esa-sceva/satcom-chunk-collection huggingface dataset:
# Cover Time in Edge-Uniform Stochastically-Evolving Graphs +
## 3 The Edge-Uniform Evolution Model  
Let us define a general model of a dynamically-evolving graph. Let \(G=(V,E)\) stand for a simple, connected graph, from now on referred to as the underlying graph of our model. The number of nodes is given by \(n=|V|\), while the number of edges is denoted by \(m=|E|\). For a node \(v\in V\), let \(N(v)=\{u:(v,u)\in E\}\) stand for the open neighbourhood of \(v\) and \(d(v)=|N(v)|\) for the (static) degree of \(v\). Note that we make no assumptions regarding the topology of \(G\), besides connectedness. We refer to the edges of \(G\) as the possible edges of our model. We consider evolution over a sequence of discrete time steps (namely \(0,1,2,\ldots\)) and denote by \(\mathcal{G}=(G_{0},G_{1},G_{2},\ldots)\) the infinite sequence of graphs \(G_{t}=(V_{t},E_{t})\), where \(V_{t}=V\) and \(E_{t}\subseteq E\). That is, \(G_{t}\) is the graph appearing at time step \(t\), and each edge \(e\in E\) is either alive (if \(e\in E_{t}\)) or dead (if \(e\
ot\in E_{t}\)) at time step \(t\).  
Let \(R\) stand for a stochastic rule dictating the probability that a given possible edge is alive at any time step. We apply \(R\) at each time step and at each edge independently to determine the set of currently alive edges, i.e., the rule is uniform with regard to the edges. In other words, let \(e_{t}\) stand for a random variable where \(e_{t}=1\), if \(e\) is alive at time step \(t\), or \(e_{t}=0\), otherwise. Then, \(R\) determines the value of \(\Pr(e_{t}=1|H_{t})\) where \(H_{t}\) is also determined by \(R\) and denotes the history length, i.e., the values of \(e_{t-1},e_{t-2},\ldots\), considered when deciding for the existence of an edge at time step \(t\). For instance, \(H_{t}=\emptyset\) means no history is taken into account, while \(H_{t}=\{e_{t-1}\}\) means the previous state of \(e\) is taken into account when deciding its current state.  
Overall, the aforementioned Edge-Uniform Evolution model (EUE) is defined by the parameters \(G\), \(R\) and some initial input instance \(G_{0}\). In the following sections, we consider some special cases for \(R\) and provide some first bounds for the cover time of \(G\) under this model. Each time step of evolution consists of two stages: in the first stage, the graph \(G_{t}\) is fixed for time step \(t\) following \(R\), while in the second stage, the agent moves to a node in \(N_{t}[v]=\{v\}\cup\{u\in V:(v,u)\in E_{t}\}\). Notice that, since \(G\) is connected, then the cover time under EUE is finite, since \(R\) models edge-specific delays.
## 4 Methodology
### Proposed Method
#### 4.1.2 Chemical Reaction Optimization Algorithm
A. Initialization of algorithm and problem parameters  
The following is a description of the optimization issue: Minimize \(f(T)\), where \(f(T)\) is an objective function, \(N\) is the number of decision variables, \(t\) is the collection of each decision variable \(t_{j}\), and \(t_{j}\) is the collection of the feasible range of values for every decision variable, i.e., \(T_{j}^{min}\) and \(T_{j}^{max}\) are the bottom and top bounds of the \(j\)'th decision parameter for real-values encoding, respectively. Various methods of encoding, such as binary, permutation, real, etc., might be needed to solve the issue. This stage additionally specifies the algorithm parameter \(ReacNum\). B. Evaluation and regulation of primary reactants  
The major reactants are uniformly initialized in the search space at this step of the method. Initial reactants can be created using the uniform population approach for the initial population generation. In general, the linear composition of the components of the base set can provide all vectors in space. If one or more items are missing from the base set, the dimension associated with that element may be lost. Thus, the primary reactants must include the reactants required to hold each member of the base set. The condition of the rule and the base set is used to ensure the regularity of the principal reactants and the preservation of the base set. The recommended approach in this study meets both instances. Producing beginning reactants using the divide and general concept is one approach to creating high-quality reactants. Initially, two reactants \(R_{0}\), \(R_{1}\) are set where \(R_{0}\) = {\(u_{1}\), \(u_{2}\), \(\ldots\), \(u_{n}\)}, \(R_{1}\) = {\(l_{1}\), \(l_{2}\), \(\ldots\), \(l_{n}\)}, \(n\) is the length of reactant and this case is considered as \(k\) =\(I\) (Figure 5.a). Then, a dividing factor, \(k\), is determined. Firstly, \(k\) = 2 and two extra \(R_{2}\), \(R_{3}\) reactants are derived from \(R_{0}\) and \(R_{1}\) (Figure 5b). \[R_{2}=\Big{\{}\ r\ *\ u_{1},\ r\ *\ u_{2},\ \ldots,\ r\ *\ u_{\frac{n}{2}}\,\ r\ *\ 1\frac{n}{2+1}\,\ \ldots,\ r\ *\ l_{n}\Big{\}}\]  
\[R_{3}=\Big{\{}\ r\ *\ l_{1},\ r\ *\ l_{2},\ \ldots,\ r\ *\ l_{\frac{n}{2}}\,\ r\ *\ u_{\frac{n}{2+1}}\,\ \ldots,\ r\ *\ u_{n}\Big{\}}\]  
where r is a random integer between \(0\leq r<1\). We take into account the size of population \(P\) as \(\mid P\mid\) and the number of elements in the created reactants \(R\) as \(\mid R\mid\), respectively. Since \(R_{0}\) and \(R_{1}\) are separated into three sections, if \(\mid R\mid<\mid P\mid\), the value of k is raised by 1, and \(2^{3}\ -2=8\ -\ 2=6\) reactants may be extracted from \(R_{0}\) and \(R_{1}\), which are not in \(R\). The following is a list of these reactants (Figure 6).
## 6 Discussion
### Landscape and Human Impact
#### 6.2.2 La Draga: Archaeopalynology High Resolution Pollen Record  
Comparing intra-site and off-site pollen records has addressed the intensity and scale of the impact of human activities in the landscape. Coeval pollen records from Lake Banyoles and La Draga confirmed the reliability of archaeological deposits in the determination of vegetal landscape evolution during several occupation phases [64], while the archaeopalynological study at La Draga provided relevant data to assess site formation processes and social use of space within the settlement [76] and plant and fungi uses at the site [101].  
The integration of palaeenvironmental data from natural and archaeological deposits assessed the intensity of human activities on the landscape at local and extra-local levels. Thus, the settlement of the first farming societies in the Lake Banyoles area was the cause of major land-cover changes, leading to deciduous oak woodland deforestation. Regarding agriculture, a high concentration of Cerealia-type pollen is documented in both occupation phases at La Draga. Crop fields could not be in the immediate surroundings of the settlement, because it was established in a swampy area unsuitable for cultivation, and high concentrations of Cerealia-t pollen within the settlement have to be understood in terms of anthropic input of spikelets, either for storage of crops or by-products. The impact of husbandry practices on the landscape shows similar trends. At the extra-local level, grazing pressure is imperceptible in off-site deposits (absence of spores of coprophilous fungi, [23]. Nevertheless, high concentrations of these spores are recorded at La Draga [64], pointing to the keeping of flocks within the settlement and to a limited impact of grazing on the landscape at a regional scale. Finally, local-scale deforestation in oak and riparian woodlands induced soil erosion episodes in the lakeshore margins, a process attested both within the site (2 nd phase, ca. 7160-6750 cal BP) and in natural deposits, progressively increasing from 7250 cal BP onwards [23].
## 3 Model Validation
### Coupling Model of Vertical Vehicle-Track-Bridge  
The analysis was performed on the Chinese high-speed ballastless railway irregularity spectrum [56], with an irregularity wavelength range of 2-120 m. Figure 3 shows the stochastic irregularity sample in the time domain and frequency spectrum simulated by SRRM. By taking 500 stochastic irregularity samples as a set, the means and standard deviations were compared with those reported in [56] in both time domain and frequency spectrum. The power spectrum of each frequency point in the sample set was tested by \(\chi^{2}\) distribution with a significance level of \(\alpha=0.05\) and two degrees of freedom, with the Kolmogorov-Smirnov method, verifying the accuracy of the random track irregularities simulated with the present method.  
Track irregularities generated by the German railway spectra of low irregularity were used as the excitation input in the vehicle-rail-bridge dynamic model. The ICE3 high-speed train was selected as the vehicle model, and the operation speed was 250 km/h. The parameters are shown in Table 1. Taking 10 spans simply supported beams as an example, the length of each span beam was 32.6 m.  
Compare the wheel load calculated using the model in this paper (PM) with that in the literature [51], as shown in Figure 4. In [51], the maximum wheel load was 119.78 kN, and the maximum vertical acceleration of the vehicle was 0.51 m/s\({}^{2}\). In PM, the maximum wheel load was 119.26 kN, which was 0.43% less than that in the literature [51]. The results of PM and the classical model differed by less than 10% in the vertical direction, confirming the accuracy of the model developed here.
## 3 Numerical Studies
### Attenuation Performance of Different Tree Distribution Areas  
As shown in Section 3.1, the attenuation efficiency varies with the relative position between the observation point and the tree grid. In this section, the influence of the tree distribution area on the mitigation performance under different train loads is further investigated. The distribution area of trees is modified by varying the number of lateral trees \(N_{y}\) and the longitudinal trees \(N_{x}\), and other geometric parameters of the tree grid are kept unchanged, as shown in Table 3. The train load is composed of eight harmonic point loads (two carriages), and the parameters of soil, the track, and the tree grid are shown in Tables 1 and 2. Figure 9 illustrates the ARS values at observation points A and C for different numbers of lateral and longitudinal trees including \(N_{x}=5\), 10, and 20 with \(N_{y}=20\) and \(N_{x}=20\) with \(N_{y}=10\), 20, and 30 in the 1-80 Hz range. As shown in Figure 9a,b, increasing the lateral extent of the tree grid leads to more effective vibration mitigation at both observation points A and C. It can also be observed from Figure 9a,b that a wider longitudinal tree distribution area results in more significant vibration mitigation, and this effect is more pronounced than that of the lateral distribution--especially at point C. In addition, significant displacement amplification is observed near \(f=16\) Hz when \(N_{x}=10\). This occurs because the longitudinal distribution of the train loads (45 m) exceeds the length of the tree grid (20 m), and the more distant observation point C further amplifies this effect.  
Figure 9: ARS values at observation points A and C for different tree counts in the lateral and longitudinal directions. (**a**) Point A for \(N_{x}=5\), 10, and 20 and \(N_{y}=20\); (**b**) Point C for \(N_{x}=5\), 10, and 20 and \(N_{y}=20\); (**c**) Point A for \(N_{x}=20\) and \(N_{y}=10\), 20, and 30; (**d**) Point C for \(N_{x}=20\) and \(N_{y}=10\), 20, and 30.
## 4 Experiments
### Experimental Evaluation
#### 4.2.1 Depth Evaluation
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **Supervise** & **Abs Rel** & **Sq Rel** & **RMSE** & **RMSE Log** & \(\delta<1.25\) & \(\delta<1.25^{2}\) & \(\delta<1.25^{3}\) \\ \hline [PERSON] [21] & Stereo & 0.169 & 1.080 & 5.104 & 0.273 & 0.740 & 0.904 & 0.962 \\ [PERSON] [18] & No & 0.155 & 0.927 & 4.549 & 0.231 & 0.781 & 0.931 & 0.975 \\ [PERSON] [19] & No & 0.147 & 0.936 & 4.348 & 0.218 & 0.810 & 0.941 & 0.977 \\ [PERSON] (Resnet) & No & 0.142 & 0.909 & 4.306 & 0.203 & 0.832 & 0.949 & 0.978 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Monocular depth results on KITTI by the split of [PERSON] et al. [10] capped 50 m. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **Supervise** & **Abs Rel** & **Sq Rel** & **RMSE** & **RMSE Log** & \(\delta<1.25\) & \(\delta<1.25^{2}\) & \(\delta<1.25^{3}\) \\ \hline [PERSON] [11] & Stereo & 0.124 & 1.076 & 5.311 & 0.219 & 0.847 & 0.942 & 0.973 \\ [PERSON] [15] & No & 0.198 & 1.836 & 6.565 & 0.275 & 0.718 & 0.901 & 0.960 \\ [PERSON] [18] & No & 0.159 & 1.231 & 5.912 & 0.243 & 0.784 & 0.923 & 0.970 \\ [PERSON] [19] & No & 0.153 & 1.328 & 5.737 & 0.232 & 0.802 & 0.934 & 0.972 \\ Ours (Resnet) & No & 0.146 & 1.253 & 5.614 & 0.224 & 0.838 & 0.941 & 0.973 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Monocular depth results on Cityscapes and KITTI by the split of [PERSON] et al. [10] capped 80 m.  
To demonstrate the generalization ability of our model, we test the model on Make3D [29] dataset which trained only on KITTI and Cityscape. The images of Make3D dataset are in different aspect ratio thus we evaluate on a central crop of these images. Errors are only computed where depth is less than 70 m in the central image crop similar as [PERSON] [11] and [PERSON] [15]. As shown in Table 5 and Figure 5, we compare with several methods including supervised ones using Make3D groundtruth depth. Though there leaves a performance gap between our method and supervised ones, our predictions beat [PERSON] [15] in three metrics, preserving well global scene layout and thin structures. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **Abs Rel** & **Sq Rel** & **RMSE** & **RMSE Log** & \(\delta<1.25\) & \(\delta<1.25^{2}\) & \(\delta<1.25^{3}\) \\ \hline No Occ-Mask & 0.161 & 1.367 & 6.017 & 0.236 & 0.805 & 0.934 & 0.972 \\ No 3D ICP Loss & 0.160 & 1.594 & 5.775 & 0.226 & 0.826 & 0.945 & 0.976 \\ No 3D Flow Loss & 0.157 & 1.353 & 5.971 & 0.232 & 0.811 & 0.938 & 0.975 \\ All losses & 0.149 & 1.223 & 5.732 & 0.225 & 0.829 & 0.944 & 0.978 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation results where individual components are left out on KITTI dataset when capped 80 m.  
Figure 3: Comparison of monocular depth estimation between [PERSON] et al. [10] (supervised by depth), [PERSON] et al. [21] (supervised by stereo), [PERSON] et al. [15] (unsupervised) and ours (unsupervised). Our method captures more details in the whole scene and particularly in thin structures for both close and distant regions.
# Password Similarity Using Probabilistic Data Structures
## 1 Introduction  
Text-based passwords are still the most common way to authenticate users against services [1]. According to a typical authorization mechanisms classification, they fall in the \"what you know\" category. The other categories, \"what you are\" and \"what you have\", are most commonly used only as a second factor in the so-called multiple factor authentication (MFA) schemes [2]. An example of MFA is the, today very popular, one time password available on a personal device (e.g., smartphone) at each login attempt into a critical service. Ideally, passwords should be easy to remember, but hard to guess. In a long term game of cops and robbers, users try to base them on dictionary words [3] in order to make them easier to remember, and system administrators write policies to block these attempts, to limit the probability of password guessing. As a common reaction, users make simple variations to hide the base, easy-to-remember common word. This makes the password harder to remember, so users try to stick to the same one forever, but system administrators add expiration times to their policies. Users then adapt by making the smallest possible change at each password update. Unfortunately this still results in an insecure behavior, making life much easier to malicious actors trying to guess current passwords, because old ones are often available through leaked databases. The countermeasure to discourage this behaviour is to prevent choosing a password too similar to the previous one(s).  
The problem with the simplest approaches to similarity estimation--for example based on the Levenshtein distance [4] between two clear-text strings--is that passwords are sensitive and personal data. Storing them to enable similarity checks exposes users to an additional high risk of breaches into their accounts. This demands for tools to implement similarity check among passwords of different age that can work without storing the password history.  
In this paper we propose a Bloom Filters based system to solve this issue, which checks passwords similarity over obfuscated data. The effectiveness of the schemes depends on the tuning of the filter,which is thoroughly analyzed to determine the values of the parameters that ensure password secrecy, yet allowing an effective detection of similarity. The scheme natively allows the integration of a cryptographic access control method, which will be investigated in a future work.  
This paper is structured as follows. Section 2 discusses a list of previous works and state-of-the-art tools that employ similar techniques to evaluate similarity between data. Some additional details of the password similarity issue, the design of our system and a possible attack on this kind of data analyzers are introduced in Section 3. Our scheme was implemented in C language for the Linux operating system, and a performance analysis is presented in Section 4. Finally we illustrate some application scenarios and propose future improvements of this work in Section 5.
## 4 Experiments and Results
### Overall Evaluation Results  
From Figure 4, the following observations can be made: (1) Among all the baselines, our NDRE achieves the best performance over the entire recall range; (2) The NDRE performs much better than PCNN+ATT, BGWA, and PCNN+ATT+SL. It indicates that our noise-detection strategy is superior to ordinary selective attention mechanism and soft labeling based on correctly labeled instances in alleviating the noisy labeling problem. (3) The NDRE substantially outperforms RESIDE and Direct+Dis, which utilize the extra information, and even achieves the higher performance over the recently promoted PCNN+PU. This demonstrates the effectiveness of our work.
## 4 Results and Discussion  
### Basic Evaluation  
The initial basic evaluation on the test data highlights the mutual dependency between the classification algorithm and the type of feature (see Figure 4). SVM and kNN perform best on text (69.1%) while ANN reaches nearly the same top score on CPC (68.1%) as Logistic Regression (LogReg) on IPC taxonomy. In contrast, the weaker base classifiers only achieve F1 values between 53.2% (CPC-kNN), 54.3% (IPC-kNN) and 60.6% for LogReg on text.  
Considering the results of prior research [12], it was to be expected that SVM would again prove to be a powerful tool for text classification in the present task. The almost equal quality of classification achieved with both patent taxonomies using ANN or LogReg is all the more remarkable, because it is only about one percentage point below the best value of SVM on text. Different from the majority of past approaches that use the taxonomies as target of automated categorization [11; 14], our results place both IPC and CPC close to the text of patent documents to be used as a valuable source of information suitable for machine learning approaches in multiclass environments.  
Since medical technology is an international domain, its technology management is forced to analyze patents worldwide originally written in many different languages. However, in many countries, such as Germany, patents are not translated into English by default. Thus, only 51.6% of all records in the international patent database provided by the European Patent Office (PATSTAT 2019 autumn version [32]) contain an English title and abstract. This is a drawback for international technology management, as automated patent categorization based on textual features usually depends on the common language of the data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Factor** & **Levels** \\ \hline Feature Source & Text; Taxonomy (IPC or CPC) \\ Base Performance Ranking & Top; Bottom \\ Fusion Method & Stacking (SVM, kNN, LogReg, ANN); \\ Fixed Combination Rules (sum, product, min, max) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Factors of the experimental design to build the varying classifier ensembles.  
In contrast, CPC or IPC codes are language independent and very frequently assigned to the patent documents. In the PATSTAT database 85.2% of documents are assigned with ICP or CPC codes. Therefore, the IPC/CPC taxonomies could potentially bridge the language gap by even replacing text features in machine learning classification systems of international patents. We have explored this prospect in a preliminary analysis, the results of which are reported in Section 4.4, Outlook.
###### Abstract  
Water color remote sensing is vital for the monitoring and quantification of marine suspended sediment dynamics and their distributions. Yet validations of these observables in coastal regions and deltaic estuaries, including the Hangzhou Bay in the East China Sea, remain challenging, primarily due to the pronounced complex oceanic dynamics that exhibit high spatiotemporal variability in the signals of the suspended sediment concentration (SSC) in the ocean. Here, we integrate satellite images from the sun-synchronous satellites, China's Huanjing (Chinese for environmental, H)-1A/B (charged couple device) CCD (30 m), and from Korea's Geostationary Ocean Color Imager GOCI (500 m) to the spatiotemporal scale effects to validate SSC remote sensing-retrieved data products. A multi-scale validation framework based on coefficient of variation (CV)-based zoning was developed, where high-resolution H) CCD SSC data were resampled to the GOCI scale (500 m), and spatial variability was quantified using CV values within corresponding H) CCD windows. Traditional validation, comparing in situ point measurements directly with GOCI pixel-averaged data, introduces significant uncertainties due to pixel heterogeneity. The results indicate that in regions with high spatial heterogeneity (CV > 0.10), using central pixel values significantly weakens correlations and increases errors, with performance declining further in highly heterogeneous areas (CV > 0.15), underscoring the critical role of spatial averaging in mitigating scale-related biases. This study enhances the quantitative assessment of uncertainties in validating medium-to-low-resolution water color products, providing a robust approach for high-dynamic oceanic environment estuaries and bays.  
GOCI; H]CCD; product validation; scale effect; suspended sediment
## 4 Simulation and Analysis
### Comparison of PLL Tracking Performance  
The steady-state tracking performance of the VLL affects the performance of the PLL, while the performance of the PLL is directly related to the bit error rate of the data demodulation. Therefore, the performance of the PLL both in the VLL-SA-PLL and HD-VLL-SA-PLL is compared and analyzed.  
By assuming the same sinusoidal motion parameters as those used in Section 4.1, the HD-VLL adopts a bandwidth of 5 Hz, the VLL adopts a bandwidth of 8 Hz, and the PLL adopts a bandwidth of 20 Hz. The PLL discrimination curves and PLL filtering curves of the two loop structures are depicted in Figure 16.  
Figure 16: _Cont._  
Figure 15: The tracking results of the HD-VLL and VLL in the fixed jerk case. (**a**) The velocity estimate curves of the HD-VLL and VLL in the fixed jerk case. (**b**) The velocity error curves of the HD-VLL and VLL in the fixed jerk case.  
It can be seen that the PLL cannot lock in the VLL-SA-PLL structure, whereas in the HD-VLL-SA-PLL structure, the PLL maintains steady-state tracking. Therefore, the tracking performance of the PLL in the HD-VLL-SA-PLL structure is superior to that in the VLL-SA-PLL structure under high dynamic conditions.