esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Load Distribution in Float Photovoltaic System
## 6 Conclusions  
The load on the PV module can be utilized as a structural design variable for the PV power plant, and although there are differences depending on the type of structure, the load distribution caused by the wind load in the floating PV system was assessed using possible parametric studies with design parameters including wind speed, wind direction, and installation angle of PV modules. The following conclusion was driven.  
Owing to wind load and flow velocity affecting the PV power plants, the load should be checked for the entire PV power plant. Wind load is evaluated as relatively low because only the projected area in the horizontal direction is considered in the design standard. Therefore, the wind load applied to all arrays of the PV power plant was evaluated through the CFD analysis. The maximum velocity for the outlet of the PV plant was confirmed through the cross-sectional velocity distribution of the flow area. In addition, the average pressure distribution for each array at the middle and end of the PV module due to wind was confirmed. To evaluate the effect of wind load, the pressure was checked based on the floating PV module.  
As a result of the CFD analysis, the largest wind load is applied to the first and second arrays for the wind in the forward direction of the solar module, the largest load is generated in the first array for the backward direction, and the load is rapidly generated from the second array. After comparing the wind loads at the center and the ends of the array, the wind load is larger at the center, so the wind load is derived as the load at the center for safety.  
The wind pressure generated in the PV module was greater than the wind speed generated from the rear when the wind speed was generated from the front. In addition, the wind pressure generated a large pressure only in the first PV panel and decreased toward the backward direction because the flow was stable.  
Therefore, it is judged that the floating PV power plant is structurally more vulnerable to the wind acting from the backward direction.  
In the future, we plan to install a solar power generation structure at an actual site and confirm the behavior of the PV module against wind pressure through field tests.  
Conceptualization, H.-J.J. and S.-J.H.; methodology, H.-J.J.; software, S.-H.K. and S.-J.H.; formal analysis, W.C. and H.-J.J.; data curation, S.-H.K.; writing--original draft preparation, W.C. and S.-H.K.; writing--review and editing, W.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Author [PERSON] and [PERSON] are employed by the company Trina Solar Co., Ltd. and SK E&S, respectively. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Experiments and Analysis
### Analysis
#### 4.3.5 Effectiveness of Text Guide Component  
To evaluate the effectiveness of the text guide component in the IT-Prompt method, we divided the CIFAR-100 dataset into three task sequences, containing 10, 20, and 50 tasks, respectively. Comparative experiments, both with and without the text guide, are depicted in Figure 6. The text guide component significantly enhanced performance in both the FAA and CAA metrics across these task sequences. This demonstrates the component's adaptability to different classifiers and task sequences.
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 1 Introduction
### Roadmap  
The paper is structured as follows: Section 2 reviews the network model and the basics of elliptic curve encryption. In Section 3, we analyze a related AKA scheme. We then describe an improved ECC-based AKA protocol in Section 4. Section 5 provides a formal proof, descriptive security analysis, and validation with ProVerif of the proposed scheme security. In Section 6, we present a performance comparison with related schemes. Finally, we conclude the paper in Section 7.
## 3 System Model and Problem Formulation
### Channel Model  
The uplink transmission link of the system includes two parts: the uplink channel from the IoT device to the UAV, and the uplink channel from the UAV to the LEO satellite. Both channels are dominated by a line-of-sight (LoS) path, and the fading follows Rician fading [42]. For the \(n\)-th IoT device of the \(t\)-th category, we used \(g_{n}^{l_{n}\to l\alpha_{tn}}\) to denote the first-layer channel gain from the IoT device to the selected UAV, where \(n\in\{1,\ldots,N_{t}\}\). Additionally, \(g_{n}^{U_{n_{m}}\to L}\) is employed to denote the gain of the second-layer channel in the uplink, specifically, the channel gain from the UAV to the LEO satellite. Drawing from refs. [29,42], considering that the carrier frequency significantly exceeds the system bandwidth, we assume that the calculation of channel gain is solely dependent on the communication distance. Therefore, the channel gain can be calculated by the following formulas:\[\begin{split} g_{n}^{l_{t}\to U_{t_{nn}}}&=\frac{g_{0}}{( \sqrt{(x_{n}^{t}-x_{\alpha_{nn}}^{l_{t}})^{2}+(y_{n}^{t}-y_{\alpha_{nn}}^{l_{t}}) ^{2}+h^{l2}})^{2}}\\ g_{n}^{l_{L_{t_{nn}}\to L}}&=\frac{g_{0}}{( \sqrt{(x_{\alpha_{nn}}^{l_{t}}-x^{l_{t}})^{2}+(y_{\alpha_{nn}}^{l_{t}}-y^{l_{t} })^{2}+h^{l2}})^{2}}\end{split} \tag{1}\]  
where \(g_{0}\) is the channel gain per unit distance. \(\gamma_{n}^{l_{t}\to U_{t_{nn}}}\) is expressed as the channel signal-to-noise ratio from the \(n\)-th device in \(I_{t}\) to the associated UAV, and \(\gamma_{n}^{U_{t_{nn}}\to L}\) is expressed as the channel signal-to-noise ratio from the selected UAV to the LEO satellite. Furthermore, we assume that the power spectral density of additive white Gaussian noise (AWGN) is \(N_{0}\). \(B_{n}^{t}=Bj_{t}k_{n}^{t}\) is used to represent the bandwidth occupied by the \(n\)-th device in \(I_{t}\). For the \(n\)-th UAV, as its forwarding power is uniformly distributed among each connected IoT device, the forwarding power allocated to the \(n\)-th device is \(P_{n}^{t}=P^{U}/C_{tn}^{U}\). Therefore, \(\gamma_{n}^{l_{t}\to U_{t_{nn}}}\) and \(\gamma_{n}^{U_{t_{nn}}\to L}\) can be obtained by the following formulas:  
\[\gamma_{n}^{l_{t}\to U_{t_{nn}}}=\frac{P^{l}g_{n}^{l_{t}\to U_{t_{nn}}}}{Bj_{t }k_{n}^{t}N_{0}} \tag{3}\]  
\[\gamma_{n}^{U_{t_{nn}}\to L}=\frac{P^{l}g_{n}^{U_{t_{nn}}\to L}}{Bj_{t}k_{n}^{t }C_{tn}^{U}N_{0}} \tag{4}\]  
According to the principles of amplification, forwarding relay, and [PERSON]'s formula, the uplink channel transmission rate of the \(n\)-th device in \(I_{t}\) can be calculated using the following formula:  
\[R_{n}^{t}=B_{n}^{t}log_{2}(1+\frac{\gamma_{n}^{l_{t}\to U_{t_{nn}}}\gamma_{n} ^{U_{t_{nn}}\to L}}{1+\gamma_{n}^{l_{t}\to U_{t_{nn}}}+\gamma_{n}^{U_{t_{nn}} \to L}}) \tag{5}\]  
For easy reference, a notation summary of the system model is presented in Table 2.  
Figure 1: A three-layer network structure consisting of multiple IoT devices, UAVs, and an LEO satellite.
## 3 Proposed Fusion Algorithm
### Abundance Estimation via ADMM
Using structure matrix (4) and Kronecker operators, the optimized solution of (17a) is rewritten [32] as  
\[\ +\mathbf{h_{2}}^{T}(\mathbf{v}-\mathbf{H}\mathbf{u})+\frac{\eta}{2 }\|\mathbf{v}-\mathbf{H}\mathbf{u}\|_{2}^{2}+\mathbf{h_{3}}^{T}(\mathbf{s}- \mathbf{x})+\frac{\eta}{2}\|\mathbf{s}-\mathbf{x}\|_{2}^{2}, \tag{14}\]7  
whose computation takes the complexity of \(\mathcal{O}(((Nr^{2})^{2}+NL)\tilde{\xi})\), much lower than that of (17a). In the same way, to reduce the high complexity of \(\mathcal{O}\left((NL)^{3}\right)\) in (17b), we define \(\tilde{\mathbf{u}}_{1}\triangleq\mathbf{v}_{1}^{j}+\widetilde{\mathbf{h}}_{2}^{j}\) and \(\tilde{\mathbf{u}}_{2}\triangleq\mathbf{s}^{j+1}+\widetilde{\mathbf{h}}_{1}^{-j}\), and then reshape these two vectors into the matrices \(\widetilde{\mathbf{U}}_{1}\) and \(\widetilde{\mathbf{U}}_{2}\), respectively. By the structure of difference matrix \(\mathbf{H}\), (17b) is simplified as  
\[\ +\mathbf{h_{2}}^{T}(\mathbf{v}-\mathbf{H}\mathbf{u})+\frac{\eta}{2 }\|\mathbf{v}-\mathbf{H}\mathbf{u}\|_{2}^{2}+\mathbf{h_{3}}^{T}(\mathbf{s}- \mathbf{x})+\frac{\eta}{2}\|\mathbf{s}-\mathbf{x}\|_{2}^{2}, \tag{14}\]8  
which involves the computation of \(\mathcal{O}\left(N^{2}L\sqrt{L}\right)\), much less than (17b). In summary, in order to obtain the abundance matrix \(\mathbf{S}\) by alternatively iterating the above original and dual variables, the resulting ADMM-based algorithm for solving (11) is presented in Algorithm 2. ```
1:Input: \(N\), \(\mathbf{Y}_{h}\), \(\mathbf{Y}_{m}\), \(\mathbf{F}\), \(\mathbf{G}\) and \(\mathbf{A}^{k}\). 2:Output\(\mathbf{S}^{k+1}\). 3:Initialize \(\mathbf{u}^{0}\), \(\mathbf{z}^{0}\), \(\mathbf{x}^{0}\),\(\mathbf{v}^{0}\) and \(\mathbf{h}_{i}^{0}(i=1,\ldots,3)\) with \(\mathbf{0}\) or warm start. 4:\(j=0\)
5:while the predefined stopping rule of the inner loop is not met do
6: update \(\mathbf{s}^{j+1}\) by (18);
7: update \(\mathbf{u}^{j+1}\) by (19);
8: update \(\mathbf{v}^{j+1}\) by (17c);
9: update \(\mathbf{x}^{j+1}\) by (17d);
10: update \(\widetilde{\mathbf{h}}_{i}^{j+1}=\boldsymbol{h}_{i}^{j+1}/\eta\;(i=1,2,3)\) by (15e) \(\cdots\) (15g), respectively;
11:\(j:=j+1\);
12:endwhile. ```  
**Algorithm 2** Solving (11) via ADMM
# Human Activity Recognition for Assisted Living Based on Scene Understanding
## 4 Used Datasets for Object Detection and HOI Detection
### Verbs in Common Objects in Context (V-COCO)  
V-COCO [55] is a dataset that builds on top of COCO for HOI detection. It splits 10,346 images into: 2.533 for training, 2.867 for validation, and 4.946 for testing. In total, there are 16.199 human instances. The dataset considers 26 different action verbs, for a few verbs it includes two types of attributes: instrument and object, resulting in a total number of 29 action classes.  
Divided by the existence of an attribute and its type, the verb classes with the associated attributes are as follows:  
* <agent, verb>: walk, smile, run, stand;
* <agent, verb, object>: cut, kick, eat, carry, throw, look, read, hold, catch, hit, point;
* <agent, verb, instrument>: surf, ski, ride, talk on the phone, work on computer, sit, jump, lay, drink, eat, hit, snowboard, skateboard.  
In the context of the SmartCare project, we split the V-COCO verb classes into three categories: essential, util, and unnecessary, according to Table 6.  
V-COCO has, on average, 1.57 people annotated per image that perform actions: over 7000 images with one annotated person, 2000 with two, 800 with three and the rest with four or more people annotated [55]. Figure 5 illustrates situations in which the person performs multiple actions at the same time.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Verbs with Associated Attribute Type** \\ \hline Essential verbs for SmartCare & walk, eat\_object, sit\_instrument, lay\_instrument, drink\_instrument, eat\_instrument, \\  & hold\_object, stand \\ \hline Util verbs for SmartCare & cut\_instrument, cut\_object, talk\_on\_phone\_instrument, work\_on\_computer\_instrument, \\  & carry\_object, smile, look\_object, point\_instrument, read\_object, run, jump\_instrument \\ \hline Unnecessary verbs for SmartCare & surf\_instrument, ski\_instrument, ride\_instrument, kick\_object, hit\_instrument, hit\_object, \\  & snowboard\_instrument, skateboard\_instrument, catch\_object \\ \hline \hline \end{tabular}
\end{table}
Table 6: V-COCO categories in SmartCare.  
Figure 5: Examples of annotations in the dataset with a human doing multiple actions at the same time: (**a**) person lying on the bed and working on a computer and (**b**) person sitting on a chair, eating a sandwich and working on a computer [55].
## 5 Fiber Transmission Performance Results and Discussion
### High-Baud-Rate 16-QAM  
For 400 Gb/s DCI applications, the industry is adopting DP-16 QAM signals. With additional FEC overhead, the DP-16 QAM signal can also fuel the 400 Gb/s metro, regional, and even long-haul DWDM applications. Figure 5a displays the fiber transmission performance of the 56 GBd 16-QAM signal with different Tx side optical power. As the optical link is power-limited, the transmission performance can be improved for longer transmission by increasing the output power of Tx. With 6-dB more optical power than \(P_{Tx\_Ref}\), the 56 GBd 16-QAM signal can transmit over 100 km with a pre-FEC BER of \(2.5\times 10^{-2}\). From the figure, one can see that, with 3-dB more optical power than \(P_{Tx\_Ref}\), it is sufficient to achieve 80 km error-free transmission after FEC. With different optical power of the Tx side, the fiber transmission performance of the 16-QAM signals at 64 GBd is shown in Figure 5b. Using 25% OH SD-FEC, the 64 GBd 16-QAM signal can still carry over 400 Gb/s net data rate. Experimental result shows that a larger than 50 km unamplified optical communication link can be achieved using \(P_{Tx\_Ref}\). With 6-dB more optical power than \(P_{Tx\_Ref}\), the system can achieve larger than 90 km transmission in the unamplified link. Consequently, enhanced output power of Tx or improved sensitivity of Rx would be beneficial for 80 km extended long reach (ZR) application [9].  
Figure 4: Experimental setup. DAC: digital-to-analog converter, IQ: in-phase (I) and quadrature (Q), VOA: variable optical attenuator, SMF: single-mode fiber, LO: local oscillator, ADC: analog-to-digital converter, DSP: digital signal processing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Length (km)** & 20 & 40 & 60 & 80 & 90 & 100 & 110 \\ \hline
**Loss (dB)** & 4.7 & 7.8 & 12.6 & 15.5 & 17.6 & 20.0 & 22.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fiber loss characterization.  
Furthermore, we compare 16-QAM signal transmission performance at various baud rates with \(P_{Tx\_Ref}\) and 6-dB more Tx side optical power, respectively. As shown in Figure 6a,b, we transmitted the 16-QAM signal with up to 86 GBd symbol rate. For the 86 GBd 16-QAM signals with \(P_{Tx\_Ref}\) and 6-dB more Tx side optical power, their reaches are around 40 km and 75 km, which are mainly limited by the increased receiver sensitivity (i.e., more receiving optical power is required) for higher baud rate system.
## 3 Finite Element Modeling (FEM) of Human-Seat Interactions  
This study employed a detailed finite element analysis (FEA) framework to investigate the interaction between a driver's body and a car-seat cushion integrated with a pneumatic spring. Following established methodologies in the literature [13], the development of the interaction model was systematically divided into the following key steps: data collection, geometric modeling, material definition, meshing, the application of boundary conditions, simulation, and validation of the results. The model focused on capturing the static contact pressure distribution influenced by the pneumatic spring, leveraging a half-symmetric approach to optimize computational efficiency and ensure convergence.
# An Autonomous Wireless Device for Real-Time Monitoring of Water Needs
## 3 Materials and Methods
### Hardware Description
#### 3.1.2 LoRa Technology  
LoRa, whose meaning is \"long range\", is a long-range communications system promoted by the LoRa Alliance [36]. This system is aimed to be used in devices where energy consumption is of the utmost importance.  
LoRa can commonly refer to two distinct layers [20]:  
1. A physical (PHY) layer, the so-called LoRa, that uses the radio modulation technique chirp spread spectrum (CSS).
2. A medium access protocol (MAC) layer in which LoRaWAN defines the communication protocol and the specific access network architecture.  
The LoRa modulation physical layer (Semtech Camarillo, CA, USA) allows a wide range at low power. We used the RN2483 transceiver module for Lora Class A, which operates in the industrial, scientific, and medical (ISM) frequency band of 868 MHz. The payload of each transmission can vary from 2 to 255 octets and the data rate can reach 50 Kbps [1].  
LoRaWAN provides a MAC mechanism, which allows many end-devices (end-nodes) to communicate with a single gateway using LoRa modulation. A typical LoRa network shows a star-of-stars topology, which includes three different types of devices--the end-devices, the LoRaWAN gateways, and the network service [20; 32].  
In the basic architecture of a LoRaWAN network (Figure 1), the end-nodes communicate with the corresponding gateway using LoRa with a LoRaWAN link, and the gateways send LoRaWAN frames to a network server via a broadband link (i.e., Ethernet or GPRS) [20].
# Laser Transmission Characteristics of Seawater for Underwater Wireless Optical Communication
## 1 Introduction
For a given number of photons and IOPs, the received power and associated transmission distance of each photon are computed to obtain the channel impulse response. Primary topics of research regarding laser transmission characteristics include power loss [14, 27, 28, 31], laser beam spreading [26, 27, 32], angle of arrival (AOA) distribution [33], and temporal dispersion [14, 30]. [PERSON] et al. [28] analyzed the power loss and BER of the three typical waters while considering the receiver aperture and FOV modulation; however, this study is solely for SISO-based UWOC links. [PERSON] et al. [9] analyzed the power loss and BER of a large divergence angle light source transmitted in four typical waters based on Monte Carlo simulation and is instructive for the deployment of relay-assisted UVLC systems. [PERSON] and [PERSON] [32] presented the received power loss in three typical waters and three harbor waters; the simulation was validated with water tank experimental results conducted by [PERSON] [23]. [PERSON] et al. [33] derived a closed-form expression of the AOA distribution in three typical waters by assuming ballistic and single scattering conditions, but the accuracy of this single scattering assumption is limited to long-distance transmission and turbid waters [34]. In summary, there is still a lack of systematic analysis of the spatial dispersion characteristics of UWOC. To deal with various application scenarios, we used the Monte Carlo ray tracing method to systematically analyze the spatial dispersion characteristics of laser transmission, including beam spreading, AOA, and channel loss at different transmission distances for typical waters. The IOPs of a water body could be crucial parameters for insight into the underwater laser transmission features. In this study, we considered the linear misalignment of the receiver by calculating the power loss at different offsets on the receiving plane; thus, this result could be applied to the transmission characteristics analysis of SISO [35] and SIMO systems [36]. In the presence of these considerations, this paper is organized as follows. Section 2 introduces Monte Carlo ray tracing technology and simulation settings including emitted laser beam width, FOV, receiver conditions, and optical properties of three typical waters. Section 3 discusses the simulated results, including the analysis of the AOA distribution, beam spreading, and channel loss, to draw a whole impression of the laser spatial dispersion. Section 4 shows the overall analysis and summary of this article.
## 3 Results and Discussion
### Measurements of Nitrite and Nitrate  
Figure 6 shows the nitrite and nitrate concentrations as a function of treatment time. In the case of oxygen and argon plasmas, nitrite was not detected, and nitrate concentration was extremely low. Since both nitrogen and oxygen species are present in the air during the plasma process, nitrite and nitrate concentrations in the air plasma are higher than nitrogen plasmas, as shown in Figure 6. Similar results associated with the produced nitrite and nitrate were reported using a needle electrode with the mixture of nitrogen and oxygen [18]. Spark and glow discharges using a needle electrode are able to generate PAW with a high acidity level (low pH level) [39]. Nitrite and nitrate concentrations rise by increasing treatment time. As mentioned before, the presence of nitrite and nitrate as oxidizing agents results in reducing the pH value of PAW [30,40].  
Figure 5: Variations of ORP level for plasma-activated water as a function of time with four gas feedings: argon, oxygen, nitrogen, and air.  
Figure 6: Nitrite and nitrate measurements inside the plasma-activated water with four gas feedings: argon, oxygen, nitrogen, and air using an ion chromatography system.
# Machine-Learning-Based Path Loss Prediction
## 2 Theory
### Machine-Learning-Based Predictive Modeling
#### 2.2.1 Artificial Neural Networks (ANNs)  
ANN is an information-processing paradigm that is inspired by the way biological nervous systems such as the brain process information [44]. The mathematical model of an ANN neuron consists of three basic components: synapses modeled as weights, an adder for summing the inputs, and an activation function. Each connection weight \(w\) represents the strength of the connection, where positive values indicate excitatory connections and negative values indicate inhibitory ones. The adder linearly combines the inputs \(x\) and their corresponding weights \(w\), as described by the equation  
\[v_{k}=\sum_{i,j}w_{ij}x_{ij} \tag{6}\]  
The activation function \(\phi\) then processes this sum to produce the neuron's output \(y_{k}=\phi(v_{k})\). This approach allows ANNs to approximate complex functions by adjusting the weights based on training data, enabling the network to learn and generalize from the input data effectively.
## 2 Materials and Methods
### Variation in the Parameter Values of SLA among Different Terrestrial Biosphere Models  
Figure 4 displayed the variation in default SLA values among seven terrestrial biosphere models within eight plant functional types, compared with the observed SLA data. Almost all default SLA value settings in the models for each PFT were in the range of the observed SLA values in China but had large differences within the same PFT. The CV of the default SLA values across models within one PFT varied from 8.7% for crop to 60.0% for NET. Particularly, the SLA value of grass in the BIOME-BGC model was higher than that in the other models by 0.024 m\({}^{2}\)/g on average, although SLA variation in grass across the seven models was relatively low. The SLA values for the PFTs of BES and NDT in the SiBCASA model were high, which were greater than that of the others by 0.016 m\({}^{2}\)/g and 0.011 m\({}^{2}\)/g, respectively. By contrast, the SLA values in the JULES model were generally lower than that in the other models by 0.011 m\({}^{2}\)/g on average for all PFTs. The variation in SLA value assigned among models for a given PFT was lower than that from the observations, except for NET, BET, and BES. Specifically, the CV of SLA values among models for NET was higher than the CV of observed values by 17.1%.  
The mean default values across models for a given PFT was greater than the observed mean by 0.004 m\({}^{2}\)/g on average (Figure 4). The difference was largest for crop, reaching 0.009 m\({}^{2}\)/g, followed by NDT, with 0.0087 m\({}^{2}\)/g. The mean default SLA values across different models were approximately equal with the observed mean for BES. In addition, the default SLA values in few models were close to the mean observed values. For example, the SLA value of BET in the CLM4.5 model was 0.012 m\({}^{2}\)/g and the average observation was 0.014 m\({}^{2}\)/g. The difference in SLA values between the hybrid model and the observed mean was only 0.001 m\({}^{2}\)/g.