esa-sceva/satcom-chunk-collection huggingface dataset:
# Inversion-Based Deblending in Common Midpoint Domain Using Time Domain High-Resolution Radon
###### Abstract  
We implement an inversion-based deblending method in the common midpoint gathers (CMP) as an alternative to the standard common receiver gather (CRG) domain methods. The primary advantage of deblending in the CMP domain is that reflections from dipping layers are centred around zero offsets. As a result, CMP gathers exhibit a simpler structure compared to common receiver gathers (CRGs), where these reflections are apex-shifted. Consequently, we can employ a zero-offset hyperbolic Radon operator to process CMP gathers. This operator is a computationally more efficient alternative to the apex-shifted hyperbolic Radon required for processing CRG gathers. Sparse transforms, such as the Radon transform, can stack reflections and produce sparse models capable of separating blended sources. We utilize the Radon operator to develop an inversion-based deblending framework that incorporates a sparse model constraint. The inclusion of a sparsity constraint in the inversion process enhances the focusing of the transform and improves data recovery. Inversion-based deblending enables us to account for all observed data by incorporating the blending operator into the cost function. Our synthetic and field data examples demonstrate that inversion-based deblending in the CMP domain can effectively separate blended sources.  
deblending; sparse radon; inversion Article
# IoT Services for Monitoring Food Supply Chains
## 2 Literature Review
### Sensor Technologies and Foodborne Illness Monitoring
This transformation not only enhances the efficiency of temperature monitoring but also reinforces the overall safety and quality of food products, ensuring they remain safe for consumption from production to the consumer's plate [10]. In recent years, advancements in wireless sensor networks have revolutionised how temperature monitoring is conducted, particularly in the food industry. This innovative approach enables the automated, real-time monitoring, measurement, and recording of temperature. By employing such an automatic system, organisations can significantly enhance food safety protocols, such as HACCP and FSMS, ensuring a continuous and reliable stream of temperature data is available around the clock--24 h a day, 7 days a week, and throughout the year. With immediate access to temperature data, companies can take swift action in the event of any temperature-related concerns. This proactive approach facilitates quicker problem resolutions and is crucial in minimising the risk of food spoilage and waste. Ultimately, implementing automated wireless temperature and humidity monitoring systems represents a significant step in maintaining high food safety standards and operational efficiency in the agrifood supply chain. Collecting temperature data is systematic and efficient, as it can be stored securely in the cloud. This cloud-based storage solution offers the convenience of accessing temperature records from any device connected to the internet, regardless of location.
# Finite Element Modeling of the Soil-Nailing Process in Nailed-Soil Slopes
## 3 FE Model of Nailed-Soil Slopes
### Nailed-Soil Slope Boundary Conditions  
The boundary conditions were applied in the finite element model, at the bottom of the model and at the left- and right-hand side of the model. The boundaries of the model were selected to avoid a size effect on the output of the models. At the bottom, the nodes were treated as fixed, i.e., with no horizontal or vertical movement or rotation. The nodes at the left and right side were free to move in the vertical direction and rotate, but they were restrained in the horizontal direction. The rest of the nodes were allowed to move in the horizontal and vertical direction and rotate.
## 3 Results and Discussion
### The Presence of Organosilane in MOC Foam Materials  
The comparison of the Fourier infrared test of F-MOC and N-MOC in Figure 7a showed that the characteristic C-F peak that should have appeared near 1170 cm\({}^{-1}\)[28] was covered by the infrared peak of the magnesium oxychloride cement itself. However, in the XPS energy spectrum test of Figure 7b, it was found that the characteristic peak of the F element appeared at 690 eV [29], indicating that the F element entered the MOC foam material.  
Figure 6: X-ray patterns of F-MOC and N-MOC.  
Figure 7: Fourier infrared test (**a**) and XPS spectrum (**b**) of F-MOC and N-MOC.  
The pH of the magnesium chloride solution was about 6.3 to 6.5. In this near-neutral environment, the hydrolysis of FAS and TEOS was relatively slow. However, when adding H2O2, the content of hydroxyl radicals in MgCl2 solution increased [30], and hydroxyl radicals significantly promoted the hydrolysis and the condensation of silane [31], thus FAS and TEOS were added to the magnesium chloride solution. The first hydrolysis could make it more fully hydrolyzed. Due to the consumption of hydroxyl radicals, part of H2O2 was decomposed in the pre-hydrolysis stage. The more TEOS content there was, the more H2O2 was consumed. As TEOS increased, this resulted in the addition of the same H2O2. The content of F-MOC in the foamed material was higher than that of N-MOC. When the magnesium chloride solution containing organosilane was mixed with active MgO, the pH value of the MOC system increased to 8-9.5 [32], and the alkaline environment further promoted the polycondensation reaction of the pre-co-hydrolyzed product of TEOS and FAS. Studies have shown [33], under normal conditions, FAS and TEOS can be copolymerized into silica spheres with 40-200 nm fluoroalkyl chains. The silica spheres with fluoroalkyl chains were uniformly dispersed throughout the MOC gelling system, resulting in the overall hydrophobicity of the foamed MOC material (Figure 8).
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 6 The 6G Network Infrastructure to Manage V2X Application Lifecycles
### Critical Evaluation:  
Feasibility in 6G: CACC leverages V2V communication, and 6G, with its URLLC and high-mobility communication capabilities, can ensure that vehicles exchange data in real-time, enabling cooperative decision-making even at high speeds.  
Advantages: Implementing CACC via 6G could enhance road safety, optimize traffic flow, and contribute to a more pleasant driving experience by reducing the burden on the driver.  
Challenges: Developing algorithms that can manage diverse and dynamic traffic scenarios while ensuring that communication between vehicles is not only fast but also exceedingly reliable is crucial.
# Biomechanical Analysis of Posterior Ligaments of Cervical Spine and Laminoplasty
## 2 Material and Methods  
### Model Development  
A 3D FE model of the cervical spine (C2-C7) was created based on the computed tomography (CT) of a 22-year-old healthy adult subject. The ethics committee approved the use of these images at the Center for Clinical Research of the corresponding author's hospital, and written informed consent was obtained. The geometry of the vertebrae was reconstructed using the CT scans which were used for reconstructing the geometry of intervertebral discs. The 3D reconstruction of cervical spine geometry from CT scans was carried out using the image segmentation software MIMICS 15. 0 (Materialise, Leuven, Belgium). The reconstructed geometry of hard and soft tissues was meshed with the hexahedral elements using the IA-FE MESH software (Iowa, United States). The meshed vertebrae/discs were exported to the ABAQUS software 6. 14 (Dassault Systemes, Simulia Inc., Providence, RI, USA) to assemble the C2-C7 cervical spine. The following ligaments were added to the model, anterior longitudinal ligament (ALL), posterior longitudinal ligament (PLL), interspinous ligament (ISL), supraspinous ligament (SSL), capsular ligament (CL), and LF using connector elements in ABAQUS. The outer 0.5 mm layer of the vertebrae represented a cortical shell, and the inside represented a cancellous bone. The intervertebral discs were composed of annulus fibrosus (50%) and nucleus pulposus (50%). The annulus consisted of a ground substance along with embedded fibers oriented at \(\pm\)25\({}^{\circ}\)[20]. The facet joints in the model were represented using the surface-surface sliding contact, whereas the [PERSON]'s joints in the lower cervical intervertebral discs were modeled using GAPUNI elements [21]. The material properties for all the structures in the FE model were taken from the literature summarized in Table 1[22; 23].  
Figure 1 represents the intact model having 213,165 elements and 173,215 nodes.
## 5 Performance Evaluation  
### Simulation Testbed  
We conduct simulation experiments using OMNeT++ [14] to evaluate the performance of the proposed approach. A 150 \(\times\) 150 m\({}^{2}\) square network area is considered, where 51 nodes including single source node are placed using a random uniform distribution. The communication range of each node is 30 m. To emulate low packet rate scenarios, an exponential packet injection rate with mean 0.1 packet/s is adopted and the size of each packet is 40 bytes. The radio model simulates CC2420 with a normal data rate of 250 Kbps, and 802.15.4 MAC/PHY operates with a default configuration in the 2.4 GHz band [40]. The channel error rate is set to 10%. We assume that the source node is always trusted, and 2-10% of nodes can be compromised and reprogrammed by an adversary to behave maliciously. The suppression attack rate varies between 0.0125 and 0.1 time/s, and the number of spoof data messages in each attack is 10 or 15. The total simulation time is 10,000 s. The simulation parameters are summarized in Table 1.  
In this paper, we measure the performance in terms of the following four major performance metrics by altering some key simulation parameters, including suppression attack rate, number of malicious nodes, observation window (\(\omega\)), number of spoof data messages (\(N_{spkt}\)), and filter gain constant (\(\alpha\)). First, the detection rate is computed as the ratio of the number of detected multicasting misbehaviors to the total number of launched multicasting misbehaviors. The objective is to show the detection efficiency of the proposed scheme. Second, the packet reception rate (PRR) is calculated as the ratio of the number of received data messages to the total number of generated data messages from source node, indicating the performance resiliency of the proposed scheme. Third, a false detection rate, namely false positive rate, measures the ratio of the number of anomalous increment of sequence number due to a sudden increase of packet injection rate to the total number of detected multicasting misbehaviors. Fourth, the change of increment rate of minimum sequence number is observed and offers an explanation of how the increment rate has changed due to the multicasting misbehaviors of malicious nodes. For performance comparison, we compare the proposed HED scheme with the standard MPL multicast protocol with and without adversary, respectively.
# Artificial Intelligence-Based Model for the Prediction of Dynamic Modulus of Stone Mastic Asphalt
## 5 Results and Discussion
### Sensitivity Analysis of the Prediction Variables  
The sensitivity analysis of the input variables used in the prediction process is presented in this section. The temperature and frequency were successively varied at their range of value, whereas other inputs were kept constant. Precisely, using the ANN-TLBO model developed previously, a new two-dimensional input space was constructed using the value of each input, recorded at ten different quantile levels: 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0. The temperature and the frequency were them successively selected, and the model was run eleven times, once for each percentile value. Principally, the method provides quantitative disclosures on the change of output when the selected input changes.  
It can be seen that the temperature was the most significant factor influencing the dynamic modulus \(|\mathbb{E}^{*}|\), followed by the frequency (Figure 9). While varying two input variables, the sensitivity index varied from [200-300] to [-100] for the temperature, whereas it varied from [0] to [100] for the frequency. Moreover, the nonlinear negative effect was observed for the temperature, whereas a positive linear effect was found for the frequency. These observations were confirmed for all mixtures.  
As indicated in many studies [82-85], exploring the relative importance of inputs might offer better in-depth knowledge of the output, which could directly assist engineers during the preparation phase. Considering the testing temperature and frequency, experimental investigations have been conducted in several works [12,80]. However, a direct linear interpolation of the as-obtained results or missing information on the range of input values could lead to inexact observations. In this work, an attempt was made from an experimental point of view, to cover a broad range of testing temperature and frequency with sufficiently grid points. The dependency of the dynamic modulus on inputs was then analyzed with the developed ML algorithm. For each mixture, a fit equation was proposed to highlight the dependency of the selected variable to dynamic modulus \(|\mathbb{E}^{*}|\). With reasonable accuracy, a linear equation was finally adopted to fit the frequency versus the dynamic modulus. On the contrary, a cubic equation was chosen to relate the temperature with the dynamic modulus. Summarized information on the sensitivity analysis of the effect of temperature and frequency is presented in Table 7, including the appropriate fit, equation form, and correlation effect. The proposed equations could facilitate the usage of asphalt mixtures in selecting the desired dynamic modulus in function of the testing temperature and frequency.  
Figure 9: Sensitivity index of frequency and temperature for four combinations of SMA, HMA, H, and W.
# The Progress of Cobalt-Based Anode Materials for Lithium Ion Batteries and Sodium Ion Batteries
## 4 Application of Cobalt-Based Anode Materials in LIBs/SIBs
### Cobalt Oxides and Its Composites
(**g**) Galvanostatic charge-discharge profiles. Points A, B, C, D and E represent the pristine state, the discharge state at 0.6 V and 0.01 V and the charged state at 1.4 V and 3 V, respectively. (**h**) Ex-situ XRD patterns corresponding to these points [124]. [PERSON] and his collogues reported a nanocomposite of N-doped carbon with Co\({}_{3}\)O\({}_{4}\) nanoparticles, exhibiting a superior sodium storage performance [124]. To further investigate the electrochemical mechanism of the electrode, five points that appeared at the first charge-discharge profile were tested by ex-situ XRD patterns (Figure 10g,h). Upon the discharge process, the phase of Co\({}_{3}\)O\({}_{4}\) gradually disappeared along with the sodiation. When the cell discharged to 0.6 V (B), a new phase of CoO came out owing to the reduction of Co\({}_{3}\)O\({}_{4}\). When fully discharged (C), only one phase of the Cu collector was retained, attributed to the poor crystallization of the Na\({}_{2}\)O phase. During the following charge process, reverse reactions occurred accompanied by the reappearance of CoO and Co\({}_{3}\)O\({}_{4}\) at 1.4 V and only Co\({}_{3}\)O\({}_{4}\) when fully charged (E). These results can be attributed to a reversible reaction of Co\({}_{3}\)O\({}_{4}+8\)Na\({}^{+}+8\)e\({}^{-}\leftrightarrow 3\)Co + 4 Na\({}_{2}\)O during cycling.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
## 3 CDL: Cooperative Downlink Listening
### Power Balancing  
One of the outstanding features of CDL is that all nodes in the network utilize a common RX window for downlink periodically triggered by the DL initiator. However, this feature might also lead to more power consumption of the DL initiator than other normal listener nodes. Therefore, in order to avoid intensive power consumption of a specific node and balance power of all nodes in the network, the DL initiator is dynamically changed. DL initiator change is based on Fcnt (Frame Counter), which is managed by network server and each node. The Fcnt is also separated into FcntUp for uplink counter and FcntDown for downlink counter. Therefore, whenever a network server receives DL request message, it checks the following:  
Figure 7: Cooperative downlink listening (CDL) time synchronization.  
\[If\ (\ {FcntUp}+FcntDown\ of\ the\ current\ DL\ initiator\geq Threshold\ )\]  
If the sum of two counter values is greater than or equivalent to the Threshold value, to elect a new DL initiator, the network server seeks a new DL candidate by the following:  
\[DL_{candidate}=\underset{\forall i\in N}{\text{argmin}}\{FcntUp(i)+FcntDown(i )\ \} \tag{2}\]  
where \(N\) is the set of nodes in the network.  
Here, the problem is what the best threshold value is. In case of taking a too small value, if the Fcnt value of all nodes in the network is greater than threshold, a new DL initiator cannot be elected. On the other hand, in case of taking too big of a threshold value, energy of original DL initiator can only be consumed intensively. Therefore, to resolve this problem, CDL changes the threshold value dynamically as follows:  
\[\text{Treshold}(\text{new})=\text{Treshold}(\text{old})\times\left(1+\frac{1}{ 2^{R}}\right) \tag{3}\]  
This results in log scale variation of threshold value as shown in Figure 8. That is, it starts with small value but threshold value is maintained with a similar value when reaching almost maximum value. This makes it possible to continuously find a candidate node, which consumed the least energy, and enables the node to become a new DL initiator. Threshold value change is also performed together with a DL initiator change event. Therefore, since CDL dynamically elects a DL initiator node considering the Fcnt value of all nodes in the network, the power imbalance problem occurring due to periodic DL request message transmission can be resolved.
## 3 Results and Discussion
### Benchmarking  
As previously mentioned, although ML techniques have not yet been applied to forecasting MEL concentrations, some studies have focused on predicting biosurfactant production. Unfortunately, none of the existing articles have published their code or datasets. In the work by [PERSON] et al. (2018), the authors predicted biosurfactant concentrations, achieving a test root mean squared error (RMSE) of 0.53, which improved to 0.31 after applying the firefly algorithm. The authors also calculated the R\({}^{2}\) for their test predictions, achieving a value of 0.98 before applying the firefly algorithm and 0.99 after. To enable a comparison, the R\({}^{2}\) values for the best result for each model and each day in the present study were also calculated and are presented in Table 6. It is also important to note that [PERSON] et al. did not provide information about the size of the dataset or the inputs given to the model. To ensure the reproducibility of the results, the methodology used in the present study has been made publicly available.  
A different study ([PERSON] et al., 2018) also explored forecasting biosurfactant concentrations using an NN. The authors provided information about the input features used and used particle swarm optimisation to optimize the weights and bias of the NN. This work was able to achieve an R\({}^{2}\) of 0.94, which is in line with the values obtained in our study.  
Finally, [PERSON] et al. (2018) used an NN to predict the biosurfactant yield. The authors used a dataset with 27 samples, with 19 used to train the model, 4 for validation, and 4 for testing. Similar to that in the previously mentioned work, an R\({}^{2}\) of 0.94 was achieved for the testing set, with an RMSE of 1.22. However, the authors noted that the weights varied with each run of the NN, resulting in inconsistent outcomes. In contrast, in the present work, all of the model weights were saved after training to ensure reproducibility and consistency in the results.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Random Forest** & **Neural Network** & **Support Vector Machine** \\ \hline R\({}^{2}\)—day 4 & 0.82 & 0.96 & \(-\)0.47 \\ R\({}^{2}\)—day 7 & 0.70 & 0.95 & \(-\)0.15 \\ \hline \end{tabular}
\end{table}
Table 6: R\({}^{2}\) values for the lowest MSE obtained for each of the models.  
Figure 4: Scatter plot of experimental values for MEL production on the seventh day of fermentation with RF, NN, and SVM predictions.
## Materials and Methods
### Analyte Identification
\begin{table}
\begin{tabular}{r r r r r r r r} \hline \hline
**Compound** & \({}^{1}\)\({}_{R}\) **(min)** & \({}^{2}\)\({}_{R}\) **(s)** & \(I^{1}\) & **Attributes** & **Odor Quality** & **OT (mg/kg)** & **Ref** \\ \hline
3-Methylbutan-1-olol & 18.35 & 0.94 & 1215 & F/M/Mo & Whiskey, malt, burnt & 0.1 & [2] \\ Ethyl hexanoate & 18.36 & 1.94 & 1216 & F & Apple peel, fruit & \\
1-Hexanol & 19.00 & 1.71 & 1231 & F & Furity, banana, soft & 0.4 & [2] \\ Styrene & 19.25 & 1.35 & 1237 & & Balsamic, gasoline & \\ Hexyl acetate & 20.33 & 2.02 & 1263 & F & Green, turity, sweet & 1.04 & [4] \\
2-Octanone & 20.83 & 2.06 & 1274 & V & Mold, green & 0.51 & \\ Octanal & 21.08 & 2.02 & 1280 & Mo/R & Fatty, sharp & 0.32 & [2] \\
1-Octan-3 one & 21.58 & 1.89 & 1292 & Mo & Mashroom, mold & 0.01 & \\ (Z)-2-Pentent-ol & 21.75 & 1.22 & 1296 & & Butter, pungent & \\ (F)-4-8-Dimethyl-1,3-7-nonatriene & 21.92 & 2.36 & 1300 & & - & \\ (Z)-3-Hexen-1-ol acetate & 22.08 & 1.68 & 1304 & & Green, banana & \\ (F)-2-Pentent-ol & 22.09 & 1.02 & 1304 & & Butter, pungent & 1.04 & [4] \\
1-Heptanol & 22.31 & 1.89 & 1309 & Herb & Herb & \\ (Z)-2-Heptanal & 22.58 & 1.77 & 1315 & R & Oxidized, fallowy & 0.042 & [2] \\ (F)-2-Heptanal & 22.67 & 1.81 & 1317 & Mo/R & - & 0.005 & [2] \\ Ethyl pentanoate & 23.08 & 2.27 & 1327 & M & - & 0.0015 & \\
6-Methylhept-5-en-2 one & 23.17 & 1.85 & 1329 & Mo/R & Pungent, green & 1 & [2] \\ (Z)-3-Hexen-1-ol & 24.08 & 1.30 & 1350 & F/R/V & Green & 1.5 & [2] \\ (E)-3-Hexen-1-ol & 24.92 & 1.35 & 1369 & V/F & Green & 6 & [4] \\
1-Octanol & 25.50 & 1.96 & 1383 & Mo & Moss, nut, mushroom & 0.1 & \\ Nonanal & 25.75 & 2.19 & 1388 & R & Fatty, wavy, pungent & 0.15 & [2] \\ (E)-2-A-Hexenad & 25.76 & 1.46 & 1388 & & Green & \\ (E)-2-Hexen-1-ol & 25.92 & 1.26 & 1392 & V & Green grass, leaves & 5 & [2] \\ (E)-2-A+Hexenad & 26.00 & 1.48 & 1395 & & & \\ (Z)-2-Octanal & 27.15 & 1.86 & 1420 & & Green leaf, walnut & \\ (E)-2-Octanal & 27.25 & 1.89 & 1424 & R & Green, nut, fat & 0.004 & [2] \\ Ethyl octanoate & 27.50 & 2.36 & 1429 & V & Fruit, fat & 10 & \\
1-Octan-3-ol & 27.83 & 1.47 & 1437 & Mo & Mold, earthy & 0.05 & \\ Acetic acid & 28.50 & 0.97 & 1453 & F/V/R & Sour, vinegary & 0.5 & [2] \\ (Z)-2-A+Heptadien & 28.58 & 1.60 & 1455 & R/Mo/F & Fatty, rancid & 0.36 & [2] \\ (E,Z)-2-A+Heptadien & 28.66 & 3.24 & 1457 & R/Mo/F & Fatty, rancid & 10 & [2] \\ (_E,E_)-2,4-Heptadien & 28.75 & 1.73 & 1459 & R/Mo/F & Fatty, rancid & 4 & [2] \\
1-Nonanol & 30.02 & 2.02 & 1487 & & Fresh, clean, floreal & 0.28 & [3] \\ Copenee & 30.16 & 2.99 & 1492 & & Wood, spice & \\ Decanal & 30.25 & 2.23 & 1494 & R & Penetrating, sweet, wavy & 0.65 & [2] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._