esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Simplified Model of the Network  
Figure 2 shows the DC distribution network implemented to analyze the different effects of the load variation. The converter has a power source with voltage \(E\) and an internal source resistor \(r_{s}\), which represent the equivalent circuit of different energy sources and their internal impedance connected to Bus 1, as shown in Figure 1. A metal-oxide-semiconductor field-effect transistor (MOSFET) works as a switch \(S\), with an internal MOSFET resistance \(r_{M}\), diode \(D\) with forward voltage diode \(V_{fd}\), filter \(LC\), internal resistance of inductor \(r_{L}\), and resistance used to measure current \(r_{Med}\). Finally, Bus 2 represents the output of the circuit where the load is connected (CPL and CIL).  
Figure 1: Simplified power network feeding direct current (DC) loads.  
A diagram of the pulse width modulation (PWM) controller connected to the buck converter and the sensors used to measure the different input signals is presented in Figure 3. An SMC with a washout filter is used to regulate the output signal. The response to the dynamics created according to the load variation (CPL and CIL) can be regulated by this SMC.  
This circuit was used to represent the mathematical and numerical models that are solved to identify the variations and response of the system. Model parameters that help represent the system are discretization, quantization, and delay effects as those obtained for real circuits.  
Figure 4 shows the complete diagram implemented in MATLAB-Simulink (version R2019a, The MathWorks, Natick, MA, USA) to control the buck converter using the SMC with a washout filter. The quantization effects are included as 12 bits for the voltage in the condenser \(v_{c}\) and the current in the inductor \(i_{L}\). A delay period was included in the numerical simulation equal to 1/Fc. The zero-order-hold block is necessary to simulate the fixed sample time and obtain \(v_{c}\) and \(i_{L}\), and the calculation of \(u\) is explained in the following mathematical model. All parameters of the buck converter and the SMC controller are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Description** & **Value** \\ \hline \(R\) & Resistance of the load & 20.44 \(\Omega\), 40.092 \(\Omega\) \\ \(C\) & Capacitance & 938 \(\mu\)f \\ \(L\) & Inductance & 53.35 mH \\ \(r_{L}\) & Internal resistance of the inductor & 1.33 \(\Omega\) \\ \(r_{Md}\) & Sensor resistance & 1.007 \(\Omega\) \\ \(r_{M}\) & Internal resistance of the MOSFET & 0.3 \(\Omega\) \\ \(r_{s}\) & Internal resistance of the source & 0.3 \(\Omega\) \\ \(E\) & Input voltage & 40 V (Dual source) \\ Fc & Commutation frequency & variable \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the circuit used in the direct current (DC) test.  
Figure 3: Block diagram of the buck converter with the pulse width modulation (PWM) controller used for the simulation.  
Figure 2: Circuit model of the buck power converter (PC) connected to the constant impedance load (CIL) (\(R\)) and constant power loads (CPLs) (\(P\)).
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 4 Content Protection with Ternary Unclonable Functions
### Protection of Digital Files Stored by IoT Terminals  
The method proposed in this section to protect digital files is a variation of that presented above. Here, the information generated at the IoT level and which is stored by the IoT for future use is protected. The objective is to prevent a third party from simply retrieving information from the memory unit of the IoT, which is equipped with its own one-way unclonable function. During the preparation cycle, as shown in Figure 7, the server generates the challenges (**T**, _IDAccess_) from the image of the PUF. The client device goes through the following steps:  
* Retrieves the challenges (**T**, _IDAccess_).
* The responses **K** are generated with the PUF, from the challenges.
* The IoT hashes **K** for the search engine.
* The file **M** is encrypted with **K** to generate the ciphertext **C**.
* The IoT stores **T**, **C**, and the message digest **H(K)**, but not _IDAccess_, which is only stored for future reference by the server.  
The IoT can only decrypt message **M** after receiving from the server the individual digital access information _IDAccess_. The server can delegate the responsibility to dispatch _IDAccess_ to a third party. If the one-way function is tamper-resistant, an opponent cannot decrypt **M** without an image of the one-way unclonable function. An example of the implementation of the decryption of **M** by the client device is as follows:  
* Receive _IDAccess_.
* Read from the memory number **T**, ciphertext **C**, and message digest **H(K)**.
* Generation of **K'** from the one-way unclonable function.
* Retrieve **K** from **K'** and **H(K)** with a search engine.
* Decrypt the digital file using **K** as a cryptographic key.  
Figure 6: Block diagram of the decryption of **M**. The server transmits _IDAccess_ to trigger the process. The challenges (**T**, _IDAccess_) generate **K’** from the PUF. The search engine finds **K** through an iterative process from **K’** and **H(K)**. The ciphertext is decrypted with **K** to recover **M**.
## 2 System Model and Analysis
### Analysis of Model Characteristics  
There is an underactuated link in the planar PPR underactuated robot system, and the control of this link can only be realized by controlling the rest of the prism-actuated link so that the rotating underactuated link is indirectly controlled and adjusted and the stability control is achieved at the same time. Therefore, it is essential to understand the control properties of planar PPR underactuated robots to achieve stability control.  
According to Equations (6)-(8), the system dynamics equation is reformulated as follows:  
\[\begin{cases}(m_{1}+m_{2}+m_{3})\dot{q}_{1}-m_{3}l_{3}\sin q_{3}\dot{q}_{3}-m_ {3}l_{3}q_{3}^{2}\cos q_{3}=\tau_{1}\\ (m_{2}+m_{3})\dot{q}_{2}+m_{3}l_{3}\cos q_{3}\dot{q}_{3}-m_{3}l_{3}q_{3}^{2} \sin q_{3}=\tau_{2}\\ (-m_{3}l_{3}\sin q_{3})\dot{q}_{1}+m_{3}l_{3}\cos q_{3}\dot{q}_{2}+(J_{3}+m_{ 3}l_{3}^{2})\dot{q}_{3}=0\end{cases} \tag{9}\]  
According to Equation (9), the underactuated equation of the planar PPR underactuated robot is as follows:  
\[(-m_{3}l_{3}\mathrm{sin}q_{3})\dot{q}_{1}+(m_{3}l_{3}\mathrm{cos}q_{3})\dot{q} _{2}+(J_{3}+m_{3}l_{3}^{2})\dot{q}_{3}=0 \tag{10}\]  
In order to analyze the integral properties of dynamic constraints of underactuated joints in planar PPR systems, the following lemma is given.  
**Lemma 1** ([29]).: _The adequate and essential conditions for the integrability of the dynamic constraint part of the underactuated joint in the underactuated system are as follows:_  
1. _The gravitational component within the dynamic constraint of the underactuated joint remains invariant;_
2. _Underactuated joint variables are absent from the inertia matrix._  
The gravity term of the dynamics equation of a planar PPR system is 0, which satisfies condition (1) in Lemma 1. However, it does not satisfy condition (2) of Lemma 1. Thus, the constraint equations corresponding to the underactuated joints in the planar PPR system are not integrable. Thus, planar PPR robot is a class of systems with the non-integrability property.  
Through Equations (9) and (10), we can obtain  
\[\ddot{q}_{3}=-\frac{M_{31}\ddot{q}_{1}+M_{32}\ddot{q}_{2}+H_{3}}{M_{33}}=- \frac{(-m_{3}l_{3}\sin q_{3})\ddot{q}_{1}+(m_{3}l_{3}\cos q_{3})\ddot{q}_{2}}{ J_{3}+m_{3}l_{3}^{2}} \tag{11}\]  
Through the integration of Equation (11), we can obtain  
\[\begin{cases}q_{3}=-\int_{0}^{t}\dot{q}_{3}\mathrm{d}t+q_{30}\\ q_{3}=\int_{0}^{t}q_{3}\mathrm{d}t+q_{30}\end{cases} \tag{12}\]  
\(q_{30}\) is the starting angle, and \(\dot{q}_{30}\) is the starting angular velocity.  
Equation (12) provides the constraint relationship between the actuated link and the underactuated link of the planar PPR system. Although Equation (12) is not integrable, according to this constraint relationship, we can indirectly control the underactuated link by controlling the actuated link.
# Observing Pictures and Videos of Creative Products: An Eye Tracking Study
## Materials and Methods
### Stimuli  
Six different creative products (see Figure 1), in which all the authors recognized original characteristics, have been exploited in the experiment. Creativity was meant as a fundamental feature of stimuli because of the reasons that follow.  
* Creative designs are supposed to attract greater interest than commonplace products and capture users' attention.
* A task was designed to keep participants' attention focused on products and their elements (see Section 3.3) and this required the use of products that are not supposedly everyday objects.
* As stated in the Introduction section, creative designs are featured by a major need for being evaluated, as they introduce novel elements or functions that deviate from people's habits.  
The creative products were selected based on the availability of videos on YouTube that illustrate these products' functioning. In addition, the authors selected products that, despite being considered creative and non-commonplace, could be used or benefitted from in everyday situations. This measure is meant to minimize the potential effects of people's age, experience, and technical background.  
The videos were first cut in order to obtain eight-second-long clips and edited to remove text messages. The employed pictures coincide with the first frame of the corresponding video and they are shown in Figure 1. Thanks to this measure, leveraged pictures and videos include a comparable amount of information about the shown products; the major difference is the fact that depicted objects, components, and/or people move in videos.  
From the pictures shown in Figure 1, it is possible to notice the original characteristics of the corresponding products; the authors deemed that their original characteristics could be understood or inferred, although with a different level of difficulty.  
The products shown in Figure 1 present the following creative characteristics:  
* Equilibrium: It is a colander integrated into a bowl. The water is kept inside the bowl; the removal of the water takes place without any need for disassembling the object while the fall of the washed food is prevented.
* Flame: it is an induction hob where the flame-shaped LED light projected on the pot indicates the intensity of the heat like in gas-powered hobs.
* Stairs: space-saving foldable stairs are integrated in the kitchen furniture.
* OnPot: it is a suction cup applied on a lid and it allows the user to open the lid over the pot and, at the same time, to put it on the edge of the pot.  
Figure 1: Pictures of the leveraged creative products.  
* Hood: it is a kitchen hood integrated in the hob so that the user can arrange easily the hob in the middle of the kitchen room.
* Tire: it is an airless tire that keeps being usable in case of an accidental puncture.
## 2 Two-Dimensional Maximum Shannon Entropy
For a threshold point (\(s\), \(t\)) in a two-dimensional image, the Shannon entropy \(H\) of the target foreground and background on a two-dimensional gray-level histogram can be represented as:  
\[\left\{ \begin{array}{l}H_{0}(s,t)=-\sum\limits_{i=0}^{s}\sum\limits_{j=0}^{t} \frac{p_{ij}}{\mathbb{P}_{0}(s,t)}\ln\frac{p_{ij}}{\mathbb{P}_{0}(s,t)}\\ H_{1}(s,t)=-\sum\limits_{i=s+1}^{L-1}\sum\limits_{j=t+1}^{L-1}\frac{p_{ij}}{ \mathbb{P}_{1}(s,t)}\ln\frac{p_{ij}}{\mathbb{P}_{1}(s,t)}.\end{array} \right. \tag{3}\]  
Hence, the criterion function threshold value \(T\) of two-dimensional maximum Shannon entropy can be defined as:  
\[T=\arg\max_{0<t<L-1}\lvert H_{0}(s,t)+H_{1}(s,t)\rvert^{2}. \tag{4}\]  
Here, a threshold value \(T\) can be acquired using two-dimensional maximum Shannon entropy to address the poor denoising of high-density salt and pepper noise. Compared with the entropy threshold method and the gray-level histogram threshold method, two-dimensional maximum Shannon entropy can express the image randomness and noise distribution more strongly to process complex high-density-noise images. Thus, it can automatically calculate the best threshold value and combine the median filter to realize self-adaptive denoising. Correspondingly, it may be more effective to enhance noise detection, denoising efficiency, and precision.
## 3 Results and Discussion
### Optimization of Experimental Conditions
#### 3.3.3 Effect of pH  
In the experimental process, the pH value of the electrolyte has an important effect on the current intensity of the electrode surface. The influence of the supporting electrolyte such as HAc-NaAc, sodium citrate, Tris-HCl, MES(2-(N-Morpholino) ethanesulfonic acid hydrate), and phosphate acid-sodium phosphate on the peak current intensity of the sensor surface was investigated. As can be perceived from Figure 5A, the electrode response decreases sharply when the pH value is below 6. This may be due to the protonation of nitrogen atoms in the H\({}_{2}\)Pdc functional groups, which weakens the interaction of UO\({}_{2}\)\({}^{2+}\) with the electron pairs of nitrogen atoms at the selective site of IIP. In the meantime, the concentration of H\({}^{+}\) in the solution increases significantly under low pH, which results in the increase of the background current of the capacitance effect generated by differential pulse voltammetry. On the other hand, when the pH value is higher than 6, the metal ions may be hydrolyzed and the formation of negatively charged salts and hydroxyl complexes will prevent the binding sites of uranium to H\({}_{2}\)pdc in the polymer interaction, resulting in lower voltametric response. Therefore, MES buffer solution with pH = 6 was identified as the best condition for this experiment.
## 4 Discussion
: conceptualization, methodology, validation, formal analysis, investigation, writing--original draft preparation, writing--review and editing, visualization; E.V.D. : conceptualization, methodology, validation, writing--review and editing, visualization, supervision, project administration; S.J. : software, resources; K.T. : writing--review and editing, methodology, formal analysis, validation; M.V.L. : conceptualization, writing--review and editing, supervision; N.V.d.B. : investigation, validation, methodology, writing--original draft preparation, writing--review and editing; F.V.K. : conceptualization, resources, writing--review and editing; supervision; M.Z. : investigation, visualization, writing--review and editing; M.D.T. : investigation, visualization, writing--review and editing; S.D.J. : project administration, writing--review and editing; W.C.: validation, investigation, writing--review and editing; C.B. : software, formal analysis, investigation, methodology, data curation; J.N. : conceptualization, resources, supervision, project administration;. L.V. : methodology, investigation, and validation; A.D.: methodology, investigation, and validation; A.D.: methodology, investigation, and validation; C.V.d.E. : methodology, investigation, and validation; M.V.G. : conceptualization, methodology, supervision, data curation, writing--review and editing; D.B. : conceptualization, resources, supervision, project administration, writing--review and editing; S.C.: conceptualization, resources, supervision, project administration, writing--review and editing; I.H. : methodology, formal analysis, validation. All authors have read and agreed to the published version of the manuscript. **Funding:** This study was supported by Janssen Research & Development, LLC, and has been executed as part of the Corona Accelerated R&D in Europe (CARE) project under grant agreement number 101005077 with the Innovative Medicines Initiative 2 Joint Undertaking (JU). The JU receives support from the European Union's Horizon 2020 research and innovation program, EFPIA, Bill & Melinda Gates Foundation, Global Health Drug Discovery Institute, and the University of Dundee. This project has also been funded in part with federal funds from the Office of the Assistant Secretary for Preparedness and Response, Biomedical Advanced Research and Development Authority (BARDA), under OTA numbers HHSO100201700018C and HHSO100201800012C. The SARS-CoV-2 screening in VeroE6-eGFP cells was performed using the 'Caps-It' research infrastructure (project ZW13-02) that was financially supported by the Hercules Foundation (Research Foundation Flanders) and Rega Foundation, KU Leuven. **Data Availability Statement:** Not applicable. **Acknowledgments:** The authors wish to express their gratitude to [PERSON] and [PERSON] for all their fruitful discussions, guidance, and support. Medical writing support was provided by [PERSON], of Envision Pharma, and funded by Janssen Research & Development, LLC. **Conflicts of Interest:** Some authors work for Janssen and hold Janssen stock.
# Differential Signalling in Free-Space Optical Communication Systems
## Appendix A
### BER Expression for Moderate to Strong Turbulence and PE  
Similar to weak turbulence with PE, the BER expression under moderate to strong turbulence with PE can be evaluated. Therefore, the PDF of \(h_{i}=h_{a}h_{p}\) is evaluated as  
\[f_{h_{i}}(h_{i})=\int_{\frac{h_{i}}{\pi_{0}}}^{\infty}f_{h_{p}}\left(\frac{h_ {i}}{h_{a}}\right)f_{h_{a}}(h_{a})dh_{a}, \tag{114}\]  
where \(f_{h_{a}}(x)\) and \(f_{h_{p}}(x)\) is given in Label (20) and Label (A22), respectively.  
In addition,  
\[E_{h_{i}}\bigg{[}Q\bigg{(}\frac{\eta h_{i}}{\sqrt{2}\sigma_{n}}\bigg{)}\bigg{]} =\int_{0}^{\infty}E_{h_{i}}\bigg{[}Q\bigg{(}\frac{\eta h_{i}}{\sqrt{2}\sigma_{ n}}\bigg{)}\bigg{]}f_{h_{i}}(h_{i})dh_{i}. \tag{115}\]  
Integral Label (A25) is determined by numerical integration using MATLAB. Substituting Label (A25) into Label (A13) gives an average BER of the considered system.
# Isoprenoids Production from Lipid-Extracted Microalgal Biomass Residues Using Engineered E. coli
## 3 Methods and Materials
### Detoxification  
To produce isoprenoids with engineered _E. coli_, inhibitors that would be generated when the microalgae biomass is hydrolyzed with sulfuric acid should be removed first. As is shown in the following, five strategies have been developed to remove the inhibitors in the acidosis hydrolysate based on previous studies with some modifications [6; 7; 8; 9; 10]:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimization Factor** & **Range of Optimization Factor** & **Constant Factors** \\ \hline Ratio of solid to acid & 1:3, 1:5, 1:7, 1:9 & 20\% (_v/v_) concentration of sulfuric acid solution, at 80 \({}^{\circ}\)C for 30 min \\ \hline Concentration of sulfuric acid & 28\% (_v/v_), 30\% (_v/v_), 32\% (_v/v_), & 1:7 ratio of solid to acid solution, at 80 \({}^{\circ}\)C for 30 min \\ \hline temperature & 80 \({}^{\circ}\)C, 90 \({}^{\circ}\)C, 100 \({}^{\circ}\)C,110 \({}^{\circ}\)C & 1:7 ratio of solid to acid solution and 32\% (_v/v_) concentration of sulfuric acid solution for 30 min \\ \hline Time & 30 min, 60 min, 90 min, 120 min & 1:7 ratio of solid to acid solution and 32\% (_v/v_) concentration of sulfuric acid solution, at 90 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Exploration of the four kinds of hydrolysis condition was explored at four kinds of factors of LMBRs.  
* The hydrolysate was adjusted to pH 10 with sodium hydroxide, and then the solution was readjusted with sulfuric acid to pH 5. Anhydrous sodium sulphite (1 g/L) was added to the solution which was heated to 100 \({}^{\circ}\)C for 15 min. Then, 1% (_w/v_) activated carbon was mixed into the solution and incubated at 40 \({}^{\circ}\)C with shaking at 200 rpm for 1 h.
* The hydrolysate was neutralized with calcium hydroxide. After that, 1% (_w/v_) activated carbon was added into the solution and incubated at 40 \({}^{\circ}\)C with shaking at 200 rpm for 1 h.
* Sodium hydroxide was used to regulate the pH of the hydrolysate to 5.0, and then 1% (_w/v_) activated carbon was added into the solution and incubated 40 \({}^{\circ}\)C with shaking at 200 rpm for 1 h.
* Anion exchange resin (D301, Tianjin, China) was added to the hydrolysate with the loading concentration of 20% (_w/v_) until the pH reached 5.5. The mixed solution was kept at 24 \({}^{\circ}\)C, with shaking 200 rpm for 1 h.
* The pH of the hydrolysate was initially adjusted to 7.0 with calcium hydroxide, after that, the pH was readjusted to 5.5 with phosphoric acid.  
All of the hydrolysated were filtered with a vacuum filter to obtain the supernatant after the inhibitors were removed with the different detoxification methods.
## 4 Evaluation of Performance and Accuracy of Different Implementations
### Database  
For the following evaluation of calculation quality and efficiency, a database with 10 fictitious example geometries, each with 100 different configurations for the cyclic properties, is used. These example geometries are represented by one dataset containing all required input values for the two notch root approximations specified in [25]. These are as follows:  
* Maximum linear-elastic stress range relevant for the strength verification \(\Delta\sigma_{\text{el,max}}\).
* Tensile strength \(R_{\text{m}}\) for estimation of Ramberg-Osgood parameters according to [25].
* Limit load factor \(K_{\text{p}}\).
* These input values are defined within the value ranges given in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline K’ in MPa & 902.2915 \\ n’ & 0.187 \\ K\({}_{\text{p}}\) & 1.7 \\ \(\Delta\sigma_{\text{el}}\) leading to numerical stable behavior in MPa & 110.022454508070 \\ \(\Delta\sigma_{\text{el}}\) leading to numerical unstable behavior in & 110.022454507071 \\ MPa & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Exemplary selection of parameters for which termination criteria 1 and 3 lead to complex values when applying the notch root approximation according to Seeger and Beste.  
The tensile strength \(\mathrm{R_{m}}\) varies within a large range, representing the range of validity within the FKM Guideline nonlinear [25] for the steel material group and is used to estimate the cyclic material properties by applying the method from [25]. The limit load factor is varied within a range that is assumed to be relevant in practice. The maximum load is based on the cyclic yield strength, which can be determined using Equation (4), and the limit load factor, which, as already mentioned, is a measure of the maximum load that can be applied.  
The dataset is constructed as follows: Every possible permutation consisting of the first two rows of Table 2 is generated. Then, for each combination, the cyclic material properties shown in Rows 3 to 5 are estimated. Next, the maximum linear-elastic stress ranges are determined based on the cyclic material properties in the range given in Table 2. Finally, for each of these combinations, notch root approximations on the hysteresis branch (since, as mentioned in Section 2, the initial load-notch-strain curve can be estimated from the load-notch-strain curve of the hysteresis branch) are performed according to [25] with 200 discrete points. In summary, this leads to \(\sim\)20,000,000 data points and notch root approximations.