esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Design Procedure  
A design procedure for narrow-beam low-sidelobe designs is given in this section. The successive steps are listed in the flowchart of Figure 3.  
The problem is defined by the statement of the LFoV prescriptions. After that, the scaling factor and the FFoV indices can be computed. Next, the designer has to choose either an aperiodic or a periodic grid for the array layout.  
In the first case, the aperture distributions have to be determined along with the aperture size. Next, a sampling procedure follows that is defined by the number of samples and the chosen sampling function.  
In the second case, in order for a FFoV design procedure to be used, the number of elements, the interelement distance, and the element excitation are determined. After that, the FFoV array is produced and, by enforcing the inverse scaling transform, we end up with an array that complies with the LFoV prescriptions. Detailed design examples are given in the next section.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**LFoV Prescription** & **Scaling Transform Equation** & **FFoV Prescription** \\ \hline From 0 to \(\theta_{0}\) & \(\left(\frac{\mu_{\rm F}}{r_{\rm F}}\right)=\frac{\sin(\pi/2)}{\sin[\theta_{0} )}\) & From 0 to \(\pi/2\) \\ \(HPBW_{\rm L}\) & \(HPBW_{\rm F}\cong\left(\frac{\mu_{\rm F}}{r_{\rm F}}\right)^{1}HPBW_{\rm L}\) & \(HPBW_{\rm F}\) \\ \(SLL_{\rm L}\) & \(SLL_{\rm F}=SLL_{\rm L}\) & \(SLL_{\rm F}\) \\ \(D_{\rm L}\) & \(D_{\rm F}=10\log_{10}\left(\frac{\mu_{\rm F}}{r_{\rm L}}\right)^{2}+D_{\rm L}\) & \(D_{\rm F}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mapping between LFoV and FFoV design requirements.
## 3 Related Work
### Privacy-Preserving Deep Learning (PPDL)
#### 3.1.5 Works Based on Trusted Execution Environments  
Trusted execution environments (TEEs) provide secure hardware environments to isolate code and data from other software, even from the operating system and hypervisor [13]. The first attempt to employ the secure enclave is Intel's Software Guard Extensions (SGX), which are a set of instructions and memory access mechanisms designed to secure running a trusted code on an untrusted platform [9]. The trusted code must be public to be inspected by the users [61]. Therefore, using simply SGX to protect privacy is insufficient from a security and privacy perspective, as the machine-learning service provider's code may not be fully trustworthy [9].  
[PERSON] et al. [61] proposed a combination of SGX and a sandbox to prevent data exfiltration, providing assurance to users that their data will not be compromised by untrusted code, even if they cannot inspect it [9]. They also introduced Chiron, a machine-learning solution within the SGX enclave, which allows data owners to train ML models without revealing their training data [61]. However, Chiron's reliance on SGX means it is restricted to CPU and cannot utilize GPUs or other machine-learning processors. [PERSON] et al. [62] expanded the use of TEEs to GPUs for machine-learning inference. They presented Slalom, a framework for evaluating deep neural networks that offloads all linear layers from a TEE, like SGX, to a GPU. While this approach enhances throughput and energy efficiency by assigning linear layers to GPU, using Slalom to train deep neural networks still presents complex, unresolved challenges [62].  
In general, the fundamental problem with TEEs is their limited memory and CPU resources, which make the training of complex deep learning models a significant challenge [46]. Furthermore, there is a risk of data leakage due to side-channel attacks [9].
# Comparative Study on Load Monitoring Approaches
## 3 Advantages and Disadvantages of Intrusive and Non-Intrusive Load Monitoring
### Advantages and Disadvantages of Intrusive Load Monitoring
#### 3.1.2 Disadvantages  
In its implementation, the intrusive approach to load monitoring is presented as expensive, complex to install, and difficult to maintain [59].  
The High acquisition cost of installing an ILM system is justified by the need to install sensors or measuring devices on each device to be monitored.  
Sub metering of the system makes it depend on the functionality of all submeters for accurate monitoring, that is, if one of the submeters is faulty, the accuracy of the monitoring system will be affected. An illustration of this situation can be seen in Figure 1.  
Intrusion: The installation of sensors on electrical circuits can be considered an intrusion into the privacy of the occupants of a dwelling or any other type of building, which may be a concern for some people.
## 2 The Algorithm
### Adaptive Consistency Filtering
#### 2.4.3 Normal Consistency Filter  
3D points within a very small region usually point in a direction with small deviation. If the normal \(\mathbf{n}_{0}\) of point \(p\) is far away from the average of its neighbor points' normal \(\mathbf{n}_{i}\), as judged by dot production in Expression (21), then it should be filtered out.  
\[\mathbf{n_{0}}\cdot\sum_{i=1}^{N_{3}}\mathbf{n_{i}}/(N_{3}+1)<th_{dp} \tag{21}\]  
In expression (21), \(N_{3}\) is the number of neighbor points within 3 \(s\) from point \(p\). \(th_{dp}\) is a threshold value.
## 2 Related Works
### Handcrafted Feature-Based Approach  
Almost all the early works rely on handcrafted feature-based approaches. Despite their limited performance, these approaches are similar to the methodology used by ophthalmologists to determine the severity of DME. In this sense, the results obtained can be interpretable for them. The authors of [8] proposed an automatic DME grading system utilizing the Particle Swarm Optimization (PSO) algorithm for HaExs segmentation. The detection of the OD and fovea relies on mathematical morphology, and the severity of the DME is determined based on the locations of lesions HaExs with respect to the OD. In reference [9], texture features are extracted from the area surrounding the macula, and DME grading is performed using a Support Vector Machine (SVM), achieving an accuracy rate of 86%. The authors of [10] utilized CLAHE (Contrast Limit Adaptive Histogram Equalization) for image enhancement. Following this, HaExs were identified through a segmentation technique, and the DME grading was conducted based on the spatial location of HaExs within the macula region. Another handcrafted-based approach outlined in [11] involves localizing the macula using its anatomical features and determining its location with respect to the OD. Subsequently, HaExs are detected using a vector quantization technique and a semi-supervised method, followed by DME grading based on the location of HaExs and the macula coordinates.
## 2 Related Works
### Existing Capacity Models and Tools  
As described in the Introduction section, extensive research has been done in network capacity. [PERSON] _et al._[30] focused on the ultra-wideband-based wireless personal area network capacity using a three-dimensional model. [PERSON] _et al._[31] solved the problem of under which scenarios a large network capacity gain can be expected by using higher transmission power. [PERSON] _et al._[32] investigated the network capacity region for a delay-tolerant mobile _ad hoc_ network. [PERSON] _et al._[33] proposed a general capacity model for both static and mobile wireless networks. Their results are valid for both finite networks and asymptotically infinite networks. [PERSON] _et al._[34] considered the deployment strategy problem for wireless sensor networks to get a large network capacity. [PERSON] _et al._[35] studied the the optimal source selection strategy to enhance the network capacity, where an optimization model is proposed to find the optimal source selection probability distribution. However, the surveyed existing literature discusses network capacity models, and prior works mainly focus on ground networks. Limited or no works have been done to develop a general analytical model for the capacity of dynamic, heterogeneous integrated sensor and satellite networks.  
Current software tools provide calculation abilities for modeling and simulating aspects of spacecraft and ground node systems. However, no integrated tool exists for network capacity study. That is, tools do not use analytical models for assessing network capacity and are unable to simulate the capacity of dynamic networks. For example, the satellite tool kit (STK) developed by Analytical Graphics Incorporated (AGI) can calculate high fidelity spacecraft and ground node information, including position, velocity, visibility, revisit times, _etc_. However, STK itself cannot calculate capacity numbers directly. In reference [36], [PERSON] _et al._ discussed the database structure of the communications system taxonomy (CommTax) toolkit, which uses STK and the QualNet network tool to model communication links among network nodes. In reference [37], [PERSON] studied the channel capacity problem in LEO remote sensing satellite systems. They improved the channel capacity of the system on the basis of STK. In our work, we will combine STK and MATLAB to quantify the trends of the capacity constraints in our capacity model. In Section 5, we will discuss this in detail.
## 3 Modelling and Parameterisation
### Gradient Boosting Decision Tree (GBDT) Model
Step 6: Output the final estimated result of the model, as shown in Equation (8) below:  
\[F(x)=\sum_{m=1}^{M}\gamma_{m}h(x,c_{m}) \tag{8}\]  
where \(F\)(x) is the final estimated function value, \(x\) is the independent variable, M is the number of decision trees, \(c_{\rm m}\) and h(x, cm) are the corresponding parameters and estimated results of the \(m\)-th tree, and \(r_{\rm m}\) is the weight of the \(m\)-th tree. In this study, the optimal parameter combinations were determined by sensitivity analysis to obtain the optimal model effect values. According to the above GBDT model algorithm flow, a grid search [17] is performed for the parameter combinations, and the model performance metrics (e.g., R\({}^{2}\)) are recorded for different parameters. Tables 2-4 show the sensitivity analyses for different numbers of decision trees (M = 100, 200, and 300) with learning rates (n) of 0.01., 0.05, and 0.10 and maximum depths of 1, 2, and 3, respectively. The model is optimal when the R\({}^{2}\) value of the model is the maximum. With the sensitivity analysis data in Tables 2-4, the computational cost was taken into account: The learning rate was determined to be \(\eta\) = 0.05, the number of decision trees M = 200, and the maximum depth of the tree as the optimal combination of parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Learning Rate (n)** & **Maximum Depth** & **R\({}^{2}\) (Total)** & **R\({}^{2}\) (Inbound)** & **R\({}^{2}\) (Outbound)** \\ \hline
0.01 & 1 & 0.83 & 0.81 & 0.84 \\
0.01 & 2 & 0.84 & 0.82 & 0.85 \\
0.01 & 3 & 0.82 & 0.8 & 0.83 \\
0.05 & 1 & 0.85 & 0.83 & 0.86 \\
0.05 & 2 & 0.87 & 0.85 & 0.88 \\
0.05 & 3 & 0.84 & 0.82 & 0.85 \\
0.1 & 1 & 0.81 & 0.79 & 0.82 \\
0.1 & 2 & 0.83 & 0.81 & 0.84 \\
0.1 & 3 & 0.8 & 0.78 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sensitivity analysis at M = 100.
## 3 Experiments  
We constructed a four-element, four-band (S, X, Ku, and K bands) phased array system, as illustrated in Figure 2. We validated the effectiveness of our joint phase-frequency distribution manipulation method through three sets of experiments: multi-band phase array radar transmission, multi-band phase array radar reception, and comprehensive transceiver validation. The system is driven by two mode-locked lasers (MLL1 and MLL2, Calmar PSL-10-TT, Palo Alto, CA, USA) with repetition frequencies of 11 GHz and 5 GHz. Baseband LFM signals with an amplitude of 1 V are generated using an AWG (KEYSIGHT M8195A, Santa Rosa, CA, USA). Photoelectric conversion and differential detection are performed using BPDs (Discovery DSC720, Ewing, NJ, USA) with a 20 GHz bandwidth. WDMs with a 200 GHz channel spacing manage optical signals, and ADCs operate at a sampling rate of 5 GSa/s. Key components, including phase shifters (DS PS6000L, Gardnerville, NV, USA, 400 MHz-6 GHz bandwidth, \(\pm 3^{\circ}\) phase accuracy) and VODLs (COF 300, Changzhou, China, maximum delay of 300 ps), are carefully calibrated to ensure precise phase control. All modulators (EOSPACE AX-1x2-0 MSS-20, Redmond, WA, USA) are maintained at quadrature bias points for optimal performance.
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
## 7 Implementation
### Package Processing Time  
Finally, we have analyzed the time it would take mobile devices to decipher the package that comes from other mobile devices. These package-processing times are really short, as evidenced by the processing time per segment shown in Figure 7.  
After analyzing the above results, it was concluded that the trend of the data follows a polynomial function of order four (see Equation (6)). This equation can be used to estimate the processing time per segment depending on the nodes of the graph representing the network.  
\[y=(2\text{e-9})x^{5}(-1\text{e-6})x^{4}+(2\text{e-4})x^{3}-0.0184x^{2}+0.673x-1.7392 \tag{6}\]  
This means that, for instance, a scheme in which the graph of the network is defined by 50 nodes and the number of challenges that defines the scheme package is six, the time a mobile device takes to process a received package to get the secret is only 48 ms.
## 4 Jasmonates: Biosynthesis, Signaling, and Functions
### Deeper Mechanistic Insights: How MeJA Modulates Trichome Development and Cannabinoid Biosynthesis  
#### 4.2.1 MeJA Signaling Pathway and Gene Regulation in C. sativa  
MeJA exerts its regulatory effects primarily via the COII-JAZ-MYC signaling pathway, which is widely conserved across angiosperms [47]. In this canonical model, exogenous MeJA enters plant cells and is frequently demethylated to JA. The free JA is then conjugated to isoleucine (Ile) by the JAR1 enzyme, producing JA-Ile, the bioactive form that binds to the SCFCOII-JAZ receptor complex [32]. When JA-Ile attaches to this complex, it triggers the ubiquitin-mediated degradation of JAZ repressor proteins, which normally sequester MYC-type transcription factors (TFs). Freed from JAZ inhibition, MYC TFs can then activate downstream jasmonate-responsive genes. Among the best-characterizedbHLH TFs in this pathway are MYC2, MYC3, and MYC4, which bind to G-box motifs in JA-responsive promoters and initiate the expression of defense- and secondary-metabolism-related genes. This activation encompasses genes associated with glandular trichome development, directly linking MeJA signaling to the production of protective and specialized metabolites [48] (Figure 3).
## 2 Design of a Structural Health Monitoring System for a Jacket Offshore Platform
### Assessment Subsystem
#### 2.3.1 Safety Monitoring and Early Warning Evaluation Using Static Measurements
The stress model of conduit leg is shown in Figure 3, in which: \(F\) is the load on the top of the conduit leg; \(M_{x}\) and \(M_{y}\) are the bending moments in the \(x\) and \(y\) directions, respectively, at the top of the leg; \(\theta\) is the included angle between the conduit leg and the \(x\)-\(y\) plane; and \(\alpha\) and \(\beta\) are the included angles between the diameter of any section position and the two coordinate axes, which can be obtained by the knowledge of material mechanics. \[N=S\times E\cdot\frac{\varepsilon_{1}+\varepsilon_{2}}{2} \tag{4}\]  
where \(N\) is the total pressure load, \(E\) is the elastic modulus, \(\varepsilon_{1}\) and \(\varepsilon_{2}\) are the strain value at both ends of any diameter on the pile section. The Equation (4) denotes that the load on the top of a guide leg can be obtained by measuring the strain of any two points on the diameter of its section; the change in the load on the top of the guide leg can then be obtained by changing Equation (4) to an incremental form:  
\[\Delta F=-\frac{SE}{\sin\theta}\cdot\frac{\Delta(\varepsilon_{1}+\varepsilon_{ 2})}{2}. \tag{5}\]  
Figure 2: Schematic diagram of pile foundation load. Therefore, the following arrangement scheme can be adopted: two strain sensors are arranged in any diameter direction on the top section of each conduit leg. The change in the value of the load on the top of the conduit leg can be obtained by monitoring the change in the strain measured by the strain sensor, and this is then multiplied by the transfer function to obtain the change in the pile foundation load. Finally, to prevent excessive pile load, it is necessary to determine a load range for safe operation of the pile foundation. In engineering, a safety factor is usually adopted to achieve this purpose. The monitoring and early warning conditions of the pile-end bearing capacity should be comprehensively evaluated according to different requirements. Here, a safety factor of 2.0 is used with reference to the American Petroleum Institute [30] specification; that is, the load on the pile foundation should be less than half of its allowable bearing capacity. This can be expressed as  
\[P\leq[P]=\frac{P_{m}}{2} \tag{6}\]  
where \(P_{m}\) is the allowable load of pile foundation.
## 6 Key Challenges and Future Directions
### Challenges in IoT Integration with WebAssembly
Enhancements can be made to the Wasm ecosystem to facilitate the verification and auditing of code. * **Improved Networking Capabilities*
* Efforts are underway to provide Wasm with direct access to networking APIs in order to eliminate the need for JavaScript intermediation and unlock the potential for more powerful and efficient networked applications. Networking is a crucial aspect of any IoT application. Devices in an IoT network communicate with both each other and with cloud-based services, necessitating reliable, efficient, and secure networking capabilities. However, Wasm's initial design does not have inherent networking capabilities, instead relying on the host environment for this. In the future, there are several directions Wasm could take to improve networking capabilities in the context of IoT. * **Networking APIs in WebAssembly*
* One of the most promising developments in Wasm is the proposal for adding networking APIs directly to the WASI. This would allow Wasm applications to establish network connections, send and receive data, and perform other networking operations without relying on JavaScript or other host environment features. This could significantly improve the efficiency of networking in Wasm-based IoT applications. * **Peer-to-Peer Networking*
* Peer-to-peer networking is an important feature for many IoT applications, especially those that involve direct device-to-device communication. Future versions of Wasm could include support for peer-to-peer networking protocols, allow
ing devices to establish direct connections with each other without needing a central server. * **Secure Networking** Security is paramount in IoT networking due to the sensitive nature of the data being transferred. Future developments could focus on enhancing the security of Wasm's networking capabilities, for instance by integrating secure networking protocols directly into Wasm or providing APIs for cryptographic operations. * **Network Performance Optimization** Efficient use of network resources is important in IoT, especially in applications where bandwidth is limited or expensive. Future iterations of Wasm could include features for optimizing network performance, such as support for data compression or efficient binary protocols. * **Advanced Networking Features** There are numerous other advanced networking features that could be beneficial in an IoT context. For example, support for multicast or broadcast communication could allow a single Wasm application to efficiently send data to multiple devices. Similarly, support for real-time communication protocols could enable use cases such as VoIP or live video streaming.
# Design of a 3-Bit Circularly Polarized Reconfigurable Reflectarray
## 3 Fabrication and Measurement Results
In order to better highlight the advantages of the 3-bit circularly polarized reconfigurable reflectarray designed in this study, a comparison of the proposed reflectarray with other works is listed in Table 2. The 3-bit circularly polarized reconfigurable reflectarray proposed in this paper has advantages of low loss, high aperture efficiency, a wide beam scanning range, and excellent stability in wide-angle oblique incidence. Compared with the traditional phased array, the 3-bit circularly polarized reconfigurable reflectarray has lower loss and lower cost because it does not need a lot of channels to control the changes in phase.
## Materials and Methods
### Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Parameter Name** & \begin{tabular}{c} **Model** \\ **Abbreviation** \\ \end{tabular} & **Mean Value** & \begin{tabular}{c} **In Situ** \\ **Range** \\ \end{tabular} & **Source** \\ \hline Prospect & Structure parameter & N & Fixed (1.5) & Fixed & \begin{tabular}{c} Fitted + \\ [42] \\ \end{tabular} \\  & Chlorophyll al+b content & Cab (\(\mu\)g/cm\({}^{2}\)) & 56.4 & 42.4–76.5 & Chl samples \\  & Caroetool content & Car (\(\mu\)g/cm\({}^{2}\)) & 3.421 & 2.398–4.579 & Chl samples \\  & Brown pigment content & Chrown & N.A. & N.A. & - \\  & Equivalent water & & & & \\  & thickness & Cw (cm) & Fixed (0.0198) & Fixed & leaf samples \\  & Dry matter content & Cm (g/cm\({}^{2}\)) & Fixed (0.0092) & Fixed & Leaf samples \\ \hline  & Dry/Wet soil fraction & & & & \\ Sail & (=1- soil moisture content) & pSoil & 0.5340 & 0.4496–0.6214 & Soil samples \\  & Leaf area index & LAI & 0.706 & 0.003-1.215 & \
\begin{tabular}{c} [57] + \\ samples \\ \end{tabular} \\  & Type of leaf angle & Lidf & Fixed(1,0) & Fixed & [59] \\  & Hotspot parameter & hspot & N.A. & N.A. & - \\  & Solar zenith angle & Tts (\({}^{\circ}\)) & N.A. & N.A. &
\begin{tabular}{c} From \\ timestamp \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ProSail model parameters. The parameter abbreviation is the abbreviation used in HSDAR for the model parameter. The mean value is the average value of the in situ measurements, fixed means it is not derived from in situ measurements. The in situ range describes the range of values (minimum-maximum) that occurred, and were used to simulate their effect on reflectance.