esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The sealing path of the alloy plug is changed from a round surface to a threaded surface, and the sealing path of the alloy plug's unit length is increased so that the casing can be completed for long-term sealing by an alloy plug with a shorter sealing length. In order to verify the feasibility of the method and research the influence of each parameter, in this paper, a small-scale experimental setup was developed for alloy plug molding and gas sealing. The researchers carried out molding and gas sealing experiments with Sn58 Bi alloy plugs inside casings of different surface morphologies, analyzed the sealing performance of the alloy plug, determined the gas leakage pathway, and analyzed the microscopic structure of the alloy plug-casing interface using an optical microscope; the effects of the inner surface roughness of the casing and threaded grooves on the gas sealing performance of the alloy plug were analyzed, and the effects of alloy plug sealing length and ambient temperature on the sealing performance of the alloy plug were researched. The research in this study could provide theoretical support for the application of the downhole CO\({}_{2}\) plugging method using Sn58 Bi in casings.
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## The Proposed Protocol
### SRPL-RP Proposal  
We present the proposed SRPL-RP to detect, mitigate, and isolate the attacks discussed in the previous section. The declared security issues for the RPL protocol can be handled by having the following features in the proposed protocol:  
1. A timestamp threshold to verify the legitimacy of the sender nodes.
2. A monitoring table during the construction of DODAG that contains information regarding the nodes.
3. Detection of rank attacks and version number attacks.
4. Mitigation of the effects of rank attacks and version number attacks.
5. Isolation of rank and version number attacks.  
The two sections below describe the protocol model flowchart and implementation, which are presented for consideration in this proposed protocol.
## 3 Results and Discussion
### Analysis of Atmospheric-Pressure Helium Dielectric-Barrier Discharges (APHeDBDs) Gas Heating
#### 3.2.3 Ion Joule Heating  
The contribution of each species to the ion Joule heating is shown in Figure 12. It is assumed that the power acquired by the ion species is transferred to background gases thoroughly due to the efficient collisions among ion species and background gases with comparable masses. Among ion species, the species N\({}_{2}\)+ and N\({}_{4}\)+ contribute 44.1% and 50.7%, respectively, to the heating source of ion Joule heating as the dominant species though the concentration of N\({}_{2}\) is much lower than that of helium.  
To further study the contribution of each ion species, Figure 13 shows the spatial average density changes of ion species during the breakdown in a typical positive HP. For the presentation purpose, the horizontal axis is rescaled. As the breakdown occurs, the number densities of He\({}^{+}\) and He\({}_{2}\)+ increase simultaneously due to:  
\[e+He\to 2e+He^{+}\quad\quad(\text{R4})\]  
\[2 He+He^{+}\to He+He_{2}^{+}\quad(\text{R17})\]  
It is observed that the number density of N\({}_{2}\)+ increases faster than those of He\({}^{+}\) and He\({}_{2}\)+ because of the high rate constant of Penning ionization:  
\[He_{m}+N_{2}\to e+He+N_{2}^{+}\quad(\text{R40})\]  
As the density of N\({}_{2}\)+ increases, the density of N\({}_{4}\)+ increases due to the fast associative process:  
\[{N_{2}}^{+}+N_{2}+He\to N_{4}^{+}+He\quad(\text{R31})\]  
Figure 11: The contribution of each background gas to the heating source of elastic collision.  
As a result, the species N4+ is the dominant ion species. Similar simulated results can be found in [13; 15; 16] showing that N4+ is the dominant ion species if the concentration of N2 admixture is higher than a certain level (e.g., \(\sim\)tens of ppm) in APHeDBDs. Therefore, the species N4+ has the highest contribution to the ion Joule heating.
# Dual-Branch Multimodal Fusion Network for Driver Facial Emotion Recognition
## 5 Limitations and Future Works  
As with any research, this study also has certain limitations. Specifically:  
Dataset Limitations: Our method was tested on only one publicly available dataset, which may introduce bias in the results and fail to fully capture the model's performance in broader real-world scenarios. Given the diversity and complexity of emotion recognition, future research could benefit from incorporating more diverse datasets, including those with varying environments, lighting conditions, driving scenarios, and emotional expressions, to better validate the model's robustness and generalization capabilities.  
Limitations of Modality Fusion: In the current network structure, modality fusion occurs after feature extraction, which may lead to information redundancy, especially when the data from different modalities are not fully complementary. This fusion strategy might not fully exploit the potential of multimodal data. Future research could explore more advanced multimodal fusion methods, such as cross-modal information sharing at earlier stages or using attention mechanisms to dynamically select and weigh information from different modalities. This could lead to more effective information fusion and improved recognition performance.
## 1 Introduction
This approach has so far been successfully applied for various purposes, from simple chemical and physical measurements of water solutions to water and food quality monitoring and disease diagnostics [6]. For example, [PERSON] et al., 2015 successfully measured concentrations of different salts using an aquaphotomics approach (with an estimated limit of detection of 1000 ppm), thus proving that, by measuring the changes in water spectra, caused by any solute, since monoatomic salts do not even absorb in near-infrared, the concentrations of solutes can be measured [10]. [PERSON] et al., 2016 reported changes in the water structure of ground water observed by NIR spectra and aquaphotomics, which occur as a consequence of the alterations in the solute content [11]. The authors concluded that the water absorbance spectral pattern could indeed serve as a measure of the qualitative change in the water molecular arrangement and a holistic marker for water quality monitoring. This is further used by [PERSON] et al., 2020 to propose the utilization of the water spectral pattern as an indicator of water quality for water produced by a purification system intended for household use [12]. Perturbation spectroscopy, on the other hand, was shown to be a very useful approach in studying systems with only subtle differences. This approach refers to the process of acquiring spectra under various perturbations, such as consecutive illuminations, a changing temperature or dilution, in order to induce spectral changes in the respective systems [7; 13; 14]. This way, by studying how respective systems react to perturbation,subtle differences between systems can be revealed. This approach was used to reveal the different functionalities of prion protein alloforms [13], and has recently been intensely used for probing the functionality of different biomolecules and increasing the accuracy of measurements [9; 15; 16]. In this paper, we present the case where the application of the usual aquaphotomics approach, as used by [PERSON] et al., 2016 [11], or [PERSON] et al., 2014, 2020 [12; 17], did not provide a clear discrimination of water samples due to their high similarity. In order to overcome this problem, we employed a temperature perturbation protocol. We propose here the application of NIR spectroscopy and aquaphotomics for the exploration of water structural changes under temperature perturbation in order to characterize different waters and discriminate between natural, processed and aged samples of drinking waters. Therefore, the objective of our research is to introduce a new temperature perturbation protocol for revealing hidden information in the spectra of water and to present this information by water absorbance spectral patterns that reflect different dynamics of different waters and, as such, can be used as an integrative marker for the comparison and discrimination of waters, as well as for water quality monitoring.
# Review on the Recent Numerical Studies of Liquid Atomization
## 2 General Overview of Liquid Atomization
### Two Atomization Regions
#### 2.2.2 Secondary Breakup  
Ligaments disintegrated from the continuous liquid jet or film may encounter further breakup depending on the relative magnitude of surface tension force, aerodynamic force, and viscous force. It is worth nothing the droplets formed from the secondary breakup may break a third and fourth time and they are still classified as a secondary breakup.  
A massive review on secondary atomization has been presented by [PERSON] et al. [34], where abundant literature on experimental methods, breakup regime, timing, droplet size and velocity distributions are discussed. Generally, the breakup regime is controlled by the relative speed between gas and droplet, or by the non-dimensional Weber number We. Also, there exists a critical We around 11, below which the breakup is suppressed by the surface tension. As the We increases from the critical value, vibration mode, bag breakup, sheet-thinning breakup, multimode, and catastrophic mode can be observed in order. The liquid viscosity, which is measured as the ratio of viscous force to surface tension force (Oh), shows a minor effect on the breakup morphology unless Oh increases to more than unit, which is not common for regular liquid. The secondary atomization regimes are schematically shown in Figure 3.
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 2 Related Works
### Producer Mobility Support Approach
However, in the process of buffering the interest packets, latency and signaling will be high before the old link is repaired due to the exchange of update and status messages other than normal interest packages. Moreover, the buffer zone requires a high amount of storage, especially in the bigger, and denser, mobile producers' environment. Furthermore, a mechanism using the spatial locality of moving producers was designed to support producer mobility. The solution maintained the reverse paths of data similar to the original ICN of NDN architecture [38]. The mechanism is modeled using hop count forwarding strategy that considered a weighted sum of the round trip time (RTT) as the average service time between the consumer and every intermediate node [38]. However, the sole use of RTT cannot determine if there is congestion among the intermediate nodes which allow the consumer to keep on sending the unsatisfied interest packets. Therefore, for ICN to support multi-access edge computing for 5G technology of anywhere-anytime connected services for mass mobile connectivity, the movement pattern of mobile content producers needs to be investigated [14].
## 2 Materials and Methods
### Hop Quality Traits
#### 2.4.5 Gas Chromatography-Olfactometry (GC-O) Analysis of HEO  
GC-O is a hyphenated technique that allows the simultaneous analysis of volatile fractions by an instrumental detector and the human nose. This allows us to separate and recognize the \"odor active\" fractions, which contribute to the odor of the extract. The result of this analysis is a double plot called \"aromagram\" (Figure 1), which represents the response of both detectors, chromatographic peaks and sensory responses, indicated as OE (odor event). GC-O analyses were performed at CREA-IT laboratory, located in Milan. The system used was composed of an Agilent 6890 N GC equipped with an FID and a DB-1 capillary column (60 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness). Helium was used as carrier gas (1.3 mL min\({}^{-1}\)). The injector and FID temperatures were set at 250 \({}^{\circ}\)C and the following column temperature program was applied: 40 \({}^{\circ}\)C for 5 min, 2.5 \({}^{\circ}\)C min\({}^{-1}\) up to 160 \({}^{\circ}\)C held for 5 min (duration: 58 min). The eluate was split 1:1 at the column outlet, linked to an olfactometric system that included the Olfactory Detector Port ODP2 Gerstel (Gerstel GmbH) equipped with the ODPneumatics module to control humidification and make up gas flows. The analyses were performed by using a direct intensity method described in [14]. The olfactometric data (intensity, duration and area of each odor event, OE) were collected through a potentiometer with the ODP recorder integrated with the GC software Chemstation Rev A 10.02. The area of each OE was calculated by the software from the intensity and duration values and shown as a chromatographic peak. The GC-O results were expressed as OE areas (A, average) and maximum intensity (Imax, median). The panel was composed of 7 panelists (1 male and 6 female), aged between 35 and 50 years, who were familiarized with the products. Before the analysis of the samples, all panelists attended two training sessions to learn to identify the main odor categories present in hop and beer products, using one representative compound for each of them. Solutions at different concentrations of the following standards were used: ethyl hexanoate (fruit), citronellol (citrus, flower), \(\beta\)-myrcene (resinous), \(\alpha\)-humulene (woody), mesifurane (caramel). For each GC-O analysis, 2 panelists were involved, sniffing was divided into two parts of 25 min and each panelist participated in the sniffing of both parts. For hop analysis, oil was diluted in hexane (1:2 \(v/v\)) and 1.5 \(\upmu\)L were injected in split mode (split flow: 12 mL min\({}^{-1}\), split ratio 1:10). GC-O results were related to composition data resulting from the GC-MS of HEO (Table 2). GC-MS was performed using an Agilent 5973N MSD connected to an Agilent 6890 GC, with the same column and chromatographic conditions of GC-O. The MS settings were as follows: filament voltage, 70 eV; scan range, \(m/z\) 45-800; scan speed, 1.4 scan/s; injector and interface temperature, 250 \({}^{\circ}\)C. Identification was performed by comparing
# A 920-MHz Dual-Mode Receiver with Energy Harvesting for UHF RFID Tag and IoT +
## 4 Proposed Dual-Mode Receiver
### IF Amplifier  
The proposed envelope detector has high output impedance, the unity gain buffer is adopted to drive high pass filter (HPF) in HS mode. Then, the IF signal is further amplified by the baseband amplifier (BB Amp). The unity gain buffer circuit and baseband amplifier are shown in Figure 10.  
The input MOSFETs \(M_{1}\) and \(M_{2}\) of the buffer are designed in minimum size to reduce the parasitic capacitance. The PMOS input baseband amplifier is adopted because the DC output voltage of HPF is zero [20].  
The simulation results of the gain and phase margin of the proposed IF amplifier is shown in Figures 11 and 12. As shown in Figure 11a, the high pass cutoff frequency is decided by \(C_{1}\) and \(R_{1}\), \(f_{\mathrm{C}}=1/(2\pi R_{1}C_{1})\) which is 5 kHz. The open-loop gain of second stage amplifier is 30 dB. The simulation result at tt corner shows that the phase margin is larger than 60\({}^{\circ}\) when gain reduced to 0. Moreover, the phase margin can guarantee the stability of the IF amplifier with corner and temperature variation. The closed-loop gain is decided by the ratio of \(R_{2}\) and \(R_{3}\), \(A_{V,\ close\ loop}=1+R_{2}/R_{3}\) which is 20 dB as shown in Figure 12a. The cascade scheme of HPF and baseband amplifier results in a band pass filter feature. The 3-dB frequency of baseband amplifier is 200 kHz.  
Figure 11: Simulation results of first stage and second stage in IF amplifier: (**a**) gain; (**b**) phase.  
Figure 10: Schematic of proposed IF amplifier.
# Experimenting with Routing Protocols in the Data Center: An ns-3 Simulation Approach
## 4 Validation and Experimental Results
### Comparison against Sibyl Framework
#### 4.2.2 Execution Environment  
All the experiments presented were executed on a server machine running Ubuntu 16.04 with 30 CPUs AMD Opteron 63 xx class and with 244 GB of RAM memory.  
We configure the simulation duration so as to allow convergence while minimizing it. For this, we studied several simulations to find the best values for each scenario. The final configurations for the simulation duration (in simulated time) for each scenario are:  
* Bootstrap: 10 s.
* Node-failure and link-failure: 20 s. This time allows for the bootstrap to finish and converge, produce the failure in the node or link and then wait again for convergence.
* Node-recovery and link-recovery: 30 s. In this case, after the failure and the convergence, the node or link is recovered and we have to wait again for convergence.  
The simulations are configured to generate traffic capture files (.pcap) for every interface of each node simulated. This files are then processed so as to count the number of BGP's UPDATE messages exchanged.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 3 Fundamentals of Channel Estimation  
As transmitting symbols propagate through the wireless communication channel, they experience several harmful effects from interaction with environmental objects such as buildings, mountains, trees, etc. This results in multipath propagation and signal attenuation. Channel estimation is a useful wireless communication technique that is adopted to ensure accurate detection of the transmitting signals. Over the years, numerous CE techniques have been proposed in the literature to combat the varying nature of the channel. In these proposed CE techniques, the emphasis has been to improve the CE performance, power and bandwidth consumption, and the computational time complexity. As such, we review the CE techniques used in wireless communication applications in general. In particular, we narrow the application of these CE technique to FBMC systems, where we introduce taxonomy to classify these CE techniques.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 3 The NDMR Approach
### NDMR Operations
#### 4.1.1 Case 1: The two-way handshake process  
The destination has found a path candidate that is node-disjoint with the first path. In this case, the candidate is chosen as the second path, and the second path selection is ended. The destination then unicasts two PREPs to the source; one contains the first path, and the other contains the second path. When an intermediate node receives a PREP, it checks if the PREP's sequence number for the originating node (i.e., the destination node in this case) is greater than that recorded by the intermediate node. If not, it means that the received PREP is a duplicate, and the intermediate node discards the PREP. If so, the intermediate node looks at the _discovered_path_ field of the PREP. The intermediate node discards the PREP if the _discovered_path_ does not contain the node. Otherwise, it means that the intermediate node is along the communication path between the source and destination, and the node then adds the corresponding forward and reverse routes into its route table. The intermediate node then forwards the PREP towards the source. Eventually, the two PREPs arrive at the source. The source sets up two node-disjoint paths to the destination based on the _discovered_path_ field of the received PREPs.  
The path discovery in this case is a two-way handshake process as shown in Figure 3(a).
## 3 Materials and Methods
### Proposed Model
#### 3.2.2 Classification  
Recently, hybrid models have been developed by combining DL and ML methods to increase the classification success [19; 26; 27]. Deep learning methods have strong representation capabilities. Gradient boosting methods provide high accuracy in classification. Therefore, a hybrid model that combines XGBoost with the customized CNN model is proposed in this study. High-level features are extracted from images with CNN, and classified with high accuracy and speed with XGBoost. The overall performance is increased by classifying with XGBoost instead of CNN's fully connected layer and softmax classifier.  
XGBoost, proposed by [PERSON] and [PERSON] [28], is created by optimizing the gradient boosting algorithm. Its most important features are high accuracy, preventing overfitting, handling empty data and speed. It uses gradient boosting decision trees as weak learners. The model works iteratively and each tree generates a new model by correcting the errors in the previous trees [29]. Error rates are reduced by using gradient descent. The goal is to minimize the loss L between the true value y and \(\hat{y}\) given a prediction model f(x). The loss is stated by Equation (1).  
\[\text{L}=\sum(y_{i}-\hat{y}_{i})^{2} \tag{1}\]  
XGBoost uses number of trees (n_estimators), learning rate (learning_rate) and tree depth (max_depth) parameters and the performance is highly dependent on these parameter values. Therefore, GridSearchCV method is used to determine the correct parameter values. GridSearchCV is a brute force approach that optimizes the hyperparameters of machine learning approaches and tries all combinations for the hyperparameter ranges specified by the users. The performance is calculated for each hyperparameter combination and the best hyperparameter set is selected. The best parameter values for XGBoost optimized with GridSearchCV are given in Table 3.
## 2 Materials and Methods
### Analytical Method Validation  
The limit of quantitation (LOQ) for the established analytical method was determined by identifying the minimum concentration at which the signal-to-noise ratio (S/N) of the matrix-matched standard solution exceeded 10. The linearity of the calibration curve was assessed using the correlation coefficient (\(r^{2}\)) derived from a first-order regression analysis. For conducting recovery tests (\(n=3\)), standard solutions with concentrations of 1 mg/L and 10 mg/L were separately spiked into 5 g samples to achieve analyte concentrations of 0.01 mg/kg and 0.1 mg/kg, respectively. Pesticides were extracted using the predefined preparation method and then matrix-matched with MeCN in a 1:1 volumetric ratio. To validate the quantitation of analytes exceeding the range of the calibration curve, a 200 mg/L standard solution was spiked into a 5 g sample to reach a concentration of 2 mg/kg. The extract solution was diluted by mixing 30 \(\upmu\)L of it with 270 \(\upmu\)L of a pesticide-free sample extract and 300 \(\upmu\)L of MeCN, followed by the calculation of the recovery rate (\(n=3\)). The matrix effect was quantified by comparing the slope of the calibration curves for the matrix-matched standard solution (_a_) to that of the solvent standard solution (_b_). This value, expressed as a percentage (ME, %), was calculated using Equation (1):  
\[\text{ME}\left(\%\right)=\left((a-b)/b\right)\times 100 \tag{1}\]