esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling a New AQM Model for Internet Chaotic Behavior Using Petri Nets
## 3 Dynamical Model for Congestion Control  
_Definition of Our Dynamical Model_  
Random early detection (RED) [32] is considered the pioneer queue-based AQM, and it is one of the most extensively investigated techniques for congestion avoidance. However, RED and its variants (see [33; 34; 35; 36; 37; 38; 39]), among others, suffer from parametrization problems, and community research is still ongoing to study new modifications, some of them using chaos theory (see [23; 24; 25; 26]). The presented dynamical model of AQM is based on a generalization of the RED algorithm that improves performance. It uses the moving average of the queue length (AQL) to detect congestion and makes possible better stability control over oscillations of the AQL. When the AQL is below a minimum threshold, the packets are put into the buffer at the router. When the AQL exceeds the maximum threshold, the packets are dropped. When the AQL is between the minimum and the maximum threshold, the packets are randomly dropped according to some control parameters of our AQM. Concretely, the linear dropped packet probability distribution \(p\) of typical RED is replaced by the beta distribution (Equation (1)), providing two additional parameters \((\alpha,\beta)\) with the aim of increasing the controllability of RED dynamics. Namely:  
\[p(x;\alpha,\beta)=\left\{\begin{array}{cc}0&x<q_{min},\\ p_{max}I_{z}(\alpha,\beta)&q_{min}\leq x\leq q_{max},\\ 1&x>q_{max},\end{array}\right. \tag{1}\]  
\[I_{z}(\alpha,\beta)=\frac{B(z;\alpha,\beta)}{B(1;\alpha,\beta)}, \tag{2}\]  
\[B(z;\alpha,\beta)=\int_{0}^{z}t^{\alpha-1}\ (1-t)^{\beta-1}dt,\ \ \ \ z=\frac{x-q_{min}}{q_{max}-q_{min}} \tag{3}\]  
where \(B(z;\alpha,\beta)\) is the incomplete beta function (Equation (3)) and \(I_{z}(\alpha,\beta)\) is the regularized incomplete beta function (Equation (2)). The value of \(x\) represents the AQL; \(q_{min}\) and \(q_{max}\) are the minimum and maximum thresholds of the queue size, respectively; and \(p_{max}\in[0,1]\) is a selected drop probability. One of the advantages of our AQM model is that for properly chosen \(\alpha\) and \(\beta\) values, the stability ranges extend beyond their bifurcation values in the original formulation (i.e., \(\alpha=\beta=1\)), which recovers the original model of RED. In Ref. [40], we provide a further mathematical perspective of our dynamical model presented in this article. Next, the Algorithm 1 shows a high-level view of how our AQM works for each packet arrival.  
```
1:New_Pkg
2:Drop = [True, False]
3:AQL = Calculate_AQL;
4:ifAQL < qmin then
5: Accept_packet;
6: Fill_buffer(New_Pkg);
7:Drop = False;
8:else
9:if(qmin \(\leq\) AQL \(\leq\) qmax) then
10: Values(\(\alpha\),\(\beta\));
11: Drop_probability = Calculate_Drop_probability(pmax,I(\(\alpha\),\(\beta\)));
12:ifDrop_probability = \"Discard\"then
13: Drop_packet;
14: Drop = True;
15:else
16: Accept_packet;
17: Fill_buffer (New_Pkg);
18: Drop = False;
19:else //AQL is greater than qmax
20: Drop = True;
21:returnDrop
```  
**Algorithm 1**Drop mechanism for each packet arrival.
# Space Efficiency of Tall Buildings in Singapore
## 5 Discussion
It is also acknowledged that differences in construction techniques, materials, and other contextual factors can introduce variability that may affect the outcomes of space efficiency calculations. To minimize these sources of error, the study applied standardized methods for measuring key variables such as the NFA and GFA across all buildings in the dataset, ensuring consistency in the evaluation process. However, it is recognized that variations in materials--such as the use of concrete, steel, or composite structures--may affect the building's structural systems, core sizes, and ultimately the spatial efficiency. Moreover, differences in construction techniques, particularly between older and more modern buildings in the sample, could also introduce subtle variations in the efficiency of space utilization. Additionally, it is important to acknowledge that this study relies on available floor plans and documentation, which may contain inherent uncertainties or incomplete information. To mitigate this, buildings lacking comprehensive data were excluded from the analysis, but some variability may still exist.
## 3 Experiments and Results
### Performance Comparison of Different Models
#### 3.7.5 Comparison of Crack Detection Performance under Different Pavement Materials  
To verify the detection performance of our proposed algorithm in the heterogeneous images of different pavement materials, we use the test set of the Crack500 dataset after image processing to detect. The test set contains a total of 285 pictures, including 84 images for asphalt, 90 images for concrete, and 111 images for gravel. MIOU and inference speed are shown in Table 5.  
The results show that RIIAnet has the best segmentation performance in asphalt and gravel materials, reaching the highest MIOU of 0.7222 and 0.7289, respectively; the MIOU of U-Net in concrete reaches the highest of 0.7252. In addition, the inference speed of RIIAnet is second only to BiseNet, with an average speed of 4.6 ms under images of 416 \(\times\) 416 resolution. More importantly, compared with other models, RIIAnet shows excellent generalization, and its MIOU value fluctuates little under the pavement of different materials.
###### Abstract  
Titanium alloys such as Ti-6 Al-4V are widely used in biomedical implants due to their excellent mechanical properties and biocompatibility, but their bioinert nature limits osseointegration and antibacterial performance. This study proposes a multifunctional surface coating system integrating a thermally oxidized TiO\({}_{2}\) interlayer with a hydroxyapatite (HAp) top layer synthesized via a green route using _Hylcoreus undatus_ extract. The HAp was deposited by electrophoretic deposition (EPD), enabling continuous coverage and strong adhesion to the pre-treated Ti-6 Al-4V substrate. Structural, morphological, chemical, and electrical characterizations were performed using XRD, SEM, EDS, Raman spectroscopy, and impedance spectroscopy. Bioactivity was assessed through apatite formation in simulated body fluid (SBF), while antibacterial properties were evaluated against _Staphylococcus aureus_. The results demonstrated successful formation of crystalline TiO\({}_{2}\) (rutile phase) and calcium-rich HAp with good surface coverage. The HAp-coated surfaces exhibited significantly enhanced bioactivity and strong antibacterial performance, likely due to the combined effects of surface roughness and the bioactive compounds present in the plant extract. This study highlights the potential of eco-friendly, bio-inspired surface engineering to improve the biological performance of titanium-based implants.  
Article  
Electrophoretic Deposition of Green-Synthesized Hydroxyapatite on Thermally Oxidized Titanium: Enhanced Bioactivity and Antibacterial Performance  
1  
[PERSON] 2  
[PERSON] 3  
[PERSON] 4  
[PERSON] 2  
[PERSON] 2  
and [PERSON] 2  
1 University of Coimbra, Physics Department, Rua Larga, 3004-516 Coimbra, Portugal;  
2 [EMAIL_ADDRESS]  
2 University of Coimbra, Centre for Mechanical Engineering, Materials and Processes, Advanced Production and Intelligent Systems, Department of Mechanical Engineering, Rua Luis Reis Santos, 3030-785 Coimbra, Portugal;  
[EMAIL_ADDRESS] (D.S.); [EMAIL_ADDRESS] (S.C.);  
2 [EMAIL_ADDRESS] (Z.B)  
3 Inovatools Portugal LDA, Rua da Indistria Metaloriga, 593 Cumeiras Embra, 2430-528 Marina Grande, Portugal;  
4 University of Coimbra, Centre for Mechanical Engineering, Materials and Processes, Electrical and Computer Engineering Department, FCTUC, Polo 2, Pinhal de Marrocos, 3030-290 Coimbra, Portugal  
5 IPN--LEDeMAT--Instituto Pedro Nunes, Laboratorio de Ensaios, Desgaste e Materiais, [PERSON], 3030-199 Coimbra, Portugal  
6 University of Stax, Laboratory of Multifunctional Materials and Applications (LaMMA), Department of Physics, Faculty of Sciences of Stax, Soukra Road km 3.5, B.P. 1171, Stax 3000, Tunisia  
* Correspondence: [EMAIL_ADDRESS] (P.F.); [EMAIL_ADDRESS] (S.D.)
# A Review of Mid-Infrared Supercontinuum Generation in Chalcogenide Glass Fibers
## 3 MIR SC Generation in ChG Fibers
### SC Generation in ChG Tapered Fibers
Unlike bare-glass-fiber tapering [154], the polymer jacket facilitates fiber drawing and helps avoid oxidation to reduce the aging of ChG fibers. Apart from improving a tapered fiber's mechanical strength, the polymer jacket can also provide dispersion engineering, as reported in the literature [69].
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 4 QoS-Aware Security Framework: Proposed Scheme
### Transport Protocol  
The framework is designed to accommodate both Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) traffic. The video streaming implemented in this work uses UDP as the preferred transport protocol. UDP uses the Internet Protocol (IP) to obtain datagrams from one node to another. It simplifies the need to have an established connection before data transmission. Although TCP is reliable and has assured delivery of packets, it is not favorable for video streaming and live conferencing applications since UDP embraces high performance and allows packets to be dropped instead of trying to process the delayed packets. Uneven delays between sections of a received message are not tolerated by real-time applications. UDP also saves bandwidth usage and is not latency-prone because there is no error checking.
# A Review of Artificial Intelligence Research in Peer-Reviewed Communication Journals
## 2 Literature Review
### The Evolution of Artificial Intelligence as a Research Area in the Communication Field
#### 2.1.3 Deepfake Technology  
A prominent and widely debated phenomenon in recent years is the emergence of deepfake technology. This entails the creation of AI-generated videos or audio recordings that convincingly replicate real individuals, posing challenges in discerning authentic content from fabricated material. Deepfake technology raises substantial ethical and misinformation concerns, particularly in the realm of communication, given its potential to disseminate false information and damage reputations. Recent research endeavors underscore the imperative of developing AI-driven tools for detecting and mitigating deepfakes [29]. Researchers are actively involved in crafting algorithms capable of identifying manipulation indicators in videos and audio recordings. Despite their progress, they acknowledge being overwhelmed by the technical challenge of detection [30; 31].
# Improvement of Surface Roughness and Cracking Study of Nb\({}_{3}\)Sn SRF Films
## 3 Study on BEP Preprocessed Nb\({}_{3}\)Sn
### Advantages of BEP Versus BCP and EP in Nb\({}_{3}\)Sn Coating  
The average roughness (R\({}_{\text{a}}\)) and Root Mean Square Roughness (R\({}_{\text{q}}\)) of the BEP-ed Nb substrates are measured, given in Table 1. The R\({}_{\text{a}}\) value is generally about 10 nm, which is much lower than the level of BCP-ed or EP-ed samples. The R\({}_{\text{a}}\) value of the niobium sample treated by EP is about tens of nanometers, while the results of BCP are even larger. Figure 6 shows the comparison of SEM pictures of BCP-ed- and BEP-ed Nb\({}_{3}\)Sn-coated sample; large non-uniformity is eliminated clearly by the BEP process. We used Dimension Icon SPM to measure the surface roughness, and AFM is one of the components; the type of SEM microscope is ThermoFisher Quattro ESEM.  
Nb samples were coated with cavities using a similar temperature process, and the roughness results of these Nb\({}_{3}\)Sn films are shown in Table 2. The grain size in the table is an average value from samples coated in the same experiment, and each datum is calculated  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **L16** & **L17** & **L18** & **L19** & **L28** & **L29** & **L30** \\ \hline Ra/nm & 8.13 & 15.63 & 8.42 & 7.78 & 6.95 & 17.2 & 6.79 \\ Rq/nm & 10.35 & 19.87 & 10.52 & 10.12 & 8.82 & 21.56 & 8.86 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Roughness results of BEP-ed Nb substrates.  
Figure 5: Mirror-like BEP polished niobium sample.  
Figure 6: BEP preprocessed and BCP preprocessed Nb\({}_{3}\)Sn (**a**) and AFM height measurement of BEP-ed substrate (**b**).  
by the line-intersection method. Samples L16-20 and L28-30 (shown in Table 1) were obtained by the BEP pretreatment, and the rest were obtained by BCP or EP. As shown in Figure 7, the roughness of Nb\({}_{3}\)Sn films is affected by the grain size and the polishing method on substrate. The Nb\({}_{3}\)Sn films on BEP-ed samples always have the smallest roughness compared with BCP-ed and EP-ed samples in the same experiments. The smallest R\({}_{\text{a}}\) of 26.0 nm was achieved with a grain size of 740 nm, using a process of 1200 \({}^{\circ}\)C 80 min coating and 1150 \({}^{\circ}\)C 60 min annealing, which reached the lowest level reported.  
vapor diffusion process, which may degrade the cavity performance. Thus, balancing grain size and surface roughness is essential to the future optimization of coating procedure.  
The uniformity of Nb\({}_{3}\)Sn is related to many factors. In our multiple coating experiments, as long as the sample nucleates uniformly in the nucleation stage, the uniformity of tin niobium sample will be relatively high, and there will be no low tin region. The smallest grain size of tin niobium that we prepared is 740 nm. This may be related to the denser film formation of niobium samples after BEP treatment.
# Validating and Testing an Agent-Based Model for the Spread of COVID-19 in Ireland
## 3 Results
### Validation
#### 3.1.3 Comparison to Real Data  
Figure 5 shows the average new cases per day across 30 simulation runs and the real Irish case counts during the same time period. Real cases are taken from the COVID-19 HPSC detailed statistics profile published by Ordnance Survey Ireland (2019). From the start of the model until April, the case counts are higher than the real cases and is likely due to a burn-in period of the model. During the summer months, from June to August, the case counts for the model are also higher than the real cases. This is likely due to the scaling of the model. From June to September, the number of new cases per day in Ireland is under 100, which is less than one agent with scaling. However, in order for the model to simulate the pandemic, there needs to be more than one agent infected and this likely leads to the difference in cases during the summer of 2020. However, we see a good match in the cases from October through December. Figure 5 also shows the confidence intervals around the average new cases per day. Although the average number of new cases per day matches closely with the real cases, the confidence intervals are relatively wide at the peaks. This  
Figure 4: Plot showing the average simulated new cases per day across 30 runs for four different levels of movement. Dashed lines represent upper and lower bound of confidence intervals.  
is likely due to the fact that the case counts are around 1000, and as we saw in the cross validation, the scaling factor leads to large variations between runs when there is a smaller number of cases. However, based on the comparison between the average simulated cases and the real case counts and that the real cases are within the confidence intervals produced by the agent-based model we can consider that the model is validated through comparison to real data when there are 100 or more cases per day.
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 1 Introduction
Aiming at the shortcomings of existing schemes, we present a new watermarking scheme based on orthogonal frequency division multiplexing (OFDM) against the background of commercial 5G systems. To improve the security of the watermark signal, the pseudo-user-bit is used as the parameter of watermark generation to ensure that different watermark signals are embedded for different data frames. The proposed watermarking scheme has better security than the schemes in [13; 14]. Similarly to [15], the watermark signal is embedded in the phase of the demodulation reference signal (DMRS) instead of the user's signal, but the effect on the channel estimation is eliminated by designing the symmetry of the watermark signal, which is not considered in [15]. The proposed scheme is based on the DMRS, but it is not limited to that situation, and we just provide one of the application scenarios. The watermarking scheme proposed in this paper has the following advantages:  
1. Channel coding is introduced into the scheme of watermark generation, which can reduce the bit error when the watermark is transmitted in the channel and improve the success rate of authentication. The receiver can use channel decoding to realize error detection and correction of the watermark signal so that even if a few watermark symbols are transmitted incorrectly, identity authentication can still be corrected. 2. The pseudo-user-bit is proposed and used as the parameter for watermark generation. To avoid a third party other than the legitimate communication parties stealing watermark information, it is necessary to ensure that different watermark signals are embedded for different data frames. The usual method is to hash the user bits contained in the data frame with the key information shared between the legitimate communication parties. The problem is that when the receiver makes an error in demodulating the user's signal, the watermark signal generated by the receiver must be inconsistent with the watermark signal generated by the sender. To reduce the influence of user's signal demodulation error on watermark authentication, the pseudo-user-bit is proposed, and it is used as the parameter of hash encryption instead of the user bit. 3. The watermark signal is embedded into the phase of DMRS, which can avoid the interference of the watermark signal in the demodulation of the user's signal so that the embedding of the watermark does not affect the SNR of the user's signal. Note that DMRS will be used to estimate the channel conditions, so the watermark should not affect the channel estimation. In this paper, we call this important condition the symmetry of the watermark and improve the channel coding algorithm to achieve the symmetry of the watermark. The structure of this paper is as follows. In Section 2, the system model is investigated in detail, including watermark generation, embedding, extraction, and authentication. In Section 3, the proposed watermarking scheme is simulated, and the authentication performance is investigated. Finally, the conclusions are summarized in Section 4.
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 4 Ground Subsidence Risk Prediction Model Based on Machine Learning
### XGBoost  
A ground subsidence risk prediction model that uses the XGB algorithm has been shown to be the most effective model [13]. XGB has the characteristic that individual models learn sequentially to produce results, and subsequent models improve the outcomes by reflecting the weights of preceding models. This is referred to as the boosting technique. XGB involves sequential multiple single tree-based learning to derive results and exhibits excellent performance in both regression and classification problems. XGB is relatively free from overfitting issues and has the advantage of being effective in handling big data [28].  
XGB uses Equation (3) for decision making. Here, \(\dot{y}_{i}\) denotes the predicted value of the i-th sample, and \(f_{k}\) denotes the predicted value of the k-th tree after applying the sigmoid function. The final result is obtained by summing all the predicted values. The predicted value is calculated using Equation (4) [29].  
\[\dot{y}_{i}=\sum_{k=1}^{K}f_{k}(x_{i}) \tag{3}\]  
\[\dot{y}_{i}=\frac{1}{1+e^{-f(x_{i})}} \tag{4}\]  
The errors in the tree are minimized by determining the weights using Equation (5). In this equation, \(\dot{y}_{i}{}^{(t-1)}\) denotes the predicted value from the previous model, \(h_{t}(x_{i})\) denotes the tree learned by the current model, and \(\eta\) denotes the learning rate, which indicates the reflection rate of the preceding model. The error can be reduced by repeating this process [30].  
\[\dot{y}_{i}{}^{(t)}=\dot{y}_{i}{}^{(t-1)}+\eta h_{t}(x_{i}) \tag{5}\]
## 4 Iv-Ssim
### Calculation of the IV-SSIM Value  
Local quality scores \(Q_{c}^{I\to J}(x,y)\) could be used for comparing the quality of different parts of the image, being potentially useful for visual localization of distortions. However, these scores have to be combined to assess the quality of the entire image. The IV-SSIM value for each color component \(c\) is calculated by averaging these scores over the entire image (entire color plane of component \(c\)):  
\[IV-SSIM_{c}^{I\to J}=\frac{1}{W_{c}\cdot H_{c}}\sum_{y=0}^{H_{c}-1}\sum_{x=0}^{ W_{c}-1}Q_{c}^{I\to J}(x,y). \tag{16}\]  
In order to produce a single IV-SSIM value, values calculated for single color components are combined using the weighted average:  
\[IV-SSIM_{YUV}^{I\to J}=\frac{IV-SSIM_{Y}^{I\to J}\cdot w_{Y}+IV-SSIM_{U}^{I \to J}\cdot w_{U}+IV-SSIM_{V}^{I\to J}\cdot w_{v}}{w_{Y}+w_{U}+w_{V}}, \tag{17}\]  
where \(w_{Y}\), \(w_{U}\), and \(w_{V}\) denote weights for luma and both chroma components. By default, \(w_{Y}=4\), \(w_{U}=1\), and \(w_{V}=1\). Such values are in line with default weights for IV-PSNR [4] and ISO/IEC MPEG VC common test conditions for MPEG immersive video (MIV CTC) [41]; they provide a high correlation with subjective quality assessment and refer to the proportion of luma and chroma samples in the most commonly used chroma subsampling format--4:2:0.