esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Considerations on a Proposed Auction for Brazilian 6G  
Using the review carried out so far and the relationships observed with regard to the practices implemented by the countries that have historically performed above average, we can start structuring a hypothetical auction aimed at the Brazilian telecommunications scenario, allowing its regulator to achieve a better result through competition and mitigation of auction risks.  
This study examined a hypothetical auction structure in Brazil. The spectrum bands related to the 700 MHz spectrum are divided into regional licenses of 5 and 10 MHz, exclusively in pairs. We used homogeneous 5 MHz blocks as a mechanism to facilitate _price discovery_, diverging from the current structure, which consists of 5 and 10 MHz blocks, without loss of generality. Each block was worth one eligibility point. Players had to make deposits in advance with the auctioneer to guarantee the amount eligible for bidding, in accordance with the minimum price set for each block.  
The selected structure for modeling was the _Combinatorial Clock Auction_, proposed by Cramton, carried out in three phases, namely _clock_, _proxy_, and _assignment_.
# Experimental Study of Complex Coupled Deep Tillage Mechanism and Drag Reduction
## 2 Materials and Methods
### Field Experiments
#### 2.4.3 Experimental Plan  
Based on the literature and previous experimental conditions, an orthogonal experimental design was used, selecting air pressure, forward speed, and subsoiler width as the main influencing factors. The air pressure was set to range from 1.4 to 2.2 MPa [38], the working speed ranged from 3.31 to 7.32 km/h [39; 40; 41], and three types of deep tillage subsoilers with widths of 150 to 200 mm were used [42]. The factor level table is shown in Table 3: Three-factor, three-level experimental design.  
Referring to the criteria outlined in DG/T 026-2019, \"Deep Tillage Machines\", published by the Ministry of Agriculture of the People's Republic of China [43], the specific  
Figure 4: (**a**) Field experiment, (**b**) testing system, (**c**) subsoiler, (**d**) point gauge.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Level**} & \multicolumn{3}{c}{**Factor**} \\ \cline{2-4}  & **Air Pressure (MPa)** & **Working Speed (km/h)** & **Subsoiler Width (mm)** \\ \hline
1 & 1.4 & Low Speed 1: 3.31 & 150 \\
2 & 1.8 & High Speed 1: 3.83 & 175 \\
3 & 2.2 & Low Speed 2: 7.32 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Three-factor, three-level experimental design.  
drag ratio was selected as the evaluation index for complex coupled deep tillage operations. The formula is as follows [31]:  
\[SDF=\frac{F}{A} \tag{10}\]  
In the equations, the following hold:  
\(F\): Traction resistance, N;  
\(A\): Disturbed area, cm\({}^{2}\).  
From the drag ratio formula, it can be inferred that, as the traction resistance decreases and the disturbed area increases, the corresponding drag ratio (SDF) will also decrease. This indicates that a smaller SDF corresponds to a higher working efficiency of the deep tillage subsoiler.
## 3 Results and Discussion
### Antimicrobial Application on Cantaloupe Fruit  
Coating treatments on cantaloupe fruits were done according to the method of [PERSON] et al. [16]. Cantaloupes were evenly dipped into various coating solutions, distilled water, 1% chitosan, 1% nano silicon dioxide, and 1% nisin and chilled at 4 \({}^{\circ}\)C on the relative humidity of 70%. After incubation at the ambient conditions (20-25 \({}^{\circ}\)C) for up to nine days, the measurements have been detected every three days. An increase in the occurred microbial population may be due to the process of slicing and the temperature condition during storage [47]. Besides, high acidity and sugar in cantaloupe fruits can enhance the microbial attachment during the storage period [1,5,48]. To avoid the survival of the microorganisms, one should take preservation methods, coating assays, and storage conditions under significant consideration [49]. The findings of the current study presented that the population reductions of approximately 0.59, 0.56, 0.54, and 0.51 log CFU/g were detected after coating treatments with distilled water only, chitosan, chitosanan/nano/SiO\({}_{2}\), and chitosan/nano/SiO\({}_{2}\)/nisin, respectively, on day zero. Yeast and mold counts increased in all treatments as the storage period increased.  
Table 2 shows a lower quality of the cubes in control samples of the fresh-cut cantaloupe fruits and reported the highest yeast and mold counts (2.94 log CFU/g) on day nine of storage compared to the other coated samples throughout the whole storage. CH samples were a little effective in maintaining the yeast and mold counts below 2.67 log CFU/g on day nine of storage. The coating treatments with chitosan/nano/SiO\({}_{2}\), and chitosan/nano/SiO\({}_{2}\)/nisin both reduced the yeast and mold counts 2.49 and 1.92 log CFU/g, respectively, on day nine. [PERSON] et al. [50] achieved a similar reduction of microbial activity, especially in _Salmonella_ spp., by coating with chitosan and nisin. The current finding established that chitosan/nano/SiO\({}_{2}\)/nisin treatment would inactivate the microbial growth, thus prolonging the shelf-life of the treated samples for nine days longer relative to the uncoated fresh-cut cantaloupe fruits.  
Figure 7: Performance characterizes of thin novel films; (**a**)turbidity and polydispersity index; (**b**) zeta potential and contact angle; (**c**) partial size distribution.
# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 4 Proposed Method
### Squeeze-and-Excitation Block  
In complex electromagnetic environments, the distinguishing features between UWB base stations and tags are weak. Traditional methods struggle with CIR data for LoS/NLoS classification due to limited feature extraction, inadequate long-term dependency capture, and high reliance on feature engineering. Research in computer vision shows that attention mechanisms, such as SE attention [39], self-attention [40], and coordinate attention (CA) [41], enhance deep neural networks' feature learning. These mechanisms adaptively weight features based on their importance. In UWB NLoS recognition, sequential signal samples with data dimensions of \(1\times 1016\) are affected by hardware defects and temporal correlations, making feature importance assessment challenging. The SE attention mechanism selectively emphasizes crucial channel features while suppressing less useful ones, maintaining a low parameter count. Given the limited computing resources of IoT devices, the proposed lightweight model, 1D-CLANet, feeds the spatiotemporal CIR features extracted by the 1D-CNN and LSTM into the SE attention module (numChannels = 512, reductionRatio = 4) to enhance feature extraction and classification accuracy.  
Figure 7 shows the details of the SE attention block. In the squeeze operation, global average pooling is applied to the channel dimensions of the feature map \(\mathbf{H}_{\text{lstm}}~{}=~{}[h_{1},h_{2},\dots,h_{t},h_{T}]\) from  
Figure 6: The architecture of a LSTM cell.  
where \(\alpha\) is a balancing factor, \(\gamma\) is a focusing parameter, and \(\hat{y}_{iy}\) is the predicted probability for class _iy_.  
The hyperparameter settings used in the training process of the 1D-CLANet model are shown in Table 4. During training, the Adam algorithm is used to optimize the parameters of each layer of the 1D-CLANet network. The batch size is set to 32, the initial learning rate is set to 0.0001, and the total number of training epochs is 20. To prevent overfitting and improve generalization ability, we apply L2 regularization, learning rate decay, and dropout during the training process.
# Precipitation Polymerization: A Powerful Tool for Preparation of Uniform Polymer Particles
## 2 Categories of Precipitation Polymerization
### Photoinitiated Precipitation Polymerization  
Lime and Irgum prepared a series of highly cross-linked micrometer-sized polymer particles made from DVB, alone or copolymerized with 2,3-epoxyproply methacrylate or styrene by irradiation from a 150 W short arc xenon lamp with AlBN as the initiator, and namely photoinitiated precipitation polymerization (PPP) [33; 34]. By using photoinitian instead of thermal initiation, it was possible to avoid coagulation and arrive at spherical particles with an exceptionally high monodispersity for particles of this size range (polydispersity index < 1.02) and with monomer loadings well above 5 % [85; 86; 87]. Moreover, the PPP method can be carried out at low temperatures with corresponding lower tendencies of aggregate formation and also allows the polymerization temperature and initiation rate to be varied independently [33]. [PERSON] et al. summarized the main cross-linkers and monofunctional monomers used in PPP, as well as the polymerization mechanism that a transient polymer surface gel layer plays a key role in the colloidal stabilization of particles by a so-called \"auto-steric\" stabilization process [88].  
[PERSON] first reported the combination of type II photoinitiation with PP for the synthesis of highly crosslinked and spherical PDVB particles with click functionality by using a benzophenone-tertiary amine initiation system [41]. [PERSON] et al. prepared the composite of acrylamide on the surface of nano TiO\({}_{2}\) under ultraviolet light by PPP, providing a facile strategy for the synthesis of organic-inorganic hybrid flocculants [89]. [PERSON] et al. prepared a new absorbent triethylene tetramine-modified crosslinked polyacrylamide-lonitrile in water at room temperature using FeC\({}_{2}\)O\({}_{4}\) as the photoinitiator and MBA\({}_{\rm m}\) as a  
Figure 4: The synthetic scheme of the iron nanozymes based on poly(\(N\)-vinyl caprolactam) (PVCL). Reprinted with permission from Ref. [76]. Copyright 2020 Royal Society of Chemistry.  
crosslinking agent, which could be used as an effective adsorbent for Cu(II) ions removal from wastewater [90].
# Biomass and Cellulose Dissolution--The Important Issue in Renewable Materials Treatment
## 2 General Characteristics of Lignocellulosic Biomass
### Lignin  
L Lignin is an amorphous heteropolymer with both aliphatic and aromatic fragments. The main precursors are monolignols: \(p\)-counaryl, coniferyl, and sinapyl alcohols (Figure 7) [39]. Depending on the source, different contents of particular precursors can be found in the lignin structure, e.g., softwood lignin contains mainly coniferyl alcohol, while in hardwood, coniferyl alcohol and sinapyl alcohol predominate. Grass lignin also contains \(p\)-counaryl alcohol. The monomers are linked by ether bonds C-O-C and C-C bonds. In biomass, lignin forms a matrix with polysaccharides, which increases the physical strength of tissue and makes it more resistant to chemical and enzymatic transformations [26; 32; 40; 41].  
Natural lignin is insoluble in water and organic solvents. A major part of it is also insoluble in acid solutions (Klason's lignin) and shows better solubility in alkaline solutions [42]. The lignin solubility can be changed via processing or chemical modification, e.g., in the Kraft process, the majority of hydroxyl groups are sulfonated, which makes it more susceptible to solubilization, even in organic solvents such as DMSO [43; 44]. Because of this, delignification is one of the main methods of lignocellulosic pretreatment. This process allows one to loosen the rigid structure of biomass and make the cellulose and hemicelluloses more accessible for chemicals and enzymes [45; 46].
# Fabric Defect Detection Based on Improved Lightweight YOLOv8n
## 2 Models and Methods  
### YOLOv8 Algorithm  
Ultralytes released YOLOv8 in early 2023, which boasts higher detection accuracy and fewer parameters compared to other single-stage detection networks in the YOLO series, such as YOLOv5 and YOLOv7. The YOLOv8 model draws on the design advantages of models like YOLOv5 and YOLOv6, offering a new state-of-the-art (SOTA) model. It is divided into five different scales--n, s, l, m, and x--to meet the requirements of various deployment platforms and application scenarios, achieving improvements in both detection accuracy and speed. Considering the characteristics of these models and to ensure the algorithm's real-time performance while accounting for the number of model parameters, this study selects the YOLOv8n version.  
The YOLOv8 network utilizes mosaic data augmentation at the input stage to enhance the network's feature extraction capability. The epoch parameter can be set to stop data augmentation to ensure model accuracy. The kernel size of the initial convolution layer has been changed from 6 \(\times\) 6 to 3 \(\times\) 3, improving computational efficiency without sacrificing accuracy. The backbone network consists of Conv, C2f, BN, and SPPF modules. The C2f module, designed based on the ELAN structure in YOLOv7, provides a richer gradient flow, enhancing the model's feature representation capability while lightweighting the model [13]. The feature fusion layer (Neck) employs an FPN-PAN structure designed based on PANet (Path Aggregation Network) [14], using upsampling and convolution layers to achieve multi-level feature fusion, strengthening the network's ability to fuse multi-scale features. The detection head (Head) uses a decoupled head structure to extract target category and location features, with an anchor-free mechanism applied to reduce regression time.
## 3 Synthetic Test
### Time-lapse Data Synthesis  
Comparing independent inversion results may be challenging due to variations in noise levels and fitting degrees. Particularly, significant contamination of the data with noise at specific instances can result in inaccurate monitoring outcomes. We introduced noise of comparable magnitude to simulate smooth monitoring, incorporate elevated noise levels to replicate noise pollution data, and simulate monitoring failures. We performed numerous tests using synthetic data to assess the algorithm's stability, its capability to address the challenge of comparing monitoring results, the reliability of pollution data, and the influence of inversion parameters on the outcomes.  
Figure 5 presents the synthetic data used for the time-lapse model and algorithm testing, with the black dots indicating the positions of the observation sites. Time-lapse Model 1t, Time-lapse Model 2t, and Time-lapse Model 3t represent the slices of the forward responses (Ex = 16 Hz) at three different time steps for the time-lapse model. Gaussian noise at levels of 2%, 8%, and 5% was added to the forward responses of the three time steps for the time-lapse model at 18 frequencies. Two different time-lapse inversions, Test 1 and Test 2, were conducted with different inversion parameters to discuss the time constraint effect of the time-lapse term. The noise level of Time-lapse Model 2t was increased to 50% for Test 3, addressing the anti-noise effect of the time-lapse inversion.  
Figure 5: Changes in the forward response of the time-lapse model electric field and noise data (Ex = 16 Hz).
## 3 Results and Discussion
### Tensile Stress-Strain Curves  
To determine the mechanical properties of the prepared P(BHMP-AM)-Zn\({}^{2+}\) hydrogel, the tensile stress-strain curves were recorded for both the P(BHMP-AM)-Zn\({}^{2+}\) and PAM hydrogels. As shown in Figure 4a, when the tensile stress exceeded 150 kPa for the P(BHMP-AM)-Zn\({}^{2+}\) hydrogel, the tensile strain reached 2000%, which was significantly improved compared to that of PAM. This was attributed to the fact that additional hydrogen bonds and metal coordination interactions formed in the hydrogel after the addition of BHMP and Zn\({}^{2+}\), which greatly increased the crosslinking density of the hydrogel and enhanced its mechanical properties. The compressive stress-strain curve of the P(BHMP-AM)-Zn\({}^{2+}\) hydrogel is shown in Figure 4b, wherein it can be seen that when the compressive stress exceeded 17 MPa, the compressive deformation rate was 85%. Cyclic tensile tests were also performed on the P(BHMP-AM)-Zn\({}^{2+}\) hydrogel to evaluate its durability and recovery. As presented in Figure 4c, it was found that at a fixed strain of 400%, the mechanical strength of hydrogels after ten loading/unloading cyclic tensile tests did not vary much.
## 5 Numerical Results
### Application in Distribution Networks  
This section presents the validation of the proposed optimization methodology in two radial distribution test feeders composed of 33 and 69 nodes to observe its efficiency when the solution space increases significantly. To calculate the dimension size of the solution space, the following formula can be used [46]:  
\[C_{a,b}=\begin{pmatrix}a\\ b\end{pmatrix}=\frac{b!}{a!(b-a)!}\]  
where \(a\) represents the number of shunt devices to be installed and \(b\) corresponds to the number of nodes of the network, excluding the slack source. This implies that for the IEEE 33-bus system with three STATCOMs, the dimension of the solution space is \(C_{32,3}=4096\), while, for the IEEE 69-bus system, the dimension of the solution space is \(C_{68,3}=50\),116.
## 10 Conclusions and Future Perspectives  
A novel, real-time GNSS positioning estimator is presented in this paper for land vehicles in challenging environments. The proposed estimation procedure is based on robust MM estimation theory. The presence of outlier observations in GNSS measurements significantly affect the positioning results. This method is capable of approximating the user position with accuracy and reliability in the presence of outlying observations by using the most trusted observations for position estimation and excluding outliers. The proposed algorithm is compared against LSE and WLSE algorithms in terms of 90% positioning accuracy. Extensive field and simulation tests are carried out to analyze the performance of the proposed scheme in diverse environments. Results demonstrate that the proposed scheme could be a promising scheme for GNSS-based positioning with high accuracy under diverse conditions, especially in harsh and challenging environments, because LSE and WLSE are also capable to provide similar accuracy in normal outlying free environments, however they are not able to maintain the accuracy in challenging environments. For future extensions, we plan to explore the applications of the algorithm in more complex GNSS-based positioning architectures such as vector-based GNSS tracking receivers and fusion of GNSS measurements with other sensors such as IMU (Inertial Measurement Unit) and vision sensors to further improve the positioning accuracy. We also want to explore the application of the proposed estimation scheme (especially therobust initialization stage) for integration with the EKF (Extended Kalman Filter) in order to enhance the filter performance.  
[PERSON] help out in resolving the research direction; [PERSON] and [PERSON] designed the experiments; [PERSON] facilitated the analysis and experimental tools; [PERSON] and [PERSON] helped to perform and log the experimental data; [PERSON] analyzed the test data; [PERSON] wrote the paper.  
The authors wish to express their gratitude for the support of Prof. [PERSON] and BATC (Brain-inspired Application Technology Center) in providing facilities and equipment to complete this research.  
The authors declare no conflict of interest.
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
## 1 Introduction
* We evaluate the feasibility and usability of different quantum immune key exchange algorithms (physical and cryptographic) for common IoT hardware platforms Raspberry Pi2 and Raspberry Pi3. * We analyse the energy efficiency of NewHope, Frodo, and the implemented physical layer security scheme. Our analysis illustrates the energy efficiency benefits for physical layer security research area. * We study the suitability of simulation-based evaluation for security algorithms. The proposed approach is a quick and flexible method for design-time energy efficiency evaluation of a large amount of solutions on various platforms. * Based on the observations, we provide guidelines for implementing energy-efficient security architectures and algorithms for quantum immune IoT. The rest of the article is organised as follows: In Section 2, we describe the quantum threat and present two threat models, encompassing issues relevant both for physical and cryptographic security. Section 3 describes prominent solutions for quantum immune key exchange: lattice-based cryptosystems and physical layer secrecy coding. We also analyse these algorithms from the energy-efficiency perspective. Section 4 reviews related work on simulation-based energy efficiency analysis. In Section 5, we describe our evaluation approach to energy efficiency evaluations. In Section 6, we present the obtained results. Section 7 discusses our main observations on the evaluation approach as well as on the evaluated quantum immune security approaches. The section also provides guidelines for security protocol implementations. Conclusions as well as future research topics are presented in Section 8.
# Dynamic Optimization Using Local Collocation Methods and Improved Multiresolution Technique
## 3 Improved Multiresolution Technique
### Slope Analysis of Control Variable  
As an example, Figure 2 shows a bang-bang control solution solved by the MRT [25], which refines the mesh using the data compression algorithm based on dyadic meshes. The method started with a uniform initial mesh consisting of \(2^{3}+1\) points and terminated with a nonuniform final mesh consisting of \(30\) points. Although the method generated only \(30\) mesh points, there are still some redundant mesh points in the solution. As shown in Figure 2, the control profile can be captured accurately by the nine initial mesh points and the two mesh points that are adjacent to the bang-bang switching location. These theoretical minimum required mesh points are denoted by the symbol \({}^{*}\) in Figure 2. In other words, most of the other 11 mesh points are redundant and thus can be removed to further improve the computational efficiency of the MRT.  
The redundant mesh points in Figure 2 can be removed by analyzing the slopes of the control variable. The left slope of the control variable at the mesh point \(\tau_{k}\), \(k=1,2,\ldots,N_{\tau}-1\), is  
\[s_{k}^{-}=\frac{u_{k}-u_{k-1}}{\tau_{k}-\tau_{k-1}}, \tag{14}\]  
where \(u_{k}\) and \(u_{k-1}\) are the control values at the mesh points \(\tau_{k}\) and \(\tau_{k-1}\), respectively.  
The right slope of the control variable at the mesh point \(\tau_{k}\), \(k=1,2,\ldots,N_{\tau}-1\), is  
\[s_{k}^{+}=\frac{u_{k+1}-u_{k}}{\tau_{k+1}-\tau_{k}}. \tag{15}\]  
If both the left slope and the right slope at a mesh point are zero, then the mesh point is determined as a redundant point and should be removed. The slope analysis is repeated for all the mesh points to remove the redundant ones. It should be noted that the initial mesh points should not be removed. Additionally, the mesh points of the highest resolution in the mesh should also be kept in the mesh to robustly capture the nonsmooth changes in the control profile.  
Figure 1: Example of a generalized dyadic mesh.  
Figure 2: Control solution from a multiresolution technique (30 mesh points).  
The abovementioned slope analysis procedure is responsible for removing the redundant mesh points generated by the MRTs of [25; 26; 27], although it can only remove the redundant mesh points in the constant control regions. This function is useful because it is very common to have some constant control regions in the control profile(s), especially for the constrained dynamical optimization problems. It is also useful but more challenging to remove the redundant mesh points in the non-constant control regions, which is beyond the scope of the present paper.