esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Nanotechnology and Nanomedicine
### Therapeutic Application of CSCaCO\({}_{3}\) Aragonite Nanoparticles in Drug Delivery  
Efficient and safe delivery systems which are capable of delivering therapeutic agents by enhancing their solubility and bioavailability to molecular or sub-cellular levels of infected cells in any diseased condition without harming normal healthy cells are of paramount importance for researchers in the field of nanomedicine [156]. Manifold trials and successes were seen in the delivery systems for therapeutics agents ranging from soluble to insoluble drugs, yet some limitations regarding biodegradability of such materials, stability in serum plasma level, inconsistent degradation of toxic by-products and high toxicity, which is mostly size and shape dependent, have prevented their smooth achievable clinical acceptance [17].  
The application of CSCaCO\({}_{3}\)NP in bone remodelling and osteoporosis therapy was documented in the previous work of [PERSON] et al. [11] who observed the safety and proliferation effects of CSCaCO\({}_{3}\)NP on osteoblast cells and concluded that CSCaCO\({}_{3}\)NP is a cytocompatible and antiosteoporotic agent. [PERSON] et al. [19] reported a positive outcome of the cytotoxicity and genotoxicity analysis of CSCaCO\({}_{3}\)NP on normal fibroblast cells, thus, a negligible increase in ROS generation after exposure of normal fibroblast cells to CSCaCO\({}_{3}\)NP at a dose of 100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL with a slight increase of ROS at a higher dose of 200-400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL after 72 h were observed. CSCaCO\({}_{3}\)NP as a bone remodelling agent was also supported and proven in previous literature [104; 143].  
CSCaCO\({}_{3}\)NP can be used in cancer research therapy. The therapeutic efficacy of Docetaxel loaded CSCaCO\({}_{3}\)NP demonstrated 22% viability on breast cancer cells (MCF-7) indicating an interesting high level of cytotoxic effects on cancer cells [18]. Purely CSCaCO\({}_{3}\)NPs were assessed based on their pH sensitivity for effective delivery of doxorubicin against a highly malignant primary bone cancer (osteosarcoma) [10]. In addition, studies reported improved therapeutic effects of anticancer drugs encapsulated with CSCaCO\({}_{3}\)NP for the treatment of osteosarcoma, breast cancer, bone tumours and acute myelogenous leukaemia [26; 84; 103; 113].  
The application of CSCaCO\({}_{3}\)NPs in nano-antibacterial therapy was successful and effective as reported in the previous work of [PERSON] et al. [97], who observed an enhanced antibacterial efficacy of ciprofloxacin encapsulated with CSCaCO\({}_{3}\)NP and its biocompatible effect on macrophages cell lines. In addition, CSCaCO\({}_{3}\)NPs were reported to efficiently encapsulate and boost the potency of vancomycin, hence, they can serve as a nanoantibiotic bone implant for the treatment of osteomyelitis [104].  
However, due to the hard and slow biodegradable nature of cockle shells, a study demonstrated the use of CSCaCO\({}_{3}\) powder particles as an alternative construction material for artificial reef which significantly improved the strength of the artificial reef [157].
# Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information
###### Abstract  
The effectiveness of depth information in saliency detection has been fully proved. However, it is still worth exploring how to utilize the depth information more efficiently. Erroneous depth information may cause detection failure, while non-salient objects may be closer to the camera which also leads to erroneously emphasis on non-salient regions. Moreover, most of the existing RGB-D saliency detection models have poor robustness when the salient object touches the image boundaries. To mitigate these problems, we propose a multi-stage saliency detection model with the bilateral absorbing Markov chain guided by depth information. The proposed model progressively extracts the saliency cues with three level (low-, mid-, and high-level) stages. First, we generate low-level saliency cues by explicitly combining color and depth information. Then, we design a bilateral absorbing Markov chain to calculate mid-level saliency maps. In mid-level, to suppress boundary touch problem, we present the background seed screening mechanism (BSSM) for improving the construction of the two-layer sparse graph and better selecting background-based absorbing nodes. Furthermore, the cross-modal multi-graph learning model (CMLM) is designed to fully explore the intrinsic complementary relationship between color and depth information. Finally, to obtain a more highlighted and homogeneous saliency map in high-level, we structure a depth-guided optimization module which combines cellular automata and suppression-enhancement function pair. This optimization module refines the saliency map in color space and depth space, respectively. Comprehensive experiments on three challenging benchmark datasets demonstrate the effectiveness of our proposed method both qualitatively and quantitatively.  
s  
saliency detection; absorbing Markov chain; depth information; cross-modal multi-graph learning
###### Abstract  
3D shape reconstruction from images has been an important topic in the field of robot vision. Shape-From-Shading (SFS) is a classical method for determining the shape of a 3D surface from a one intensity image. The Lambertian reflectance is a fundamental assumption in conventional SFS approaches. Unfortunately, when applied to characterize the reflection attribute of the diffuse reflection, the Lambertian model is tested to be inexact. In this paper, we present a new SFS approach for 3D reconstruction of diffuse surfaces whose reflection attribute is approximated by the Oren-Nayar reflection model. The partial differential Image Irradiance Equation (IIR) is set up with this model under a single distant point light source and an orthographic camera projection whose direction coincides with the light source. Then, the IIR is converted into an eikonal equation by solving a quadratic equation that includes the 3D surface shape. The viscosity solution of the resulting eikonal equation is approximated by using the high-order Godunov-based scheme that is accelerated by means of an alternating sweeping strategy. We conduct the experiments on synthetic and real-world images, and the experimental results illustrate the effectiveness of the presented approach.  
3D reconstruction; shape-from-shading; Oren-Nayar reflection model; eikonal equation; Godunov-based scheme; alternating sweeping strategy +
Footnote †: journal:  
Article  
A New Oren-Nayar Shape-from-Shading Approach for 3D Reconstruction Using High-Order Godunov-Based Scheme
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 4 Multi-Queue Model and Priority-Based Transmission
### Mathematical Analysis
#### 4.2.1 Average Queue Waiting Time in Q\({}_{1}\)
Using Equation (16), the effective arrival rate \(\overline{\alpha_{1}}\) can be given as:  
\[\overline{\alpha_{1}}=\sum_{k=0}^{K}\alpha_{1k}\,p_{k}=\sum_{k=0}^{K-1} \alpha_{1}p_{k}, \tag{17}\]  
Figure 7: The state-transition diagram for the finite Markov chain of \(\text{Q}_{1}\). where \(p_{k}\) is the steady-state probability to find \(k\) packets in \(\text{Q}_{1}\). Solving the equilibrium equations for the queuing system in Figure 7, we obtain:  
\[p_{k}= \begin{cases}p_{0}\left(\dfrac{\alpha_{1}}{\mu}\right)^{k}&0\leq k\leq K \\ 0&k>K.\end{cases}  \tag{18}\]  
Using Equation (18) along with the conservation relation \(\sum_{k=0}^{\infty}p_{k}=1\), we solve for \(p_{0}\):  
\[p_{0} =\dfrac{1}{1+\sum_{k=1}^{\text{K}}\left(\dfrac{\alpha_{1}}{\mu} \right)^{k}}=\left[1+\dfrac{\left(\dfrac{\alpha_{1}}{\mu}\right)\left(1-\left( \dfrac{\alpha_{1}}{\mu}\right)^{K}\right)}{1-\dfrac{\alpha_{1}}{\mu}}\right]^ {-1} \tag{19}\] \[=\dfrac{1-\dfrac{\alpha_{1}}{\mu}}{1-\left(\dfrac{\alpha_{1}}{ \mu}\right)^{\left(K+1\right)}}.\]  
Then, \(p_{k}\) in Equation (18) can be rewritten as:  
\[p_{k}= \begin{cases}\dfrac{\left(\dfrac{\alpha_{1}}{\mu}\right)^{k}\left(1- \dfrac{\alpha_{1}}{\mu}\right)}{1-\left(\dfrac{\alpha_{1}}{\mu}\right)^{ \left(K+1\right)}}&0\leq k\leq K\\ 0&k>K.\end{cases}  \tag{20}\]  
Applying Equation (20) in Equation (17), we get:  
\[\overline{\alpha_{1}} =\alpha_{1}\sum_{k=0}^{\text{K}}\dfrac{\left(\dfrac{\alpha_{1}}{ \mu}\right)^{k}\left(1-\dfrac{\alpha_{1}}{\mu}\right)}{1-\left(\dfrac{\alpha_ {1}}{\mu}\right)^{\left(K+1\right)}} \tag{21}\] \[=\alpha_{1}\dfrac{1-\left(\dfrac{\alpha_{1}}{\mu}\right)^{K}}{1- \left(\dfrac{\alpha_{1}}{\mu}\right)^{\left(K+1\right)}}\] \[=\alpha_{1}\dfrac{1-\left(\alpha_{1}T_{c}\right)^{K}}{1-\left( \alpha_{1}T_{c}\right)^{\left(K+1\right)}}.\]  
Based on Equations (13)-(15), and (21), \(\overline{W_{\text{Q}_{1}}}\) can be given as:  
\[\overline{W_{\text{Q}_{1}}}=\dfrac{T_{c}}{2}\left(3\left(\left(\alpha_{1} \dfrac{1-\left(\alpha_{1}T_{c}\right)^{K}}{1-\left(\alpha_{1}T_{c}\right)^{ \left(K+1\right)}}\right)\left(\max\left\{0,\left(\overline{W_{\text{Q}_{1}}} -D_{\text{Q}_{1}}\right)\right\}+\min\left\{\overline{W_{\text{Q}_{1}}},D_{ \text{Q}_{1}}\right\}\right)\right)+1\right). \tag{22}\]
## 2 Background
### Interference Estimator  
The received signal \(y[j,k]\) was supplied in two parts. The first is fed to the VI/HI estimator to predict VI and HI. The second is the equalizer part, which converts \(y[j,k]\) into \(z[j,k]\). Signal \(z[j,k]\) is approximate to the desired signal \(d[j,k]\), which is interfered with by the 2D target **G**. After equalizing the received signal \(y[j,k]\) into \(z[j,k]\), we can present the signal \(z[j,k]\) as follows:  
\[\begin{array}{rcl}z[j,k]&\approx&d[j,k]\\ &\approx&a[j+1,k+1]rp+a[j+1,k]pl_{2}+a[j+1,k-1]rp\\ &+&a[j,k+1]rl_{1}+a[j,k]l_{1}l_{2}+a[j,k-1]rl_{1}\\ &+&a[j-1,k+1]rp+a[j-1,k]p_{2}+a[j-1,k-1]rp\end{array}. \tag{22}\]  
The interferences \([r_{1}\,l_{1}\,l_{2}\,r_{1}]\) and \([p_{2}\,l_{1}\,l_{2}\,p_{2}]\) are from the horizontal and vertical main tracks, respectively. The interference \([rp\,p_{2}\,rp]\) is from the upper and lower tracks. In this study, we define \([rp\,p_{2}\,rp]\) as VI. The interference \([rp\,r_{1}\,rp]^{T}\) is from the left and right tracks and assigned as HI. Then, we define a vector **v** as follows:  
\[\textbf{v}=\Big{[}v[j,k+1]\quad v[j,k]\quad v[j,k-1]\Big{]}, \tag{23}\]  
where  
\[v[j,k+1]=a[j+1,k+1]rp+a[j,k+1]rl_{1}+a[j-1,k+1]rp, \tag{24}\]  
\[v[j,k]=a[j+1,k]pl_{2}+a[j,k]l_{1}l_{2}+a[j-1,k]p_{2}, \tag{25}\]  
\[v[j,k-1]=a[j+1,k-1]rp+a[j,k-1]rl_{1}+a[j-1,k-1]rp, \tag{26}\]  
Using the values in Table 1, we can detect **v** to obtain HI by employing a trellis with 36 states for \([v[j,k+1],v[j,k]]\), where each state has six output branches for \(v[j,k-1]\). The Viterbi algorithm is then applied to this trellis to detect the values of \(v[j,k]\). This process is referred to as hard detection or hard output. We further modified these detected values into the probabilities of \(v[j,k]\), which are analogous to the extrinsic information in the MAP algorithm. The modified method is detailed in Section 3.3. Similarly, the vector **v** can be grounded as shown below to detect VI.  
\[\textbf{v}=\Big{[}v[j+1,k]\quad v[j,k]\quad v[j-1,k]\Big{]}, \tag{27}\]  
where  
\[v[j+1,k]=a[j+1,k+1]rp+a[j+1,k]pl_{2}+a[j+1,k-1]rp, \tag{28}\]  
\[v[j,k]=a[j,k+1]rl_{1}+a[j,k]l_{1}l_{2}+a[j,k-1]rl_{1}, \tag{29}\]  
\[v[j-1,k]=a[j-1,k+1]rp+a[j-1,k]pl_{2}+a[j-1,k-1]rp, \tag{30}\]
## 3 Simulation and Discussions
### Discussions  
From the above simulation and training results, it can be seen that the proposed method in this paper has a certain improvement in accuracy and a shorter training time compared with the traditional algorithm, indicating a certain reliability. However, limited by the capacity of the simulation data and the performance of the machine, the span of the percentage band in this paper is large, reaching 10%. As a result, it leads to an uneven distribution of correct predictions in the same class of data sets, and there are some misjudgments at the occupancy threshold. If conditions allow, after acquiring more data, and then using the feature variability of improved energy entropy concentrated in some data segments to reduce the length of feature vectors in the training samples and reduce redundant information, this algorithm should be able to further improve the prediction accuracy and reduce the training time. Meanwhile, considering that the regression model can more accurately describe the proportion of renewable energy, the further study is considered.  
Therefore, the following advantages and disadvantages should be pointed out. Regarding the advantages:  
1. There is no need to specifically analyze the topology and power flow of the power grid, and the required data types are easy to obtain;
2. The intelligent algorithm has higher fault tolerance and can adapt to various network structures and operating conditions;
3. Compared with the traditional algorithm, the algorithm in this paper can highlight the power quality characteristic level with renewable energy more. However, this paper also has the following shortcomings:  
1. The training of neural network requires a large amount of data and has certain requirements for sampling devices and storage capacity;  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **R1** & **R2** & **R3** & **R4** & **R5** \\ \hline Accuracy & 100\% & 92.5\% & 97.5\% & 100\% & 97.5 \\ \hline Total Accuracy & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test accuracies for each type of occupancy.  
Figure 6: Comparison between predicted results and accurate values.  
At present, the proportion cannot be given very accurately, only the general scope can be specified.  
Based on the above analysis and simulation verification, this method has certain practical application value. In the context of global promotion of low carbon emissions, this method can judge the proportion of renewable energy components from the user side, so as to provide a more accurate basis for carbon emissions for power users. Electricity enterprises can use it as a basis to choose cleaner energy.
# Deduplication-Aware Healthcare Data Distribution in IoMT
## 2 Literature Review
### Sub-Chunk Deduplication-Based Schemes
In [33], the chunking algorithm incorporates a cut point positioned to the right of a fixed-size window, as in LMC. This technique is named the Asymmetric Extremum (AE) or Rapid Asymmetric Maximum (RAM). In place of hashing, byte values are considered to determine cut points. To manage this, the RAM utilizes both fixed and variable-sized windows to find the byte with the highest value, which then functions as the cut point for the chunk. Ultimately, this results in fewer comparisons, which assists in lessening computational energy consumption. This method provides a higher or larger value situated at crucial points among two consecutivewindows. The fundamental difference is that, contrary to former techniques, it displays a window with a variable size on one side and a fixed size on the other side. When the cut points are determined, the hashing procedure is omitted [34]. In [35], to obtain an appropriate window size, the CWCA scheme is introduced in the domain of IoT systems. Patient data aggregation is performed through sensing devices. Afterward, the data are forwarded to a fog server, where they are checked for duplicate values. Healthcare data, including body temperature, glucose, BP, sugar level, and cholesterol data, exist in string format. The CWCA dynamically controls the size of the chunks by splitting the data into segments based on a delimiter and window size w. Three core conditions are imposed for the selection of the chunk size. If the array size exceeds w, then the last item is eliminated, ensuring that the chunks fit within the window. If the size of the selected array is equal to w, the chunks are returned without any adjustment. Finally, if the array size is equal to or greater than 75% of \(w\) and less than \(w\), the array is returned without deleting any items. This technique ensures that the chunking process adjusts to the dynamically controlled window size, enabling the recognition of duplicates and enhancing the effectiveness in processing duplicate-free healthcare data. [PERSON] et al. presented a window size chunking algorithm with a biased sampling-based Bloom filter using Advanced Signature-Based Encryption (WCA-BF + ASE) [35]. The Bloom filter is applied to its output to further detect duplication. The data elements are added, and membership verification is performed using a hash function. The bit positions in the Bloom filter, equivalent to the hashed values, are set as 1, demonstrating the insertion of the new element. This procedure guarantees that if the same data element is found in the data stream again, the resultant bit positions are already set as 1, indicating duplicates. To enhance privacy, ASE is used so that only legal authorities can obtain access to the data. This scheme bears a high computational cost [36].
## 2 Characteristics of the Joint Drawing Process for TP2 Copper Tubes  
The joint drawing process reduces the wall thickness and diameter of copper tubes using the coordinated operation of two or three drawing machines. To successfully implement drawing with a floating core, the following two considerations must be addressed:  
1. Forces Acting on the Floating Core: During the joint drawing process, when the floating core is in a stable position, it is subjected to normal pressures N1 and N2, as well as friction forces f1 and f2 on the surfaces of the sizing section and the tapered section, as shown in Figure 2. The equilibrium equations governing these forces are as follows: \[\sum N_{2}sin\alpha-\sum f_{2}cos\alpha-\sum f_{1}=0\] (1) or \[\sum N_{2}(sin\alpha-\mu cos\alpha)=\sum f_{1}\] (2)  
Figure 2: Equilibrium condition of forces.  
Since \(\sum N_{2}\) and \(\sum f_{1}\) are both greater than zero,  
\[\frac{sina}{cosa}>\mu \tag{3}\]  
This implies that  
\[tg\alpha>\mu\text{ or }\alpha>\tau \tag{4}\]  
where \(\alpha\) is the taper angle of the core, \(\mu\) is the friction coefficient between the core and the tube, and \(\tau\) is the friction angle between the core and the tube.  
The taper angle of the floating core must be greater than the friction angle, as indicated by (4). If this condition is not met, the core will be pulled into the sizing section, leading to tube breakage.  
2. Taper Angle Requirements: The taper angle of the floating core must be smaller than the taper angle of the outer die:  
\[\alpha\leq\beta \tag{5}\]  
If this condition is not satisfied, the core will not be able to establish a force in the opposite direction during the joint drawing process. As a result of changes in axial force, the reciprocating movement of the core may cause distinct annular marks to be squeezed out on the inner surface of the tube, and in severe cases, this can lead to tube breakage.
# CNN-BiLSTM-DNN-Based Modulation Recognition Algorithm at Low SNR
## 4 Experiment and Conclusion Analysis  
### Experimental Data and Parameter Settings  
The experiment was conducted using the well-known open-source benchmark datasets RadioML2016.10a and RadioML2016.10b [20]. Eleven widely used modulation signals, including WBFM, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, 4 PAM, 16 QAM, 64 QAM, QPSK, and 8 PSK, are included in the RadioML2016.10a dataset. Every modulation signal has a signal-to-noise ratio between \(-20\) and 18 dB. A total of 220,000 modulated signals were generated with 1000 samples at 2 dB intervals. On the other hand, the 1,200,000 signals in the RadioML2016.10b dataset are an expansion of the RadioML2016.10a dataset and include 10 modulation signals (not AM-SSB). These datasets contain signals with lengths of 128 that combine the components of the real part (I) and the imaginary part (Q) to form complicated groups. These signals, which include Gaussian white noise, multipath fading, sampling rate shift, and center frequency shift, were generated in difficult propagation settings simulations.  
Based on a single SNR for each modulation style, the signals in the dataset were randomly divided into training sets, verification sets, and test sets at a ratio of 6:2:2. Gradient updates with a 400-batch size were optimized using the Adam optimizer and the cross-entropy loss function. In cases where the verified loss did not decrease within five epochs, a multiplier of 0.8 was applied to enhance training efficiency. If the verification loss remained stagnant for 60 consecutive epochs, the training process was halted, and the model with the lowest verification loss was selected for predicting the modulation type of each test signal. All experiments were conducted on a Windows 10 operating system utilizing the Pycharm software platform. The hardware setup included a robust configuration featuring a 36-core Intel (R) Xeon (R) CPU E5-269 v4 @ 2.10 GHz processor, 160 GB of memory, and an NVIDIA Titan Xp graphics card with a total video memory of 36 G.
## 3 System Model and Performance Analysis
### Fairness Analysis  
This paper uses the fairness index proposed by [PERSON] to measure the fairness of low-priority users [28]. The fairness index ranges from 0 to 1. The index close to 1 means it is very fair, and close to 0 indicates unfairness. If the system allocates resources to \(n\) users and the \(i\)th SU is assigned \(x_{i}\), the fairness index is expressed by \(f(x)=\frac{\left[\sum_{i=n}^{n}x_{i}\right]^{2}}{n\sum_{i=1}^{n}x_{i}^{2}}\).  
In this paper, \(x_{i}\) equals 1. If user \(i\) successfully competes for subchannels, \(x_{i}\) equals 0 if the competition fails. Since the value of \(x_{i}\) is random, the average value of \(x_{i}\) is considered. If user \(i\) is PU, \(E(x_{i})=\theta(1-p_{1})+\frac{\theta p_{1}}{r-n\theta(1-p_{1})}\), because PU has the opportunity to obtain the remaining subchannels. If user \(i\) is SU, \(E(x_{i})=\theta(1-p_{1})\), where SU only obtains the subchannel through random competition, because SU is opportunistic to use the subchannel of other users in the backoff phase. Thus, the fairness index can be expressed as follows:  
\[f(x)=\frac{\left[n_{1}\bigg{(}\theta(1-p_{1})+\frac{\theta p_{1}}{r-n\theta(1- p_{1})}\bigg{)}+n_{2}\theta(1-p_{1})\right]^{2}}{n\bigg{(}n_{1}\bigg{(}\theta(1-p _{1})+\frac{\theta p_{1}}{r-n\theta(1-p_{1})}\bigg{)}^{2}+n_{2}(\theta(1-p_{ 1}))^{2}\bigg{)}}. \tag{39}\]  
According to the above analysis, a conclusion can be drawn. \(\theta p_{1}/(r-n\theta(1-p_{1}))\) will affect the fairness of the protocol. \(\theta p_{1}/(r-n\theta(1-p_{1}))\) is larger; this protocol is less fair.