esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Formulation
### Dynamic Linearization Scheme  
From a holistic perspective, considering the impact of governor dead zones and physical limitations, the power system can be characterized as a profoundly intricate nonlinear system. The LFC subsystem model (7) can be redefined as a comprehensive nonlinear function:  
\[y(k+1)=f_{\alpha(k)}(y(k),u(k)) \tag{8}\]  
In the equation, \(f_{\alpha(k)}(\cdot)\) denotes an unknown nonlinear function. Before linearizing the nonlinear power system, we establish the following two assumptions.  
**Assumption 1**.: _The partial derivative of \(f_{\alpha(k)}(\cdot)\) with respect to the variable \(u(k)\) is continuous at any given sampling instant \(k\)._  
**Assumption 2**.: _The nonlinear system (8) adheres to the generalized Lipschitz condition, which implies that for all instances of \(k>0\) and \(\Delta u(k)\
eq 0\), the following condition is met:_  
\[|\Delta y(k+1)|\leq b|\Delta u(k)| \tag{9}\]  
_where \(\Delta y(k+1)=y(k+1)-y(k),\Delta u(k)=u(k)-u(k-1),b>0\) is a constant._  
**Remark 1**.: _From a practical standpoint, the two assumptions above on the power system (3) are both reasonable and fulfilled. Assumption 1 is a common restriction for a general nonlinear system and the continuity of \(f_{\alpha(k)}(\cdot)\) can be inferred from Equation (3). Assumption 2 restricts the maximum pace at which the system output can vary. If the change in \(\Delta P_{ci}\) is limited, the change in output energy \(\Lambda\mathrm{CE}_{i}\) generated by the power system is also limited, from an energy utilization standpoint._  
**Theorem 1**.: _For subsystems (7) meeting Assumptions 1 and 2, under the condition \(|\Delta u(k)|\
eq 0\), there exists a time-varying model called the parameter pseudo-partial derivative (PPD). Thisparameter ensures that the nonlinear power subsystem model is equivalent to the following compact form dynamic linearization (CFDL) data equations:_  
\[\Delta y(k+1)=\phi_{\alpha(k)}(k)\Delta u(k) \tag{10}\]  
_The parameter \(\phi_{\alpha(k)}(k)\) is defined as time-varying and remains bounded at every instant._
# Design and Fabrication Technology of Metal Mirrors Based on Additive Manufacturing: A Review
## 2 Additive Manufacturing Technology for Metal Mirrors
### Additive Manufacturing Metal Mirror Fabricating Considerations
#### 2.2.3 Connectivity Constraints  
The connectivity constraint requires no enclosed voids inside mirrors. For PBF, there should be no residual powder inside parts. Compared to open-back mirrors and arch mirrors, sandwich mirrors are closed-back, so connectivity constraints must be considered in the design stage. In the mirror design, the structure walls are perforated to present complete airflow channels inside the mirror, meeting the connectivity of the airflow and facilitating the removal of residual powder (Figure 12) [50; 51; 52]. Finally, ultrasonic cleaning and other methods are used to remove the powder from the interior of the parts altogether.
# A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks
## 2 Related Works
### Neural Networks for Optimizing Hetnets  
Many researchers focused on solving heterogeneous network problems with neural networks. Different from Refs. [4; 5], [PERSON] et al. [29] use convolutional neural networks (CNN) and deep neural network (DNN) network structure as actor part and critic part of the AC algorithm to optimize heterogeneous network resources. [PERSON] et al. [30] proposed an access network modeling and an adaptive parameter adjustment algorithm based on a neural network model. The algorithm was applied to the source and destination switching networks, and the input parameters of users in HetNets were dynamically adjusted according to the required QoS. [PERSON] et al. [31] proposed a fuzzy neural network based on RL to optimize antenna tilt and power to achieve automatic collaborative optimization of power and antenna tilt. Self organizing network entities used cooperative Q-learning and reinforcement back-propagation methods to obtain and adjust their optimization experience to achieve cooperative learning. Similar to [29], some schemes combining neural network withreinforcement learning ware proposed in [32; 33]. Since traditional iterative optimization methods, whether optimal or heuristic, usually needed a lot of iterations to achieve satisfactory performance, and led to considerable computational delay, from the perspective of in-depth learning, [PERSON] et al. [32] proposed a feasible cache optimization method which was to train the optimization algorithms through a DNN in advance, instead of directly applying them in real-time caching or scheduling. The computational burden was transferred to a DDN training phase to reduce the complexity of delay sensitive operation phase significantly. Considering the complexity of base station power optimization in multi-layer heterogeneous networks, [PERSON] et al. [33] proposed a dynamic pico-cell base stations (PBS) operation scheme based on CNN, which dynamically changed the on/off state of PBS according to the user's real-time position, thus reducing the total power of BSS.  
As shown in the above analysis, with the increasing complexity of heterogeneous network, more and more parameters are needed to be jointly considered to optimize the network system. It is more difficult to directly solve the network optimization problem by using the traditional optimization scheme. Recently, machine learning technologies have become popular and are applied to the optimization of heterogeneous network resources. Model-free learning brings the convenience of solving non convex problems. However, the acquisition of samples, the design of data labels, and the establishment of Markov decision-making process are all great challenges. In this paper, our proposed TQL algorithm which combines Model-free QL with multistage decision process to optimize the allocation of network resources.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 3 Detailed Description of the Proposed Localization Scheme
### Calculating the Node Location  
In this section, the location of the sensor node is determined using the obtained projection in Section 3.2 and perpendicular distance in Section 3.3. We first transform the three-dimensional (3D) underwater sensor node localization problem into a two-dimensional (2D) localization problem. Then, we describe in detail the calculation process of localization.
## 2 Materials and Methods
### Preliminaries
#### 2.2.5 Threshold EIGamal Encryption Scheme  
Based on the Shamir secret-sharing scheme [39] and the threshold Paillier encryption scheme proposed by [PERSON] et al. [28], we present a threshold ElGamal encryption scheme. The basic idea of the threshold ElGamal encryption scheme is to divide the private key of the ElGamal encryption algorithm into \(n\) copies using the Shamir threshold secret-sharing scheme. Decryption can then be achieved when at least \(t\) copies are gathered together.  
For a \((t,n)\) threshold ElGamal encryption algorithm, where \(n\) is the total number of participants and \(t\) is the minimum number of participants required for decryption, the basic scheme is described as follows:  
Key generation: Randomly select a large prime \(p\), and then, choose a prime element \(g\) modulo \(p\). Randomly pick a \(sk=d\) as the private key for the ElGamal algorithm such that \(2\leq d\leq p-2\). Compute \(y=g^{d}\ mod\ p\) and \(pk=y\) as the public key. The private key is shared as follows: let \(a_{0}=d\), and generate the polynomial \(f(x)=\sum\limits_{i=0}^{t-1}a_{i}x^{i}\ mod\ p\); the key sharingof the \(j\)-th participant is \(sk_{j}=f(j)\ mod\ p\), where \(j\in\{1,\ldots,n\}\).  
Encryption: Assume that the message to be encrypted is \(M\). Choose a random \(r\in Z_{p}^{*}\), and compute the ciphertexts \(c_{1}=g^{r}\) and \(c_{2}=My^{r}=Mg^{dr}\).  
Share decryption: Among the \(t\) participants involved in decryption, the \(i\)-th participant calculates \(c_{1,i}=(c_{1})^{\Delta_{i}\cdot sk_{i}}\), where \(\Delta_{i}=\prod\limits_{j=1,j\
eq i}^{t}\frac{j}{j-i}\).  
Combination calculation (Comb): The results of decryption are obtained by performing ElGamal homomorphic multiplication operations on the shared decryption results of the \(t\) participants:  
\[\prod\limits_{i=1}^{t}c_{1,i}=(c_{1})^{\sum\limits_{i=1}^{t}\Delta_{i}\cdot f(i)} \tag{10}\]  
From the Shamir threshold secret-sharing scheme (Section 2.2.4), it is known that restoring the key \(sk=d\) is calculated as follows:  
\[sk=f(0)=d=\sum\limits_{i=1}^{t}(f(i)\cdot\prod\limits_{j=1,j\
eq i}^{t}\frac{j }{j-i})=\sum\limits_{i=1}^{t}\Delta_{i}\cdot f(i) \tag{11}\]  
where \(\Delta_{i}=\prod\limits_{j=1,j\
eq i}^{t}\frac{j}{j-i}\).  
Thus, the decryption result can be obtained as follows:  
\[M=\frac{c_{2}}{c_{1}^{2}}=\frac{c_{2}}{\frac{c}{\sum\limits_{i=1}^{t}\Delta_{ i}\cdot f(i)}}=\frac{c_{2}}{\prod\limits_{i=1}^{t}c_{i,j}} \tag{12}\]
# A Framework for Attribute-Based Access Control in Processing Big Data with Multiple Sensitivities
## 2 Materials and Methods
### Cybersecurity Vulnerability Evaluation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Title** & **Summary** & **ATT\&CK\({}^{\text{th}}\)** \\  & & **Reference ID** \\ \hline \multirow{4}{*}{Bypass} & Accessing data through underlying operating systems or & \multirow{4}{*}{\begin{tabular}{c} Bypass User Account Control, T1088 \\ Credentials in Registry, T1214 \\ Credentials in Files, T1081 \\ Credential Dumping, T1003 \\ Hocking, T1179 \\ Account Manipulation T1098 \\ \end{tabular} } \\  & & \begin{tabular}{c} Leveraging access permissions using a relationship with \\ a third party, exploiting an existing connection or \\ authorized access by an unauthorized user/process. \\ \end{tabular} & \multirow{4}{*}{\begin{tabular}{c} Trusted Relationship \\ T1199 \\ \end{tabular} } \\  & & \begin{tabular}{c} Analyzing legitimately obtained data to obtain \\ unauthorized knowledge based upon characteristics of \\ groups or instances \\ \end{tabular} & Related to Data Obfuscation, T1001 \\ \hline \multirow{4}{*}{Unauthorized use of Valid Accounts} & Unauthorized use of valid compromised credentials & \multirow{4}{*}{\begin{tabular}{c} Valid Accounts, T1078 \\ Logon Scripts, T1037 \\ Pass the Hash, T1075 \\ Pass the Ticket, T1097 \\ \end{tabular} } \\  & & \begin{tabular}{c} Discovering and using an unauthorized account before \\ the account status is propagated and synchronized across \\ all the systems in the environment. \\ \end{tabular} & Related to Account Discovery, T1087 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Attribute Proliferation and \\ Assurance \\ \end{tabular} } & Poor maintenance of or overly complex attributes and \\  & metadata tags can lead to errors hard to detect and lead & Related to File Deletion, T1107 \\  & & to unauthorized access. \\ \hline \multirow{4}{*}{Denial of Service} & \multirow{4}{*}{
\begin{tabular}{c} Locking out a valid user account or causing excessive \\ process or memory/storage utilization resulting in the \\ disabling of normal operations. \\ \end{tabular} } & Related to Jamming or Denial of Service \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of Threats to AC Systems. the ATT&CK website in the techniques and software sections. Researchers have published reports identifying these vulnerabilities in thousands of online Hadoop systems [46; 47; 48; 49].
## 3 Results and Discussion
### Effect of VPD Regulation on Tomato Crops in Soil
#### 3.1.2 Effect of VPD Regulation on Yield  
Figure 5a shows the results of the yield evolution accumulated expressed as average values of the fresh weight of harvested fruits in each episode of fruit harvests and each evaluated experimental treatment of commercial and total fruit yield (commercial fruits + non-commercial fruits) harvested during the trial. In the same way, Figure 5b shows the results of the accumulated number of fruits produced (expressed as average values of the number of fruits harvested in each episode of multiple harvests of fruit and in each experimental treatment evaluated) from the commercial and total yields of fruits (commercial fruits + non-commercial fruits) obtained during the trial.  
The accumulated total yield measured in treatment T2 was 13.4% higher on average than the accumulated total yield measured in treatment T1. The commercial yield measured in treatment T2 was 18.6% higher on average than the accumulated commercial yield measured in treatment T1. However, the number of total fruits measured in T1 was higher than that in T2. At the initial stage of harvest, the number of total fruits harvested in T1 was more than 100% higher than that in T2, but the gap between T1 and T2 decreased progressively from 89 days. This result agrees with the published observations of [28; 29],  
Figure 4: Growth parameters of tomato crops grown in soil: (**a**) evolution of crop height; (**b**) evolution of leaves developed; (**c**) evolution of fruit setting; (**d**) evolution of phenological growth stages.  
who observed that a high humidity, which means a low VPD, produced smaller fruits and thereby a lower yield compared with a high VPD. Additionally, as the standard error bars in Figure 5a-c show, there were statistically significant differences. T2 was slightly better than T1 in the number of commercial fruits, but no significant difference was found.  
The results measured for the four yield evolutions had a normal distribution, and the \(p\)-values of the \(t\)-tests were greater than 0.05, so there were no statistically significant differences between the two treatments. H1 for the yields is not true.
# An Improved Dynamic Model of the Vanadium Extraction Process in Steelmaking Converters
## 2 Improved Mathematical Model of the Vanadium Extraction Process  
When extracting vanadium in a converter (Figure 1), vanadium-containing molten steel and a coolant are put together into the converter, following which an oxygen lance is inserted into the converter to blow oxygen at a high pressure. The high-speed oxygen jets interact with the metal melt in the converter, reacting with the elements in the molten steel which, in turn, oxidizes the vanadium to stable vanadium oxides, causing them to group into slag. The element oxidization process in the converter mainly consists of three series links: the diffusion of the reactants to the phase interfaces, interfacial chemical reactions, and the diffusion of the resultants away from the phase interfaces. Among these steps, the one with the lowest rate is called the rate limiting step.  
As the slag in the vanadium extraction process is acidic, and as phosphorus and sulfur in the molten pool have been shown to not be involved in the reaction [20], we mainly consider the elements iron, carbon, vanadium, titanium, silicon, manganese, and oxygen in the molten steel. In this paper, substances in the metal phase (molten steel) are denoted by [M], where M represents Fe, C, V, Ti, Si, Mn, O; the substances in the gaseous phase (gas) are denoted by {G}, where G represents O\({}_{2}\) and CO; and the substances in the slag phase (slag) are denoted by (S), where S represents FeO, \(\rm V_{2}O_{3},TiO_{2},SiO_{2},MnO\). Note that vanadium has many oxidation states; however, vanadium mainly has been shown to have a valence of \(+3\) in the slag [21], which we assume for convenience.
# A Cross-Layer Optimization QoS Scheme in Wireless Multimedia Sensor Networks
## 3 Problem Formation and Dynamic Algorithm
### Comparison of Performance
According to the characteristics of wireless sensor networks, CLADS-BCD adopts distance coefficients of energy queues in optimization to reduce the energy consumption of nodes in the area surrounding the sink node. This can prolong the lifetime of the nodes near the sink node, as well as increase the network lifetime. The average number of packets of non-real-time and real-time services arriving at the sink node are compared in Figure 10 and Figure 11, respectively. From these two figures we can see that, for both real-time service and non-real-time service, the number of packets arriving at the sink node under CLADS-BCD was higher than the number under EASYO. This implies that wireless sensor  
Figure 8: Average end-to-end delay of non-real-time sessions under different numbers of nodes. Figure 1:0 Average end-to-end delay of real-time sessions under different numbers of nodes. networks using CLADS-BCD can collect more information than networks using EAYSO. The reason is that the network lifetime of CLADS-BCD was much higher than that of EASYO. Thus, the sink node can collect data packets for a much longer time under CLADS-BCD than under EASYO. Figure 1:1 Average number of packets of non-real-time services arriving at the sink node. Figure 1:2 Network lifetime. The simulations results indicate the conclusions as follows. (i) The network lifetime under ACH is the longest. However, the average throughput under ACH is the lowest. Therefore, ACH is suitable for low-rate wireless sensor networks. (ii) CLADS-MAC is suitable for real-time applications in wireless networks with nodes that have adequate energy. (iii) Though CLADS-BCD performs worse than EASYO in QoS performances, it can obtain a longer network lifetime than EASYO. Thus, networks under CLADS-BCD can collect more data through more working time than networks under EASYO. This indicates that CLADS-BCD achieves the best trade-off between network lifetime and QoS performances in these schemes.
## 1 Introduction
A study realized in Russia considering seven different climates (Monsoon-influenced warm-summer humid continental Dwb, Subarctic climate Dfc, Warm-summer humid continental climate Dfb, Tundra climate ET in the Koppen climate classification) show that all of the case studies complied with the Passivhaus standard with regards to the primary energy demand [26]. In general, different studies [22; 23; 24; 25; 26; 27; 28; 29; 30] have proven that the Passivhaus standard is suitable for different climates. Thus, Passivhaus could provide a useful guide to support the development of energy-efficient housing in Chile. However most of the studies are focused on the technical aspects, benefits of thermal comfort, and energy performance while approaches based on the costs associated with the construction of a Passivhaus are scarce [31; 32]. Some authors have mentioned that economic aspects represent the greatest challenge for the application of the Passivhaus standard [33; 34] and this challenge must be addressed individually in each country because the costs are not comparable between countries. A previous study assessed the barriers and opportunities associated with the implementation of energy efficiency standards in Chilean social housing and determined that their implementation would not be economically feasible [4]; the study found that building in compliance with the Passivhaus standard has a higher cost per square meter [4] than a conventional house (ca. 30%). This has also been found in other countries with limited resources [35; 36; 34]. However, in the current market there is a wide variety of increasingly affordable insulation materials with low thermal conductivity. This work aimed to evaluate the technical and economic feasibility of a Passivhaus dwelling in Chile, considering the current level of state subsidy for housing construction. We developed a design for a passive house in Chile and evaluated its affordability. In cases where construction costs are unaffordable to families, it is of vital importance to know what construction standards can be achieved. In this sense, this study aims to show the possibility of building an affordable dwelling with low energy demand.
# 3D DNA Nanostructures: The Nanoscale Architect
## 2 3D DNA Nanostructure Design
### Fundamental Techniques for Design of 3D DNA Nanostructures  
#### 2.2.1 DNA Origami Blocks  
Following the advent of DNA origami, the [PERSON] group made the first foray towards a general design method for 3D DNA origami [9]. This strategy can be thought of as folding a flat 2D DNA origami at creases between adjacent helices to create a stacked structure of multiple planes of 2D DNA origami and adding crossovers that span both in-plane and between planes to hold the structure together. Further work by the [PERSON] and [PERSON] groups also introduced various different organizations of the cross-section of DNA origami blocks, such as the honeycomb lattice, as initially demonstrated, but also square [24] and hybrid (of both square and honeycomb) [25] lattices. Around this time, unique designs showcasing a growing proficiency for designing DNA origami nanostructures were also demonstrated. For example, using flat 2D DNA origami as the faces of simple polygonal shapes such as cubes [7; 26] or tetrahedrons [27], or unique artistic structures such as a Mobius strip [28] and tensegrity structures [29], which are truly a testament to the structural versatility of DNA nanostructures. As with the other techniques we will discuss here, the increasing scale of 3D structures also solicited the creation of CAD software to assist in their design,and the software package caDNAAno [30] has been the longstanding, dominant option for structures with cross-sections of parallel helices.
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 2 Technology Overview
### LoRaWAN  
The LoRaWAN MAC protocol is an open source protocol standardized by the LoRa Alliance which operates above the LoRa physical layer. The LoRaWAN MAC layer specifies the MAC control process which allows data transmission between several end nodes and gateways. The MAC protocol provides LoRa end nodes with channel access, ADR, security and energy saving services [19]. The deployment of thousands of end nodes requires extensive access to improve simultaneous transmissions and circumvent packet collisions. The LoRaWAN MAC uses Aloha to coordinate the links, dividing airtime among end nodes to handle packet collisions. The Aloha MAC permits end nodes to forward data packets once they wake up and in the event of any collisions the exponential back-off is applied. The LoRaWAN protocol is responsible for device class allocation. The LoRa specification defines three device classes that the end nodes must operate in, Class A, Class B and Class C. The end node initiates Class A communication which is fully asynchronous. The UL message can be transmitted at any instant, followed by two short DL windows, providing a prospect for bi-directional communication. After an end node has sent a confirmed message, the end node expects an Acknowledgement (ACK) from the NS during the two pre-set timeslots known as \"Receive Windows (RW)\". The gateway either responds with the first RW or the second RW. Unconfirmed messages from the end nodes do not receive acknowledgement from the NS. Periodic beacons are used to synchronise Class B devices to the network, which opens DL 'ping slots' at programmed intervals. The network is provided the capacity to transmit DL messages with a predetermined latency but results in added energy consumption in the end node. Class A and B end nodes are largely battery-powered but Class A utilises less power than Class B. Class B devices do not support Class C functionality. Class C devices, additional to the Class A structure of UL followed by two DL windows, further reduce latency on the DL by maintaining the end node receiver continuously listening for responses from the gateway. Because these devices are continually listening, they consume more energy and hence need to be mains powered. Class C devices do not support Class B functionality.  
A LoRaWAN network will always use LoRa as its PHY layer. The LoRaWAN protocol is the suitable MAC layer but different MAC layer protocols can be used. LPWAN requirements such as ACKs, firmware updates, localisation, roaming, and security are all addressed in the LoRaWAN standard.