esa-sceva/satcom-chunk-collection huggingface dataset:
# Preparation and Chemical Protective Clothing Application of PVDF Based Sodium Sulfonate Membrane
## 3 Results  
### Synthesis and Characterization of PVDF-g-SSS  
The PVDF-g-SSS membrane was prepared by a one-pot reaction. Firstly, TMAH methanol (10 wt%) solution was used to partially eliminate HF from PVDF and generate carbon-carbon double bonds (Scheme 1a). Then the SSS monomer was grafted to the modified PVDF via free radical polymerization initiated by AIBN (Scheme 1b). The volatile solvent was evaporated at 70 \({}^{\circ}\)C to obtain the PVDF-g-SSS membrane.  
Figure 3 depicted the FT-IR spectra of pure PVDF and TMAH treated PVDF. Compared with pure PVDF (a), the TMAH treated PVDF (b) shows a new band at 1610 cm\({}^{-1}\) is representative of the stretching vibration of the carbon-carbon double bond, indicating the elimination of HF by TMAH and generation of carbon-carbon double bond active site. In the FTIR spectrum of the PVDF-g-SSS membrane, absorption bands at 1007 and 1034 cm-1 are representative of the symmetric telescopic vibration of sulfonate [36], suggesting the successful grafting of SSS onto the PVDF backbones, as shown in Scheme 1.  
Figure 3: FT-IR spectra of (**a**) PVDF, (**b**) modified PVDF, and (**c**) PVDF-g-SSS.  
The SEM image in Figure 4 shows the surface and cross-section morphology of M3. It can be seen from the figure that the membrane material is uniform, dense, and non-porous. The morphologies of other membrane samples are similar to M3, which is also uniformly dense.
# Floral Scent Variation in the Heterostylous Species _Gelsemium sempervirens_
## 2 Results
### Overall Scent Profile
\begin{table}
\begin{tabular}{|c c|c|c|c|c|} \hline \multirow{2}{*}{**\#**} & \multirow{2}{*}{**Compound**} & \multicolumn{2}{c|}{**Percent Occurrence (\%)**} & \multicolumn{2}{c|}{**Mean Relative Amount (\%)**} \\ \cline{3-6}  & & **L Style** & **S Style** & **L Style** & **S Style** \\  & & **(N = 38)** & **(N = 36)** & **(N = 38)** & **(N = 36)** \\ \hline \multicolumn{6}{|c|}{**Benzenoids**} & & & **77** & **52** \\
2 & Benzaldehyde * & 100 & 100 & 55 & 37 \\
20 & Benzyl benzoate * & 100 & 75 & 8.4 & 5.3 \\
13 & 4-Anisaldehyde * & 100 & 64 & 6.1 & 3.6 \\
5 & Acetophenone & 87 & 89 & 2.6 & 2.5 \\
3 & 4-Methylanisole & 95 & 64 & 2.0 & 1.3 \\
4 & Benzyl alcohol * & 84 & 56 & 2.0 & 1.1 \\
**Terpenoids** & & & **14** & **30** \\
29 & (\(Z\))-\(\alpha\)-Ocimene * & 32 & 58 & 0.02 & 16 \\
38 & \(\alpha\)-Farnesene * & 74 & 44 & 5.4 & 4.0 \\
39 & \(\beta\)-Ionone & 26 & 22 & 3.1 & 2.6 \\
32 & Dihydro-\(\beta\)-ionone * & 66 & 81 & 0.8 & 3.1 \\
34 & \(\alpha\)-Ionone * & 97 & 61 & 1.8 & 1.3 \\
21 & \(\alpha\)-Pinene & 68 & 67 & 1.0 & 0.3 \\
**Fatty Acid Derivatives** & & & **0.5** & **9.7** \\
73 & 2-Pentadecanone * & 0 & 42 & - & 5.8 \\
70 & 2-Tridecanone * & 0 & 33 & - & 1.8 \\
74 & 2-Heptadecanone * & 42 & 75 & 0.2 & 1.1 \\
**Yeast Compounds** & & & **7.8** & **8.1** \\
76 & Ethanol & 92 & 94 & 7.7 & 8.0 \\  & & & & **49.6 ± 4.9** & **36.8 ± 4.9** \\  & & & & **million** & **million** \\ \hline \end{tabular}  
* significantly different relative amounts in L and S flowers.  
\end{table} 
Table 2: Major (\(>\)1%) floral scent compounds in _G. sempervirens_ in order of relative amounts. Compounds with significantly different relative amounts in L verses S flowers (\(p<0.05\)) are indicated with an asterisk.
## 5 Results and Discussion
### Descriptive Analysis from XAI
#### 5.2.2 LIME  
Using the means and standard deviation from the training data, The LIME image explainer was used to extract numerical features and perturbations, as sampled from a normal (0, 1) image, and perform the inverse operations of mean-centering and scaling. The explainer was adjusted so that it calculated the first 100 features using simple linear iterative clustering (SLIC) [61] to define the highest contributing boundaries and embed them in the image. The second column of Table 3 shows the original test images for each  
Figure 8: Based on the high concentration of red pixels in the second explanation image (third column), we determined the CT image was normal.  
Figure 10: Based on the high concentration of red pixels in the fourth explanation image (fifth column), we determined that the CT image indicated the presence of a tumor.  
Figure 7: Based on the high concentration of red pixels in the first explanation image (second column), we determined that the CT image indicated the presence of a cyst.  
Figure 9: Based on the high concentration of red pixels in the third explanation image (fourth column), we determined the CT image indicated the presence of a stone.  
category; these results defined the masks in the third column of Table 3. In the fourth column of Table 3, the segmented image portion, resulting from the mask, reflected the LIME results.  
As shown in Table 3, LIME provided a visual explanation of the model's decision-making process and highlighted the regions of the image that contributed significantly to a specific class prediction. For example, in Table 3, the segmented LIME results showed only those portions of the image that contributed to the image being classified as a cyst.
# A Blockchain-Based Audit Trail Mechanism: Design and Implementation
## 4 Audit Trail Mechanism Description
### Smart Contracts for Audit Trail
#### 4.2.3 Authorized User Functionalities  
Authorized users are those who can use the audit trail mechanism by recording or retrieving the audited data. A specific audit trail data model is needed. The following fields are proposed as an example for the audit trail data model:  
* ID: This is an internal ID to identify the audit trail record in the Blockchain. It is automatically generated by the smart contract.
* Owner: This refers to the authorized user ID (authorized user Blockchain address) who has registered the audit trail. The owner is the only one with permission to update or retrieve this information. It is automatically detected by the smart contract.
* Data: This refers to the audit trail registered in the system for auditing purposes. The data could be provided by the authorized user as a name-value, to allow the registration of different types of information and provide a general-purpose solution (not limited to a specific application or domain). The value can be encrypted if necessary.
* creationTimestamp: This refers to the timestamp of the audit trail data registering process in seconds since the epoch. It is automatically generated by the smart contract.
* updatedTimestamp: This refers to the timestamp of the audit trail data last updating process in seconds since the epoch. It is automatically updated by the smart contract.  
The functionalities related to the audit trail mechanism for authorized users include (but are not limited to) the following:  
* Register a new audit trail (data information is needed).
* Update an existing audit trail (ID of the audit trail and new data are needed).
* Obtain an existing audit trail (ID of the audit trail is needed).  
It is not possible to delete information recorded in the Blockchain, which is why the \"delete\" functionality has not been included.  
Every time an operation is executed in the smart contract, a Blockchain-based event is generated to feed the Blockchain monitor. The Blockchain-based events to be generated include (but are not limited to) the following:  
* A new administrator is added.
* An existing administrator is removed.
* A new user is authorized.
* An existing user is deauthorized.
* A new audit record is registered.
* An existing audit record is updated.
###### Abstract  
In this study, the preservation of As(III) in model solutions and natural groundwater samples from four locations in Croatia was conducted. Model laboratory samples were spiked with As(III) and As(V), and different complexing agents. Solutions were analysed in intervals of 24, 48 h and during ten days after preparation. Model samples containing citric acid, sodium citrate, sodium oxalate and potassium sodium tartrate in combination with acetic acid, spiked with As(III)and As(V), showed good species preservation. As(III), in model samples, was preserved for 7 days with citric acid, and citric acid in combination with acetic acid, as well as with tartrate. As(III), in natural samples, was preserved for 6 to 12 days with potassium sodium tartrate, citric acid, and citric acid in combination with acetic acid and showed improvement, compared with unpreserved samples (oxidation in 3 days). The results showed that acetic acid alone was not successful in preserving As speciation. Good resolution of inorganic arsenic species was achieved using differential pulse anodic stripping voltammetry technique (DPASV). Since this technique is comparatively cheaper and more convenient to use than other available techniques it could become a method of choice for arsenic speciation in water.  
inorganic arsenic species; preservation; sample stability; anodic stripping voltammetry 2021  
17 September 2021  
1 Department of Natural Sciences in Pharmacy, Faculty of Pharmacy, University of Sarajevo, 71000 Sarajevo, Bosonia and Herzegovina; [EMAIL_ADDRESS] Department of Environmental Protection and Health Ecology, Teaching Institute of Public Health \"[PERSON]\", 1000 Zagreb, Croatia; adela.krivch@kstampar.hr1  
[PERSON], [PERSON] Novel Preservation Methods for Inorganic Arsenic Speciation in Model and Natural Water Samples by Stripping Voltammetric Method. _Appl. Sci._**2021**, _11_, 8811. [[https://doi.org/10.3390/app11198811](https://doi.org/10.3390/app11198811)]([https://doi.org/10.3390/app11198811](https://doi.org/10.3390/app11198811))
## 3 The Desired Solutions
Figure 14: Evolution of solutions \(z\), \(\Phi\), and \(\eta\) throughout time \(\tau\) at \(c_{3}(=0.19,0.2,0.21)\), \(c_{1}=0.01\), \(c_{2}=0.02\), \(\omega_{1}=3.354\), and \(\omega_{2}=3.131\): (**a–c**) at \(\tau\in[0,100]\) and (**d–f**) at \(\tau\in[0,1000]\). Figure 15: Progression of solutions \(z,\Phi\), and \(\eta\) over time \(\tau\) when \(\omega_{1}(=3.316,3.354,3.391)\), \(\omega_{2}=3.131\), \(c_{1}=0.01\), \(c_{2}=0.02\), and \(c_{3}=0.2\): (**a–c**) at \(\tau\in[0,100]\) and (**d–f**) at \(\tau\in[0,1000]\). Based on the sketched curves of the solutions \(z,\Phi\), and \(\eta\), we observe that the waves describing these solutions have a periodic manner in which the number of oscillations and their wavelengths remain stationary to some extent with the variation of \(c_{j}\left(j=1,2,3\right)\) values. Parts (a) of these figures have an explicitly periodic form for the wave of the solution \(z\). It is notable from parts (b) of these figures that each period of the wave contains a constant number of vibrations that are repeated for each period. This is due to the analytical form of the rotation angle \(\Phi\), in which its behavior has a spinning form. On the other hand, the wave describing spring's elongation \(\eta\) experiences rapid oscillations at the beginning of the motion due to the absorber's effect and damping impact on the investigated dynamical system, in which it settles down after that and vanishes at the end of time interval, as seen in parts (c) of these figures. According to the calculations of Figures 15 and 16, we get to the conclusion that the change of the \(\omega_{k}\left(k=1,2\right)\) values has a considerable impact on the attitude of the describing waves of the attained solutions. Regardless of the fact that the wave's behavior of the solutions is periodic, we observe that the amplitudes of these waves increase and decrease with the increasing of \(\omega_{k}\) as seen in Figures 15a-f and 16a-f, respectively.
## 2 Related Work
### Attention Mechanism  
The attention mechanism used in transformer-based models is a key technology in modern deep learning that focuses attention on the important elements in the data. It consists of three components--Query, Key, and Value--and learns how each element in the input data relates to all the others to emphasize the most relevant information [6].  
In the transformer model, attention is not limited to a single layer but uses multi-head attention to learn different patterns and relationships. Multi-head attention runs multiple independent attention mechanisms in parallel and then combines the results to maximize information representation. This approach helps the model learn complex correlations in the input data and capture multidimensional patterns in the data [44]. Transformer models originated in natural language processing (NLP) but have recently been widely used in time series data analysis. Modern models, such as the Time Transformer, Informer, and Autoformer, are designed to reflect the characteristics of time series data and excel at anomaly detection and predictive maintenance in manufacturing environments. These models have efficiently improved self-attention to reduce computational cost and improve the ability to handle large-scale data.  
The self-attention mechanism is a particularly powerful tool in time series data analysis. By learning how each point in time in the input data interacts with all other points in time, it captures the complex temporal correlations in time series data [43]. The self-attention mechanism thus overcomes the limitations of traditional recurrent neural network (RNN) or LSTM-based models and can be computed in parallel, greatly improving computational efficiency. One of the key challenges of the attention mechanism is its high computational complexity. As the computational cost increases rapidly when processing long-sequence data, efficient mechanism designs such as Log-sparse Attention, Sparse Attention, and Autocorrelation Attention have been studied. Sparse Attention, which is a recent representative design, increases computational efficiency by selectively learning the interactions between important elements without computing the relationships between all elements in the input data. These techniques are particularly useful in tasks that deal with large amounts of data, such as manufacturing environments [45].
# Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation
## 3 Preliminary Work
### GMF Structure Based on Multiple Hidden Layers  
The traditional matrix factorization-based recommendation model cannot capture the complex non-linear interaction between users and items because of adopting the linear inner products. In response to this problem, many related works have been done, among which the GMF model is particularly conspicuous. By introducing a single layer neural network on the second-order interaction between the user and the item, the activation function and the bias term are employed to endow the model with the ability to model non-linear interaction between users and items. However, GMF is a shallow model, which can well model the low-order interaction features of user and item, it is not effective to capture the more abstractive features that contain more rich information, namely high-order interaction information. For example, if we enter the interaction information between the user and the music into the recommendation model with a single layer neural network, we could capture that the user prefers classical music. Then if we enter the obtained results into the next layer neural network for learning, we could get the result that the user tends to listen to [PERSON]'s music in classical music. Therefore, we introduce the GMF structure based on multiple hidden layers in the IFDNAMF model, so that the model can not only model non-linear second-order interaction between users and items but also capture the high-order interaction between users and items. We can define the formulation as follows:  
\[s_{1}=\varphi(p_{u},q_{i})=p_{u}\odot q_{i} \tag{7}\]  
\[Z_{1}=\phi_{1}(s_{1})=a_{1}(W_{1}^{T}s_{1}+b_{1}) \tag{8}\]  
\[Z_{2}=\phi_{2}(Z_{1})=a_{2}(W_{2}^{T}Z_{1}+b_{2})\]  
\[......\]  
\[Z_{L-1}=\phi_{L-1}(Z_{L-2})=a_{L-1}(W_{L-1}^{T}Z_{L-2}+b_{L-1})\]where \(a_{1}\) is the activation function. \(W_{1}\) and \(b_{1}\) is the connection weight matrix and bias vector. \(Z_{L-1}\) is the output of the \(L-1 th\) layer network. \(a_{2}\) and \(a_{L-1}\) are the activation functions of each hidden layer. \(W_{2}^{T}\), \(W_{L-1}^{T}\) and \(b_{2}\), \(b_{L-1}\) are the connection weight matrix and bias vector of each hidden layer, respectively.
# Machine Learning Applied to the NOx Prediction of Diesel Vehicle under Real Driving Cycle
## 6 Model Application
### Analysis of Relative Variable Importance  
The Garson methodology was applied in this study to calculate variable importance by using the R software package \"Garson\". The Garson algorithm is mostly used with ANN models to provide the relative importance for the prediction when using hidden inputs and outputs [57, 58, 59].  
Figure 4: Comparison of estimated and actual values.  
Figure 5 presents the relative variable importance results in ANN, all weights specific to a predictor were generated as values between 0 and 1 to determine the relative predictor importance. Figure 5 displays the relative importance of each predictor in determining the \(\text{NO}_{\text{x}}\) concentration. The effects of each input variable (road environment, atmospheric, and after-treatment performance factors) on \(\text{NO}_{\text{x}}\) concentrations were considered. The results imply that the weights of the EGR rate had the greatest effect on the \(\text{NO}_{\text{x}}\) concentrations value (approximately 46%). The features ranked second, third, and fourth were the ambient temperature, exhaust temperature, and relative humidity with 16.2%, 15.8%, and 15%, respectively. The VSP had the smallest effect on the \(\text{NO}_{\text{x}}\) concentration value (approximately <1% of the total effect), unlike in diesel vehicles compliant with previous standards.  
In summary, \(\text{NO}_{\text{x}}\) emissions from Euro 6 light-duty diesel engines were significantly affected by EGR rate and exhaust temperature, which are after-treatment performance factors. On the other hand, the road environment had the smallest effect on \(\text{NO}_{\text{x}}\) concentrations. The \(\text{NO}_{\text{x}}\) emissions of a Euro 6 light-duty diesel engine are the least affected by the road environment conditions (road grade, vehicle speed, and acceleration) because of the development of after-treatment technologies, which adhere to the stringent Euro-6 standards.
# On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks +
## 5 Evaluation
### Simulation on Various Topology  
We run simulations on Cooja [40] with four different topologies to compare the three algorithms and see the impact of routing topology. For the first three, nodes are deployed in a grid manner, and only the root's position is varied to generate different routing topologies (Figure 7).  
Last one is the random topology scenario where random topologies are generated by Cooja simulator.  
Figure 8 presents the results when the root is deployed on the top-left corner of the deployment. The routing tree on this topology has the longest hop count. Therefore, total amount of traffic in the network is the largest due to forwarding, and results in more link congestion and queue losses. In the figures, \(F\) stands for \"Flooding + RPL-collection\" and \(T\) is \"Trickle + RPL-collection\". The numbers after each letter denotes response random jitter \(T_{R}\). PRR and the number of packet retransmissions becomes better as the \(T_{R}\) increases in both \(F\) and \(T\) cases, from 50% to 90%. However, increasing \(T_{R}\) causes long network latency as shown in Figure 8b.  
On the other hand, SCoRe achieves 99% PRR while keeping the latency near 2 s, only about 7% packets exceed 2 s among 1000 packets. Furthermore, SCoRe has the least number of packet retransmissions compared to others. The few losses come from occasional \(N_{\text{dslot}}\) inconsistency and collisions with routing control packets such as DIO and DAO. From the results, we see that SCoRe improves PRR and latency significantly compared to legacy schemes.  
Figure 6: A 3D plots representing Packet Reception Ratio with varying parameters.  
Figure 7: Three-simulation topology with change of root’s position: top-left, top, and middle.  
Figure 9 plots the result of simulation with the root at the center of the deployment. Because the network depth is smaller than top-left scenario, all algorithms have better performance than the previous topology. SCoRe can achieve 99% PRR, 1.5 s latency, and the number of response retransmissions decreases close to 0.  
Figure 10 is the result from random topology scenario. Because distance between nodes in the topology is longer than grid topology, the performance of each algorithm is better overall including SCoRe. SCoRe still keeps 99% PRR and 2 s average latency, but the number of retransmissions decreases evidently. From the result we can see that SCoRe adapts well to other topology, and can expect to have consistent performance in real-world deployments as well.  
Figure 8: Simulation results when the root is placed at the top-left corner of the grid topology with \(M=3\), \(T_{C}=200\) ms, \(K=5\) and varying \(T_{R}\).  
Figure 10: Simulation results from random topologies with \(M=3\), \(T_{C}=200\) ms, \(K=5\) and varying \(T_{R}\).  
Figure 9: Simulation results when the root is placed in the middle of the grid topology with \(M=3\), \(T_{C}=200\) ms, \(K=5\), and varying \(T_{R}\).