esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory
### The Cause of Branch Ambiguity
#### 2.1.2 Surmounting the Branch Ambiguity Issue: Phase Unwrapping as Analytic Continuation  
As discussed in [30], the branch ambiguity problem afflicting \(n_{eff}(\omega)\) is caused by the incorrect handling of Equation (1). Here, we summarize the main theoretical considerations and results from that study. If we set the following:  
\[w=iN_{eff}(\omega)k_{0}d_{eff} \tag{7}\] \[z=\frac{S_{21}(\omega)}{1-S_{11}(\omega)R_{01}(\omega)} \tag{8}\]  
relation (1) then becomes  
\[e^{w}=z. \tag{9}\]  
Now, if the complex exponential \(e^{(\cdot)}\) turns out to be a univalent function [32], it will be invertible, and its inverse will be unique [32]. Accordingly, relation (9) can be solved without ambiguity. However, it has been highlighted in [30] that \(e^{(\cdot)}\) holds this property only when the path (8) lies strictly inside a semi-open stright-line strip of \(\mathbb{C}\), with width \(2\pi\), parallel to \(\mathbb{R}^{+}\). However, since the place where this image lies is tied to \(N_{eff}(\omega)\), which is the unknown we are searching for, we must handle \(e^{(\cdot)}\) as if it was a non-univalentfunction [30]. Therefore, relation (9) can be only _right-inverted_[30; 35]; i.e., relation (9) can be solved by evaluating an analytic function \(h(\cdot)\) such as the following.  
\[e^{(h(z))}=z\quad\forall z\in\mathbb{D} \tag{10}\]  
Unlike the inverse function, right-inverse function \(h(\cdot)\) is _not unique_[35]. Therefore, when \(e^{(\cdot)}\) is handled in this way, it is fundamental to use a suitable constraint condition to obtain a unique solution from inverting Equation (9) (i.e., from inverting (1)) [30]. In [30], it has been demonstrated that \(h(\cdot)\) can be computed via an analytic continuation of the principal complex logarithm \(log(\cdot)\) on the path traced in \(\mathbb{C}\) by term (8), which guarantees the fulfillment of condition \(\mathsf{Im}(h(0))=0\) that ensures the causality of the refractive index [30]. As a further result, in [30], it has been shown that the analytic continuation operation can be carried out by a phase unwrapping method using the algorithm developed by [PERSON] and [PERSON] for the homomorphic filtering, PUNWOS, [36]. It restores the continuity of the imaginary part of \(h(\cdot)\) along path (8) [30], thus unambiguously providing \(n_{eff}(\omega)\) as follows.  
\[n_{eff}(\omega)=\frac{1}{k_{0}d_{eff}}\mathsf{Im}[h(z)] \tag{11}\]
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 3 Classification and Evolution of Interference Management Strategies in B5G Networks
### Classification and Analysis of Interference Management Strategies in B5G Networks
#### 3.2.2 Heterogeneous Networks
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Issue** & **Methodologies** & **Advantages** & **Limitations/Future Works** & **Ref.** \\ \hline Interference in NOMA-based HetNets. & Joint optimization of NTBS position and RIS. & Improved DL SINR extended coverage area. & Evaluation under user mobility (UM) and high-density traffic needed. & [108] \\ \hline Joint RA and POC in dense HetNets. & Deep RL-based multi-agent framework. & Higher EE, interference-aware learning in dynamic settings. & High training cost and lack of cross-tier synchronization considered. & [109] \\ \hline Reducing the impact of interference signals from macro-cell receivers on femto-cell users. & \(\bullet\) & Cross-tier UL IA scheme. Zero-forcing (ZF) technique. & Maximizing the capacity of the system and SIR compared to other IA schemes. & \(\bullet\) The average selection IA applying ZF scheme at the femto-BSs will be applied. & [116] \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline To maximize the user data-rate and network resources. & Communication strategy based on relay selection scheme. & Increasing the network throughput and the number of concurrently served IoT devices by 44\% and 20\%, respectively. & An algorithm that uses a multi-hop relay will be developed to enhance common available and accommodate a greater number of IoT terminals concurrently. & [117] \\ \hline Mitigate the ICI and jammer interference. & RFA scheme. & Improving SIR of the proposed scenario. & A sophisticated wideband jammer will be evaluated with variable PT to reduce the probability of coverage. & [118] \\  & & & & \\ \hline To improve the security rate of the cellular device in the presence of an espionage attack. & A m-MIMO technique is applied to BSs. & Improving the data rate secrecy of the proposed system. & The UL scenario was not considered. & [119] \\ \hline Controlling the interference in DOL HetNets. & Dynamic eICIC and CoMP-based hybrid IM technique. & Improving the SE and throughput of the proposed scenario. & A novel technique, like selecting coordinated BSs, will be considered. & [120] \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of previous studies of HetNets in literature.
## 3 Performance Evaluation and Discussion
### Geometric Error Evaluation
The histogram and statistics of chord length corresponding to the different angles above are shown in Figure 11 and Table 1. It can be clearly seen that the standard deviations \(\mathcal{SD}\) and RMSE with respect to columns Mean and \(Ref\) are monotonically increasing, reaching maximum 2.8 mm and 3.2 mm, respectively. This implies that the errors of cross sections become prominent when the chord length gradually increases. Note that the standard deviation of the chord length with a central angle of 180\({}^{\circ}\) reaches the maximum value of 2.8 mm, which is exactly twice the number of standard deviations of the radius (1.4 mm). This means that the deviation of chord length with a center angle of 180\({}^{\circ}\) is consistent with twice the deviation of the radius. In addition, compared with the references of chord length calculated using the design radius of 2.75 m, the deviation of the chord length is extremely small, just reaching the maximum of 3.2 mm. Therefore, we can make a safe conclusion that our created cross sections have high geometric accuracy by indirectly evaluating four levels of chord lengths. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline _Central Angles_ & \(Min\) & \(Max\) & Mean & \(Ref\) & \(\mathcal{SD}\) & RMSE \\ \hline
30\({}^{\circ}\) & 1.4189 m & 1.4261 m & 1.4231 m & 1.4235 m & 0.0008 m & 0.0009 m \\
60\({}^{\circ}\) & 2.7414 m & 2.7544 m & 2.7492 m & 2.7500 m & 0.0015 m & 0.0017 m \\
90\({}^{\circ}\) & 3.8762 m & 3.8948 m & 3.8880 m & 3.8891 m & 0.0020 m & 0.0023 m \\
180\({}^{\circ}\) & 5.4817 m & 5.5075 m & 5.4984 m & 5.5000 m & 0.0028 m & 0.0032 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chord length statistics for 1258 cross sections. The symbols \(Min\), \(Max\), Mean and \(\mathcal{SD}\) represent the minimum, maximum, mean and standard deviation of chord length. ‘\(Ref\)’ means the reference (standard) of chord length calculated using the design radius of 2.75 m. ‘\(\mathcal{SD}\)’ and ‘RMSE’ denote the standard deviations which are calculated using the numbers in the columns ‘Mean’ and ‘\(Ref\)’, respectively. Figure 10: Histogram of chord length. (**a–d**) are histograms of different chord lengths with respect to central angle of 30\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\) and 180\({}^{\circ}\), respectively.
## 3 Purification Routes Applied to Lignocellulosic Acid Hydrolysates  
Monomeric sugars are the target molecules to valorize from lignocellulosic acid hydrolysates. Depending on the concentration of the mineral acid, it can be of interest to recycle it. The lignocellulosic  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reaction** & **Reactant** & **\% Converted to Product** \\ \hline (Glucan)\({}_{n}\) + H\({}_{2}\)O \(\rightarrow\) & n Glucose & Glucan & 9.9\% \\ (Glucan)\({}_{n}\) + H\({}_{2}\)O \(\rightarrow\) & n Glucose Oligomer \({}^{a}\) & Glucan & 0.3\% \\ (Glucan)\({}_{n}\) \(\rightarrow\) & n HMF + 2n H\({}_{2}\)O & Glucan & 0.3\% \\ Sucrose \(\rightarrow\) & HMF + Glucose + 2 H\({}_{2}\)O & Sucrose & 100\% \\ (Xylan)\({}_{n}\) + n H\({}_{2}\)O \(\rightarrow\) & n Xylose & Xylan & 90.0\% \\ (Xylan)\({}_{n}\) + m H\({}_{2}\)O & \(\rightarrow\) & m Xylose Oligomer \({}^{a}\) & Xylan & 2.4\% \\ (Xylan)\({}_{n}\) \(\rightarrow\) & n Furfural + 2n H\({}_{2}\)O & Xylan & 5.0\% \\ Acetate \(\rightarrow\) & Acetic Acid & Acetate & 100\% \\ (Lignin)\({}_{n}\) \(\rightarrow\) & n Soluble Lignin & Lignin & 5.0\% \\ \hline \hline \end{tabular} \({}^{a}\) Sugar oligomers are considered soluble, but not fermentable.  
\end{table}
Table 2: Dilute acid pretreatment of corn stover - hydrolysis reactions and assumed conversions based on the NREL technical report NREL/TP-5100-60223 (2013) [19].  
acid hydrolysates are also constituted of other molecules, which are fermentation inhibitors, such as acetic acid, decomposition products from monosaccharides (furans and aliphatic carboxylic acids), and phenolic compounds.  
Purification requirements (purity, yield, and cost) have to be considered regarding the final objective of the process. For some applications, the removal of fermentation inhibitors before fermentation is not necessary. For instance, the purification of a hemicellulose acid extract by several methods (overlapping, adsorption on charcoal or on resins) did not lead to higher xylitol yields by fermentation, compared to a simple pH adjustment to 5.5 before fermentation [68]. For other fermentation reactions, for instance glucose or xylose to ethanol, concentrations of furans greater than 1 g/L strongly inhibits the enzymes [69], so lower concentration for these molecules are targeted after the purification step. Since the detoxification process can be expensive and take a large portion of the whole ethanol production cost, the selection of the detoxification method is of major importance [1].  
The purification cost can be compensated by the value of the molecules to remove. Their valorization would contribute to the global efficiency of the lignocellulosic biorefinery.
# Vibroacoustic Optimization Study for the Volute Casing of a Centrifugal Fan
## 1 Introduction
The integration of structural-acoustic optimization correctly eliminates these drawbacks. This method has been used in the automotive field, and shows that the sound radiation generated by the excitation of body surface vibrations on the engine is substantially reduced [24; 25; 26; 27] after optimization. Based on the aforementioned advantages of optimization, the authors proposed a vibration-acoustic integrated optimization design method that is suitable for turbomachinery volute. In investigating the vibrational noise of the studied marine centrifugal fans, the following three aspects were the focus:  
(1) In this study, we proposed a numerical method for one-way fluid-solid-acoustic coupling. The rationality of this one-way coupling is verified by a volute wall vibration test. (2) To analyze the influence factors of vibrational noise and reduce the vibrational sound radiation induced by unsteady flow in the fan, a detailed theoretical derivation of vibration noise is put forward. (3) To control the vibrational noise of a certain type of marine centrifugal fan volute, an optimization method considering the influence of vibroacoustic coupling is proposed. Under the premise of whether volute total mass constraints, accordingly taking the panel thickness of the volute casing (FT: the front panel thickness, ST: the side panel thickness, BT: the back panel thickness) as the design variable, this study conducted low vibrational noise single target (taking the volute vibrational radiated sound power as the target function) and multi-target optimization (taking the volute vibrational radiated sound power and total mass as the target function).
## 2 Routing Protocols Overview
### BGP Features and Characteristics  
BGP (Border Gateway Protocol) is essential for enabling communication between multiple autonomous systems (ASs) on the internet. As an exterior gateway protocol, BGP is responsible for exchanging routing information and determining the best routes for data and internet traffic across diverse network domains. It must handle complex routing decisions based on policies, such as path attributes and network policies, to optimize traffic flow and ensure efficient load sharing. In the context of the load sharing and link-failover system being studied, BGP plays a critical role by influencing routing decisions that guide traffic between autonomous systems, thereby affecting the overall performance of the network. BGP is divided into two primary types:  
1. Internal Border Gateway Protocol (iBGP): iBGP is used for communication between routers within the same autonomous system (AS). It facilitates the distribution of routing information within the boundaries of a single AS, ensuring consistent and effective routing decisions across the internal network. iBGP helps maintain a coherent view of the network's topology, enabling routers within the AS to make informed decisions about traffic routing.
2. External Border Gateway Protocol (eBGP): eBGP is utilized for communication between routers in different autonomous systems. It ensures that networks managed by separate administrative entities can exchange routing information, thereby enabling inter-AS communication. eBGP plays a pivotal role in the broader internet infrastructure by facilitating the exchange of routing data across the global network, allowing data to traverse multiple autonomous systems efficiently.  
Both iBGP and eBGP contribute to BGP's ability to manage and optimize the flow of traffic across the internet, making it a crucial protocol for global network connectivity and stability.
# Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment
## 3 Materials and Methods
### Performance Evaluation  
In order to evaluate the reliability of the model, in a simplified and analytical way, it was decided to calculate some statistical indicators [92; 93; 94; 95]:, mean bias (_MB_), normalized mean bias (_NMB_), root mean squared error (_RMSE_), normalized mean squared error (_NMSE_), index of agreement (_IOA_) and the fraction of predictions within a factor or two (_FAC2_). The following equations report the calculation methods:  
\[MB\ =\ \frac{1}{N}\ \sum_{i\ =\ 1}^{N}E_{i}\ =\ \overline{M}-\overline{O} \tag{31}\]  
\[NMB\ =\ \frac{\sum_{i\ =\ 1}^{N}(M_{i}-O_{i})}{\sum_{i\ =\ 1}^{N}O_{i}}\ =\ \frac{ \overline{M}}{\overline{O}}-\ 1 \tag{32}\]  
\[RMSE\ =\ \sqrt{\frac{1}{N}\ \sum_{i\ =\ 1}^{N}(M_{i}-O_{i})^{2}} \tag{33}\]  
\[NMSE\ =\ \frac{\frac{1}{N}\ \sum_{i\ =\ 1}^{N}(M_{i}-O_{i})^{2}}{\overline{ PO}} \tag{34}\]  
\[IOA\ =\ 1-\ \frac{\sum_{i\ =\ 1}^{N}(M_{i}-O_{i})^{2}}{\sum_{i\ =\ 1}^{N}(\left|M_{i}-\overline{O}\right|\ +\ \left|O_{i}-\overline{O}\right|\ )^{2}} \tag{35}\]  
\[FAC2\ :\ 0.5\ \leq\ \frac{M_{i}}{O_{i}}\ \leq 2 \tag{36}\]  
where \(M_{i}\) is the single modelled value and \(O_{i}\) is the single observed value. All these parameters have ideal values: _MB_ = 0, _NMB_ = 0, _RMSE_ = 0; _FAC_2 = 1; _IOA_ = 1. We also used regression lined scatter and quantile-quantile plots [96].
## 3 Results
### Oxidative Stability  
FFA and IFA mixtures were heated to evaluate their thermal stability and to investigate the eventual protective role of LDH on the oxidative processes. Then, aliquots of the thermally stressed mixtures (FFA and IFA samples) were withdrawn periodically after seven days for analysis. The primary and secondary oxidative products, in addition to the FA composition, were evaluated. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**A**} & **Intercalation** & \multirow{2}{*}{**[Mg\({}_{0.63}\)Al\({}_{0.37}\)(OH)\({}_{2}\)](A)\({}_{x}\)(NO\({}_{3}\))\({}_{y}\)-n\({}_{2}\)O**} & \multirow{2}{*}{**mmol A/g**} \\ \cline{3-3}  & **Solvent** & & **x** & **y** & **n** \\ \hline \multirow{2}{*}{Oleate} & H\({}_{2}\)O & 0.23 & 0.14 & 0.44 & 1.64 \\  & H\({}_{2}\)O:EtOH (1:1, \(\
icefrac{{y}}{{c}}\)) & 0.24 & 0.13 & 0.45 & 1.68 \\ \hline \multirow{2}{*}{Linoleate} & H\({}_{2}\)O & 0.25 & 0.12 & 0.32 & 1.76 \\  & H\({}_{2}\)O:EtOH (1:1, \(\
icefrac{{y}}{{c}}\)) & 0.25 & 0.12 & 0.30 & 1.76 \\ \hline \multirow{2}{*}{\(\alpha\)-Linolenate} & H\({}_{2}\)O & 0.28 & 0.09 & 0.38 & 1.88 \\  & H\({}_{2}\)O:EtOH (1:1, \(\
icefrac{{y}}{{c}}\)) & 0.29 & 0.08 & 0.38 & 1.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Molar fractions of O, L and Ln intercalated in LDH-NO\({}_{3}\) using different solvents. Figure 3: XRPD (**a**); TGA and DTA, operative conditions: air, 10 \({}^{\circ}\)C/min (**b**) of MgAl-OLLn. The evaluation of the primary oxidation products was carried out using the FOX spectrophotometric assay. Figure 4 shows the evolution of peroxide values (PV, meq/kg) during the heating of FFA and IFA samples. The determination of PV is the typical and official method to evaluate the primary oxidative lipid process, including in vegetable foods [8]. Fatty acid oxidation has been recognized as a free radical chain reaction, with the abstraction of hydrogens at the allylic position of the double bond of unsaturated fatty acids to form hydroperoxides and free radicals on a new fatty acid. Hydroperoxides are considered the primary oxidation products; therefore, the PV parameter represents an indicator of initial fat autoxidation [8; 9]. The results showed that the hydroperoxide content was always lower for the IFA series than for the corresponding FFA, at different heating times. These results are very significant because they demonstrate the protective effect of LDH on the oxidation. In this work, the secondary oxidative compounds of the thermally stressed FA mixture were evaluated using SPME-HRGC-MS analysis, a procedure used in a previous paper to evaluate the volatile profile of vegetable foods and FA oxidative status [6; 33]. Table 2 shows the main volatile categories detected for FFA and IFA series. Aldehydes were the most represented compounds in FFA and IFA series, among the many categories of volatiles. Alcohols and ketones were the second most represented category in FFA and IFA series, respectively. Moreover, alkylfurans and hydrocarbons were also detected, together with carboxylic acids in FFA series.
# Enhanced Label Noise Filtering with Multiple Voting
## 5 Experimental Work
### Experimental Investigation
(2) CF\({}_{ODP}\) and MF\({}_{ODP}\) adapted to various noise ratios. In particular, even in a high noise ratio environment, the cost increases of CF\({}_{ODP}\) and MF\({}_{ODP}\) were not great. (3) Under different cost matrices, CF\({}_{ODP}\) and MF\({}_{ODP}\) consistently outperformed other filters. The advantages of CF\({}_{ODP}\) and MF\({}_{ODP}\) were more obvious when the difference between \(C_{01}\) and \(C_{10}\) was big. (4) Given a noisy training dataset, our proposed approach proved to be effective under different noise ratios and cost matrices if two conditions hold: (a) the noise ratio of this dataset is known; (b) there exists another noise-free training dataset that is drawn from the same distribution as this noisy dataset.
## 3 Results and Discussion
### Sorbent Comparison Study  
The extraction efficiency of the novel poly(n-VIM/C\({}_{6}\)-bis-VIM) polymer with a monomer to crosslink ratio of 11.50 to 1 was evaluated by comparing it with Oasis\({}^{\circledR}\) MAX from  
Figure 6: Binding results after performing multiple DSPE cycles with the poly(n-VIM/C\({}_{6}\)-bis-VIM) polymer with a monomer to crosslink ratio of 11.50 to 1. Standard deviations are given as error bars.  
WatersTM. Oasis(r) MAX is a strong mixed-mode anion exchange and reversed-phase polymeric sorbent with a quaternary amine as the anion exchange function. WatersTM recommends it for the separation of acidic compounds [50]. Therefore, it was selected as a comparative polymer in this study.  
Using the developed SPE protocol, extraction recoveries above 90% could be obtained with both sorbents. In addition, no phenolic acid residues were detected in the supernatants. Significantly higher extraction recoveries for all analytes were found with the synthesized sorbent in comparison with the Oasis(r) MAX. The poly(n-VIM/C6-bis-VIM) polymer gave recovery results ranging between 97.2 and 98.5%, which were on average 5% higher than with the commercial solid sorbent. All results are given in Table 2.  
Since quantitative adsorption was achieved for both polymers, the differences in analyte recovery can only be due to differences in elution efficiency. Either the binding of phenolic acids with Oasis(r) MAX was too strong or the eluting conditions too weak to obtain higher recovery results.  
Results with the recommended SPE standard protocol from WatersTM showed lower extraction recoveries for all phenolic acids compared to the developed protocol in this study. Furthermore, two new signals appeared in the chromatogram and the background noise increased considerably. It is very likely that the phenolic acids degraded during the washing step, due to the use of an ammonia solution and the instability of phenolic acids under alkaline conditions.  
It has to be mentioned that the backpressure during the SPE experiments with the poly(n-VIM/C6-bis-VIM) polymer was very high probably due to swelling effects. Therefore, the use of DSPE was clearly at an advantage in comparison to conventional SPE.  
The developed DSPE and adapted SPE procedure with the poly(n-VIM/C6-bis-VIM) polymer were compared with different solid-phase extraction methods and sorbents described in the literature (Table 3). It could be shown that the novel resin can compete with commercial sorbents as well as other synthesized SPE materials.