1, or U2 snRNP, thereby influencing the selection of splice sites and thus altering splicing decisions [17]. The aberrant expression of SR and hnRNP proteins in cancers suggests that dysregulation of these two types of splicing factors plays an important role in tumor progression. In addition, RNA splicing is frequently dysregulated in a variety of cancers, and hotspot mutations affecting key splicing factors, SF3B1, SRSF2, and U2 AF1 are commonly enriched across cancers [19; 20], strongly suggesting that aberrant RNA splicing is a new class of hallmark that contributes to the initiation and/or maintenance of cancers. These mutations in genes encoding splicing factors are commonly identified in a variety of hematologic malignancies [19; 20; 21], as well as in solid tumors such as breast cancers, lung cancers, and pancreatic cancers [22; 23; 24; 25; 26]. The pathogenic roles of recurrent mutations affecting these splicing factors [27; 28; 29; 30] and the therapeutic strategies against cancers bearing these mutations [12; 27; 31] have been extensively studied and reviewed elsewhere [32; 33; 34].
## 5 Conclusions  
In this investigation, we proposed a coordinated LVRT control method using a PMSG WT and an ESS. Many previous studies on LVRT focused on improving its transient response. However, we focused on coordinating the WPS (pitch and inertia modulated) and ESS controls. Before applying the coordinated control method, we evaluated whether an ESS LVRT response is required using power equation analysis. At high wind speeds, an ESS LVRT response is required in addition to the WPS LVRT response to account for the rotor speed limit and converter current limits. If no ESS LVRT response is required, the WPS LVRT response is enough to handle the LVRT during the grid fault. When an ESS LVRT response is required, the power reference considering the pitch dynamics and rotor speed limit is used to evaluate the proper power reference of each unit in the proposed coordinated LVRT method. Using the proposed method, the ESS required power output for LVRT support was obtained. Thus, the ESS energy was conserved according to the WPS operational status and it can result in more cost effective ESS SoC management. From the two case studies, we validated the effectiveness of the proposed method by satisfying the operational constraints of the WT rotor speed limit and the ESS SoC limit.  
Figure 19: Power coefficient variation during a balanced voltage sag (80%).  
Figure 18: Rotor speed variation during a balanced voltage sag (80%).  
**Author Contributions:** C.K. designed the algorithm and developed the simulation; Y.G. provided guidance in designing the algorithm; H.Z. and W.K. verified the simulation model and results; and all authors reviewed and approved the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Energy Cloud R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (NRF-2019M3F2A1073313).  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DFIG & Doubly-fed induction generator \\ ESS & Energy storage system \\ GSC & Grid-side converter \\ LVRT & Low-voltage ride through \\ MPPT & Maximum power point tracking \\ PCC & Point of common coupling \\ PI & Proportional-integral \\ PMSG & Permanent magnet synchronous generator \\ RSC & Rotor-side converter \\ SoC & State of charge \\ WP & Wind power \\ WPS & Wind power system \\ WT & Wind turbine \\ \end{tabular}
###### Abstract  
Artificial neural network (ANN) is widely applied as a predictive tool to solve complex problems. The performance of an ANN model is significantly affected by the applied architectural parameters such as the node number in a hidden layer, which is largely determined by the complexity of cases, the quality of the dataset, and the sufficiency of variables. In the present study, the impact of variation/response space complexity and variable completeness on backpropagation (BP) ANN model establishment was investigated, with a steel ladle lining from secondary steel metallurgy as the case study. The variation dataset for analysis comprised 160 lining configurations of ten variables. Thermal and thermomechanical responses were obtained via finite element (FE) modeling with elastic material behavior. Guidelines were proposed to define node numbers in the hidden layer for each response as a function of the node number in the input layer weighted with the percent value of the significant variables contributing above 90% to the response, as well as the node number in the output layer. The minimum numbers of input variables required to achieve acceptable prediction performance were three, five, and six for the maximum compressive stress, the end temperature, and the maximum tensile stress.  
keywords: backpropagation artificial neural network; space complexity; variable completeness; lining concept; steel ladle; thermomechanical responses +
Footnote †: journal: Applied Sciences
# An Approach to Designing Critical Railway Voice Communication
## 3 Reusable Functionality Related to Security Services
### Management of FRMCS User and Application Data
A network operator authorizes the communication in order to avoid disruptions or interruptions to users (e.g., drivers during train movement). The permission or denial of voice and data communications is also based on functional identities related to the user, user identity, equipment functional identities, and subscriber identity. Multiuser talker control is an FRMCS application for multiparty voice communications that restricts the number of simultaneous talkers based on the identities and applications of the involved users. The number of simultaneous talkers, initial talker permissions, and priorities are configurable as part of the application data. Arbitration between simultaneous communications competing for the same user's attention is another FRMCS application that relies on data configured to the application. According to [3], it is the FRMCS Service Server that stores the user profile application data and performs service-level registration and deregistration, whereas the FRMCS Mobile Gateway is responsible for the authorization of applications, which is represented through the FRMCS Mobile Application Client. To unify the access to FRMCS data from different applications, the functionality of FRMCS data management can be synthesized as RESTful microservices. The microservices' design principles are described in Section 4.
## 2 Overview of the TMM
### The Theory of the MVTMM
#### 2.3.1 The Construction of the Mixed Variable Transfer Matrix
Therefore, employing mixed variable transfer matrices instead of traditional transfer matrices effectively mitigates high-frequency instability issues commonly encountered in long pipelines.
# A Hybrid Control Approach for the Swing Free Transportation of a Double Pendulum with a Quadrotor
## 4 Experimental Results  
### Experimental Setting  
A computational experiment based on accurate simulations of the system has been carried out in order to test the efficiency and validity of the model. We compare our MPC-PD control proposal with PD-PD control configuration. The simulation experiment was carried out using a discretization of the time variable and all the simulations cover a time of 32 s, including the transient state of the experiment. The time increment used to compute simulation steps is 0.01 s. Experiments have been coded in house in Matlab. The MPC control horizon is 1 s and the selected integration step is 0.1 s. This is, the control horizon consists of 10 steps.  
Dynamic parameters of the quadrotor are assumed as in Table 1. Initial angles of the drone are set to 0. Inner-loop PD controller parameters are computed as described in [53]. For experiments in this paper are set as follows: \(K_{P}=130\) and \(K_{D}=39\), which are the same for MPC-PD and PD-PD control configuration. In the case of PD-PD control configuration, \(K_{P1}\) and \(K_{D1}\) values are set to 0.25 and 1.6, respectively. Thrust of the drone is limited by hardware to 20 N. Drone starting coordinates in the \(XZ\) plane are \((0,0)\).  
The control efficiency to follow a path and adjust correctly the quadrotor parameters for the minimization of the double pendulum swing is going to be tested. A parabolic path was planned for the quadrotor to follow. The quadrotor is carrying a double pendulum and both systems are joined by an ideal punctual link. Table 2 gives the parameter setting of the double pendulum. The equation of the parabolic path is \(z=2x-0.066\cdot x^{2}\). This parabolic path introduces tangential inertial forces affecting the double pendulum that are not considered in [40]. Double pendulum dynamics during take-off maneuvers from the ground is not considered. Initial values of the pendulum \(\theta_{1}\) and \(\theta_{2}\) are set to 0.
## 4 Analysis and Results
### Informal Security Analysis
#### 4.1.3 Resistance to Eavesdropping Attacks  
This attack allows the adversary to intercept the communication between two entities and possibly manipulate the messages exchanged between these entities who believe that they are directly communicating with each other, as in a man-in-the-middle attack, which is considered a type of eavesdropping attack. In the presented authentication scheme, an eavesdropping attack can be resisted, as there is no sensitive or valuable information transmitted during the authentication phase, which is conducted on an unsecure channel. This is because the messages are not exchanged in plain text and the parameters involved in these messages cannot be extracted. For instance, as shown in Figure 11, let us assume that the adversary obtains the C1, C2, and C3. The parameters such as the secret key SKAM_C or H (MID\({}_{\text{C}}\)), which are used to generate these ciphers, cannot be extracted, as they are merged in an XOR operation with other parameters, resulting in ciphertext. Thus, the adversary cannot interpret the message and its sensitive contents. On the other hand, in cases where the adversary modifies the contents of transmitted messages, such as a digest hash, which is involved in each authentication message, the receiving entity of the message will detect the attack. For example, if the adversary changes the Na that was used to generate Hash5, the digest hash result, Hash5', computed by B will not match the received Hash5, and thus, the authentication process will be terminated.
# Sequential Polar Decoding with Cost Metric Threshold
## 3 Our Contribution
### Key Difference from Basic SC-Creeper Decoder  
When considering SC-Creeper with a cost metric, one may consider the fundamental differences between the proposed approach and the basic SC-Creeper algorithm. In this subsection, we list the key differences in the two approaches.  
1. Penalty for the wrong node: The basic SC-Creeper has the ability to move in the opposite direction, but backward movement does not always help to find the correct T-node or a valid node. In this case, an algorithm with an additional metric may decide not to use backward movement, but, for example, to continue moving in the direction of the largest LLRs.
2. Memory optimization: Although an additional metric must be calculated periodically, this action prevents the decoder from following a potentially incorrect path. Thus, instead of storing the stack of the entire path and recalculating it at a later stage, it is necessary to store only two values of the \(\Gamma(\cdot)\) metric in order to control and optimize the variation of the stack.
3. Purpose of threshold values: For SC-Creeper, values such as \(\Delta\) and \(T\) are the algorithm step and the degree of quantization when moving to a new tree node. However, in the modified version, the step plays a less significant role (that is, for different values of \(\Delta\), the performance variation is much lower), however, instead of the constant variable \(T\), the function \(T(\cdot)\) is used, which serves as an upper threshold value for the searching of T-nodes. A more flexible search allows us to achieve better performance results.
# Non-Probabilistic Reliability Analysis of Slopes Based on a Multidimensional Parallelepiped Model
## 5 Conclusions  
In this paper, a non-probabilistic reliability analysis method of slopes based on a multidimensional parallelepiped model is proposed. The distinctive and significant findings of the study are as follows: first, the feasibility of the method is verified via case analysis, which indicates the multidimensional parallelepiped model is suitable for non-probabilistic reliability analysis of slope engineering. Second, the calculation results of this method are intermediate between those based on a traditional interval model and those based on an ellipsoid model, which are more in line with engineering practice. Third, the correlation between the geotechnical parameters and its own variability have a greater impact on the non-probabilistic reliability indexes of the slope, which must be taken into account when using the proposed non-probabilistic reliability method to analyze the stability of slopes. The research's significance and implications are as follows: The proposed method only needs a small number of geotechnical samples to judge the slope stability state, without the need to assume the probability distribution type of geotechnical parameters through the use of a large number of samples statistically as in the case of the probabilistic reliability method, and the correlation of the geotechnical parameters can be taken into account. Moreover, the multidimensional parallelepiped model is combined with non-probabilistic theory for slope engineering, which expands the application field of calculation methods based on non-probabilistic theory and provides a new feasible scheme for slope stability analysis.  
Conceptualization, S.S.; methodology, J.Q.; software, J.Q.; formal analysis, Z.Y. and W.G.; investigation, Z.Y.; resources, S.S. and W.G.; data curation, K.P.; writing--original draft preparation, J.Q.; writing--review and editing, S.S.; supervision, S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 52008319).  
Not applicable.  
Data Availability Statement: Data is unavailable due to privacy or ethical restrictions.  
The authors declare no conflict of interest.