esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Emerging Nuclear Sensor Technologies: Challenges, Advancements and Applications
## 4 Challenges and Future Directions
### Sensor Size  
The physical dimensions of nuclear sensors play a critical role in their integration into environments limited in space, like nuclear power plants and security installations. Traditional sensor systems typically include not only sensitive elements but also supporting circuitry, packaging, and thermal management components, which together can result in a bulky overall design. Recent advancements in micro- and nano-fabrication techniques [89] have enabled the development of sensor elements with dimensions on the order of micrometers [90]. For example, printed electronics and flexible substrates have been used to fabricate sensor arrays with significantly reduced footprints [91], while system-in-package (SiP) [92] and wafer-level packaging [93] approaches further minimize the overall size of the sensor module. These miniaturization strategies improve the ability to integrate sensors into existing systems without requiring substantial modifications. Compact sensor designs not only address environments with limited space but also enhance system integration and potentially lower material and assembly costs through scalable manufacturing processes.
# Power Dissipation and Wear Modeling in Wheel-Rail Contact
###### Abstract  
This paper is concerned with the modeling of power dissipation due to friction and its relation with wear estimation in wheel-rail contact. Wear is a complex multi-scale and multi-physical phenomenon appearing in rolling contact. Wear is generated by high contact stress and the work of friction forces. This phenomenon leads to the appearing of the worm material in the form of wear debris between contacting surfaces. In contact models, wear is usually described in terms of the wear depth function. This function modifies the gap between the contacting bodies as well as the shape of the surfaces of the wheel and rail in contact. In this paper, besides the wear depth function, the dissipated energy, rather than the contact stress, is taken into account to evaluate the wear impact on rail or wheel surfaces. The dissipated energy allows us to more precisely evaluate the wear debris amount as well as the depth of wear and its distribution along the contact interface. A two-dimensional rolling contact problem with frictional heat flow is considered. The elasto-plastic deformation of the rail is considered. This contact problem is governed by a coupled system of mechanical and thermal equations in terms of generalized stresses, displacement and temperature. The finite element method is used to discretize this problem. A discretized system of equations with nonpenetration and friction conditions is transformed and formulated as a nonlinear complementarity problem. The generalized Newton method is applied to numerically solve this mechanical subproblem. The Cholesky method is used to find the solution of the heat-conductive problem. The dissipated power is evaluated based on the resultant force and slip at a reference point. Numerical results including the distribution of slip velocity, power factor and wear rate are provided and discussed.  
rolling contact; elasto-plastic material; energy dissipation; wear +
Footnote †: journal: Applied Sciences
# Adaptive Authentication Protocol Based on Zero-Knowledge Proof
## 2 Analysis of Authentication Protocols Used to Ensure Confidentiality in VANET
The use of encryption systems allows for a sufficiently high level of confidentiality when authenticating vehicles in VANET, but requires the creation of an additional key management system. At the same time, compromising the keys will lead to a decrease in the level of protection of the transmitted data in VANET. 2. The development of zero-knowledge authentication protocol, which ensures a high level of vehicle confidentiality without the use of encryption methods, is an urgent task.
# Multi-View Visual Relationship Detection with Estimated Depth Map
## 4 Experimental Procedure
### Visual Relationship Detection
#### 4.1.4 Ablation Study
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Pre. Det.(\(R_{506\text{-}100}\))**} & \multicolumn{2}{c}{**Phr. Det.(\(R_{50100}\))**} & \multicolumn{2}{c}{**Rel. Det.(\(R_{50100}\))**} \\ \cline{3-8}  & & **Unseen** & **All** & **Unseen** & **All** & **Unseen** & **All** \\ \hline a. & RDBN[without BC] [7] & 21.0 & 49.9 & 7.0/7.6 & 16.2/17.7 & 6.4/6.9 & 14.5/15.7 \\ b. & RDBN[without BC](\(10<|\Delta\text{Depth}|<30\)) [7] & 19.3 & 51.7 & 9.2/10.1 & 17.7/19.3 & 8.6/9.5 & 15.8/17.2 \\ c. & RDBN[without BC](\(|\Delta\text{Depth}|>100\)) [7] & 39.3 & 54.3 & 9.8/9.8 & 19.3/20.2 & 6.6/6.6 & 14.4/15.0 \\ \hline d. & **Multi-view[Img]**[without BC’] & 22.0 & 50.4 & 7.1/7.6 & 16.3/17.8 & 6.5/6.9 & 14.6/15.8 \\ e. & **Multi-view[Img]**[without BC’](\(10<|\Delta\text{Depth}|<30\)) & 19.8 & 52.0 & 9.2/10.1 & 17.9/19.3 & 8.9/9.5 & **15.9**/17.2 \\ f. & **Multi-view[Img]**[without BC’](\(|\Delta\text{Depth}|>100\)) & 41.0 & 55.2 & 9.8/9.8 & 19.6/20.2 & 4.9/4.9 & 14.4/14.7 \\ g. & **Multi-view[Img]**[with BC’] & 22.7 & 50.2 & 7.4/8.2 & 16.1/17.9 & 6.8/7.3 & 14.4/15.9 \\ h. & **Multi-view[Img]**[with BC’](\(10<|\Delta\text{Depth}|<30\)) & 21.3 & 52.0 & 9.5/10.3 & 17.6/19.4 & **9.2**/**9.8** & 15.8/**17.2** \\ i. & **Multi-view[Img]**[with BC’](\(|\Delta\text{Depth}|>100\)) & **44.3** & **55.8** & **11.5**/**11.5** & **19.6**/**20.9** & 6.6/6.6 & 14.1/15.3 \\ \hline j. & **Multi-view[Reg]**[without BC’] & 22.0 & 50.4 & 7.0/7.5 & 16.3/17.8 & 6.4/6.8 & 14.5/15.9 \\ k. & **Multi-view[Reg]**[without BC’](\(10<|\Delta\text{Depth}|<30\)) & 21.0 & 52.2 & 9.2/10.1 & 17.7/19.4 & 8.9/9.5 & 15.7/**17.3** \\ l. & **Multi-view[Reg]**[without BC’](\(|\Delta\text{Depth}|>100\)) & 39.3 & 54.3 & 9.8/9.8 & **19.9**/20.6 & 4.9/4.9 & 14.7/15.0 \\ m. & **Multi-view[Reg]**[with BC’] & 22.9 & 50.3 & 7.0/7.9 & 16.1/17.8 & 6.4/7.0 & 14.4/15.8 \\ n. & **Multi-view[Reg]**[with BC’](\(10<|\Delta\text{Depth}|<30\)) & 22.4 & 52.2 & 9.5/10.6 & 17.8/19.5 & **9.2**/**10.1** & **15.8**/17.2 \\ o. & **Multi-view[Reg]**[with BC’](\(|\Delta\text{Depth}|>100\)) & **42.6** & **55.5** & **11.5**/**11.5** & 19.6**/**20.6** & 6.6/6.6 & 14.1/15.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation study for multi-view information and proposed novel visual relationship balanced classifier BC’ (BC represents the balanced classifier proposed in RDBN [7]). [without BC’] represents VRD framework with traditional predicate classifier. Multi-view[Img] and Multi-view[Reg] represent multi-view VRD framework with ImageNetM and RegionNorm normalization strategy, respectively. The best performances of the framework with these two normalization strategies is highlighted in boldface. In conclusion, multi-view information and the novel balanced classifier can both improve the detection accuracy and generalization ability of the VRD framework.
## 2 Study Site and Datasets  
### In Situ Water Data  
A source of in situ water quality data in NYS is the Citizens Statewide Lake Assessment Program (CSLAP). CSLAP is a volunteer lake monitoring program run by the NYS Department of Environmental Conservation (NYSDEC) and the NYS Federation of Lake Associations, Inc. that was modeled after similar programs in New England and the Midwest ([[https://dec.ny.gov/environmental-protection/water/water-quality/cslap/sampling-activities](https://dec.ny.gov/environmental-protection/water/water-quality/cslap/sampling-activities)]([https://dec.ny.gov/environmental-protection/water/water-quality/cslap/sampling-activities](https://dec.ny.gov/environmental-protection/water/water-quality/cslap/sampling-activities)), accessed on 19 September 2024). The CSLAP dataset for 2016-2020 (May to October) includes a range of lake characteristics and water quality observations for 172 lakes, with some lakes having multiple sample locations and multiple sampling dates throughout the year. The lakes are diverse: they represent oligo-, meso- and eutrophic states; have watersheds dominated by either urban, agricultural, or forest; and vary in depth from <2 m to 215 m. In this study, we utilized CSLAP data for 2019 (381 samples for Chl-a and 376 samples for phycocyanin) and 2020 (499 samples for Chl-a and 498 samples for phycocyanin) due to the correspondence to available corresponding Sentinel-2 image data. This project used observations of Chl-a (extracted and benchtop fluoroprobe) and phycocyanin (benchtop fluoroprobe). [PERSON] et al. [59] contains a detailed discussion of the derivation and use of these parameters. Figure 1 shows the distribution of New York State lakes where in situ CSLAP water quality samples were collected.
## 3 Macroscopic Transmission Control: Virtual Hub-Based Transmission  
The proposed macroscopic transmission control consists of a transmission request, hub topology construction, and a hub-path decision.
## 3 PDT in Veterinary Oncology
### Matsocytomas  
The proliferation of neoplastic mast cells characterizes mast cell tumors, frequently affecting the skin and, more rarely, systemic organs.  
A cat with lip mastocytoma was submitted to HPD-based PDT. After the first treatment, complete tumor response was seen in ten days, plus total re-epithelialization within 15-25 days. There was a recurrence in 5 months. A second treatment was performed, and relapse occurred two months later [101]. The same study submitted a dog with a lip mastocytoma to PDT after surgical excision. The animal was considered disease-free for a period of six months [101].  
A dog with a cutaneous mast cell tumor was orally given 5-ALA (40 mg/kg). Irradiation was performed with LED light (60 J/cm\({}^{2}\)). The complete response was reported, and the disease was considered stable [71].  
The PAD-S31 was used in two dogs with cutaneous mast cell tumors. The site of irradiation showed slight depigmentation immediately after laser irradiation. Two days after treatment, a scar progressively formed over its surface but decreased in size to 50% by the seventh to the tenth day. Local recurrence of tumors receiving PDT was not observed during the sixth and seventh months of follow-up [107].  
The photosensitizer EtNBS was administered intravenously (2.0 mg/kg) in two dogs with eye neoplasms: a subconjunctival mast cell tumor and an eyelid mast cell tumor. PDT was completed with 100 and 400 J/cm\({}^{2}\), respectively. The dog with a subconjunctival mast cell tumor briefly responded to therapy. The dog with eyelid mast cell tumor showed a complete response but initiated chemotherapy for two cutaneous mast cell tumors at other sites for four months; thus, PDT efficacy was difficult to ascertain [96].  
A dog affected on the skin's right flank, subcutaneous cervical, and submandibular region was submitted to CASPc-based PDT. The right flank tumor was surgically reduced before PDT, remaining a lesion with less than 1 cm. Complete response was reported in all injuries [113].  
A phase I clinical trial with a mast cell tumor in the gingiva showed a partial response after ZnPcS\({}_{4}\) (4 mg/kg)-based PDT [84]. PDT protocols used to treat mastocytomas are presented in Table 2.
## 2 Materials and Methods
### Influence of the Damping Factor on the Synchronism Phenomenon  
In this sub-section, the effect of the damping factor on the synchronism phenomenon is studied, i.e., when \(\rm Td=Tw\). From Equation (7), the component of the induced motion between waves and with resistance is obtained:  
\[\theta_{\rm f}=\frac{\theta_{\rm MW}\cdot\cos\beta\cdot\sin\left[\frac{2\pi} {\rm Tw}\cdot\rm t-\beta\right]}{1-\frac{\rm Td^{2}}{\rm Tw^{2}}}, \tag{9}\]  
After carrying out mathematical operations, the equation of the induced rolling motion as a function of the ship's natural roll period (\(\rm Td\)) and wave period (\(\rm Tw\)) is as follows [27]:  
\[\theta_{\rm f}=\frac{\theta_{\rm MW}\cdot\sin\left[\frac{2\pi}{\rm Tw}\cdot \rm t-\beta\right]}{\sqrt{\left(1-\frac{\rm Td^{2}}{\rm Tw^{2}}\right)^{2}+ \left(1-\frac{\rm Td^{2}}{\rm Tw^{2}}\right)^{2}\cdot\frac{\lambda_{1}^{2}}{ \pi^{2}}\cdot\frac{\frac{\rm Td^{2}}{\rm Tw^{2}}}{\left(1-\frac{\rm Td^{2}}{ \rm Tw^{2}}\right)^{2}}}}=\frac{\theta_{\rm MW}\cdot\sin\left[\frac{2\pi}{ \rm Tw}\cdot\rm t-\beta\right]}{\sqrt{\left(1-\frac{\rm Td^{2}}{\rm Tw^{2}} \right)^{2}+\frac{\lambda_{1}^{2}}{\pi^{2}}\cdot\frac{\rm Td^{2}}{\rm Tw^{2}}}}, \tag{10}\]  
Now, Equation (10) is introduced in Equation (7) in order to obtain the equation of the rolling angle of a ship sailing in beam waves in a resistant environment, which allows analysis of the influence of the damping factor on the synchronism phenomenon.  
\[\theta=\theta_{\rm M}\cdot\rm e^{\frac{-\lambda_{1}\rm t}{\rm Td}}\cdot\left[ \cos\frac{2\pi}{\rm Td}\cdot\rm t+\frac{\lambda_{1}}{2\pi}\cdot\sin\frac{2\pi} {\rm Td}\cdot\rm t\right]+\frac{\theta_{\rm MW}\cdot\sin\left[\frac{2\pi}{ \rm Tw}\cdot\rm t-\beta\right]}{\sqrt{\left(1-\frac{\rm Td^{2}}{\rm Tw^{2}} \right)^{2}+\frac{\lambda_{1}^{2}}{\pi^{2}}\cdot\frac{\rm Td^{2}}{\rm Tw^{2}}}}, \tag{11}\]
## 2 Materials and Methods  
### Study Region  
Our study region covers all deciduous forest ecosystems in China, including natural forests and planted forests, which account for 81% and 19% of the deciduous forest ecosystems in China, respectively (Figure 2). We extracted the deciduous forest map from the China land cover dataset [37], based on Landsat TM/ETM and HJ-1 satellite data of 30 m resolution in 2010, and a large amount of field investigation data. [PERSON] et al. (2022) provided a planted forest map with 30 m spatial resolution, using the time-series Landsat archive from 1982-2020 and the LandTrendr algorithm to generate global maps of planting years [22]. Excluding the planted forests, we assumed that the remaining part comprised natural forests. Then, we resampled the deciduous forest map to 0.05 degree. Descriptions of the climatic information for China's deciduous forest ecosystems are given in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Annual Mean** & **Annual Sum** & **Annual Sum Global** \\  & **Temperature (\({}^{\circ}\)C)** & **Precipitation (mm)** & **Radiation (MJ/m\({}^{2}\)/yr)** \\ \hline Natural forests & 3.16 & 747.46 & 5960.07 \\ Planted forests & 6.34 & 924.58 & 6135.60 \\ China’s deciduous forest ecosystems & 4.75 & 836.02 & 6047.83 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A description of the climatic information.  
Figure 1: Conceptual figure of the maximum value of gross primary productivity (GPP) and the growing season length (GSL) in determining changes in the annual GPP.  
Figure 2: The spatial pattern of natural and planted forests in China’s deciduous forest ecosystems. The inset shows the percentage of natural and planted forests in China’s deciduous forest ecosystems.
## 2 Materials and Methods
### Multispectral Image Analysis
#### 2.4.4. NBR (Normalized Burn Ratio)  
The main index used to monitor forest fire areas is the NBR, also called the burn index. NBR was developed to observe areas digested by large fires, such as forests fires [33]. It mainly provides visual confirmation of fire areas and is designed to estimate fire severity. NBR characterizes the extent of the fire and determines the degree of vegetation regeneration after the fire. It combines visible (RED) and short-wave near-infrared (SWIR) bands, as shown in Equation (4).  
\[NBR=\frac{NIR-SWIR}{NIR+SWIR} \tag{4}\]  
Fire severity (i.e., difference normalized burn ratio (dNBR)) can be estimated using two NBR images, one taken before and one after the fire, and is calculated by the formula shown in Equation (5). The higher the dNBR value, the more severe the fire damage, and the lower the dNBR value (the more negative), the more the vegetation regenerated after the fire. The U.S. Geological Survey (USGS) classifies fire severity into seven categories [34]. The dNBR ranges from -500 to 1300, corresponding to the high severity level and to the enhanced regrowth, high (post-fire) level, respectively. Table 2 is a fire severity classification table proposed by USGS.  
\[dNBR=PrefireNBR-PostfireNBR \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Severity Level** & **dNBR Range** & **dNBR Range** \\  & **(Scaled by 1000)** & **(not Scaled)** \\ \hline Enhanced regrowth, high (post-fire) & \(-500\) to \(-251\) & \(-0.500\) to \(-0.251\) \\ Enhanced regrowth, low (post-fire) & \(-250\) to \(-101\) & \(-0.250\) to \(-0.101\) \\ Unburned & \(-100\) to \(+99\) & \(-0.100\) to \(+0.099\) \\ Low severity & \(+100\) to \(+269\) & \(+0.100\) to \(+0.269\) \\ Moderate-low severity & \(+270\) to \(+439\) & \(+0.270\) to \(+0.439\) \\ Moderate-high severity & \(+440\) to \(+659\) & \(+0.440\) to \(+0.659\) \\ High severity & \(+660\) to \(+1300\) & \(+0.660\) to \(+1.300\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: U.S. Geological Survey (USGS) fire severity level.
## 3 Validation of the Numerical Method
### Validation of Numerical Simulations  
As mentioned previously, a few experimental studies on active flexible flapping foil can be compared, so in this section, we chose an experiment of the rigid foil with a passive motion under waves, carried out by [PERSON] [26] in 1984, to validate the numerical simulations. [PERSON]'s experiment was carried out in a tank 25 m \(\times\) 1 m \(\times\) 0.71 m (length \(\times\) breath \(\times\) depth) and the foil had a profile of NACA0015 and 0.4 m chord length. The foil was attached to a carriage by springs and the carriage moved horizontally with constant velocity. It is known that any object's motions have a one-to-one correspondence with its mechanical performance. Therefore, although the foil in this experiment was moving passively, we could still use the active moving foil in numerical simulations on the condition that they all followed the same motion law. The passive and active foil can then obtain the same thrust force. In our simulations, \(V_{0}\) is equal to the constant velocity of the carriage in the experiments, and the distance between the pitching center and the leading edge point of the foil is 0.12 m, as set in [PERSON]'s experiment. In addition, the motion frequency of the foil is set equal to its encounter frequency, while \(a=0.048\) m.  
The numerical results by our method and experimental results from the reference [26] are compared in Figure 5, in which the vertical coordinate represents non-dimensional thrust force and the horizontal coordinate is the product of the wave number and chord length of the foil. For comparison, other numerical results based on the linear theory by [PERSON] [24], the nonlinear theory by [PERSON] et al. [27], and the viscous flow theory by [PERSON] [30] are also given in Figure 5.  
From Figure 5, we see that the non-dimensional thrust force first increases and then decreases in the experiments. Comparing four numerical results, we note that all of are in good agreement with [PERSON]'s experimental results at larger wavelengths (smaller \(kc\)), but when wavelengths are smaller (larger \(kc\)), only the results by the method proposed in this paper and [PERSON]'s method are in good agreement with it. Therefore, considering the general trend, the numerical method proposed in this paper seems better and will be used in the next sections.  
Figure 4: The free surface time series at (**a**) \(x=2\) m and (**b**) \(x=6\) m, and (**c**) the waveform at \(t=20\) s.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 3 Simulation Platform Design
### Upstream Network
#### 3.1.1 Background Voltage Unbalance  
In LV networks, three-phase voltages and currents often exhibit unbalance due to the extensive connection of single-phase devices and their unequal distribution among the phases. To analyze this unbalance, the method of symmetrical components is employed, which decomposes the unbalanced system into three sequence components: positive, negative, and zero.  
Voltage unbalance (\(k_{u}\)) is quantified as the ratio of the negative sequence component (\(\underline{U}_{\text{neg}}\)) to the positive sequence component (\(\underline{U}_{\text{pos}}\)), expressed as \(k_{u}=\mid\underline{U}_{\text{neg}}\mid/\mid\underline{U}_{\text{pos}}\mid\).  
These symmetrical components can be calculated from the phase-to-neutral voltages using (1) [17].  
\[\begin{bmatrix}\underline{U}_{\text{pos}}\\ \underline{U}_{\text{neg}}\\ \underline{U}_{\text{zero}}\end{bmatrix}=\frac{1}{3}\cdot\begin{bmatrix}1& \underline{a}&\underline{a}^{2}\\ 1&\underline{a}^{2}&\underline{a}\\ 1&1&1\end{bmatrix}\cdot\begin{bmatrix}\underline{U}_{a}\\ \underline{U}_{b}\\ \underline{U}_{c}\end{bmatrix}\quad with\quad\underline{a}=-\frac{1}{2}+j \frac{\sqrt{3}}{2}. \tag{1}\]  
Voltage unbalance in most applications is less than 5%, with the IEC 61000-2-2 standard [18] recommending a 2% limit. Although the focus is on industrial LV networks, specific data on voltage unbalance in these settings is limited. Therefore, data from surveys conducted in public LV networks are used. For instance, a study involving 127 public LV networks in Germany [17] provides typical values of voltage unbalance, as shown in Table 2. This approach is justified because further surveys, including industrial and mixed LV networks, have demonstrated consistency and similar results in voltage unbalance levels [17]. Moreover, industrial LV networks are often connected to public LV networks, implying that the background voltage unbalance experienced in industrial networks can be similar to that in public networks. The referenced survey reports the 95 th percentiles of 10 min aggregated measurements collected over two weeks, along with the respective percentiles from 127 different grids.  
The simulation platform in this paper considers background voltage unbalance by computing phase-to-neutral voltages for each phase of the three-phase source, based on predefined symmetrical component values. Only the negative sequence voltage is considered for voltage unbalance due to the delta winding of transformers, which prevents zero-sequence voltage transfer from the upstream network to LV networks [19].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Percentile Over 127 Grids** & **1** & **5** & **50** & **95** & **99** \\ \hline \(\mid\underline{U}_{\text{neg}}\mid\) (V) & 0.28 & 0.31 & 0.57 & 1.12 & 1.44 \\ \(k_{u}\) (\%) & 0.12 & 0.13 & 0.25 & 0.49 & 0.63 \\ \hline \end{tabular}
\end{table}
Table 2: Percentiles of \(\mid\underline{U}_{\text{neg}}\mid\) for 10 min average values and 127 LV networks (Data from ref. [17]).