esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Encrypted SFC Architecture
### The Infrastructure
#### 4.1.1 Overlay Network Topologies  
This structured setup of the networking components ensures that the routing of the data packets is not only controlled by flow-rules, but it is also controlled by how the network topology is designed. This is the main objective behind the design of the structured hierarchy of the NSH headers. The structured network topology disallows unencrypted data traffic between an SF and an EF to be routed out of the Compute Node and out on the network.  
Figure 5 shows the main difference between using and not using an additional NSH header. Within the current NSH RFC [17] (no additional NSH header), the EF must be treated as a regular SF on one NSH layer (Figure 5 (1, 2)). The VL is perceived as encrypted and protected if both the EF and SF is located on the same Compute Node. However, if the EF is migrated to another host (Figure 5 (2)), the non-encrypted traffic (between SF-A and EF-X) is in fact allowed to flow both between different Compute Nodes or between different infrastructure domains. Hence, enabling the EF in a separate network layer (Figure 5 (3)) makes the network topology more secure. Using two NSH layers ensures that the SF never can be distributed in a way where non-encrypted traffic can leave the Compute Node. The VM, such as the EF-X (Figure 5 (3)), is in this case also open for VM migration, but, if the VM  
Figure 4: The layered network architecture.  
EF-X is migrated to another Compute Node, the next-hop network destination is unavailable due to a header mismatch between the two types of NSH headers. Hence, the VM's EF-X and SF-A must be migrated in pairs for allowing the communication between them.  
Dedicated networking components per Compute Node that are responsible for the inner and the outer NSH headers logically separate the EFs and the SFs. Therefore, we implemented two separate virtual switches on each compute node that is responsible for the routing of the two NSH layers. This structured setup of Compute Node components (Figure 3) ensures that the EFs must be co-located with the SF.
# Multi-Phase Stator Current Tracking with Gradual Penalization of Commutations
## 1 Introduction
The deadbeat concept has been exploited to (1) eliminate the combinatorial search of the optimization phase of the predictive method and (2) somehow get rid of the problem posed by \(x-y\) currents. It has been pointed out that the elimination of \(x-y\) voltages is not perfect in practice since it operates in an open loop. On the other hand, single-vector approaches have been hindered by the lack of fast methods for VV computation. Several methods have been proposed to diminish the computational load: the reduction of the control set [25], the simplification of the combinatorial search [26], and the use of a region identification algorithm to derive the control action [27]. Recently, a region-based method for multi-phase systems was presented [28]. This constituted an extension of previous works, using geometrical insights for VV determination [29; 30]. With this technique, it is possible to compete with multi-vector approaches. From the state of the art presented above, it is clear that single-vector predictive solutions need some advancement. The goal is to compete with fast multi-vector solutions while retaining the advantages of single-vector schemes. Among these advantages, the most important are as follows:  
1. The possibility of more complex cost functions;
2. The appropriate consideration of the whole \(x-y\) plane in a closed loop;
3. Attaining a low switching frequency despite the sampling period used. Having this in mind, the novelty of the paper lies in that it solves the above problem. This is achieved by means of a fast procedure for the optimization phase of MPC. The method employs a weighted metric to consider the \(x-y\) components entirely. In this way, \(x-y\) content is considered in a closed loop. Also, the allowable \(x-y\) content can be decided using a weighting factor in the CF (unlike multi-vector approaches). Furthermore, the proposal constitutes a novel method for ASF reduction. The method considers the switch changes required by the VV selection. The cost of using this selection is compared with the cost of not commuting. A trade-off solution is found with the flexibility to be adapted to the requirements of the actual VSI used. The proposal is tested in an experimental laboratory setup using a five-phase induction machine (5 pIM). The rest of the paper is organized as follows. Section 2 details the PSCC control structure considered for the five-phase induction machine. Later, in Section 3, the proposal is introduced, presenting some background on the fast optimization method and detailing the new control block for ASF reduction. Experimental results are provided to assess the proposal and compare it with previous approaches. In the last section, the main conclusions drawn are presented.
## 4 Concluding Remarks  
This work was conducted with the goal of assessing non-ionic surfactants as potential solvents for extractive lactic acid fermentation. Compared to other solvents that have been evaluated for the targeted extraction process, non-ionic surfactants (particularly alcohol ethoxylates and alcohol alkoxylates) are known to be biodegradable and do not affect microbial activities. An initial assessment indicated that all the ternary systems containing lactic acid, water, and non-ionic surfactant are of the Type 1 (Treybal classification) ternary system with a water-surfactant binary pair exhibiting partial miscibility. In addition, the size of the phase envelope and stability of the phases formed suggested that the alcohol ethoxylate can possibly be utilized for the extraction process.  
The phase diagram of the system containing lactic acid, water and alcohol ethoxylates was established at 308.15 K. The estimated distribution coefficients and separation factors suggested that the use of alcohol ethoxylate as solvent for lactic acid extraction will involve a compromise between the size and number of extraction units needed for the operation. An extraction efficiency of 36.7-47.6% was calculated, which is similar to that of tri-\(n\)-butyl phosphate. The reliability of the LLE data was tested using both Hand and Bachman correlations, showing satisfactory results.  
This study presents the first attempt to utilize non-ionic surfactants as solvents for the separation of lactic acid from aqueous solutions. Although the obtained extraction efficiencies were not that high, AE with different alcohol lengths as well as different ethoxylate/propoxylate numbers and combinations can possibly be exploited for process improvement. The capacity of the surfactant to extract or solubilize lactic acid could be a combination of micelle formation, hydrogen bonding, or other interactions. The actual mechanism is out of the scope of this work. However, understanding the molecular interactions among lactic acid, surfactant and water could provide a deeper insight of the extraction phenomenon and will be the subject of further experimentation.  
Conceptualization, E.D.R., R.H., and M.E.Z.; Methodology and Investigation, R.A., W.H., S.L., C.S., S.B., and B.L.; Data curation, R.A. and E.D.R.; Writing-original draft preparation, R.A.; Writing-review and editing, E.D.R., R.H., and M.E.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was supported by the Energy Institute of Louisiana (EIL) and the University of Louisiana at Lafayette. Appreciation is felt toward the staff and undergraduate students of the EIL for their able assistance with the performance of this project. The authors declare no conflict of interest.  
Figure 5: (**a**) Hand; and (**b**) Bachman correlation plots of the lactic acid + water + AE ternary system at \(T\) = 308.15 K and \(P\) = 0.1 MPa (atmospheric pressure).
## 3 Results and Discussion
### Results and Discussion of Experiment 1
In simulations 1 and 2, from period 200, it took an additional 4 periods (6 min) for the vehicles still circulating in the network to leave it, whereas in simulation 3, an additional 58 periods were required (87 min), since S3 experiences congestion (i.e., it reaches the critical density from which flow decreases; see Figure 6). This implies that queues are formed at the entry links (not all vehicles are able to enter the network at the time when are spawned, as dictated by the input flow) and that the network flow goes up and down during the congestion (in a period range when input flow = high_flow; see Figure 3). In Figure 6, it is appreciated that in S3, the critical density is reached at 0.0258 veh/m (according to the modeled data); Figure 2 shows that from approximately period 97 to 204 in S3, the critical density is surpassed (in that range, if density increases, flow decreases). From period 81 (i.e., since the input flow at each network entrance is equal to high_flow) it takes around 25.5 min (the time that elapses between period 81 and period 97) for the congestion to emerge; in the range of periods described in [97; 204], the speed is notoriously lower in S3 than in S1 and S2 (see Figure 1), the density is higher (see Figure 2), and the flow is (repeatedly) lower (see Figure 3).
# Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic
## 1 Introduction
Additionally, we analyzed the importance of individual features for the classification accuracy. For our experiments, we used the UNSW-NB15 dataset, which consists of 100 GB of labeled traffic and contains legitimate, normal communications mixed with attacks and anomalies that belong to nine different families of malicious activities. This dataset was published in 2015 to become a replacement for the old, widely used KDD dataset family. Results showed that the most effective feature set in terms of classification performance is based on modeling the behavior of sources instead of separately checking communication flows (note that, in the given context, _sources_ are meant to be any network device that sends and receives traffic, either servers, clients or routers. For such _sources_, only the sending activity is profiled, vectorized, and analyzed). On the other hand, a feature set designed from a consensus of the most important field research for the last few years obtains good trade-offs between classification and computational costs. Finally, the analysis of features shows that calculations based on packet lengths are determining for classifying traffic and detecting attacks, followed--but with lower relevance--by calculations based on the use of TTLs (Time to Live), number of packets, and number of bytes. The conducted experiments and the observed results are useful to face the design of improved feature vectors for applications related to both network security and network traffic analysis. The rest of the paper is organized as follows: Section 2 describes the studied feature vectors, Section 3 presents the dataset used for evaluation, Section 4 explains the conducted experiments, Section 5 shows and discusses results, Section 6 faces results from the perspective of data encryption, and Section 7 summarizes findings and concludes the work.
# Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era
## 5 Discussion  
Taking into account the conclusions from the literature (see Section 2.3) and the novel results shown in Section 4, it is overall agreed that the improved PHY layer of LTE-V2X allows for achieving the desired delivery rate at a longer distance in most scenarios, compared to the standard IEEE 802.11p. However, whereas this appears clear using Mode 3 (i.e., when the resources are assigned by the network), doubts arise assuming Mode 4 (i.e., when the selection is autonomous). Indeed, up to some distance and subject to an optimal choice of the MCS, the standard IEEE 802.11p might behave similar to LTE-V2X in terms of PRR and better in terms of UD. These considerations are valid also at higher distances when an improved PHY is used in IEEE 802.11p.  
In general, LTE-V2X is anyway expected to provide a longer range than the standard IEEE 802.11p and overall significant performance improvement in most scenarios, especially if Mode 3 is assumed. However, it should be noted that tests with real hardware are still at an initial stage. In addition, aspects that are of simple management or have been deeply investigated in IEEE 802.11p still need insights in LTE-V2X. For example, the congestion control mechanisms designed for IEEE 802.11p might not be optimal for LTE-V2X, as early works suggest [45; 46]. As another example, dealing with messages of different sizes is trivial in IEEE 802.11p; in LTE-V2X, instead, given the time-frequency organization of the resources and the SPS procedure, variable size messages need specific solutions, as discussed for example in [7; 36].
# Design and Stability Analysis of Six-Degree-of-Freedom Hydro-Pneumatic Spring Wheel-Leg
## 4 Analysis of Horizontal Rotational Stability
### Displacement Load Variation of Hydro-Pneumatic Springs
#### 4.1.1 Displacement Load Variation of Left Hydro-Pneumatic Spring  
At the static equilibrium position, the horizontal distance between the upper and lower ball joints of the left hydro-pneumatic spring is given as  
\[a_{0}=\sqrt{s_{0}^{2}-h_{c}^{2}}. \tag{16}\]  
The angle between the line connecting the lower ball joint and the center of rotation and the line connecting the upper ball joint and the center of rotation at the static equilibrium position is given as  
\[\varphi=\arccos\left(\frac{l_{c}^{2}+r_{c}^{2}-a_{0}^{2}}{2l_{c}r_{c}}\right). \tag{17}\]  
After rotation by an angle \(\theta\), the horizontal projection length of the hydro-pneumatic spring is given as  
\[a_{1}=\sqrt{l_{c}^{2}+r_{c}^{2}-2l_{c}r_{c}cos(\varphi-\theta)}. \tag{18}\]  
Thus, the changed length of the hydro-pneumatic spring is given as  
\[s_{1}=\sqrt{a_{1}^{2}+h_{c}^{2}}. \tag{19}\]  
The rotational lever arm corresponding to the left hydro-pneumatic spring is given as  
\[\left\{\begin{aligned} \alpha=\arccos\left(\frac{\frac{l_{c}^{2}+a_{1} ^{2}-l_{c}^{2}}{2l_{c}a_{1}}}{2l_{c}a_{1}}\right).\\ L_{1}=l_{c}sin\alpha\end{aligned}\right. \tag{20}\]  
According to Equation (18), the load on the left hydro-pneumatic spring after rotation by an angle \(\theta\) is given as  
\[F_{1}=\frac{P_{A0}\cdot S_{A}}{\left(1-\frac{(s_{0}-s_{1})D^{2}}{h_{0}d^{2}} \right)^{n}}. \tag{21}\]  
Thus, the unstable rotational torque provided by the left hydro-pneumatic spring is given as  
\[N_{1}=-F_{1}L_{1}\frac{a_{1}}{s_{1}}. \tag{22}\]
## 1 Introduction
Thirdly, the estimated DOA parameters are used to construct the beamformer, which can achieve interference suppression and signal enhancement. The contribution and novelty are reflected in the following aspects:  
1. The DOA parameter of the GNSS signal can be estimated accurately in strong interference scenarios. It solves the problem that it is difficult to achieve both interference suppression and DOA estimation with traditional methods. 2. The proposed method can realize DOA parameter estimation with low computational complexity, and thus stable DOA tracking in dynamic scenes. It is very suitable for the GNSS receiver, which needs real-time processing. 3. The blind adaptive beamformer is constructed using the estimated DOA parameter, which can realize interference suppression and navigation signal enhancement. It can achieve almost the same performance as the MVDR method. The remainder of this paper is organized as follows: Section 2 presents the signal model. Section 3 presents the proposed blind adaptive beamforming method based on DiLL. Section 4 gives the simulation results, and Section 5 concludes this paper.
# Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry
## Materials and Methods
### Empirical Satellite-Derived Bathymetry (SDB)  
Satellite-derived Bathymetry started during the 1970s with empirical methods that used spectral bands at the visible wavelength; blue, green, and red have been widely used as the independent variables in a multiple regression approach, and the in-situ data, the depth soundings with known depths, as the dependent variables. [PERSON] [9] (hereafter Lyzenga85) was the first that developed the equation of the estimation of bathymetry through the aforementioned approach assuming that the relationship between the log-transformed bands and known depth via multiple regression is linear. The coefficients of the regression are applied to the satellite data for the calculation of the bathymetry [19]. For the five images, multiple linear regressions, using the package \"_car_\" of R [22], have been performed for the calculation of the coefficients. Based on the lower value of the corrected  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Scene ID** & **Date** & **Time (UTC)** & **Sun Azimuth** & **Sun Elevation** \\ \hline
20170706\_082033\_103e\_3B\_AnalyticMS\_SR & 6 July 2017 & 8:20 & 106.24 & 59.69 \\
20170711\_082041\_1012\_3B\_AnalyticMS\_SR & 11 July 2017 & 8:20 & 107.14 & 59.27 \\
20170720\_082118\_101d\_3B\_AnalyticMS\_SR & 20 July 2017 & 8:21 & 109.4 & 58.37 \\
20170725\_082251\_1011\_3B\_AnalyticMS\_SR & 25 July 2017 & 8:22 & 111.02 & 57.78 \\
20170730\_082318\_102e\_3B\_AnalyticMS\_SR & 30 July 2017 & 8:23 & 112.69 & 57.17 \\ \hline \end{tabular}
\end{table}
Table 1: The PlanetScope CubeSat images used in the analysis.  
Figure 2: The selected daily images of the PlanetScope. All panels (**A–E**) share scale and north arrow. All maps share the same grid as of panel **D**.  
Akaike Information Criterion (AICc) [23; 24] calculated by using the R package \"_AICcmodavg_\" [25], the selected image has been further analyzed by applying a low pass filter of \(3\times 3\) to reduce the potential radiometric anomalies between pixels. Two regressions have been applied using the training data; the first for the depth zone between 0-10 m and the second for the zone between 10-25 m; the respected coefficients have been applied to the selected image and for each depth zone the metrics \"coefficient of determination (R\({}^{2}\))\", \"Standard Error (SEz)\" and the \"Root Mean Square Error (RMSEz)\" have been estimated using the validation points. The comparison of the RMSEz value with the Category Zone of Confidence (CATZOC) values provides insights into the Zones of Confidence and the reliability of the bathymry product for implementation in navigation charts produced by hydrographic offices [15].
# Hybrid Adaptive Bias OFDM-Based IM/DD Visible Light Communication System
## 2 System Model  
In this section, the transmitter and receiver of the proposed HABO-OFDM scheme are investigated concretely. In addition, the adaptive bias is also introduced specifically.
## 1 Introduction and Aims
In depleted gas fields, the high diffusivity of hydrogen could be the reason for hydrogen loss (iii). The hydrogen is dissolved in the formation water of the cap rock and diffuses through the cap rock [2]. [PERSON] et al. [2] stated that a significant loss of hydrogen can be expected. The high diffusivity, low viscosity, and low density of hydrogen leads to a high mobility and therefore the potential loss due to leakage should be considered [1]. A natural analog for an unintended and unpredictable leakage of hydrogen can be found in hydrogen anomalies associated with faults. These so called natural hydrogen seeps have been described by [PERSON] et al. [30], [PERSON] et al. [31], [PERSON] et al. [32], [PERSON] et al. [33], and [PERSON] et al. [34]. In these cases, faults act as \"fluid conduits\" [30]. An increased CH\({}_{4}\) concentration is also observed within these anomalies because of the increased microbial activity stimulated by the increased amount of hydrogen [30]. If hydrogen reaches the surface, it could inhibit the growth of trees, underbrush, and grass [30]. There are experimental studies concerning kinetics of pyrite and pyrrhotite reduction by hydrogen at high temperatures [35], the reactivity of hydrogen in sandstones [36], and the petrographic and petrophysical variation in reservoir sandstones due to hydrogen storage [37]. However, modeling studies of hydrogen storage to predict \"long-term\" behaviors are still rare. Therefore, this study is performed to model the loss of hydrogen (and related energy loss) as a combination of (i) the conversion by bacteria, (ii) gas-water-rock interactions in the reservoir and cap rock, which are connected with porosity changes, and (iii) loss by aqueous diffusion along a gradient of decreasing pressure and temperature conditions. These processes are retraced by a one-dimensional reactive mass transport model to simulate gas-water-rock interactions resulting from hydrogen storage in depleted gas fields. The aim of this study is to investigate the basic mechanisms of BSR and methanogenesis in an integrated way over storage times at the reservoir scale and to describe qualitatively and quantitatively which reservoir rock and cap rock minerals dissolve or precipitate because of hydrogen storage as well as the related porosity changes. Furthermore, the parameters that influence the loss of hydrogen are determined. Therefore, a model is presented, in which the hydrogeochemical mechanisms of underground hydrogen storage are simulated in a reference scenario (Sections 3.1-3.3). In further scenarios, single parameters will be changed in the model to show their effects on the modeling results and to identify the parameters that are most sensitive for underground hydrogen storage (Section 3.4).
# Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
## 4 Experimental Results
### Compression Performance Evaluation
In the R-D curves of Figure 4, the x-axis represents the total attribute bitrates, while the y-axis represents the combined PSNR values at each testing bitrate point. In Figure 4, consistent R-D performance gains are evident with the proposed scheme across the majority of datasets at various bitrate points. While BAAC achieves comparable coding performance with a slight BD-BR loss compared to our scheme on MPEG solid point cloud datasets, the performance gap widens significantly when compressing MPEG dense point clouds. This discrepancy stems from the increased geometry precision of dense point clouds, leading to a more intricate distribution of textures in geometry space. BAAC, relying solely on geometry information for graph construction, fails to adequately capture attribute correlations. Consequently, the decorrelation capability of the generated graph transform for point cloud attribute compression is diminished. These results highlight our scheme's versatility in handling different types of point clouds and its ability to achieve substantial coding performance improvements over state-of-the-art methods at various bitrate points. The presentation of Table 3 and Figure 4 offers a comprehensive evaluation of the coding performance from different aspects, providing insights into the compression efficiency and improvements achieved by our proposed scheme. The consistent and substantial attribute BD-BR gains achieved by our scheme across all tests highlight its effectiveness and robustness in compressing point clouds with diverse attribute features. However, it is important to note that the introduction of our proposed texture-guided graph transform optimization scheme comes with an increase in computational complexity compared to existing platforms. Unlike previous approaches [16; 18], which mainly rely on geometry information for graph sparsity optimization, our scheme incorporates texture analysis of point cloud local regions to extract graph sparsity parameters for guiding graph optimization. This process may lead to increased computational demands, especially when dealing with complex texture distributions in point cloud local regions that contain numerous details. To address this potential issue, we plan to design a more efficient texture analysis scheme in the future, aimed at enhancing the efficiency of the proposed texture