esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Main Oxidant Production versus Radiation Composition  
#### 3.1.1 Neutral Water  
Figure 2 shows the steady-state concentrations of O\({}_{2}\), H\({}_{2}\)O\({}_{2}\), \({}^{\bullet}\)OH, and HO\({}_{2}\)\({}^{\bullet}\) as a function of the fraction of \(\gamma\) rays (\(f_{\rm g}\)) in the mixed gamma-neutron radiolysis of neutral water.  
The steady-state concentrations of O\({}_{2}\), H\({}_{2}\)O\({}_{2}\), and HO\({}_{2}\)\({}^{\bullet}\), obtained for fast neutron radiolysis (\(f_{\rm g}=0\)), are higher than the concentration of \({}^{\bullet}\)OH. The increasing fraction of \(\gamma\) rays decreases the concentrations of the molecular oxidants but increases the concentration of \({}^{\bullet}\)OH. This trend is expected since the flux of 2 MeV neutrons produces recoil protons of mean linear energy transfer (LET) 20, 27, 34.5, and 44 eV\(\cdot\)nn\({}^{-1}\)[16], whereas \(\gamma\) rays are representative for low-LET radiation (LET\(-\)0.2 eV\(\cdot\)nm\({}^{-1}\)), producing higher yields of radical than molecular radiolytic products. The number of generated oxidants depends on the dose rate. A particularly significant increase with increasing dose rate was obtained for \({}^{\bullet}\)OH and H\({}_{2}\)O\({}_{2}\).  
Figure 1: Concentration profiles of the radiolytic species calculated for neutral deaerated water exposed to (**a**) gamma and (**b**) fast neutron radiation at 300 \({}^{\circ}\)C and dose rate 1 kGy\(\cdot\)s\({}^{-1}\).  
Figure 2: The simulated steady state concentrations of oxidants formed in neutral water vs. fraction of \(\gamma\)-rays (\(f_{\rm g}\)) in the mixed gamma-fast neutron radiation: solid lines indicate dose rate of 1 kGy\(\cdot\)s\({}^{-1}\); dashed + symbol indicates 10 kGy\(\cdot\)s\({}^{-1}\).  
The hybrid method allows one to assess the change in pH under exposure to ionising radiation. The simulation showed a slight decrease in pH, which is more pronounced for fast neutron radiation--namely, the 100 s exposure to fast neutrons leads to a decline in pH by 0.0037 at the radiation dose of 1 kGy\(\cdot\)s\({}^{-1}\) and by 0.0063 at 10 kGy\(\cdot\)s\({}^{-1}\), whereas exposure to \(\gamma\) rays leads to a decline in pH by 0.0020 at 1 kGy\(\cdot\)s\({}^{-1}\) and by 0.0041 at 10 kGy\(\cdot\)s\({}^{-1}\).
## 4 Simulation Model of DQPSK Modulated Data Transmission
Figure 9: Bit error rate (BER) performance of an 80 Gb/s RZ-DQPSK signal after a 390-km transmission over a three-segment hybrid fiber core structure and a conventional single fiber core structure. The three-segment hybrid fiber core structure had better BER performance and reduced the required OSNR of 2.71 dB at the target BER of \(10^{-9}\). Figure 11 shows a summary of BER performance for various data rates, comparing the BER performance results between the three-segment hybrid fiber effective core area structure and the conventional single-segment single fiber core structure when the required signal-to-noise ratio per bit (Eb/No) is set to 18 dB for both cases. The three-segment hybrid fiber core structure shows superior transmission performance across all data rates. For any given data rates, the transmission impairment of the span with the three-segment hybrid fiber core structure is smaller than the span with the conventional single fiber core structure. This excellent transmission performance is due to a higher delivered optical signal-to-noise ratio (OSNR\({}_{\text{Del}}\)) at the receiving end and a smaller Kerr nonlinear penalty accumulated along the transmission span, as predicted by previous analysis and illustrated in Figures 5 and 6. In general, excluding the impact of polarization mode dispersion and intra/inter-channel interaction on single-channel single-span transmission links with short to medium length, the bit error rate result is promising for a data rate of 40 Gb/s, but will require FEC for higher data rates to improve the BER performance sufficiently for field applications. Figure 11: Performance summary for various data rates of an RZ-DQPSK modulated signal, comparing the three-segment hybrid fiber core structure and the conventional single fiber core structure after a 390-km transmission. Figure 10: Bit error rate (BER) performance comparison between various data rates (40, 60, 80, and 100 Gb/s) of an RZ-DQPSK modulated signal after a 390-km transmission using a three-segment hybrid fiber core structure.
# Real-Time Eye Diagram Monitoring for Optical Signals Based on Optical Sampling
###### Abstract  
A real-time eye diagram monitoring method for optical signals is proposed and experimentally demonstrated based on optical sampling. In the system, the optical signals under test are directly sampled by an optical sampling pulse train with a narrow pulse width and a high repetition frequency. The sampling pulse train is achieved in a Mach-Zehnder modulator (MZM), gated on-off by an electrical pulse. The sampled optical signals are then broadened and detected by a photodetector (PD). A low-speed electrical analog-to-digital converter (ADC) will then quantify the detected electrical signals. Combining with an algorithm based on the infinitesimal calculus, the quantified data is then used to achieve the eye diagram, according to which more time-domain parameters, such as period, time jitter, \(Q\) value, and bit error rate (BER) for the optical signals under test, are obtained. Thanks to the high repetition rate of the optical sampling pulse train, the eye diagram and the time-domain parameters of the optical signals are observed in real time. Experimental results show that a real time of about 350-us eye diagram monitoring for a 2.5-Gb/s optical signal with a dynamic range from \(-10\) to \(-22\) dBm is achieved. In addition, time jitters are measured to range from 4.3 to 49.8 ps. \(Q\) values are estimated to range from 20.4 to 4.3, corresponding to BERs ranging from \(2.3\times 10^{-92}\) to \(8.5\times 10^{-6}\). The results are also verified by a commercial real-time oscilloscope.  
microwave photonics; signal processing; eye diagram; optical sampling +
Footnote †: journal: Applied Physics Letters  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Real-Time Eye Diagram Monitoring for Optical Signals Based on Optical Sampling. _Appl. Sci._**2023**, _13_, 1363. [[https://doi.org/10.3390/app13031363](https://doi.org/10.3390/app13031363)]([https://doi.org/10.3390/app13031363](https://doi.org/10.3390/app13031363))
# Optimisation of Segregation Distances between Electric Cable Bundles Embedded in a Structure
## 5 Application to a Realistic Test-Case
### Optimisation Strategy Results  
The two proposed algorithms presented in Section 4 were implemented to estimate the optimal segregation distance under the EMC constraint. The algorithms were validated for various configurations of the use-case, chosen in terms of increasing complexity. The validation process consisted of comparing the value of \(d_{opt}\) obtained by both algorithms to the value obtained by a brute force Monte Carlo analysis, for which the distance parameter was varied step by step. Figure 5 illustrates the variation of the \(G\) function for more than 1000 computed geometrical configuration samples with the brute force analysis in a narrow frequency band below resonances \(F=[1.99;2.066]\) MHz and for fixed heights of the bundles. The \(G\) function is distributed in well-defined strips and can be considered as a monotonic decreasing function with the distance of segregation.  
Table 1 summarises the results of the optimisation algorithm compared to the brute force analysis and validates the implementation of the algorithms. The gain in terms of the number of calls to CRIPTE, that is to say in terms of the computational resources to solve this problem, is obvious, and the optimal segregation distance estimation was very well estimated by the proposed algorithm. The linearity of the \(G\) function facilitates its approximation by a surrogate model. Thus, very few calls to \(G\) are required to obtain an accurate evaluation of the probability \(\widehat{\mathbb{P}}^{\mathrm{MC}}_{d}\) for different values of \(d\).  
Figure 4: Examples of currents induced on bundle \(B\).
# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 3 Parameters That Affect Thermal Conductivity
### Nanofluids  
#### 3.3.1 Preparation Methods  
Two methods of preparing nanofluids are commonly used by the scientific community, the one-step, and two-step method. The one-step method involves the production and dispersion of the NPs in the BF at the same time. For this reason, the process reduces the NPs agglomeration and increases stability in the NFs [3]. In contrast, the high cost and level of impurities on the NFs, according to [PERSON] et al. [51], are the main disadvantages of this process. In the two-step method, first, the NPs are obtained and then added to the BF. This simplifies the manufacturing process while reducing its cost. However, maintaining the stability of the NFs is one of the challenges of this method. Some alternatives to solve the problem include the use of ultrasonic treatment, altering the pH of the suspension, adding surface activators or dispersants or a mixture of both [51, 52].  
The high production cost of the nanofluids has been another limitation, which may require a more demanding maintenance of a cooling system and, consequently, raise the operating costs. In addition, the preparation of NFs on a larger scale requires further reduction of its costs since the most cost-effective techniques are the ones considered for commercialization purposes. One way to reduce these costs is by recycling and recover NPs from industrial wastes.
## 4 Additive Entropy  
Background on entropy can be found e.g., in [13]. Following [PERSON]'s 1928 treatment of equiprobable events, [PERSON] in 1948 developed his theory around expectation. In 1960 [PERSON] developed a recursive rather than linear estimation. Various other forms of entropy are discussed in [14].  
Consider density \(f\) with support in \(\mathbb{R}^{m}\). Then:  
* Shannon entropy: \(H_{S}=-\int f(x)\log f(x)dx\)
* Renyi entropy: \(H_{R\alpha}=\frac{1}{1-\alpha}\log\int f(x)^{\alpha}dx\) for \(\alpha>0,\alpha\
eq 1\).  
We have: \(\lim_{\alpha\longrightarrow 1}H_{R\alpha}=H_{S}\). So \(H_{R1}=H_{S}\). We also have: \(H_{R\beta}\geq H_{S}\geq H_{R\gamma}\) for \(0<\beta<1\) and \(1<\gamma\) (see e.g., [15], section 3.3). When \(\alpha=2\), \(H_{R2}\) is quadratic entropy.  
Both Shannon and Renyi quadratic entropy are additive, a property which will be availed of by us below for example when we we define entropy for a linear transform, i.e., an additive, invertible decomposition.  
To show this, let us consider a system decomposed into independent events, \(A\), \(B\).  
So \(p(AB)\) (alternatively written: \(p(A\&B)\) or \(p(A+B)\)) \(=p(A)p(B)\). Shannon information is then \(I_{S}^{AB}=-\log p(AB)=-\log p(A)-\log p(B)\), so the information of independent events is additive. Multiplying across by \(p(AB)\), and taking \(p(AB)=p(A)\) when considering only event \(A\) and similarly for \(B\), leads to additivity of Shannon entropy for independent events, \(H_{S}^{AB}=H_{S}^{A}+H_{S}^{B}\).  
Similarly for Renyi quadratic entropy we use \(p(AB)=p(A)p(B)\) and we have: \(-\log p^{2}(AB)=-2\log p(AB)=-2\log p(A)p(B)=-2\log p(A)-2\log p(B)=-\log p^{2} (A)-\log p^{2}(B)\).
## 4 Combinatorial Auction Algorithm
### Algorithm for D2D Relay Selection  
At the beginning of the auction, the eNB collects the location information of UEs and gets the channel state information (CSI) for all the links between UEs and the eNB. The bidders, _i.e._, the NACK-UEs evaluate all their D2D links connecting to the ACK-UEs to obtain their valuations. In the first round, all bidders submit bids for each ACK-UE. Let \(\{b_{k},i_{m},R_{h_{k},i_{m}}\}\) denote the submitted bid by bidder \(b_{k}\) and \(\mathcal{V}(b_{k})\) the bid equal to its valuation for item \(k\). The auctioneer finds the highest bid \(\{b_{k}^{*},i_{m}^{*},R_{h_{k},i_{m}}^{*}\}\), and sells the ACK-UE \(i_{m}^{*}\) to bidder \(b_{k}^{*}\). Then \(b_{k}^{*}\) is added to package \(\mathcal{D}_{m}\). The auction process moves on until all the bidders obtain an item. Then, the auction enters the second round.  
In the auction, the ACK-UEs is allowed to be sold more than once, but the bidder can only be allocated one item. That means, at the end of the first round, we can get a provisional allocation \(\{\mathcal{D}_{1},\mathcal{D}_{2},...,\mathcal{D}_{M}\}\), in which each element corresponds to a bidder package (possibly empty). To improve the outcome of the first round, the auctioneer adjusts the auction results in the second round. Given each combinatorial valuation \(\wp^{\{h_{k}\}}(\mathcal{D}_{m})\) calculated according to Equation (2), the available adjustment strategies of the auctioneer and bidders are classified as follows  
1. Try to remove each bidder \(b_{k}\) (\(k=1,2,...,N\)) from its package \(\mathcal{D}_{m}\) and calculate the consequent combinatorial valuation \(\wp^{\{h_{k}\}}(\mathcal{D}_{m})\).
2. Find the maximum value from the set \(\{\wp^{\{h_{k}\}}(\mathcal{D}_{m})-\wp^{\{h_{k}\}}(\mathcal{D}_{m}),\forall k= 1,2,...,N\}\) and select the corresponding \(b_{k}\) which is marked as \(b_{k^{\dagger}}\).
3. Bidder \(b_{k^{\dagger}}\) submits bid \(\{b_{k^{\dagger}},i_{m}^{*},R_{h_{k^{\dagger}},i_{m}}\}\), and then the auctioneer tries to add the bidder \(b_{k^{\dagger}}\) into package \(\mathcal{D}_{\widetilde{m}}\) (\(\widetilde{m}\
eq m,\widetilde{m}=1,2,...,M\)) and calculates the consequent combinatorial valuation \(\wp^{\{h_{k^{\dagger}}\}}(\mathcal{D}_{\widetilde{m}})\).  
is obvious that for sufficient large values of \(M\) and \(N\), a finite number of \(T\), a lower complexity is obtained by using the proposed iterative CA scheme. That is, \(\mathcal{O}(N^{M})>\mathcal{O}(NM^{2})\).
# Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities
## 5 Scalability Model
### Model Steps
#### 5.1.2 Achievable Range  
The maximum range that a device can achieve (\(d_{MAX}\)) is one of the main indicator to study how scalable a solution can be. Known the shape of the cell, e.g., square, circle or hexagonal, it is possible to obtain the area that can be covered for specific UWB settings.  
Figure 4: System diagram for UWB indoor scalability model.  
The formula to compute \(d_{MAX}\) is function of the transmission power (\(P_{TX}\)), receiver sensitivity (\(S_{i}\)), channel (\(ch\)) and mainly varies based on the path loss (PL) model that is adopted to study the system (Figure 5). The output of this step is also an important parameter to evaluate coverage capabilities of UWB.
## 6 Lexicon Comparison and Validation  
We compared our bilingual lexicon with two recently proposed emotion lexicons, one in the Arabic language proposed by [PERSON] [35] and the second one, an English language lexicon, Moodbook. Table 6 highlights the characteristics of these two lexicons along with the proposed AEELex.  
[PERSON]'s Arabic language lexicon comprises six categories of emotions: namely, _Fear_, _Anger_, _Sadness_, _Joy_, _Surprise_, and _Disgust_. However, AEELex has two additional categories i.e., trust and anticipation. Furthermore, AEELex has a comparatively larger number of emotion words than the former. Therefore, the proposed lexicon provides a much more fine-grained study of emotions and can cover many numbers of tweets where the existing lexicons failed to mine any emotions. We performed a comparative analysis by identifying the emotion categories of 1000 tweets in the Arabic language collected from two cities in Saudi Arabia and applying it to AEELex (Arabic version) and the lexicon proposed by [PERSON]. It was observed from the results that both lexicons were able to identify all the emotion categories. However, [PERSON]'s lexicon was not able to mine emotions from \(\sim\)60 percent of total tweets; contrastingly, AEELex was not able to mine only 17 percent of total tweets. [PERSON]'s lexicon combined several emotions into a single category, which led to a lack of essence for particular emotion words such as _Joy_, _Trust_, and _Anticipation_--emotion categories that were combined into one category (_Joy_). For example, the emotion  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mood Category** & **Mood Category** & **Mood Words** & **Mood Words** & **Example Sentence (From the Collected Dataset)** \\
**(English)** & **(Arabic)** & & **[PERSON]** & **Ailet** & **Ailet** \\ \hline \multirow{2}{*}{_Fear_} & \multirow{2}{*}{\(\lambda\)\(\lambda\)\(\lambda\)\(\lambda\)} & \multirow{2}{*}{darkness} & \multirow{2}{*}{\(\lambda\)\(  
word \" 5.3\(\mid\)e\"(worship) was placed under the _joy_ category by [PERSON], whereas AEELex classified the same word under the _anticipation_ category rather than _joy_.  
Furthermore, we compared AEELex with an English language lexicon, Moodbook, by analyzing 1000 tweets in the English language collected from Saudi Arabia. Both lexicons have eight emotion categories and were able to identify all the categories in the user tweets. However, it was observed that AEELex outperformed Moodbook by being able to identify a greater number of emotions in the tweets.  
Thus, we can conclude that AEELex is efficient as a bilingual dictionary in identifying more emotions from text in both English and Arabic.
## 2 Problem Description
### 3D Fuzzy Modeling  
The 3D fuzzy model is a DPS modeling approach that combines time-space separation and synthesis. It has linguistic interpretability and does not require model reduction. The 3D fuzzy system also has universal approximation, but unlike the traditional fuzzy system, the 3D fuzzy system is constructed based on a 3D spatial fuzzy set [24].  
The 3D fuzzy model framework is illustrated in Figure 1. The antecedent set calculates the time coefficients, and the consequent set characterizes the spatial basis functions.  
These components achieve spatiotemporal separation and synthesis through fuzzy inference mechanisms. Traditional offline 3D fuzzy models cannot effectively cope with the dynamic changes and abnormal situations of a DPS because they rely on historical data to capture system behavior and cannot be adjusted in real time to cope with equipment wear, partial link damage, or unexpected disturbances.  
In contrast, the proposed online 3D fuzzy spatial MSVR learning method can dynamically update the model parameters and reflect changes in the system in real time. When the system faces equipment failures or deviations from normal operating conditions, the model parameters are adjusted using real-time data. This allows the model to adapt promptly to new environments and abnormal conditions. As a result, the system model can continuously track the actual operating status. This adaptive capability not only improvesthe robustness of the model in complex dynamic environments but also provides higher accuracy and reliability for DPS monitoring and prediction.
# Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure
## 4 Simulation Experiments on Robustness
### Experiment Results and Analysis
#### 4.2.1 Robustness with Different Attack Modes  
On the basis of the default parameters, we change the attack mode to make the nodes fail in the DGCN of CSOS. The combat networks are attacked when the communication net and the functional net are ER-net, Goh-net and NW-net, respectively. To study the influence of asymmetric attack on the robustness of the DGCN of CSOS, the variations of the robustness of the combat networks under six different attack modes are shown in Figure 8. As can be seen from the figure, the robustness of different attack modes with different model network structures is quite different. For attacks on the communication net and when the model network is the ER-net and the NW-net, whether it is a random or intended attack mode, the impact on the robustness of the system is almost the same. When the model network is Goh-net, the deliberate attack on the communication net has the greatest impact on the robustness of the DGCN of CSOS, although the ISFA mode has a better attack effect for a short period of time. For attacks on the functional net and when the model network is the ER net and the NW net, the influence on robustness caused by ISFA mode is the greatest. When the model network is the Goh-net, the effect of ISFA mode is not as good as that of ISPA mode. Generally, deliberate attacks on double-layer networks have an impact on the robustness between that with ISPA mode and ISFA mode. For several types of network models to be attacked in different modes, the influence on the robustness of the combat network is complex, but the common feature is that the impact of deliberate attacks on the robustness is greater than that of random attacks, and random attacks on the double-layer network have a negative impact on the combat network's robustness. Therefore, it is necessary to fully understand the architecture of the attacked object during combat, so that the attack strategy can be reasonably developed to achieve a better combat effect.
# A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection
## 2 Proposed Framework
### Data Filtering  
The data of the 3D reconstructions are saved in formats unique to these structures, highlighting the Wavefront (.obj), Polygon File Format (.ply), or COLLADA (.dae). These formats present the data, with their peculiarities, in the following form: x position, y position, z position, normal vector [nx, ny, nz], and color [r,g,b] of each point. The positions are the representation--in our case, in ECEF or GPS, of the position of the point.  
Note the necessity of these points to be filtered to create a mission with this data as a reference, removing outliers and decreasing the optimization algorithms' points. Some algorithms for this task are the Convex Hull and Concave Hull. The convex hull of geometric objects is the smallest convex set that contains the objects. There are algorithms for points in the literature in two, three, and even Euclidean spaces of higher dimension [36]. The concave hull is an algorithm that finds a concave object that surrounds all points, using methods such as Alpha Shapes or K-nearest neighbor algorithms [37]. Figure 2 shows both algorithms at points in a reconstruction, looking at only one height of the entire reconstruction. The points are green, with the blue line formed by the convex hull and the concave hull's red line. Although the concave hull has better results in representing the surface, the algorithm's computational time is higher than the convex hull.