esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In the presented study, ultrasound homogenisation contributed to a decrease in the stability of ice cream mixes. The samples after the ultrasound treatment had a higher value of TSI. The elevated value of TSI was noted for sample IU (with the addition of iota carrageenanan) and BU (hydrolysate of iota carrageenanan) and was at a level of 4. The addition of stabilisers in ice cream mixes improved stability during maturation. Moreover, the ultrasound treatment in ice cream mixes reduced the particle size; however, it was visible after maturation. Additionally, the combination of ultrasound and the use of stabilisers gave better results in reducing the size of the particle, than only using stabilisers and the smallest particles were noticed in the BU sample--9.76 \(\upmu\)m. The effect of ultrasound treatment on the consistency index was only significant for ice cream mixes IU and BU and the consistency index of these samples decreased after ultrasound treatment. Based on the microscopic photos, the agglomeration of fats and considerable diversification was visible in all ice cream mixes. While analysing the microstructure, the effect of ultrasound treatment was not noticeable for most samples and only in the IU sample, after maturation, were the differences visible.  
Due to the specific mechanism of ultrasound homogenisation, it may be contemplated as an interesting tool for ice cream applications. This method can be effectively used for reducing the size of particles. Moreover, it may be used in improving the stability of the emulsion in comparison to the traditional form of homogenisation. This method has the potential in enhancing the quality and performance of vegan ice cream mixes. However, although a plethora of advantages to using this method in production are acknowledged, wide-ranging research is still required to further industrial applicability.  
Conceptualization, A.K. and A.K.-D.; methodology, A.K. and E.J.; software, A.K.; validation, A.K., A.K.-D., E.J.; formal analysis, A.K. and A.K.-D.; investigation, A.K., A.K.-D. and E.J.; resources, E.J.; data curation, A.K., A.K.-D. and E.J.; writing--original draft preparation, A.K., A.K.-D. and E.J.; writing--review and editing, A.K., A.K.-D. and E.J.; visualization, A.K.; supervision, E.J.; project administration, A.K.-D. All authors have read and agreed to the published version of the manuscript.  
The research equipment purchased as part of the \"Food and Nutrition Centre--modersisation of the WULS campus to create a Food and Nutrition Research and Development Centre (C-Zi-Z)\" was co-financed by the European Union from the European Regional Development Fund under the Regional Operational Programme of the Mazowieckie Voivodeship for 2014-2020 (Project No. RPMA. 01.01.00-14-8276/17).  
Not applicable.  
The authors declare no conflict of interest.
# Beam Dynamics Design of a Multi-Ion RFQ for Medical Application
## 3 Beam Dynamics Design
### Multi-Particle Simulation Results  
To verify the reliability of the new design strategy, additional multi-particle simulations were performed using two widely used programs, TRACEWIN [48] and TOUTATIS [49], for cross-checking. These programs provide validation of PARAMETQM simulation results by employing different field-solving methods. Unlike PARAMETQM, which models RFQ fields using a truncated cylindrical-harmonic expansion confined to a cylindrical region, TRACEWIN employs a full 3D particle-in-cell Poisson solver, while TOUTATIS solves the full Poisson equation on an adaptive multigrid mesh that precisely follows the vane geometry. By running identical RFQ geometries and beam parameters through all three codes and comparing key outputs--such as transmission efficiency and emittance--solver or algorithmic biases can be identified and mitigated, yielding more robust and reliable beam dynamics predictions. The new RFQ beam dynamics design was simulated using both programs. Each simulation employed 10,000 particles, with the RFQ input beam distributions configured as a 4D water-bag and a 3D Gaussian distribution, respectively.  
Figure 8: New design simulation results for He\({}^{2+}\). Plots from top to bottom show the beam envelope evolution along the RFQ in the x and y planes, and the phase and energy spectra, respectively (color online).  
Figure 9: Comparison results between the general design and the new design. (**a**) Area of the separatrix. (**b**) Longitudinal emittance growth (color online).  
The results obtained from the three multi-particle simulations are very close, as listed in Tables 3 and 4. All deviations in transmission efficiency and emittance are small and within acceptable tolerance ranges, confirming the reliability of the proposed strategy.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 7 Challenges/Future Directions of CR-LPWAN
### PHY Layer Challenges
#### 7.1.6 Other Challenges  
Other PHY layer challenges that may confront the development of effective CR-LPWAN systems are listed as follows:  
1. Improving scalability to admit additional devices;
2. Control and mitigation of interference issues between CR and PU operators [4; 5];
3. Development of improved and higher data rate techniques for CR-LPWAN systems[4; 5];
4. Interoperability between different CR-LPWAN technologies;
5. Localization, link optimization, and adaptability issues [150];
6. Developing functional testbeds and simulators;
7. Security, mobility, roaming, and co-existence issues with other wireless technologies [4; 5];
8. Support for data analytics, efficient spectral utilization, and reliability [5].
# Using a Deep Quantum Neural Network to Enhance the Fidelity of Quantum Convolutional Codes
## 2 Basic Concepts  
We hope to enhance the fidelity of quantum convolutional codes with a quantum neural network in order to ensure the reliabiliy of the code. In this section, we will introduce the concepts of quantum convolutional code and a quantum neural network.
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 1 Introduction  
In 2019, the U.S. horticulture sector reported a total of $13.8 billion in sales of plants and products [1]. The U.S. turfgrass industry maintains an estimated worth of $40-60 billion per year [2] and accounts for 50 million acres, making it the fourth largest crop and the largest irrigated crop in the U.S. [3]. horticultural plants and turfgrass are hosts to several different insect pests, including caterpillars, mole crickets, mites, and various fly and beetle species. Of these, scarab beetles (Coleoptera: Scarabaeidae) are arguably the most damaging [4, 5]. The majority of damage occurs from white grub (larval) feeding on the roots of plant hosts but adult defoliation can also cause economic damage and aesthetic damage on the foliage of over 300 species of plants. The most damaging endemic turf-feeding species in U.S. include: _Aphodius_ beetles (_Aphodius granularis_), black turfgrass _Attenius_ (_Attenius spretulus_), June beetle (_Phylophaga_ species), masked dater (_Cyclocphala_ species), false Japanese beetle (_Strigoderma arboricola_), and green June beetle (_Cotinis nitida_). Turf damage can also be caused by exotic species, such as Japanese beetle (_Popilla japonica_), oriental beetle (_Anomala orientalis_), European dater (_Amphimallon majalis_), and the Asiatic garden beetle (_Maladera castanea_) and tend to cause more damage than endemic species [4]. Of these species, masked chafers, _Attenius_, _Aphodius_ species, European dater, and oriental beetle are larval pests, while June beetle, false Japanese beetle, Japanese beetle, green June beetle, and the Asiatic garden beetle are pests at both larval and adult stages [6].
## 3 Dynamic Area Optimization Based on an NN Considering Intercell Relationships
### Utilizing CC to Learn Cell Relationships  
To account for the interactions among adjacent cells, we introduce Classifier Chain (CC) [32] into the NN learning process. The CC chains multiple classifiers to learn output relationships. Particularly from the second classifier onward, the estimates produced by previous classifiers are incrementally added to the input data, which effectively exploits the estimated information. This operation facilitates the consideration of relationships among outputs. The input for the \(m\)-th model (\(\mathbf{x}_{m}^{\text{CC}}\)) and all the beam directions predicted by the CC models (\(\mathbf{a}^{\text{CC}}\)) are expressed as follows:  
\[\mathbf{x}_{m}^{\text{CC}}=\left\{\begin{array}{ll}\left[\mathbf{x}_{m}^{\text{NN}},a _{1},a_{2},...,a_{m-1}\right],&\text{if }\ m>1\\ \mathbf{x}_{m}^{\text{NN}},&\text{otherwise}\end{array}\right. \tag{18}\]  
\[\mathbf{a}^{\text{CC}}=\left[a_{1},a_{2},...,a_{m-1},a_{m}\right], \tag{19}\]  
where \(a_{m}\) is the beam direction predicted by the \(m\)-th CC model. The loss function remains the same as the one used in the NN, as shown in Equation (17). This method enhances the accuracy of estimations by accounting for intercell relationships.  
Figure 6 illustrates the model structure when CC is incorporated into the horizontal beam directions across three cells. Here, the inputs are the user densities mentioned earlier. The CC captures the relationship between neighboring beams by adding the solutions to the following models. As depicted in Figure 6, the output for cell #2 is predicted based on its relationship to cell #1, while the output for cell #3 is predicted based on its relationship to cell #1 and cell #2.  
Figure 4: Neural network (NN) learning the solutions generated by GA.  
Figure 5: Challenge encountered in area optimization using individually trained NNs.
## 3 Results and Discussion
### Neural Network
The primary objective of these predictive models was to assess and contrast the predicted values of NOx emission factors established by the US EPA. Long Short-Term Memory Networks represent a particular kind of Recurrent Neural Network designed to tackle the issue of the vanishing gradient problem that plagues conventional Recurrent Neural Networks. The authors of [43] developed a long short-term memory (LSTM) neural network to improve and optimize the NOx emissions of a fossil fuel power plant based on a \"t-fired coal boiler\" running under a steady state. The authors claimed to achieve an improvement of approximately 5%. However, the researchers did not sufficiently discuss how the overfitting or hypermeter tuning issues, which are commonly encountered for LSTMs, are addressed. LSTMs may not be an ideal machine learning model where robust and time-efficient models are required (e.g., for real-time evaluation of carbon emissions in power plants), as they require long training times and are computationally complex.
## 3 Results and Discussion
### Coral Health Predictions Overview  
The primary goal of this work was to use protein signatures to predict whether a coral would later bleach under high temperatures. First, we used lab data exclusively to ensure that the AI could resolve differences in fragment health designations, and the corresponding PLS-based stepwise DA (Figure 4a) with a subset of only 5 of the 86 lab coral proteins that passed all QC [11] could differentiate coral biopsies of the three phenotypes with 100% accuracy. Even when withholding data from the AI, this simple model could accurately call the fragment health designation of laboratory tissue biopsies based on their protein signatures alone.  
However, differentiating a bleaching coral biopsy from a healthy one is computationally simple vs. building a robust predictive model for a property of a colony or genotype in which disparate phenotypes must be considered. The colony health designation predictions included all possible fragment health designations for each queried colony (Figure 2d,e);  
Figure 4: Progressive training of the AI with coral proteomic data. In the stepwise, discriminant analysis (SDA) canonical correlation plots, inner and outer ellipses represent 50 and 95% confidence, respectively, and for the latter, rays are shown only in Supplementary File S2. Note that for step 4, we have shown only one example of the “lab-field” approach in which lab-derived protein profiles were used to predict field coral bleaching susceptibility (“CHD-field”); models trained with field data exclusively are instead shown in Table 5. Note that in step 4, a third, intermediately bleaching-susceptible phenotype was considered as a model Y (vs. only bleaching-susceptible [BLS] vs. bleaching-resistant [BLR] as possible colony health designations [CHD] in the lab samples). AB = actively bleaching, HC = healthy control, HTA = high-temperature-acclimating, SLS = sub-lethally stressed.  
the AI then had to search for patterns through the \"noise\" associated with the significant differences in phenotypes that emerged due to temperature stress in the instance of the intermediate and bleaching-susceptible corals. This was less of an issue with the bleaching-resistant corals since, theoretically, their protein signatures would be more consistent across temperatures (Figure 2d). This is one reason why DA, MANOVA, and other descriptive approaches excel in _differentiating_ fragment health designations, but not in _predicting_ intrinsic properties of the colonies/genotypes from which these biopsies were taken. To resolve such complexity in a way that is useful for predicting the behavior of coral genotypes about which nothing is known, more advanced predictive modeling approaches are warranted. In the lab samples only, a neural network (Figure 4b and Table 3) could resolve colony health designations with 100% accuracy; regardless of the fragment health designation of the biopsy, the AI could accurately determine whether it was from a bleaching-resistant or bleaching-susceptible genotype (no intermediately bleaching-susceptible genotypes were identified in the lab study).
Digital pathology tools, able to create and store single-point annotations, have been on the field for a long time. It is safe to assume that there are many projects where such annotations were used to designate objects of interest. Generating a reference dataset is one of the main expenses in an image analysis (object detection, segmentation) project. It seems logical to explore the possibility of re-using them, in some cases, when creating the annotation from scratch is a costly solution, or data are scarce, similarly to this project. Based on the pair of reference and measurement results, each object can be classified into four classes: true positive (TP), true negative (TN), false positive (FP) and false negative (FN). This information is collected into a confusion matrix, and algorithm quality measures are calculated from that. This gives the framework of comparison for the algorithms examined. Image processing on fluorescent samples is quite different from analyzing brightfield samples. Fluorescent samples are generated in smaller quantities, caused by the cost of the technique itself. Digitization time is heavily dependent on the appliance and is mostly governed by the excitation time. Glass slide samples can take more than an hour, or even multiple hours to digitize. The data to measure are usually separated into single image channels, which simplifies the image processing task. In the case of this project, low exposure times and single channel demand resulted in a 5-10-min long digitization process. Sample thickness and light scatter in the sample makes the object boundaries harder to discern. Generally, the task of detecting/segmenting objects on fluorescent samples can be a less complex problem, that enables the usage of algorithms of simpler construction. This  
Figure 2: Schematic representation of the project this investigation is part of. The topic of this article is highlighted in blue. is fortunate, because more complex supervised deep learning AI models need considerably more annotated training data, and usually unsupervised methods need even more data. Achieving improvements in that direction seems to be an endeavor gradually more and more complicated. Transfer learning might be an option to consider, using available models and using and extending their training set with task-specific samples. In diagnostics, AI image analysis is less widespread/accepted now, but this is changing rapidly. Explainability and tractability are among the causes (through regulatory aspects). There are projects investigating better explainable segmentation algorithms [11], that try to deal with these aspects of modern AI. The algorithms we selected have the advantage of still being simple in concept and solving only the segmentation part of the problem, thus the risks involved remain controllable, while maintaining the equilibrium of cost and performance.
## 2 Pumping Intensity Distribution and Derivation of the Spatial Rate Equations
### The Simulation of the Pumping Intensity Distribution in the Side-Pumped Laser Module
The alteration of the rod diameter results in a notable shift in the intensity distribution of the pumping power, as depicted in Figure 3. The intensity distribution is uniform when the diameter of the rod is small. When the rod's diameter is large, the intensity distribution is uneven, with high intensity in the core and low power in the periphery. The intensity distribution even appears in a pentagram when the section diameter of the Nd:YAG rod is 5 mm or 6 mm, which can affect the spatial overlap efficiency and laser oscillated mode in the resonator [25; 26; 27]. We assume that the Gaussian distribution of pumping power is utilized to calculate the spatial overlap efficiency and subsequent calculations. Under such circumstances, the following calculated theoretical values will diverge significantly from the experimental values. Consequently, the subsequent analyses will fully account for our simulations of the pumping power distribution that are more representative of the actual situation. Figure 3: The LD side-pumped module’s intensity distribution of pumping power at various Nd:YAG rod diameters. The rod section diameter is 3 mm, 4 mm, 5 mm and 6 mm, respectively. Figure 2: Simulation of the pumping power distribution across the section of the Nd:YAG with a 5 mm side-pump module rod diameter.
## 2 System Model
### Transmitter and Receiver Model
When considering an FC structure with SPS, the \(\mathbf{F}_{\text{RF}}\) will be dense with all elements having unit amplitude. Nevertheless, if we consider the case of the AoSA architecture, the \(\mathbf{F}_{\text{RF}}\) elements also have unit amplitude, but the matrix structure is \[\mathbf{F}_{\text{RF}}=\text{blkdiag}\{\mathbf{f}^{RF,1},\dots,\mathbf{f}^{RF,N_{ RF}}\}=\left[ \begin{array}{ccc}\mathbf{f}^{RF,1}&\dots&0\\ \vdots&\ddots&\vdots\\ 0&\dots&\mathbf{f}^{RF,N_{RF}}\end{array} \right], \tag{2}\]  
with \(\mathbf{f}^{RF,j}=\left[f_{1}^{RF,j},\dots,f_{N_{RF}}^{RF,j}\right]\in\mathcal{ U}_{N_{\text{Iz}}/N_{RF,1}}\) for \(i=1,\dots N_{RF}\). In the case of the DAoSA architecture, the matrix is similar to the case of the AoSA but can have a number of non-null columns in each row of up to the maximum number of subarrays that can be connected to an RF chain (\(L_{\text{max}}\)). If we consider DPS-based architectures, the amplitude of the elements becomes less than or equal to 2. Figure 1: Transmitter and receiver structure of a multiuser OFDM mmWave/THz MIMO system with hybrid precoding for an InD scenario. Several schemes were proposed in the literature to calculate the matrices \(\mathbf{F}_{\text{RF}}\) and \(\mathbf{F}_{\text{BB}}\), such as [12; 29]. The approach suggested in [15] can calculate \(\mathbf{F}_{\text{RF}}\) and \(\mathbf{F}_{\text{BB}}\) by the approximation of the digital F for any of these four structures. The overall optimization problem can be then expressed as:  
\[\min_{\mathbf{F}_{\text{RF}},\mathbf{F}_{\text{BB}}}\sum_{k=1}^{ F}\left\|\mathbf{F}_{\text{opt}_{k}}-\mathbf{F}_{\text{RF}}\mathbf{F}_{\text{BB}_{k}} \right\|_{F}^{2} \tag{3}\] \[\text{subject to }\mathbf{F}_{\text{RF}}\in\mathcal{C}_{N_{tx} \times N_{RF}^{nz}}\] (4) \[\left\|\mathbf{F}_{\text{RF}}\mathbf{F}_{\text{BB}_{k}}\right\|_{F }^{2}=N_{u}N_{s}. \tag{5}\]  
In this formulation, the matrix \(\mathbf{F}_{\text{opt}_{k}}\) denotes the fully-digital precoder and Equation (5) enforces the transmitter's total power constraint and \(\mathcal{C}_{N_{tx}\times N_{RF}}\) is the set of feasible analog precoding matrices, which is defined according to the adopted RF architecture. In order to enforce \(\mathbf{F}_{\text{RF}}\mathbf{F}_{\text{BB}_{k,u}}\) to lie in the null space of \(\mathbf{H}_{k,u}\in\mathbb{C}^{(N_{u}-1)N_{rx}\times N_{rx}}\), we write the following restriction to the overall optimization problem expressed in (3) to (5):  
\[\mathbf{F}_{\text{RF}}\mathbf{F}_{\text{BB}_{k,u}}\in\mathcal{N} \left(\overline{\mathbf{H}}_{k,u}\right), \tag{6}\]  
with \(k=1,\ldots,F\) and \(u=1,\ldots,N_{u}\). \(\overline{\mathbf{H}}_{k,u}\) is a matrix corresponding to \(\mathbf{H}_{k}\) with the \(N_{rx}\) lines of user \(u\) removed, which we denote as \(\mathcal{N}\left(\overline{\mathbf{H}}_{k,u}\right)\). Other RF constraints can be directly integrated into the objective function of the optimization problem in order to cope with the different RF architectures.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 5 Battlefield Simulation Model
### Related Work on Battlefield Simulations for MANET
Whilst the scenario and tactics are realistic to a point, the simulation uses random waypoint and reference point group mobility models (which are incorrectly called \"radio propagation models\" in the paper). [PERSON] and [PERSON] (2011) [44] specifically focus on battlefield scenarios, with the suggestion that each soldier's weapon and vehicle has an embedded \"wireless card\" to provide node communications. Despite this focus, the model consists of a rectangular grid 1500 m \(\times\) 300 m with 50 nodes. Speeds are set to 1 ms\({}^{-1}\) or 25 ms\({}^{-1}\), presumably representing soldiers on foot and vehicles, although this is not stated. The actual mobility model utilised is not mentioned. [PERSON] and [PERSON] (2011) [179] state that a battlefield monitoring wireless sensor network is used in their research. They suggest four scenarios wherein none, one, or two different node types (all vehicular) are mobile, but there is no mention of the mobility pattern of these nodes. The use of robot sensor nodes, which can adjust their position to enable connectivity in an urban battlefield is discussed by [PERSON] et al. (2013) [180]. Their simulation uses a 1 km square, 100 randomly distributed nodes, and radio ranges of 200 m, 400 m and 800 m. There is no justification given for the world size. It is claimed that these radio ranges are based on 802.11 standard, which is contentious, because this standard requires a 300-m free space range, but a significant practical reduction in this range will occur in an urban environment. In February 2012, NATO releases a report on the use of modelling and simulation for their network-enabled capability (NATO, 2012, [181]), but it merely covers standard infrastructure networks with no mention of MANETs. Although not a battlefield scenario, [PERSON] and [PERSON] (2012) [182] present a realistic world and mobility model based on a building (a health centre) with observed people movement patterns (nodes) within the health centre. [PERSON] et al. (2013) present a set of three modelsthat is derived from published military tactics. Each of these models covers one specific military tactic rather than a complete modelling of a particular operation. A comparison with conventional mobility models (Random Waypoint (RWP), Reference Point Group Mobility (RPGM), and Manhattan) is provided, with the conclusion that these models do not represent realistic mobility patterns in a military scenario. To date, there is a limited number of published models based on genuine military tactics deployed for military operations in buildings or urban warfare. This paper presents a novel simulation based on published military tactics for an operation within a building.