esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Policy/Intent-Based Security in SDN and NFV Networks
### Enhancing Security in SDNs and NFVs through PBN
Like in previous examples of applied policy enforcement, this enforcement is carried out dynamically by adapting to network conditions or threat landscape changes by configuring SDN switches in response to detected threats or policy changes. Using MaxSMT, the framework guarantees the formal correctness of the security conditions applied, ensuring that the implemented security measures match the specified policies without needing post-configuration verification. This methodology also provides some advantages to previously discussed examples, since it optimises the placement and configuration of SDN switches to minimise resource usage while maximising security effectiveness, which includes minimising the impact of attacks and enhancing bandwidth allocation by placing SDN switch rules as close to the traffic source as possible.
## 3 Results and Discussions
### Reaction Mass Efficiency (RME) or Material Efficiency (ME)  
The results shown in Figures 6 and 7 were calculated using Equations (7) and (8), respectively. The results shown in Figure 7 are based on the _E-factor_, but a similar trend was followed using both equations. As shown in Figure 7, the reaction mass efficiency percentages were higher when based on the _E-factor_, which could be due to the fact the _E-factor_ is solely based on the amount of waste produced. Therefore, more than one alternative is available to calculate _RME_. The results favored the transesterification reaction using methanol for both expressions. In addition, the differences between _MI_ and _MP_ values are due to the differences in environmental factors calculated in these reactions.  
Figure 5: Mass Productivity for the simultaneous effect of MW/US in WCO transesterification. Effect of (**a**) different reaction time; (**b**) alcohol to oil molar ratio; (**c**) catalyst concentration and (**d**) sample (oil) volume.  
Figure 6: Reaction mass efficiency for the simultaneous effect of MW/US in WCO transesterification. Effect of (**a**) different reaction time; (**b**) alcohol to oil molar ratio; (**c**) catalyst concentration and (**d**) sample (oil) volume.
# Parameterized Algorithms in Bioinformatics: An Overview
## 3 Genome Assembly and Sequence Analysis
### Identifying Common Patterns
#### Shortest Common Supersequence  
**Problem Description.**: In Shortest Common Supersequence, we now want to find a larger string of which every input string is a subsequence (see Figure 6(right)).  
Shortest Common Supersequence (SCS)  
**Input:** Strings \(s_{1},\ldots,s_{k}\), all of length at most \(\ell\), and some \(m\in\mathbb{N}\)  
**Question:**\(\exists\) string \(s^{*}\) of length at most \(m\) such that each \(s_{i}\) is a subsequence of \(s^{*}\)?  
**Results.**: As for LCS, Shortest Common Supersequence is W[1]-hard for parameter \(k\) even on binary alphabets [56], and does not admit an \(\ell^{o(k)}\) algorithm [68]. It does admit an FPT algorithm for the number of _repetitions_, \(m-|\Sigma|\) when \(\mathsf{occ}=1\) (each character appears at most once in any string) [69].  
**Open Problems.**: Can the FPT algorithm for \(\mathsf{occ}=1\) be extended to more general strings?
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 3 Network Model and Design Requirements
### Network Model
#### 3.2.4 Network Utility  
Network utility is the sum of the weighted logarithm of the allocated bandwidth of all STAs, which also is the overall channel utilization rate. It is expressed by Equation (4).  
\[U=\sum_{i=1}^{n}w_{i}\log b_{i} \tag{4}\]  
From AP utility, since \(\text{b}_{\text{i}}=\frac{r_{i}\cdot\text{t}_{i}}{\text{I}_{\text{j}}}\), and \(r_{i}\) and \(T_{j}\) are constants, the network utilization can be expressed by Equation (5).  
\[U=\sum_{i=1}^{n}w_{i}\log t_{i}= \sum_{j=1}^{n}\left(\sum_{i\in A}w_{i}\log t_{i}\right) \tag{5}\] \[=\sum_{j=1}^{n}\log(\prod_{i\in A}\left(t_{i}\right)^{w_{i}})\] \[=\sum_{j=1}^{n}U_{A_{j}}=\log(\prod_{j=1}^{n}U_{A_{j}})\]  
So, Equation (5) can be transformed into Equation (6).  
\[U\leq n\log(\frac{1}{n}\sum_{j=1}^{n}U_{A_{j}})=n\log\overline{U_{A_{j}}} \tag{6}\]  
The equation holds if and only if \(U_{A_{1}}=U_{A_{2}}=\cdots=U_{A_{m}}=\overline{U_{A_{j}}}\) when \(U=n\log\overline{U_{A_{j}}}\), which means the load of APs is balanced and the channel is utilized fully.
## 3 Proposed Detection Mechanism
### Features Extraction  
Once the data packets are captured by the BS, these are analyzed in the second stage. The packet contents, the data about the nature and features of packets, are recorded. The metadata about the packets is analyzed in this stage. The required parameters, i.e., IP addresses of the source and destination nodes, the size of the payload, and time of reception, are calculated and maintained by the BS.
# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 4 G.727 DPCM Encoder
### Short-Term Predictor  
The short-term predictor \(P_{\rm s}(z)\) is a 2-pole and 6-zero backward adaptive predictor. The two pole coefficients (\(a_{i}\)) of the predictor are adapted by [24; 25]:  
\[a_{1}(k)=(1-2^{-8})a_{1}(k-1)+(3\times 2^{-8})\text{sgn}[p(k)]\text{sgn}[p(k-1)], \tag{1}\]  
and  
\[a_{2}(k)=(1-2^{-7})a_{2}(k-1)+2^{-7}\{\text{sgn}[p(k)]\text{sgn}[p(k-2)]-f[a_{1 }(k-1)]\text{sgn}[p(k)]\text{sgn}[p(k-1)]\}, \tag{2}\]  
where  
\[p(k)=e(k)+\sum_{i=1}^{6}b_{i}(k-1)u(k-i) \tag{3}\]  
\[f(a_{1})=\left\{\begin{array}{ll}4a_{1},&|a_{1}|\leq 2^{-1}\\ 2\text{sgn}(a_{1}),&|a_{1}|>2^{-1}\end{array}\right. \tag{4}\]  
The six zero coefficients (\(b_{i}\)) of the predictor are adapted by:  
\[b_{i}(k)=(1-2^{-8})b_{i}(k-1)+2^{-7}\text{sgn}[u(k)]\text{sgn}[u(k-i)] \tag{5}\]  
for \(i=1,2,\ldots,6\).
### Radionics  
Radiomics involves the extraction of quantitative features from medical images, such as those from MRI or Computed Tomography (CT), to uncover patterns and insights not visible to the naked eye [9]. Before feature extraction, the region of interest (ROI) is segmented to focus the analysis and reduce the volume of data that needs processing, thereby enhancing computational efficiency. This process can be performed manually, which is considered the gold standard. In this approach, a medical expert segments the region of interest (ROI). This time-consuming task heavily relies on the level of expertise of the individual performing the segmentation. Furthermore, there are fully automated methods for ROI segmentation. However, they struggle in cases with lesions with indistinct borders and are highly dependent on the quality of the image. For that reason, semi-automatic methods are preferable. These methods have minimal user interaction for seed identification or manual correction [22]. After the ROI segmentation, several different features can be extracted. Commonly, these features are divided into four categories [22]:  
1. Shape-Based Features: numeric information respecting geometric characteristics, like shape and size.
2. First-Order Features: distribution of voxel values without spatial information, generally histogram-based.
3. Second-Order Features: \"texture\" features, focus on the spatial relationships between voxels with similar grey levels.
4. High-Order Features: filters are used to extract patterns from the images. From the resultant images, first- and second-order features are extracted.  
Traditional ML models are frequently used to classify radiomic features of regions of interest and have achieved high classification performance in different anatomical regions [9; 23]. These models are used instead of conventional statistical methods as they perform better with large amounts of data [5].
# Formation of Conductive Networks in Polysulfone Filled with Graphite-Derived Materials
## 3 Results and Discussion
### Mechanical Properties
but adding CB to a PSU-based composite filled with AG led to an increase in the flexural  
Figure 14: Flexural test curves of PSU-based composites with unfilled PSU solution (1); 2 (2), 4 (3), 6 (4), 8 (5) wt. % of CB. Figure 15: Flexural test curves of PSU-based composite filled with (1) 28 wt. % NG + 2 wt. % CB, (2) 26 wt. % NG + 4 wt. % CB, (3) 68 wt. % NG + 2 wt. % CB, (4) 66 wt. % NG + 4 wt. % CB, (5) 28 wt. % AG + 2 wt. % CB, (6) 26 wt. % AG + 4 wt. % CB, (7) 68 wt. % AG + 2 wt. % CB, (8) 66 wt. % AG + 4 wt. % CB. strength of the composite using 2 wt. % of CB; however, as we used 4 wt. % of CB, the flexural strength decreased as well as for the composite filled with 70 wt. % AG. To clarify the effect of additives on mechanical properties, tables were created showing the highest values of flexural strength depending on the filler content. The mechanical properties of the studied composites are given in Table 4. As can be seen, the flexural strength of unfilled PSU is about 124 MPa and by using graphite materials as fillers the flexural strength dropped to 51 MPa max. As we saw, adding graphite to a polymer can increase electrical and thermal conductivity. However, it can also make the composite more brittle or fragile due to the rigid nature of graphite particles. The flexural strength of graphite is about 34 MPa. The highly filled polymeric composite material was expected to be more brittle than the unfilled polymer, but as the results show we can assume that the flexural strength of the highly filled composite is not bad compared to the graphite properties, such as high brittleness.
## 2 Material and Methods
### The Relationship between H\({}_{2}\)O/CO\({}_{2}\) Flux and Environmental Factors  
Because the eddy covariance can provide continuous measurements of the net ecosystem exchange (NEE) and evapotranspiration (ET), gross primary productivity (GPP) and ecosystem respiration (RECO) were derived from NEE measurements according to the method provided by the Tovi 2.4.0 software. A negative NEE value was defined as the CO\({}_{2}\) fixation from the atmosphere, while a positive NEE value was defined as the CO\({}_{2}\) emission into the atmosphere [47]. The continuous NEE at 30 min intervals was calculated as follows:  
\[\mathrm{NEE}=\mathrm{RECO}-\mathrm{GPP} \tag{2}\]  
In addition, the continuous NEE, GPP, RECO, and ET measurements at 30 min intervals were summed to generate the daily, monthly, and annual values of these CO\({}_{2}\) and H\({}_{2}\)O flux components.  
The net ecosystem production (NEP) was the negative value of NEE [52; 54].  
\[\mathrm{NEP}=-\mathrm{NEE} \tag{3}\]  
In order to examine the inherent differences in the response of H\({}_{2}\)O/CO\({}_{2}\) flux to environmental factors, a regression model between H\({}_{2}\)O/CO\({}_{2}\) flux (NEP, GPP, RECO, and ET) and environmental factors was established.  
Firstly, the exponential regression model was used to quantify the relationship between environmental temperature and CO\({}_{2}\)/H\({}_{2}\)O flux at annual timescale [4; 31]. The fitting equation was as follows:  
\[f(x)=a\times e^{bT} \tag{4}\]  
where \(T\) was the TA (\({}^{\circ}\)C) or TS (\({}^{\circ}\)C); \(f(x)\) represented H\({}_{2}\)O/CO\({}_{2}\) fluxes (ET, NEP, GPP, and RECO).  
Then, we explored the relationship between RECO and temperature (TA (\({}^{\circ}\)C) and TS (\({}^{\circ}\)C)) in two forest ecosystems. The temperature sensitivity (\(Q_{10}\)) of ecosystem respiration (RECO) indicated that the RECO increased exponentially for every 10 \({}^{\circ}\)C increase in temperature, which was expressed as [31; 55]  
\[Q_{10}=e^{10b} \tag{5}\]  
where \(b\) is the constant obtained by the Equation (3).  
Furthermore, the linear regression model was used to quantify the relationship between PAR, RH, and VPD and H\({}_{2}\)O/CO\({}_{2}\) flux at an annual timescale in two different forest ecosystems [4; 31]. The fitting equation was as follows:  
\[f(x)=a+bx \tag{6}\]  
where \(f(x)\) represented H\({}_{2}\)O/CO\({}_{2}\) flux (ET, NEP, GPP and RECO); \(x\) was PAR, RH or VPD.
## 2 Design and Modeling
(**c**) Detailed cross section of the layer structure. This corresponds with the unit cell in the simulations. cell as shown in Figure 1b,c. Then, periodic boundary conditions applied on the sides of this unit cell to repeat it infinitely. The optical source is placed on top of the whole structure with a customized orientation that resembles the incidence conditions. The optical model evaluates absorption at each active layer, as well as the total reflection of the tandem cell. A detailed description of the optical model and the underlined equation are discussed and reported elsewhere [41]. The optical absorption in the perovskite and c-Si active layers is weighted with the spectral solar irradiance and is used to calculate the short-circuit current of each cell [7]. The structure is illuminated with transverse magnetic polarized light (TM) and transverse electric polarized light (TE) separately. Finally, we averaged both contributions to consider the unpolarized character of the solar irradiation.
## 3 Methods
### Mann-Kendall Significance Analysis  
The Mann-Kendall method is a technique used to test the significance of temporal changes. It does not depend on the overall distribution of the data and can handle time series data with missing values [28].  
\[\text{SV}=\sum_{\text{af=1}}^{l-1}\sum_{\text{bf=af+1}}^{l}\text{sgn}(WUE_{ \text{af}}-WUE_{\text{bf}}) \tag{3}\]  
\[\text{sgn}(\text{WUE}_{\text{af}}-\text{WUE}_{\text{bf}})=\left\{\begin{array}[ ]{l}1,\text{WUE}_{\text{af}}-\text{WUE}_{\text{bf}}>0\\ 0,\text{WUE}_{\text{af}}-\text{WUE}_{\text{bf}}=0\\ -1,\text{WUE}_{\text{af}}-\text{WUE}_{\text{bf}}<0\end{array}\right. \tag{4}\]  
\[\text{SI}=\left\{\begin{array}{l}\frac{\text{SV}-1}{\sqrt{\varphi(\text{SV}) }},\text{SV}>0\\ 0,\text{SV}=0\\ \frac{\text{SV}+1}{\sqrt{\varphi(\text{SV})}},\text{SV}<0\end{array}\right. \tag{5}\]  
\[\varphi(\text{SV})=\left(l(l-1)(2l+5)-\sum_{r=1}^{R}\text{WUE}_{r}(\text{WUE} _{r}-1)(2\text{WUE}_{r}+5)\right)/18 \tag{6}\]  
where \(l\) represents the length of the time series, af and bf are the indices of the variables, \(R\) is the number of repeated data groups in the sequence, and \(r\) is the number of repeated data. \(S\) is the test statistic, SI represents significance, and \(\varphi(\text{SV})\) is the variance in SV. If the absolute value of SI is greater than 1.96, it indicates a significant change; otherwise, it indicates no significant change.
## 3 Methodology
### Experimental Design and Survey Development
#### 3.3.1 Interaction Mode Design  
The chatbot implemented three distinct interaction modes, as shown in Figure 2, to accommodate varying query complexities and user preferences:* Text-Based Mode: This mode allows users to type free text for open-ended interaction, leveraging natural language processing (NLP) to handle both simple and complex queries. For example, when asked _\"What options are available for academically struggling students, and how can GPA be improved while considering college policies?\"_, the chatbot interprets the query through its NLP pipeline, identifies the intent (e.g., academic support), and generates a detailed response based on pre-trained models.
* Menu-Based Mode: Utilizing a hierarchical navigation structure with predefined choices, this mode guides users from broad categories (e.g., \"Academic Support\") to increasingly specific topics (e.g., \"Options for GPA Improvement\"). For complex queries like the example above, users would navigate step by step: 1. Select _\"Academic Support\"_ from the main menu.
* Choose _\"GPA Improvement Strategies\"_ from the subsequent submenu.
* Access tailored resources or instructions provided for struggling students, with options to explore policies further.
* Hybrid Mode: This mode combines text-based and menu-based interactions, offering the flexibility to switch between free-text input and predefined menus. For the complex query example, users could type part of their question (_\"What options are available for GPA improvement?\"_), allowing the chatbot to generate an initial response while also presenting related menu options like _\"Academic Policies\"_ or _\"Guidance and Counseling\"_ for further exploration. This dual approach ensures effective handling of diverse query complexities and user preferences.
## 3 Background
### Product Life Cycle Model
The non-parametric Mann-Kendall test is employed to detect monotonic trends in time series data. The null hypothesis posits that the observations are independent and identically distributed, indicating no trend. In contrast, the alternative hypothesis suggests the presence of a monotonic (either increasing or decreasing) trend in the data. The Mann-Kendall test is a non-parametric method used to identify significant trends in time series data. It is specifically designed to detect monotonic trends--either consistently increasing or decreasing--over the observed period. The core principle of the test involves ranking the observations and evaluating the relative ordering of data points to determine the direction and significance of the trend. \[x_{i}\ =\ f(t)+\epsilon_{i} \tag{8}\]  
where  
\(f\) (\(t\))--is a continuously monotonically increasing or decreasing function of time,  
\(\epsilon_{i}\)--the residuals are assumed to originate from the same distribution with a mean of zero. Therefore, the variance of the distribution is assumed to be constant in time. The Mann-Kendall test statistic s is calculated using the given equation:  
\[S\ =\ \sum_{i\ =\ 1}^{n-1}\sum_{j\ =\ i+1}^{n}sgn\big{(}x_{j}-x_{i}\big{)} \tag{9}\]  
where  
\(n\)--total number of data points in the time series,  
\(m\)--cluster of data points with the same data value,  
\(t_{i}\)--total observation of range i at a specific time. When the total sample size \(n\) exceeds 10, the standardized test statistic \(Z\)s is calculated using the following formula:  
\[Z_{s}\ =\ \begin{cases}\frac{s-1}{\sqrt{Var\ (s)}}&\text{If S}>0\\ \begin{array}{cc}0&\text{If S}\ =\ 0\\ \frac{s+1}{\sqrt{Var\ (s)}}&\text{If S}<0\end{array}\\ \end{cases}  \tag{10}\]  
A positive value of \(Z_{s}\) indicates an increasing (positive) trend in the time series, whereas a negative \(Z_{s}\) value signifies a decreasing (negative) trend.
# Fatigue Crack Growth in Managing Steel Obtained by Selective Laser Melting
## 2 Fatigue Crack Growth Analysis Based on \(\Delta\)K  
### Material  
Table 1 presents the chemical composition of the AISI 18 Ni300 managing steel. This steel contains nickel as the primary strengthening source rather than carbon, promoting superior strength and toughness. In addition to its high strength, the18 Ni300 steel can be easily machined or formed; afterwards it can undergo an aging heat treatment step, which forms intermetallic precipitates involving cobalt, molybdenum and titanium that aid in increasing the tensile strength. Samples were manufactured using Lasercusing(r) technology, with layers growing in the same direction as the load application in the mechanical tests. The equipment for selective laser melting is of the mark \"Concept Laser\" and model \"M3 Linear\". This apparatus comprises a laser type Nd:YAG with a maximum power of 100 W in continuous wave mode and a wavelength of 1064 nm. The scan speed was 200 mm/s. The material layers were found to have about 40 \(\upmu\)m of thickness. The analysis by scanning electron microscope (SEM) showed the presence of a significant amount of small porosities and the formation of martensitic needles [8]. Additionally, the increase of laser speed was found to increase the level of porosity.
## 2 Materials and Methods
### Methods
#### 2.3.4 Machine Learning Method Integration Analysis  
We introduced four machine learning methods, decision trees, random forests, GBDT, and XGBoost, to identify and analyze the complex influences of WUE [52]. Decision trees predict target variables by learning decision rules from the features of the utilized data. This method is advantageous because the generated model is easy to understand, interpretable, and does not require extensive preprocessing, making it suitable for directly revealing the main factors affecting WUE [53]. Random forests improve model accuracy and the robustness of a model by constructing multiple decision trees and combining their predictions, with each tree using randomized data samples and subsets of features for training. The approach significantly reduces the variance of the model and prevents overfitting [54]. As a result, random forests are well suited for handling large datasets with high-dimensional features, offering improved stability and accuracy in detecting WUE drivers with minimal parameter tuning [55]. The gradient boosting decision tree (GBDT) corrects errors from the previous tree by sequentially adding decision trees, with each step aimed at reducing the overall prediction error. This enables the model to maintain high prediction accuracy under the influence of complex natural factors, especially in capturing nonlinear changes in WUE [56]. GBDT enhances the model accuracy by optimizing a derivable loss function, which enhances predictive performance and is particularly effective when applied to structured data [57]. XGBoost (Extreme Gradient Boosting), an implementation of the gradient boosting framework, is designed to provide high prediction performance while maintaining computational speed and efficiency. This enabled our study to process large-scale WUE data quickly and detect subtle changes in WUE across different temporal and spatial scales [58]. By integrating these methods, the complex response patterns of WUE under the action of multiple natural factors were revealed, and the advantages of these methods provided novel and effective analytical tools for WUE research [52].
# Correction of Directional Effects in VEGETATION NDVI Time-Series
###### Abstract  
Land surface reflectance measurements from the VEGETATION program (SPOT-4, SPOT-5 and PROBA-V satellites) have led to the acquisition of consistent time-series of Normalized Difference Vegetation Index (NDVI) at a global scale. The wide imaging swath (>2000 km) of the family of VEGETATION space-borne sensors ensures a daily coverage of the Earth at the expense of a varying observation and illumination geometries between successive orbit overpasses for a given target located on the ground. Such angular variations infer saw-like patterns on time-series of reflectance and NDVI. The presence of directional effects is not a real issue provided that they can be properly removed, which supposes an appropriate BRDF (Bidirectional Reflectance Distribution Function) sampling as offered by the VEGETATION program. An anisotropy correction supports a better analysis of the temporal shapes and spatial patterns of land surface reflectance values and vegetation indices such as NDVI. Herein we describe a BRDF correction methodology, for the purpose of the Copernicus Global Land Service framework, which includes notably an adaptive data accumulation window and provides uncertainties associated with the NDVI computed with normalized reflectance. Assessing the general performance of the methodology in comparing time-series between normalized and directional NDVI reveals a significant removal of the high-frequency noise due to directional effects. The proposed methodology is computationally efficient to operate at a global scale to deliver a BRDF-corrected NDVI product based on long-term Time-Series of VEGETATION sensor and its follow-on with the Copernicus Sentinel-3 satellite constellation.  
NDVI; VEGETATION; BRDF +
Footnote †: journal: Remote Sensing  
12 February 2021  
Accepted: 11 March 2021  
Subject head **Publisher's Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.