esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure LoRa Firmware Update with Adaptive Data Rate Techniques +
## 2 Materials and Methods  
The proposed firmware update process is applicable to any device that uses the LoRa protocol and is equipped with additional memory to store the new code. The memory can be external to the processor or internal if the criteria in Equation (1) is met. Here, \(S_{Device}\) designates the total amount of flash memory, \(S_{Application}\) represents the flash memory requirements of the application, and \(S_{Bootloader}\) represents the requirements for the boot loader. Figure 2 depicts the hardware components associated with the proposed system, and includes components to carry out RF processing, battery monitoring, and an external flash (if required). The battery monitor can vary in complexity but must be capable of approximating the remaining battery capacity, as discussed below in Section 2.3.  
\[S_{Device}>=2S_{Application}+S_{Bootloader}. \tag{1}\]  
The code resides in flash and is composed of a boot loader and the application. The application code is responsible for implementing the device functionality and must be re-based above the boot loader. The boot loader has the ability to modify and/or erase the application code residing in the flash. The boot loader must also be able to start executing the application code at different addresses depending on where the most recent application update was programmed. The firmware update process is defined as a sequence of five steps outlined below, with most of the functionality residing in the application code. The device will not execute the boot loader code to update the firmware unless it has validated the integrity of the new image and confirmed that there is sufficient battery power to rewrite the flash.  
1. Initial Exchange: Establishes status and general configuration information.
2. ADR Rate Optimization: Finds the optimal communication setting with which to exchange the firmware.
3. Battery Approximation: Estimates total energy consumption for completing the update process and terminates the process if the battery capacity is insufficient.
4. Firmware Transfer: Transfers the encrypted firmware image over the RF link.
5. Commit Code to Flash: After confirming that the firmware image is valid, the application hands control over to the boot loader to load the new application code, and then returns control to the new application.  
We elaborate on the details of these five steps in the following sections.
## 1 Introduction  
Over the past few decades, polymer-based materials have rapidly developed and are used wildly in all areas. Especially biodegradable polymers, have been used extensively in fields such as food packaging, tissue engineering and biomedical application. They promote societal development and have greatly changed our lives [1; 2; 3]. Alongside the performance requirements of materials becoming increasingly strict and the rise of environmental consciousness, biodegradability is already not only a functional need, but also an important environmental requirement [4]. Therefore, more and more biodegradable polymers are starting to become a concern for people, especially a polymer that could control the rate of degradation [5; 6]. Amino acid-based polyamide-imides (PAIs) are a type of high-performance (HP) materials that combine the thermal resistance and mechanical properties of polyimides, and the easy machinability of PA [7; 8], as well as having improved degradation properties [9] and cell compatibility [10]. This is due to the introduction of natural chiral sections, which is obviously of great significance for the development of HP, high-processability, and biodegradable polymer materials.  
The strong chiral preference for amino acids and other essential biomolecules is one of the characteristics of a biological system. Many biological events are greatly influenced by the chirality of molecules [11,12], such as the specific rotational directions of some flowers, and the different tastes or smells of a pair of enantiomers [13]. This inspired the research of introducing chiral effects into biomaterial [14]. [PERSON] et al. modified the chiral surface with NIBC enantiomers and found that on the L surface, the extent of absorption is higher than on the D surface [15]. [PERSON] et al. proved that proteinase K preferentially degrades (L)-PLA as opposed to (D)-PLA [16]. Mastering chiral signals and translating them into macroscopic properties or functions of materials, will bring great advantages to many applications, particularly for degradable materials [17]. In addition, it was also necessary to elucidate the relations between the chemical structure and degradation properties in order to design and develop a new biodegradable polymeric material.  
In this paper, PAIs derived from six amino acids with different configuration were prepared. The PAIs were characterized and subjected to biodegradation experiments in buffer solution (containing proteinase-K). The effects of stereochemical composition on the biodegradation performance are discussed.
# Multiparametric Monitoring in Equatorial Tomato Greenhouses (II): Energy Consumption Dynamics
###### Abstract  
Tomato greenhouses are a crucial element in the Equadorian economy. Wireless sensor networks (WSNs) have received much attention in recent years in specialized applications such as precision farming. The energy consumption in WSNs is relevant nowadays for their adequate operation, and attention is being paid to analyzing the affecting factors, energy optimization techniques working on the network hardware or software, and characterizing the consumption in the nodes (especially in the ZigBee standard). However, limited information exists on the analysis of the consumption dynamics in each node, across different network technologies and communication topologies, or on the incidence of data transmission speed. The present study aims to provide a detailed analysis of the dynamics of the energy consumption for tomato greenhouse monitoring in Ecuador, in three types of WSNs, namely, ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology. The networks were installed and maintained in operation with a line of sight between nodes and a 2-m length, whereas the energy consumption measurements of each node were acquired and stored in the laboratory. Each experiment was repeated ten times, and consumption measurements were taken every ten milliseconds at a rate of fifty thousand samples for each realization. The dynamics were scrutinized by analyzing the recorded time series using stochastic-process analysis methods, including amplitude probability functions and temporal autocorrelation, as well as bootstrap resampling techniques and representations of various embodiments with the so-called M-mode plots. Our results show that the energy consumption of each network strongly depends on the type of sensors installed in the nodes and on the network topology. Specifically, the CO\({}_{2}\) sensor has the highest power consumption because its chemical composition requires preheating to start logging measurements. The ZigBee network is more efficient in energy saving independently of the transmission rate, since the communication modules have lower average consumption in data transmission, in contrast to the DigiMesh network, whose consumption is high due to its topology. Results also show that the average energy consumption in WiFi networks is the highest, given that the coordinator node is a MeshiumTM router with larger energy demand. The transmission duration in the ZigBee network is lower than in the other two networks. In conclusion, the ZigBee network with star topology is the most energy-suitable one when designing wireless monitoring systems in greenhouses. The proposed methodology for consumption dynamics analysis in tomato greenhouse WSNs can be applied toother scenarios where the practical choice of an energy-efficient network is necessary due to energy constrains in the sensor and coordinator nodes.  
Keywords:wireless sensor networks; stochastic process; energy consumption; tomato greenhouse; M-mode plots.
## 2 Preliminaries
### A Basic Review of Intuitionistic Hesitant Fuzzy Sets  
[PERSON] [9] proposed the theory of IFS as an extension of FS. While FS provides the degree of membership of an object in a specific set [0, 1], IFS provides both the degree of membership and the degree of non-membership simultaneously.  
**Definition 1** ([9]).: _An IFS \(\mathcal{W}\) on \(U\) is denoted via the two mappings \(m(e)\) and \(n(e)\). Mathematically, it is shown by the following structure:_  
\[\mathcal{W}=\langle e,\;m_{\mathcal{W}}(e),n_{\mathcal{W}}(e)\rangle|e\;\in\;U\rangle \tag{1}\]  
_where \(m_{\mathcal{W}}(e):U\rightarrow[0,\;1]\) and \(n_{\mathcal{W}}(e):U\rightarrow[0,\;1]\) represents the MG and NMG including the condition \(0\leq\;m(e)+n(e)\leq 1\), for each \(e\in U\)._  
For every IFS \(\mathcal{W}\) in \(U\), we denote \(p_{\mathcal{W}}(e)=1-m_{\mathcal{W}}(e)-n_{\mathcal{W}}(e),\;\forall\;e\in U\). Then \(p_{\mathcal{W}}(e)\) is called as the indeterminacy grade of \(e\) to \(\mathcal{W}\).  
[PERSON] et al. [14] introduced a more generalized version of IFSs by combining them with HFS, known as IHFSs. In IHFSs, the MG and NMG represent the collection of elements from [0, 1]. The fundamental definition and operations are provided below.  
**Definition 2** ([14]).: _An IHFS \(\mathcal{W}\) on \(U\) is symbolized via the two mappings \(m(e)\) and \(n(e)\). Mathematically, it is shown by the following structure:_  
\[\mathcal{W}=\langle e,\;m_{\mathcal{W}}(e),n_{\mathcal{W}}(e)\rangle|e\;\in\;U\rangle \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbols** & **Description** & **Symbols** & **Description** \\ \hline
**FS** & Fuzzy Set & \(\mathcal{A}_{i}\) & Alternatives \\ \hline
**IHFS** & Intuitionistic Hesitant & \(\Omega\) & State of Yes for Action \\  & Fuzzy Sets & \(\Omega\) & State of No for Action \\ \hline
**RS** & Rough Set & \(-\Omega\) & State of No for Action \\
**DTRS** & Decision-Theoretic & MG & Membership Grade \\  & Rough Set & MG & Membership Grade \\
**3 WD** & Three-Way Decision & NMG & Non-Membership Grade \\ \hline
**DM** & Decision-Making & \(\xi\) & Cost Parameter Values \\
**Scr** & Score Function & _Hac_ & Accuracy Function \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of abbreviations.  
* [(iv)] _Drastic triangular co norm:_  
\[B^{\prime}_{\text{dra}}(l,\ m)=\begin{cases}l,&if\ m=0\\ m,&if\ l=0\\ 1.&otherwise\end{cases}\]  
**Definition 9**[23].: _Aczel-Alsina presented novel \(T\).\(\mathcal{N}\)s and \(T\).\(\mathcal{CN}\)s which are represented as_  
\[A^{\prime\varphi}_{\text{A}}(l,\ m)=\begin{cases}\begin{array}{cc}A^{\prime} _{\text{dra}}(l,\ m),&if\ \varphi=0\\ min(l,\ m),&if\ \varphi=\infty\\ e^{-((-logl)^{\varphi}+(-log\ m)^{\varphi})^{\varphi}/\varphi},&\text{ otherwise}\end{array}\end{cases}\]  
_and_  
\[B^{\prime\varphi}_{\text{A}}(l,\ m)=\begin{cases}\begin{array}{cc}B^{\prime} _{\text{dra}}(l,\ m),&if\ \varphi=0\\ max(l,\ m)&if\ \varphi=\infty\\ 1-e^{-((-log(1-l))^{\varphi}+(-log\ (1-m))^{\varphi})^{\varphi}/\varphi},&\text{ otherwise}\end{cases}\end{cases}\]
# Atomic Layer Deposition (ALD) of Metal Gates for CMOS
## 1 Introduction
### Metal Gate for FinFETs and GAA-FETs
The advantages of using ALD to deposit metal gate can be summarized as: excellent thin film thickness uniformity, excellent composition control (doping by inserting dopant layers), little plasma damage to gate oxide (especially for thermal ALD) compared to PVD, lower  
Figure 9: The relation between the threshold voltage and the work function of the metal gate for different devices. deposition temperature with low impurity compared to CVD, and conformality at nanoscale structures, especially for 3D devices [59].
## 2 Long-Gauge FBG Sensing Principle  
A long-gauge FBG sensor was developed to measure the average strain over extended gauge lengths [41; 42]. This sensor can be customized for specific lengths, spanning from several centimeters to meters, making it ideal for the on-site monitoring of large civil structures. Installing a long-gauge FBG sensor involves the insertion of a bare fiber with FBG into an embedded tube and fixing both ends to ensure that the measured value represents the average strain over the measured length. The bare optical fiber, including the FBG, serves as the sensing component and operates effectively, apart from temperature compensation. This compensation issue can be resolved by incorporating a dummy-reference Bragg grating subjected to the same thermal environment but free from any load. Temperature compensation for the long-gauge strain sensor can be achieved by subtracting the response of the FBG temperature sensor from the response measured by the strain sensor.  
The internal structure of the long-gauge FBG sensor is shown in Figure 1a. The long-gauge FBG sensor outputs static and dynamic structural strains related to the structural angle. For a beam element with two local degrees of freedom per node (vertical displacement \(w\) and angle \(\theta\)), the long-gauge strain (macro strain) of the sensing element can be expressed as follows:  
\[\epsilon_{j}(t)=\upmu_{j}\big{[}\theta_{o}(t)-\theta_{p}(t)\big{]} \tag{1}\]  
where \(\upmu_{j}=\up h_{j}/L_{j}\), \(j\) represents the \(j\)th sensor unit; \(h\) is the distance from the sensor to the neutral axis of the beam section; \(L\) is the length of the sensing unit; \(\theta_{o}(t)\) and \(\theta_{p}(t)\) are the angular degrees of freedom of o and \(p\) at both ends of the sensor unit at time \(t\), respectively.  
The accuracy of strain measurement is consistently maintained at 1-2 \(\upmu\epsilon\) when utilizing commercial demodulators. Additionally, this device can achieve a dynamic measurement frequency of up to 1000 Hz. The long-gauge FBG strain sensor examined in this study was initially calibrated in the laboratory to determine the calibration coefficient. The measured responses from sensors could then be calibrated using this coefficient. Multiple long-gauge FBG sensing units were connected in series to cover a critical sensing area. These key areas can then be interconnected to form a distributed sensor network for macro-strain measurement in structural regions. Figure 1b illustrates the area sensing capabilities of the long-gauge FBG sensor.
# Design Procedure for Motion Profiles with Sinusoidal Jerk for Vibration Reduction
## 3 Parameter Design for Sinusoidal Jerk Profile
### Optimization Process for Vibration Suppression
\right., \tag{34}\]  
\[\hat{t}_{2}=\left\{ \begin{array}{ll}\max\big{\{}\hat{t}_{1},t_{2}\big{\}},& \textbf{C}(2)=0\\ \text{ceil}\Big{(}\frac{\max\big{\{}\hat{t}_{1},t_{2}\big{\}}}{T_{d}}\Big{)}T_ {d},&\textbf{C}(2)=1\end{array} \right., \tag{35}\]  
\[\hat{t}_{4}=\left\{ \begin{array}{ll}\max\big{\{}\hat{t}_{1}+\hat{t}_{2},t_{4 }\big{\}},&\textbf{C}(3)=0\\ \text{ceil}\Big{(}\frac{\max\big{\{}\hat{t}_{1}+\hat{t}_{2},t_{4}\big{\}}}{T_ {d}}\Big{)}T_{d},&\textbf{C}(3)=1\end{array} \right., \tag{36}\]  
where \(\text{ceil}(\cdot)\) rounds a number up to the nearest integer greater than or equal to the given argument, and \(\max(\cdot)\) returns the largest element among a collection of input values. This modulation ensures both the suppression of the residual vibration and the minimization of the introduced time delay without violating any constraints. The conformity to the actuator limits is always ensured, as the values of the parameters \(\hat{t}_{1}\), \(\hat{t}_{2}\), and \(\hat{t}_{4}\) after modulation are not smaller than their initial values computed in Table 1. This is evident from the relationship between these parameters and the peak kinematic values:  
\[\hat{V}_{peak}=\frac{D}{\hat{t}_{4}},\ \hat{A}_{peak}=\frac{D}{\hat{t}_{2}\hat{t}_{4 }},\ \hat{f}_{peak}=\frac{\pi D}{2\hat{t}_{1}\hat{t}_{2}\hat{t}_{4}}. \tag{37}\]  
All possible options in S should be computed to find the optimal selection \(\mathbf{C}\) that minimizes the total motion duration. Finally, once the optimal values of \(\hat{t}_{1}\), \(\hat{t}_{2}\), and \(\hat{t}_{4}\) are obtained, the time parameters \(\hat{T}_{1}\), \(\hat{T}_{2}\), and \(\hat{T}_{3}\) are computed via \(\hat{T}_{1}=\hat{t}_{1}\), \(\hat{T}_{2}=\hat{t}_{2}-\hat{t}_{1}\), and \(\hat{T}_{3}=\hat{t}_{4}-\hat{t}_{2}-\hat{t}_{1}\), respectively. So far, a closed-form solution for the optimal sinusoidal jerk profile has been derived. The optimized motion profile minimizes the execution time to achieve fast movement while considering the actuator capacities, vibration cancelation, and robustness constraints. The final implementation procedure for practical applications of the developed methodology is as follows: (1) user: definition of the desired displacement and physical limits according to the machine specifications and task requirements; (2) host computer: computation of the optimal profile parameters through computing tools such as Matlab or Python and the generation of the motion profile; (3) machine controller: reception of the motion profile data from the host computer and their transfer to driving commands; (4) driver: execution of the motion profile according to the driving commands sent by the controller.
# Study on the Damage Process and Numerical Simulation of Tunnel Excavation in Water-Rich Soft Rock
## 2 Staged Softening Process of Water-Rich Soft Surrounding Rock Based on Tunneling
### Evolution Equation of Damage Variables Considering Dual Softening Coupling
#### 3.1.1 Water-Softening Damage in Tension  
The approach of [PERSON] to extend the one-dimensional damage to three dimensions is adopted, and the equivalent tensile strain \(\overline{z}\) is used to replace the uniaxial tensile strain as the softening parameter [35; 63; 64; 65; 66]. Concerning [PERSON] approach, the expression for the damage threshold and damage variable \(D_{t}\) of the tension element are given as:  
\[D_{t}=\left\{\begin{array}{cc}0&\overline{z}\leq\varepsilon_{t}\\ 1-\frac{\sigma_{tr}}{E_{0}\overline{z}}&\varepsilon_{t}<\overline{z}< \varepsilon_{tu}\\ 1&\overline{z}\geq\varepsilon_{tu}\end{array}\right. \tag{12}\]  
\[\overline{z}=\sqrt{<\varepsilon_{1}>^{2}+<\varepsilon_{2}>^{2}+<\varepsilon_ {3}>^{2}} \tag{13}\]  
where, \(\sigma_{tr}\) is the residual tensile strength of rock; \(\varepsilon_{t}\) is the maximum elastic tensile strain; \(\varepsilon_{tu}\) is the ultimate tensile strain; \(E_{0}\) is the initial (undamaged) elastic modulus of rock; \(\overline{z}\) is the equivalent strain, and \(\varepsilon_{1},\ \varepsilon_{2},\ \varepsilon_{3}\) are the three principal strain components. The angel brackets \(<\) > is a function, which is defined as follows:  
\[x=\left\{\begin{array}{cc}x&x\geq 0\\ 0&x<0\end{array}\right. \tag{14}\]  
Combining Equations (13) and (14), it can be seen that: when the main strain is tensile strain, it is included in the equivalence strain calculation; when the main strain is compressive strain, it is not considered.  
Considering the coupling of unloading and water-softening effects, the tensile damage variable \(D_{t12}\) is deduced using Equation (11) as follows:  
\[D_{t12}=D_{t1}+D_{t2}-D_{t1}D_{t2}=\left\{\begin{array}{cc}0&\overline{ \varepsilon}\leq\varepsilon_{t}\\ 1-\frac{\sigma_{t1}\sigma_{t2}}{E_{0}\overline{\varepsilon}^{2}}&\varepsilon_ {t}<\overline{\varepsilon}<\varepsilon_{tu}\\ 1&\overline{\varepsilon}\geq\varepsilon_{tu}\end{array}\right. \tag{15}\]  
where, \(D_{t1}\), \(D_{t2}\), \(D_{t12}\) are the rock tensile damage variables caused by unloading softening, water-softening, and the coupling of unloading and water-softening, respectively; \(\sigma_{tr,1}\) and \(\sigma_{tr,2}\) are the residual tensile strength of the rock in a natural state and saturated state, separately; \(E_{0}\) is the initial elastic modulus of rock; \(\varepsilon_{tu}\) is the ultimate tensile strain, which can be derived from the test.
# C1 DR-Net: Channel-Time Dense Residual Network for Detecting Crew Overboard Behavior
## 3 DR-Net and CTAM
### DR-Net
#### 3.1.1 Dense Blocks Design  
The output of the previous layer is only used as the input of the next layer in traditional convolutional neural networks, which constitutes less feature reuse and is not applicable to the detection applications with sparse features in this paper. The dense block structure consists of multiple dense layers, and the input of each dense layer is composed of the outputs of all preceding layers. In this structure, shallow features can participate in deep feature extraction, which avoids information sparsity and increases the efficiency of feature utilization.  
Considering the characteristics and computational complexity of crew overboard behavior feature extraction, two dense blocks are added to the DR-Net to extract shallow image features in the video frame sequence. Figure 2 shows a sketch of the dense blocks, where each dense block contains six dense layers, with a transition layer introduced between the two dense blocks.  
The new feature map is formed by concatenating the original input of the dense block with the output of all the previous dense layers, and serves as the input for the current dense layer. Each dense layer produces k output feature maps; the size of the input and output feature maps remains consistent. As we can see from Figure 2, the \(l^{\text{th}}\) dense layer has \(k_{0}+k\times(l-1),l=1,\,\ldots,6\) input feature maps, where \(k_{0}\) is the number of original input channels in the dense block. In each dense layer, the number of the input feature maps is compressed to \(4\times k\) through \(1\times 1\times 1\) convolution operation, where \(k=32\). This compression operation reduces the number of input features in dense layers and reduces the computational complexity of the network. \(K\) pseudo-convolutions of size \(1\times 3\times 3\) and \(3\times 1\times 1\) are utilized to replace the traditional convolution of size \(3\times 3\times 3\), which can improve the real-time detection performance. The \({(l+1)}^{\text{th}}\) dense layer has\(k_{0}+k\times l\), \(l=1,\ \ldots,6\) input feature maps, which are composed of the outputs of all previous layers. After feature extraction in six dense layers, \(6\times k\) feature maps are added to each dense block.  
The transition layer connecting two adjacent dense blocks is composed of a \(1\times 1\times 1\) convolutional layer and a \(2\times 2\times 2\) average pooling layer. The \(1\times 1\times 1\) convolutional layer reduces the number of channels of the dense block output feature maps from 256 to 128, which reduces the computational complexity and maintains feature extraction accuracy. The \(2\times 2\times 2\) pooling layer reduces the size of the output feature map from \(56\times 56\) to \(28\times 28\), which preserves important information and reduces redundancy.