esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Comparison with Other Emerging Interconnect Technologies
### Applicability of Inter-Chip Interconnects in Industry 4.0  
Modern industrial revolution is seeing an increasing push towards automation, use of Artificial Intelligence (AI) and novel methodologies to combine manufacturing and material movement. Industry 4.0 will require automated and connected industrial elements such as material handling agents, robots, and sensing apparatus [113]. Robotic arms and material handling agents such as fork lifts have parts that need to communicate across moving joints and those with relative motion. Video sensors to aid in  
Figure 19: Performance Comparison for the 4C4M system using emerging technologies.  
localization of robots require capturing high-speed video feeds and transmitting them via high-speed cables to compute nodes. Cables hinder range of motion of robotic elements. Using short-range high bandwidth wireless interconnects video feeds from sensor cameras can be sent across moving parts to the computational elements inside chips directly. Modern and future 3D printing devices have robotic arms with moving parts similar to material handling agents. Wireless inter-chip connectivity between sensors, actuators and microcontrollers in 3D printers can also provide a low-power alternative to cables while increasing the range of motion between the parts with relative motion.  
The future of material handling is going to include small-scale distributed warehouses in urban centers and material handling agents that are small and are able to move only light packages such as drones [114]. The weight and power budget on such agents will be more stringent than on larger agents such as forklifts. High-speed communication cables for communication between sensors and controller or compute nodes are generally metallic and can be heavy depending upon the required length. These can be eliminated by using wireless short-range communication. Therefore, short-range wireless interconnects have the potential of reducing the power requirement of small-scale distributed material handling agents such as drones or Unmanned Aerial Vehicles (UAV). In addition to providing a high speed communication channel, using the unlicensed 60 GHz mm-wave frequency, indoor localization of autonomous industrial agents such as robots have been investigated. Mm-wave frequencies enable high-precision localization in the order of a few millimeters, which is impossible to achieve using lower frequency radar technologies [115].
# Emerging Trends in Pharmacotoxicological and Forensic Sample Treatment Procedures
## 3 Main Solid Matrices for Forensic Purposes
### Brain Tissue  
The brain is the primary site of action for many drugs, particularly lipophilic substances that accumulate in central nervous tissue. For this reason, high concentrations of these types of drugs can be observed in brain specimens from acute fatalities. [PERSON] et al. have validated a method for quantification of 30 common pharmaceuticals and metabolites in brain tissue from a forensic case. They use a fully automated robotic system to perform a solid-phase extraction. In this way, manual labor and human errors are minimized [63]. [PERSON] et al. have used brain tissue in their study about the distribution of synthetic opioids in postmortem specimens. The sample was first diluted in distilled water and homogenized, then pH 6 phosphate buffer was added, vortexed, and centrifugated. The supernatant was added on to a UTC Xcel I cartridge and driven through the column at 1-2 psi after washing and conditioning the column. The samples were eluted with ethyl acetate:ammonium hydroxide (98:2, _v:v_), and the eluate was dried under nitrogen [64].  
[PERSON] et al. used a classic solid-phase extraction to extract analytes of interest from brain tissue. The aim of their work was to analyze cocaine adulterants in the human brain in cases of drug-related death [65]. Two extraction techniques used, one after the other, have been necessary for [PERSON] et al. to develop and validate a new method for the quantification of polycyclic aromatic hydrocarbons for forensic assessment. The brain was also used as a matrix for the evaluation of these. The first step was to add 3 mL of water and 3.5 mL of acetonitrile in 300 mg of tissue, and after manual agitation, the organic and aqueous phases were separated by addition of 0.5 g NaCl (Salting-out Liquid-Liquid Extraction, SALLE). The supernatant (2 mL of acetonitrile) was used for Dispersive Liquid-Liquid Microextraction (DLLME), and this was added 50 mL of carbon tetrachloride (CCl\({}_{4}\)) was. The mixture was injected into 9 mL water containing 5% NaCl  
Figure 1: Classical operations in solid–liquid extraction low temperature partitioning.  
(_w:v_). After centrifugation, the portion precipitated by CCl\({}_{4}\) was recovered and injected in GC [66]. [PERSON] et al. developed an innovative and simple technique in their study about the quantification of serotonin uptake inhibitors in plasma, urine, and brain tissue. Stir bar sorptive extraction has been employed as a new sample pretreatment and consists of a stir bar coated by a thin layer of polydimethylsiloxane (PDMS) and used to stir samples and simultaneously extract analytes. After the extraction step, the bar was removed from the sample, dried, and then the analytes were removed by thermal or liquid desorption. Before starting the extraction, brain tissue was homogenized with 1 mL of water using an ultrasonic cell disruptor, was centrifugated, and supernatant was subjected to extraction [67].
## 4 Discussion
### Lichen Colonization and Penetration in the Rocks  
The extensive lichen colonization observed on St. Simeon Church was distributed with high coverage values, ranging from 50% in the North-East to 80-100% on the remaining sides, but with a vicariant role of certain species. These findings show how lichen colonization varies across different sides in terms of coverage and species diversity, being influenced overall by exposure and inclination [20; 21; 24].  
In fact, the consequent variations in solar lighting and the effects of the prevailing winds can differently favor evaporation and influence water content, arising from the wind-driven rain (WDR) amounts. This dynamic interplay between solar exposure, wind patterns, and WDR contributes to the observed differences in lichen colonization and deterioration processes across various sides of the Church. For instance, in North-facing exposures, which receive less direct sunlight, the evaporation is lower, and once wet, these surfaces remain damp for much longer than other sides, but a difference exists among NE and NW in the results of prevailing winds and WDR. This slow-drying condition could be responsible for the higher and diversified lichen colonization on northern faces, compared with the rapid wetting and drying cycles on the South surfaces [3]. In particular, the north-west (NW) exposure is particularly favourable to the lichens' growths, along with some bryophytes, and hosted foliose lichen species such as Physconia grisea and Xanthoparmelia verrucigera. These cryptogams, due to their more complex thallus structure compared with crustose lichens, have higher demands regarding water availability dynamics [38; 39]. Their presence indicates the persistence of more pronounced moisture conditions throughout the day and year, potentially leading to greater physiological activity for these species [38]. Within general xerophylous conditions and with tolerance to the high direct solar radiation in a relatively wide pH range of low to moderate eutrophication, the specific composition and growth forms of lichen colonization showed some clear differences, with an almost complete turnover rate between exposures [38].  
In all the collected samples, the SEM analyses confirmed some previous data [17], revealing a severe deterioration of the internal part of the rock surfaces, characterized by dense networks of fungal hyphae. These observations indicate an extensive penetration of lichens into the rock matrix, leading to the embedding of mineral grains and subsequent fragmentation and crumbling. The variation in hyphal density across different lichen species suggests varying capabilities in terms of their spread and penetration into the substrate, influenced by both intrinsic factors related to the species and extrinsic environmental factors.
## 2 Materials and Methods  
### Study Area  
The study area encompasses the northern part of the Middle East, situated between the western portion of Asia and the northeast boundary of Africa, within latitudes 25\({}^{\circ}\)06\({}^{\prime}\)N and 42\({}^{\circ}\)07\({}^{\prime}\)N and longitudes 24\({}^{\circ}\)41\({}^{\prime}\)E and 63\({}^{\circ}\)17\({}^{\prime}\)E (see Figure 1). Specifically, we focused on seven countries in this region: Iran, Iraq, Jordan, Lebanon, Palestine, Syria, and Turkey. This area covers approximately 3160.873 thousand km\({}^{2}\), accounting for 45.6% of the entire Middle East. According to the FAO Global Land Cover SHARE dataset [51], one-third of the region is covered by bare soil lands, while shrubland covers 22.5%. Tree cover and cropland dominate the western and central parts, representing 9.9% and 20% of the total area, respectively. Forested areas are mainly found in northern and southern Turkey and the northern and western regions of Iran (see Figure 1b).  
Figure 1: (**a**): Spatial distribution of climate types over the northern Middle East based on Köppen-Geiger climate classification (see [PERSON] et al. [52]). (**b**): Land cover types (see FAO. [51]): cropland (Cr); grassland (Gr); tree-covered area (TCA); shrub-covered area (SCA); herbaceous vegetation aquatic (HVA); sparse vegetation (SV); and bare soil (BS) water bodies (W); artificial surfaces (AS).  
The climate of the Middle East falls into a transitional category, with characteristics of hot-dry climates in the Arabian Peninsula and temperate-humid or cold-humid climates in Eastern Europe and Eurasia. The western part experiences high seasonality in the rainfall regime. Rainy winters occur predominantly between November and April along the Mediterranean Sea and in western Iran, influenced by Mediterranean cyclones. The northern region, on the other hand, exhibits a slight seasonal pattern, with rainfall distributed throughout the year and the highest precipitation occurring during the spring season. Based on the Koppen-Geiger climate classification [52], the area is grouped into three main categories (B, C, and D) and 14 subtypes. Group B includes arid (desert) and semi-arid (steppe) climates, represented by BS and BW climates in the south, eastern side of Iran, northern Iran, and central Turkey. Group C comprises Mediterranean temperate climate types (Cs, Cf) along the Mediterranean coast in the Aegean Sea, near the Black Sea, the Levant, and northern Iraq, encompassing 15.4% of the area. The remaining region is characterized by cold climate patterns (Ds, Df) found in the eastern Anatolian plateau, the Taurus Mountains in Turkey, and the Zagros Mountains in western Iran (see Figure 1a).
# Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification
## 3 Classification of IR Radiation Intensity Sequence Based on IRRNN
### Structure of IndRNN  
In this section, we will introduce the structure of IndRNN. The main difference with RNNs is the way the hidden layer is connected. According to the calculation formula of the traditional RNNs, we describe the IndRNN using the following formula:  
\[\mathbf{h}_{t}=f\Big{(}\mathbf{W}^{HI}\mathbf{x}_{t}+\mathbf{w}\odot\mathbf{ h}_{t-1}+\mathbf{B}^{H}\Big{)}. \tag{10}\]  
The weight \(\mathbf{w}\) is a vector, consisting of all diagonal elements of \(\mathbf{W}^{HH}\) in the Equation (8), and the dimension is H. Symbol \(\odot\) indicates the Hadamard product, which is the corresponding elements of the two matrices multiplied. It can be seen from Equation (8) that each neuron in the hidden layer is independent of other neurons in the layer. So for the \(j\)th neuron, the state of its hidden layer \(h_{j,t}\) can be expressed as:  
\[h_{j,t}=f\Big{(}\mathbf{W}_{j}^{HI}\mathbf{x}_{t}+w_{j}h_{j,t-1}+B_{j}^{H} \Big{)}, \tag{11}\]  
where \(\mathbf{W}_{j}^{HI}\) and \(w_{j}\) are the \(j\)th row and the \(j\)th element of the weight matrix and the weight vector, respectively. Each hidden layer neuron only receives the information from the input and its own state at a previous moment. So each hidden layer neuron in the IndRNN processes a spatial-temporal pattern independently.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 4 Decision-Making Algorithm Based on Anti-Jamming Knowledge Graph
### Anti-Jamming Knowledge Graph Search Statement Design
#### 4.2.2 Question Classification  
In the process of question answering module design, we classify questions and combine/match them with the corresponding query template. In practical application, the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Question Template** & **Example** & **Tag** \\ \hline Jamming patterns & What jamming patterns do pattern features correspond to? & 1 \\ Anti-jamming decision-making & What are the anti-jamming strategies? & 2 \\ Parameters to be adjusted & What parameters do anti-jamming decision-making adjust? & 3 \\ Parameter adjustment target & Which frequency band is the communication frequency adjusted to? & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Anti-jamming knowledge question answering (Q&A) template.  
proposed method utilizes the machine language as the inputs of the anti-jamming knowledge graph, and its question directivity is obvious. Moreover, because of the small scale of the anti-jamming problem template, questions can be manually classified. The question and the corresponding query template are listed in Table 2.  
For example, in Table 2, the question 'How to counter sweep jamming?', we take the frequency sweep jamming as the query condition, fill the query template, and then map it into a complete Cypher query statement. The query result is returned after the query in the Neo4j graph database.
# Tropical Cyclone Winds from WindSat, AMSR2, and SMAP: Comparison with the HWRF Model
## 5 Discussion and Conclusions
In general, good agreement was found between the three sensors and the HWRF model for the 19 storms examined between 2017-2020, with the absolute values of the average biases being less than 1 m/s and standard deviations being below 4 m/s for all tropical cyclone winds between 10 and 60 m/s. It was discovered that biases and standard deviations between the three sensors and HWRF were in better agreement in the Atlantic Ocean than the Pacific. Since the algorithms for SMAP, AMSR2, and WindSat are the same everywhere across the globe, these differences are attributable to differences in the HWRF model. We also assessed the impact of rain on the TC-wind retrievals. No systematic differences in the performance of the three sensors were found. In the future, it is desirable to more closely examine the residual biases between the TC-winds and those of HWRF in order to help determine how either product can be improved. This includes further study of the biases between the satellites and HWRF in different rain regimes. We also wish to extend our comparisons of the satellite TC-winds with other external sources, such as the Synthetic Aperture Radar (SAR) aboard the Sentinel and RADARSAT-2 satellites [29; 30; 31], as well as other models. **Author Contributions:** Conceptualization, A.M., L.R. and T.M. ; methodology, A.M., L.R. and T.M. ; software, A.M., L.R. and T.M. ; formal analysis, A.M., L.R, and T.M. ; investigation, A.M., L.R. and T.M. ; resources, A.M., L.R. and T.M. ; data curation, A.M., L.R. and T.M. ; writing--original draft preparation, A.M.; writing--review and editing, A.M., L.R. and T.M. ; visualization, A.M. and L.R. ; supervision, T.M. and L.R. ; project administration, T.M. ; funding acquisition, T.M. and L.R. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded under NASA contracts NNH17 CA04C (SMAP Science Utilization), 80 HQTR19C0003 (Ocean Vector Winds Science Team), NNH15 CN50C (Earth Science U.S. Participating Investigator), and 80 HQTR18C0035 (The Science of Terra Aqua Suomi NPP). **Data Availability Statement:** The Remote Sensing Tropical Cyclone wind speeds for SMAP, AMSR-E, AMSR2 and WindSat are publicly available at [[http://www.remss.com/tropical-cyclones/tc-winds/](http://www.remss.com/tropical-cyclones/tc-winds/)]([http://www.remss.com/tropical-cyclones/tc-winds/](http://www.remss.com/tropical-cyclones/tc-winds/)) (accessed 10 May 2021). **Acknowledgments:** The authors would like to thank [PERSON], [PERSON], and [PERSON] from NOAA EMC for their help with acquiring several previous years of HWRF data for this project. The authors would also like to thank [PERSON] (University of Michigan, now SRI) for his helpful discussion and for providing HWRF data for the 2017 storms used in this work. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Theoretical Background
### Curvature Ratio and Conformity Effects
However, this study demonstrates that strategic exploitation of material property asymmetry in non-conforming geometries can achieve superior performance under extreme conditions. This represents a theoretical advancement that extends Hertzian contact principles to dissimilar material combinations, establishing a new sealing model specifically designed for ultra-high-pressure pilot-operated valves and contributing to the fundamental understanding of extreme pressure contact mechanics. While the mathematical models above quantitatively explain the influence of key design variables, they have several important limitations. Hertzian contact theory assumesideal elastic contact, but in actual ultra-high-pressure environments, nonlinear material behavior and elastoplastic deformation occur. Additionally, the complex geometric shape of the concave contact geometry and the behavior changes across a wide pressure range (2.0 MPa to 87.5 MPa) are difficult to fully explain with a single theoretical model. Therefore, to overcome these theoretical limitations and precisely analyze the key performance metrics of contact pressure at low pressure and maximum equivalent stress at high pressure, numerical analysis through FEA is essential.
## 3 Case Studies
### Economic Indicators  
Levelised cost of electricity (LCOE) has been given in Table 3 and was calculated using:  
\[LCOE=\frac{\sum_{t=0}^{N}\frac{I_{\text{t}}+O_{\text{t}}}{(1+r)^{\text{t}}}}{ \sum_{t=0}^{N}\frac{E_{\text{t}}}{(1+r)^{\text{t}}}}\]  
where \(\text{I}_{\text{t}}\) = investment, \(\text{O}_{\text{t}}\) = O&M, \(\text{E}_{\text{t}}\) = electricity delivered, r = 6% discount rate, t = year.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**System** & **CAPEX Items** & **Amount (INR)** & **LCOE (INR/kWh)** \\ \hline Solar PV (rooftop, 150 kWp, & 500 \(\times\) 300 W modules— & & \\ Solar PV (rooftop, 150 kWp, & 3 \(\times\) 50 kVA DSP Grid-Support & & \\
300 W BHEL modules) & Conditioners & \(\approx\)44.1 million (\(\approx\)2.94 lakh/kWp) & 2.4 \\  & (inverters/UPS)—3 \(\times\) 240 V, & 150 Ah OPzS battery strings & \\ Solar-thermal CPC dish (3.5 kW, & Steel paraboloid structure, & 37,400 per dish (MNRE & \\ Solar-thermal CPC dish (3.5 kW, & aluminum receiver tube \(\mathcal{O}\) & benchmark) to \(\approx\)2.8 lakh & \\
4.4 m\({}^{2}\) aperture) & 90 mm single-axis tracker & (hostel cooking system & \\  & and mount & analogue) & \\ Biogas CHP set (pilot, 4 kWe) & 5 kVA biogas genset (Indo & 90,000 & \\ Digester balloon replacement & Engineering Works) & & 4.1 \\ (\(\mathcal{O}\): 7 m, H: 2 m) & Thermoplastic membrane + fittings & 179,000 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: CAPEX, O&M, and LCOE.  
Figure 4: Integrated energy vector layout for renewable systems at IIT Roorkee and DEI Agra.
## 4 Discussion
### Biochemical Composition  
Since microalgal species can vary in nutritional value as a function of culture conditions, several studies have attempted to manipulate them with the objective of increasing biochemical products [2; 40; 41], protein [13], carbohydrate [35; 42], lipid [43; 44; 45] and fatty acid [16; 46; 47] contents. Many microalgal species, respond to nitrogen deficiency by increasing their lipid content. A lack of nitrogen and carbon availability allows microalgae to switch from synthetizing proteins to lipids, with correspondingly slower growth rates and lower biomass production [48]. Some strains of the genus _Thalassioira_ have been described as potentially harmful [49] but the production of allelochemical compounds from _T. weisflogii_ strains with allelobatic effects against potentially harmful microalgae, such as _Phaacystis globosa_, has also been reported [50].  
Chlorophyll is utilized as an intracellular nitrogen pool to support further cell growth and biomass production as nitrogen in the media becomes depleted [47; 51]. Chlorophyll \(a\) follows a similar trend to biomass production, since chlorophyll \(a\) is a nitrogen-rich compound that contains four nitrogen atoms. This suggests that cells accumulate large quantities of chlorophyll molecules when a nitrogen source is abundantly available [52]. This pattern has been described in a previous research [13].  
The protein content revealed maximum values in the exponential growth phase, decreasing throughout the stationary phase. [PERSON] [13] found the same trend for five genera of microalgae from the Brazilian coast (_Bellerochea_ sp., [PERSON] 1885; _Chaetoceros_ sp., [PERSON] 1844; _Chorella_ sp. [PERSON] 1890; _Rhodomonas_ sp. [PERSON] 1898 and _Thalassioira_ sp. [PERSON] 1873). [PERSON] [53] also reported a peak in the protein content of _Rhodomonas_ in the transitional growth phase and a declination in the stationary stage.  
Generally, as microalgae enter the stationary stage due to limited nutrients, cells accumulate carbon in the form of lipids and carbohydrates [13; 54]. [PERSON] [55] observed that the lipid content tends to increase when cultures reach the stationary phase, especially when silicate and nitrogen are limited. This study proved that lipid content rises as nitrogen concentration declines in the cultures around day nine, i.e., at the onset of the stationary phase. This phenomenon was clearly observed in cultures with lower nitrogen concentrations (111.25, 222.50 uM). [PERSON] [56] studied the effect of KNO\({}_{3}\) concentration on the lipid concentration. Different KNO\({}_{3}\) concentrations of 0.2, 1.0, 3.0 and 5.0 mM were applied to _Chlorella vulgaris_ cultures and demonstrated that the lipid content decreased as the KNO\({}_{3}\) concentration increased.