esa-sceva/satcom-chunk-collection huggingface dataset:
# Life-Cycle Assessment of Biofortified Productions: The Case of Selenium Potato
## 3 Results and Discussion
In order to exceed these methodological weaknesses, the intention is to implement new research increasing the number of farms under investigation and adopting the protocol of the Consortium \"Patata Italiana di Qualit \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Impact Category** & **Unit** & \(P\)**Se** & \(P\)**Conv** & **Difference \%** \\ \hline Global warming & kg CO\({}_{2}\) eq & 7584.8975 & 9384.7258 & \(-\)23.73 \\ Stratospheric ozone depletion & kg CFC11 eq & 0.1058336 & 0.1510982 & \(-\)42.77 \\ Ionizing radiation & kBq Co-60 eq & 173.72061 & 186.66713 & \(-\)7.45 \\ Ozone formation, human health & kg NOx eq & 27.854544 & 31.219681 & \(-\)12.08 \\ Fine particulate matter formation & kg PM\({}_{2.5}\) eq & 17.107643 & 19.37635 & \(-\)13.26 \\ Ozone formation, terrestrial ecosystems & kg NOx eq & 28.274082 & 31.6718 & \(-\)12.02 \\ Terrestrial acidification & kg SO\({}_{2}\) eq & 33.290309 & 42.953039 & \(-\)29.03 \\ Freshwater eutrophication & kg P eq & 1.8246778 & 1.8339521 & \(-\)0.51 \\ Marine eutrophication & kg N eq & 2.2722197 & 2.3203019 & \(-\)2.12 \\ Terrestrial ecotoxicity & kg 1,4-DCB & 51,387.258 & 57,943.158 & \(-\)12.76 \\ Freshwater ecotoxicity & kg 1,4-DCB & 545.14385 & 677.70278 & \(-\)24.32 \\ Marine ecotoxicity & kg 1,4-DCB & 665.66013 & 796.10175 & \(-\)19.60 \\ Human carcinogenic toxicity & kg 1,4-DCB & 179.5491 & 202.2912 & \(-\)12.67 \\ Human nonarcinogenic toxicity & kg 1,4-DCB & 11660.33 & 13,581.233 & \(-\)16.47 \\ Land use & m\({}^{2}\)a crop eq & 681.77309 & 673.79335 & 1.17 \\ Mineral resource scarcity & kg Cu eq & 85.130212 & 89.970775 & \(-\)5.69 \\ Fossil resource scarcity & kg oil eq & 1540.868 & 1803.6372 & \(-\)17.05 \\ Water consumption & m\({}^{3}\) & 2489.3864 & 2519.9408 & \(-\)1.23 \\ \hline \hline \end{tabular} (*) Our elaboration.  
\end{table} 
Table 2: Characterization factors and environmental impact per unit of stressor in selenium and conventional potato cultivation (*).
# PrECast: An Efficient Crypto-Free Solution for Broadcast-Based Attacks in IPv4 Networks
## 5 Setting up the PrECast Infrastructure
### The PrECast Algorithm
#### 5.1.2 A Host Obtained Its IP Address through a DHCP Server  
In this case, after booting the system, Host A will broadcast a DHCP-discover packet. This packet in general is sent to every host in the network. The DHCP server available in the host's IP network will unicast its reply to the host with the IP address and other relevant network information.  
We modify this traditional scheme to eliminate broadcasting due to the DHCP-discover process in the network as follows. Like the previous subsection, the switch port \(S_{A}\) receiving this DHCP-discover packet will note down Host A's MAC address and unicast to the DHCP server available in the network.  
The DHCP server information is known to the switch through the manual configuration process outlined in \"the boot strap process\". The switch will wait for the DHCP-offer message from the server. Whenever the DHCP server offers the lease of an IP address to the requested host, the switch will complete the MCAM table that will now contain the port ID (which is \(S_{A}\)), the MAC address and the IP address of Host A.  
Since there are only two different ways of obtaining the IP address for a host, in both schemes, the switch will transparently build its MCAM table.  
The state transition diagram of the MCAM table build-up process of a switch is presented in Figure 3.
## 4 Proposed Technique
### Dual Decomposition  
To obtain the optimal transmit power, the PA problem can be converted into Lagrangian optimisation and the problem can be solved using the Lagrangian dual method. It is worth noting that this method was used in the author's previous work to solve a similar problem [12]. First, the Lagrangian function of the PA optimisation problem is formed as in Equation (5). Then, the dual problem is developed accordingly as shown in Equation (6). Due to its convex characteristic, the dual decomposition method is used to solve the Lagrange function. Consequently, the dual problem is determined by solving the corresponding Karush-Kuhn-Tucker (KKT) conditions [32] as in Equation (8). Finally, the optimal transmit power, which is obtained by applying the dual decomposition method, is expressed in Equation (9).  
\[\begin{split}\mathcal{L}(p_{ji},b_{j})=&\sum_{i}B_{ o}\log_{2}\left(1+\frac{\sum_{j}a_{ji}b_{j}p_{ji}\delta_{ji}}{B_{o}N_{o}}\right)- \tau\sum_{j}\frac{b_{j}}{\lambda_{j}}\sum_{i}a_{ji}p_{ji}-\tau\sum_{j}b_{j}P_{ j}^{cir}+\tau\sum_{j}b_{j}\rho E_{j}\\ &-\alpha_{j}b_{j}\sum_{j}a_{ji}p_{ji}+\alpha_{j}b_{j}P_{j,max}- \beta_{j}SNR_{i,thr}+\beta_{i}\frac{\sum_{j}a_{ji}b_{j}p_{ji}\delta_{ji}}{B_{o} N_{o}}\end{split} \tag{4}\]  
Figure 4: EE observation of 100 realisations to compare between binomial and distance-based BS-Sw techniques for different scenarios.  
\[\begin{split}\mathcal{L}(p_{ji},b_{j})=&\sum_{i}B_{o} \log_{2}\big{(}1+\frac{\sum_{j}a_{ji}b_{j}p_{ji}\delta_{ji}}{B_{o}N_{o}}-\tau \big{(}\sum_{j}b_{j}\big{[}\frac{1}{\lambda_{j}}\sum_{i}a_{ji}p_{ji}+P_{j}^{ cir}-\rho E_{j}\big{]}\big{)}\\ &-\alpha_{j}b_{j}\big{[}\sum_{i}a_{ji}p_{ji}-P_{j,max}\big{]}- \beta_{i}\big{[}SNR_{i,thr}-\frac{\sum_{j}a_{ji}b_{j}p_{ji}\delta_{ji}}{B_{o}N_{ o}}\big{]}\end{split} \tag{5}\]  
where \(\alpha_{j}\) and \(\beta_{i}\) are nonnegative Lagrange multipliers and \(\tau\) denotes EE based on [PERSON]'s method [28] (see Appendix A).  
\[\min_{\alpha_{j},\beta_{i}\geq 0}\max_{p_{ji}}\mathcal{L}(p_{ji},b_{j}) \tag{6}\]  
To obtain an optimal power allocation for BS \(j\) serving user \(i\), Equation (5) is derived with respect to \(p_{ji}\) and equated to zero:  
\[\begin{split}\frac{d\mathcal{L}(p_{ji},b_{j})}{dp_{ji}}=& \frac{\sum_{i}B_{o}\sum_{j}a_{ji}b_{j}g_{ji}}{(\ln 2)\big{(}B_{o}N_{o}+ \sum_{j}a_{ji}b_{j}p_{ji}\delta_{ji}\big{)}}-\tau\sum_{j}\frac{b_{j}}{\lambda_{ j}}\sum_{i}a_{ji}-\alpha_{j}b_{j}\sum_{j}a_{ji}+\beta_{i}\frac{\sum_{j}a_{ji}b_{j} \delta_{ji}}{B_{o}N_{o}}\end{split} \tag{7}\]  
\[\begin{split}\frac{d\mathcal{L}(p_{ji},b_{j})}{dp_{ji}}=0\end{split} \tag{8}\]  
\[\begin{split} p_{ji}^{*}=\Bigg{[}\frac{B_{o}}{(\ln 2)\big{(} \frac{\tau}{\lambda_{j}}+\gamma_{j}+\alpha_{j}-\beta_{i}\frac{\sum_{j}\delta_{ ji}}{B_{o}N_{o}}\big{)}}-\bigg{(}\frac{B_{o}N_{o}}{\sum_{j}\delta_{ji}}\bigg{)} \Bigg{]}^{+}\end{split} \tag{9}\]  
where \(p_{ji}^{*}\) represents the optimal power allocation.
## 4 Understanding of TBM Performance
### Current Developments in Performance Prediction
#### 4.2.1 Theoretical Model  
Theoretical models in TBM performance prediction are based on the analysis of cutter breaking mechanisms and cutting forces acting on a single cutter. Indentation tests or indoor full-scale cutting tests are conducted to derive the cutter force balance equation. One well-known theoretical model is the CSM model, developed by the Colorado School of Mines [9]. Although the CSM model is primarily theoretical, the equation for calculating the baseline pressure in the crush zone is obtained through multiple regression analysis. The model was developed based on data from indoor full-scale linear cut tests. However, the predictions of TBM performance using the CSM model tend to be conservative due to discrepancies between the rock samples used in the indoor tests and the actual rock conditions encountered during TBM field excavations. Moreover, the model disregards the influence of rock discontinuities, such as joints, on TBM construction performance. Researchers have recognized this limitation and made improvements accordingly. [PERSON] [39] developed an enhanced PR prediction model based on TBM construction performance data and geological information from the Queens Tunnel in New York. The model incorporates additional indicators characterizing rock fragmentation and brittless into the original CSM model. Building upon this work, [PERSON] [40] developed a new model for predicting length per revolution, considering data from 11 TBM tunnels with a total length exceeding 60 km and taking into account the influence of rock parameters on TBM construction performance. This model significantly improves prediction accuracy. Indoor full-scale linear cut tests provide the closest approximation to the field conditions in which TBMs break rocks. [PERSON] [10] developed formulas to predict cutter breaking performance in laminated and flaky rocks based on simple theoretical analysis and tests, assuming that the primary cutter breaking mechanism is tensile damage. The Sanio model accounts for the effects of rock anisotropy and discontinuities on cutter breaking performance. However, it relies only on point load strengths in different directions to predict the degree of penetration per revolution, resulting in limited accuracy. Additionally, [PERSON] [41] utilized quantitative analysis to predict PR. The [PERSON] model addressed issues related to inconsistent magnitudes in regression analysis and fuzzy neural network modeling. However, it lacks detailed specific energy values for each rock type, and the machine efficiency factor must be carefully considered.
# Confidence Measures for Deep Learning in Domain Adaptation
## 4 Experimental Setup
### Domain Adaptation Network  
The experiments were based on the domain adaptation network proposed in [13], whose architecture is depicted in Figure 1.  
Specifically, the network employs a classifier \(C\), which is attached on the top of a feature extractor \(E_{S}\), and trained on the source dataset (Step 0). After that, \(S\), the generator of a conditional GAN (CGAN [45]), is used to learn the distribution of the features extracted by \(E_{S}\) from the source dataset (Step 1). Finally, a second GAN is used to train another feature encoder, \(E_{I}\), aimed at extracting the same feature distribution from both the source and the target domain (Step 2). In this step, \(E_{I}\) acts as the GAN generator and learns to match the feature distribution produced by \(S\) (its weights are not updated in this phase). The procedure aims at extracting features from the target domain that are indistinguishable from those extracted from the source domain. Therefore, based on the features extracted by \(E_{I}\), the classifier \(C\) can be used to classify images in the target domain. The training procedure proposed by [PERSON] et al. [13] was modified as follows:  
* Step 0. The validation set of the source domain is used to early stop the training.
* Step 1. The CGAN generator, \(S\), produces features for a given class; then, every 1000 training steps, \(C\) is engaged in classifying the features generated by \(S\), using the conditioning labels to evaluate the error and early stop the training.
* Step 2. Every 1000 iteration, the classifier \(C\) is used to classify the features extracted from \(E_{I}\). The proposed confidence measures are used to evaluate the performance of the classifier to early stop the training.  
Figure 1: Domain adaptation network architecture.  
In the task of SVHN \(\rightarrow\) MNIST, the network hyper-parameters (number of layers, initial weights, learning rate, batch size, etc.) are the same as in [13], while for CIFAR \(\rightarrow\) STL, which is not utilized in [13], the same network structure is maintained, changing only the weight initialization (truncated norm), the pooling kernel size (from 2 to 3), and using the padding \"same\" instead of \"valid\" in the feature extractor convolutions. (The type \"same\" means using zero-padding in order to have an output feature map with the same size of the input. Instead, \"valid\" means no padding is added and the convolution is applied only inside the feature map; in this case, the resulting feature map size is reduced depending on the size of the convolutional kernel.)
## 2 Materials and Methods
### Superparamagnetic Iron Oxide Nanoparticles (SPIONs) Synthesis  
The superparamagnetic iron oxide nanoparticles (SPIONs), in the form of maghemite (\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)), were prepared by co-precipitation of Fe\({}^{2+}\) and Fe\({}^{3+}\) ions from an aqueous solution, as reported elsewhere [28; 29; 30; 31]. Briefly, ferrous sulfate (FeSO\({}_{4}\)) and ferric sulfate (Fe\({}_{2}\)(SO\({}_{4}\))\({}_{3}\)) were dissolved in distilled water in order to obtain final concentrations of 0.027 mol/L of Fe\({}^{2+}\) and 0.014 mol/L of Fe\({}^{3+}\). Then, precipitation was triggered with aqueous ammonia (\(\sim\)25%) in two steps. First, the pH was adjusted to 3 and kept constant at that value for 30 min. After that time period, the pH was set to 11.6. After additional 30 min, the formed nanoparticles were collected with a magnet and washed five times with an aqueous ammonia solution at pH 10.5, then finally dispersed in 120 mL of water. The washed nanoparticles were further functionalized with citric acid. A volume of 5 mL of a 0.5 g/mL citric acid aqueous solution was added to the nanoparticle suspension in 120 mL of water, and the pH was adjusted to 5.2 with aqueous ammonia. The reaction mixture was then magnetically stirred at 450 rpm in an oil bath at 80 \({}^{\circ}\)C for 90 min. After that, the pH was set to 10.2 with aqueous ammonia. Finally, the obtained suspension was centrifuged at 8000 \(g\) for 5 min to remove and discard any sediment of aggregated nanoparticles (approximately 3-5% of all nanoparticles) while the supernatant, representing the colloidal suspension, was used for further procedures.
## 4 Attack Surface Analysis
### Radio Access Control
#### 4.1.2 Formal Model for Attack Scaling
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Password Length** & **Number of Possible Passwords** & **Time 67 CPU (h)** & **Time (RTX 3070 GPU) (h)** & **Time (RTX A6000 GPU) (h)** \\ \hline
8 & \(2.18\times 10^{14}\) & \(1.83\times 10^{6}\) & \(8.07\times 10^{4}\) & \(4.22\times 10^{4}\) \\ \hline
9 & \(1.34\times 10^{16}\) & \(1.12\times 10^{8}\) & \(4.96\times 10^{6}\) & \(2.11\times 10^{6}\) \\ \hline
10 & \(8.39\times 10^{17}\) & \(7.06\times 10^{9}\) & \(3.10\times 10^{8}\) & \(1.31\times 10^{8}\) \\ \hline
11 & \(5.20\times 10^{19}\) & \(4.37\times 10^{11}\) & \(1.92\times 10^{10}\) & \(8.11\times 10^{9}\) \\ \hline
12 & \(3.22\times 10^{21}\) & \(2.71\times 10^{13}\) & \(1.19\times 10^{12}\) & \(6.23\times 10^{11}\) \\ \hline
13 & \(2.00\times 10^{23}\) & \(1.68\times 10^{15}\) & \(7.41\times 10^{13}\) & \(3.87\times 10^{13}\) \\ \hline
14 & \(1.24\times 10^{25}\) & \(1.04\times 10^{17}\) & \(4.59\times 10^{15}\) & \(2.40\times 10^{15}\) \\ \hline
15 & \(7.68\times 10^{26}\) & \(6.46\times 10^{18}\) & \(2.84\times 10^{17}\) & \(1.48\times 10^{17}\) \\ \hline
16 & \(4.67\times 10^{28}\) & \(3.93\times 10^{20}\) & \(1.72\times 10^{19}\) & \(7.41\times 10^{18}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Brute-force attack times for password lengths (8–16 characters). The table indicates that employing a format consisting of three words and three numbers yields performance comparable to utilizing a 12-character passphrase. This presents a superior solution, as passwords are easier to recall than passphrases. During our investigations, we evaluated a single GPU node. However, these attacks can escalate considerably with a higher number of nodes. For example, in a system that houses 1000 nodes, the attack speed could potentially decrease by a factor of 1000. Additionally, it is worth noting that in this study, we exclusively employed general-purpose computers. APTs might possess specialized hardware tailored for password attacks.
# 3D Skeletal Joints-Based Hand Gesture Spotting and Classification
## 4 Experiments and Results
### Training Process  
* **Network training for hand gesture spotting:** To train the Bi-LSTM network for segmentation of continuous gestures, we firstly use a pre-trained 3D human pose extraction network (on Human3.6M dataset) and a pre-trained 3D hand pose extraction network (on Stereo Hand Pose Tracking dataset) to extract the 3D position of key poses. The quality between human and hand pose extraction algorithms are demonstrated in Figure 4. Using those extracted input features for the network, we train the Bi_LSTM network with the provided gesture segmentation labels by a training set of Chalearn LAP ConGD dataset.  
Bi-LSTM network is trained with CTC loss for predicting the sequence of binary output values to classify whether the frame belongs to gesture frame or transition frame. In Bi_LSTM, the input layer has 20 time-steps, the hidden layer has 50 memory units, and the last fully connected layer output has one binary value per time-step with a sigmoid active function. The efficient ADAM optimization algorithm [36] is applied to find the optimal weight of the network. The spotting output of the Bi-LSTM network by a given speed input is displayed as in Figure 5.  
* **Network training for hand gesture classification:** The single-stream network (pre-trained on Sports-1M dataset) is separately fine-tuned on the huge dataset Chalearn LAP ConGD dataset. Each fine-tuned stream 3D_CNN network weights is learned  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Number of Classes** & **Number of Videos** & **umber of Videos for Train, Validation, Test Set** & **Gesture Segmentation Task** \\ \hline
20 BN\_Jester & 27 & 148,092 & 118,562 114,787 114,743 & No \\ NVIDIA Hand Gesture & 25 & 1532 & 1050 \(|-\) 1428 & Yes \\ Chalearn LAP ConGD & 249 & 22,535 & 14,314 4179 \(|\) 4042 & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation studies on the ISBI 2016 and ISBI 2017 datasets.  
Figure 4: (**a**) The 2D and 3D human pose estimation examples and (**b**) The 2D and 3D hand pose estimation examples.  
using ADAM optimization, learning rate with an initial value of 0.0001 reducing by half for every 10 epochs on 200 epochs. Ensemble modeling with 5 3D_ResNet models is applied to increase the classification accuracy. The LSTM network parameters are selected through the observations of experimental results. The optimal LSTM model parameters are 3 memory blocks, and 256 LSTM Cells per memory block. The pre-trained LSTM network is trained with a learning rate of 0.0001 on 1000 epochs. After pre-training of each streaming network, we retrain these networks with a specific dataset. Finally, we concatenate the prediction probability outputs of these trained models to train the weights of the last fusion fully connected layer for gesture classification. Besides training with the 3D_ResNet framework, we also train with the 3D_CNN framework to prove the effectiveness of the proposed algorithm.
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 3 Results
### Validation of the Estimated Yield
All the coefficients in the figure passed the significance test at the 0.01 confidence level. Figure 5: City-level comparison of the statistical and estimated yields: (**a**) shows the model’s overall performance and (**b–f**) display the correlation coefficients (slope, R\({}^{2}\), RMSE, MAPE, and d\({}_{t}\)) between the statistics and estimates on the yield from 2001 to 2019. All the coefficients in the figure passed the significance test at the 0.01 confidence level. Figure 6: Scatter plots on the statistics and estimated yield at the county and city level: (**a–e**) show the relationship between the statistics and estimates on the yield during 2001-2019 in HB, HN, SD, AH, and JS provinces, respectively. The green and yellow dots represent the county-level and city-level yield, respectively. Green and yellow represent regression lines at the county and city levels, respectively. Dotted line indicates 1:1 line.
# A Hyperledger Fabric-Based System Framework for Healthcare Data Management
## 4 System Design and Implementation
### System Overview  
The existing literature has largely used a patient-driven access control policy to overcome privacy issues. A recent innovation by [PERSON] and [PERSON] employed a hierarchical access control approach in the E-medicine system [40]. This approach overcomes the efficiency problem of dynamic access problems and enhances interoperability. Hence, this study decided to adopt this method and apply it to blockchain system design.  
After synthesizing the findings, a system design along with the mechanisms of agent interactions during data sharing was proposed. The design is based on the Hyperledger Fabric framework, using its pluggable member management services, consensus, and ordering services to simplify deployment and development. Specifically, the raft consensus mechanism recommended by Hyperledger is adopted. Furthermore, a series of smart contracts are designed to provide privacy-preservation, access control, and other functions.  
The proposed scheme is based on the Australian medical process practice (covered by Medicare) and legal requirements. Healthcare data archiving and sharing are necessary parts of the medical practice. In the diagnosis and treatment process, the healthcare data that the practitioner has gathered about a patient could help understand the history and determine the treatment strategies of that patient. Specific examples includes information provided by the patient, x-rays, pathology and other test results, referral letters, prescriptions, and other relevant healthcare information is the main object of this research. Such information has the following characteristics:  
* Healthcare data is collected and managed in a timely manner.
* There are multiple types of data that are stored in existing health information systems including text and pictures, as well as handwritten and electronic versions.
* The structure, accuracy, and comprehensiveness of the data depend on the medical service providers.
* The law requires retaining the medical records for at least seven years and it is recommended that all doctors keep the medical data for as long as possible.
* The data is scalable and must be easy to share.
* Healthcare information is important privacy for patients.  
Based on the above characteristics, the improved data management scheme is required to (1) follow the traditional medical recording habits and patterns; (2) be integrated with the existing healthcare information systems; (3) improve the convenience of patients and doctors; (4) strengthen the privacy protection strategies, especially for reviewers, process, and purpose.  
According to the above requirements, Figure 1 outlines the key system functions for the proposed scheme. In the process of generating, using, and storing healthcare information, there are three stages.