esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study addresses the challenges of high joint noise, slow movement, and difficulty in obstacle crossing during the inspection of high-voltage transmission cables by a snake-like robot. A helical rolling-gait control method, driven by the output of UA-CPGs, is proposed based on the Hopf-CPGs controller theory. This approach effectively overcomes the limitations of traditional Hopf-CPGs controllers, such as poor adaptability and the inability to generate obstacle-crossing gaits for obstacles with varying diameters. The inspection process for obstacles of different diameters is simulated using the Open Dynamic Engine (ODE) simulator, and the method's effectiveness is validated through experiments on a physical prototype. To further enhance the practical application of the snake-like robot  
Figure 19: Snake-like robot prototype winding inspection joint signal change curve. **(a)** Deflection joint angle signal curve. **(b)** Rotary joint angle signal curve.  
Figure 20: Deflection joint 2 velocity curves of UA-CPGs and Hopf-CPGs within 0-5 s.  
in high-voltage transmission line inspections, future research will focus on addressing environmental disturbances such as wind speed, line oscillations, and temperature fluctuations, which may affect the stability and accuracy of the robot's movement. In this regard, control strategies based on nullclines and event-driven phase compensation methods proposed in [32], as well as the discrete-time barrier control functions discussed in [33], could provide effective solutions for improving real-time adaptability and enhancing system robustness. By integrating these techniques, future research aims to improve the reliability and scalability of the helical rolling-gait UA-CPG control strategy, further advancing its practical application in real-world scenarios.  
Conceptualization, Z.Y.; Funding acquisition, Z.Y.; Data curation, Z.F.; Formal analysis, Z.F.; Writing--original draft preparation, Z.F.; Writing--review and editing, Z.F.; Investigation, S.Y.; Software Z.F.; Methodology, Y.X.; Resources, D.Z.; Supervision, D.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by (1) Key Projects of Hubei Provincial Department of Education, D20221404; (2) Hubei Provincial Talent Plan for Scientific and Technological Innovation Project, Grant No. 2023 DJC092; (3) Hubei Provincial Natural Science Foundation Innovation Team Project, Grant No. 2023 AFA037; (4) National Natural Science Foundation of China, 51907055, 52075152.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available as the study will continue on this basis.  
The authors declare no conflicts of interest.
# An Integrated Photorefractive Analog Matrix-Vector Multiplier for Machine Learning +
## 3 Results  
### Characterization of the GaAs-Based Integrated Photonic Platform  
The prototype chip (Figure 8c) features test structures to probe the optical and electrical performance of the GaAs photonic circuits. The characterization was done for a wavelength range of 1260 to 1360 nm. The results are shown in Figure 10. Comments on the individual graphs are listed below, respectively:  
* Figure 10a: The GaAs/SiO\({}_{2}\) waveguides display a typical propagation loss of less than 5 dB/cm. The extinction coefficient was obtained by comparing the transmission through different lengths of waveguide.
* Figure 10b: 1 \(\times\) 2 MMIs display the correct approximate \(-\)3 dB attenuation compared to a reference waveguide.
* Figure 10c: The electric tungsten-heater-based thermo-optic phase modulator performs as intended, as demonstrated by its deployment in a Mach-Zehnder amplitude modulator. The response times are in the order of \(\mu\)s (Table 1).
* Figure 10d: This graph displays the power transmission through the photorefractive processor (Figure 8a). The experiment was performed by inserting light via a single input channel. Specifically, the input was not split up by an MMI into both input branches as denoted in Figure 8a. The black line corresponds to a waveguide that follows a similar trajectory as the light inside the processor and acts as a reference. The thick purple line denotes the signal from the designated output channel, whereas the thin lines denote the output from the neighboring output channels.  
The input channel is correctly transmitted to the designated output channel. The key performance indicators are the contrast between the designated output channel and its neighbors and the attenuation compared to reference. Both are indicative of the correct collimation and refocusing of the input beam and the undistorted propagation  
Figure 10: Performance characterization measurements of the GaAs/SiO\({}_{2}\) integrated photonic chip (Figure 8c).  
through the GaAs slab waveguide. The contrast between the target output channel and its neighboring output channels is retrieved at around 20 dB. The transmission through the processor yields less than \(-6\) dB power attenuation at wavelength bands along the entire spectral range.
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
## 3 System Deployment and Motivation
### Predicting Node Status  
After verifying the delay components in IT-WSNs, we find that the key to optimizing delay is to identify node status, e.g., node availability and its link quality, in advance. As described above, predicting the status when nodes will be aerially exposed is difficult. By analyzing the running process of nodes in the Zhoushan system, we identify several metrics that are related to the node status. For example, during the high tide period, the constant submersion impacts link quality between nodes, especially for those deployed lower on the shore that are submerged first. Thus, a sudden change of link quality and the number of neighbors may reveal the change of water level to a certain extent. These characteristics can be well-organized and utilized as good indicators for node status prediction.  
In summary, IT-WSNs suffer from queuing delay due to the intermittent connections and buffer condition, and non-ignorable transmission delay caused by the poor link quality. Considering these factors comprehensively, we design Pido to optimize delays for IT-WSNs. In the following sections, we first provide an overview of our design (Section 4) and then describe modeling in detail in Section 4.2.
# Capturing the Swirling Vortex and the Impact of Ventilation Conditions on Small-Scale Fire Whirls
## 2 Mathematical Formulation
### Soot Formation and Radiation Models  
The soot particles contributed significantly in augmenting the overall radiative heat transfer from a fire [33]. Tracking the evolution of soot volume fraction was thereby essential in the current study. The two-equation semi-empirical soot model by [PERSON] et al. [34], that incorporates the essential physical processes of soot nucleation, coagulation and surface growth, was adopted. This model is attractive as it can use PDF in terms of mixture fraction to predict soot formation [35]. The major parameters utilized to determine soot formation were normalized radical nuclei concentration \(b^{*}_{muc}\) and soot mass fraction \(Y_{soot}\). The Moss-Brookes model solves two more transport equations for these two parameters. \[\frac{\partial}{\partial t}(\rho Y_{soot})+\frac{\partial}{\partial x_{i}}( \rho u_{i}Y_{soot})=\frac{\partial}{\partial x_{i}}\Bigg{(}\frac{\mu_{t}}{Pr_{ soot}}\frac{\partial Y_{soot}}{\partial x_{i}}\Bigg{)}+\frac{dM}{dt} \tag{18}\]\[\frac{\partial}{\partial t}(\rho b^{*}_{muc})+\frac{\partial}{\partial x_{i}}(\rho u _{i}b^{*}_{muc})=\frac{\partial}{\partial x_{i}}\bigg{(}\frac{\mu_{t}}{Pr_{soot}} \frac{\partial b^{*}_{muc}}{\partial x_{i}}\bigg{)}+\frac{1}{N_{norm}}\frac{dN} {dt} \tag{19}\]  
where \(Y_{soot}\) is soot mass fraction and \(Pr_{soot}\) is the turbulence Prandtl number for soot transport that equals to 0.4 [36]. \(M\) is soot mass concentration, \(b^{*}_{muc}\) is normalized radical nuclei concentration, \(N\) is soot particle number density, and \(N_{norm}\) equals \(10^{15}\) particles. The last term in these two equations describes the instantaneous soot mass concentration and instantaneous production rate of soot particles, respectively. \[ \begin{array}{c}\frac{dM}{dt}=M_{p}C_{a}\bigg{(}\frac{X_{prec}P}{RT}\bigg{)} ^{l}exp\bigg{(}-\frac{T_{a}}{T}\bigg{)}+C\bigg{(}\frac{X_{seg}P}{RT}\bigg{)}^{ m}exp\bigg{(}-\frac{T_{y}}{T}\bigg{)}\bigg{\{}(\pi N)^{1/3}\bigg{(}\frac{6M}{ \rho_{soot}}\bigg{)}^{2/3}\bigg{\}}^{n}\\ \hskip 14.226378 pt-C_{oxid}C_{\omega}\eta_{coll}\big{(}\frac{X_{ pol}P}{RT}\bigg{)}\sqrt{T}(\pi N)^{1/3}\bigg{(}\frac{6M}{\rho_{soot}}\bigg{)}^{2/3} \end{array}  \tag{20}\]  
\[\frac{dN}{dt}=C_{a}N_{a}\bigg{(}\frac{X_{prec}P}{RT}\bigg{)}^{l}exp\bigg{(}- \frac{T_{a}}{T}\bigg{)}-C_{\beta}\bigg{(}\frac{24 RT}{\rho_{soot}N_{a}}\bigg{)} ^{1/2}d_{p}^{1/2}N^{2} \tag{21}\]  
where \(X_{prec}\) is the mole fraction of the soot precursor, which is assumed to be acetylene [37]. The mass density of soot \(\rho_{soot}\) is assumed to be 1800 \(kg\cdot m^{-3}\), and the activation temperature \(T_{a}\) for nucleation reaction is that proposed by [PERSON] [38]. Furthermore, in the soot mass concentration equation, \(X_{sgg}\) is the mole fraction of the participating surface growth species. [PERSON] and [PERSON] [39] assumed the collision efficiency (\(\eta_{coll}\)) to be 0.04. \(C_{a}\), \(C_{\beta}\), \(C_{r}\), \(M_{p}\), \(C_{oxid}\), \(C_{\omega}\), and \(T_{y}\) are model constants.
## 3 Comprehensive Antioxidant Characteristics
### Antioxidants  
Antioxidants represent a diverse group of compounds, classified based on their chemical structure, solubility (either water-soluble or fat-soluble), and the kinetics of their interactions in biological processes. Fat-soluble antioxidants include \(\alpha\)-tocopherol, \(\beta\)-carotene, lipoic acid, and coenzyme Q10 (ubiquinone), while water-soluble antioxidants include glutathione (GSH) and ascorbic acid [21].  
Antioxidants can be categorized into enzymatic and non-enzymatic groups. Enzymatic antioxidants include superoxide dismutase (SOD)--which exists as manganese SOD (MnSOD) in the mitochondria, copper-zinc SOD (Cu/Zn SOD) in the cytoplasm, and extracellular SOD--catalase (CAT), and enzymes reliant on GSH such as glutathione peroxidase (GP\(\times\)1-GP\(\times\)8), glutathione transferase (GST), and glutathione reductase (GR).  
Non-enzymatic antioxidants are diverse and include compounds like GSH, uric acid, melatonin, metal chelators (such as transferrin and lactoferrin), lipoic acid, ubiquinone, trace metals (zinc, copper, selenium), vitamins E and C, \(\beta\)-carotene, and polyphenolic substances [21].  
Exogenous antioxidants primarily originate from dietary sources and medicinal plants, including fruits, vegetables, cereals, mushrooms, beverages, flowers, spices, and traditional herbal medicines. Additionally, industries that process agricultural by-products represent a significant potential source of natural antioxidants. These plant-derived antioxidants predominantly consist of polyphenols (such as phenolic acids, flavonoids, anthocyanins, lignans, and stilbenes), carotenoids (including xanthophylls and caroten), and vitamins (such as vitamins E and C) [22].
# SDMw: Secure Dynamic Middleware for Defeating Port and OS Scanning
## 5 SDMw: The Secure Dynamic Middleware Architecture
### The SDMw Architecture
#### 5.1.4 Connectionless Datagram Service from Inside Hosts  
UDP and IP datagrams offer connectionless services. The term connectionless means that UDP and IP do not maintain any state information about successive datagrams. Each datagram is handled independently from all other datagrams. For connectionless UDP services, source IP address, source port, destination IP address and the destination ports (called a socket) information are important. Each UDP connection is uniquely identified by this 4-tuple. An attacker may not be able to fingerprint a system just based on this parameter. Thus, the SDMw system preserve this 4-tuple without modification. If the source IP address and the source port information is changed (as in the case of a Network Address Translation (NAT) or a Port Address Translation (PAT) system), a reference translational table must be maintained in order for the reply to be forwarded to the right host. If a NAT/PAT is not used along with SDMw, the proposed SDMw system will not modify the original 4-tuple. Thus, it will improve the system performance by reducing a table-lookup latency.  
At Layer-3, IP-ID field, TTL, Explicit Congestion Notification (ECN) and Fragment flags are traditionally used for fingerprinting a host. These parameters are chosen by the host's OS on per packet basis, and they need to be changed in order for a host to be protected from fingerprinting. If we assume that IP fragmentation is not needed along the path between a host and the SDMw, then changing them by the SDMw is not going to affect the communication between the end-hosts. Since these parameters are applied on per packet basis, there is no need for the SDMw to maintain a translational table. Our proposed SDMw architecture will generate these parameters according to the chosen UHGA algorithm and replace the one in the IP packet.  
Figure 1: The Secure Dynamic Middleware black box.
## 3 Results and Discussion  
### Gross Primary Productivity  
Gross primary production was higher in fringing forests than scrub forests and higher still in the P fertilized scrub forests (Table 2). A larger portion of GPP was attributed to the microbial mat production in the scrub (~27%) compared to the fringing forest (~2.5%) and the P fertilized trees (~2%). The contribution to GPP of the epiphytic macroalgal community that adheres to the roots (_i.e._, _Bostrychia_ and _Catenella_) was low in the interior scrub forest and highest in the seaward fringing forest where tidal inundation is frequent. The mean biomass of these communities was 8 \(\pm\) 8 g\(\cdot\)m\({}^{-2}\) (mean \(\pm\) SE) for the scrub forests and 40 \(\pm\) 20 g\(\cdot\)m\({}^{-2}\) in the fringing forest (Table 1). The macroalgal community contributes 3.7% of total community GPP in P fertilized scrub, 5.6% in the scrub and 8.9% in the fring forest.  
Within the same island, different habitats (seaward fringing forests and scrub or dwarf habitats) can have vastly different levels of productivity and have differences in the sources and fates of fixed C. The mangrove canopy is responsible for most GPP, but soil surface microbial communities accounted for 27% of GPP in the scrub compared to 2.5% in the fringing forest. These values are consistent with the limited development of the canopy in scrub forests, which at this site is due to P limitations to tree growth [14, 34, 52].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Budget Components**} & \multicolumn{3}{c}{**Forest Type**} \\ \cline{2-4}  & **Fringe** & **Scrub** & **Fertilized P Scrub** \\ \hline
**INPUTS** & & & \\ \hline Mangrove trees & 378.0 & 89.0 & 555.0 \\ Epiphytic algal community & 38.0 & 7.5 & 22.0 \\ Microbial mat & 11.0 & 36.5 & 11.0 \\
**TOTAL INPUTS** & **427.0** & **133.0** & **588.0** \\ \hline
**OUTPUT** & & & \\ \hline Canopy respiration & 132.0 & 51.0 & 286.0 \\ Soil respiration & 47.0 & 8.6 & 81.5 \\ Aboveground root respiration & 108.0 & 29.0 & 168.0 \\ Stem respiration & 18.6 & 3.2 & 36.5 \\ Algal respiration & 8.2 & 1.6 & 4.8 \\ Wood production & 11.6 & 0.1 & 15.0 \\ Herbivory & 1.2 & 0.4 & 1.6 \\ Burial in soil & 17.6 & 2.8 & 12.9 \\ Export (litter) & 13.1 & 0.8 & 13.1 \\
**TOTAL OUTPUS** & **357.3** & **97.5** & **619.4** \\ Net Ecosystem Production (NEP) (\% GPP) & 69.6 (16.3) & 35.5 (26.7) & \(-\)31.4 (\(-\)5.39) \\ \% C of GPP buried & 4.1 & 2.1 & 2.2 \\ Production:Respiration ratio (P:R) & 1.19 & 1.36 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary carbon budget (mol C m\({}^{-2}\)\(\cdot\)year\({}^{-1}\)) of fringing and scrub _Rhizophora mangle_ forests and phosphorus fertilized scrub forests for Twin Cays, Belize.
## 5 Conclusions and Future Work  
A new method based on gray-level morphological reconstruction and multi-feature analysis is proposed in this paper to detect small ship targets under heavy maritime background clutter. The proposed TIR ship target detection method can automatically segment the small ship target from the sea background clutter which has intricate texture and strong contrast. By using the opening or closing based GMR, the intricate sea clutter is removed while the intensity, shape, and contour information of ship target are retained, so the proposed ship target detection method is robust to heavy sea clutter. Considering the intensity and contrast features of TIR ship targets after GMR-based pretreatment, the IFSM and BCSM are computed and fused, so the potential ship targets can be well highlighted and the complex clutter can be suppressed simultaneously. Furthermore, considering the contour and shape features of TIR ship targets, a two-step ship verification strategy including STAEM-based contour descriptor and the statistical shape knowledge constraint is constructed, so the true ship targets are efficiently extracted from residual non-ship clutter. Moreover, the dual approach is applied by directly adding bright and dark ship target maps, so both bright and dark ship targets in TIR image can be simultaneously detected. Extensive experiments verify that the proposed small ship detection algorithm has a better detection performance than compared state-of-the-art methods, including THF/BHF, 2 DME, 2 DO, MSS, IMFS, IFCM, CVM, and HCST.
## 2 Background
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Data** & \multicolumn{2}{c}{**Pe-Proessing**} & \multicolumn{2}{c}{**Features Engineering**} & \multicolumn{2}{c}{**Techniques**} & \multicolumn{1}{c}{**Ref**} \\ \hline Open-source data of Edwards labeled-space & anonymization, leveraging, kernelizing, kernelizing, kernelizing, kernelizing & CNN, RF, CR, energy and stack ensemble & [24] \\ \hline TIL: CRM department & memory swapping step words, spectral hashing, kerneling, kerneling, kerneling & Inspike Features & T-EDFM and KNN, domain tree, NR, LR, SVM, QROSSK,COSS & [25] \\ Fast-fold restaurant data & DukeTime domain named-based local cluster information from struggling & feature extraction was performed based on daily data, kernel features extracted new embedded. The feature was selected based on publicly deep. & NR, LR, and paired knowledge decision tree model & [24] \\ \hline IT department from a high-national country & labelization, stage word and digits removal, word attention, and part-speech filtering by selecting only open & word count per solution category-joint text data, & T-ED and multimodal naive Bayes SVM, KNN, decision tree, and logistic regression & [27] \\ \hline IBM: NVIDIA training system & No & No & & \\ \hline German: Jordanian University, & remove HTML tags and special characters & T-IDF feature vectorization & SVM, AR, rule-based, and decision tree & [28] \\ \hline IT: international incident data & remove two words, special characters, and time, phone & Chi-boardboard system used to detect important feature using & NR, SVM, and Ada SVM & [29] \\ \hline IBM: CMBD dataset & keywords and their annotations as classification features & Selecting up Configuration from records from CMBD to per-word & SVM & [27] \\ \hline IBM: CMBD dataset & keywords and their annotations as classification features & SVM complexity, T-IDF was used to give inputspace & & \\ \hline Incident user tracking system of labelled-Techniques & partitioning of tickets from HTML, and numerical expression using user content & bag of crowds using TF-IDF & decision trees, SVM, KNN, and NB & [27] \\ \hline Confidential data of IT Company & kernelization, two words removal, and kerneling & part of speech hugging was performed to filter out vocabulary & SVM & [29] \\ \hline Real-world incident data & labelization, step words removal and kerneling & TFIDF & SVM & [29] \\ \hline Real-world IT infrastructure service disk ticket data & resource map words, special characters, and time, phone & TF-IDF & LR, KN-NN, MBN and SVM & [27] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing implementations of automated IT incident prediction utilizing AI models.
# Biocompatibility of Membranes Based on a Mixture of Chitosan and _Lythri herba_ Aqueous Extract
## 3. Results
### Biological Analysis  
#### 3.3.1 The Genotoxic Assay of the Aqueous Solution of Chitosan and _Lythri herba_ Extract  
The genotoxicity of the aqueous extract and of the mixtures of chitosan and aqueous extract was detected via SOS-Chromo test. The test consists of colorimetric assays of enzymatic activities (\(\beta\)-_galactosidase_ and _alkaline phosphatase_) after incubating the tester strain in the presence of various amounts of compound [66]. All samples showed a non-genotoxic effect compared with 4-NQO (4-hydroxyquinoline 1-oxide) and 2-AA (2-aminoanthracene), which were used as positive controls (Table 1).  
Additionally, the potential indirect mutagenic effect was evaluated through metabolic products, where all the samples analyzed with the microsomal enzyme activator (+S9) showed a lack of genotoxicity due to the values of the induction factor (IF) being lower than 1.5 (Table 1). A significant decrease in genotoxicity was observed especially in combination with CS-Ly with microsomal activation (+S9) compared with the same non-activated combinations (-S9). In the presence of microsomal enzymes, mixtures of CS-Ly were ten times less genotoxic. Consequently, indirect mutagenesis was non-existent, and this will allow safe applications without systemic effects.  
The results of testing both genotoxic activity (with and without microsomal activation) evaluated from a quantitative point of view (IF) were adapted after our previous work, [PERSON] et al. [67].  
Figure 4: FT-IR spectra of the obtained membranes.
## 7 Quinolone Inactivation/Modification
### Fungi  
Thus, in the 1990s, the ability of several wood-rotting fungi, including _Irpex lactaeus_, _Gloeophyllum striatum_ or _Phanerochaete chrysosporium_, among others, to degrade enrofloxacin was first described [154]. Thereafter, the ability of other fungi, such as _Clitocyhe odora_, _Coriolopsis gallica_, _Cyathus stercoreus_, _Irpex lactaeus_, _Xylaria longipes_ or others, to degrade or modify quinolones has been largely described [155; 156; 157; 158]. Of note, the resulting metabolites may retain a degree of antibacterial activity, which varies between processed quinolones and fungi species [156].  
The fungi degradation/modification routes of quinolones are diverse. Thus, working with _Gloeophyllum striatum_, [PERSON] et al. proposed four possible degradation routes: oxidative decarboxylation, defluorination, hydroxylation at C-8 or oxidation of the amino moiety (Figure 4) [158]. Data regarding the exact enzymes involved in these degradation/modification routes are scarce. Nevertheless, the role of enzymes such as cytochrome P450, or those possessing lactose-like and peroxidase-like activity, has been explored [155; 156; 159]; of note, the two latter types of enzymes are considered ligninolytic enzymes [160], in agreement with the apparent facility of wood-rooting fungi to degrade or modify quinolone. Along this line, a recent study analyzing the ability of _Coriolopsis gallica_ to degrade levofloxacin suggested that enzymes with lactose-like and peroxidase-like activity may play a role due to their prevalence in fungi secretome [155]. Similarly, the chloroperoxidase of _Leptoxylpium fumago_ (formerly _Caldarionyces fumago_) is also able to degrade norfloxacin [161]. On the other side, other authors have proposed that several peroxididases, such as for instance manganese peroxididases, play no role in the detoxification of quinolones, limiting this role to cytochrome P450 and lacases [159; 162]. Regarding this latter issue, as mentioned previously, it should be considered that different amino acid sequences may result in different activity.