esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectrum Slicing for Multiple Access Channels with Heterogeneous Services
## 4 Orthogonal Multiple Access
### Latency-Oriented System
We can then define the set \(\mathcal{H}_{\ell}^{(n,d)}\), which contains the elements \(\mathbf{g}\in\mathcal{G}_{\ell}^{(n)}\) for which the considered packet is transmitted at the \(\ell\)-th opportunity:  
\[\mathcal{H}_{\ell}^{(n,q)}=\Big{\{}\mathbf{g}\in\mathcal{G}_{\ell}^{(n)}: \psi_{\ell}^{(\mathbf{g},q)}-\sum_{k=1}^{\ell-1}\psi_{k}^{(\mathbf{g},q)}=1 \Big{\}}. \tag{27}\]  
The maximum value of \(\ell\) is \(q+1\), as by that point the packet has either been transmitted or dropped. Consequently, the success probability \(p_{s,l}(n,q)\) for an intermittent user arriving \(n\) slots after an allocated one and finding a queue of \(q\) packets ahead of it is given by  
\[p_{s,l}(n,q)=\sum_{\ell=1}^{q+1}\sum_{\mathbf{g}\in\mathcal{H}_{\ell}^{(n,q)} }p_{\text{gen}}(\mathbf{g};\ell,n)(1-\epsilon_{l})p_{\text{free}}. \tag{28}\]  
The packet can only be received correctly if the channel is free and there are no channel errors, as we assume totally destructive interference. The probability of having a free channel is equal to the probability that none of the other intermittent users in the same group have any packets in their queues:  
\[p_{\text{free}}=(\pi_{0}^{(n-1)})^{\frac{U_{l}}{\mathrm{c}}-1} \tag{29}\]  
We can then compute the conditioned latency distribution:  
\[p_{T}(\ell GT_{\text{int}}-n;q,n)=\frac{\sum_{\mathbf{g}\in\mathcal{H}_{\ell} ^{(n,q)}}p_{\text{gen}}(\mathbf{g};\ell,n)}{p_{s,l}(n,q)}. \tag{30}\]  
Knowing that packet generation probability is the same for every slot, we can now use (23) to derive the overall success probability. \[p_{s,l}=\sum_{n=1}^{T_{\text{int}}}\sum_{q=0}^{Q}\frac{\pi_{q}^{(n-1)}p_{s,l} (n,q)}{GT_{\text{int}}}. \tag{31}\]  
In the same way, we derive the latency pmf:  
\[p_{T}(t)=\sum_{n=1}^{GT_{\text{int}}}\sum_{q=0}^{Q}\frac{\pi_{q}^{(n-1)}p_{T} (t;\min(q,Q-1),n)p_{s,l}(n,q)}{GT_{\text{int}}p_{s,l}}. \tag{32}\]The 90 th percentile of the packet delivery latency \(T_{90}\) can be derived by applying the definition in (12).
## 3 Results
### Characterization of the Materials
#### 3.1.5 FTIR Spectra Analysis  
FTIR spectra obtained for P25 and the two TiLAC composites are presented in Figure 5. A medium sharp peak at around 3700 cm\({}^{-1}\) can be ascribed to the stretching vibration of the O-H bond. The absorption band of water molecules adsorbed from the environment appeared at 1680 cm\({}^{-1}\)[46]. Two bands at the range 2200-2300 are associated to the carbon dioxide vibration band and medium C=C=C stretching band. A strong absorption peak is observed around 850-1200 cm\({}^{-1}\) and is attributed to the Ti-O-Ti bond [47]. The wavelength is slightly shifted to a lower wavenumber after the addition of LAC, indicating that the mass of this molecule is reduced, being the frequency of vibration inversely proportional to the mass of a vibrating molecule [48].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**BE (eV) C 1\(s\) Signal**} & \multicolumn{4}{c}{**BE (eV) O 1\(s\) Signal**} \\ \hline \multirow{2}{*}{LAC} & \multirow{2}{*}{284.8} & \multirow{2}{*}{286.2} & \multirow{2}{*}{288.2} & \multirow{2}{*}{290.0} & & 531.6 & 533.4 \\  & & & & & & (33.2) & (66.8) \\ TiLAC-7/3 & 283.6 & 284.8 & 286.7 & 288.9 & & 529.7 & 531.2 & 532.9 \\  & & & & & & (82.3) & (14.0) & (3.7) \\ TiLAC-9/1 & 283.6 & 284.8 & 286.5 & 288.5 & & 529.7 & 531.2 & 532.8 \\  & & & & & & (85.0) & (12.3) & (2.7) \\ Ti & & & 284.9 & 286.6 & 288.7 & & 529.7 & 532.0 & \\  & & & & & & (57.1) & (42.9) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: BE values corresponding to C 1\(s\) and O 1\(s\) signals (the relative contribution, in percentage, for the oxygen deconvoluted peaks appears in brackets).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **C (\%)** & **O (\%)** & **P (\%)** & **Ti (\%)** \\ \hline LAC & 93.25 & 6.13 & 0.62 & - \\ TiLAC-7/3 & 38.80 & 43.02 & 0.93 & 17.26 \\ TiLAC-9/1 & 26.92 & 51.34 & 0.37 & 21.36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Atomic surface composition in (%) from XPS spectra.
## 3 The Basic of GNSS  
The performance and quality of modern train control systems depend mainly on the localization equipment and systems used. The main functions of such solutions are to determine the absolute position of the train, including the safety-appropriate confidence interval, speed, acceleration, and the assignment of trackside and mobile resources. The result of determining the location of a given rail vehicle is used for the needs of the traffic control and management system, on the basis of which railway traffic is carried out in a collision-preventing manner and optimized use of infrastructure, which has a significant impact on the safety and capacity of railway lines [21]. Fulfiment of the above criteria  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & & & & & & & Time Interval & & & \\ \cline{2-10}  & **2000–2002** & **2003–2005** & **2006–2008** & **2009–2011** & **2012–2014** & **2015–2017** & **2018–2020** & **2021–2023** & **2024** \\ \hline Number of publications & 30 & 32 & 90 & 134 & 193 & 289 & 456 & 623 & 93 \\ Trend & - & 2 & 58 & 44 & 59 & 96 & 167 & 167 & - \\ \hline \end{tabular}
\end{table}
Table 1: Overview cross-section of literature items published since 2000 by three-year period.  
with regard to positioning functions can potentially be met through applications of GNSS functionality. The general assumptions for GNSS are that it is possible to extract GNSS position records and then use the data for analysis to determine the position of a rail vehicle on the rail infrastructure. The above is achieved by extracting features in the form of spatial and temporal information, such as, for example, speed, acceleration, direction, and distance for each vehicle individually or collectively for all in the localization area. The extracted features can serve as batch data for the rail traffic guidance and control systems to define a dynamic graph of rail traffic in the study area [18; 22].
# Atom Probe Tomography for Catalysis Applications: A Review
## 3 Case Studies
### Model Catalysts as Tips  
_Focus on the Surface and Bulk_  
Using tip-samples as model catalysts, this section addresses the global behavior of Pt-based catalysts, concentrating on both the surface and the bulk rather than on a facet-to-facet analysis as was the case in Section 3.1. This research is driven by the challenge of minimizing the amount of expensive platinum group metals used as catalysts, without affecting the catalytic activity. One option is the design of nano-engineered catalysts for which a thorough atomic scale understanding of the catalytic materials is required.
## 2 Materials and Methods
### Accuracy Metric  
To verify the effectiveness of the proposed network and the improvements made to sea ice classification, we employed evaluation metrics commonly used in the field of image semantic segmentation, including the overall accuracy, kappa coefficient, Mean Intersection Over Union (MIoU), and _F1_score_ to enact a performance analysis. We started by calculating the confusion matrix to obtain True Positives (TPs--those samples correctly predicted and judged as positive), True Negatives (TNs--those correctly predicted and  
Figure 8: The overall process of post-processing based on morphological methods.  
judged as negative), False Positives (FPs--incorrectly predicted but judged as positive) and False Negatives (FNs--incorrectly predicted but judged as negative). Then, four evaluation metrics were calculated:  
\[OA=\frac{TP+TN}{TP+FP+FN+TN} \tag{3}\]  
\[Kappa=\frac{OA-P_{e}}{1-P_{e}} \tag{4}\]  
\[MIoU=\frac{1}{k+1}\sum_{i=0}^{k}\frac{TP}{TP+FP+FN} \tag{5}\]  
\[F1\_score=\frac{2\times TP}{2\times TP+FP+FN} \tag{6}\]  
where \(OA\) refers to the proportion of all the correctly judged results out of the overall number, and the formula is the same as that shown in Equation (3). The Kappa coefficient is used to measure the accuracy of the image algorithm, which is defined in Equation (4). Here, \(P_{e}\) indicates the overall and desired classification accuracy, and the closer the Kappa coefficient to 1, the more effective the segmentation. \(P_{e}\) represents the sum of the \"product of actual and predicted quantities\" for all categories, divided by the \"square of the total number of samples\". \(MIoU\) represents the ratio of intersection and concatenation between the segmentation and real results of a category following the segmentation algorithm, and the closer the \(IOU\) is to 1, the more effective the segmentation; the formula is the same as that shown in Equation (5). \(k\) is the number of categories classified. The \(F1\_score\) measures the relationship between the segmentation results and the recall, and the formula is the same as that shown in Equation (6).
## 4 Data Analysis in Maritime IoT Applications
### Case Studies of Successful Data Analysis in Maritime IoT
#### 4.1.5 Case Study 5  
Application of Internet of Things (IoT) and Big Data in the Maritime Industries: Ship Allocation Model is a research paper by [PERSON] that investigates the usage of IoT and Big Data in maritime industries, with a specific focus on a ship allocation model.  
IoT, as a gateway to digital transformation, alongside Big Data, AI, and blockchain, relies heavily on digitized data. The study posits that the transformation of physical framework information into digital data opens up possibilities to improve vessel operations. Moreover, several stakeholders, including consignees, shipyards, shippers, manufacturers, and classification societies, are interested in maritime Big Data due to its potential to uncover hidden information useful for operations.  
The research illustrates the significant changes in the world's ship logistics industry due to global cargo movement, which in turn has led to an exponential growth in available Big Data. The effective utilization of this Big Data and IoT could lead to major innovations in the shipping industry.  
The paper presents a ship allocation model developed using maritime Big Data and data extracted from IoT. The model uses a Machine Learning Database (MLDB) with multiple types of data, such as AIS data, ship operational data, ship data, port data, and route data. Three distinct models were utilized to develop the ship allocation model, namely the shipper, shipowner, and operator models.  
The paper highlights the development of a system for ship allocation using maritime Big Data and IoT data. The allocation model effectively balances supply and demand, identifies effective vessel specifications, and gauges the impact of vessel efficiency on demand. A 210,000 DWT vessel was found to be the most competitive for iron ore transport from Australia to Japan. The study suggests that future research should focus on simulating the automatic ship allocation model based on global ship allocations for different cargoes, ship sizes, and types [70].
# Structure-Aware Trail Bundling for Large DTI Datasets
## 8 Conclusions  
In this paper, we have presented a set of techniques for the simplified visualization of large 3D trail sets produced by tractography on DTI volume data. We perform simplification jointly both in the geometric space and image space. For the geometric simplification, we extended the state-of-the-art CUBu bundling method to handle 3D trail sets, and also proposed several bundling adaptations to handle the specifics of DTI fiber tracks. For the image-space simplification, we proposed several splat-based rendering methods that merge the rendered trails in the resulting image to create more compact sheet-like structures, add shading, and delineate bundles by outlines. The geometric and image-space simplifications can be used jointly but also independently. We implemented the entire pipeline on the GPU using a combination of CUDA and pixel shader techniques. Our end-to-end method can create simplified visualizations at interactive frame rates and allow interactive parameter manipulation.  
We studied the parameter space of the proposed method by identifying good presets for most of them. For the remaining free ones, we explored their various combinations by an internal and an external evaluation, the latter involving five professionals working with DTI data. The combined evaluations show that both bundling and shading have perceived added value and are seen as creating images that have less occlusion, are clearer, and show the brain connectivity better than other comparable techniques.  
Several directions of future work are possible. First, we aim to further explore the design space spanned by our various bundling and shading techniques, so as to find different combinations of these and/or their parameters that lead to visualizations with better occlusion reduction, clarity, and ability to depict complex brain structures. Secondly, we consider adapting our pipeline to incorporate (and measure) quality criteria to show and control the level of permissible deformation caused by bundling, thereby increasing the confidence of users in the depicted structures. Last, a low-hanging fruit is to use our pipeline to create simplified visualizations of other large 3D trail sets such as aircraft trajectories, fluid flow data, or graph layouts.  
[PERSON] was responsible for the implementation and testing of the presented set of techniques (Section 3). [PERSON] was responsible for executing the user evaluation (Sections 5 and 6). [PERSON] was responsible for the overall research ideas, methodology, and formalization of the reported results. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Covert Surveillance Video Transmission with QAM Constellations Modulation
## 2 Transmission Scheme and Hypothesis Test
### Hypothesis Test  
To determine whether a surveillance video is transmitted, the adversary performs a binary hypothesis test [22] based on \(n\) successive observations \(\mathbf{Z}_{w}^{n}=[\mathbf{Z}_{w,1},\ldots,\mathbf{Z}_{w,n}]\in\mathbb{R}^{K \times n}\). Let \(\mathcal{H}_{0}\) denote the null hypothesis and \(\mathcal{H}_{1}\) denote the alternative hypothesis. Then, the total detection error probability of the adversary is defined as [39; 40; 41]  
\[\Pr(D_{1}|\mathcal{H}_{0})+\Pr(D_{0}|\mathcal{H}_{1})=1-\mathbb{V}(Q_{1,w}^{n} \|Q_{0,w}^{n}), \tag{7}\]  
where \(\Pr(D_{1}|\mathcal{H}_{0})\) denotes the probability of a _false alarm_ (i.e., rejecting \(\mathcal{H}_{0}\) when it is true) and \(\Pr(D_{0}|\mathcal{H}_{1})\) denotes the probability of a _missed detection_ (i.e., accepting \(\mathcal{H}_{0}\) when it is false). Moreover, \(Q_{1,w}^{n}\) and \(Q_{0,w}^{n}\) denote the likelihood functions of \(\mathbf{Z}_{w}^{n}\) under \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\), while \(\mathbb{V}(Q_{1,w}^{n}\|Q_{0,w}^{n})\) denotes the total variation distance between \(Q_{1,w}^{n}\) and \(Q_{0,w}^{n}\).  
Similar to Equation (4), the probability density functions of \(Q_{1,w}^{n}\) and \(Q_{0,w}^{n}\) at the adversary can be expressed as  
\[Q_{0,w}=\frac{1}{2\pi\sigma_{w}^{2}}\Big{[}\exp\Big{(}-\frac{x^{2}+y^{2}}{2 \sigma_{w}^{2}}\Big{)}\Big{]}, \tag{8}\]  
and  
\[Q_{1,w}=\sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}\cdot q_{j,k,w}(x,y), \tag{9}\]  
where  
\[q_{j,k,w}(x,y)=\frac{1}{2\pi\sigma_{w}^{2}}\Bigg{[}\exp\Big{(}-\frac{(x+g_{w} \mathbf{d}[j]\beta)^{2}}{2\sigma_{w}^{2}}-\frac{(y+g_{w}\mathbf{d}[k]\beta)^{ 2}}{2\sigma_{w}^{2}}\Big{)}\Bigg{]}. \tag{10}\]  
Figure 2: Constellation points corrupted by AWGN channels.  
Based on the Pinsker inequality [22; 29; 42], we can obtain the upper bound of total variation  
\[\mathbb{V}(Q_{1,w}^{n}\|Q_{0,w}^{n})\leq\sqrt{\frac{1}{2}\mathcal{D} (Q_{1,w}^{n}\|Q_{0,w}^{n})}, \tag{11}\]  
where \(\mathcal{D}(Q_{1,w}^{n}\|Q_{0,w}^{n})\) is the KL divergence between \(Q_{1,w}^{n}\) and \(Q_{0,w}^{n}\). Let  
\[\mathbb{V}(Q_{1,w}^{n}\|Q_{0,w}^{n})\leq\epsilon, \tag{12}\]  
where \(\epsilon\in[0,1]\) denotes the covertness parameter. With some transformation, the covertness constraint can be expressed as  
\[\mathcal{D}(Q_{1,w}^{n}\|Q_{0,w}^{n})\leq 2\epsilon^{2}. \tag{13}\]
## 3 Results and Discussions
### Energy Harvesting Study
#### 3.3.2 Energy Harvesting Results
The values of 123, 241, and 320 pA were obtained, respectively, with 0.25%, 0.50%, and 0.75% of strain using EVA 0% at 4 Hz. In the case of EVA 37%, harvested current values at 4 Hz of 371, 620 and 1027 pA were obtained, respectively, with 0.25%, 0.50% and 0.75% of strain. For EVA 65%, harvested current values at 4 Hz of 497, 938 and 1692 pA were obtained, respectively, with 0.25%, 0.50% and 0.75% of strain. Figure 11 shows a linear relationship between electrical properties and porosity. At the microscopic level, each cell is regarded as a generator containing a number of macro-dipoles. EVA 65% composition has more gas foams than EVA 37% composition. As a result, raising the proportion of space content in gas cells increases the number of macro-dipoles. For that, EVA 65% is a more sensible mechanical force compared to EVA 37% and EVA 0%. However, the increase in harvested current of the copolymer with an increase in the porosity that could be captured could be confirmed by the theoretical model with an error range between 0.3% and 5%. The harvested current significantly increases with increasing strain or porosity, achieving a high current value up to 1627 pA for EVA 65%. The same dependence is observed for the voltage harvested. These results demonstrate that porous materials are electrically and mechanically stable. As a result, it is evident that these porous polymers will be used in mechanical applications, particularly in the manufacture of energy harvesters and smart sensors based on vibration effect. The results' analysis makes it possible to study the influence of the pores created in the polymer matrix. It also makes it possible to examine the dependence of these parameters on the mechanical applied stress and frequency range. This analysis was carried out with the aim of optimizing the operating interval for a better application in the field of sensor, actuation or vibratory energy harvesting.
## 5 Smart-BEEM For IoT Systems in 5G Scenarios
### Analysis on Existing Communication Interfaces  
At present, many communication interfaces are available on IoT devices, including Zigbee, Bluetooth, WiFi, LTE, etc. New machine type communication, like LTE-M, NB-IoT and DSRC are also invented to support machine type communication. The communication interfaces all have their own characteristics, such as frequency, data rate, transmission range and energy consumption, which will also affect the throughput, network delay and in turn user utility. For example, Table 4 lists some most concerned features and the documented values for several popular communication technologies. As we can see, every single one is unique. Comparing with LTE and WiFi, Zigbee, bluetooth and NB-LTE have lower data rate. The transmission range for Zigbee, bluetooth and WiFi is shorter than NB-LTE and LTE. Apart from that, Zigbee has low energy consumption, supporting devices working for years without recharging. While the energy consumption of WiFi and LTE are relevantly high, with which IoT devices may only last for a few days.
## 2 Experimental Setup
In this scheme, the resonance took place only for the signal beam. The DP-SRO design can effectively improve the oscillator stability, increase the idler MIR output, reduce the oscillation threshold and suppress the back conversion [23]. To lower the oscillation threshold and increase the output pulse energy, the cavity length was as small as possible to increase the number of signal wave roundtrips and reduce the diffraction losses in the OPO cavity. Therefore, the total OPO cavity including the BGSe crystal was about 30 mm in physical length. To ensure only MIR radiation at 6.45 \(\upmu\)m was detected by an energy meter, a germanium (Ge) wafer with a transmittance of \(\sim\)42% at 6.45 \(\upmu\)m was set in the front of the energy meter to cut out signal and surplus pump beams. All measured OPO output results are corrected for these transmission losses. In brief, the large-sized pump beam with high peak power and a high gain DP-SRO configuration were employed to achieve high pulse energy and high conversion efficiency MIR laser at 6.45 \(\upmu\)m.
# Enhanced Radiation Therapy of Gold Nanoparticles in Liver Cancer
## Materials and Methods
### Clonogenic Cell Survival Measurement  
H22 cells were treated with PEG-GNPs of 14.4 or 30.5 nm diameter at the desired concentration (25 \(\mu\)g/mL, Au atom concentration). After 24 h, these cells were irradiated by \({}^{137}\)Cs at the following doses: 0, 1, 2 and 4 Gy, then the culture medium was replaced with fresh one containing 10% FBS. H22 cells were suspended in 0.3% agar and then plated over underlay of 2.0% agar. The medium used in the assay was RPMI-1640 (HyClone) with 10% FBS, penicillin and streptomycin at the concentration of 45 IU/mL. Colonies were counted at day 7.  
HepG2 cells were seeded in 6-well and grown for 12 h. PEG-coated GNPs of 14.4 or 30.5 nm diameter in the colloid solution were added to a final concentration of 25 \(\mu\)g/mL (Au atom concentration) and incubated for a further 24 h. Cells were irradiated by \({}^{137}\)Cs, the irradiated doses were 0, 1, 2 and 4 Gy. After gamma irradiation, the medium containing colloidal nanoparticles were replenished with freshly prepared medium, and culture media were changed every 3 days. After a two-week incubation, cells were rinsed with phosphate buffered saline containing 2 mL ethanol and stained by crystal violet. Plates were kept in air until dry, then colonies were counted. Each condition was performed in triplicate.