esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
However, due to the physiological structure of vegetation itself, and the strength of photosynthesis and respiration caused by the changes in environmental factors, scholars have rejected the constant value of CUE = 0.5 [19]. Thus, the study of WUE and CUE can help human beings to better understand climate change and CO\({}_{2}\) emissions, which will be important for managing ecosystems in the future. In the last decades, there has been extensive research interest in the relationship between plant C assimilation and water depletion, especially with regard to the relationship between WUE and CUE and meteorological factors, as well as techniques with which to enhance vegetative WUE and CUE [28]. With advancements in observation techniques and the improvement of plant physiological models, scholars have gained a better understanding of the interaction of WUE and CUE with environmental factors at different scales [20; 21; 22; 24; 25; 26; 29]. Nevertheless, traditional site observation, modeling, and experimental methods have difficulties meeting the needs of large-scale research. These methods have large uncertainties in the conversion process from sample points to regions, making the study of the water-carbon cycle at large spatial scales challenging [15]. Fortunately, access to various biophysical parameters and hydrologic indices have become feasible with the support of remote sensing-based public domain data, providing unique opportunities for regional- and global-scale studies. The MRYR are located on the Loess Plateau, the largest in the world. The region has experienced severe ecological damage [30]. In response, China has implemented many ecological restoration measures over the past 40 years, and the Loess Plateau has shown an obvious greening trend [30; 31; 32; 33]. Nevertheless, the impact of anthropogenic vegetation restoration on the key processes of water and carbon cycling in terrestrial ecosystems are still poorly understood [34]. Therefore, it remains a challenge to study the changes in ecosystem productivity, WUE, and CUE during vegetation restoration and to link such changes to various natural factors. To address this challenge, the main objectives of this study were the following: (1) to analyze the characteristics of the changes in the climate variables in the MRYR during 2001-2022; (2) to quantify the spatial-temporal interannual variations in the NEP, WUE, and CUE; (3) to analyze the relationship between the NEP,WUE, and CUE and climate factors (PRE, TEM, and DSI) to determine how they are controlled by environmental variables; and (4) to quantify the impact of land cover change on the NEP. The main contribution of this paper is its study of the NEP, WUE, and CUE in the context of anthropogenic restoration in the MRYR. It makes up for the fact that there are fewer studies of this area. Further, this study analyzes the WUE and CUE based on the NEP, which is more in-depth than previous studies based on the NPP.
## 2 Materials and Methods
### Calculation of Seasonal Cycle Amplitude of XCO\({}_{2}\)  
The SCA of XCO\({}_{2}\) is determined by CO\({}_{2}\) uptake and release by terrestrial biosphere. The temporal variation of XCO\({}_{2}\) can be divided into two components, long-term yearly increases mainly caused by anthropogenic emissions [3] and inherent seasonal cycle mainly resulted from photosynthesis and respiration by the terrestrial biosphere [23]. Fitting formulas for the XCO\({}_{2}\) seasonal cycle have been proposed in many different forms [13; 14; 20; 54; 55]. In this study, we use the typical one, adopted by [PERSON] et al. [54] and [PERSON] et al. [55], to statistically fit the seasonal cycle and yearly increases of XCO\({}_{2}\), which is given by the Equation (1) below,  
\[\mathrm{x(t)}=\mathrm{k_{0}}+\mathrm{k_{1}}\times\mathrm{t}+\sum_{i=1}^{2}(a_{ i}\cos(2\pi it)+b_{i}\sin(2\pi it)) \tag{1}\]where \(k_{0}\) is the XCO\({}_{2}\) at the start time, \(k_{1}\) is the temporal growth rate of XCO\({}_{2}\), and the annual harmonic functions, \(\sum_{i=1}^{2}(a_{i}\cos(2\pi it)+b_{i}\sin(2\pi it))\), describe the seasonal variation of XCO\({}_{2}\). The harmonic functions include an annual seasonal cycle and semi-annual seasonal cycle [56]. We fit Equation (1) to the five-year time series of GM-XCO\({}_{2}\). However, the inter-annual variations in the seasonal cycle shape is not included in the fitted curves. In this study, the yearly increase of XCO\({}_{2}\) in each grid is removed to obtain the seasonal GM-XCO\({}_{2}\) data (hereafter referred to as dXCO\({}_{2}\)), which are used in analyzing the seasonality in the following sections. The SCA is calculated as the difference between maximum and minimum value of dXCO\({}_{2}\) from five years GM-XCO\({}_{2}\) data from Jun 2009 to May 2014. An example of fitting GM-XCO\({}_{2}\) using Equation (1) in a selected grid (46.5\({}^{\circ}\)N, 111.5\({}^{\circ}\)E) is shown in Figure 2.
## 2 Materials and Methods
### Statistical Analyses  
Statistical analyses were done using SPSS v. 26 (IBM, New York, NY, USA). Risk factors for traumatic and overuse injuries were analyzed separately. The eight test time points resulted in repeated measurements (up to eight) in the athletes. Ordinary binary regression analysis is expected to produce biased estimates of the regression analysis. Therefore, generalized estimating equations (GEE) were used to model the binary outcome in which 0 represents no injury, and 1 represents at least one injury. GEE was preferred compared to ordinary binary regression analysis since GEE accounts for the inter-correlation of repeated observations in the same subjects over time and was used in the injury context previously [27]. A working correlation matrix with exchangeable structures was used. The first step of the primary statistical analysis consisted of a series of simple GEE models using all potential associated factors separately. In detail, the factors tested on association with injury risk were sex, relative age quarter, age, maturity group, puberty status, core flexion to extension strength ratio group, height percentile group, and weight percentile group. The odds ratio (OR) including 95% confidence intervals (95% CI) was calculated for each factor. In a second step of the primary statistical analyses, a multiple GEE model with all significant associated factors in the simple analysis was conducted. Quasi-information criterion (QIC) was given for the multiple GEE model. A lower QIC value indicates a better model fit [28].  
As an additional analysis, a series of multiple binary regression analyses with stepwise backward elimination was performed separately for each test time point. The regression analysis is regarded as secondary and as a supplement to the GEE, since it cannot consider repeated observations across athletes. \(p\)-values \(<\) 0.05 were considered as significant and \(p\)-values ranging from 0.05 to 0.10 were considered as tendencies.
## 4 Discussion
### Limitations and Future Directions  
While our study expands the current knowledge by exploring evaluations of different LLMs and comparing them with qualitatively developed Personas in two contexts and in a relatively large sample of experts, some limitations need to be mentioned. While the Persona Perception Scale [34] is an instrument with generally good psychometric properties, the consistency and credibility subscales exhibited a low internal consistency for evaluating LLM-generated Personas, most likely due to the exclusion of items that referred to the picture of the Persona that our LLM-generated descriptions did not entail. Therefore, some caution is advised in interpreting the results regarding these two dimensions of Persona perception.  
In addition, while LLMs are imposed by heavy safety restrictions that aim to prevent misuse, the generation of specific Personas might be limited, particularly those exhibiting harmful or unethical behaviors. Namely, the limited ability to study negative Personas may, for instance, hamper research on detecting and mitigating harmful AI behaviors. Moreover, the inability to generate certain Personas could create blind spots in safety testing and the evaluation of AI systems' responses to problematic interactions. Future work should consider methods to ethically study these edge cases while maintaining appropriate safeguards.  
Furthermore, based on the results of this study, we corroborate further research implications for LLM-generated Personas put forward by [PERSON] et al. [17]. These include verifying the LLM-generated Persona descriptions using subject-matter experts, adjusting the prompts if observing challenges, and comparing LLM-generated Personaswith additional sources when possible, such as with Personas developed via the qualitative way, as performed in the present study. Additionally, we recommend exploring the use of various LLMs for developing the Persona description in question, as they can provide qualitatively different Persona descriptions.
## 2 Materials and Methods
### Nanotoxicological Bioassays
#### 2.3.5 Hemocompatibility Assay  
Human red blood cells were extracted from the peripheral blood of a healthy donor with normal cell morphology. The hemocompatibility assessment followed ISO 10993-4 standards. Blood was collected in sodium heparin-containing tubes (BD USA), red blood cells (RBCs) were isolated by centrifugation, then cells were rinsed with 10 mL of 150 mM NaCl, followed by washes with 1x PBS. The RBCs were then diluted 1:50 in PBS, and 100 \(\upmu\)L of the solution was placed into each well of a 96-well plate. Fe\({}_{3}\)O\({}_{4}\) or Fe\({}_{3}\)O\({}_{4}\)-Ag nanoparticles were added at 0.1, 1, 5, 10, 25, 50, and 100 \(\upmu\)g/mL concentration. After incubation for 1 h at 37 \({}^{\circ}\)C, samples were centrifuged at 10,000 rpm for 5 min. The absorbance of the supernatant was measured at 550 nm. The hemolysis percentage was calculated using the formula: Hemolysis (%) = (OD 550 nm sample \(-\) OD 550 nm negative control)/(OD 550 nm positive control \(-\) OD 550 nm negative control) \(\times\) 100. PBS-treated RBCs served as the negative control, showing no hemolysis. RBCs treated with 0.1% Triton X-100 in PBS were the positive control, inducing 100% hemolysis. Background signal at 550 nm was subtracted for each experiment. According to ISO standards, acceptable hemolysis of RBCs is \(\geq\)5%.
## 4 Results and Discussion  
### Validation of Simulation Models  
For the simulation validation, the thermal environment of the meeting room (see Figure 3) predicted by TRNSYS was compared with that observed in the experiment. The outdoor conditions and the indoor thermal environment were measured with a temperature  
Figure 6: Outdoor conditions considered in the simulation.  
and humidity sensor (Testo 610), and the measurement accuracy was within 0.5 \({}^{\circ}\)C for the temperature and 2.5% for the relative humidity.  
The ambient conditions used for the validation of the simulation models are shown in Figure 7a. In the simulation, outside air conditions similar to those of the experiments conducted for 4 h from 13:00 to 17:00 on 9 July 2019, were considered for comparing the operation results. In addition, the target temperature was set to 27 \({}^{\circ}\)C.  
Figure 7b compares experimental and simulated changes in the indoor thermal characteristics of the meeting room after the operation of the HDCS. Both scenarios enter the steady-state operation mode through a transient section of approximately 30 min. The steady-state temperature was accurately predicted (within 0.3 \({}^{\circ}\)C).  
As shown in Figure 7b, it takes about 30 min to reach the steady state after start-up. The simulation reproduced the changes in the indoor thermal environment from the initial conditions to the steady state very well. At all moments within the initial 30-min transient interval, the difference between the experiment and the simulation is within the measurement error range for both temperature and humidity.  
In the steady state, the temperature was predicted well within the measurement error (0.5 \({}^{\circ}\)C) by the simulation (compare the red symbol and line in Figure 7b), but the humidity was underpredicted by the simulation in the initial steady-state region. The maximum error occurred a little over 1 h after the simulation start-up, and the measured humidity ratio of 10.5 g/kg was predicted as 9.6 g/kg. When driving for 4 h, the humidity ratio obtained in the simulation converged to the same value as in the experiment. The TRNSYS simulation results for the indoor thermal environment (presented later) were extracted from the results of operation for 6 h from start-up.
## 2 Methods
### Cluster Validation Indices  
From now on, for a given number of clusters \(K\), we denote, by \(\{\mathbf{c}_{k}\}\) and \(\{\mathbf{C}_{k}\}\), \(k=1,\ldots,K\), the best prototypes and divisions obtained after a fixed number of repeated applications of Algorithm 1. When \(K=1\), we denote the prototype, i.e., the mean, median, or spatial median, of the whole data with \(m\). Moreover, we let \(\mathcal{J}_{K}\) denote the corresponding clustering error over the whole data and \(\mathcal{J}_{K}^{k}=\sum_{\mathbf{x}_{i}\in\mathbf{C}_{k}}\|\mathbf{x}_{i}- \mathbf{c}_{k}\|_{p}^{d}\) to refer to the corresponding final within-cluster errors. Cluster validation considers the quality of the result of a clustering algorithm, attempting to find the partition that best fits the nature of the data. The number of clusters, given as a parameter for many clustering algorithms (such as the ones presented in Section 2.1), should be decided based on the natural structure of the data. Like the best clustering solution, the number of clusters is also not always clear and many 'right' answers can exist (see, e.g., ([PERSON] et al., 2016), Figure 5). The number can also depend on the resolution, i.e., whether the within- and between- cluster separabilities are considered globally or locally. Here, we focus on the CVIs based on the internal criteria. The validation indices measure how well the general goal of clustering--high similarity within clusters and high separability between clusters--is achieved. These are considered with measures of within-cluster (Intra) and between-cluster (Inter) separability, for which lower and higher values are better, respectively. Normally, a division between Intra and Inter is made and the optimal value is at the minimum or maximum, based on the order of the division. In Table 1, the best internal validation indices (as determined for the K-means-type of clustering in ([PERSON] et al., 2016), augmented with ([PERSON] et al., 2016)) are introduced in a general fashion for the \(l_{p}^{q}\)-norm setting. All except one of the indices have been modified in such a way that the optimal number of clusters can be found at the minimal value. Only the Wemmert-Gancarski index, which has a unique pattern of the general formula, is given in the original form, where the maximum value indicates the optimal number of clusters. In Table 1, if the formula that combines Intra and Inter depends on the generated clusters, then this is indicated with the corresponding parameters. As can be seen from the formulas of the indices, there are a lot of similarities in how different indices measure the within- and between-cluster separability. For example, the clustering error straightforwardly measures the similarity within clusters and, therefore, almost all of the indices include it in their measure of Intra. In case of between-cluster separability, it is common to measure, for instance, the distance between cluster prototypes or between cluster prototypes and the whole data prototype. The rationale behind the index structures and their more detailed descriptions can be found in the original articles.
## 4 Calibration Experiment
### Repeated Positioning and Calibration Experiment of Single Robot
The definitions of the position error \(\Delta_{p}\) and the angle error \(\Delta_{a}\) are followed:  
\[\Delta_{p}=\sqrt{\left(p_{x_{1}}-p_{x_{2}}\right)^{2}+\left(p_{y_{1}}-p_{y_{2 }}\right)^{2}+\left(p_{z_{1}}-p_{z_{2}}\right)^{2}} \tag{12}\]  
\[\Delta_{a}=\sqrt{\left(\alpha_{1}-\alpha_{2}\right)^{2}+\left(\beta_{1}-\beta _{2}\right)^{2}+\left(\gamma_{1}-\gamma_{2}\right)^{2}} \tag{13}\]  
In Formula (12), \(p_{x_{1}}\) and \(p_{x_{2}}\) are the distances on \(X\)-axis of the two groups data. \(p_{y_{1}}\), \(p_{y_{2}}\) and \(p_{z_{1}}\), \(p_{z_{2}}\) are the distances on \(Y\)-axis and \(Z\)-axis. In Formula (13), \(\alpha_{1,2}\), \(\beta_{1,2}\) and \(\gamma_{1,2}\) are Euler angles of the two groups data. According to Formula (2), the calculation formulas of Euler angles \(\alpha\), \(\beta\), \(\gamma\) can be obtained:  
\[\alpha=\tan^{-1}(n_{21}/n_{11}) \tag{14}\]  
\[\beta=\sin^{-1}(-n_{31}) \tag{15}\]  
\[\gamma=\tan^{-1}(n_{32}/n_{33}) \tag{16}\]Based on Formulas (14)-(16), Euler angles of the two groups data can be obtained by \(H_{B_{1}^{1}}^{G}\) and \(H_{B_{1}^{1}}^{G}\). \[[\alpha_{1},\beta_{1},\gamma_{1}]=[86.087^{\circ},-4.416^{\circ},2.070^{\circ}]\]  
\[[\alpha_{2},\beta_{2},\gamma_{2}]=[86.708^{\circ},-3.038^{\circ},1.837^{\circ}]\]  
Though Formulas (12) and (13), the error of this calibration experiment \(\Delta\) can be obtained:  
\[\Delta=[5.603\;\text{mm},1.529^{\circ}]\]
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 2 Background
### Applicability of TCP in the IoT
#### 2.2.1 Maximum Segment Size  
During the TCP handshake a host can announce the maximum TCP segment size it can accept. The host can arbitrarily choose the value of the Maximum Segment Size (MSS). The option does not incur any additional overhead in the TCP header. Smaller segment sizes are useful when constrained devices only have limited memory resources. Long segments require not only big TCP send and receive buffers but also increase the 6 LoWPAN fragmentation rate. Constrained devices need to allocate additional buffers to store the 6 LoWPAN fragments; the loss of a single fragment causes a retransmission of the entire TCP segment.
## 3 DCPSO with Cooperation
### Distributed Cooperative Particle Swarm Optimization with Cooperation  
The PH path is adopted as the parameterized path of a UAV in this paper. According to the theory of the PH curve, a path is determined by the lengths of direction vectors, namely \(m_{0}\) and \(m_{1}\), under certain poses at initial and final locations. So, the path-planning for \(N\) UAV formation rendezvous means getting each UAV a pair of \((m_{0},\quad m_{1})\) so that the corresponding PH path can meet those constraints (including safety requirements, kinematic constraints, cooperation requirements, and others) and the total cost of all paths reaches the minimum or second minimum.  
Here, the possible solutions of \(N\) UAVs are regarded as \(N\) sub-swarms or subpopulations. A distributed cooperative particle swarm optimization (DCPSO) with cooperation mechanism is proposed to optimize the \(N\) PH paths cooperatively. The particles in each sub-swarm update their positions and velocities and finish their fitness computations independently, which is the same process as the standard PSO. In order to achieve cooperation within UAVs, each sub-swarm communicates with the others to transmit their representative individuals, then \(N\) cooperative groups are formed. Then, in each new-forming group, fitness of particles in the current subpopulation is modified and the personal and group best locations are also modified. Therefore, particles in each sub-swarm will move to the optimal or suboptimal solutions with cooperation between UAVs. The flow chart of the DCPSO with cooperation is given in Figure 2. It can be seen that information interaction between UAVs only happens in forming cooperative groups and fitness modification, which means the proposed algorithm has a distributed structure. To improve searching efficiency, an elite keeping strategy is adopted.  
Figure 2: Flow chart of distributed cooperative particle swarm optimization (DCPSO) with cooperation.
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 3 Proposed Method
### Reversible Information Embedding  
#### 3.3.1 Difference Image Compression  
During the watermark embedding stage, the watermark information \(W_{o}\) is embedded into the cover image \(I_{o}\) to obtain the reconstructed watermarked image \(I_{e}\). To recover \(I_{o}\), the difference image \(D\) between \(I_{e}\) and \(I_{o}\) is used as compensation information and embedded into \(I_{e}\) using a reversible watermarking method. Ideally, we can losslessly compress \(D\) to obtain its corresponding binary sequence and then reversibly embed it into \(I_{e}\) to obtain the watermarked image \(I_{m}\). In the absence of attacks, the encoded information can be extracted from \(I_{m}\) and decompressed to obtain \(D\) for lossless restoration of \(I_{o}\). However, lossless compression of \(D\) typically results in a large amount of data, leading to the low visual quality of \(I_{m}\). To reduce the amount of compensation information, We use a combination of DCT transformation and Huffman coding to compress \(D\). Specifically, we divide \(D\) into blocks of size \(8\times 8\) and apply DCT transformation to each block to obtain the DCT coefficients \(F\), determined by the following equation,  
\[\begin{split} F_{uv}=\frac{1}{4}C(u)C(v)\Big{[}\sum_{x=0}^{7} \sum_{y=0}^{7}I(x,y)\cos\Big{[}\frac{(2x+1)u\pi}{16}\Big{]}\cos\Big{[}\frac{(2 y+1)v\pi}{16}\Big{]}\Big{]}\\ \text{  
coefficients excluding the DC coefficient. Since the values of \(D\) are usually small, the DCT coefficients need to be amplified and then quantized using the quantization matrix \(Q\) and the scaling factor \(n\), as shown in the following equation.  
\[F_{q}=round\left(\frac{F_{up}\times 2^{n}}{Q}\right) \tag{3}\]  
The quantized DCT coefficients \(F_{q}\) and the scaling factor \(n\) are Huffman coded to obtain the binary sequence \(D_{c}\), as shown in the following equation.  
\[D_{c}=H\big{(}F_{q},n\big{)} \tag{4}\]  
where \(H\) represents the Huffman coding function. Since quantization is a loss compression process, there may be differences between the decompressed difference image \(D^{\prime}\) and \(D\). To eliminate these differences, we calculate the error matrix \(e=D-D^{\prime}\) and directly add it to the watermarked image \(I_{e}\) to obtain \(I_{e}^{\prime}\).
###### Abstract  
The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.  
CNN; human action recognition; spatiotemporal feature; embedded system; real-time