esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Research  
IPv6 has existed for almost two decades and a lot of technical research has been conducted in this area. However, in the area of managing and supporting the secure introduction of IPv6 into existing networks, literature is very scarce. Currently, we are not aware of a deep and systematic comparison of the important guidelines that support practitioners during this process. Concerning general literature, most relevant information on IPv6 can be found in the RFCs published by IETF, see the list in A, which is updating and extending an older list published by NIST [3]. Those RFCs also cover important research in the field of IPv6, including experimental methods and protocols. The RFCs were also used as a starting point of our work, extended to a large extent during the literature review. To the best of our knowledge, no evaluation of security guidelines for the deployment of IPv6 has been conducted before that was based on relevant RFCs published by the IETF.  
Concerning other general literature, [PERSON] gives a comprehensive overview of the IPv6 in her books [4, 5] as do older reference works such as [6, 7]. High level introductions to IPv6 security are given by [8, 9, 10, 11, 12]. More detailed discussions on IPv6 security include [10, 13, 14], as well as books such as [15, 16]. Another very detailed introduction to IPv6 security in German is [17]. Reference [18] gives a comparison of IPv4 with IPv6 security and threats. Reference [19] focuses on network auto configuration and related security issues. A survey of secure protocols for Mobile IPv6 is presented by [20]. Moving-target defense based on IPv6 is the topic of [21]. Reference [22] present the result of a survey (with 11 usable responses) on security issues during transition to IPv6 as well as some limited practical security tests on production networks. In comparison to this paper, the number of respondents in our paper is larger and the result more detailed.  
With respect to security, our current paper does not aim at providing a concise survey of IPv6 security issues and details of recent exploits. Such a work would be an important complement to our article. Instead, we focus on the management aspects of secure IPv6 deployment and the question to what extent the relevant RFCs are reflected in the two most prominent guidelines for practitioners.
# A New Model of Quasigeoid for the Baltic Sea Area
## Data
### GNSS and Leveling Data  
To assess the accuracy of the calculated quasigeoid model, it is necessary to compare quasigeoid anomalies from GNSS observations and leveling \(\zeta^{\text{GNSS/lev}}\) with height anomalies calculated from anomalies \(\zeta^{\text{grav}}\) from gravity data on the points of the permanent ASG-EUPOS network.  
The ASG-EUPOS permanent network was established in June 2008. The ASG-EUPOS is a multifunctional system for precise satellite positioning [44]. This network covers the entire Polish territory and consists of a total of 393 GNSS/leveling points, shown in Figure 4. The results of the observation, i.e., the ellipsoidal coordinates \(\phi\), \(\lambda\), and h of the reference stations in the PL-ERTF2000 system are given on the web page ASG-EUPOS website and they are probably in the tide-free system.  
All ASG-EUPOS stations are connected to the vertical reference system Kronstadt86. The Kronstadt86 system is the result of the measurement and adjustment of the leveling network measured from 1974 to 1982 [45]. The final adjustment of the entire network is the tide gauge in Kronstadt and was accepted in 1985. The standard deviation of adjusted height difference is equal \(\pm 0.844\text{mm}\)\(\sqrt{L_{km}}\), where \(L_{km}\) is the length of the leveling line and the standard deviation of adjusted heights is between 6.5 and 11 mm [45]. Heights in the Krostadt86 reference system are tide-free heights.
## 2 Experimental Setup
### Breakaway Force  
To identify the breakaway force, a force controller was designed to subject the sealing ring to a linearly increasing force. Therefore, the signal from one of the force sensors is used as feedback. The breakaway point can be identified by the movement of the sealing ring, which is detected by a displacement sensor. First, the breakaway force was investigated for PTFE. Due to the high stiffness of the material and thus the modesty of its dynamics, the settings of the controller's parameters were very simple. The increasing force and the displacement signals are shown in Figure 9a. The slope was set to a value of 0.03 N/s for the sake of a good identification of the characteristic movement of the sealing ring on the metal surface. The displacement was measured at different ultrasonic amplitudes. The excitation of the contact partners led to earlier breakaway of the seal, i.e., with a lower breakaway force. The sliding process of the seal was stopped after approximately 12 mm due to a current or movement limitation in order to protect the components of the test rig from damage. At the time of breakaway, the value of the force, and thus the breakaway force, was determined; see Figure 9b. For an exact determination of the breakaway force, it was necessary to consider the characteristic movement of the sealing ring in the groove; see Figure 10a-c.  
In the ranges I, II and III, static friction is still present. The change in the displacement signal can be explained by the movement of the sealing ring in the groove and its elongation as a result of the static friction force. With the aid of a transparent housing, this displacement could be determined as 0.4 mm for PTFE and 0.6 mm for NBR70; see Figure 11a,b. Thus, sliding friction did not prevail until area IV. The transition from area III to area IV can be identified as the breakaway point. The breakaway force of 0.3 N (without ultrasonic excitation) could already be reduced by 33% with a vibration amplitude of 8 \(\upmu\)m. An increase to 16 \(\upmu\)m resulted in a reduction of the breakaway force by 67%. With NBR70 as the seal's material, far higher amplitudes were necessary for significant reductions of the breakaway force. The initial breakaway force of 0.6 N could only be reduced by 17% at a vibration amplitude of 8\(\upmu\)m. A reduction of 75% could be achieved at a vibration amplitude of 16 \(\upmu\)m. As already seen in dynamic operation mode in Section 4.1, a higher excitation amplitude was needed for softer materials to cause a significant effect.  
Figure 8: Force –displacement curves for NBR70 at \(\upnu_{\text{rel}}\) of 50 mm/s.
## 4 Modeling  
### Biased Diversity Combination  
A diversity combination approach is proposed for the fusion of the ITD and ILD cues for the lateralization decision in the brain. The lateralization decision could be considered as an estimation problem under additive white Gaussian noise for each of the cues as,  
\[\hat{\Delta}_{L}=\Delta_{L}+n_{L}\pm\beta_{L}\quad\text{and}\quad\hat{\Delta}_{T }=\Delta_{T}+n_{T}\pm\beta_{T}, \tag{17}\]  
where \(\hat{\Delta}_{L}\) and \(\hat{\Delta}_{T}\) respectively represent observed lateralization changes due to the ILD and ITD cues while \(n_{L}\) and \(n_{T}\) are zero-mean Gaussian random noise sources with \(\sigma_{L}^{2}\) and \(\sigma_{T}^{2}\) variances, and \(\beta_{L}\) and \(\beta_{T}\) are the positive bias factors for LO and TO conditions representing the inward and outward decisions respectively with additive (plus) and subtractive (minus) contributions. The fusion of the ITD and ILD cues for the lateralization decision is defined in the diversity combination framework as follows:  
\[ \begin{array}{rcl}\hat{\Delta}_{TL}&=&\lambda\hat{\Delta}_{L}+(1-\lambda) \hat{\Delta}_{T}\\ &=&\lambda(\Delta_{L}+n_{L}\pm\beta_{L})+(1-\lambda)(\Delta_{T}+n_{T}\pm\beta_ {T})\\ &=&\Delta_{TL}+\lambda n_{L}+(1-\lambda)n_{T}\pm\lambda\beta_{L}\pm(1-\lambda),\beta_{T}\end{array}  \tag{18}\]  
where \(\Delta_{TL}=\Delta_{L}=\Delta_{T}\) is the noise-free lateralization change, \(\hat{\Delta}_{TL}\) is the observed lateralization change due to weighted fusion of ILD and ITD cues with weights \(\lambda\) and \((1-\lambda)\), respectively. The weight parameter \(\lambda\) should be in \((0,1)\) interval. Assuming that the noise sources are uncorrelated, the estimated SNR of the observed lateralization change of the combined cues were found as,  
\[SNR_{\overline{TL}}^{\pm}=\frac{(\Delta_{TL}\pm\lambda\beta_{L}\pm(1-\lambda) \beta_{T})^{2}}{\lambda^{2}\sigma_{L}^{2}+(1-\lambda)^{2}\sigma_{T}^{2}}, \tag{19}\]  
where the plus representing the inward and the minus representing the outward decisions. Under the biased diversity combination assumption, the task is to find the optimal model parameters in order to minimize the difference between the estimated and experimental SNR  
Figure 3: Box-plots of the \(SNR_{T+L}\) and \(SNR_{TL}\) values computed from the re-sampled error rates of T+L and TL cases, respectively, for the four azimuthal angles studied. Note that the SNR values of time and level cues are combined into \(SNR_{TL}\) super-additively for \(0^{\circ}\) and \(10^{\circ}\), whereas their combination turns to be additive and sub-additive for \(20^{\circ}\) and \(30^{\circ}\), respectively. values. These parameters are noise variances, bias factors and the fusion weight.
## 4 Chitosan as Catalytic Support
### Catalytic Metal-Chitosan Composites  
The lives of NPs can be divided into three stages: preparation, storage and application. The stabilization of particles' physical and chemical characteristics is critical and must be carefully controlled [132]. Metal deposition on CS is a promising area of research in the field of catalysis, since CS is a source of various functional groups that can effectively bind metal ions or NPs [133; 134]. In the resulting catalytic materials, CS can serve as a reducing agent (due to the presence of hydroxymethyl and amino groups) and stabilizer as well, which is able to control the sizes of metal NPs to some extent. Any modifications to CS influence the concentration of functional groups and contribution of different interactions (hydrogen bonds, electrostatic interactions, \(\pi\)-\(\pi\) stacking interactions, etc.). For example, it was found that the DD and molecular weight of CS influences the sizes of Au NPs. At low CS concentration (0.005% and 0.01% (\(w/r\))), individual spherical Au NPs with a mean diameter of about 10 nm were obtained regardless of chitosan DD and Mw, while anisotropic Au NPs were obtained at a concentration above 0.05% (\(w/v\)) [135]. The growth of larger polygonal Au NPs was promoted as the higher concentration and lower DD CS was used. Chitooligomers (2.4 kDa, DD 94%) showed the highest reduction ability for Au(III) and the synthesized Au NPs exhibited aggregation. In general, it was concluded that the DD and concentration of CS is more important than Mw [135]. CS and its derivatives can be used as stabilizers of various catalytically active metal ions and NPs (Mn [136], Co [112; 137], Cu [138; 139; 140; 141; 142; 143; 144; 145; 146], Ag [147; 148; 149], Pd [150; 151; 152; 153; 154; 155], Pt [156], etc.) and metal oxides [133; 157; 158; 159; 160]. Since CS-containing materials require relatively mild reaction conditions, their catalytic applications are also limited. In the last years, attention was mainly paid to the hydrogenation of nitro-compounds, catalytic oxidation, cross-coupling reactions, click chemistry and some other processes. The hydrogenation of nitro-compounds is one of the most widespread
## 3 Methodology
### Data Preprocessing
#### 3.2.5 Class Balancing
\[X_{syn}=X_{i}+\gamma\times\left(X_{j}-X_{i}\right) \tag{4}\]  
where \(X_{syn}\) represents the synthetic sample generated to balance the class distribution, \(X_{j}\) is a randomly chosen neighbor from the k-nearest neighbors of \(X_{i}\), the minority sample, and the term \(\gamma\) is a random value between 0 and 1, ensuring that the synthetic sample is created along the line segment connecting \(X_{i}\) and \(X_{j}\). Following the ADASYN step, SMOTE is applied. When a sample \(X_{I}\) from the minority class is provided, SMOTE generates a synthetic sample by selecting one of its k-nearest neighbors, \(X_{zi}\). The new synthetic sample \(X_{new}\) is then created by interpolating between \(X_{I}\) and \(X_{zi}\), as described in Equation (5) [45]. \[X_{new}=X_{I}+\sigma\times\left(X_{zi}-X_{I}\right) \tag{5}\]  
where \(\sigma\) is a random number between 0 and 1. This formula ensures that the generated sample \(X_{new}\) lies on the line segment connecting \(X_{I}\) and \(X_{zi}\), effectively creating a synthetic data point that better represents the minority class within the feature space. By emphasizing the feature relationships within the minority class, SMOTE helps balance the dataset, thereby improving model performance in binary classification tasks. An initial imbalance existed in the binary classification of the CICIDS2017 dataset, with 79,112 samples in the Normal class and 62,497 in the Attack class. To address this, the hybrid ADASYN-SMOTE technique was applied to generate synthetic samples for the minority Attack class, first increasing its count to 79,296 with ADASYN and then balancing both classes to 79,296 using SMOTE, as shown in Table 8. This balanced distribution ensured more reliable training and evaluation by providing equal representation for both classes.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 4 Constructing the Multispectral Datasets
### Ground-Truth Labels and Their Usage  
For the source domain, if Sentinel-2 data are used, then Landsat 9 data are used for the target domain. Likewise, if Landsat 9 data are used for the source domain, then Sentinel-2 data are used for the target domain.  
To train the source cloud detector (Section 3.2), source data cubes were assigned a binary label (_cloudy_ vs. _not cloudy_) by thresholding the number of cloudy pixels in the cloud masks. We followed [5] by applying thresholds of 30% and 70% to produce labelled source datasets \(\mathcal{D}^{s}_{\text{TH30}}\) and \(\mathcal{D}^{s}_{\text{TH70}}\), respectively. Each of \(\mathcal{D}^{s}_{\text{TH30}}\) and \(\mathcal{D}^{s}_{\text{TH70}}\) was further divided into training and testing sets.  
To adapt the source cloud detector in the offline adaptation setting (Section 3.3.1), target data cubes were assigned a binary label by applying a 70% cloudiness threshold on the cloud masks to produce a labelled target dataset \(\mathcal{D}^{t}_{\text{TH70}}\). This dataset was further divided into training and testing sets. Recall in the online setting (Section 3.3.2), an unlabelled target dataset \(\hat{\mathcal{D}}^{t}\) is only required for adaptation.
# Riverbank Following Planner (RBFP) for USVs Based on Point Cloud Data
## 4 Experiments
### Simulation Results
#### 4.2.3 Results for Concave Riverbank  
Concave banks present intricate challenges on the bank, as follows. During the USV's scanning process, it frequently detects the opposite bank, which can hamper its ability to accurately trace the intended bank. The riverbank following planner mitigates this by utilizing a wide-angle LiDAR scan, ensuring a comprehensive capture of the surroundings. This allows for the precise identification of the riverbank's shape and discernment of whether the opposite side is navigable, circumventing scenarios where one bank side is untraceable. In Figure 8a, positions 1 and 4 denote the USV's entry and exit points of the concave bank, respectively. As the USV transitions between positions 2 and 3, its inherent momentum brings it closer to the riverbank. Concurrently, the PID controller intervenes to rectify the USV's heading, minimizing path deviations. Should the USV, while withinthe concave bank, detect the opposite bank, the planner strategically disregards it and prioritizes the USV's proximal bank for navigation. Figure 8b showcases the USV's planned trajectory within the concave bank under varied wind influences. Due to both its dynamics and wind impact, the USV tends to veer toward the riverbank, especially during turns.
## 5 Conclusions and Future Work  
Numerous elements add to the efficiency of sentiment analysis, and this research proposed a mechanism for dialectal Arabic which takes these into account. The presented study extended a sentiment LX that is accompanied by a complete overview of multidialectal sentiment alternatives for the target given the problem of unemployment. We employed a feature-sentiment association technique to weed out thoughts that were not significant to the problem domain and an effective light-stemming approach to associate the given feelings with the associated term root in the multidialect LX.  
In order to test the precision of our multi-intensity lexical sentiment analysis technique, we performed experiments. Findings showed that, in conjunction with light stemming, the collaborative considerations of numerous factors of emoji, intensification, negation, supplication, and interjection helped in improving the proposed algorithm in the sentiment categorization of TTs. The employed algorithm's classification efficiency and F-SC were enhanced from 83.84% to 89.80% and from 73.47% to 84.70%, respectively. Our approach was compared with two existing research studies, and results showed that MULDASA outperformed the existing approaches.  
Our future work involves investigating the integration of our lexical algorithm with ML techniques in a hybrid approach that could further improve overall classification accuracy. This is particularly useful for problem domains where it is difficult to recognize beforehand a comprehensive set of domain key ideas (features) that could be associated with the sentiment LX. An example of such a domain is \"hate speech\", where the feature set and expressed sentiments cover a wider pool of terminology and dynamically change. We are also planning to explore the effectiveness of the proposed approach on neutral text.  
Data curation, G.A.; formal analysis, G.A.; investigation, M.E.H.; supervision, T.O. and M.E.H.; writing--original draft, G.A.; writing--review and editing, S.A., M.H. and N.U.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 3 Results
### Hunter's Color Properties of Yanggaeng  
Figure 1 and Table 4 shows the colorimetric properties of Yanggaeng. The \(L^{\star}\) values, indicating the brightness of the subject Yanggaeng's color, were highest for the CON (45.73). However, as the amount of CQ powder increased, the \(L^{\star}\) values decreased for CQ2, CQ4, and CQ6 to 21.3, 13.73, and 9.73, respectively. Contrastingly, the \(a^{\star}\) parameter, responsible for the red or green color, had the lowest value for the CON (\(-0.60\)). The CQ added Yanggaeng exhibited an increase, with CQ2, CQ4, and CQ6 having values of 5.53, 5.37, and 4.67, respectively. Notably, the \(b^{\star}\) parameter, describing the presence of blue or yellow color, significantly increased with the addition of CQ powder, with CQ2 having the highest value (14.10).  
Figure 1: Appearance of Yanggaeng treated with _C. quadrangularis_ powder.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Items**} & \multicolumn{4}{c}{**Treatments 1**} \\ \cline{2-5}  & **CON** & **CQ2** & **CQ4** & **CQ6** \\ \hline pH & 6.97 \(\pm\) 0.03 \({}^{\text{a}}\) & 6.65 \(\pm\) 0.05 \({}^{\text{b}}\) & 6.46 \(\pm\) 0.02 \({}^{\text{c}}\) & 6.29 \(\pm\) 0.03 \({}^{\text{d}}\) \\ \({}^{\circ}\)Brix & 2.50 \(\pm\) 0.00 \({}^{\text{b}}\) & 2.80 \(\pm\) 0.00 \({}^{\text{a}}\) & 2.60 \(\pm\) 0.06 \({}^{\text{b}}\) & 2.10 \(\pm\) 0.00 \({}^{\text{c}}\) \\ WHC 2 & 33.91 \(\pm\) 1.30 \({}^{\text{a}}\) & 28.95 \(\pm\) 0.70 \({}^{\text{b}}\) & 23.09 \(\pm\) 5.38 \({}^{\text{bc}}\) & 21.35 \(\pm\) 5.77 \({}^{\text{c}}\) \\ \hline \hline \end{tabular} \({}^{\text{1}}\) Treatments: CON; control, 0% CQ powder, CQ2; 2% CQ powder, CQ4; 4% CQ powder, CQ6; 6% CQ powder. \({}^{\text{2}}\) WHC; Water holding capacity. Values are presented as means with standard deviation (\(n=3\)). All data were ranked and subsequently analyzed using one-way ANOVA followed by [PERSON]’s multiple range test. Means with different superscript letters \({}^{\text{a}-\text{d}}\) in the same row significantly differ (\(p<0.05\)).  
\end{table}
Table 3: pH, \({}^{\circ}\)Brix, and water holding capacity of Yanggaeng with treated _C. quadrangularis_ powder.  
Furthermore, the brightness index (BI) was calculated using the acquired \(L^{*}\), \(a^{*}\), and \(b^{*}\) values, and it was higher in Yanggaeng than in CON. During the drying process of CQ, the color typically shifts from green to brown or yellowish [39]. Moreover, the color alterations observed during the drying process of CQ in [PERSON]'s research mirror these transformations, indicating possible compositional changes during drying. This is likely due to interactions among the diverse chemical components present in CQ and the transformations that occur throughout the drying process [40].
# Research on Price Prediction of Construction Materials Based on VMD-SSA-LSTM
## 4 Experimental Data Processing and Analysis  
### CMP Analysis  
CMP is influenced by various factors, including market demand, supply chain fluctuations, and policy adjustments. The cost of building materials plays a significant role in construction projects, and fluctuations in material prices directly impact project budgets and cost control. Figure 6 shows the price of some construction materials in Hengyang City. The data source is the price information from October 2020 to May 2024 for Hengyang City, as per the General Station of Cost Management of Hunan Department of Housing and Construction. The data are sourced from the Hunan Provincial Construction Engineering Cost Management Station [[https://zjt.hunan.gov.cn/zjt/hnweb/index.html](https://zjt.hunan.gov.cn/zjt/hnweb/index.html)]([https://zjt.hunan.gov.cn/zjt/hnweb/index.html](https://zjt.hunan.gov.cn/zjt/hnweb/index.html)) (accessed on 15 September 2024), which operates under the National Construction Project Cost Data Monitoring Platform. It consists of biweekly data collected and calculated by the cost management departments of various cities and districts in the province, as outlined in the \"Biweekly Price Trends for Rebar, Cement, Sand, Gravel, and Concrete Materials\". The prices include the base cost, handling fees, insurance fees, and transportation costs, but exclude VAT. The author compiled and summarized the material price trends specifically for Hengyang City.  
This study was approved by the Ethics Committee of the School of Civil Engineering, University of South China. The data were analyzed anonymously and therefore no additional informed consent was required.  
From the Figure 6, it can be seen that the CMPs show an irregular and fluctuating phenomenon subject to market constraints. In construction projects, material costs typically account for a large proportion of the project cost. Considering the characteristics of general  
Figure 6: The prices of some construction materials in Hengyang. (**a**) The graph shows historical information price data for Rebar HRB400E 18–25. (**b**) The graph shows historical information price data for Ordinary Silicate Cement (P-O) 42.5 (bulk). (**c**) The graph shows historical information price data for Natural Sand. (**d**) The graph shows historical information price data for Gravel 10–20 mm.  
construction projects, it is evident that construction project investment is substantial and difficult to manage. Many project contractors and builders are prone to fall into a passive state of cost control, resulting in serious cost issues [38]. Since material price fluctuation is one of the most important contributors to deviations from the initial estimated cost in construction projects, the accurate prediction of material costs is essential for the proper management and budgeting of construction projects.  
Hence, it is essential to conduct the prediction and analysis of CMP, which is of crucial significance for rationally controlling project costs, ensuring project quality, and guaranteeing project progress.