esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this study, a functional relationship between the solid particle density (\(\rho_{s}\)), void ratio (\(e\)), and dry density (\(\rho_{d}\)) of mechanically and biologically treated (MBT) municipal solid waste (MSW) was examined. In total, 60 waste specimens were tested with an air pycnometer device and corresponding triplets (\(e\), \(\rho_{d}\), and \(\rho_{s}\)) of values were obtained. In addition, a long-term oedometer test with an allowed decomposition process was also conducted. Based on the obtained results, the variability in the solid particle density caused by heterogeneity and decomposition, as well as its influence on the corresponding void ratio and dry density values, were critically evaluated. The obtained results showed that the variability in the solid particle density caused by waste heterogeneity had a significant influence on the initial void ratio value. Furthermore, the obtained results also showed that the change in the solid particle density, caused by the degradation process, had a significant impact on the final void ratio and dry density values. In addition, an empirical relationship, \(\rho_{d}=f(e)\), was proposed. The proposed function allows a landfill operator to establish the corresponding dry density at an arbitrary chosen void ratio, and vice versa, without having the exact knowledge of the corresponding solid particle density value.  
air pycnometry; dry density; municipal solid waste; solid particle density; void ratio Article
# Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer
## 2 Methods
### Algorithm Principle
#### 2.5.2 Principle of Bidirectional Long Short-Term Memory  
LSTM (long-short term memory) is an improved recurrent neural network (RNN) introduced by [PERSON] and characterized by its ability to maintain dependencies over long time sequences. Unlike traditional RNNs, where neurons are connected sequentially, LSTM introduces a memory cell and gating mechanisms (input gate, output gate, and forget gate) that enable selective memory retention and information flow control (Figure 4). This structure allows LSTM to extract sequential dependencies in data effectively, solving the vanishing and exploding gradient issues commonly faced in standard RNNs. As a result, LSTM improves prediction accuracy and enhances the model's generalization capability [28]. The mathematical formulations of the LSTM model are as follows:  
\[i_{t}=\delta(W_{i}\cdot[h_{t-1},x_{t}])+b_{i} \tag{6}\]  
\[f_{t}=\delta(W_{f}\cdot[h_{t-1},x_{t}])+b_{f} \tag{7}\]  
\[o_{t}=\delta(W_{o}\cdot[h_{t-1},x_{t}])+b_{o} \tag{8}\]  
\[\hat{c}_{t}=tanh(W\cdot[h_{t-1},x_{t}])+b_{c} \tag{9}\]  
\[c_{t}=f_{t}\bigodot c_{t-1}+i_{t}\bigodot\hat{c}_{t} \tag{10}\]  
\[h_{t}=o_{t}\bigodot tanh(c_{t}) \tag{11}\]  
where  
\(i_{t},f_{t},o_{t}\): Input gate, forget gate, and output gate, respectively;  
\(c_{t}\): Cell state at time \(t\);  
\(h_{t}\): Hidden state at time \(t\);\(W_{X}\) and \(b_{X}\): Weight matrix and bias term corresponding to a specific gate or state \((X=i,f,o,c,h)\);  
\(\sigma\): Sigmoid activation function;  
_tanh_: Hyperbolic tangent function;  
\(\bigcirc\): Element-wise multiplication (Hadamard product).  
BiLSTM (bidirectional long short-term memory) improves upon the traditional LSTM by incorporating a forward LSTM layer and a backward LSTM layer. BiLSTM reads both past and future sequence information through its hidden layers, enabling bidirectional fitting of the data. This design allows the model to better capture hidden state information, with both forward and backward sequences influencing the output. This dual consideration of past and future data helps further improve prediction accuracy [29; 30]. The structure of BiLSTM is shown in Figure 5, where \(x_{i},y_{i},\overrightarrow{h}_{i}\), \(\overleftarrow{h}_{i}(i=1,2,\cdots,t)\) represent the sequence input and output data and the forward and backward hidden states of the LSTM, respectively.
# Speeding up Statistical Tolerance Analysis to Real Time
## 5 Methods: Detailing the Approach to Speeding Up Tolerance Simulations
### Procedure for the Statistical Tolerance Analysis  
The application of Monte Carlo simulation [29] in statistical tolerance analysis carries out a virtual reproduction of the manufacturing, assembly and inspection of a large number of products. Figure 4 illustrates these three steps. First, a defined number \(n\) of variants of each individual part are generated virtually, which differ from each other in various properties (e.g., dimensions), which are subject to tolerances [30]. The value of this deviation is determined taking into account the associated probability distribution of each tolerance. This step reproduces virtually the production of \(n\) single parts. In the next step, the single parts generated are assembled into \(n\) final products. This corresponds to the \(n\)-fold virtual assembly of the final product and is based on the \"pulling without adding back\" of the individual parts according to [PERSON]'s urn model [31]. Finally, the relevant key characteristic is determined using the vector-chain and documented for each of the \(n\) virtually assembled products. This corresponds to the inspection of the products after their assembly.
## 2 Regional Settings and Methods
### Methods
#### 2.2.2 Mineral Separation and Identification  
Mineral separation and identification conducted as follows. Approximately 100-200 g of each sediment sample were wet sieved to obtain the very fine sand fraction (0.063-0.125 mm) via nylon mesh screens. Additionally, the very fine sand fraction of each subsample was dried and weighed. Then, each subsample was placed into bromoform (CHBr3, \(\rho=2.65\)-3.15 g/cm\({}^{3}\)) and centrifugal to separate the detrital micas. This was repeated until almost no mica could be separated. Then, the obtained micas were manually purified by removing other minerals under the microscope. Additionally, micas left in the very fine sand fraction subsample were manually selected under the microscope. After repeatedly rinsing with alcohol, all the obtained detrital micas were dried at 60 \({}^{\circ}\)C and weighed to calculate the mass percentage. Finally, the Gazzi-Dickinson method [52] was used for the mica identification under the microscope. In detail, line counts of 300-500 grains were identified under a binocular microscope with incident light. Particles of biotite, weathered mica, and muscovite were classified (Figure 2) and counted.  
The particle percentages of biotite, weathered mica, and muscovite were separately calculated as follows: particle percentage of biotite or weathered mica or muscovite = 100% * particle number of biotite or weathered mica or muscovite/total particle number of mica. Additionally, the mass percentage of mica was calculated as follows: mass percentage of mica = 100% * total mica mass/total mineral mass.
# FSOpt_k: Finding the Optimal Anonymization Level for a Social Network Graph
## 6 Experimental Results
### Comparison of Optimization Algorithms
#### 6.3.2 Evaluation Results of General Graph Properties  
In order to evaluate the optimization algorithms and select the best ones, values of 0.05, 0.1, and 0.2 were considered for the parameter \(\mathcal{L}\) and then averaged from the obtained error values. The average evaluation results of the optimization algorithms on the Dolphins dataset are shown in Figure 17(a). These results show that the NaFa algorithm performs better than the other algorithms for both APL and modularity criteria, and the error is less. For the T and ACC criteria, the results of the evaluation of the Bat algorithm show less error. Figure 17(b) denotes the average of the evaluation results of the optimization algorithms on the netscience dataset. In this figure, in general, the amount of error generated in the social network graph features by applying the NaFa algorithm is less than Bat and PSO. We obtained similar results for the power grid dataset. Figure 17(c) shows the evaluation results of the algorithms on this dataset. As can be seen in this figure, the best algorithm for minimally changing the social network graph is the NaFa algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Data Set** & & & & **UMGA** & & & **NaFa** & & & **PSO** \\ \hline \multirow{3}{*}{} & \(\mathcal{L}\) & **Desired** & **Utility** & **\(k\) Value** & **Utility** & **Obtained** & **Obtained** & **Utility** & **\(k\) Value** & **Utility** & **\(k\) Value** & **Utility** \\ \hline \multirow{3}{*}{ Dolphins} & 0.05 & 0.9746334 & 8 & 0.973607 & 10 & 0.94134 & 12 & 0.9346 & 11 & 0.9395 \\  & 0.1 & 0.9436949 & 13 & 0.938416 & 14 & 0.935483 & 9 & 0.9422 & 12 & 0.9332 \\  & 0.2 & 0.9161290 & 20 & 0.914956 & 20 & 0.9149560 & 15 & 0.9227 & 28 & 0.9104 \\ \hline \multirow{3}{*}{netscience} & 0.05 & 0.997917 & 13 & 0.9979023 & 10 & 0.9971 & 32 & 0.9862 & 22 & 0.9948 \\  & 0.1 & 0.992014 & 106 & 0.9913992 & 101 & 0.99005 & 146 & 0.981 & 110 & 0.9861 \\  & 0.2 & 0.9842668 & 227 & 0.9844766 & 219 & 0.9802 & 321 & 0.9772 & 288 & 0.9684 \\ \hline \multirow{3}{*}{Power Grid} & 0.05 & 0.9985949 & 65 & 0.9984933 & 79 & 0.993636 & 105 & 0.9892 & 61 & 0.9924 \\  & 0.1 & 0.9933436 & 224 & 0.992938 & 248 & 0.992 & 301 & 0.9912 & 346 & 0.9916 \\ \cline{1-1}  & 0.2 & 0.9869257 & 516 & 0.9861476 & 520 & 0.9846 & 699 & 0.9821 & 598 & 0.9864 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The evaluation results of optimization algorithms with respect to the obtained k value and utility.  
In order to conclude the evaluation performed on all datasets, the mean values of the errors obtained from the datasets were computed. Figure 19 shows the results. As can be deduced from this figure, on average, the NaFa algorithm performed fewer changes to the social network graph than the other two algorithms.
# Role of Blockchain Technology in Combating COVID-19 Crisis
## 4 Results
### Applications of Blockchain Technology to Fight against COVID-19
#### 4.4.3 Immunity and Vaccination Certificate  
Immunizer, serology, or antibody tests are the tests that determine if someone has gained immunity to the virus (e.g., COVID-19) upon complete healing from the infection. The immunity (vaccination) certification records the viruses or diseases that he/she have been protected or vaccinated against. The certificate helps limit and restrain the scope of COVID-19 by letting the officials and authorities form plans by supporting cross-border transportation from one place to another for those who hold this certification [49]. Accordingly, the certificate's fraud security, robust privacy means, and cost-effectiveness are the governments' fundamental responsibilities to reduce frauds relative to traveling. Employing blockchain-based vaccine certification and immunity testing implements an adequate and reliable data administration policy that is straightforward to manage, non-vulnerable, and cost-effective [5].  
Blockchain manipulates asymmetric encryption and decryption patterns [50], as well as uses digital signatures to preserve both tests and certificates. Additionally, the decentralization characteristic confirms the security of such data against malicious attacks or a single point of failure by enhancing users' confidence by maintaining information authenticity and protection.  
The above-mentioned certifications can be confirmed in a reliable, advanced manner, and secure the secrecy of the user information. For example, at resuming marketplaces after the COVID- 19 crisis, many businesses can promote and adopt the policies to allow only those workers to turn to work who hold a legitimate electronic vaccination license (immunity passport) to get back to work. Here, blockchain deployment guarantees that only \"COVID-19 clear case\" employees could resume working in the company. The essential traceability and transparency hallmarks of blockchain support building the data origin of the COVID-19 lab outcomes. It can also considerably help the businesses check the PCR testing kits' validity for the COVID-19 examination. It generates an immunity passport for the user to register it on the ledger.
###### Abstract  
This paper proposes a novel receiver coil configuration referred to as the Vertical Coil (VC), which substantially improves energy transfer efficiency in Dynamic Wireless Power Transfer (DWPT) systems for electric vehicles (EVs). Unlike the commonly used horizontally oriented coil layout (DDQ), the VC design employs multiple coils wound vertically around a ferrite core, thereby capturing magnetic flux more effectively over an extended spatial range. Through comprehensive simulations on full-scale models and corroborating small bench model experiments, we demonstrate that the VC configuration not only achieves a higher peak efficiency exceeding 90% but also widens the efficient power transfer zone by approximately 50% compared to DDQ. This broader coverage translates into an overall energy capture increase of more than 50% during a full pass of the vehicle over the transmitter coils, a significant step toward facilitating the implementation of DWPT technology. While mutual coupling among the VC coils can alter the system's resonance frequency, our results confirm that this effect does not impede the VC's superior energy transfer performance. Consequently, the new VC receiver design offers a promising pathway for advancing DWPT technology for reducing EV battery size and extending driving range.  
wireless power transfer; dynamic wireless power transfer; EV; vertical coil; inductive charging 2025 IEEEexample:BSTcontrol
## 2 Results
### Immunological Localisation of Ab-FAR-1 Asp  
A previous study showed that _Ab-far-1_ mRNA was located in the hypodermis and other part ovaries of nematodes by in situ hybridisation [26]. Signal peptide-carrying protein was transported into the endoplasmic reticulum and Golgi for processing and modification, and it would be cut off during the process of being secreted out of the cell [40]. To further determine the tissue localisation of the Ab-FAR-1 protein and the secretion process after nematode infection, we performed immunofluorescence localisation analysis of Ab-FAR-1 without a signal peptide (Ab-FAR-1\({}^{\Delta\text{SP}}\)) on RWTN bodies and plant sections from _A. thaliana_ infected with RWTNs at 3 dpi. First, the 32.14 kDa rAb-FAR-1\({}^{\Delta\text{SP}}\) protein containing the trxA tag was obtained by prokaryotic expression. We produced polyclonal antibodies against rAb-FAR-1\({}^{\Delta\text{SP}}\) by immunising rabbits, and the antibody titres were all greater than 512 K (Figure S1a). Western blotting showed that the anti-rAb-FAR-1B polyclonal antibody specifically detected rAb-FAR-1\({}^{\Delta\text{SP}}\) at the expected size of \(\sim\)32 kDa and  
Figure 1: Expression changes of _Ab-far_ genes in the process of nematode infection. (**a**) The expression changes of _Ab-far-1_ gene during RWTN infection. (**b**) The expression changes of _Ab-far_ genes (Ab-far-1 to Ab-far-8) 3 days post-infection (dpi). To determine the silence efficiency of gene, the gene expression from 0 dpi was normalized as 1, and fold change was calculated using the 2\({}^{\Delta\Delta\text{CI}}\) method to determine a change in expression of the genes of interest. Values are presented as the means \(\pm\) standard error (SE), \(n=9\). Asterisks denote significant differences (\({}^{\ast}\)_p_ < 0.05, \({}^{\ast\ast}\)_p_ < 0.01, \(t\)-test) from the 0 dpi treatment.  
rAb-FAR-1\({}^{\mathrm{Asp}}\) in nematode proteins at the expected size of \(\sim\)19 kDa (Figure S1b). No signal was detected in the total proteins from healthy leaves. The size of the band was consistent with the predicted size, indicating that anti-rAb-FAR-1B could specifically recognise Ab-FAR-1\({}^{\mathrm{Asp}}\). Immunolocalization was performed on the nematode bodies and sections of _A. thaliana_ infected with RWTNs. Strong red fluorescent signals of Ab-FAR-1\({}^{\mathrm{Asp}}\) were observed in the body walls of nematodes (Figure 2f,j) and plant tissue sections (Figure 2j) treated with anti-rAb-FAR-1B serum, but not in the control nematodes (Figure 2c) and plant tissue sections (Figure 2n) treated with pre-immune serum. These results suggest that Ab-FAR-1 is secreted by the body walls of RWTNs and is likely targeted to host tissues. Therefore, it may play an important role in the parasitic processes of RWTNs.
### Paper Contribution  
Researchers have expressed considerable interest in vehicular networks, their applications (safety and non-safety), traffic conditions, network topology, protocols, and enhanced network performance. This survey provides the latest update on applications, standards, and MAC protocols for efficient vehicular communication. The state-of-the-art covers the essential aspects of vehicular communications, including architecture, applications, challenges, vehicular networks, standards in different countries, and classification of MAC protocol based on the channel access mechanism. To the best of our knowledge, this survey is the first of its kind to classify MAC protocols based on channel access mechanisms, including the latest protocols. Apart from the classification, the paper presents the details of ITS standard deployments in different regions, followed by a detailed explanation and implementation of the latest-generation technologies (C-V2X, 5G, and SDN). The article commences from a clear description of vehicular communication, architectural overview, objectives, and the state-of-the-art and concludes with the future scope and research directions.
## 3 Other Applications
### Environmental Monitoring Other Than Rainfall Estimation
#### 3.1.4 Monitoring Phenomena Related to Air Refractivity  
Since local temperature and humidity variations due to turbulent eddies lead to fluctuations in air refractivity, which cause the microwave signal of CMLs to scintillate, the sensible and latent heat fluxes associated with the air refractivity can be estimated by CML signals. On the basis of this principle, [PERSON] et al. [122] proposed a method for estimating the sensible and latent heat fluxes and, hence, the areal evaporation using CMLs with consideration of the surface energy budget constraints. In addition, since temperature inversions, which are the main cause of poor air quality, lead to multipath and ducting phenomena in electromagnetic waves, thereby relatively amplifying or attenuating RSLs, [PERSON] et al. [123] utilized CMLs to detect temperature inversions and, thus, air pollution. Similarly, the literature [124] measured wildfire smoke particulate matter concentrations using CMLs and found a power-law relationship between RSL fluctuations and PM10/PM2.5 (particulate matter concentration less than 2.5/10 \(\upmu\)m). In addition, given the relationship between pollutant washout and rainfall, CMLs have the potential to point out areas with improved air quality [125].
## 5 Experiments  
In this section, we present a comprehensive security assessment of the smart contract. The evaluation employs a hybrid methodology that integrates both automated analysis tools and structured manual testing. The assessment was carried out under the assumption that the smart contract had been verified and published in Etherscan block explorer [44]. This assumption is aligned with best practices in decentralized application development, where developers verify the contract to improve transparency and community trust. A verified contract on Etherscan allows users to inspect Solidity source code, analyze function-level behavior, and understand input/output relationships without relying on reverse engineering. While code verification promotes openness and facilitates third-party audits, it also increases the attack surface, as malicious actors can more easily identify flaws. In our study, this setup enabled efficient manual security testing and provided a realistic context for simulating both valid and adversarial interactions with the smart contract. The general testing framework applied in this study is shown in Figure 6. It combines symbolic analysis with manual testing in a structured pipeline, enabling comprehensive vulnerability detection. The detected issues were classified using the SWC registry [45], assigned severity levels, and summarized in structured tables (Tables 2-6). Figure 6: Security assessment methodology combining static analysis, symbolic execution, and scenario-based manual testing. \begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**No.** & **Function** & **Scenario** & **Expected Result** & **Result** \\ \hline
1 & _subscribe(email, firstName, lastName)_ & The user inputs correct arguments for the function. & The function executes correctly, and the provided data is saved on the blockchain. & Positive A “Subscribed” event is emitted. \\ \hline
2 & _subscribe(email, firstName, lastName)_ & The user invokes the function with an incorrect transaction fee. & The function returns an error and does not save the provided data. A “Subscribed” event is not emitted. \\ \hline
3 & _unsubscribe()_ & A user with an active subscription invokes the function. & The function returns an error and does not save the provided data. A “Subscribed” event is not emitted. \\ \hline
4 & _updateSubscriptionFee(newFee)_ & The contract owner invokes the method with a correct new fee value. & The function call succeeds. The subscription fee is updated. & Positive A “Subscribed” event is not emitted. \\ \hline
5 & _updateSubscriptionFee(newFee)_ & The contract owner invokes the method with an incorrect fee value, e.g., an empty value. & The function call fails. The fee is not updated. & Positive A “Subscribed” event is not emitted. \\ \hline
6 & _withdrawFunds()_ & The contract owner invokes the method. & The function executes successfully. Funds are transferred to the owner’s account. & Positive A “Subscribed” event is not emitted. \\ \hline \end{tabular}
\end{table} 
Table 2: Successfully completed manual testing scenarios for the implemented contract methods.