esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Histology plays a crucial role in medical research, primarily focusing on the detailed microscopic examination of cells and tissues [1]. Tissue processing involves tissue preservation, dehydration, clearing, and paraffin infiltration, which is fundamental for the preparation of fixed tissues [2]. The essential purpose of this tissue processing is to provide structural support for morphological preservation during sectioning, and it can be achieved via embedding the tissue in a liquid paraffin. This also acts to protect the tissue for long-term storage for future analysis [3]. In contrast to solid organ tissue, lung tissue processing can be challenging owing to its structural fragility. This necessitates elevated standards for providing comprehensive and uniform support during the sectioning procedure, thereby imposing heightened demands on the quality of tissue processing.  
An essential and efficient factor contributing to paraffin penetration into tissues is tissue clearing. Therefore, the selection of a tissue-clearing agent holds particular significance in this context.  
Xylene and isopropanol have traditionally been used as clearing reagents in tissue processing with xylene achieving more widespread use [4; 5]. Xylene is an aromatic hydrocarbon compound solvent that is widely used in industry and medicine [6]. It has a crucial role in histological laboratories, aiming to render alcohol to be removed from tissue, enhancing tissue transparency, and contributing to better paraffin penetration and the observation of the morphological structure of the tissues, for which is it widely employed in tissue processing [4; 7; 8]. However, xylene also has a detrimental impact on the environment, potentially introducing a hazard to human beings and pollutions to our ecosystem [6; 9; 10; 11; 12]. Moreover, prolonged exposure, particularly through occupational means, like researchers and technicians in the lab, leads to inhalation risk, skin and eye irritation, central nervous system depression, and even long-term health impacts [6; 13; 14; 15; 16; 17; 18]. Isopropanol is a clear, colorless, flammable liquid with an odor similar to a mixture of ethanol and acetone [19]. It is metabolized mainly in the liver by alcohol dehydrogenase to acetone, which is the primary metabolite of isopropanol [20; 21], and it is less toxic than xylene [22; 23].  
The aim of our study was to evaluate the differences, if any, between xylene and isopropanol for tissue processing and the imaging of human lung tissue, including brightfield imaging of hematoxylin and eosin (H&E) stains, epi-fluorescent imaging of a molecular lectin stain, and confocal imaging of a multiplex antibody stain.
## Influence of \(\omega_{K}\)
### Probe \(K\) Value  
In combination with the results and analysis in Section 3.1, the probe with a \(K\) value of 0.8 was selected to analyze wave heights from six prefabricated cracks (C-1 to C-6). The tested \(\omega_{K}\) values  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Tests**} & \multicolumn{3}{c}{**Crack Width/mm**} \\ \cline{2-4}  & **0.1** & **0.15** & **0.2** \\ \hline \(R\)/\% & 26.5 & 25 & 25 \\ \(R_{\text{max}}\)/\% & 74.5 & 76 & 74 \\ \(\omega_{K}\) & 0.355 & 0.328 & 0.338 \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(\omega_{K}\) values corresponding to different crack widths.  
Figure 6: Crack depth wave height curve.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Tests**} & \multicolumn{5}{c}{**Crack Depth/mm**} \\ \cline{2-6}  & **3** & **5** & **7** & **9** & **11** & **13** \\ \hline \(R\)/\% & 30 & 28 & 29 & 26 & 25 & 26 \\ \(R_{\text{max}}\)/\% & 83 & 84 & 86 & 80 & 80 & 82 \\ \(\omega_{K}\) & 0.361 & 0.342 & 0.346 & 0.325 & 0.315 & 0.315 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(\omega_{K}\) values corresponding to different crack depths.  
were compared with that of the probe with a \(K\) value of 2. The influence of different \(K\) values \(\omega_{K}\) was analyzed (Table 5). From Table 5, it may be seen that the \(\omega_{K}\) value calculated by probes with different \(K\) values was different: when \(K\) was 0.8, the \(\omega_{K}\) value increased with increasing crack depth, and the average value was approximately 0.43. When \(K\) was 2, the \(\omega_{K}\) value decreased with increasing crack depth, and the average value was approximately 0.34. Therefore, a reasonable probe \(K\) value should be selected according to the structural details of the test before using any \(\omega_{K}\) value to judge the crack tip position.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Key Metrics
#### 5.1.6 Spice  
SPICE (Semantic Propositional Image Caption Evaluation) is a metric for assessing image caption quality by analyzing semantic content, utilizing a graph-based semantic representation called a scene graph to encode objects, attributes, and relationships present in captions, thus abstracting away lexical and syntactic idiosyncrasies of natural language. It calculates an \(F\)-score over logical tuples to reflect semantic propositional similarity, providing a more human-like evaluation.  
To generate a scene graph, SPICE employs a two-stage approach: first, a dependency parser establishes syntactic dependencies between words in the caption, resulting in a dependency syntax tree; second, this tree is mapped to a scene graph using a rule-based system, consisting of object nodes, attribute nodes, and relationship edges [13].  
SPICE computes a similarity score between a candidate caption \(c\) and reference captions \(S=\{s_{1},s_{2},\ldots,s_{m}\}\) by extracting logical tuples from their parsed scene graphs using a function \(T\):  
\[T(G(c))=O(c)\cup E(c)\cup K(c), \tag{19}\]  
where \(G(c)\) is the scene graph for caption \(c\), \(O(c)\) is the set of object nodes, \(E(c)\) is the set of relationship edges, and \(K(c)\) is the set of attribute nodes. Each tuple contains one, two, or three elements, representing objects, relationships, and attributes, respectively.  
SPICE then computes precision \(P(c\), \(S)\), recall \(R(c\), \(S)\), and \(F\)-score \(F_{1}\) based on the matching tuples between the candidate scene graph and the reference scene graphs:  
\[P(c,S)=\frac{|T(G(c))\otimes T(G(S))|}{|T(G(c))|}, \tag{20}\]  
\[R(c,S)=\frac{|T(G(c))\otimes T(G(S))|}{|T(G(S))|}, \tag{21}\]\[SPICE(c,S)=F_{1}(c,S)=\frac{2\cdot P(c,S)\cdot R(c,S)}{P(c,S)+R(c,S)}, \tag{22}\]  
where \(\otimes\) denotes the tuple matching operator, which returns the set of matching tuples between two scene graphs. For matching tuples, SPICE uses WordNet synonym matching, similar to METEOR.
# Influence of Yeast Strain on Odor-Active Compounds in Fiano Wine
###### Abstract
Barriques, oak barrels of a well-defined volume, can be used by adding wine after the alcoholic fermentation, or as occurs in the production technology of Chardonay from Burgundy, immediately after mashing the grapes, allowing the must to ferment into them [22; 23; 24]. In the latter case, the yeasts will change their chemical composition, compared to the same must fermented in a traditional inert tank. Therefore, the choice of the yeast strain could be more complex, and the greater complexity of the must has to be taken into account. Whilst wine fermentation in wooden barrels is traditionally carried out for red wines, it has also spread to the production of Italian white wines obtained from native grape varieties [25]. Other recent research evaluated the impact of different fermentation technologies of another typical white wine, namely Palomino Fino, a neutral variety, reporting significant changes of the volatile composition, Odor Activity Values (OAV) and sensory profiles [26]. However, information on the influence of specific yeast strains on the aroma characteristics of wine fermented in barrique is lacking regarding Italian native grape varieties. Therefore, the aim of this study was to determine the influence of four yeast strains on the volatile composition of white wine, whose grape-must was fermented in oak barriques. The experiment was conducted on the \"Fiano\" cultivar, the most representative white wine variety in the Campania region of Italy.
# A Capacity Enhancement Method for Frequency-Hopping Anti-Jamming Communication Systems
## 3 Results
### Comparison with OFDM Signals
#### 3.5.1 Comparison of FH Capacity Enhancement Signals with OFDM Access Signals  
OFDM access signals, typically used in multi-user access scenarios, transmit narrowband information such as access signaling, with the sub-bandwidth typically on the order of kHz. Within the communication bandwidth, \(W_{s}\), the frequency resource is divided into a number of sub-bandwidths, and each sub-bandwidth is divided into a number of time slots. At the time of access, each user is assigned to a time slot within a sub-bandwidth to communicate.  
The spectral structure of the OFDM access signal is shown in Figure 17.  
With the same bandwidth resource-limiting parameter (bandwidth \(W_{s}\)) and power resource-limiting parameter (BER threshold Th\({}_{A}\)), according to Equation (22), FH capacity-boosting signal A is able to obtain the same anti-jamming gain when the information rate of the FH capacity-boosting signal A is the same as the information rate of the OFDM access signal. At this time, the FH capacity enhancement signal is able to transmit Information B in addition to Information A. Therefore, the FH capacity enhancement signal transmission rate is higher than the OFDM access signal.  
FH capacity-boosting signals and OFDM access signals both suffer from random variations in frequency parameters. The FH capacity-boosting signals use the frequency parameter variations for transmitting Information B, and the OFDM access signals are used to indicate multiple access information.
# An Intelligent Access Channel Algorithm Based on Distributed Double Q Learning
###### Abstract  
Aiming at the problem of mutual interference between nodes and external malicious jamming in wireless communication networks, this paper proposes an intelligent communication anti-jamming algorithm based on distributed double Q-learning. First of all, the proposed algorithm anti-jamming problem is modeled as a time-frequency two-dimensional optimization problem. The decoupled relationship between subframe and channel is used. Each node uses two Q-learning. According to the automatically received feedback signal, users choose their subframe and channel to avoid malicious external jamming and mutual interference between users. It maximizes the sum of all user throughput. Simulation results show that the proposed algorithm can effectively shorten the convergence time and improve the performance of the system.  
communication anti-jamming; double Q-learning; distributed learning; channel access Article
## 1 Introduction
Employing different monitoring methods, it examined the weakening mechanisms of weakly cemented sandstone under prolonged water immersion from macro, meso, and micro perspectives. Initially, thin section analysis and XRD testing were performed on sandstone samples at varying immersion durations to determine the variations in internal mineral composition. The variations in pore structure under different immersion times were analyzed using SEM. Next, uniaxial compression tests combined with acoustic emission monitoring were performed to observe the evolution of mechanical parameters in sandstone samples after different immersion periods. The failure mode served as a link to correlate micro, meso, and macro acoustic emission signals. Finally, the mechanistic reasons for all observed patterns were summarized.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 4 Results
### Principal Component Analysis (PCA)  
The results of the PCA are displayed in Figures 6 and 7. To obtain the greatest visualization results, we further allocated these PC bands to RGB band combinations for color composite production as shown in Figures 6 and 7. The ASTER RGBs include PC1, PC4, PC6 (Figure 6a) and PC3, PC5, PC7 (Figure 6b). The OLI RGBs include PC1, PC3, PC4 (Figure 7a) and PC2, PC4, PC7 (Figure 7b). These PC band RGB combinations of ASTER and OLI are shown to significantly discriminate the lithologies in the study area (Figures 6 and 7).
# ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles
## 6 Conclusions  
In this paper, we proposed the ARNS method for the relay-node selection in complex road scenarios. To the best of our knowledge, it is the first time developing an adaptive relay-node selection mechanism considering the road structure within the communication range of the sender in each hop. ARNS adopts the favorable relay-node selection method according to the road structure. In addition, the effect of obstacles was considered. It was demonstrated through simulation that ARNS is superior to methods based on 3P3B [23] and RTAD [20] in terms of the end-to-end delay and PDR, and superior to the complete method [25] in terms of the broadcast coverage and one-hop delay. In a real-world road scenario, we showed that ARNS reduces end-to-end delay by at least 13.8% compared to the beacon-based method, and the broadcast coverage of ARNS was increased by 3.6-7% compared with the complete method.  
In the future, we plan to extend our work to relay-node selection on 3D road structures, such as overpass structures and parking lot structures, and utilize AI [46; 47; 48; 49; 50; 51] to optimize the method [52; 53] in complex 3D scenarios.  
**Author Contributions:** [PERSON] conceived the mechanism design, [PERSON] and [PERSON] performed the simulation of Section 4.1, [PERSON]. performed the rest of the simulations and wrote the paper, [PERSON], [PERSON], [PERSON], and [PERSON] revised the manuscript and improved the mechanism on the intersection, and [PERSON] contributed to the refinement of the article. All authors have read and agreed to the published version of the manuscript.  
Figure 10: End-to-end delay on the curved road.  
Figure 9: Contention delay.  
Funding: This work was funded by the National Natural Science Foundation of China (Grant No. 61902041 and No. 61801170), Project of Education Department Cooperation Cultivation (Grant No. 201602011005 and No. 201702135098), China Postdoctoral Science Foundation (Grant No. 2018M633351), the National 13 th Five National Defense Fund (Grant No. 6140311030207), and the Scientific Research Fund of Hunan Provincial Education Department (Grant No.17B011). This work is funded by the Researchers Supporting Project No. (RSP-2019/102) King Saud University, Riyadh, Saudi Arabia.  
We thank Researchers Supporting Project No. (RSP-2019/102) King Saud University, Riyadh, Saudi Arabia, for funding this research.  
The authors declare no conflict of interest.
## 6 Experimental Results and Analysis
As shown in Table 3, the average processing time for a frame at both server side and agent side increases with more pedestrians. By comparison, the recognition time of the server is much shorter than the processing time of one agent. When dealing with pedestrians in a frame, the agent needs to detect, track, and extract features from a pedestrian, and then send the features to the server for identification. Afterwards, the same operation is performed on the next pedestrian in the frame. The operation loops until the features of all the pedestrians in a frame are extracted and transmitted to the server. The more pedestrians, the more tasks the agent needs to process and transmits to the server, and the more pedestrians the server needs to identify. Therefore, the total time for an agent processing a frame and the recognition time of the server are longer.
## 6 Conclusions
\begin{table}
\begin{tabular}{l l c c l l} \hline \hline
**Reference** & **AI Model** & **Precision** & **Recall** & **F1-Score** & **Case** \\ \hline This Work & KNN & 1 & 1 & 1 & Spoofing or Jamming, Benign \\ This Work & Logistic regression & 0.40 & 1 & 0.58 & Spoofing or Jamming, Benign \\ This Work & Random forest & 1 & 1 & 1 & Spoofing or Jamming, Benign \\ This Work & Decision tree & 1 & 1 & 1 & Spoofing or Jamming, Benign \\ This Work & LSTM & 0.40 & 0.52 & 0.45 & Spoofing or Jamming, Benign \\ [PERSON] et al. [23] & Support vector machine & 0.69 & 0.96 & 0.80 & Spoofing (Malicious) \\ [PERSON] et al. [23] & Support vector machine & 0.99 & 0.99 & 0.99 & Spoofing (Benignign) \\ [PERSON] et al. [23] & Local outlier factor & 0.04 & 1 & 0.08 & Spoofing (Malicious) \\ [PERSON] et al. [23] & Local outlier factor & 1 & 0.57 & 0.72 & Spoofing (Benign) \\ [PERSON] et al. [23] & Auto encoder neural network & 0.64 & 0.99 & 0.78 & Spoofing (Malicious) \\ [PERSON] et al. [23] & Auto encoder neural network & 0.99 & 0.98 & 0.99 & Spoofing (Benign) \\ [PERSON] et al. [28] & Support vector machine & NA & NA & 0.81 & Spoofing (Malicious) \\ [PERSON] et al. [28] & Support vector machine & NA & NA & 0.99 & Spoofing (Benign) \\ [PERSON] et al. [28] & Local outlier factor & NA & NA & 0.08 & Spoofing (Malicious) \\ [PERSON] et al. [28] & Local outlier factor & NA & NA & 0.73 & Spoofing (Benign) \\ [PERSON] et al. [28] & Autoencoder neural network & 0.69 & 0.99 & 0.85 & Spoofing (Malicious) \\ [PERSON] et al. [28] & Autoencoder neural network & 0.99 & 0.99 & 0.99 & Spoofing (Benign) \\ [PERSON] et al. [29] & Support vector machine & 1.00 & 0.66 & 0.80 & Spoofing (Malicious) \\ [PERSON] et al. [29] & Support vector machine & 0.94 & 1 & 0.97 & Spoofing (Benign) \\ [PERSON] et al. [29] & Local outlier factor & 0.92 & 0.76 & 0.83 & Spoofing (Malicious) \\ [PERSON] et al. [29] & Local outlier factor & 0.96 & 0.98 & 0.97 & Spoofing (Benign) \\ [PERSON] et al. [29] & Auto encoder neural network & 0.74 & 0.96 & 0.84 & Spoofing (Malicious) \\ [PERSON] et al. [29] & Auto encoder neural network & 0.99 & 0.94 & 0.97 & Spoofing (Benign) \\ [PERSON] et al. [29] & Support vector machine & 0.98 & 0.07 & 0.13 & Jamming (Malicious) \\ [PERSON] et al. [29] & Support vector machine & 0.99 & 0.99 & 0.99 & Jamming (Benign) \\ [PERSON] et al. [29] & Local outlier factor & 0.98 & 0.46 & 0.63 & Jamming (Malicious) \\ [PERSON] et al. [29] & Local outlier factor & 0.86 & 0.99 & 0.92 & Jamming (Benign) \\ [PERSON] et al. [29] & Auto encoder neural network & 0.84 & 0.99 & 0.91 & Jamming (Malicious) \\ [PERSON] et al. [29] & Auto encoder neural network & 0.99 & 0.94 & 0.97 & Jamming (Benign) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between this work and the previous literature on attack detection. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
## 3 Results and Discussion
### Cone Calorimetric Analysis
#### 3.1.3 Rate of Smoke Production (SPR)  
SPR is defined as the ratio of extinction area to mass loss rate. The greater the SPR is, the quicker the smoke is produced when the material is burned, and the greater the risk of the material becomes in the fire [10; 25].  
The SPR curves for different samples are shown in Figure 2a-c are the contrast analysis curves of the three-phase material, the two-phase material, and the pure EP; Figure 2d compares the SPR of the three-phase composites with different content reinforcement. It can be seen from Figure 2a-c that the smoke rate of CF/EP is less than that of EP and the time to smoke is delayed. In contrast, the rate of smoke generated by the three-phase composites has been reduced, but the time to initially produce smoke has been advanced and the time from the beginning of the production of smoke to the peak has been significantly shortened. This shows that a certain amount of CNTs and CF can synergistically form the denser protective char layer of EP as a physical barrier to prevent gas transfer compared to CF/EP and EP. From Figure 2d, the peak value of F7N7 is the smallest, and the total smoke production is 29.24% lower than that of EP, while the total smoke production of F5N5 is the smallest, which is decreased by 43.04% than EP.  
Figure 2: Rate of smoke production (SPR) of different samples. (**a**) SPR of epoxy resin (EP), F5, F5N5, and F5N10; (**b**) EP, F7, and F7N7; (**c**) EP, F10, and F10N5; and (**d**) EP, F5N5, F5N10, F7N7, and F10N10.  
It can be seen from Table 2 that, mCOY of F10 is minimum which is decreased by 6.85% compared to EP, while that of F7 and F15 are increased in the two-phase composite. It can be seen from Table 2 that mCOY of the three-phase composite is reduced compared to CF/EP. F8N10 is reduced the most, which is decreased by 12.33% than the EP. This shows that CNTs and CF can co-prevent the transfer of CO, thereby weakening the toxicity of blue gas.
# Uncertainties and Perspectives on Forest Height Estimates by Sentinel-1 Interferometry
## 2 Materials and Methods
### Modelling \(dh\) Uncertainty  
Once the above-mentioned condition has been satisfied, one can proceed to estimate the expected uncertainty of \(h^{FP}\), taking care of the contribution of the involved parameters. \(h^{FP}\) uncertainty (\(\sigma_{dh}\)) can be estimated by Equations (3) and (4), assuming \(h^{RP}\) uncertainty as a priori known. Equation (3) is the one commonly adopted in the literature to estimate \(\sigma_{dh}\)[42]:  
\[\sigma_{dh}=\omega\cdot\sigma_{d\Delta\varphi} \tag{3}\]  
where \(\sigma_{dh}\) is \(dh\) uncertainty and \(\sigma_{d\Delta\varphi}\) is the interferometric phase difference uncertainty.  
It is worth highlighting that this formula does not consider for \(\omega\) uncertainty; this has to be further considered to properly model error propagation that can be proficiently estimated by the Variance Propagation Law (VPL, [43]). VPL is a statistical tool (Equation (4)) useful to estimate the a priori variance of a statistical variable (\(y\)) depending on some other independent ones (\(x_{i}\)):  
\[\sigma_{y}^{2}=\left(\frac{\partial y}{\partial x_{1}}\right)^{2}\cdot \sigma_{x_{1}}^{2}+\left(\frac{\partial y}{\partial x_{2}}\right)^{2}\cdot \sigma_{x_{2}}^{2}+\ldots+\left(\frac{\partial y}{\partial x_{n}}\right)^{2} \cdot\sigma_{x_{n}}^{2} \tag{4}\]  
where \(y\)=\(f\left(x_{1},\,x_{2},\,\ldots,\,x_{n}\right)\) is the dependent variable, \(x_{i}\) the independent one, and \(\sigma_{x_{n}}^{2}\) their variance (supposed to be known).  
Application of VPL to Equation (3) results in Equation (5):  
\[\sigma_{dh}=\sqrt{\left(\frac{\partial dh}{\partial\omega}\sigma_{\omega} \right)^{2}+\left(\frac{\partial dh}{\partial d\Delta\varphi}\sigma_{d\Delta \varphi}\right)^{2}} \tag{5}\]  
where \(\sigma_{\omega}\) and \(\sigma_{d\Delta\varphi}\) are the expected uncertainties for \(\omega\) and \(d\Delta\varphi\), respectively.
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 3 Energy Storage System for the Marine Power System
### Lithium Iron Phosphate (LiFePO4, LFP)  
LiFePO4, with its unique phosphorus olivine structure, offers a cathode chemistry that is distinct from the layered metal oxides of NMC. This structural arrangement significantly reduces the risk of thermal runaway due to its inherent oxygen deficiency [111]. LiFePO4 presents a discharge potential of 3.4 V and a theoretical capacity of 170 mAh.g-1 [114; 115]. The lower discharge voltage reduces specific energy value [111]. However, its key advantage lies in the stability of the cathode during charge-discharge cycles, effectively safeguarding the battery against thermal runaway. Additionally, LiFePO\({}_{4}\)'s longer cycle life compared to LiCoO\({}_{2}\) is a testament to its durability despite its lower energy density [116].