esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Mechanical Testing  
Fabricated implant specimens were tested under axial compression. Such type of implants can be used for compression tests because the top and the bottom contact faces of the implants are parallel. The bottom face has a tight contact with the compression plate of the mechanical press. The top surface contacts the compression plate at a number of points.  
We tested specimens at the Instron 5969 electromechanical testing machine equipped with a 50 kN load cell. We used two 150 mm diameter hardened, polished compression plates, with an adjustable upper plate. Before the test, the plates were brought together with a small load and the upper plate was adjusted so as to ensure parallelism. To avoid premature failure of the ceramic specimens at spots of contact with the metal, thin cardboard pads were glued onto both compression plates with double-sided adhesive tape. For full-field strain measurements, we applied speckles of black acrylic paint onto the specimens, using a spray bottle.  
The upper plate was brought to the position of approx. 5 mm above a specimen, then the load was balanced, and the test started with the constant traverse speed of 0.2 mm/min. Tests were stopped manually, on visible signs of specimen fracture. We used a Digital Image Correlation (DIC) system from Correlated Solutions, taking one photograph a second from a 5 MP camera with a Tokina 100 mm F2.8 Macro D lens with simultaneous recording of force via a National Instruments Data acquisition (DAQ). Speckle correlation was performed in the VIC-2D-2009 program. After obtaining the strain field, we applied a virtual extensometer incorporated in the program to extract the tabulated data of engineering strain vs. force relationship.  
In this study, we do not consider the testing of specimens with cylindrical base plates at both faces of a cylinder due to the following reasons. The proposed design with a single base plate represents a real implant to be used in further clinical testing. As the mechanical performance of such items is a very important part of the engineering design for biomedical applications, the real implants need to be tested \"as is\", without artificial parallelization of surfaces.
# Research Progress on Flexible Oxide-Based Thin Film Transistors
## 2 Device Fundamental Concept and Conventional Configuration  
### Carrier Transport Mechanism of Oxide Semiconductor Materials  
Compared to monatomic silicon, oxide semiconductors are more complex materials as these are built up from several chemical compounds and can contain more different kinds of defects and impurities. Hence, it is beneficial for fabrication to comprehend the carrier transport mechanism of oxide semiconductor materials. In ternary and multinary compound semiconductors (e.g., indium zinc oxide (IZO), indium gallium zinc oxide (IGZO)), zinc (Zn) is replaced by indium (In), which generates excess electrons, resulting in most oxide semiconductors exhibiting n-type characteristics. However, in binary semiconductors (e.g., ZnO, In\({}_{2}\)O\({}_{3}\)), the mechanism of conduction is unintelligible. [PERSON] [132], [PERSON] [133], and [PERSON] [134] considered the mechanism to be oxygen vacancy doping; one of the most popular explanations. Calculation results show that at a high Fermi level oxygen vacancy has a low formation energy, which proves that the oxygen vacancy concentration indeed determines the conductivity and the charge carrier concentration. However, other theoretical calculations [72] show that oxygen vacancies form deep donors make it difficult to excite electrons to the conduction band. Another possibility for the explanation of n-type conductivity was reported by [PERSON] and [PERSON] et al. [135; 136]. These authors proposed that unintentional hydrogen incorporation could be the reason for the observed n-type characteristic of oxide semiconductors. Such an assumption, however, fails to explain the high conductivity of the high purity of In\({}_{2}\)O\({}_{3}\). Besides, some other propositions still fail in persuasiveness [137; 138; 139].  
In the case of the doped oxide semiconductors (e.g., a-IGZO), In has the electron structure of (n \(-\)1) d10 ns0 (n \(>\) 5), where the electronic channel would be generated by overlapping the adjacent 5s electron orbit. The 5s electrons are spherically symmetric and insensitive to direction. Hence, the degree of ion alignment has little influence on electron transportation. As the conduction band minimum (CBM) is contributed by the In 5s orbital, rather than from Zn 4s or Ga 4s. The mobility and effective masses of monocrystalline and amorphous IGZO are relatively close due to the similar bandwidth of the conduct band level [140] and the close effective mass of 0.18 me and 0.2 me [141]. However, the mobility of IGZO depends on the electron concentration, which can be explained by the osmotic conduction model [142; 143; 144]. In this model, the center energy (\(\phi_{0}\)) of a-IGZO is getting closer to the Fermi level (E\({}_{\text{F}}\)), with rising electron concentrations, thus, leading to a narrower distribution width (\(\sigma_{\phi}\)).
## 4 Results: Manual Service Composition
### Summary  
The manual service composition hypotheses posited at the beginning of this article were reviewed in light of the evidence; see Table 8. Hypotheses H1b \(\rightarrow\) H1e were fully supported, while H1a was partially supported. Thematic analysis results mirrored and affirmed the quantitative results and showed that the non-programmers' struggle to define conditions was caused by their weak conceptual understanding of the service elements, such as parameters and operators, that constitute a conditional statement, and their inability to differentiate between multiple operators and match the correct operator to the intended action. This was aggravated by the technical language used to express these composition concepts. The expert sample, however, was mainly hindered by their unfamiliarity with the syntax of the Process Editor tool to formulate conditions and dataflow connections.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Manual Composition Hypothesis** & **Differences between the Groups** & **Verdict** \\ \hline \multirow{6}{*}{H1a Non-programmers will find it more difficult to understand service terminologies compared to programmers} & -Statistical differences in satisfaction scores of composition notations (Independent samples \(t\)-tests)—Figure 5 & \\  & -Percentage of conceptual problems discussing the complexity of terminologies and notations (thematic analysis)—Table 5 & \\ \hline \multirow{6}{*}{H1b Non-programmers will find it more difficult to define conditional flows (IF \(\ldots\) ELSE) between services compared to programmers} & -Statistical differences in performance results, i.e., correctness of (if \(\ldots\) else) specifications, task completion rates (\(t\)-tests)—Table 3 & Supported \\  & -Statistical differences in satisfaction scores of condition definition (\(t\)-tests)—Figure 5 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Review of the manual service composition hypotheses (H1a \(\rightarrow\) H1e).  
Figure 5: Average usability rating of manual service composition by non-programmers and programmers; all dimensions differed statistically.  
In addition to the hypotheses listed above, various interesting findings emerged with respect to manual service composition. Non-programmers were found to spend more time and needed more substantial guidance to define the parallel processes compared to programmers (\(t\)-tests). This was also evidenced by the percentage of conceptual problems emerging in the themes.
## 1 Introduction
### Main Contribution and Overview  
To address the problem of the POD computation of multiple non-ideal planes, this study proposes a point-by-point-contact-based approach. The six-point positioning principle is employed to determine the cases in which DOFs are constrained by the contact points on each mating plane. The surface deviations of each constraint plane are modeled from the perspective of manufacturing errors. Next, the DOFs corresponding to each mating plane are constrained successively to determine each contact point by progressively approaching position and orientation and by recursion of the contact points. As a result, the PODs corresponding to the given surface deviations are acquired.  
The remainder of this paper is organized as follows. Section 2 discusses the principle of POD computation by successively determining contact points. Based on the discussion in Section 2, the process of computing the POD of multiple mating planes is introduced in Section 3. Next, this approach is verified by computing the PODs of an assembly with multiple mating planes and a sliding workbench, and the efficiency of the approach is discussed. Finally, conclusions are drawn.  
Position and Orientation Deviation Computation Principle by Successively Determining Contact Points
# A Reliability Scheduling Algorithm for the Static Segment of FlexRay on Vehicle Networks +
###### Abstract  
FlexRay is a next-generation in-vehicle communication protocol which works in real time with flexibility. The most common applications in FlexRay are high bandwidth. X-by-wire applications, such as brake by wire and throttle by wire. However, there is no mechanism which can prevent transient faults in the application layer of FlexRay. If a transient fault occurs during driving, this would be very dangerous; therefore, we propose a fast reliability scheduling algorithm (FRSA) to improve the communication reliability of FlexRay. The proposed method reduces the probability of transient faults in one clock cycle by using a retransmission mechanism to recover the transient errors, and further improves computational complexity using the lookup table method to ensure system reliability. In this paper, we analyze a related literature to establish the system reliability constraints needed to evaluate the necessary time and slot usage, and the proposed cost function is used to evaluate the performance and efficiency when the number of messages is increased. Experimental results show that the proposed FRSA reduces execution time by an average 70.76% and cost by an average 13.33% more than the other existing methods. This method can be useful to others, especially regarding research about periodic time-triggered communication systems.  
FlexRay; transient fault; scheduling; re-transmission mechanism; communication reliability; time-triggered +
Footnote †: This work is an extended version of the conference paper published in the 7 th IEEE International Symposium on Next Generation Electronics entitled “Fast Reliability Scheduling Algorithm for the Static Segment of FlexRay for Vehicle Network”.
# Binary PSO with Classification Trees Algorithm for Enhancing Power Efficiency in 5G Networks
## 3 System Model
The motion of the particle is based on its local best position, and the best-known position of all the other particles [9; 52]. The fitness value of every particle is calculated using a fitness function that is optimized in every iteration [9; 52]. The set of particles X = {\(x_{1},\;x_{2},\;\ldots,\;x_{N_{par}}\)} is defined, where \(x_{N_{par}}\) represents one possible status for the SCs, while \(N_{par}\) is the swarm size. In BPSO algorithm, the population is randomly initialized as binary values. For every particle, the population binary value of 1 signifies the active SC, while 0 signifies the sleeping SC. The velocity of the particle \(j\) is initialized as [9; 58]:  
\[v_{j}=v_{min}+(v_{max}-v_{min})\;a_{1} \tag{6}\]  
where \(v_{min}\) and \(v_{max}\) denote the minimum and the maximum velocity of the particle, respectively, while \(a_{1}\) is a random number uniformly distributed between 0 and 1 [9]. The velocity and the position of the particles are updated in each iteration. The velocity of the particle \(j\) in iteration \((z+1)\) is updated as [9; 26]:  
\[v_{j}^{\ z+1}=\omega_{z}\ v_{j}^{\ z}+c_{1}\ a_{2}\Big{(}P_{best_{j}}-x_{j}^{\ z} \Big{)}+c_{2}\ a_{3}\Big{(}G_{best}-x_{j}^{\ z}\Big{)} \tag{7}\]  
where \(\omega_{z}\) is the IW in the \(z^{th}\) iteration, while \(v_{j}^{\ z}\) and \(x_{j}^{\ z}\) are the velocity and position of the particle \(j\) in the \(z^{th}\) iteration, respectively. The best position of the particle \(j\) is denoted as \(P_{best,j}\), while the global best position of all the particles is denoted as \(G_{best}\). Additionally, \(c_{1}\) and \(c_{2}\) denote the acceleration parameters [9]. Moreover, \(a_{2}\) and \(a_{3}\) are two random numbers uniformly distributed between 0 and 1. It is noted that the particle \(x_{j}\) is a binary vector and the velocity \(v_{j}\) is also a vector. The sigmoid function \((Sig\big{(}v_{j}(m)^{z}\big{)})\) is given as [52; 55]:  
\[Sig\big{(}v_{j}(m)^{z}\big{)}=\frac{1}{1+e^{-v_{j}(m)^{z}}} \tag{8}\]  
Thus, the on/off state of the SC \(m\) in particle \(j\) in the \(z\)-th iteration is calculated as [52; 55]:  
\[x_{j}(m)^{z}=\left\{ \begin{array}{ll}1,&a_{4}<Sig\big{(}v_{j}(m)^{z}\big{)} \\ 0,&otherwise\end{array} \right. \tag{9}\]  
where \(a_{4}\) is a random number uniformly distributed between 0 and 1. Since the IW is the pivotal factor in the convergence of the PSO, it should be carefully adjusted. Thus, linearly increasing IW is utilized in this paper, where the IW linearly increases every iteration from \(\omega_{min}\) to \(\omega_{max}\). The IW in the \(z^{th}\) iteration is given as [33]:  
\[\omega_{z}=(\omega_{max}\ -\omega_{min})\bigg{(}\frac{z-1}{Z_{max}\ -1}\bigg{)}+ \omega_{min} \tag{10}\]  
where \(\omega_{max}\) and \(\omega_{min}\) denote the maximum and minimum IW, respectively [33], and \(Z_{max}\) is the maximum number of iterations [59].
## 3 Results and Discussion
### Performance Indices  
AE, PE and EQ were calculated for both normal and faulty operation cases. They were determined as a mean value over 28 days, i.e., for the period of the days ranging from day 140 to day 168. Table 2 shows the values of the AE, PE and EQ for the normal operation and for the six different faults.  
It can be observed that for the bias, loss of accuracy, fixed value and complete failure maximum types of faults, the PE and EQ indices values are much higher than the normal operation values, but AE is lower. The AE values can be explained by the fact that the four fault types are characterized by high values of the DO sensor signal, sent as a wrong feedback signal to the DO controller. As a result, the DO controller diminishes the aeration with the aim of reducing the value of the faulty DO signal, and the aeration energy drops.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Operating Regime** & **AE** & **PE** & **Total Energy Demand** & **EQ** \\  & **(kWh/day)** & **(kWh/day)** & **(kWh/day)** & **(kg PU/day)** \\ \hline Normal operation & 16,992 & 1329 & 18,321 & 16,852 \\ Bias fault & 14,206 & 2415 & 16,621 & 21,461 \\ Drift fault & 15,569 & 1746 & 17,315 & 17,134 \\ Wrong gain fault & 15,866 & 1593 & 17,459 & 16,706 \\ Loss of accuracy fault & 9150 & 2403 & 11,553 & 219,189 \\ Fixed value fault & 1968 & 2415 & 4383 & 338,737 \\ Complete failure & 23,537 & 1039 & 24,576 & 19,804 \\ minimum fault & 1968 & 2415 & 4383 & 338,750 \\ Complete failure & & & & \\ maximum fault & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of the performance indices for the normal and faulty operation cases.  
This is followed by a decrease in nitrification, which induces a lower concentration of nitrates and nitrites in the aerated bioreactor and, subsequently, in the anoxic bioreactor. Consequently, the NO control loop reacts to this change by increasing the internal recycle flow rate, which determines higher pumping energy. Additionally, the quality of the effluent drastically deteriorated due to the inefficiency of the treatment process, affected by the air-lacking circumstances, and higher amounts of pollutants are discharged with the clean effluent. The high AE value in the case of the complete failure minimum type of fault is due to the increase in the DO controller manipulated airflow rate as a response to the low but faulty value of the DO sensor signal.  
It is worth mentioning that in cases of the faulty operation of the DO sensor that is characterized by reduced values of the DO signal, compared to the true one, the DO controller will drive the aeration to increased airflow rates with straightforward consequences on the rise of the AE index values.
# Solid-Phase Microextraction Arrow for the Sampling of Volatile Organic Compounds in Milk Samples
## 3 Results and Discussion
### Evaluation of the SPME Arrow Procedure
#### 3.1.6 Selection of Salt Addition  
The effect of salt addition in the SPME Arrow extraction procedure was also studied. Addition of sodium chloride was tested to check its effect on the HS-SPME procedure. The salting-out method is widely used in SPME methods, since it is known to enhance the distribution constant between the analytes and the fiber by minimizing the interaction of the analytes with water and by helping the analytes to go to the headspace [16]. For this purpose, different quantities (i.e., 0, 5, 10 and 20% _w/w_) of sodium chloride were added to the sample. Figure 5 shows the effect of addition of sodium chloride in the extraction procedure. As can be seen, no significant benefit from salt addition was observed. Although salt addition has often been observed to enhance extraction efficiency, this is not the case for volatiles analyzed in milk. This is believed to be related to milk being an emulsion rather than a homogenous solution. The volatile compounds are mainly enriched in the lipid phase of the milk while the salt dissolves in the aqueous phase, thus not affecting the response of the volatiles. For purely aqueous solutions, the addition of salt could cause an increase in the viscocity of the aqueous  
Figure 4: Evaluation of the stirring rate for SPME Arrow.  
Figure 3: Evaluation of the extraction time for SPME Arrow.  
sample solution which might reduce the mass transfer of the analytes to the headspace and result in a reduction in the extraction efficiency [17]. As a result, further experiments were carried out with no salt addition.
## 2 Materials and Methods
Figure 2: (**a**) Positional deviation, \(\Delta z(t)\), from a constant-motion reference of \(v_{0}\approx 520\) m/s, where \(\Delta z(t)=v_{0}t-z(t)\) and actual position, \(z(t)\), is measured from a multi-exposure stroboscopic image, as in Figure 1b. (**b**) Velocity-position data corresponding to the data in (**a**) fit with an exponential decay fitting function (Equation (1)). Error bars correspond to a 95% confidence interval (\(\pm 2\) standard deviations). of 0.018, 0.009 and 0.18, respectively. Each flight yielded a single, constant \(\beta\) value, which is consistent with the methods of prior \(C_{D}\) studies [15; 17; 18; 19; 20]. By derivation from the drag force equation (Equation (2)), \(\beta\) is directly proportional to \(C_{D}\) by Equation (3), in which \(\rho\), \(A\), and \(m\) correspond to air density, particle cross-sectional area, and particle mass. The added mass and Basset history forces were neglected due to the high density ratio between the particle and the surrounding gas [21]. \[v(z)=v_{0}e^{-\beta z} \tag{1}\]  
\[F_{D}=m(d\
u/dt)=-\frac{1}{2}C_{D}\rho Av^{2} \tag{2}\]  
\[C_{D}=2\beta m/\rho A \tag{3}\]
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### Analytical Comparison of the Models
#### 5.2.3 Scalability  
The scalability of the two approaches can vary significantly. As shown in Table 5, the multi-layered structure of HSALA offers a higher degree of modularity and adaptability.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **HSALA** & **Three-Layered Model** \\ \hline Security Integration & Each functional layer is paired with a security layer, providing a defense-in-depth approach. & Security measures are often added \\ \hline Proactive Measures & Incorporates security from the ground up, including data encryption, secure coding practices, and continuous monitoring. & May lack dedicated security layers, leading to gaps in protection and inconsistent security practices. \\ \hline Regulatory Compliance & Facilitates adherence to data protection laws and industry standards (e.g., GDPR, HIPAA). & Ensuring regulatory compliance can be more complex due to the absence of integrated security considerations. \\ \hline Security-Aware Development & Developers are guided to consider security implications at every stage due to the integrated security layers. & Security depends heavily on individual developers’ awareness and practices, leading to potential oversights. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the two architectural models considering cybersecurity features.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **HSALA** & **Three-Layered Model** \\ \hline Integration Level & Accessibility is embedded within the architecture through the Accessibility Enhancement Layer. & Accessibility features are often added after the core development is completed. \\ \hline Compliance with Standards & Ensures adherence to WCAG and uses WAI-ARIA for assistive technologies. & May not fully comply with accessibility standards due to retrofitting challenges. \\ \hline Holistic Approach & Accessibility is considered at every stage, from data representation to user interaction. & Lacks a dedicated layer, leading to possible inconsistencies and gaps in accessibility. \\ \hline Accessibility Security & The Accessibility Security Layer & Accessibility features might \\  & addresses potential security risks introduced by accessibility features, ensuring secure and inclusive access. & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the two architectural models considering accessibility features.  
HSALA's modular design facilitates scalability and adaptability, allowing the application to evolve with changing demands and technologies. The three-layered model may face challenges in scaling and updating due to its less flexible architecture.
# Comparison of Distributed Tamper-Proof Storage Methods for Public Key Infrastructures
## 3 Concepts and Evaluation Methods
### Public IPFS-Filecoin
#### Sia  
Sia is a decentralized cloud storage platform that implements a different storage scheme than Filecoin or IPFS in general. Instead of lending storage space from a central provider, Sia allows clients to lend storage space to each other. The underlying Sia Blockchain only stores the storage contracts the clients agreed on. Each contract has a storage interval in which the data storage provider needs to guarantee the storage of the data. To prove this, the provider executes a _Proof-of-Storage_ algorithm, thereby validating the contract.  
Additionally, the storage providers get compensated for each successful proof using the native _SIA Token_. In return, the data client will have to spend some of his SIA Tokens for the data to be stored on the network. After the process is complete, chunks of the data will be replicated to 30 different peers within the Sia network and, using error correcting code (ECC), can be reconstructed using only ten unique peer responses [19].