esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Reconstruction of bone tissue defects is a problematic area of the modern world. Temporary \"platforms\" of various materials for improving cell adhesion and proliferation have been extensively researched in recent decades. \(\beta\)-tricalcium phosphate (\(\beta\)-TCP) is a suitable biocompatible, biodegradable material used for bone regeneration. The creation of scaffolds with specifically designed surface structures will enable bone engineering applications that require navigated cell proliferation on a substrate with pre-set geometric limits. In this study, an innovative laser-based technique for surface modification was applied to improve the morphological properties of the surface of \(\beta\)-TCP pellets for proper cell surface environment creation. The obtained topographies with diverse processing parameters were compared. Homogenous microgroove structures, less than 100 \(\upmu\)m, without the onset of melting and crack formation, were produced. The contribution from the accumulation effect of a diverse number of laser pulses (N = 1-100) on the final structure dimensions was examined. The microstructured scaffolds were investigated by confocal laser scanning microscopy (CLSM), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) analyses. We studied the effect of the patterned surface of the material on the mouse calvaria osteoblast (MC3T3) cells' viability and cytotoxicity from 1 to 7 days. The results indicated that cell behavior was affected by microscale dimensions of the surface.  
\(\beta\)-tricalcium phosphate; bioceramics; femtosecond laser processing; bone tissue engineering: temporal scaffolds +
Footnote †: journal: Physics Letters B
## 3 Methodology
### DESTformer Architecture
#### 3.2.4 Decoder  
In the decoder, a frequency domain transform module is introduced to further obtain clearer seasonal-trend representations. Suppose there are \(M\) decoder layers. In each decoder layer, cross MVI-Attention and cross MSC-Attention are first used to fuse the output representation of the encoder with the input representation of the decoder. Then, two interactive frequency domain decomposition modules are used to further complete time series decomposition and aggregation. Finally, seasonal-trend representations are learned through a feedforward network and residual connection. Formally, the \(l\)-th decoderlayer is represented as \(X_{de}^{l}=Decoder(X_{de}^{l-1},X_{en}^{N})\). The detailed process of each decoder layer is represented as  
\[S_{des}^{l,1},T_{des}^{l,1}=FreDecomp(MVI-Attention(S_{de}^{l-1,2},S_{en}^{N2})), \tag{4}\] \[S_{det}^{l,1},T_{det}^{l,1}=FreDecomp(MSC-Attention(T_{de}^{l-1,2},T_ {en}^{N2})),\] \[S_{de}^{l,1}=S_{des}^{l,1}+S_{det}^{l,1},\] \[T_{de}^{l,2}=T_{des}^{l,1}+T_{det}^{l,1},\] \[S_{de}^{l,2}=S_{de}^{l,1}+FeedForward(S_{de}^{l,1}),\] \[T_{de}^{l,2}=T_{de}^{l,1}+FeedForward(T_{de}^{l,1}),\]  
where \(S_{de}^{0,2}=X_{des}\) and \(T_{de}^{0,2}=X_{det}\). In contrast to the encoder, seasonal and trend representations are only added in the last layer \(X_{de}^{M}=S_{de}^{M,2}+T_{de}^{M,2}\), and the final prediction is made through a linear layer in the decoder.
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
## 3 Framework Design
### Operation of Framework
#### 3.4.1 Framework Workflow
```
\(\langle\mathcal{N}\rangle\) : classified network trace data \(QoE_{nom}\) : normalized QoE \(th\) : threshold for normalized QoE \(Bw\) : network bandwidth \(\langle\mathcal{S}\rangle\) : streaming trace data \(\langle\mathcal{D}\rangle\) : duplicate trace check vector \(\langle\mathcal{A}\rangle\) : waiting agent \(\langle\mathcal{T}\rangle\) : each agent's trace data \(1\) : Initialize the \(k=0,\ i=1\) \(2\) : While Streaming session \(3\) : Compute Network bandwidth \(Bw_{i}\) \(4\) : Compute Normalized QoE \(QoE_{nomi}\) \(5\) : If\(\langle\mathcal{S}\rangle\)'s length \(<2\) - minutes \(6\) : Enqueue \(\langle Bw_{i}\), \(QoE_{nomi}\rangle\) to \(\langle\mathcal{S}\rangle\) \(7\) : Else \(\mathcal{S}_{1}\) \(9\) : Enqueue \(\langle Bw_{i}\), \(QoE_{nomi}\rangle\) to \(\langle\mathcal{S}\rangle\) \(10\) : Compute Average and variance of \(\mathcal{S}(Bw_{i})\) in the form of \(Bw_{avg_{i}}\) and \(Bw_{var_{i}}\) \(11\) : Leveling the \(Bw_{avg_{i}}\) and \(Bw_{var_{i}}\) to \(\langle\hat{Bw}_{avg_{i}},\hat{Bw}_{var_{i}}\rangle\) \(12\) : If Average of \(\mathcal{S}(QoE_{nom})<\)\(th\) & \(\langle\hat{Bw}_{avg_{i}},\hat{Bw}_{var_{i}}\rangle\) is not in \(\mathcal{D}\) \(13\) : Enqueue \(\langle\hat{Bw}_{avg_{i}},\hat{Bw}_{var_{i}}\rangle\) to \(\mathcal{D}\) \(14\) : If\(\mathcal{A}\)'s length is less than \(\#\) of thread that can be used \(15\) : Training _Agent_ using \(\mathcal{N}\)that matches \(\langle\hat{Bw}_{avg_{i}},\hat{Bw}_{var_{i}}\rangle\) \(16\) : Else \(17\) : Enqueue \(\langle\hat{Bw}_{avg_{i}},\hat{Bw}_{var_{i}}\rangle\) to \(\mathcal{T}\) \(18\) : Enqueue Agent to \(\mathcal{A}\) \(19\) : Dequeue \(\mathcal{A}(1)\) and \(\mathcal{T}(1)\) & training \(\mathcal{A}(1)\) using \(\mathcal{T}(1)\)
```  
**Algorithm 1** HTTP Adaptive Streaming with Online Reinforcement Learning
## 3 Results and Discussion
### Performance Evaluation Results  
#### 3.2.1 Viscosity  
The viscosity test was conducted according to the KS M ISO 2555:2002 Plastics--Resins in the liquid state or as emulsions or dispersions--Determination of apparent viscosity by the Brookfield Test method. The viscosity test result of each W-SPRG sample mixed according to the ratio of waste oil and waste rubber showed that the highest viscosity was measured in sample A, mixed with a ratio of 2:1 of waste oil and waste rubber, as shown in Figure 8. This confirmed that a viscosity below the quality standard was measured in samples D and E, mixed with ratios of 5:1 and 6:1, because the contents of waste oil were high based on the contents of waste rubber, and this was reflected in the viscosity value of W-SPRG in proportion to the mixture viscosity value of waste oil and waste rubber. This showed that waste oil and waste rubber are not just playing the role of fillers to increase the volume but also as waterproofing materials that can adjust the viscosity of synthetic polymerized gel waterproofing materials. It was also possible to secure the quality of viscosity when the ratio between waste oil and waste rubber was below 4:1. Because the higher the content of waste oil, the lower the viscosity value based on the waste rubber content, it was difficult to secure the quality, and samples D and E were subsequently excluded from the experiment.  
Figure 6: Variation in viscosity with melting time.  
Figure 7: Viscosity value with ratio of waste oil and waste rubber.
# A Game Theoretic Framework for Surplus Food Distribution in Smart Cities and Beyond
## Discussion  
The results presented in Section 4 reveal and reinforce certain preferable policies to be followed by any such approach, in general, towards the matching of donors and receivers:  
* **The availability of volunteers matter:** This is intuitive and clearly evident. As volunteer availability goes up, so does the number of matching sets. However, this happens relatively faster at the start followed by a plateau thereafter. Thus, a basic minimal availability of volunteers is required for the mechanism to produce good results.
* **Receiver requirement end timings dictate the matching order:** Although it seems quite counter-intuitive to address receiver requests that end first, it is very essential to do so. It is clearly evident that this receipt window is not the window in which the donated food is actually consumed. It is rather a food accumulation window right before the food consumption begins. Therefore, by satisfying the requirements whose accumulation windows are about to end, we are actually reach the requirement request whose food consumption window starts first. In terms of food utilization, this is most important.
* **The dynamic nature of the market should be captured:** While the agents provide their preferences with a static image of the market, it is very important to capture the dynamism of the same. Real-time situations such as unavailability of preferred agent requests and efficacy constraints such as distance limitations arising from the perishable nature of the donation can change the scenario quite a bit. By modifying the preference list of the agents to include the eligible options, the allocation, and thereby the utilization, of surplus food can be drastically improved.
* **A regret-free platform for fair donation allocation:** While it is a great facility to be able to provide preferences by agents, it can also be heavily abused. This flexibility that the mechanism provides has to be safeguarded from being over-utilized, leading to the deprivations of some agents from being mapped at all. To ensure that the provided preferences do not have an adverse effect on the mapping process, and that the same cannot be intentionally achieved by agents, provides a regret-free preference manipulation-independent platform for agents to utilize.
# Activated Carbons as Effective Adsorbents of Non-Steroidal Anti-Inflammatory Drugs
## 3 Results
### Adsorption Equilibrium
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**System** & **Isotherm Type** & **a\({}_{\text{m}}\)a** & **m b** & **n b** & **logK c** & **R2 d** & **SD(a) e** \\ \hline IBP/GAC\_15 \({}^{\circ}\)C & T & 0.50 & 1 & 0.77 & 2.57 & 0.939 & 0.042 \\ IBP/GAC\_25 \({}^{\circ}\)C & T & 0.41 & 1 & 0.61 & 3.93 & 0.954 & 0.045 \\ IBP/GAC\_35 \({}^{\circ}\)C & T & 0.31 & 1 & 0.17 & 8.45 & 0.909 & 0.085 \\ D/GAC\_15 \({}^{\circ}\)C & GF & 0.59 & 0.36 & 1 & 0.36 & 0.955 & 0.048 \\ D/GAC\_25 \({}^{\circ}\)C & GL & 0.48 & 0.54 & 0.61 & 0.64 & 0.985 & 0.033 \\ D/GAC\_35 \({}^{\circ}\)C & GF & 0.39 & 0.37 & 1 & 0.16 & 0.902 & 0.088 \\ NPX/GAC\_15 \({}^{\circ}\)C & GF & 0.64 & 0.39 & 1 & \(-\)0.28 & 0.978 & 0.047 \\ NPX/GAC\_25 \({}^{\circ}\)C & GF & 0.87 & 0.45 & 1 & 0.07 & 0.972 & 0.061 \\ NPX/GAC\_35 \({}^{\circ}\)C & GF & 1.09 & 0.47 & 1 & 0.12 & 0.985 & 0.048 \\ \hline \hline \end{tabular}  
* a\({}_{\text{m}}\)—sorption capacity, b m, n—heterogeneity parameters, c logK—logarithm of the adsorption equilibrium constant, d R2—determination coefficient, e SD(a)—standard deviation.  
\end{table} 
Table 2: Parameters of the generalized Langmuir equation for the adsorption of ibuprofen sodium, diclofenac sodium, and naproxen sodium on GAC activated carbon. Figure 2: The adsorption isotherms for ibuprofen sodium (**a**), diclofenac sodium (**b**), and naproxen sodium (**c**) on GAC activated carbon at 15, 25, and 35 \({}^{\circ}\)C.  
Thermodynamic parameters were also determined, which are critical design variables for sorption and the characterization and optimization of this process. The Gibbs free energy \(\Delta\)G\({}^{\circ}\), enthalpy \(\Delta\)H\({}^{\circ}\), and entropy \(\Delta\)S\({}^{\circ}\) were estimated from the relationship:  
\[\ln(\text{Kc}^{\text{o}})=\frac{\Delta\text{S}^{\text{o}}}{\text{R}}-\frac{ \Delta\text{H}^{\text{o}}}{\text{RT}} \tag{13}\]  
where Kc\({}^{\circ}\)--equilibrium constant scaled with reference to standard conditions by using the standard concentration c\({}^{\circ}\) = 1 mol/L R--gas constant, T--temperature [8; 34]. The obtained values of thermodynamic parameters are summarized in Table 3.  
It was observed that for all studied systems, the values of \(\Delta\)G\({}^{\circ}\) were negative, which means that the adsorption processes were spontaneous and thermodynamically favored (Table 3). Moreover, it was noticed that \(\Delta\)H\({}^{\circ}\) was positive, indicating that the sorption of ibuprofen sodium, diclofenac sodium, and naproxen sodium on GAC activated carbon is an endothermic process. Moreover, both the above-mentioned values indicate that physisorption occurred in the tested experimental systems (\(\Delta\)H \(<\) 40 kJ/mol [35] and 0 \(<\)\(\Delta\)G \(<\) 20 kJ/mol [36]). As one can see, the entropy value was also positive, which indicates that the number of degrees of freedom increased in the studied systems. This fact can also be referred to as the increase in disorder at the phase boundary in the sorption process.
## 6 Simulation Results
### Robustness against Common Signal-Processing Effects  
In this section, the robustness of our scheme is analyzed against common signal-processing attacks.  
Figure 15: (a–c) Original and reconstructed bio-signal frames using 2 DHMs.
# Testing Accuracy and Repeatability of UAV Blocks Oriented with GNSS-Supported Aerial Triangulation
### Description of the Tests  
The general goal of the experiments is to evaluate the accuracy obtained on checkpoints with GNSS-AT and compare it with the accuracy of block orientation performed with GCPs only. The accuracy is evaluated computing the differences between the coordinates of the checkpoints (CP) estimated in BBA and those measured with a GNSS receiver. Besides the Root Mean Square Error (RMSE) of the differences, also the mean values and the standard deviations are computed.  
The influence on the results of the software package used, particularly as far as the assignment of the standard deviations of the camera positions in GNSS-AT is concerned (Section 2.4.1), has been investigated. SfM has been performed separately in each software package, setting a high accuracy level in tie point extraction. To the contrary, in order to render the comparison among the three packages independent of the manual collection of image observations, the image coordinates of the 26 targets were measured with PhotoScan, imported in Pix4D and MicMac, and then used as GCP or CP according to the specific characteristics of the experiment.  
To find out whether or under which conditions GNSS-AT delivers the same accuracy as InSO, three experiments were performed varying the block control configuration (Section 2.4.2).  
In all cases, a self-calibrating BBA has been executed. Table 11 in Appendix A reports the list of parameters that have been estimated: in PhotoScan and Pix4D the same set has been considered, while in MicMac a polynomial model has been used. The interaction between interior and exterior orientation parameters has been investigated to highlight the behavior differences of the three packages.
## 3 Efficient OFDM BER Floor with Joint Near-Optimal Time Sampling and CFO
### Simulation Setup
This further implies that we can calculate the CFO-exclusive BER values, as offsets with respect to the first value (for 0 Hz) in each column of Table 7.  
This way obtained entries of Table 8 are BER penalties of the five selected exemplar CFO values, for 16 QAM modulation. As we can see from Tables 7 and 8, there is excellent matching between the estimated and the simulated CFO-induced BER floor values, which validates the CFO abstraction by the time dispersion model proposed here. Somewhat different values coming out of the AWGN abstraction (which is computationally demanding but we used it here as just another means of verification), are still in accordance with the ones coming out of the model as well as the simulator. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Top** & **Delay (ns)** & **Power (dB)** \\ \hline
1 & 0 & 0.0 \\
2 & 40.01 & \(-\)1.0 \\
3 & 93.33 & \(-\)2.0 \\
4 & 120.01 & \(-\)3.0 \\
5 & 146.67 & \(-\)8.0 \\
6 & 253.33 & \(-\)17.2 \\
7 & 546.67 & \(-\)20.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Modified EPA delay profile; CFO = 5 kHz; mean = 0.3406, variance = 0.1091. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**CFO** & **BER Estimated (21)** & **BER Estimated (18)** & **BER Simulated** \\ \hline
0 Hz (EPA) & \(1.2969\times 10^{-2}\) & \(1.3141\times 10^{-2}\) & \(1.2827\times 10^{-2}\) \\
5 Hz & \(1.2990\times 10^{-2}\) & \(1.3182\times 10^{-2}\) & \(1.2901\times 10^{-2}\) \\
70 Hz & \(1.3096\times 10^{-2}\) & \(1.3244\times 10^{-2}\) & \(1.2978\times 10^{-2}\) \\
300 Hz & \(1.3498\times 10^{-2}\) & \(1.3711\times 10^{-2}\) & \(1.3415\times 10^{-2}\) \\
1 kHz & \(1.4767\times 10^{-2}\) & \(1.5013\times 10^{-2}\) & \(1.4681\times 10^{-2}\) \\
5 kHz & \(2.3081\times 10^{-2}\) & \(2.3454\times 10^{-2}\) & \(2.2901\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Inter-symbol interference (ISI) and (time-dispersion-abstracted) CFO-based BER floor; 16 QAM, EPA profile. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Top** & **Delay (ns)** & **Power (dB)** \\ \hline
1 & 0 & 0.0 \\
2 & 32.01 & \(-\)1.0 \\
3 & 74.67 & \(-\)2.0 \\
4 & 96.11 & \(-\)3.0 \\
5 & 117.33 & \(-\)8.0 \\
6 & 202.67 & \(-\)17.2 \\
7 & 437.33 & \(-\)20.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Modified EPA delay profile; CFO = 1 kHz; mean = 0.2725, variance = 0.0698. As we can see, only for the largest selected CFO of 5 kHz, supposedly acting on the very first subcarrier (\(m=1\)) (i.e., for the worst-case CFO), the BER change with respect to the EPA profile is still minor, from 0.012969 to 0.023081.
## 1 Introduction
It offers comprehensive information about their integration and highlights opportunities for further research to enhance current crop yield prediction approaches and crop traits retrieval. While this article reviews the application of RTMs alone or in conjunction with other techniques for predicting crop yield on a large scale, it acknowledges the limited availability of studies on the integrated use of RTMs for crop yield prediction. It is important to note that this article primarily focuses on information extraction and techniques for improving crop yield prediction and crop traits retrieval using RTMs. Moreover, research gaps will also be identified to suggest future areas of improvement in this field. Crop yield prediction and crop traits retrieval using an RTM, alone or via its integration with a CGM or other advanced techniques, has the potential for improved prediction accuracy, process-based understanding, sensitivity analysis, remote sensing capabilities and enhanced decision support. These approaches offer valuable insights into crop growth dynamics, enable spatially explicit predictions and facilitate targeted interventions for optimizing agricultural productivity. The above discussion indicates the requirement for a systematic review to provide insight into research studies about RTM applications for crop yield prediction and crop traits retrieval. Hence, the contribution of this article is described here with the below given objectives:  
* To observe the current status of research studies pertaining to RTM applications for crop yield prediction and retrieval of crop traits. * To evaluate the effect of integration of RTMs and CGMs on crop yield prediction
* To assess RTM-based crop traits retrieval.
## 2 Related Work
### Rule- and Dictionary-Based Approaches  
The early named entity recognition methods mainly relied on the rules and dictionaries that were designed by combining the experience of experts in the medical field to create recognition rules and construct medical dictionaries covering entity words. Although this approach can achieve good results in some applications, it also has clear limitations. Firstly, entities in the medical field are diverse and constantly updated, making it difficult for rules and dictionaries to cover all of them. Secondly, with the continuous development of medical research, emerging medical entity words continue to appear, leading to gaps in existing dictionaries. [PERSON] et al. [6] combined medical dictionaries for medical entity extraction. [PERSON] et al. [7] explored combining BiLSTM-CRF models with lexical resources. By introducing medical lexical resources, the model improves the recognition accuracy of medical terms using prior knowledge. [PERSON] et al. [8] performed the entity extraction of medical text through grid tagging and semantic segmentation. [PERSON] et al. [9] embedded multi-feature fusion to design class-specific NERs, which are better recognized but not scalable enough.
# Role of Nicotinamide in Genomic Stability and Skin Cancer Chemoprevention
## 2 The Influence of NAD+ Status on Genomic Stability and Cell Senescence
### NAD+ Influences Energy Production by Acting as Co-Enzyme  
NAD+ is a co-enzyme in several redox reactions which release energy from oxidized nutrients (e.g., glucose, fatty acids). The energy is transferred from NAD+ to NADH by reduction, as part of glycolysis, fatty acid \(\beta\)-oxidation, and the tricarboxylic acid cycle processes. NADH is oxidized to NAD+ by anaerobic glycolysis and mitochondrial oxidative phosphorylation (OXPHOS) that generates ATP. NAD+ is also a substrate for the biosynthesis of NADP+ by NAD kinase (NADK)-mediated phosphorylation. NADP+ has an analogous role for electron transfer in biosynthetic reactions such as fatty acid and cholesterol synthesis. Moreover, NADPH provides reducing equivalents to regenerate the cellular detoxifying and antioxidative defense systems. NADPH is also a substrate of NADPH oxidases (NOXs) that generate reactive oxygen species (ROS). Thus, the NAD(P)+/NAD(P)H ratio controls cellular redox status [2; 5; 13].  
NAD+ level is critical in preserving genomic integrity of the cells, since DNA repair mechanisms are highly energy-dependent [11]. In human HaCaT keratinocytes, NAD+ depletion increased spontaneous DNA damage through up-regulation of NOX activity and subsequent increase in ROS production [14]. ROS increase induces DNA lesions, such as 8-oxo-2'deoxyguanosine (8-oxo-dG), that may result in GC to TA transition. This modified base is removed by the 8-oxoguanine-DNA glycosylase 1 (OGG1), an enzyme of the BER system [9]. NAD+ replenishment by NAM treatment completely reversed ROS accumulation and DNA damage in skin cells [14].  
Low NAD+/NADH ratios promote cellular senescence, at least in part, by limiting glycolysis and ATP production. Indeed, senescent cells display decreased cytosolic NAD+/NADH ratio and increased AMP/ATP and ADP/ATP ratios. Lower energetic status activates the AMP-activated protein kinase (AMPK) that phosphorylates and activates p53, and, in turn, promotes senescence but suppresses the IL1R signaling arm of the SASP. AMPK also inactivates HuR, which increases the stability of transcripts of p21 and p16 INK4a--two cyclin-dependent kinase inhibitors that mediate the senescence proliferative arrest through the activation of pRB tumor suppressor [13]. Furthermore, NAMPT inhibition induces senescence without the IL1R arm of the SASP [15]. Conversely, NAMPT overexpression suppresses cell senescence [16].
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 4 A Proposed New Secured Unclonable Medical Watermarking Scheme
### The Proposed Embedding and Extraction of Clone-Resistant Watermarking
#### 4.2.2 Procedure of Extraction and Verification of the Watermark  
The procedure of extraction and verification of the watermark is the inverse of the watermark embedding and signing phase. Such a process is illustrated in Figure 10. The process starts with extracting the signed watermark Z and recovering the watermark.  
Figure 10: Watermark extraction process.  
Figure 9: Clone-resistant watermark generation and embedding process.  
During this process, the stored used plaintext \(X_{A}\) is obtained from the TA server to complete the verification process. The receiver should compare the unsigned WM with the extracted WM again. The verification (comparison) can be done before clinical procedures and diagnosis.