esa-sceva/satcom-chunk-collection huggingface dataset:
# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
## 4 Discussion
### Prescriptive Maintenance Actions  
The prescriptive dimension of PsM is characterised by the ability to convert predictive insights (e.g., failure diagnosis, RUL) into actionable recommendations. These recommendations outline _what_ activities should be taken, _when_, _by whom_, and _under what constraints_. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Study** & **MLP-ANN** & **LSTM** & **DNN** & **CNN** & **VAE** & **TRN** & **RNN** & **GAN** & **ESN** \\ \hline
[81] & & & & & & & & ✓ & & & \\
[78] & ✓ & & & & ✓ & ✓ & & & ✓ & \\
[55] & & & & & & & & & ✓ & \\
[64] & ✓ & ✓ & & ✓ & & & & & & \\
[86] & & & & ✓ & & & & & & \\
[75] & & & ✓ & & & & & & & \\
[87] & & & ✓ & & ✓ & ✓ & ✓ & & & \\
[63] & ✓ & ✓ & & & & & & & & \\
[83] & & & & ✓ & & & & & & \\
[62] & & & ✓ & & & & & & & ✓ \\
[72] & ✓ & ✓ & & & & & & & & ✓ \\
[65] & ✓ & & & & & & & & & \\ \hline \end{tabular} NOTE: MLP-ANN (MultiLayer Perceptron ANN); LSTM (Long Short-Term Memory); DNN (Deep Neural Network); CNN (Convolutional Neural Network); VAEs (Variational AutoEncoders); TRNs (Transformer Networks); GAN (Generative Adversarial Network); ESN (Echo State Network).  
\end{table} 
Table 4: Deep learning algorithms used in PsM applications. In contrast to traditional maintenance strategies, PsM extends this pipeline by embedding decision-making logic into the maintenance execution process. This marks a decisive shift from mere prognostics to prescriptive intelligence. To capture this dimension, we show how PsM is operationalised in practice (Table 5). The spectrum of PsM activities spans from localised decisions--component repair versus replacement--to systemic interventions including fleet-level scheduling and co-optimisation. This is supplemented by prioritising and planning necessary tools and resources to resolve specific failures using optimal strategies (e.g., corrective vs. preventive). Consequently, the prescriptive part aims to execute actions that will be self-regulated with minimal human interventions. These can be within the operational segment (e.g., adjusting machine settings, reducing speed, or load) [9; 95; 96], input segment (e.g., resource allocation, spare parts supply, workforce prioritisation) [65; 66; 68; 97], environmental segment (e.g., adjusting env. temperature) [72], and output segment (e.g., modifying production targets or quality criteria) [3]. In contrast to traditional maintenance practices that focus solely on machine-level availability, PsM considers other elements of overall equipment effectiveness, enabled by I4.0 technologies such as AI, ML, and H&VI (Horizontal and Vertical Integration), which are crucial for automating PsM actions. Thus, switching
###### Abstract  
A reflectarray antenna with an optimized sectorial beam is designed for the surveillance channel of a DVB-S-based passive radar (PR). The employment of satellite illuminators requires a high gain antenna to counteract the losses due to the great distance from the transmitter, but without forgetting a beamwidth wide enough to provide angular coverage. A method based on optimizing the position of several contiguous beams is proposed to achieve the required sectorial pattern. Different reflectarray elements are designed to achieve S-curves with smooth slopes and covering all the required phases (the S-curve represents the reflection phase of a single element, as a function of size, rotation and incidence angle). The real phase and modulus of the reflection coefficient of each element are considered in the optimization process to achieve the best real prototype. Geometry has been studied and adapted to employ commercial elements for the feed, feed-arm and the structure that holds the aperture. The designed prototype has been characterized in an anechoic chamber achieving a stable gain greater than 19 dBi in almost the complete DVB-S band, from 10.5 GHz to 12 GHz with a sectorial beam of 8.7\({}^{\circ}\)\(\times\) 5.2\({}^{\circ}\). The prototype has also been validated in PR trials in terrestrial scenarios allowing the detection of cars at distances up to 600 m away from the PR, improving the performance achieved with commercial parabolic dish antennas.  
antenna; reflectarray; passive radar; terrestrial targets; optimization; sectorial beam; simulation; measurements +
Footnote †: journal: Sensors  
Accepted: 30 July 2021  
Published: 4 August 2021
# Methods for Determining Polycyclic Aromatic Hydrocarbons (PAHs) in Milk: A Review
## 2 Polycyclic Aromatic Hydrocarbons
### PAHs Impact on Human Health  
PAHs are non-polar and lipophilic, so they are readily soluble in fat and bind to cell membranes, disrupting their structure and function. The most hazardous PAHs in food products typically contain 2-to-7 aromatic rings. Among these, benzo[a]pyrene is the most lipid-soluble, binding readily to chloromicrons and lipoproteins. This enables it to penetrate lipid absorption and distribution systems, leading to bioaccumulation in the liver and small intestine, i.e., the key organs in these processes [42; 43]. Its accumulation in adipose tissues  
also leads to the suspicion that exposure to PAHs and the formation of PAH-DNA adducts may contribute to an increased risk of breast cancer. However, the evidence remains inconclusive due to the complex nature of these interactions [44]. Children are potentially at risk of developing childhood cancers, which, according to the World Health Organization (WHO), may also be associated with exposure to PAHs. It is important to emphasize that the ongoing research refers to PAHs from food and environmental PAHs [45].  
The effects of PAHs on human health are related to the exposure type, concentration, and relative toxicity. Many of them are identified as carcinogenic, mutagenic, and teratogenic [46]. Benzo[a]pyrene, identified as a highly potent carcinogen, has been classified as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC). It is widely used as a marker for assessing PAH contamination levels and in clinical studies exploring links between PAHs and disease [40,47]. This compound forms metabolites capable of creating DNA adducts and causing chromosomal aberrations. If these DNA lesions are not effectively repaired before replication, they can lead to mutations and DNA strand breaks [48]. Additionally, benzo[a]pyrene induces oxidative stress, chronic inflammation, and inflammatory cascades, all of which are associated with cancer initiation through non-genotoxic mechanisms [49].  
PAHs, although their carcinogenic effects are the most widely discussed, also cause non-genotoxic effects. PAHs are positively associated with the release of pro-inflammatory cytokines, leading to vascular inflammation and remodeling. These processes may accelerate the progression and instability of atherosclerotic plaques, contributing to the pathogenesis of atherosclerosis [50]. Exposure to PAHs may increase the risk of cardiovascular disease, including atherosclerosis, hypertension, thrombosis, and myocardial infarction [46]. The WHO has further associated PAHs with neurodevelopmental issues in children. Animal studies suggest adverse effects on neuromuscular function, coordination, and sensorimotor development, as well as impaired cognitive function in young individuals [45].  
Given these findings, it is clear that addressing PAHs in food is essential, particularly in staple items like milk. Milk is a dietary mainstay, often recommended for its positive effects on development, making the presence of PAHs in such products a critical concern.
## 4 Discussion
### Voxel Size  
Voxel size has little impact on accuracy but this somewhat favours the 2.0 mm voxel size over 0.5 or 1.0 mm for a number of reasons. The largest voxel size correspondingly produced the smallest number of data points and therefore the smallest and fastest classifiers with the shortest training times. Metal additive manufacturing is a high-value application where the computing power required for any of these models could be justified, so none of the models should be excluded based on these criteria. However, this still represents a significant practical advantage of the larger voxel size. The downside of the larger voxel size is the reduced spatial resolution of porosity predictions; however, 2.0 mm should be sufficiently fine for many applications.  
The choice of voxel size has a significant impact on the balance between porous and non-porous voxels in the dataset, which also has interactions with the discretisation approach and class-balancing approach. Pores represent a small percentage of the volume of a specimen and their distribution is fixed regardless of voxel size, so increasing the voxel size increases the probability that any given voxel will include a pore. This leads to the very biased non-porous to porous ratio seen in Figures 7 and 8 compared to the more balanced ratio in Figure 9. The skew towards non-porous regions is expected and relevant to real-world applications, since manufacturer-recommended process parameters were used, which are naturally chosen to minimise porosity. However, it presents a challenge to training classifiers and some approaches such as class-balancing must be used to avoid the classifier becoming very biased towards the majority class in the training data.  
The relationship between class balance and voxel size for the defect-centric discretisation approach is very different since in this approach, there is one porous data point created for each pore, regardless of voxel size. In contrast, the number of non-porous data points increases significantly as voxel size decreases, so the dataset goes from non-porous-biased at 0.5 mm voxel size to porous-biased at 2.0 mm voxel size and is quite balanced at 1.0 mm voxel size.  
Another advantage of the 2.0 mm voxel size is that it achieved higher accuracy than the smaller voxel sizes. While the magnitude of the advantage varied by classifier type and other factors, the high accuracy combined with the computational advantages at this voxelsize is clearly desirable. A possible reason for the improved accuracy at this voxel size is that the mean values per voxel for each of the features are calculated using more individual data points, helping to filter out noise in the sensor signals. The significant overlap between porous and non-porous classes in the histograms of Figures 7-12 supports this.
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IoT & Internet of Things \\ IIoT & Industrial Internet of Things \\ INC & In-Network Computing \\ DPP & Data Plane Programming \\ P4 & Programming Protocol-Independent Packet Processors \\ MQTT & Message Queuing Telemetry Transport \\ IP & Internet Protocol \\ TCP & Transmission Control Protocol \\ XMPP & Extensible Messaging and Presence Protocol \\ OPC UA & OPC Unified Architecture, Object Linking and Embedding for Process \\  & Control (OPC) \\ CoAP & Constrained Application Protocol \\ UDP & User Datagram Protocol \\ SCTP & Stream Control Transmission Protocol \\ TLS & Transport Layer Security \\ ASIC & Application-Specific Integrated Circuit \\ NPU & Network Processing Unit \\ FPGA & Field-Programmable Gate Array \\ GPU & Graphical Processing Unit \\ CPU & Central Processing Unit \\ OVS & Open vswitch \\ SNI & Smart Networks 4 Industry \\ NFV & Network Function Virtualization \\ SDN & Software Defined Networking \\ MAC & Media Access Control \\ EIB & Escuela de Ingenieria de Bilbao \\ CFAA & Centro de Fabricacion Aeronautica Avanzada \\ WAN & Wide Area Network \\ ONOS & Open Network Operating System \\ OSM MANO & Open Source MANO, Management and network orchestration (MANO) \\ ISP & Internet Service Provider \\ KPI & Key Performance Indicator \\ RTT & Round Trip Time \\ \end{tabular}
###### Abstract  
All-optical encoders and comparators are essential components for high-speed optical computing, enabling ultra-fast data processing with minimal latency and low power consumption. This paper presents a numerical analysis of an all-optical encoder and comparator architecture operating at 120 Gb/s, based on carrier reservoir semiconductor optical amplifier-assisted Mach-Zehnder interferometers (CR-SOA-MZIs). Building upon our previous work on all-optical arithmetic circuits, this study extends the application of CR-SOA-MZI structures to implement five key logic operations between two input signals (A and B): \(\overline{\text{AB}}\), \(\overline{\text{AB}}\), AB (AND), \(\overline{\text{A}}\)\(\overline{\text{B}}\) (NOR), and AB + \(\overline{\text{A}}\)\(\overline{\text{B}}\) (XNOR). The performance of these logic gates is evaluated using the quality factor (QF), yielding values of 17.56, 17.04, 19.05, 10.95, and 8.33, respectively. We investigate the impact of critical design parameters on the accuracy and stability of the logic outputs, confirming the feasibility of high-speed operation with robust signal integrity. These results support the viability of CR-SOA-MZI-based configurations for future all-optical logic circuits, offering promising potential for advanced optical computing and next-generation photonic information processing systems.  
Keywords:optical encoder and comparator; carrier reservoir semiconductor optical amplifier; Mach-Zehnder interferometer; quality factor +
Footnote †: journal: _Nanomaterials_
# Effect of Biostimulants on the Yield and Quality of Selected Herbs
## 2 Materials and Methods  
### Study Site and Experimental Factors  
The experiment was conducted in a heated greenhouse equipped with movable flood tables, at the University of Warmia and Mazury in Olsztyn (20\({}^{\circ}\)29\({}^{\prime}\) E, 53\({}^{\circ}\)45\({}^{\prime}\) N; 125 m a.s.l.), between 28-30 April and 30 June 2017-2018. The experiment had a randomized block design with three replications. The first experimental factor was plant species: summer savory (_Satureja hotensis_ L.) var. _Saturn_, marjoram (_Origanum majorana_ L.) var. _Miraz_, and lemon balm (_Melissa officinalis_ L.) (Seed Company W. Legutko, Nad Stawem, Poland). The herbs selected for the study are most widely cultivated in Poland. The second experimental factor was the effect exerted by two biopreparations, Calleaf Aminovital (Calfert\({}^{\circledgtr}\), Warsaw, Poland), and Maximus Amino Protect (Ekoplon, Grabki Duze, Poland). The products were used in diluted form.  
Calleaf Aminovital (according to the product label) is an organic foliar fertilizer in liquid form, with an amino acid-balanced composition, which contains large amounts of polypeptides and free amino acids. The product is obtained in the process of the hydrolysis of natural proteins.  
Maximus Amino Protect (according to the product label) is a combination of free amino acids, peptides, and potassium phosphate, which has strong biostimulatory and antistress effects. Potassium phosphite stimulates the immune system of plants and prevents fungal diseases. Peptides improve the biological effectiveness of the fertilizer--they support the active uptake and penetration of micronutrients and plant protection products. It is practically advisable to use this fertilizer as an addition to treatments against fungi attacking plants (fungicide treatments), as it significantly improves the effectiveness of these treatments. In the control treatment, plants were sprayed with water.  
In 2017-2018, in the last days of April, 10 seeds of each herb species were sown in 0.7 dm\({}^{3}\) pots. Each treatment consisted of 64 pots and had an area of 1 m\({}^{2}\). The experiment was performed in triplicate. The horticultural substrate used in the experiment was characterized by salt concentration of 1.5 g dm\({}^{-3}\), pH 5.9, and the following chemical composition: N-NO\({}_{3}\)--112 mg dm\({}^{-3}\), P--80 mg dm\({}^{-3}\), K--143 mg dm\({}^{-3}\), Ca--1240 mg dm\({}^{-3}\), and Mg--383 mg dm\({}^{-3}\). Crop protection chemicals were not applied. Yellow sticky traps were placed over the pots to control the number of pests (_Trialeurodes vaporarium_ and _Sciara militaria_). _Encarsia formosa_ (three individuals per m\({}^{2}\)) were used for the biological control of _Trialeurodes vaporarium_. Leaves were harvested at maturity.
# Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural Network
## 3 Methods and Material
### Scaling and Standardization  
Continuous values, such as weight, age, and measures of the laboratory were scaled to a range of 0 to 1, as shown in Equation (1).  
\[x_{s}=\frac{x_{i}-min(x)}{max(x)-min(x)} \tag{1}\]  
where \(x_{s}\) denotes a scaled value, \(x_{i}\) represents the original value, and min and max are the minimum and maximum values of a particular feature. Scaling prevents variables from over-dominating in the supervised learning algorithms.
# Synthesis and Selected Properties of Ester Elastomer Containing Sorbitol
## 3 Results and Discussion
### Dynamic Thermomechanical Analysis (DMTA)  
DMTA results are shown in Figure 4. The relaxation behavior exhibited by PSBS 1 h, PSBS 2.5 h and PSBS 3.5 h was tested. Loss tangent (tan delta), loss modulus E\({}^{\prime}\) and storage modulus E\({}^{\prime}\) as a temperature function were measured.  
Figure 3: First-heating differential scanning calorimetry (DSC) thermograms of PSBS 1 h, PSBS 2.5 h and PSBS 3.5 h.  
Figure 4: Storage modulus (E\({}^{\prime}\)) (**A**), loss modulus (E\({}^{\prime\prime}\)) (**B**) and loss tangent (tan delta versus temperature) **(C)** for PSBS 1 h, PSBS 2.5 h and PSBS 3.5 h.  
Three temperature ranges for the storage modulus can be observed. In the first temperature range (\(-100\)\({}^{\circ}\)C to \(-40\)\({}^{\circ}\)C), the storage modulus function does not show significant changes because polymers are in the glassy state. The initial value of the storage modulus for PSBS 3.5 h is significantly higher compared to its value for PSBS 2.5 h and PSBS 1 h. In the second temperature range (\(-40\)\({}^{\circ}\)C to \(-10\)\({}^{\circ}\)C), the value of the storage modulus decreases, which corresponds to a process of viscoelastic relaxation associated with the amorphous phase undergoing the glass-transition. In the third temperature range (\(0\)\({}^{\circ}\)C to \(50\)\({}^{\circ}\)C), the storage modulus of PSBS 2.5 h and PSBS 3.5 h is constant, and a flexibility plateau can be observed. PSBS 1 h in the temperature range \(0\)\({}^{\circ}\)C to \(50\)\({}^{\circ}\)C exhibits the subservience of elastic to viscous properties and a rapid decline of the storage modulus. This phenomenon is not observed for PSBS 3.5 h and PSBS 2.5 h. The loss modulus E\({}^{\prime\prime}\) = f(T) and loss tangent tan delta = f(T) functions exhibit peak maximum corresponding to alpha relaxation. It is associated with the amorphous phase undergoing the glass-transition. The glass-transition temperature value measured by DMTA is \(10\)\({}^{\circ}\)C higher than the T\({}_{\rm g}\) value measured by DSC. Such discrepancy in results is typical for those methods.
# Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization
## 4 Simulation
### Simulation Experiment 2  
In Experiment 2, we simulate the scene of bistatic sea surface target detection, as depicted in Figure 6. In actual sea surface target detection, sea clutter is not only affected by the ionosphere and bistatic angle, but also influenced by ocean currents. The impact of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{x}\)} & **Bragg Peak Doppler Frequency** & **Interference Reduction** & **Difference Value of Power between the** \\  & **Estimation Error (Hz)** & **Amplitude (dB)** & **Target and the Maximum Clutter (dB)** \\ \hline Root & 0.35 & 27.2 & \(-2.6\) \\ SVD & 0.15 & 26.7 & \(-1.9\) \\ Proposed & 0.05 & 32.3 & 3.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison.  
Figure 5: The comparison of echo power spectra before and after sea clutter suppression using different algorithms under the condition of \(\text{SCR1}=-30\) dB: (**a**) comparison of root method before and after suppression, (**b**) comparison of SVD method before and after suppression, (**c**) comparison of the proposed method before and after suppression.  
ocean currents on the sea clutter spectrum can be approximated as a process of spectrum shift, resulting in the formation of sea clutter interference in a larger frequency band. The basic simulation parameters are: radar operating frequency \(f_{c}=12\) GHz; the distance between the sea surface target and the receiver is \(R=4000\) m; the target Doppler frequency is \(f_{\text{tar}}=40\) Hz. We simulate and generate the bistatic first-order sea clutter broadened by interference, and the signal-to-clutter ratio of target reflection signal and sea clutter is \(\text{SCR}=-40\) dB. Figure 7 shows the range Doppler spectrum without sea clutter suppression processing. In order to clearly demonstrate the comparison of results before and after sea clutter suppression, Figure 8 shows the power spectrum at the distance point where the target is located.  
Figure 6: The diagram of bistatic sea surface target detection.  
Figure 7: The range Doppler spectrum without sea clutter suppression processing.  
From Figure 7, it is apparent that there are distinct bands of sea clutter present in the range Doppler spectrum prior to sea clutter suppression. The interference caused by the sea clutter is quite substantial, completely obscuring any target signal within these bands.  
Figure 8a shows the results without sea clutter suppression. As can be seen, the target is entirely obscured by the sea clutter, making it challenging to distinguish from the surrounding clutter. Figure 8b-d depicts the results after sea clutter suppression using root method, SVD method, and the proposed method, respectively. In comparison, the method proposed in this paper demonstrates superior suppression performance and more prominent target detection effect.
# Immuno-PET for Glioma Imaging: An Update
## 2 Gliomas  
### Classification  
In 2021 the World Health Organization (WHO) published the fifth edition of the Classification of Tumors of the Central Nervous System (CNS), which comprises many important data related to molecular features and updates pathologic diagnoses with respect to the previous version [9; 10]. In order to provide a more biologically and molecularly defined grouping of those neoplasms, our most recent categorization combines histopathological data with the most recent developments in understanding the molecular etiology of brain tumors. Additionally, the available of more natural history data has led to a major improvement in the most recent classification of CNS tumors [11]. Because histopathological categorization alone is insufficient to predict the clinical course of gliomas, researchers additionally depend on genetic classification to inform clinical management and therapy decisions. Mutations in IDH1 and IDH2 (two very similar genes) represent the majority of lower-grade gliomas in adults and establish a subtype that is associated with a favorable prognosis. In comparison to diffuse gliomas without these changes, lower-grade gliomas that have both an IDH mutation (in either IDH1 or IDH2) and the deletion of chromosome arms 1p and 19q (1p/19q codeletion), which is most common in oligodendrogliomas, respond better to radiochemotherapy and are linked to longer survival. Astrocytomas are more likely to have TP53 and ATRX mutations, which are significant indicators of clinical behavior [12].