esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Implementation of the Barcelona Basic Model Based on Return-Mapping Integration
## 2 Implicit Integration of the BBM
### Equations of the BBM  
The stress state in the BBM is described by the net mean stress in the form of  
\[\mathbf{\sigma}^{\prime}=\mathbf{\sigma}-u_{a}\mathbf{I} \tag{1}\]  
in which the volumetric stress \(p\) and deviatoric stress \(q\) are defined as  
\[p=\frac{1}{3}tr\big{(}\mathbf{\sigma}^{\prime}),\ \ q=\sqrt{\frac{3}{2}}\|\mathbf{ \xi}\|, \tag{2}\]  
here, \(\mathbf{\xi}=\mathbf{\sigma}^{\prime}-1/3 tr(\mathbf{\sigma}^{\prime})\mathbf{I}\).  
The elastic strain of volumetric and shear components are expressed as  
\[de_{v}^{\epsilon}=\frac{\kappa}{v}\frac{dp}{p}+\frac{\kappa_{s}}{v}\frac{ds}{( s+p_{a}t)} \tag{3}\]  
\[de_{s}^{\epsilon}=\frac{dq}{3G} \tag{4}\]  
It is notable that the BBM has two yield surfaces in the form of  
\[f_{1}(p,q,s,p_{0}^{*})\equiv q^{2}-M^{2}(p+p_{s})(p_{0}-p)=0 \tag{5}\]  
and  
\[f_{2}(s,s_{0})\equiv s-s_{0}=0 \tag{6}\]  
Figure 1: Three-dimensional representation of the yield surface in the thermo-elasto-plastic BBM.  
where  
\[p_{s}=k_{s}s \tag{7}\]  
\[\frac{p_{0}}{p^{c}}=\left(\frac{p_{0}^{*}}{p^{c}}\right)^{\left\lfloor\lambda(0)- \kappa\right\rfloor/\left\lceil\lambda(s)-\kappa\right\rfloor} \tag{8}\]  
\[\lambda(s)=\lambda(0)[(1-r)\exp(-\beta s)+r] \tag{9}\]  
The yield surface of the BBM is determined by the hardening parameters \(p_{0}^{*}\) and \(s_{0}\), which depend on the total plastic volumetric strain increment \(de_{v}^{p}\) in the form of  
\[\frac{dp_{0}^{*}}{p_{0}^{*}}=\frac{v}{\lambda(0)-\kappa}de_{v}^{p} \tag{10}\]  
\[\frac{ds_{0}}{s_{0}+p_{at}}=\frac{v}{\lambda_{s}-\kappa_{s}}de_{v}^{p} \tag{11}\]
## 2 Related Work
### Continual Learning
#### 2.1.3 CLIP-Based Continual Learning Methods  
For the continual learning of CLIP, Continual-CLIP [39] demonstrated impressive performance in downstream continual learning tasks by leveraging the IT classifiers from the pretrained CLIP model without employing any specific continual learning methods. Conversely, VR-LwF [14] and ZSCL [15] utilized mainstream continual learning approaches, such as replay and knowledge distillation for incremental learning in downstream tasks, achieving notable results. Additionally, AttriCLIP [17] employed a text-prompt-based approach for continual learning in downstream tasks, also yielding promising outcomes. However, these methods have not fully exploited the inherent transferability of the pre-trained CLIP model. CLAP [40] enhances continual learning with CLIP through probabilistic finetuning, improving calibration, mitigating forgetting, and enabling better uncertainty estimation for tasks like novel data detection and exemplar selection. DDAS [41] enables parameter-efficient continual learning for vision-language models by integrating Mixture-of-Experts (MoE) adapters and a Distribution-Discriminative Auto-Selector (DDAS), mitigating forgetting, preserving zero-shot recognition, and reducing training costs by 60%. TMM-CLIP [42] enhances rehearsal-free class-incremental learning by preserving discriminative visual representations, using dynamic prompt tuning to reduce intra-task confusion, and employing contrastive inter-task learning to improve task separability, outperforming state-of-the-art methods on multiple benchmarks. In summary, while these approaches have shown effectiveness in continual learning for CLIP, there remains room for enhancing the utilization of CLIP's pre-existing transfer capabilities to achieve more robust continual learning performance.
# Random-Specific Activity in Drinking Water and Radiological Health Risk Assessment: A Case Study
## 4 Conclusions  
In this paper, the evaluation of the radon content in groundwater samples for human use coming from nine selected locations of Calabria, southern Italy, representative of the investigated area, is reported as a case study.  
The radon-specific activity was measured by means of a liquid scintillation counting setup, and the experimental results provide evidence that the activity concentration of radon is below the parameter value indicated by the current Italian legislation (100 Bq L\({}^{-1}\)), except for the site ID1. In this latter case, the corresponding annual effective doses for infants, children and adults due to the ingestion of _Rn_-222 were shown to fall within the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Site ID** &
\begin{tabular}{c} **C\({}_{\textit{Rn}\text{-}222}\)** \\ **(Bq L\({}^{-1}\))** \\ \end{tabular} \\ \hline
1 & 164 \(\pm\) 46 \\
2 & 7.4 \(\pm\) 1.4 \\
3 & 13.1 \(\pm\) 2.4 \\
4 & 9.1 \(\pm\) 1.6 \\
5 & 76 \(\pm\) 18 \\
6 & 38.9 \(\pm\) 8.3 \\
7 & 15.5 \(\pm\) 2.8 \\
8 & 64 \(\pm\) 15 \\
9 & 32.4 \(\pm\) 6.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The annual mean value (\(\pm\)standard deviation) of the _Rn_-222-specific activity in the investigated drinking water samples, for each collection site.  
acceptable range of 0.2 mSv y\({}^{-1}\)-1.8 mSv y\({}^{-1}\) reported in Recommendation 2001/928/Eu- ratom. Moreover, the total (ingested and inhaled) annual effective doses for infants, children and adults were found to be below the 1 mSv y\({}^{-1}\) limit value recommended by the World Health Organization (WHO), thus reasonably ensuring the safety of the analyzed samples for drinking purposes, and no remedial actions are demanded.  
Although these results display low levels of radon-specific activities and doses, in complete accordance with official institutional guidance, it should be highlighted that regular monitoring is necessary to ensure the safety of drinking water. In light of this, the data reported in this paper will be supplemented in the near future by increasing the sampling points and the number of analyzed drinking water samples. Moreover, it should be remarked that the approach reported in this article might be applied, in principle, for the assessment of any potential radiological hazard for human beings due to the presence of radioactive elements in drinking water, by constituting a guideline for investigations focused on the monitoring of the radiological quality of these samples.  
Conceptualization, F.C. and V.V.; methodology, F.C.; validation, D.M.; formal analysis, M.D., S.M. and A.B.; investigation, F.C. and V.V.; resources, G.P., G.S. and D.M.; data curation, F.C.; writing--original draft preparation, F.C.; supervision, G.B., D.M. and V.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Technological Development and Application of Photo and Video Theodolites
## 2 Technological Development
### Development from 2001 until Today
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specifications** & **Leica MS60** & ** Trimble SX 12** & **Topcon GTL-1000** \\ \hline
**Camera/sensor** & 2\(\times\)/CMOS & 3\(\times\)/CMOS & 1\(\times\)/CMOS \\
**Resolution** & 5 MPx & 8.1 MPx & 5 MPx \\
**Fps** & 20 Hz & 15 Hz & Live video \\
**FOV overview/telescope** & 15.5\({}^{\circ}\)\(\times\) 11.7\({}^{\circ}\)/1.3\({}^{\circ}\)\(\times\) 1.0\({}^{\circ}\) & 2 Total 360\({}^{\circ}\)\(\times\) 300\({}^{\circ}\) & 270\({}^{\circ}\)\(\times\) 360\({}^{\circ}\)/\(\dot{X}\) \\
**Zoom overview/telescope** & 8\(\times\)/30\(\times\) & 107\(\times\) 3 & 1 \\
**Accuracy distance (prism)** & 1 mm + 1.5 ppm & 1 mm + 1.5 ppm & 1 mm + 2 ppm \\
**Accuracy distance (non-prism)** & 2 mm + 2 ppm & 2 mm + 1.5 ppm & 2 mm + 2 ppm \\
**Accuracy Hz and V** & 1\({}^{\prime\prime}\) & 1\({}^{\prime\prime}\) & 1\({}^{\prime\prime}\) \\
**Scan rate** & 30,000 Hz & 26,600 Hz & 100,000 Hz \\
**Year released** & 2020 & 2021 & 2019 \\ \hline \hline \end{tabular}  
* Specification is not available; 2 FOV using all three cameras; 3 main camera zoom; X—not integrated.  
\end{table} 
Table 2: Main specifications of currently available IASTS [51; 53; 54]. All currently available commercial IATS and IASTS instruments use recorded images and videos for additional attributes of the measured point, i.e., primarily for documentation and colorizing the scanned scenes. Standard measurement methods in the field have been largely developed and improved by technological developments of TS. Manual focusing has been replaced by auto-focusing; moreover, using remote control, an expert can select the point of interest defined by a pixel from a live video/image on a display, thereby pointing the instrument to a signalized or unsignalized point. For pointing the instrument to the point of interest and for tracking the reflector in the field, instruments use motorization, which is currently realized by piezo, magnetic, or ultrasonic drives depending on the manufacturer. The recorded images can be automatically downloaded for each point or for a set of points for documentation purposes. This way, it is possible to visualize these points. More importantly, we can perform photogrammetric processing of collected georeferenced images afterward and, e.g., use them for structural and geo-monitoring, leveling, and fusion of laser scan and image data for deformation monitoring. However, commercial IATS and IASTS instruments still do not use the full potential and functions of these instruments [8]. In the next section, the potential for application of the latest state-of-the-art IATS is presented.
## 3 Mesh Stiffness of Internal Spur Gears with Tooth Wear
### Multi-Tooth Mesh Stiffness  
#### 3.2.1 Multi-Tooth Mesh Rule  
For internal spur years, the contact ratio is usually 1-2, which means the single-tooth and double-teeth of the gear pair alternately engage during the engagement process. In this section, the multi-tooth mesh rule which describes the meshing relationship between single and double teeth is deduced to obtain the law of gear meshing with tooth wear.  
With the expressions of the new single-tooth mesh relationship deduced in Section 2.1, the mesh relationship between the driving and driven gears rotational angles can be gained, which are represented by \(\varphi_{1}\) and \(\varphi_{2}\). The multi-tooth meshing can be obtained by superimposing the single-tooth meshing, and the formula can be expressed as:  
\[\left\{\begin{array}{l}\varphi_{1}\big{(}2^{\text{nd}}\big{)}=\varphi_{1}(1^ {\text{st}})+\frac{2\pi}{2_{1}}\\ \varphi_{2}\big{(}2^{\text{nd}}\big{)}=\varphi_{2}(1^{\text{st}})+\frac{2\pi}{ 2_{2}}\end{array}\right.,\left\{\begin{array}{l}\varphi_{1}\big{(}3^{\text{rd }}\big{)}=\varphi_{1}(1^{\text{st}})+2\times\frac{2\pi}{2_{1}}\\ \varphi_{2}\big{(}3^{\text{rd}}\big{)}=\varphi_{2}(1^{\text{st}})+2\times \frac{2\pi}{2_{2}}\end{array}\right.,\ldots,\left\{\begin{array}{l}\varphi_ {1}\big{(}n^{\text{th}}\big{)}=\varphi_{1}(1^{\text{st}})+(n-1)\times\frac{2 \pi}{2_{1}}\\ \varphi_{2}\big{(}n^{\text{th}}\big{)}=\varphi_{2}(1^{\text{st}})+(n-1)\times \frac{2\pi}{2_{2}}\end{array}\right. \tag{59}\]  
where \(n^{\text{th}}\) is the nth tooth pair, and \(z_{1}\) and \(z_{2}\) are the tooth numbers of the driving and driven gears, respectively.  
Figure 14 presents the single-tooth and multi-tooth mesh relationship when the tooth profile is prefect, in which the second gear pair single-tooth meshing is presented in Figure 14b. The meshing process consists of three parts in the multi-tooth meshing: double/single/double, in which single means that a pair of teeth mates and double indicates that two pairs of teeth mesh at the same time. Meanwhile, the multi-tooth mesh rule is  
Figure 13: Single-tooth mesh stiffness with different non-uniform wear depths: (**a**) tooth angle \(\alpha_{1}\); (**b**) rotational angle.  
proposed to acquire the multi-tooth mesh relationship of internal spur gear with tooth wear, and its two principles are as follows:  
1. The tooth pair whose \(\varphi_{2}\) is larger meshes first in the initial double-tooth meshing range with the increase of \(\varphi_{1}\).
2. Two tooth pairs mesh concurrently when \(\Delta\varphi_{2}<\textit{Threshold}\) for the same \(\varphi_{1}\) where _Threshold_ represents the threshold of the tolerable error of rotational angle.  
Furthermore, the multi-tooth mesh relationship with tooth wear is presented using the multi-tooth mesh rule. The new single-tooth and multi-tooth mesh stiffness are discussed in both prefect and worn cases.
## 5 Experimental Results and Analysis
### Simulation Results
#### 5.2.1 Dopra Performance Analysis
As the number of feasible links formed between UNs increases, the UNs can select the most appropriate relay node for cooperative information transmission. Consequently, the delay gap between the cooperative and non-cooperative modes becomes more pronounced with an increasing number of UNs. Therefore, with a large number of UNs, the DOPA proves to be more effective in reducing latency. Figure 8: Delay variation with different numbers of UNs. Figure 6: Delay variation for different AP transmission power values. Figure 7: Delay variation under different path attenuation coefficients.
# Mapping Based Quality Metrics for Mesh Deformation Algorithms Using Radial Basis Functions
## 4 Results and Discussions  
In this section, three test cases have been proposed with the RBFs' method based on generalized TPS including (1) the translation and rotation of a rectangle block with square mesh, (2) mesh deformation due to a rigid body rotation of airfoil NACA0012 with unstructured mesh, and (3) mesh transformation from airfoil NACA0012 to NACA4412 with structured mesh. Initial meshes are all generated in software Pointwise and the deformed mesh is generated by a program in C++ language. The mapping-based metrics are used to evaluate the mesh deformation algorithm. A geometrical change of elements is also measured to check the accuracy of the mapping-based metrics method proposed.
# Static and Moving Target Imaging Using Harmonic Radar
The automobile industry has been investigating harmonic tags as early as the 1970s for avoiding collisions with other automobiles and humans carrying nonlinear tags at X-/Ku-Bands [31; 32; 33]. Such a system has also been used by biologists and entomologists interested in tracking insects at 917/1834 MHz [34; 35] and at 5.8/11.6 GHz [36; 37; 38]. Since most systems traditionally used a single frequency waveform and a single transmit and receive antenna, only detection of nonlinear targets was performed. No ranging, tracking, imaging or identification of nonlinear targets was possible. For two-dimensional localization of nonlinear targets, a wide bandwidth and a large aperture are needed to achieve good down range and cross range resolutions, respectively. To address the shortcomings mentioned above, we have constructed a wideband harmonic radar operating transmitting over the 800-1000 MHz range and receiving over the fundamental frequency range as well as the second harmonic (1600-2000 MHz) frequency range [39]. In this paper, we present the basic principles of harmonic radar, and provide experimental evidence of its ability to suppress linear clutter, perform detection and indication of slow moving nonlinear targets, and perform high resolution synthetic aperture imaging. While our work on moving target and synthetic aperture radar imaging has been published in conference proceedings, this paper combines the two imaging modalities and presents a unified treatment of the two topics.
# Adaptive Hybrid Storage Format for Sparse Matrix-Vector Multiplication on Multi-Core SIMD CPUs
## 4 Our Approach  
### HYB5 Overview  
HYB5 is a hybrid sparse matrix storage scheme that combines SELL-C-\(\sigma\)[9] and CSR5 [8]. With HYB5, an input matrix is partitioned into two portions in the vertical direction: the left regular portion is stored in the SELL-C-\(\sigma\) format, and the right irregular portion is stored in the CSR5 format. Figure 4 shows how a sparse matrix A of size 16 \(\times\) 16 with 76 nonzero elements is represented in the HYB5 format. In this example, K is set to be 3, and matrix A is first divided into two sub-matrices of the same size as A. The left sub-matrix is stored in the SELL-C-\(\sigma\) format. Due to the fact that the right portion becomes even more irregular than it was, it is necessary to compress this part to reduce the processing overhead and save storage space.Thus, the right sub-matrix is compressed first, then the rows without non-zero entries are removed (i.e., _empty rows_) and stored in the CSR5 [8] format.  
Compared to the native SELL-C-\(\sigma\) format, HYB5 can further reduce the storage and processing overhead. As an illustrative example in Figure 5, the same matrix A is represented in SELL-4-8. We see that there are fewer padding zeros when K = 3 compared to K = 8 with the same configurations. Then, the remaining sparse rows with various lengths are handled by the CSR5 format. By doing so, HYB5 can make use of the best of both worlds. Note that HYB5 is not a simple combination of the two native formats, and we have to choose the right partitioning position between the two portions on a per matrix basis (Section 4.2).  
Figure 4: Represent a sparse matrix A with the HYB5 format. How a sparse matrix A is represented in HYB5 and SELL-C-\(\sigma\). In this example, matrix A is of 16 \(\times\) 16 size and has 76 non-zeros. Colored cells denote non-zero elements, and white cells are zeros. The arrows indicate the memory accessing order.
## 2 Overall Analysis Approach and Implemented Methods
### Data and Assumptions on LH\({}_{2}\) Technology
#### 2.1.2 L\({}_{2}\) Storage
The types are a polystyrene foam, a polymethacrylimide foam, and a polyurethane foam, with the characteristics summarised in Table 3. Data regarding polystyrene foam are taken from [16], whereas data about polymethacrylimide and polyurethane foams are from [25]. Thermal conductivity data about foams are taken from [16; 25] and reported in Figure 4. It can be noted that, for this kind of material, the thermal conductivity strongly depends on the temperature. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & \(\rho_{i}\)**Density [kg/m\({}^{3}\)]** & \(K_{i}\)**, Conductivity [W/(m K)]** \\ \hline
**Polystyrene foam (PS)** & 25.6 & 0.015–0.045 \\ \hline
**Polymethacrylimide foam (PMI)** & 51.1 & 0.005–0.037 \\ \hline
**Polyurethane foam (PRU)** & 32.0 & 0.005–0.025 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Properties of insulant materials. Figure 3: Temperature–pressure (**a**) and density–pressure (**b**) relations for saturated H2. As in [24], in order to keep the design of aircraft-borne LH\({}_{2}\) tanks as general as possible and applicable to different integration solutions, non-integral tanks with inner metallic structural vessel and external foam insulation were taken into account. Since more tanks can be installed in the same fuselage portion, the thermodynamic analysis should take their mutual interaction into account. To reduce the computational time, this problem was simplified assuming that each tank is thermodynamically isolated from the others and that the foam insulation is in contact with the air flowing at the aircraft flight speed. This means that the insulation effect of the calm air inside the fuselage as well as the additional thermal resistance of the fuselage skin are neglected, leading to an overestimation of the heat exchange and providing conservative results on aircraft performance. Concerning the structural materials, three metal alloys were considered: the stainless steel grade 301 (SSS301), the aluminium alloy 2219 (AA2219), and the titanium alloy Ti-5 Al-2.5 Sn. The mechanical characteristics at cryogenic temperature conditions (20 K) were extrapolated from [37] and are here summarised in Table 4. These materials have been considered in several of the studies cited in the present section, and details are not reported for sake of conciseness. Figure 4: Thermal conductivity of insulation foams.
## 3 Results and Discussion
### Single-Factor Analysis of Bond Strength  
Based on the results of the center pull-out tests, the bonding lengths, rebar diameters, and concrete types were found to be the most important factors affecting bond strength. Single-factor analyses of the three main variables were carried out to quantify the incidence of each variable. Typical specimens with rebar pull-out damage and their mean bond strength values were analyzed.
## 4 Performance Evaluation
### End-to-End Transmission Delay  
End-to-end transmission delay represents the unit time that the data packet takes from source to sink. If the ACK model does not receive the ACK packet by the timeout, after transmitting the data packet, it provides reliability using a retransmission mechanism. GF-HOP in Figure 7(a) attributes the lengthened delay to the timeout resulting from numerous retransmissions. Conversely, in GF-HOP with DRDT, the delay decreases due to the reduction of the number of retransmissions. The longer the timeout setting, the bigger the difference between performance of GF-HOP with DRDT and that of the former schemes would be made. In the case of GF-ETX, in Figure 7(b), it is noticeable that there is a decline of delay due to the reduction in the number of retransmissions. However, in certain network regions (e.g., a network boundary) every node can be in a transmission region with low PRR. Therefore, it is identified that GF-HOP and GF-ETX may both experience noticeably prolonged delay.
# Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
## 2 Materials and Methods
### Statistical Features
#### 2.4.2 Shannon Entropy  
Shannon entropy (ShEn) is a statistical measure of signal complexity. It was estimated by computing the signal histogram using a bin size 0.01 times the signal range, with its value expressed in absolute terms (mV). This histogram configuration has been used in previous studies of atrial fibrillation electrograms [17]. It is mathematically defined as follows:  
\[ShEn=-\frac{1}{\log_{2}(N)}\sum_{i=1}^{N}p_{i}\log_{2}(p_{i}), \tag{7}\]  
where \(p_{i}\) is the probability of occurrence of the \(i\)-th interval of values in a given signal, and \(N\) is the number of possible amplitude values. If a specific amplitude value occurs in 100% of the samples, indicating complete predictability, the ShEn value will be 0. If all amplitude values have an equal chance of occurring, the entropy value will be 1.