esa-sceva/satcom-chunk-collection huggingface dataset:
# Antenna-on-Chip for Millimeter Wave Applications Using CMOS Process Technology
## 3 Results  
Figure 20 shows the antenna-on-chip designed in this paper, using the GSG probe to perform needle insertion and using the large magnification to measure frontal photos. The measured reflection coefficient (S11) of the antenna designed in this paper was measured at the Taiwan Semiconductor Research Institute of the National Research Institute (Taiwan Semiconductor Research Institute), using a vector network analyzer (Agilent E8361C) as the feed system and using a spacing of 100 \(\upmu\)m; the GSG probe transmits signals for the antenna. Figure 21 shows the simulation and measurement results of the millimeter wave band antenna-on-chip designed in this experiment, which have the same trend. However, the difference between the position of the GSG probe and the simulation during the measurement affects the bandwidth result of the measurement, and the bandwidth under the standard reflection coefficient of \(-10\) dB has a higher application frequency than the simulation, covering millimeter wave application frequencies of 28 GHz, 38 GHz, and 60 GHz. The measurement results after the frequency of 67 GHz are unknown, but it can be inferred from the previous measurement trend that the measurement results will be similar to those of the simulation.  
Figure 19: The surface current of the antenna-on-chip: (**a**) 38 GHz; (**b**) 60 GHz.  
Figure 20: GSG connector and actual measurement diagram.  
This paper simulated the radiation direction through high-frequency electromagnetic simulation software regarding the antenna radiation pattern. Through the 2D field pattern, the radiation directions in the xz and yz planes can be analyzed, and the radiation directions of the chip antenna designed in this document at 38 GHz and 60 GHz can be better understood. Figures 22 and 23 are the 2D plane field radiation diagram and the 3D field radiation diagram of 38 GHz. When the theta angle is 1 degree, the maximum gain is \(-12.2\) dBi. The xz plane field radiation diagram shows that when the Theta angle is 2 degrees, the maximum gain is \(-12\) dBi. Figures 24 and 25 are 2D planar field radiation diagrams and 3D field radiation diagrams of 60 GHz. It can be known that when the theta angle is 2 degrees, the maximum gain is \(-2.64\) dBi. The xz plane field radiation diagram shows that when the theta angle is 2 degrees, the maximum gain is \(-2.56\) dBi. According to the above xz, yz plane radiation pattern simulation diagram and 3D radiation simulation diagram, it can be known that the radiation pattern of this antenna is upward, and the maximum gain is \(-2.64\) dBi.  
Figure 21: Simulation and measurement of reflection coefficient S11.  
Figure 22: 2D field simulation at 38 GHz: (**a**) yz plane; (**b**) xz plane.  
Figure 23: The 3D field simulation diagram at 38 GHz.  
Figure 24: 2D field simulation at 60 GHz: **(a)** yz plane; **(b)** xz plane.  
Figure 25: The 3D field simulation diagram at 60 GHz.
# Autonomous Mutual Authentication Protocol in the Edge Networks
## 1 Introduction
### Objectives  
Authentication is considered to be one of the key components for performing secure communication within distributed systems. Therefore, it must be included within the recent autonomous edge networks for better security. There are multiple authentication protocols in the distributed system, but the process is incomplete without servers. Therefore, this work proposes a novel protocol that can authenticate multiple devices by mutual authentication without the need of any server for initiation, authentication, and management.  
1. A novel protocol design for the mutual authentication in the edge networks: This work demonstrates an idea for the challenge-response model by establishing an authentication process within the autonomous devices performing the session key exchange as the part of valid response by the requesting party. Therefore, mutual authentication is achieved after solving challenges, which are provided by both the parties to each other. The protocol design has included use of a unique session key, different time-quarter-based PRNG random values, time-stamps, and the transitions within the group of octet's position.
2. Server-less mutual authentication within the independent devices: Authorization is selected as the basic requirement for this work. Thus, authorized devices can only initiate and implement the authentication protocol. Autonomous operations make the multiparty authentication independent of the external server. Thus, the traditional approach of using server or third-party systems within the mutual authentication protocol limitations is resolved by this work. Therefore, complete autonomy on the operations is achieved.
3. Multiple IoT device authentications within the network: Multiple IoT devices exist within a network with the mobile workstation as a portable device. This work presents the communication within the autonomous edge devices that can iterate,process, and authenticate each other without any fixed system/server for achieving complete autonomous status. Successively, autonomous devices with public keys can mutually authenticate each other and can form a trusted network. The IoT devices can connect to multiple devices within the wireless local network for a single communication session.
* No additional support of infrastructure and IoT resource-constrained device utilization: Architecture of several authentication protocols consists of a service server for key distribution, ticket-granting server (TGT), for process validation and authentication server for confirming the process and declaring successful authentication. Inclusion of many servers creates a bottleneck in the system. Additionally, several calculations with different servers are not suitable for IoT resource-constrained devices. In this work, there is no need for a ticket-granting (TGT), service server and authentication server. Whereas, the need for registration server after registration phase is completely eliminated. Minimal calculations and no secret key leads to reduced overhead on resource-constrained devices.
## 4 Experiment and Analysis
### Comparison Methods  
In the experimental validation of the proposed method for OLD in Malay, several comparison models are set up for comparison:  
1. LR (Logistic Regression): Utilizing TF-IDF for text feature extraction, followed by LR detector for constructing an offensive language classifier. LR is a linear model, simple and interpretable. In specific parameter settings, feature extraction employs TF-IDF with a maximum of 5000 features and an n-gram range of (1, 2). The minimum document frequency (min_df) is set to 0.05, and the maximum document frequency (max_df) to 0.95. For LR, the penalty type is \"12\" with a regularization strength of 1.0, using the solver \"liblinear\" with a maximum of 100 iterations [64].
2. SVM: Extracting features from ext using TF-IDF and feeding them into a SVM detector to learn text features. SVM classifies text by finding the optimal hyperplane in feature space. SVM classifies text by finding the optimal hyperplane in the feature space. The regularization parameter for SVM is set to 1.0, with a radial basis function kernel (rbf) and a gamma coefficient set to \"scale\" [65].
3. mBERT (Unsupervised): Training mBERT model on labeled source language dataset and leveraging mBERT's multilingual transfer capabilities for direct application in OLD task in the target language. However, limitations exist due to language differences, task specificity, label bias, and constraints of transfer learning.
4. MMSE [66]: Pretraining a multilingual neural translation model and transferring through the encoder for model transfer.
5. MDD-VAT [36]: Enhancing mBERT using unsupervised domain adaptation with target language unlabeled samples through virtual adversarial training.
6. mBERT (Supervised): Training mBERT model on labeled target language dataset for benchmark performance reference. In supervised settings, mBERT undergoes end-to-end training using labeled data for OLD tasks in the target language.
## Literature  
* Newborn screening for CF in Switzerland--Consequences after analysis of a 4 months pilot study. _J. Cyst. Fibros._**2013**, \(6\), 667-674.
* One-year evaluation of a neonatal screening program for cystic fibrosis in Switzerland. _Dtsch. Arztebl. Int._**2013**, _110_, 356-363.
* Newborn screening for cystic fibrosis--The parent perspective. _J. Cyst. Fibros._**2016**, _15_, 443-451.
* Measurement of fecal elastase improves performance of newborn screening for CF. _J. Cyst. Fibros._**2016**, _15_, 313-317.  
_L-06. Newborn Screening for Vitamin B6 Non-Responsive Classical Homocystinuria--Systematical Evaluation of a Two-Tier Strategy_
* [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]  
University Children's Hospital Heidelberg, Division of Neuropediatrics and Metabolic Medicine, Dietmar-Hopp-Stoffwechselzentrum, Im Neuenheimer Feld 669, 69120 Heidelberg, Germany  
Background: In classical homocystinuria (HCU, MIM# 236200), due to deficiency of cystathionine \(\beta\)-synthase (EC 4.2.1.22), there is clear evidence for the success of early treatment. The aim of this study was to develop and evaluate a two-tier strategy for HCU newborn screening.  
Patients/Methods/Results: We reevaluated data from our newborn screening program for Qatar, in a total number of 125,047 neonates, including 30 confirmed HCU patients. Our hitherto existing screening strategy involves homocysteine (Hcy) measurements in every child, resulting in a unique dataset for evaluation of two-tier strategies. Reevaluation included methionine (Met) levels, Met to phenylalanine (Phe) ratio, and Hcy. Four HCU cases identified after database closure were also included in the evaluation. In addition, dried blood spot samples selected by Met values >P97 in the newborn screening programs in Austria, Australia, The Netherlands, and Taiwan were analyzed for Hcy. Met to Phe ratio was found to be more effective as a first sieve than Met, sorting out nearly 90% of normal samples. Only 10% of the samples would have to be processed by second-tier measurement of Hcy in dried blood spots. As no additional patients with HCU were found neither in the samples investigated for HCU, nor by clinical diagnosis in the other countries, the generalization of our two-tier strategy could only be tested indirectly.  
Conclusions: The presented two-tier algorithm using Met to Phe ratio as first and Hcy as second-tier requires 10% first-tier positives to be transferred to Hcy measurement, resulting in 100% sensitivity and specificity in HCU newborn screening.
# Predicting the Compressive Strength of Concrete Using an RBF-ANN Model
## 1 Introduction
Recently, other advanced algorithms have been employed in determining the RBF centers and their shape parameters in the RBF-ANNs which are widely used in various research areas [28; 29; 30; 31; 32; 33; 34]. In this paper, an RBF-ANN model for predicting the 28-day compressive strength of concrete is established. The database in [26] which was also used by [27] for establishing their model is expanded by adding more data listed in [35; 36; 37; 38; 39]. The Stochastic Gradient Approach presented in [40] is employed for determining the centers of RBFs and their shape parameters. Further validations are implemented by using the data of [25; 26]. The results are compared with actual values as well as with the results of [27].
# Green IoT: A Review and Future Research Directions
## 5 Energy-Efficient and Eco-Sustainable Wireless Sensor Networks
### Aggregation and Reduction of the Data
\begin{table}
**Energy-Efficient Technique** & **Energy Savings** & **Advantages** & & **Disadvantages** \\ \hline  & & - & Reducing energy consumption and & - & Increasing complexity and overhead of network protocols. \\ _Transmission power control:_ & Up to 50\% compared & - & Improving network & - & Potentially increasing end-to-end latency and reducing throughput. \\ Dynamic adjustment of & to a constant & & scalability and coverage & - \\ transmission power to & transmission power & - & Improving network reliability by reducing & - & Depending on the specific algorithm used, it may require additional \\ consumption [46, 88, 89]. & & - & Adapting to changes in network topology and environmental conditions & - \\ \hline \hline \end{tabular} \
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{_Cooperation between wireless sensors: Collaborative data processing, routing, and sharing among neighboring sensors [89, 90]._} & Up to 80\% compared & - & Reducing redundancy and improving data accuracy. & - \\  & & to independent sensor & - & Enhancing security and fault tolerance. & - \\  & & & & application, it may require additional communication overhead or latency. \\ \hline \hline \multicolumn{1}{c}{_Spatial diversity:_ Involves using multiple antennas to transmit the same signal from different locations, which can reduce the transmission power required to achieve the same level of communication performance, leading to energy savings [91, 92]. & - & Increased energy efficiency and network lifetime. & - \\  & & - & Improved network coverage and reliability. & - \\  & & - & Reduced interference and signal fading. & - \\ \hline \multicolumn{1}{c}{_Modulation optimization:_} & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \hline \multicolumn{1}{c}{_Modulation optimization:_} & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \hline \multicolumn{1}{c}{_Modulation optimization:_} & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{_Topology control:_ Adjusting the transmission range; nodes can avoid unnecessary communication and reduce energy consumption [93, 90, 94].} & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & \\ \hline \hline \multicolumn{1}{c}{_Duty cycling:_ In which a sensor node is put into sleep mode for a certain period of time, known as the “duty cycle”. During this sleep period, the node does not transmit or receive data, and its radio is turned off. When the sleep period ends, the node wakes up and resumes its normal operations [96, 97, 92].} & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A summary of energy-saving estimates for different green WSNs techniques.
# Attosecond Pulse Shaping by Multilayer Mirrors
## 1 Introduction
### HHG as Major/Only Attosecond Pulse Source  
There are many publications on the generation of attosecond pulses by HHG which deliver a very intuitive theoretical description [26] of this process, as well as describe the required experimental setups [27] and generated radiation spectra and pulses [28].  
The process of HHG in atoms and molecules is theoretically best described by the three-step model of [PERSON] [29]. The model separates the process into different steps. It starts with laser-field induced tunnel ionization of electrons during a fraction of a laser period. This step is followed by the acceleration of the free charges in the ponderomotive laser field. Finally, the rescattering of the returning electrons with their parent cores occurs.  
It is important to note that this process delivers a coherent (spatially and temporally) radiation spectrum with multiple odd overtones (harmonics) of the driving laser frequency. The generated spectrum ranges from the laser's fundamental wavelength (with a Ti:sapphire laser usually in the near-infrared (NIR) at 780 nm) over the strongly modulated plateau region toward the weakly modulated cut-off regime. The cut-off regime constitutes the shortest HHG wavelengths.  
The spectral position of the HHG cut-off is determined by the laser's pulse intensity \(I\) and carrier frequency \(\omega_{L}\), as well as the ionization potential \(I_{p}\) of the target atom, and is given by the following formula:  
\[E_{\text{cut-off}}=I_{p}+3.17\times U_{p}\,, \tag{1}\]  
with the ponderomotive potential of the laser:  
\[U_{p}=\frac{Ie^{2}}{2m_{e}e_{0}c\omega_{L}^{2}}\,. \tag{2}\]  
The forming of single isolated attosecond pulses from such broadband HHG radiation spectra is performed by filtering out the shortest-wavelength part of the HHG spectrum in the cut-off regime. This is typically achieved by transparent filter foils, grazing incidence or multilayer reflectors, or a combination of those elements.  
Thin-film filters and grazing incidence optics are not very versatile for tailor-made spectral filtering and spectral phase manipulation. In contrast, multilayer optics, such as those described herein, allow for a wide range of tailor-made spectra and phases, thus providing the best flexibility for temporal pulse shaping.
## 3 Results and Discussion
### Influence of Soil Properties on Zn Absorption by Rice Grains
metals, thereby reducing the bioavailability of heavy metals like Zn and Cd and inhibiting their absorption by plants. Iron (Fe) and manganese (Mn) readily form iron and manganese oxide minerals in surface environments [52]. Zn and other heavy metals may be incorporated into the crystal lattice of these minerals [53], reducing their bioavailability. Additionally, iron and manganese hydroxide colloids have a significant effect on the adsorption of Zn\({}^{2+}\)[54]. SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) represents the degree of silicon depletion and aluminum enrichment in the soil. A higher SiO\({}_{2}\) content indicates a higher sand fraction in the soil and fewer clay minerals and organic matter, which reduces the soil's ability to adsorb Zn [55; 56]. Phosphorus (P), as a nutrient element, shows a high negative correlation with the bioaccumulation factor of Zn in rice grains. Many studies suggest that P and Zn exhibit antagonistic effects [57; 58]. Studies has found that high concentrations of P significantly reduced the Zn content and bioavailability in rice grains. [PERSON] et al. [59] observed that applying phosphorus fertilizer reduced the Zn content in wheat grains by 17% to 56%.
# A Cross-Layer Media Access Control Protocol for WBANs
## 3 System Model
### Superframe Structure  
The time is divided into periodic superframes, beginning with the beacon frame. As shown in Figure 2, the superframe consists of the Beacon Period (BP), Contention-Free Period (CFP), Ack Period (AP), and Contention-Based Period (CBP). During each BP, the coordinator broadcasts a beacon packet containing control information. The CFP is further divided into multiple time slots and sensors upload their data during assigned time slots. At the end of the CFP, the coordinator broadcasts a combined Ack packet to inform all sensors about the successful reception of their uploaded packets. If a packet uploaded by sensor \(n_{i}\) is lost during the CFP, \(n_{i}\) decides whether to retransmit the packet during the CBP based on its priority. The CBP is primarily used for sensors to upload event-triggered and emergency information. If these packets are generated during the CBP, sensors access the channel immediately using a CSMA/CA scheme.  
Figure 1: Network model.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Default**} \\ \hline  & \multicolumn{3}{c}{**Train**} & \multicolumn{3}{c}{**Test**} \\  & adj R\({}^{2}\) & R\({}^{2}\) & RMSE (\(\mu\)g L\({}^{-1}\)) & adj R\({}^{2}\) & R\({}^{2}\) & RMSE (\(\mu\)g L\({}^{-1}\)) \\ SVR & 0.81 & 0.82 & 1.255 & 0.63 & 0.71 & 1.273 \\ RF & 0.96 & 0.96 & 0.23 & 0.15 & 0.33 & 1.929 \\ MLP & 1 & 1 & 0.04 & 0.02 & 0.23 & 2.068 \\ \hline  & \multicolumn{3}{c}{**Optimized**} & \multicolumn{3}{c}{**Test**} \\  & adj R\({}^{2}\) & R\({}^{2}\) & RMSE (\(\mu\)g L\({}^{-1}\)) & adj R\({}^{2}\) & R\({}^{2}\) & RMSE (\(\mu\)g L\({}^{-1}\)) \\ SVR & 0.77 & 0.77 & 1.424 & 0.77 & 0.78 & 1.113 \\ RF & 0.81 & 0.81 & 0.495 & 0.74 & 0.75 & 1.189 \\ MLP & 0.75 & 0.75 & 0.56 & 0.76 & 0.76 & 1.144 \\ \hline  & \multicolumn{3}{c}{**Linear (Base Model)**} & \multicolumn{3}{c}{**Test**} \\  & adj R\({}^{2}\) & R\({}^{2}\) & RMSE (\(\mu\)g L\({}^{-1}\)) & adj R\({}^{2}\) & R\({}^{2}\) & RMSE (\(\mu\)g L\({}^{-1}\)) \\  & 0.74 & 0.76 & 1.47 & 0.65 & 0.73 & 1.227 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of nonoptimized (default) and optimized model performances for predicting chlorophyll-a concentration during training (train) and testing (test) steps. The linear model serves as a base model. 0.76 for MLP, while for SVR the result improved from 0.63 to 0.77 and from 0.15 to 0.74 for RF in the test dataset. For the independent validation, a chl-a dataset acquired by HPLC, the predictions had better RMSE and R\({}^{2}\) than the test datasets (Figure 6). Again, the higher values had limitations in prediction, but the lower variance compared with the training and testing datasets allowed for better evaluation indicators, with RMSE for all algorithms in the order of 0.3 \(\upmu\)g L\({}^{-1}\) and R\({}^{2}\) reaching approximately 0.90. Figure 5: Results of prediction (black dashed) and comparison with the observed test dataset (gray solid). For the three algorithms, R\({}^{2}\) is higher than 0.7 and RMSE lower than 1.2 \(\upmu\)g L\({}^{-1}\). (**a**) SVR, (**b**) MLP and (**c**) RF. Figure 6: Results of prediction (black dashed) and comparison with the validation dataset (gray solid). For the three algorithms, R\({}^{2}\) is approximately 0.9 and RMSE lower than 0.3 \(\upmu\)g L\({}^{-1}\). (**a**) SVR, (**b**) MLP and (**c**) RF. The iterative SARIMA parameters selection uses much more computer processing time compared with the GridSearchCV method in machine learning. The latter is a scale of seconds to minutes while the former hours to days. It took around two weeks to select the best p, q, P and Q parameters in the daily data considering a yearly seasonality. Fitting the test dataset with the SARIMA model gave the worst results when compared with the ML models (Figure 7).
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 2 Materials and Methods
### Quantitative Analysis of the Main Bioactive Substances  
Eight bioactive compounds (eleutheroside B and E, protocatechic acid, chlorogenic acid, rutin, hyperoside, isofravidin, and quercetin) were simultaneously quantified using ultra-performance liquid chromatography (UPLC-UV, Waters Corporation, Milford, MA, USA). Standards were obtained from Shanghai Yuanye Bio-Technology Co., Ltd. (Shanghai, China) Sample extraction followed a modified method [20]. A 1 g sample was extracted with 30 mL ethanol-water (80:20, _v/v_) for 1 h (25 \(\pm\) 2 \({}^{\circ}\)C), centrifuged (1880\(\times\) g, 4 \({}^{\circ}\)C, 15 min), and filtered through a 0.22 um PVDF membrane. Chromatographic separation was achieved on an ACQUITY UPLC(r) BEH C18 column (100 mm \(\times\) 2.1 mm, 1.7 um, Waters Corporation, Milford, MA, USA) at 27 \({}^{\circ}\)C, using gradient elution (Table S1) with mobile phases consisting of solvent A (acetonitrile-phosphoric acid, 980:20, _v/v_) and solvent B (acetonitrile) at 0.3 mL/min. Detection was performed at 210 nm. Standard solutions and sample extracts were injected separately, with quantification based on external standard calibration curves.