esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Sequential Models  
Building on the advances in matrix factorization methods, researchers have explored sequence prediction models to capture more complex temporal patterns in POI recommendation. The Spatio-Temporal Recurring Neural Network (ST-RNN) model [11] incorporates spatio-temporal contextual information, extending RNNs with time-specific and distance-specific transition matrices. To enhance Long Short-Term Memory (LSTM) networks, the Spatio-Temporal Gated Network (STGN) [12] introduces spatio-temporal gates to capture relationships between successive check-ins. Addressing the limitations of existing RNN-based methods, the Long- and Short-Term Preference Modeling (LSTPM) method [13] combines a non-local network for long-term preference with a geo-dilated RNN for learning short-term preferences.  
While sequence prediction models have proven effective, the introduction of attention mechanisms and Transformer models [18] has further advanced the accuracy and performance of POI recommendations. The Long- and Short-Term Preference Learning (LSPL) model [14] leverages attention mechanisms and LSTM to capture users' contextual and sequential behavior. Addressing spatio-temporal sparsity and relations, the Spatio-Temporal Attention Network (STAN) [15] employs self-attention layers to capture non-trivial correlations from non-adjacent locations and non-consecutive visits. The Personalized Long- and Short-Term Preference Learning (PLSPL) model [16] combines user-specific prefer ences through a linear combination unit and utilizes attention mechanisms to capture contextual features. The Auto-Correlation Enhanced Multi-Modal Transformer Network (AutoMTN) [17] uses the Transformer network with an auto-correlation mechanism to model intricate dependencies and cross-modal interactions between POI and category sequences. SelfTrip [27] introduces a self-supervised representation learning framework that employs a two-stage contrastive learning mechanism and four trip augmentation methods to capture the diversity and uncertainty of users' travel needs. INSPIRE [28] focuses on modeling users' interest sustainability over time for sequential POI recommendation, addressing the dynamic nature of user preferences. Counterfactual user sequence synthesis [29] employs counterfactual data augmentation to alleviate noise and sparsity issues in sequential POI recommendation, enhancing the robustness of the models.
## 3 Results and Discussion
### Continuous LBI Measurement
#### 3.2.3 Correlation between Backscattering Images and Oil Interface Height  
Figure 7 illustrates the correlation between the height of the oil-sediment interface and the backscattering images. The scattering phenomenon decreased as the interface moved to a lower position, particularly when the interface height ranged between 0 and \(-\)100 mm. When the interface was lower, e.g., at \(-\)450 mm, a laser signal fluctuation could be observed. An exponential curve was initially used to fit this correlation, resulting in an R\({}^{2}\) value of 0.73 (Figure 7a). As the sedimentation process progressed, the influence on the scattering spot located on the oil surface diminished, thereby increasing the difficulty of predicting the interface height based on the scattering spot. However, fitting the data with interface heights only between 0 and \(-\)100 mm using a power function resulted in an R\({}^{2}\) of 0.97 (Figure 7b). This indicates that the scattering spots at the oil surface were more suitable for measuring the sedimentation process in the early stage of the diluted sedimentation. The Tyndall effect (Figure 3) may be more suitable for monitoring the entire sedimentation process. The laser's traveling depth should be crucial for observation, indicating that decreasing the height of the sedimentation tank may not only speed up the sedimentation process [15] but also make laser scattering technology more suitable for monitoring the sedimentation process.
# A Fast Hole-Filling Method for Triangular Mesh in Additive Repair
## 6 Experimental Results and Discussion
### Advantages and Disadvantages  
The proposed method provides a systematic solution to close the holes with different sizes in additive repair. In the case of small-sized holes, our hole-filling technique is very simple and enough robust, which ensures this technique to be smoothly applied in many practical applications. As for middle-sized holes, the presented algorithm achieves a good balance between the hole-filling efficiency and the ability to shape recovery. Regarding large-sized holes, our method can not only close the holes with very low time complexity but also ensure the filling patches to be compatible with surrounding meshes without any further refinement and fairing steps. Our approach can be used in the fields of geometry measurement, geometry modeling, intelligent manufacturing, and additive repair, etc.  
Our method might not work well on some complex or special holes. To sum up, there are two main limitations. The first one is that it is difficult for our approach to handle holes with islands. Another limitation is that the filling patch may be a nonplanar surface if the shape curve of the hole is approximately planar by using our approach.  
In future work, a novel algorithm will be developed to deal with nonplanar issues. In the case of the holes with islands, they will be first transformed into regular ones by removing the detected islands, and then be filled by our method presented in this work.
## 3 Results and Discussion
### Microstructural Analysis
Other than that, the presence of high Si elements on the surface of the coir fibre has enhanced the formation of the bond that is strongly wrapping around the admixture. This is in line with the high increase in strength registered by the soil stabilization mixture, relative to the untreated soil specimen. Table 7 shows the weight percentages and the corresponding ratios of all the major elements found in the samples for EDX test. Of all of the element composition, the major relative concentrations found are alumina (Al), silica (Si) and sodium (Na) for the untreated soil clay which are elements existing in nature. From Table 7, it can be observed that the presence and increment percentage of Si for the treated specimens with lime and alkaline activation confirms that there is formation of cementitious compound occurring caused by the pozzolanic reaction after 28 days curing periods compared to the untreated clay soil. Similarly, the percentage of Al for the treated sample happens to be increased in contrast to the untreated clay soil. In addition, from the calculated ratio of Ca:Si, and Al:Si illustrate that there be changes on the compositions for the surface particles due to the coating of pozzolanic reaction or new minerals products formed between the soil with lime or with alkaline activation interactions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Element** & **Si** & **Al** & **Ca** & **Si/Al** & **Ca/Si** & **Al/Si** \\ \hline Clay & 21.49 & 7.86 & 0.99 & 2.73 & 0.05 & 0.37 \\ CLF & 19.87 & 3.85 & 4.73 & 2.88 & 0.72 & 0.35 \\ CFAF & 24.36 & 6.19 & 3.33 & 3.3 & 0.31 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Energy Dispersive X-Ray Element for All Specimens. Figure 8: FESEM images for treated soil with inclusion of fibre (CFAF). The increment of the chemical percentage was due to the commencement of the pozzolanic reaction in conjunction with the time elapse through dissolution of alumina and silica discovered in the soil and calcium presented in lime and alkaline activation stabilizer at higher pH value (\(>\)10) for both, confirming the significant increase in strength. This observation similarly was reported by [52]. In addition of lime attributed to the increment of chemical percentage after 7 days curing period. Besides that, the ratio of the Ca:Si, and Al:Si show increase and decreases with the curing periods relative to the untreated clay soil. This was enclosed with the cementitious reaction products of calcium- silicate hydrate (CSH), calcium aluminate hydrate (CAH) and calcium aluminium silicate hydrate (CASH) in which the reaction indicates high peak element of Si, Al and Ca from EDX analysis. This observation on cementitious reaction is similar to that reported by [53; 54]. Ratio of Ca/Si was found to be in the range of 0.24 to 1.73 by [54] where it contributes to the reduction in linear shrinkage values and where the changes in plasticity properties of clay could be predictable.
## 2 WOA and VMD Algorithms  
### VMD Algorithm  
Variational mode decomposition algorithm is an adaptive decomposition algorithm based on one-dimensional Hilbert transform and frequency mixing [15]. Its essence is to solve the variational problem, by iteratively calculating the center frequency and bandwidth of each mode function, which completes the signal frequency domain decomposition. The constrained variational equation is constructed as follows.  
\[\begin{array}{c}\min\limits_{\{u_{k}\},\{\omega_{k}\}}\left\{\sum\limits_{k} \|\partial_{t}\Big{[}\Big{(}\delta(t)+\frac{j}{\pi t}\Big{)}*u_{k}(t)\Big{]}e^ {-j\omega_{k}t}\|_{2}^{2}\right\}\\ s.t.\sum\limits_{k=1}^{K}u_{k}=x(t)\end{array} \tag{1}\]  
where \(x(t)\) is original signal; \(\{u_{k}\}\) is the intrinsic mode function (IMF) component; \(\{\omega_{k}\}\) is the center frequency; \(\delta(t)\) is the impulse function.  
In order to solve Equation (1), the penalty coefficient \(\alpha\) and Lagrange operator should be introduced to relieve the constraint of equation. Solve the extending Lagrange formula by alternating parameters of \(u_{k}^{n+1}\), \(\omega_{k}^{n+1}\), and \(\lambda^{n+1}\)  
\[\begin{array}{c}\hat{a}_{k}^{n+1}(\omega)=\frac{\hat{f}(\omega)-\sum\limits _{i\
eq k}\hat{a}_{i}(\omega)+\frac{\lambda(\omega)}{2}}{1+2\alpha(\omega- \omega_{k})}\\ \omega^{n+1}=\frac{\int_{0}^{n}\omega|\hat{a}_{k}(\omega)|^{2}d\omega}{\int_{ 0}^{n}|\hat{a}_{k}(\omega)|^{2}d\omega}\end{array} \tag{2}\]  
The original signal \(x(t)\) and the reconstructed signal \(\hat{x}(t)\) can be expressed as follow.  
\[x(t)=\sum\limits_{k=1}^{K}u_{k}+u_{r}=\sum\limits_{k=1}^{K}IMF_{k}+u_{r} \tag{3}\]  
\[\hat{x}(t)=\sum\limits_{k=1}^{K}IMF_{k} \tag{4}\]
## 3 System Models  
In the context of this article, the three systems are regional, the relationship between each is relatively simple, and the distance is very close. For this reason, we have made these assumptions: (1) ignore the changes in the voltage of each system, that is, the normalized value takes (1); (2) no need to consider the influence of reactive power; (3) only active power flow constraints are considered. Therefore, there are only three power interaction constraints between systems in our model.
# Extending the Quality of Secure Service Model to Multi-Hop Networks
## 4 Case Studies of Multi-Channel and Multi-Hop QoS
### Three-Channel, Point-to-Point Network  
A third example network incorporates the concept of multiple channels between the two end-points, as described by the theory of Perfectly Secure Message Transmission [11, 12, 13, 14] and as shown in Figure 6. This third example network follows the calculations for multi-channel QoSS as detailed in [4]. In this example, there is \(\rho=1\) listener and \(\sigma=1\) disruptor with access to the network, and they may have access to any of the channels. The data are fragmented across the three channels such that the \(AL\), the average percent of the message on any of the channels, is 0.66 and the \(DF\), the average number of times a given fragment is transmitted, is 2. The QoSS is similar to that of Figure 3 with some minor variations in the characteristics to highlight the possibility that the three channels cross different network infrastructures. The network equivalent, shown in Figure 7 is represented as a single-channel network with average QoSS characteristics of \(P(C)=0.833\) and \(P(I)=0.789\). Compared to the single-channel point-to-point network in Figure 3 or the single-channel three-hop equivalent network in Figure 5, this network has a significantly improved probability of confidentiality and probability of integrity despite being more complex.  
Figure 4: A Single-Channel, Three-Hop Network and QoSS Characteristics.  
Figure 5: A Single-Channel, Point-to-Point Equivalent of a Single-Channel, Three-Hop Network.  
Figure 6: A Three-Channel, Point-to-Point Network with QoSS Characteristics.  
Figure 7: A Single-Channel, Point-to-Point Equivalent of a Three-Channel, Point-to-Point Network.
## 3 Methodology
### Prototype Implementation and Testing of Proposed Model Using Real-Time Database
#### 3.6.2 Prediction of Diabetes Using Proposed Model Using Real-Time Dataset  
We tested our optimized proposed model using a real-time dataset, where data were imported from the AWS server in the form of chunks, each of which was tested with our optimized CNN-Bi -LSTM model, and the results are updated after each instance. The entire method is demonstrated in Figure 10. referred to through Algorithm 1.  
Figure 10: Real-time data processing using optimized proposed model CNN-Bi-LSTM.  
```
Require:\(C_{Size}\), \(D_{Size}\), \(n,i\), \(R_{i}\), \(R_{c}\) initialization:\(i=0,R_{i}=0\) Ensure:\(n=\frac{D_{size}}{C_{size}}\), \(n\geq 0\) if\(D_{size}\geq C_{size}\)then while\(n\geq 1\)do  Step1: Import chunk \(i\) of size \(C_{size}\)  Step2: Perform testing using proposed model.  Step3: Obtained result \(R_{c}\) of chunk \(i\).  Step4: Update previous results \(R_{i}\): \(R_{i}\gets R_{i}\diamond R_{c}\) \(i\gets i+1\) \(n\gets n-1\) endwhile endif \(D_{size}\gets D_{size}-C_{size}=0\)
```  
**Algorithm 1**Algorithm to fetch real-time data  
Notations used in Algorithm 1:  
\(D_{size}\) : Size of database \(C_{size}\) : Size of chunks \(n\) : Number of chunks \(R_{i}\) : Initial results of model \(R_{c}\): Results of the model at an instance \(i\) \(\diamond\) : used for updating of previous results  
In the above algorithm complete set of steps are as follows:  
* Step1: Real-time dataset of size \(D_{size}\) is imported from the cloud server in the form of \(n\) chunks such as \(n=\frac{D_{size}}{C_{size}}\);
* Step2: We check if the size of the dataset is greater than or equal to chunk size. Then, each chunk \(i\) is tested over our optimized proposed model;
* Step3: For every instance, previous results \(R_{i}\) were updated with a new result \(R_{c}\);
* Step4: Lastly, we updated the size of the dataset \(D_{size}\), \(\gets D_{size}-C_{size}\). If it was still greater, then the chunk size algorithm starts from step1 again.
# Simultaneous Analysis of Heavy Metal Concentration in Soil Samples
## 1 Introduction
A review of the spectral preprocessing methods is presented by [PERSON] et al. (2016) and [PERSON] et al. (2017). The methods that are frequently combined with infrared spectroscopy are partial least square regression (PLS), principal component analysis (PCA), and artificial neural networks (ANN). A literature review showed that extensive research was performed in the field of spectral rapid assessment of soil properties, especially with emphasis on properties that are important for agriculture-organic matter content, nitrogen content, pH etc. A much narrower group of papers considered heavy metal concentration in soil. Measurements were conducted on NIR, Vis-NIR, and less often on MIR. The far-IR region was not investigated in term of its usefulness for soil metal analysis. Frequencies of oscillations of heavy metal bonds are lower because of the mass. The crystal structure of inorganic compounds can be affected in the presence of heavier cations. Thus, analyzing the possibility of use of the FIR region for metal concentration analysis is justified. The aim of this paper was construction of the PLS models for simultaneous analysis of various heavy metal concentrations in soil samples based on FT-IR spectra. Two spectral techniques, namely ATR and DRIFT, as well as the whole infrared region, were investigated during the study. Accuracy and prediction abilities of models were compared for as many as 13 harmful metals. Samples were taken from the polluted area, thus the range of concentrations of metals is very large. Moreover in this paper, the region of far-IR spectroscopy was considered as the method to improve the detection of heavy metals in soil. According to the knowledge of the authors, this region had not been investigated before in terms of soil metal concentration prediction.
# Modified Index Policies for Multi-Armed Bandits with Network-like Markovian Dependencies
## 1 Introduction
### Main Findings  
This paper makes the following theoretical contributions:  
* We demonstrate that for each arm, represented as an irreducible, finite-state, aperiodic, and reversible three-state Markov chain, simple sample mean-based index policies can achieve logarithmic regret uniformly over time, even in interconnected settings resembling networked dependencies.
* We simplify the analysis of state transition probabilities by modeling the arms as Markov chains with identical rewards that capture basic network-like structures in which transitions are dependent on state dynamics.
* We present a numerical comparison of the regret incurred by our sample mean-based index policy and evaluate its performance relative to other policies.
## 4 Trajectory Tracking Problem Statement  
This section states the tracking problem based on the identification structure proposed in Equation (25). The control objective is to design an indirect adaptive control law \(u(\zeta)\) based on the approximation given by the neural network Equation (26) and the parametric estimation of \(b\) and to force the state of the second-order system Equation (24) to track a desired trajectory \(\boldsymbol{\zeta_{ref}}=[\xi_{1,},\ \xi_{2}]^{\top}\in\mathbb{R}^{2}\). The reference trajectory represents the solution of the nonlinear reference model:  
\[\begin{split}\dot{\zeta}_{1,}=&\xi_{2},\\ \dot{\xi}_{2,}=& f_{2,}(\zeta_{1,},\zeta_{2,},t)\end{split} \tag{37}\]  
By design, we assume that the reference signal is bounded, such that  
\[\|\boldsymbol{\zeta_{ref}}\|^{2}\leq\gamma_{ref},\quad\gamma_{ref}>0. \tag{38}\]  
Now, let us define the tracking error \(\boldsymbol{e}\) as  
\[\boldsymbol{e}=\boldsymbol{\tilde{\zeta}}-\boldsymbol{\zeta_{ref}}=\begin{bmatrix} e_{1}\\ e_{2}\end{bmatrix}=\begin{bmatrix}\xi_{1}-\zeta_{1,}\\ \xi_{2}-\xi_{2,}\end{bmatrix}_{.} \tag{39}\]From Equations (25) and (37), the tracking error dynamics are given by  
\[\dot{\mathbf{e}}=\left[\begin{array}{c}\xi_{2}-\xi_{2},\\ \hat{f}_{\mathbf{e}}\Big{(}\hat{\mathbf{\zeta}},t\Big{)}+\hat{b}(t)u(\mathbf{\zeta})+\mathbf{K}_ {id}{}^{\top}\bar{\mathbf{\zeta}}-f_{2},(\zeta_{1},\zeta_{2},t)\end{array}\right], \tag{40}\]  
Adding and subtracting the term \(a_{1}\zeta_{1},+a_{2}\zeta_{2},\) in the second line of Equation (40), we obtain for the tracking error dynamic  
\[\dot{\mathbf{e}}=\mathbf{A}\mathbf{e}+\mathbf{B}\Big{[}a_{1}\zeta_{1},+a_{2}\zeta_{2},+\mathbf{W}( t){}^{\top}\mathbf{\
abla}\big{(}\hat{\mathbf{\zeta}}\big{)}+\hat{b}u(\mathbf{\zeta})+\mathbf{K}_ {id}{}^{\top}\bar{\mathbf{\zeta}}-f_{2},(\zeta_{1},\zeta_{2},t)\Big{]}. \tag{41}\]  
The tracking problem is solved by stabilizing the dynamic in Equation (41), which implies that the tracking error \(\mathbf{e}\) converges to an arbitrary small neighborhood of zero. If we can ensure that the error \(\mathbf{e}\) converges to a bounded region, then we can guarantee that the system Equation (24) tracks the reference given by Equation (37).
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 4 Algorithm Design
### Reward Function and Penalty  
The reward function is a critical parameter in the Q-learning algorithm, which is denoted as \(r_{t}\). It signifies the reward that the agent obtains when transitioning from the current state \(s_{t}\) to the next state \(s_{t+1}\) after executing an action. The agent's actions are guided by the reward function, and the goal of Q-learning-based routing algorithms is to maximize the reward by successfully transmitting data from the source to the destination. Therefore, the ability to accurately find the destination depends entirely on the reward function. The reward function is defined as follows:  
\[r_{t}=\begin{cases}LDF(l)\times RSSF(l)\times\varphi,&\text{if $s_{t+1}$ is destination}\\ 0,&\text{otherwise}\end{cases}. \tag{15}\]  
In order to ensure that the nodes are within the stable communication range of the destination node and have a sufficient signal strength, \(LDF\) and \(RSSF\) are introduced, with \(\varphi\) serving as a reward factor. If the next-hop node to which a data packet is forwarded is the destination node, the reward is \(LDF(l)\times RSSF(l)\times\varphi\). Otherwise, the reward is 0.  
Q-learning is capable of establishing an effective path by providing rewards. To further enhance the effectiveness of finding the best forward relay node, a forward-oriented reward function has been introduced. The reward function can be redefined as follows:  
\[r_{t}=\begin{cases}LDF(l)\times RSSF(l)\times\varphi,&\text{if $s_{t+1}$ is destination}\\ max(0,1-\frac{DIST^{t}(w,dest)}{DIST^{t}(w,dest)})\times\phi,\ w\in\mathcal{N}_{ \triangleright}&\text{otherwise}\end{cases}, \tag{16}\]  
where \(DIST^{t_{i}}(w,dest)\) represents the distance from the next-hop node to the destination node. If the next-hop node's position is closer to the destination node than the current position, the reward is calculated based on the forwarding progress. \(\phi\) serves as a constraint factor, where \(\phi\ll\varphi\), thereby restraining the forward-oriented reward function to avoid disrupting the reward effect of the target node.  
The proposed protocol updates the Q table through broadcast, thereby preventing the occurrence of routing voids, as there is no requirement for an exploration and exploitation process. This is due to the difficulty of adapting routing exploration and exploitation processes to rapid changes in the network topology, which can easily lead to local optima or link disconnections. Periodic beacons allow for adaptive responses to environmental changes, thereby automatically adjusting the route selection. However, even in this update process, a specific error scenario can still arise, where nodes consider each other as the next-hop node with the highest Q value. To prevent this error, we introduce the penalty value \(-\psi\), where \(\psi>0\):