esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### GPP Prediction Model  
This study utilizes GPP data from four distinct sites, along with corresponding EVI, NDVI, NIRv, SIE, and PAR data. All datasets underwent necessary preprocessing, including the removal of NaN values, to ensure data quality and consistency. The data were aggregated into eight-day composites, resulting in a total of 753 samples for modeling.  
To predict GPP, we employed a generalized linear model (GLM) combined with a mixed-effects model [49; 50]. The GLM offers flexibility in handling different types of dependent variables (e.g., continuous, binary, count), making it suitable for our GPP prediction task. The mixed-effects model accounts for potential heterogeneity across different sites, thereby effectively capturing both fixed and random effects. The model structure is defined as follows:  
\[\mathrm{Y_{ij}}=\beta_{0}+\beta_{1}\mathrm{EVI_{ij}}+\beta_{2}\mathrm{NDVI_{ij }}+\beta_{3}\mathrm{NIRv_{ij}}+\beta_{4}\mathrm{SIF_{ij}}+\beta_{5}\mathrm{PAR _{ij}}+\mathrm{b_{i}}+\epsilon_{ij} \tag{4}\]  
where \(\mathrm{Y_{ij}}\) represents the GPP observation for the ith site at the jth time point; \(\beta_{0}\) is the intercept; \(\beta_{1}\),\(\beta_{2}\), \(\beta_{3}\), \(\beta_{4}\), and \(\beta_{5}\) are the fixed effect coefficients for EVI, NDVI, NIRv, SIF, and PAR, respectively; \(b_{i}\) is the random intercept for the ith site; and \(\in_{ij}\) is the error term. We utilized the maximum likelihood estimation (MLE) method for parameter estimation in the mixed-effects model.  
To assess the predictive performance and explanatory power of the model, we implemented the following steps.  
Data Splitting: The dataset was randomly divided into a training set (600 samples) and a test set (153 samples), ensuring a proportional representation of each site in both sets.  
Model evaluation: The performance of the model is primarily evaluated by the proportion of the relative root mean square error (rRMSE) to the sample mean [51].  
\[\mathrm{rRMSE}=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{n}}\div \overline{y} \tag{5}\]  
Here, \(y_{i}\) is the observed value, \(\hat{y_{i}}\) is the predicted value of the metric, and \(\overline{y}\) is the average of the observed value. The rRMSE measures the proportion of deviation between observed and true values. The smaller the rRMSE value is, the smaller the deviation between the predicted and true values, indicating higher model accuracy. Additionally, we compared the annual GPP predicted by the model with the flux observations to further validate the model's accuracy.  
These methodologies were aimed at constructing a robust and efficient GPP prediction model, providing a comprehensive understanding of the influence of various vegetation indices on GPP.
## 7 Conclusions
Moreover, interdisciplinary collaborations among researchers from different fields, such as materials science, electrical engineering, computer science, and physics, are essential to accelerate RIS research and development. Furthermore, future RIS research should also consider the impact of RISs on society, including ethical and regulatory implications, as RISs may have significant implications for privacy, security, and social equity. Therefore, it is crucial to involve stakeholders from diverse backgrounds in RIS research, including industry, academia, government, and civil society, to ensure the responsible and sustainable development of RISs. Finally, the potential impact of RISs on future technologies is immense, and the ongoing research and development in this field hold great promise for advancing various fields and improving our daily lives. With continued efforts and collaborations, RISs can potentially revolutionize how we communicate, sense, and interact with the world around us. This work was partially supported by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brazil (CAPES) and RNP, with resources from MCTIC, under Grant Nos. 01250.075413/2018-04, 01245.010604/2020-14, and 01245.020548/2021-07 under the Brazil 6G project of the Radiocommunication Reference Center (Centro de Referencia em Radiocomunicacoes--CRR) of the National Institute of Telecommunications (Instituto Nacional de Telecomunicacoes--Inatel), Brazil; by Huawei, under the project Advanced Academic Education in Telecommunications Networks and Systems, Grant No. PPA6001 BRA2302110257684; by Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG) via grant number 2070.01.0004709/2021-28; by FCT/MCTES through national funds and when applicable co-funded EU funds under the Project UIDB/EEA/50008/2020; and by the Brazilian National Council for Research and Development (CNPq) via Grant Numbers 313036/2020-9 and 403827/2021-3; and by the MCTI/CGI.br and the Sao Paulo Research Foundation (FAPESP) under Grant No. 2021/06946-0. The author declares no conflict of interest.
## 2 Overview of Multi-Link Operation in IEEE 802.11 be WLANs
### Multi-Link Transmission and Reception  
IEEE 802.11 defines a traffic identifier (TID) to classify a packet for differentiated service. The TID is represented as a four-bit number (0-7) identifying the desired quality of service (QoS) for the traffic. In MLO, the TID is used to determine which link(s) to use for traffic with a specific QoS. If a specific TID is mapped to a set of links, then any link within that set can be used to transmit data frames from that TID. By default, after multi-link setup, all TIDs are mapped to all established links. However, the TID-to-link mapping can specify either the same or different link sets for each TID. After multi-link setup, the TID-to-link mapping can be updated through negotiation.
### Background and Related Work
#### 1.1.3 IEEE 802.11 ad Network Performance, Transceivers and Radios  
System-level simulations for wireless fiber using IEEE 802.11 ad are presented in [45], A receiver design for an IEEE 802.11 ad radio is presented in [46], and a configurable transmitter architecture is presented in [47]. A fully integrated transceiver chipset is presented in [48]. In [49], the authors investigate the performance of different modulation and coding schemes (MCS) in a Gaussian noise and multipath fading channel, concluding that the single-carrier modes (MCS 1 to 12) outperform the orthogonal frequency division multiplex (OFDM) modes in most cases. The performance of the IEEE 802.11 ad medium access control (MAC) layer is discussed in [50]. Throughput measurements are presented in [51; 52], and transmission control protocol (TCP) performance in an office room is discussed in [53]. In [54], a channel sounder is presented for mmWave radio channel modeling and IEEE 802.11 ad system validation, which is being used by different research institutions [55; 56; 57; 58; 59; 60; 12].
Remote Sens._**2015**, \(7\), 7597-7614; doi:10.3390/rs70607597  
_remote sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 3, [PERSON] 1 and [PERSON] 1**  
1 Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China; E-Mails: [EMAIL_ADDRESS] (H.G.); [EMAIL_ADDRESS] (Q.L.)  
2 German Remote Sensing Data Centre (DFD), German Aerospace Centre (DLR), D-82234 Wessling, Germany; E-Mail: [EMAIL_ADDRESS]  
3 Department of Geography, University of South Carolina, Columbia, SC 29208, USA; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-10-8217-8102; Fax: +86-10-8217-8177.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 11 February 2015 / Accepted: 2 June 2015 / Published: 9 June 2015_  
**Abstract:** Understanding the spatial and temporal dynamics of vegetation is essential in drylands. In this paper, we evaluated three vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Surface-Reflectance Product in the Xinjiang Uygur Autonomous Region, China (XUAR), to assess index time series' suitability for monitoring vegetation dynamics in a dryland environment. The mean annual VI and its variability were generated and analyzed from the three VI time series for the period 2001-2012 across XUAR. Two phenological metrics, start of the season (SOS) and end of the season (EOS), were detected and compared for each vegetation type. The mean annual VI images showed similar spatial patterns of vegetation conditions with varying magnitudes. The EVI exhibited high uncertainties in sparsely vegetated lands and forests. The phenological metrics derived from the three VIs are consistent for most vegetation types, with SOS and EOS generated from NDVI showing the largest deviation.  
**Keywords:** MODIS; vegetation index; dryland; vegetation dynamics; time series; phenology
## 2 Related Work  
IoT-cloud, in other words, the sensor-cloud, has been recently proposed as a promising approach that has been receiving great interest [1, 2, 3, 5, 6, 7] from researchers. Although there are several main studies discussing architecture designs of IoT-cloud, their basic design is quite similar. In the IoT-cloud, physical sensors are virtualized into virtual sensors on the cloud. Physical sensor nodes are responsible for sensing and forward their sensing data to the cloud. The IoT-cloud provides sensing services directly to multiple users/applications through virtual sensors. Users/ applications request for sensing services on demand from the IoT-cloud. A number of initial studies have been conducted toward detailed design for the IoT-cloud.  
In [5], [PERSON] et al. propose an architecture for the sensor-cloud based on virtual sensors that are built on top of physical sensors. The purposes are for enhancing sensor management capability and enabling sensing data shared across multiple applications. In [2], [PERSON] et al. propose two modelsfor IoT-cloud including a data-centric model and a device-centric model. In the former, the IoT-cloud offers only data to its users without exposing lower physical networks. In this model, the cloud collects physical sensing information and structures them using a uniform format to deliver to end users. In the latter, the IoT-cloud allows its users to customize virtual sensing infrastructure based on their purposes.  
To optimize sensing data delivery from physical wireless sensor networks to the IoT-cloud, several studies have been investigating the issue. [PERSON] et al. [10] design a framework using a zero-sum model for sensors to select an optimal gateway to forward their data to the cloud. The gateway selection model is mainly built based on the available bandwidth. [PERSON] et al. [11] discuss an optimal decision rule to select intermediate nodes for sensors to forward sensing data to the cloud. The decision rule is made based on different factors including Euclidean distance and energy budget. In another work [12], the same authors propose using internal and external caching techniques to save energy.  
For operations of the IoT-cloud, an efficient composition method for virtual sensors is required. In [8], the authors propose two different approaches to efficiently virtualize physical sensors into virtual sensors. An optimal pricing model for the IoT-cloud is also introduced in [9]. According to the pricing model, the price of a sensing service is proportional to a number of resources it consumes. As sensors are resource constrained devices, efficient usage of sensor resources is very important to make sensing services competitive.  
To further optimize resource consumptions of sensors, this paper discusses a location-based interactive model for the IoT-cloud, which enables on-demand interaction between cloud and WSNs-based location of mobile users, and exploits cloud capabilities to save energy for sensor nodes by on-demand scheduling.
## 4 Conclusions  
This paper develops two networks for measuring the strain field in the case of crack occurrence during laser beam welding. The first network StrainNetR uses ResNet as the feature extractor. The second network StrainNetD applies DenseNet, which is an improvement of ResNet with more tight connections. The networks are trained and tested in the real welding dataset collected using the CTW test. The strain prediction results demonstrate that StrainNetD is slightly better than StrainNetR in terms of the AEE. On the other hand, the feature maps of DenseNet are larger, and it requires more memory accesses. The processing time of StrainNetD is nearly twice that of StrainNetR. Compared with traditional methods, both networks can achieve approximately real-time strain prediction within the tolerable error, which enables the timely identification of crack defects in the welding process.  
We have shown early results in the prediction of the strain field based on deep learning methods, and further optimization is still necessary. For instance, the data set critically affects the final results. The data in this paper is obtained under several different strain rates. In order to enrich data diversity and improve model robustness, synthetic data are a good choice. Generative adversarial networks (GAN) [46] are a widely used technology to generate desired images. We are considering to use a GAN to generate more weld defect images. On the other hand, the accuracy of strain prediction can be further improved. Transformer networks [47], which have achieved great success in natural language processing, have been gradually explored in computer vision tasks. Next, we plan to combine or replace the CNN model with a transformer model to enhance the accuracy and training efficiency.  
Figure 10: Visual examples of predicted strain fields.  
**Author Contributions:** Conceptualization, W.H. and N.B.; Methodology, W.H. and N.B.; Software, W.H. and N.B.; Validation, W.H.; Writing--original draft, W.H. and N.B.; Writing--review & editing, A.G., M.R. and K.W.; Supervision, K.W.; Project administration, N.B. and K.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the China Scholarship Council file Nr. 202008610227 and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) project Nr. 434946896 and project Nr. 465316565.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyzes, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 System Structures
### IMU Factor and Bias Factor Modeling
\begin{split}\mathbf{\alpha}_{B_{k+1}}^{B_{k}}=\iint\limits_{t\in[k,k+ 1]}(\mathbf{R}_{B_{k}}^{B_{t}}(\hat{\mathbf{a}}_{t}^{B}-\mathbf{b}\mathbf{a}_{k}- \mathbf{n}_{\mathbf{a}}))dt^{2}\\ \mathbf{\beta}_{B_{k+1}}^{B_{k}}=\int\limits_{t\in[k,k+1]}(\mathbf{R}_ {B_{k}}^{B_{t}}(\hat{\mathbf{a}}_{t}^{B}-\mathbf{b}\mathbf{a}_{k}-\mathbf{n}_{ \mathbf{a}}))dt\\ \mathbf{\gamma}_{B_{k+1}}^{B_{k}}=\int\limits_{t\in[k,k+1]}\frac{1}{2} \mathbf{R}_{B_{k}}^{B_{t}}\otimes(\hat{\mathbf{\omega}}_{k}^{B}-\mathbf{b}\mathbf{ g}_{k}-\mathbf{n}_{\mathbf{g}})dt\end{split}  \tag{7}\]  
where \(\mathbf{R}_{B_{k}}^{B_{t}}\) represents the transfer matrix from the body frame at time \(k\) to time \(t\). Then, the generated measurements by IMU pre-integration can be defined as follows:  
\[\mathbf{Z}_{B_{k+1}}^{B_{k}}=[\mathbf{\alpha}_{B_{k+1}}^{B_{k}},\mathbf{\beta}_{B_{k+1 }}^{B_{k}}\mathbf{\gamma}_{B_{k+1}}^{B_{k}},\mathbf{b}\mathbf{a}_{k+1},\mathbf{b} \mathbf{g}_{k+1}]^{\text{T}} \tag{8}\]  
According to Figure 2, the factor node of IMU can be constructed as a binary factor, and the residual function \(\mathbf{r}_{imu}(\mathbf{Z}_{B_{k+1}}^{B_{k}},\mathbf{X}_{k})\) can be defined as follows:  
\[\mathbf{r}_{imu}(\mathbf{Z}_{B_{k+1}}^{B_{k}},\mathbf{X}_{k})= \begin{bmatrix} \delta\mathbf{\alpha}_{B_{k+1}}^{B_{k}}\\ \delta\mathbf{\gamma}_{B_{k+1}}^{B_{k}}\\ \delta\mathbf{\beta}_{B_{k+1}}^{B_{k}}\\ \delta\mathbf{b}\mathbf{a}\\ \delta\mathbf{b}\mathbf{g}\end{bmatrix} = \begin{bmatrix}\mathbf{R}_{W}^{B_{k}}( \mathbf{P}_{B_{k+1}}^{W}-\mathbf{P}_{B_{k}}^{W}-\mathbf{v}_{B_{k}}^{W}\Delta t -\frac{1}{2}\mathbf{g}_{k}^{W}\Delta t^{2})-\mathbf{\alpha}_{B_{k+1}}^{B_{k}}\\ 2[(\mathbf{\gamma}_{B_{k+1}}^{B_{k}})^{-1}\otimes(\mathbf{R}_{B_{k}}^{W})^{-1} \otimes\mathbf{R}_{B_{k+1}}^{W}]\\ \mathbf{R}_{W}^{B_{k}}(\mathbf{v}_{B_{k+1}}^{W}-\mathbf{v}_{B_{k}}^{W}\Delta t )-\mathbf{\beta}_{B_{k+1}}^{B_{k}}\\ \mathbf{b}\mathbf{a}_{k+1}-\mathbf{b}\mathbf{a}_{k}\\ \mathbf{b}\mathbf{g}_{k+1}-\mathbf{b}\mathbf{g}_{k}\end{bmatrix}  \tag{9}\]  
The errors of IMU are corrected during the global factor graph optimization process.
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
## 7 Implementation
### Package Size  
Given the format that has been used to represent the elements that are part of the proposed scheme, optimal package sizes have been achieved.  
The message size is given by the dimension of the graph used to represent the network. The more nodes a graph has, the more space is needed. This involves the segments being larger, resulting in larger package sizes.  
Therefore, we have analyzed which sizes per segment are recommended depending on the number of nodes in the graph, which are shown in Figure 5.  
This clearly reveals a polynomial tendency of order two that represents the relationship between the size of the segments and the number of nodes of the graph that represents the network. Thus, the following polynomial function (see Equation (4)) has been calculated that defines the data, so that segment sizes for graphs with more nodes can be estimated.  
\[y=0.9765x^{2}+5.8046x-1.1513 \tag{4}\]  
Thus, 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 would require a package size of 16 kilobytes without including the size of the shared secret. Thus, in order to send a message in a single package securely by using this scheme, we have used six challenges for 50 nodes, and the sent message has an overhead of only 16 kilobytes.
## 3 Results and Discussion
### Protein Solubility  
Protein solubility is used as the primary indicator of functionality and is subjected to the state of denaturation or aggregation. The percentages of soluble proteins of the UPG and the CG are shown in Table 1. Concerning the CG, solubility significantly increased for all gelatins except for UPG-52, and no significant differences were found between the rest of the treatments. The increase in the solubility of proteins has been related to changes in conformation that are caused by cavitation (as mentioned above), by the exposure of more internal hydrophilic residues to the water surface, by the interruption of hydrophobic interactions in insoluble aggregates, and also by changes in the electronic environment of the acid residues related to the effect of pH. This behavior in protein solubility has been previously reported in HIU studies, although it has also been reported that the prolongation of these treatments can generate insoluble aggregates [5; 9; 10; 22].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Treatment** & **pH** & **Soluble** & **Surface** & **ABTS (\(\mu\)M** & **FRAP (\(\mu\)M** \\  & **Protein (\%)** & **Hydrophobicity** & **TE/mg gelatin)** & **TE/mg gelatin)** \\ \hline Control & 7.00 \(\pm\) 2.0 \({}^{\mathrm{a}}\) & 27.6 \(\pm\) 8.3 \({}^{\mathrm{b}}\) & 62.1 \(\pm\) 7.7 \({}^{\mathrm{c}}\) & 539 \(\pm\) 73 \({}^{\mathrm{c}}\) & 112 \(\pm\) 32 \({}^{\mathrm{c}}\) \\ UPG-42 & 6.40 \(\pm\) 0.2 \({}^{\mathrm{b}}\) & 43.6 \(\pm\) 1.2 \({}^{\mathrm{a}}\) & 71.7 \(\pm\) 6.5 \({}^{\mathrm{b}}\) & 301 \(\pm\) 88 \({}^{\mathrm{d}}\) & 244 \(\pm\) 50 \({}^{\mathrm{b}}\) \\ UPG-52 & 6.30 \(\pm\) 0.2 \({}^{\mathrm{b}}\) & 27.4 \(\pm\) 3.9 \({}^{\mathrm{b}}\) & 75.2 \(\pm\) 4.9 \({}^{\mathrm{b}}\) & 209 \(\pm\) 28 \({}^{\mathrm{d}}\) & 293 \(\pm\) 15 \({}^{\mathrm{b}}\) \\ UPG-71 & 6.08 \(\pm\) 0.3 \({}^{\mathrm{b}}\) & 43.7 \(\pm\) 1.6 \({}^{\mathrm{a}}\) & 66.5 \(\pm\) 4.0 \({}^{\mathrm{b}}\) & 698 \(\pm\) 65 \({}^{\mathrm{b}}\) & 385 \(\pm\) 143 \({}^{\mathrm{b}}\) \\ UPG-84 & 6.10 \(\pm\) 0.2 \({}^{\mathrm{b}}\) & 42.0 \(\pm\) 2.9 \({}^{\mathrm{a}}\) & 81.4 \(\pm\) 6.3 \({}^{\mathrm{a}}\) & 1001 \(\pm\) 38 \({}^{\mathrm{a}}\) & 732 \(\pm\) 91 \({}^{\mathrm{a}}\) \\ \hline \hline \end{tabular} Values are the mean of n = 6 \(\pm\) the standard deviation. Different superscripts in each column indicate significant differences between ultrasonic pulsed gelatin (\(p<0.05\)).  
\end{table}
Table 1: Antioxidant activity, pH, protein solubility, and surface hydrophobicity of tilapia (_Orechromis niloticus_) skin gelatins that were treated with high-intensity ultrasound pulses.
# Automated Cart with VIS/NIR Hyperspectral Reflectance and Fluorescence Imaging Capabilities
## 2 Materials and Methods
### System Validation  
Dairy manure was acquired from the United States Department of Agriculture (USDA) Beltsville Dairy Facilities. Bagged baby spinach leaves were acquired from a local grocery store. Dollops of manure and leaves were placed on a non-reflective plastic surface which was then placed on an asphalt road surface. The cart was programmed to take images with a step size of 2.5 mm. The pixel resolution was about 1.5 mm/pixel. Reflectance line-scan images were acquired using an exposure time of 0.1 ms and a camera gain of 3000. Fluorescence images were acquired using a laser attenuation of 150 and a camera gain of 3000 and two different exposure settings: 5 ns delay with 25 ns gate width and 20 ns delay with 10 ns gate width.
# Removing Land Subsidence Impact from GPS Horizontal Motion in Tianjin, China
## 2 Materials and Models
### Models
#### 2.2.3 Training  
Since the target model aims to predict the horizontal motion as a single feature and the number of samples in the dataset is limited, after trial calculations, a shallow structure of 2 layers of GRUs was used in the model. The shallow model helps to avoid overfitting and speeds up training. The number of GRUs in each layer was set to 32, and finally, a Dense layer was used as the output layer. The structure of the model is shown in Figure 4.  
The model used the Adam optimizer and the mean square error (mse) as the loss function. The models were trained on the training sets, and their performance was then evaluated on the test sets.  
In the initial stages of model training, the number of training rounds was set to 20. Early stopping and the restoring of the best weights were used: when the performance of the model on the test set no longer improved, training was terminated early, and the weights of the model were restored to the optimal state. The loss curve during the training process is shown in Figure 5. Upon the completion of training for each target model, the mean square error on each training set and test set was found to be less than 0.03.