esa-sceva/satcom-chunk-collection huggingface dataset:
# Bridging the Gap: Enhancing Maritime Vessel Cyber Resilience through Security Operation Centers
## 6 Methodological Discussion and Limitations  
Most of the SOC studies that we reviewed have used respondents from diverse domains [77; 78; 79; 80]. While this can help provide a broad perspective of the challenges SOCs are facing, their results cannot be said to be representative of any specific domain. By only focusing on M-SOC respondents, we can claim that our findings are representative of the maritime domain.  
This study also had to contend with the small sample size for several reasons. The first is that M-SOC adoption has been a recent undertaking. As such, there was a very limited number of either in-house M-SOCs or MSSPs we could contact. Additionally, the sensitive nature of the work that these entities are tasked with performing meant that some of the M-SOCs contacted were uncomfortable participating in the study. This further reduced the pool of potential respondents. Similar SOC studies in other domains have had to contend with participant pool challenges but have managed to obtain meaningful results despite the small sample sizes [78; 80; 81]. However, by utilizing semi-structured interviews, we were able to obtain sufficient depth in the responses.  
Constant comparison guided the data collection and coding process, which helped minimize the impact of bias. To ensure quality, as viewed from the interpretive lens, was maintained, the coding process was documented by the primary author through memos. These were reviewed by the co-authors to ascertain the representativeness and confirmability of the findings. Visualizing the output of the coding process through Tables 2-5 and Figure 2 helped ensure the dependability of the study. The findings on connectivity and contextual information, as presented, are specific to the maritime domain.
## 3 Results and Discussion
### Gas Diffusion Coefficient  
Figure 8 displays the relationship between the gas diffusion coefficient and moisture content (after different times of drying with the initial moisture content of 25.6%) of the compacted clay after ZP modification. The relationship of GCL \(\mathrm{D_{P}}\) and moisture content can be divided into two stages in Figure 8. The first trend in stage I represents \(\mathrm{D_{P}}\), which has no changes with the increasing moisture content, ranging from 8% to 42%. The second trend in stage II shows \(\mathrm{D_{P}}\), which decreases sharply with the increasing moisture content of 58-100%. The relationship between CCC \(\mathrm{D_{P}}\) and moisture content follows the linear decreasing law; \(\mathrm{D_{P}}\) decreases sharply with the increasing moisture content of 0-25.6%.  
As the content of ZP ranged from 0.2% to 1.0%, the gas diffusion coefficient decreased with the rise in moisture. According to the analyses of the gas diffusion test results of [PERSON] and [PERSON] [19; 56] on the GCL, when the moisture content of the GCL was 8-100%, the gas diffusion coefficient was between 10\({}^{-6}\) and 10\({}^{-9}\) m\({}^{2}\)/s, much smaller than that of compacted clay with the same moisture content.  
As shown in Figure 8, when the moisture content was 25.6%, the gas diffusion coefficients of clay mixed with ZP at the contents of 0.2%, 0.4%, 0.6%, 0.8%, and 1.0% remained in the same order of magnitude, which was 4.56 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/s, 5.05 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/s, 4.72 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/s, 4.56 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/s, and 4.51 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/s, respectively. When the drying starts, the CCC samples present different gas barrier properties. The \(\mathrm{D_{P}}\) of the CCC sample with 0.2% ZP content after 17.5 h drying is 1.37 \(\times\) 10\({}^{-4}\) m\({}^{2}\)/s, while the \(\mathrm{D_{P}}\) of the 1.0% ZP content sample is 3.06 \(\times\) 10\({}^{-5}\) m\({}^{2}\)/s. This is because the CCC samples mixed with high content ZP have high WRC, which can reduce the microstructure of clay particles and enhance the barrier property of CCC.  
The CCC sample's total drying time is 17.5 h. After drying, the CCC's final state can be compared to unveil the barrier performance under different ZP contents. Figure 9 presents the relationship between the gas diffusion coefficient and moisture content of the compacted clay after drying for 17.5 h with the change in ZP content. It can be observed that the \(\mathrm{D_{P}}\) decreased with increasing moisture content. When the ZP content was 1%, the gas diffusion coefficient of the clay was 3.06 \(\times\) 10\({}^{-5}\) m\({}^{2}\)/s, which was only 35% of that at the ZP content of 0.2%. Hence, ZP can effectively improve compacted clay's gas-barrier and anti-diffusion performance, and its modification effect increases with the rise in ZP admixture content.  
Figure 8: Relationship between the gas diffusion coefficient and moisture content of the compacted clay after PAM modification [11; 57].
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Peak Name**} & \multirow{2}{*}{
\begin{tabular}{c} **Peak Position** \\ **(eV)** \\ \end{tabular} } & \multicolumn{2}{c}{**Concentration (at. \%)**} & \multirow{2}{*}{**Corresponding Structure(s)**} \\ \cline{3-3}  & & **S-0** & & \\ \hline
**C 1s** & & **75.16** & **64.91** & \\ \hline C 1s (A) & 284.7 & 58.43 & 55.15 & C–C, C–H \\ C 1s (B) & 286.3 & 28.87 & 25.73 & C–O–C, C–OH, C–N \\ C 1s (C) & 287.4 & 1.82 & 8.88 & C=O \\ C 1s (D) & 289.0 & 10.88 & 10.24 & O=C–O\({}^{-}\) \\ \hline
**O 1s** & & **21.84** & **32.61** & \\ \hline O 1s (A) & 531.1 & 4.29 & 8.13 & C=O \\ O 1s (B) & 532.2 & 61.99 & 68.90 & C–OH \\ O 1s (C) & 533.5 & 33.72 & 22.97 & O=C–O\({}^{-}\) \\ \hline
**N 1s** & & **3.00** & **2.48** & \\ \hline N 1s (A) & 399.0 & absent & 20.84 & C=N (times) \\ N 1s (B) & 400.0 & 43.36 & 57.91 & amines \\ N 1s (C) & 401.5 & 56.64 & 21.25 & N–O \\ \hline  & **O/C ratio:** & 0.291 & 0.502 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of XPS analysis of the non-modified sample and the sample modified for 10 s.  
Figure 7: Force needed to detach the modified sample from the unmodified one. further increase in the modification time did not significantly increase the joint's durability, and at 0.9 N the material was destroyed. Thus, the prolongation of the modification time to longer than 10 s is not profitable from a technological point of view.
## 2 Various Strategies for Enhancing the Production of Biofuel  
### Microbial Engineering for Enhanced Lignocellulosic Biomass Utility  
Being one of the most abundant polysaccharides on the earth, cellulose can be an apt substrate for biofuel production. Most of the plant biomass is made up of lignocellulosic. Lignocellulosic biomass is composed of lignin, cellulose, and hemicellulose. There are numerous cultivable and uncultivable microbes that have the capability to degrade lignocellulosic biomass and produce fermentable sugars [14]. These microbes produce lignolytic enzymes to degrade lignin, cellulases to degrade cellulose, and hemi-cellulaases to degrade hemicellulose. Cellulose is a polymer of \(\beta\)-D glucose with \(\beta\)1\(\rightarrow\)4 linkages. Cellulolytic enzymes can be broadly classified into three types, endoglucanes, exoglucanases, and \(\beta\)-glucosidase [15]. Endoglucanases hydrolyse the glycosidic linkages in an erratic manner, generating saccharides of varying size, whereas exoglucanases hydrolyse the non-reducing/reducing ends of cellulose to generate disaccharides (cellobiose) and glucose. \(\beta\)-glucosidase convert the cello-biose generated by exoglucanases into glucose. These three different types of cellulases hydrolyse cellulose in a collective manner to generate sugars required for the microbial growth and survival [15]. As hemicellulose is chiefly composed of sugars like xylan, mannan, and arabinose, hemi-cellulaases like \(\beta\)-xylosidases, \(\beta\)-mannanases, xylanases, etc., are required for its degradation into simple sugars [16]. A humongous amount of research has been performed on lignocellulosic microbes in the past few decades, and now, with the help of metabolic engineering, there is a viable chance to enhance their cellulolytic potential [17].  
It was observed in few microbes that these enzymes, which were usually produced individually, were produced in a cluster complex called cellulosome. In cellulosomes, along with the catalytic enzymes, there are other proteins like carbohydrate-binding modules to hold the substrate [18]. For the more efficient degradation of cellulose, scientists have been designing artificial cellulosomes, too. In a study by [PERSON] et al., the \(\beta\)-glucosidase and cello-biose transporter genes were over expressed in \(S\). _cerevisiae_; this resulted in enhanced utilization of cello-biose followed by fermentation [19]. [PERSON] et al. cultivated a consortium of yeast, which were cellulolytic in nature and were producing either mini CipA, a scaffoldin, or different cellulases. This engineered consortia, when supplemented with cellulose substrate, produced ethanol directly [20]. [PERSON] et al. inferred that enhanced cello-biose degradation was achieved when enzymes were used in combination with thermophilic scaffolding [21].
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 3 Overview of Network Protocols
### Internet Layer  
The Internet layer, often referred to as the network layer, plays a pivotal role in the TCP/IP protocol stack. The Internet Protocol (IP) holds paramount significance at this layer. The layer's fundamental role entails facilitating the transmission and reception of data packets across interconnected networks, encompassing tasks such as packet forwarding, logical addressing, routing and error handling. Unlike connection-oriented protocols, the IP protocol operates in a connectionless manner, foregoing the use of acknowledgements or connection establishment. These responsibilities are delegated to the protocols situated above and below its layer. Consequently, the IP protocol is sometimes perceived as lacking reliability on its own [48]. Nevertheless, the IP protocol remains indispensable in today's communication landscape, particularly considering the proliferation of networked devices.  
The surge in Internet connectivity and IoT deployments has impacted the availability of Internet Protocol version 4 (IPv4) addressing schemes. As a result, IPv4 has exhausted its available addresses for internet nodes. To mitigate this issue, the IETF has introduced a new version, Internet Protocol Version 6 (IPv6) [76]. In addition to the introduction of IPv6 as a solution for addressing the shortage, several protocols have been developed to support efficient communication in constrained networks. IPv6 over Low-Power Wireless Personal Area Networks (6 LowPAN), Routing Protocol for Low-Power, and Lossy Networks (RPL) are notable protocols in IoT's Internet layer to support efficient transmission and routing in a resource-constrained environment, respectively.
# UAV Charging Station Placement in Opportunistic Networks
## 6 Clustering Approaches
### K-Means Clustering  
The K-means algorithm is an iterative parametric algorithm that aims to divide a dataset into K distinct non-overlapping clusters where each data point of the dataset should  
Figure 2: Archimedes spiral with density d and maximum radius as R.  
belong to only one cluster. The algorithm starts by having K randomly chosen centroids for each cluster. Each data point of the dataset is then assigned to the nearest cluster centroid. The centroids are calculated again based on the newly formed clusters, which, in return, are calculated by obtaining the sum of the squared distance between the data points and the cluster's centroid. The process is repeated until the new centroids are no longer changed. As the algorithm proceeds, homogeneous data points come closer to each other and they are contained within the same cluster. The way K-means works is depicted in Algorithm 1.  
```
\(X=\{x_{1},x_{2},...,x_{n}\}\) A set of data Points \(k\) number of clusters Output:\(C=\{c_{1},c_{2},..,c_{k}\}\) Cluster centroids for\(j\gets 1\)to\(k\)do  Randomly initialize cluster centroids \(c_{i}\)  end for while\(C\) of newly formed clusters do not changedo for\(i\gets 1\)to\(n\)do for\(j\gets 1\)to\(k\)do  find closest centroid \(c_{j}\in C\) to \(x_{i}\)  Assign \(x_{i}\) to cluster \(j\);  end for  end for foreachCentroid\(c_{j}\)do  Recalculate \(c_{j}\) with the mean of points in \(X\) assigned to cluster \(j\);  end foreach  end foreach
```  
**Algorithm 1** K-means clustering algorithm  
The locations of the UAV charging stations are determined using the K-means clustering on the dataset by taking the number of clusters to be created as a parameter and returns the cluster center locations of the created clusters. These centers act as the charging locations.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 4 Hyperbranched Polymer Toughening
### Current Research  
Current research indicates that significant progress has been made in using hyperbranched polymers to toughen epoxy resins. Researchers are focusing on optimizing the toughening effect by adjusting the structural design of hyperbranched polymers, such as core-shell structures and branched architectures. This includes incorporating terminal groups that can react with the epoxy resin to enhance dispersion and compatibility within the resin matrix. These advancements aim to improve the toughening effect and achieve multi-faceted control over the material properties of epoxy resins, thereby meeting the diverse needs of different application fields [5; 78].
# Laser Cooling beyond Rate Equations: Approaches from Quantum Thermodynamics
## 2 Materials and Methods
### Master Equations for Laser Cooling  
We consider a model in which the system Hamiltonian is given by Equation (2). We suppose that there is a continuum of phonons responsible for transitions between the states of the ground-state manifold. This phonon bath will be described by Equations (3) and (4), with coupling operator \(O=|g_{l}\rangle\langle g_{u}|+|g_{u}\rangle\langle g_{l}|\). For the spectral density of this bath, we take the super-Ohmic form with an exponential high-frequency cut-off, \(J(\omega)=2\kappa(\omega^{3}/\omega_{c}^{2})\exp(-\omega/\omega_{c})\). We are not targetting a detailed model of a real system, and this form is chosen largely for illustrative purposes. It may, however, be noted that it corresponds to that for acoustic phonons coupling to localized impurities such as the silicon-vacancy center in diamond [22] or a quantum-dot exciton [20]. \(\kappa\) is a dimensionless measure of the coupling strength, and \(\omega_{c}\) is a high-frequency cut-off. Such cut-offs arise from the size of the electronic states and correspond roughly to the phonon frequency at a wavelength given by that size.  
We also consider, in the following, an alternative form of dissipator, of standard Lindblad form. For a transition caused by a jump operator \(A\), with rate \(\gamma_{A}\), the standard Lindblad form is  
\[\frac{d\rho_{S}(t)}{dt}=\gamma_{A}\mathcal{L}_{A}\rho_{S}(t)=\gamma_{A}\bigg{(} A\rho_{S}(t)A^{\dagger}-\frac{1}{2}[\rho_{S}(t),A^{\dagger}A]_{+}\bigg{)}. \tag{17}\]  
Thus the natural phenomenological form, capturing the processes shown in Figure 1, is to combine two of these dissipative terms, one for phonon absorption, with rate \(\gamma_{+}\) and jump operator \(\sigma_{+}=|g_{u}\rangle\langle g_{l}|\), and one for phonon emission, with rate \(\gamma_{-}\) and jump operator \(\sigma_{-}=\sigma_{+}^{\dagger}=|g_{l}\rangle\langle g_{u}|\). Such a form corresponds to Equation (10) when the eigenstates of \(H_{S}\) are simply \(|g_{l}\rangle\) and \(|g_{u}\rangle\), which is resonable for weak driving. This comparison allows us to identify the appropriate rates, from Equation (8), as \(\gamma_{-}=2\pi(n(E_{\text{man}})+1)J(E_{\text{man}})\) and \(\gamma_{+}=2\pi n(E_{\text{man}})J(E_{\text{man}})\).  
In addition to the phonon dissipation, our model involves the radiative decay of the excited state, \(|e\rangle\) to the ground state \(|g_{l}\rangle\). We model this as a Lindblad form with jump operator \(|g_{l}\rangle\langle e|\), and rate \(\gamma\).
## 3 Circuit Design
### Design of Inter-Stage and Input Matching Network  
There are usually two ways to design an IMN. Method 1 is by transforming Impedance A to 50 \(\Omega\) and then transforming it from 50 \(\Omega\) to Impedance B, and Method 2 is transforming Impedance A to Impedance B directly, as shown in Figures 9a and 9b, respectively.  
The advantage of Method 1 is that both Impedances A and B can be considered to be transformed from the 50 \(\Omega\) standard impedance, which is relatively stable and reduces the influence of the optimal source/load impedance change caused by the change in the working state of the HEMT on the matching accuracy; the disadvantage is that the matching network will become complex, thereby increasing the chip area. The advantages of Method 2 are that Impedances A and B are directly transformed from each other, the matching network is simple, and the chip area is saved; however, the difficulty of the matching network design will also increase. After, comprehensive consideration, this design adopts Method 2 to design the IMN. Simultaneously, in order to improve the efficiency, the second-harmonic control network is used in the IMN, which is composed of an inductor and a capacitor in series. The circuit structure of the IMN is shown in Figure 10a. It can be observed that the structure of the matching network between the two stages is simple and occupies a small chip area. The design of the IMN takes into account the same factors as the design of the OMN. Figure 10b presents the structure of the proposed IMN.  
The insertion loss and return loss simulation results of the ISMN are shown in Figure 11a. With a return loss better than 12 dB, the insertion loss is 1.4-0.7 dB across the frequency range of 7-13 GHz; the simulation result of the IMN is shown in Figure 11b. With a return loss better than 10 dB, the insertion loss is 2.8-1.2 dB across the frequency range of 7-13 GHz.  
Figure 8: Simulation result of the OMN: (**a**) insertion loss and return loss; (**b**) points of actual load impedance.  
Figure 9: Impedance transform diagram: (**a**) indirect change; (**b**) direct change.
## 2 Background
### Kernel Extreme Learning Machine (KELM)  
The extreme learning machine (ELM) [71] is a class of machine learning systems or methods based on feed-forward neural networks. The traditional ELMs have a single hidden layer, which is considered to have a low learning rate and generalization when compared with other shallow learning systems, such as the single-layer perceptron and support vector machine (SVM). The ELM algorithm randomly generates the connection weight between the input layer and the hidden layer and the threshold of the neurons in the hidden layer. Therefore, in the process of training, one only needs to set the number of neurons in the hidden layer, and the unique optimal solution can then be obtained.  
KELM was designed by adding a radial basis function (RBF) kernel that is based on ELM [72]. The selection of RBF kernels aims to map samples into a high-dimensionalmapping space, further solving nonlinear problems. In addition, the RBF kernel has few parameters; only the penalty factor and kernel parameters need to be considered. However, the choice of hyperparameters has a certain impact on the effect of model fitting. Therefore, it is necessary to select an appropriate hyperparameter optimization method according to the specific problem.
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
## 3 Type of Sensor Used to Detect PD Signals
### Electrical-Electromagnetic Sensor
The sensor provides alternative methods for estimating PD, and the illustrated setup, referred to as a bushing tap, is specifically designed for measuring galvanically connected decoupling. Figure 7c depicts a configuration of an inductive UHF sensor used to take a reading on the power cable termination [109]. Figure 8a illustrates three distinct UHF sensors that are suitable for the purpose of PD detection on both power transformers and gas-insulated switchgear. Conventionally, the three types of sensors utilized for PD detection, according to reference [110], are disc-type, monopole-type, and spiral-type sensors. Recent research has revealed that these sensors accumulate a significant amount of energy, demonstrating their excellent sensitivity in detecting emitted signals [110]. Meanwhile, Figure 8b depicts a capacitive coupler used in a high-voltage cable for PD detection. The capacitive sensor is linked through a 40 mm tin tape that encircles the uncovered cable. The insulation measurement on the cable remains unaffected by the capacitive sensor coupler due to its effective connection at UHF, wherein it functions as the power frequency ground, as stated in reference [111].
# Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM +
### Discrete Signaling and OFDM  
Our main interest is discrete-alphabet precoding for multipath channels with OFDM as in 5G wireless systems. Precoding for OFDM is challenging because the alphabet constraint is in the time domain after the inverse discrete Fourier transform (IDFT) rather than in the frequency domain. We further focus on using information theory to derive achievable rates. For this purpose, we consider two types of channel estimation at the receivers: pilot-aided channel estimation via pilot-aided transmission (PAT) and data-aided channel estimation.  
Discrete-alphabet precoding for OFDM was treated in Ref. [37], who used QLP and low resolution DACs. A more sophisticated approach appeared in Ref. [38], who applied a squared-infinity norm Douglas-Rachford splitting (SQUID) algorithm to minimize a quadratic cost function in the frequency domain. The performance was illustrated via bit error rate (BER) simulations with convolutional codes and QPSK or 16-quadrature amplitude modulation (QAM) by using 1-3 bits of phase quantization.  
The paper [39] instead proposed an algorithm called multi-antenna greedy iterative quantization (MAGIQ) that builds on [19] and uses coordinate-wise optimization of a quadratic cost function in the time domain. MAGIQ may thus be considered an extended version of [4] for OFDM and discrete alphabets. Simulations showed that MAGIQ outperforms SQUID in terms of complexity and achievable rates. Another coordinate-wise optimization algorithm appeared in [40; 41] that builds on the papers [21; 22]. The algorithm is called constant envelope symbol level precoding (CESLP) and it is similar to the refinement of MAGIQ presented here. The main difference is that, as in [38], the optimization in [40; 41] uses a cost function in the frequency domain rather than the time domain. We remark that processing in the time domain has advantages that are described in Section 3.1.  
The MSM algorithm was extended to OFDM in [42]. MSM works well at low and intermediate rates but MAGIQ outperforms MSM at high rates both in terms of complexity and achievable rates. Finally, the recent paper [43] uses generalized approximate message passing (GAMP) for OFDM.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Precoding Algorithm**} \\ \cline{3-6} \multirow{3}{*}{**Modulation**} & \multirow{3}{*}{**Fading**} & **QLP** & **Convex** & **Coord.-Wise** & **Other (MSM,** \\  & & & **Relaxation** & **Optimization** & **BB, NN, etc.)** \\ \hline \multirow{2}{*}{1 Carrier} & Flat & [8; 9; 10; 11; 12; 13; 14; 15] & [16; 17; 18] & [19; 20; 21; 22; 23; 24; 25] & [26; 27; 29; 30; 31; 32; 33; 34; 35; 36] \\  & Freq.-Sel. & & & & [28] \\ \hline OFDM & Freq.-Sel. & [37] & [38] & [39; 40; 41] & [42; 43] \\ \hline \hline \end{tabular}
\end{table}
Table 1: References for quantized precoding.