esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Modeling of Heat and Mass Transfer during Cryopreservation Using Interval Analysis
## 4 Discussion  
In this paper, the numerical analysis of heat and mass transfer proceeding in an axially symmetrical articular cartilage sample subjected to the cryopreservation process was presented. The problem discussed was solved using the interval FDM with the rules of directed interval arithmetic. The mathematical model is based on Fourier and mass diffusion interval equations, assuming interval values of thermophysical parameters.  
This approach allows the mathematical model to include imprecise values that are closer to reality, as these parameters are determined by experimental means. The use of interval values allows obtaining solutions in the form of intervals, which better reflect the analyzed process. The obtained interval temperatures and concentrations were compared with the results of the experiment and [21]. While satisfactory results were obtained for temperatures, different results were obtained for concentrations than in the experiment, probably because of estimation of the diffusion coefficient by the Einstein-Stokes model differently than in [28].
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 3 Experiments
### Results
#### 3.3.1 Results for Multivariate Setting  
As presented in Table 1, our method outperforms existing baselines across all of the settings except for the \"weather\" dataset at horizon lengths of 96 and 192. Our proposed model is second only to SCINet in terms of mean squared error (MSE) for the 'weather' dataset, and only for forecast horizons of 96 and 192 time steps. However, our model outperforms all other models in terms of mean absolute error (MAE) in these settings. This observation indicates that for the 'weather' dataset and shorter forecast horizons (96 and 192 time steps) our model exhibits a more consistent error distribution but is more susceptible to outliers than SCINet under these specific conditions. The average relative improvements in mean squared error (MSE) compared to the previous SOTA are 13.7% on ETTm2, 20.26% on electricity, 56.45% on exchange, 16.5% on traffic, 0.3% on weather, and 21.7% on the ILI dataset. The overall average improvement in MSE is 21.5%, and the most significant improvement seems to be in the exchange dataset. The best result obtained in any particular setting also seems to be on the exchange dataset with a horizon length of 720, with a 64.82% relative improvement. In contrast to other methods, there does not seem to be any significant drop-off in performance as the length of theprediction horizon increases. The plots of the forecasts for these datasets in the multivariate setting are shown in Figure 2. In Figure 2, we can visualize the superiority of the results of our model as compared to those of the SOTA Autoformer. Our model seems to forecast seasonal data such as traffic and electricity very well. In the case of data that do not exhibit seasonality, the plots are not groundbreaking; however, our model still outperforms the existing SOTA Autoformer. We attribute the success of our model to its ability to make careful consideration of the non-stationary property of the time series and better generalization of the complexities in inter- and intra-series relationships in the multivariate time series.
# FollowMe: One Social Importance-Based Collaborative Scheme in MONs
## 6 Experimental Results and Analysis
On the other hand, FollowMe introduces the social-related signal game strategy, which allows data packets to be relayed mainly by nodes of high social importance that could contact a greater number of other nodes. This helps to increase the probability of data packets reaching their destination. Figure 4: PDR of FollowME and Random within SimBet under three scenarios. Figure 3: PDR of FollowME and Random within PRoPHET under three scenarios. Figure 2: Cooperation rate of FollowME and Random with different routing protocols under three scenarios. Figure 5 shows the number of hops of the two incentive mechanisms with ProPHT. It is obvious to see that FollowMe generally has a smaller hop than Random. For example, in the SLAW scenario, the length of forwarding path generated by FollowME within ProPHT tends to an average of 4.9, while that of Random within ProPHT is about 5.7 and higher as shown in Figure 5c. The reason is similar as those of PDR. Recall that FollowME employs social nodes to forward packets, which naturally reduces the number of hops.
## 5 Conclusions  
To precisely predict manufacturing quality, we considered a manifold learning-based deep regression network (MDRN), which integrates the Isomap algorithm for learning manufacturing multi-parameter information and the AE-based DRN model for extracting sophisticated patterns between manufacturing multi-parameter inputs and quality. The three steps of the MDRN are as follows: (1) the Isomap is operated to reduce the multi-parameter dimension from a high-dimensional to a low-dimensional space; (2) the DL technique, the first part in the AE-based DRN, is applied to extract the features in the low-dimensional space; and (3) the ANN, the second part in the AE-based DRN, is then used to construct the relationship between the low-dimensional features and the manufacturing quality, so as to achieve the manufacturing quality prediction. To investigate the prediction capacity of the proposed model, two cases with the same manufacturing process are considered, and comparisons with other methods are also studied. The results show that the proposed method with a generalization ability and robustness exhibits the best performance among all of the peer methods, and these models are also significantly different in statistics.  
The innovation of this work is combined with the deep framework through the ML technique to understand sophisticated features of multi-parameter manufacturing quality. Based on this philosophy, specific constituents, i.e., the Isomap and the AE-based DRN, may be replaced with other techniques. Therefore, the present approach is general enough to be developed as a series of models for the multi-parameter manufacturing quality prediction. In the future, we will focus on the theoretical method of the model's parameter definition and practical application in different manufacturing.  
Conceptualization, Y.B.; methodology, J.D. and Y.B.; data curation, J.D.; writing (original draft preparation), J.D.; writing (review and editing), Y.B.; funding acquisition, Y.B. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant 71801044 and 51775112, the China Scholarship Council under grant 201908500020, the Guangdong Research Foundation under grant 2019B1515120095, and the DGUT Research Project under grant KCYKYQD2017011.  
The authors acknowledge the Tianchi Big Data Competition for data provision and the anonymous reviewers for manuscript improvement.  
The authors declare no conflict of interest.
## 2 The Phase-Locked Loop and Normalized Fokker-Planck Equation
### Nonmodulo-\(2\pi\) Phase-Error PDF  
An initial distribution of the dependent variable and two sets of boundary conditions are required for a complete description of the problem. Equation (7) can be solved for the PDF of phase error with the initial condition  
\[\underset{t\to 0}{\lim}p(\phi,t)=\delta(\phi-\phi_{o}) \tag{9}\]  
and normalizing condition  
\[\int_{-\infty}^{\infty}p(\phi,\tau)d\phi=1 \tag{10}\]where \(\delta(\phi-\phi_{o})\) is the unit impulse function, also known as the Dirac delta function possessing the following property  
\[\delta(\phi-\phi_{o})=\left\{\begin{array}{cc}0,&\phi\
eq\phi_{o}\\ \infty,&\phi=\phi_{o}\end{array}\right.;\;\int_{-\infty}^{\infty}\delta(\phi- \phi_{o})d\phi=1 \tag{11}\]  
The PDF \(p(\phi,\tau)\) can be written as  
\[p(\phi,\tau)=g(\phi,\tau)e^{k(\phi)} \tag{12}\]  
which can be substituted into Equation (7) and lead to the following equation  
\[\frac{\partial g(\phi,\tau)}{\partial\tau}e^{k(\phi)}=\frac{\partial}{ \partial\phi}[(\sin\phi-\gamma)g(\phi,\tau)e^{k(\phi)}]+\frac{1}{\alpha}\frac {\partial^{2}[g(\phi,\tau)e^{k(\phi)}]}{\partial\phi^{2}} \tag{13}\]  
The function \(k(\phi)\) can be found to be [31]  
\[k(\phi)=\int-\alpha(\sin\phi-\gamma)d\phi=\alpha(\cos\phi+\gamma\phi) \tag{14}\]  
and consequently, Equation (12) becomes  
\[p(\phi,\tau)=g(\phi,\tau)e^{\alpha(\cos\phi+\gamma\phi)} \tag{15}\]  
where the density function \(g(\phi,\tau)\) satisfies the following one-dimensional advection-diffusion parabolic PDE, called the modified FP equation  
\[\frac{\partial g(\phi,\tau)}{\partial\tau}=(\gamma-\sin\phi)\frac{\partial g (\phi,\tau)}{\partial\phi}+\frac{1}{\alpha}\frac{\partial^{2}g(\phi,\tau)}{ \partial\phi^{2}} \tag{16}\]  
Solving for \(g(\phi,\tau)\) is computationally simpler than solving for \(p(\phi,\tau)\), mainly due to the fact that coarser grid spacing is allowed for \(g(\phi,\tau)\). Additionally, \(p(\phi,\tau)\) can be reconstructed readily using Equation (15) without difficulty when \(g(\phi,\tau)\) is obtained.
# Humans and Automation: Augmenting Security Operation Centers
## 3 Results and Findings
### SOC Challenges Necessating Automation
#### 3.1.4 Alert Reporting Constraints  
Studies suggested that alert reports lacked context and were inconsistently formatted. One study indicated that security analysts face the cognitive task of piecing together the fragments of intrusion information while still considering the broader organizational impact [35]. Further literature reports that a lack of contextual information elongates response initiatives as analysts need to source information not presented to help inform their response decisions [49, 50]. Thus, the process of gaining contextual knowledge and generating actionable information to accompany current alert reports is largely a manual undertaking [51]. Additionally, the lack of industry and tool standardization regarding alert reports makes collecting and interpreting information more difficult [31]. The technical jargon of alert reports inhibits analysts from a comprehensive understanding, with studies characterizing alert reports as lacking human-readable information. Conversely, [32] argue that report information should be machine-readable to automate responses. These challenges were mentioned in 12/49 (24%) articles analyzed.
## 3 The Relay Experiment and Parameters Analysis
### Parameter Analysis
Hence, the temperature should be taken into account when the performance parameters of relay are used to predict the lifetime. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Temperature** & **Times** & **\(t_{T}\) (ms)** & **\(t_{D}\) (ms)** & **\(V_{m}\) (V)** & **\(V_{C}\) (V)** \\ \multicolumn{1}{c}{(\({}^{\circ}\)C)} & \multicolumn{1}{c}{(Ten Thousands Times)} & \(t_{T}\) (ms)** & **\(t_{D}\) (ms)** & **\(V_{m}\) (V)** & **\(V_{C}\) (V)** \\ \hline
55 & 10 & 0.34 & 0.86 & 1.20 & 0.96 \\
55 & 50 & 0.53 & 0.55 & 1.40 & 0.98  
Table 6 presents that the maximum number of iterations is set to 2000 and the test dimension is 100. \(\mathit{Seed}_{N}\) is the number of fireworks. \(\mathit{Son}_{N}\) is the number of sparks. \(\mathit{Muta}_{N}\) is the number of variant sparks. The three optimization algorithms are tested repeatedly for 15 times using three standard test functions. The test results are shown in Table 7.  
Table 7 presents the optimal values of the three standard test functions are all 0. It is found that the optimal convergence, the worst convergence, and the average convergence of the IFWA in Table 7 are the closest to 0. The 15 tests of IFWA for function Y\({}_{3}\) converge to the optimal value of 0. Compared with the traditional FWA, the convergence accuracy of IFWA has been obviously improved. The average running time of IFWA is the shortest after 15 repeated tests of the three algorithms. Through comparison, the results show that the convergence accuracy and running speed of IFWA are improved compared with FWA.
## 3 Autonomous Vehicles and Monitoring Platforms
The peculiarity of this AUV is the ability to move using two different modes. The first, thruster mode, allows it to move in a precise and controlled way to perform transects parallel to the seabed and collect data with great accuracy. The second mode is called glider mode and allows it to cover larger areas for a longer time, as consumption is significantly reduced [66; 67; 68]. The USV autonomously follows the AUV, providing information for accurate geo-localization of the acquired data. Data transfers between the AUV and the USV are realized through acoustics communication or through a wireless connection [53]. It is also important to establish threshold values at the beginning of the mission for correct data processing. The last phase is represented by adaptation, in which the mission plan can be redesigned by changing the detection scheme and the trajectory of the vehicle [69]. The AUTOSUB Long Range 1500, which is being designed, built, and operated by the National Oceanography Center, is a highly capable AUV with the potential of providing measurements that would have been previously impossible to collect, therefore allowing key advances in marine ecology studies. This vehicle will be built to be able to reach a depth of 1500 m [70; 71]. Finally, an underwater \"glider\" (Table 1) is a specific type of AUV which employs variable-buoyancy propulsion instead of traditional propellers or thrusters. It houses sensors capable of making multidisciplinary oceanographic observations with long-term deployments (months) and has the ability to cover large distances (hundreds to thousands of kilometers) because it has significantly greater endurance compared with traditional AUVs [72]. The typical up-and-down, sawtooth-like profile followed by a glider can provide data on temporal and spatial scales unattainable by powered AUVs and which are much more costly to sample using traditional shipboard techniques. Four commercially available electric underwater gliders represent the main opportunities in this field: the Slocum electric [73], the Seaglider [34], the Coastal glider [74], and the Sea Explorer [75]. In addition, other gliders are under development, including Spray [76]. Coastal gliders are designed to be applied in the littoral zone (they are self-ballasting from essentially fresh to full ocean water) with a faster maximum speed (2 knots, according to [PERSON] and [PERSON] [74]). The Deep Glider, on the other hand, is designed to operate at depths of 6000 m [77]. These vehicles mostly extract energy from wave motion and convert it directly into forward motion. The vehicles also use solar or wind power to charge batteries used to power the navigation systems and the sensor payload.
# Bike-Sharing Travel Demand Forecasting via Travel Environment-Based Modeling
## 2 Literature Review
### Geographically Weighted Models  
The geographically weighted regression (GWR) model has been increasingly employed to predict the demand for shared bicycle trips. The GWR-based model can consider spatial variations, thus allowing for exploring the spatial variations in a research object at a certain scale. [PERSON] et al. [21] employed both a global regression model and a GWR model to analyze the global and local effects of the built environment on bicycle use. [PERSON] et al. [22] built geographically and temporally weighted regression models to examine the influence of bike-share stations, socio-demographic factors, and land use factors on user demand for all bike-sharing systems.  
Although the GWR model is capable of capturing the spatial variation in all influencing factors, it cannot reflect the spatial variation in the range of the single influencing factor, which affects the prediction accuracy [23]. Some researchers proposed the multiscalegeographically weighted regression (MGWR) model based on the GWR model. This model can calculate the bandwidth of each influencing factor and has the advantage of analyzing the spatial variation in these factors and differences among adjacent areas [24]. [PERSON] et al. [25] exploited the MGWR-based model to examine the impact of the density of intersections and points of interest on bike-sharing trip demand at various spatial scales. [PERSON] et al. [26] utilized multivariable linear regression-based and MGWR-based models to quantify the spatial and temporal heterogeneity in the impact of the built environment on the vitality of different age groups in cities. Their study revealed that the MGWR-based model is capable of more precisely predicting riding demand in refined areas [27].  
Based on the above summary, we utilized the MGWR model to analyze the influence of spatial variability in non-motorized transport facilities and land use on riding demand.
# Ontology Modeling Using Fractal and Fuzzy Concepts to Optimize Metadata Management
## 3 The FFOM Methodology
### Foundational Concepts and Formal Definition
FFOM also conceptually supports linguistic fuzzy modifiers, such as'very' or'slightly', whose application typically involves transforming the membership function of the base fuzzy set they modify, for instance, by concentrating the membership degrees for'very'. Furthermore, to determine the strength of derived or composed relationships where intermediate links are expressed as TFNs, FFOM accommodates the use of fuzzy operators. A common method for such relational composition is fuzzy multiplication, often applying the product t-norm component wise to the TFNs. For example, if \(\text{TFN}_{\text{A}\rightarrow\text{B}}\) = (a\({}_{1}\), b\({}_{1}\), c\({}_{1}\)), which means that the TFN values from fractal A to B are a\({}_{1}\), b\({}_{1}\), and c\({}_{1}\), and \(\text{TFN}_{\text{B}\rightarrow\text{C}}\) = (a\({}_{2}\), b\({}_{2}\), c\({}_{2}\)), then the composed \(\text{TFN}_{\text{A}\rightarrow\text{C}}\) can be approximated as (a\({}_{1}\)\(\cdot\)a\({}_{2}\), b\({}_{1}\)\(\cdot\)b\({}_{2}\), c\({}_{1}\)\(\cdot\)c\({}_{2}\)). The execution of complex fuzzy IF-THENrules, the operationalization of fuzzy modifiers, and the computation using fuzzy operators are generally managed by external fuzzy reasoning engines or specialized application logic, for which FFOM's OWL representation provides the structured fuzzy data input. To ensure compatibility with existing semantic web technologies, FFOM also defines an OWL transformation pathway. This consists of a clear methodology and dedicated mapping strategies to translate both its fractal structural and fuzzy semantic layers into OWL 2, the Web Ontology Language standard. Through this transformation, detailed in Section 3.4, the full semantic richness and flexibility of FFOM can be formally represented in a machine-interpretable and interoperable format. Overall, FFOM is proposed as a structured and extensible modeling framework that supports the scalable evolution of ontology structures through fractal principles, and effectively manages semantic imprecision through these defined fuzzy logic mechanisms, offering a practical and formalized approach to ontology engineering for domains demanding both adaptability and uncertainty tolerance.
## 1 Introduction
However, it remains unclear how to combine the physical and deep learning models and whether CBH can be effectively inferred using only infrared observations from geostationary satellites. To retrieve CBH diurnally from geostationary satellite infrared observations, we propose a hybrid algorithm that synergistically combines deep learning and a physical model. Within this framework, a CNN-based model is first employed to retrieve CTH and CWP from satellite infrared radiances. Subsequently, these intermediate cloud products serve as inputs to the physically based model, which estimates CBH by utilizing an effective cloud water content (ECWC) look-up table (LUT). Finally, the retrieved CBH is validated against active measurements from both space-borne and ground-based cloud radars. The remainder of the paper is organized as follows. Section 2 describes the dataset used in this study and the development of the CBH retrieval algorithm. Section 3 presents the results, and Section 4 provides the discussion. The conclusion and future perspectives are provided in Section 5.
## 2 Laser Acceleration Simulations
### Description of Few-Cycle Chirped Focused Laser Pulses
The electromagnetic fields are derived from \(\mathbf{E}=-\partial\mathbf{A}/\partial t-\
abla\phi,\mathbf{B}=\
abla\times\mathbf{A}\): \(\mathbf{E}=\mathbf{E}^{(\hat{x})}+\mathbf{E}^{(\hat{y})}\), \(\mathbf{B}=\mathbf{B}^{(\hat{x})}+\mathbf{B}^{(\hat{y})}\), herewith,  
\[E_{x}^{(\hat{x})} = \frac{-E_{1}}{C_{1}^{2}}\left[C_{1}^{3}-f^{2}C_{1}\frac{x^{2}}{z_ {r}^{2}}+\frac{fC_{1}}{kz_{r}}-\frac{2 ifkx^{2}}{z_{r}\left(2 kC_{2}+\eta\right) ^{2}}\right]\] \[E_{y}^{(\hat{x})} = \frac{E_{1}fxy}{C_{1}^{2}z_{r}^{2}}\left[fC_{1}+\frac{2 ikz_{r}}{ (2 kj_{z}+\eta)^{2}}\right]\] \[E_{z}^{(\hat{x})} = \frac{-E_{1}fx}{C_{1}^{2}z_{r}^{2}}\left[\frac{ifC_{1}}{k}-C_{1} z_{r}\left(i+\frac{if^{2}r^{2}}{4z_{r}^{2}}-\frac{\eta}{s^{2}}\right)-\frac{iC_{1}z_ {r}}{s^{2}+i\eta}-z_{r}C_{2}\right] \tag{1}\] \[B_{x}^{(\hat{x})} = 0\] \[B_{y}^{(\hat{x})} = E_{1}\left(\frac{if}{2 kzr}-i-\frac{if^{2}r^{2}}{4z_{r}^{2}}+ \frac{\eta}{s^{2}}+\frac{1}{is^{2}-\eta}\right)\] \[B_{z}^{(\hat{x})} = \frac{E_{1}fy}{z_{r}}\]  
and, furthermore,  
\[E_{y}^{(\hat{y})} = \frac{-E_{1}}{C_{1}^{2}}\left[C_{1}^{3}-f^{2}C_{1}\frac{y^{2}}{z_ {r}^{2}}+\frac{fC_{1}}{kz_{r}}-\frac{2 ifky^{2}}{z_{r}\left(2 kC_{2}+\eta\right) ^{2}}\right]\] \[E_{x}^{(\hat{y})} = \frac{E_{1}fxy}{C_{1}^{2}z_{r}^{2}}\left[fC_{1}+\frac{2 ikz_{r}}{ (2 kj_{z}+\eta)^{2}}\right]\] \[E_{z}^{(\hat{y})} = \frac{-E_{1}fy}{C_{1}^{2}z_{r}^{2}}\left[\frac{ifC_{1}}{k}-C_{1} z_{r}\left(i+\frac{if^{2}r^{2}}{4z_{r}^{2}}-\frac{\eta}{s^{2}}\right)-\frac{iC_{1}z_ {r}}{s^{2}+i\eta}-z_{r}C_{2}\right] \tag{2}\] \[E_{x}^{(\hat{x})} = \frac{-E_{1}}{C_{1}^{2}}\left[C_{1}^{3}-f^{2}C_{1}\frac{x^{2}}{z_ {r}^{2}}+\frac{fC_{1}}{kz_{r}}-\frac{2 ifkx^{2}}{z_{r}\left(2 kC_{2}+\eta\right) ^{2}}\right]\]0 \[E_{x}^{(\hat{x})} = \frac{-E_{1}}{C_{1}^{2}}\left[C_{1}^{3}-f^{2}C_{1}\frac{x^{2}}{z_ {r}^{2}}+\frac{fC_{1}}{kz_{r}}-\frac{2 ifkx^{2}}{z_{r}\left(2 kC_{2}+\eta\right) ^{2}}\right]\]1 \[E_{x}^{(\hat{x})} = \frac{-E_{1}}{C_{1}^{2}}\left[C_{1}^{3}-f^{2}C_{1}\frac{x^{2}}{z_ {r}^{2}}+\frac{fC_{1}}{kz_{r}}-\frac{2 ifkx^{2}}{z_{r}\left(2 kC_{2}+\eta\right) ^{2}}\right]\]2  
where \(E_{1}=E_{0}\psi e^{i\left(\eta+b\eta^{2}\right)}/S_{0}\), with the normalization parameter \(S_{0}=[-s_{1}^{2}-1/(kz_{r})]/s_{1}\), \(s_{1}=i[1+1/(2 kz_{r})+1/s^{2}]\), \(j_{z}=z+iz_{r}\), and the following shorthand notations have been introduced:  
\[E_{x}^{(\hat{x})} = \frac{-E_{1}}{C_{1}^{2}}\left[C_{1}^{3}-f^{2}C_{1}\frac{x^{2}}{z_ {r}^{2}}+\frac{fC_{1}}{kz_{r}}-\frac{2 ifkx^{2}}{z_{r}\left(2 kC_{2}+\eta\right) ^{2}}\right]\]3 \[E_{x}^{(\hat{x})} = \frac{-E_{1}}{C_{1}^{2}}\left[C_{1}^{3}-f^{2}C_{1}\frac{x^{2}}{z_ {r}^{2}}+\frac{fC_{1}}{kz_{r}}-\frac{2 ifkx^{2}}{z_{r}\left(2 kC_{2}+\eta\right) ^{2}}\right]\]4These field expressions are in concordance with the sub-cycle pulse field of [36] and the long-pulse field of [37]. Such fields were also employed recently to simulate quantum radiation reaction effects for an ensemble of electrons interacting with ultra-short pulses [34]. For the much slower ions studied here, the influence of radiation reaction can be neglected [15].