esa-sceva/satcom-chunk-collection huggingface dataset:
# Strontium Ferromolybdate-Based Magnetic Tunnel Junctions
## 2 Theoretical Considerations
### Interface Dead Layer  
The magnetic properties of ultrafine granular systems are size-dependent. FM properties appear when the layer thickness exceeds a critical value denoted as a dead layer (DL). For instance, a drastic drop in the magnetization, when the particle size of La\({}_{0.8}\)Sr\({}_{0.2}\)MnO\({}_{3.\delta}\) nanoparticles decreases to 8 nm, indicates the formation of a DL at the surface [31]. The thickness of this DL was found to have a logarithmic dependence on the particle size. In La\({}_{0.7}\)Sr\({}_{0.3}\)MnO\({}_{3}\)/depleted manganite/MnSi/SiO\({}_{2}\) structures, DL formation at the upper interface is avoided by using the depleted manganite layer as a tunneling barrier [8]. A similar DL was obtained in metallic Fe [32] and Ni films [33], both deposited by electroplating onto Cu, Ag or Au substrates. The existence of such DLs at \(T=0\) was attributed to a transfer of electrons from the \(s\)-band to the \(d\)-band in the neighborhood of a surface, thus affecting the magnetic moment. A special form of a dead layer is a magnetically disordered layer.  
The magnetization averaged over the film thickness in the presence of a DL amounts to [33]  
\[M=M_{bulk}\left(1-\frac{\delta}{d}\right), \tag{11}\]  
where \(\delta\) is the DL thickness and \(d\) is the film thickness. In [34], the DL at the surface of Fe nanoparticles was assigned to a 1 to 2 nm thick coating with Fe oxides. DLs related to a distorted structure near the surface were also found in thin films of yttrium-iron garnet [35] and DyTiO\({}_{3}\)[36], both deposited by PLD. In the latter case, the DL with a thickness of 4-5 nm was located at the film surface where magnetic Ti\({}^{3+}\) ions are replaced by nonmagnetic Ti\({}^{4+}\) ions, leading to a paramagnetic response. In the case of FM La\({}_{0.7}\)Sr\({}_{0.3}\)MnO\({}_{3}\) (LSMO) thin films grown by PLD, interfacial DLs were formed due to phase separation [37]. They had a thickness of 3-5 nm, 3-5 nm, and 5-8 nm for films grown on STO (001), NdGaO\({}_{3}\) (110) and LaAlO\({}_{3}\) (001) substrates, respectively. On the other hand, LSMO films deposited by PLD onto STO (001) with thicknesses ranging from 3 to 70 unit cells exhibited a reduction of the saturation magnetization below a thickness of 4.8 nm, a thickness of a nonmetallic interface layer of 3.2 nm, and vanishing of ferromagnetism below 1.2 nm [38]. The difference in the critical thicknesses to produce ferromagnetism and metallicity was explained by phase separation into FM/metallic and non-FM/nonmetallic regions with a loss of percolation at a critical thickness. A recent study of PLD deposited LSMO thin films revealed a dependence of the \(\delta\) parameter on the oxygen pressure \(p_{\text{O2}}\) during film growth, with \(\delta\) ranging from 2.4 nm for 0.05 Pa to 1.1 nm for 26.6 Pa [39].
# Performance and Scalability Analysis of SDN-Based Large-Scale Wi-Fi Networks
###### Abstract  
The Software-Defined Networking (SDN) paradigm is one that is utilized frequently in data centers. Software-Defined Wireless Networking, often known as SDWN, refers to an environment in which concepts from SDN are implemented in wireless networks. The SDWN is struggling with challenges of scalability and performance as a result of the growing number of wireless networks in its coverage area. It is thought that SDN techniques, such as Mininet-Wi-Fi and Ryu Controller for wireless networks, can overcome the problems with scalability and performance. Existing Wi-Fi systems do not provide SDN execution to end clients, which is one reason why the capability of Wi-Fi is restricted on SDN architecture. Within the scope of this study, we analyzed Wi-Fi networks operating on SDN using the Transmission Control Protocol (TCP) and User Datagram Protocol (UDP). By utilizing a testbed consisting of Ryu Controller and Mininet-Wi-Fi, we were able to test Wi-Fi over SDN and evaluate its performance in addition to its scalability. When evaluating the performance of a network, we take into account a number of different metrics, such as bandwidth, round-trip time, and jitter. In order to assess the level of performance, the SDN-based Wi-Fi controller Ryu is linked to an increasing number of access points (1, 2, 3, and 4) as well as stations (10, 30, 50, and 100). The experimental findings obtained using Mininet-Wi-Fi indicate the scalability and dependability of the network performance provided by the SDN Wi-Fi network controller Ryu in an SDN environment. In addition, the round-trip time for TCP packets grows proportionally with the number of hops involved. A single access point is capable of simultaneously supporting up to fifty people at once.
# FTGM: Fast Transformer-Based Global Matching for Particle Image Velocimetry
## 4 Experiments
### Efficiency  
The FTGM series of models has demonstrated not only improved performance but also reduced computational overhead. As shown in Table 4, the parameters of our models are 4.68 M and 4.72 M, which is a reduction of 0.7 M (10%) and 0.6 M (11%) compared to RAFT256-PIV (5.3 M). We tested the inference time of different models on a single NVIDIA RTX 3080 GPU 16 GB for images sized 256 \(\times\) 256, and the average time spent on a single pair of images decreased from 66 ms for RAFT256 to 30 ms (a 58.5% improvement), and with some performance trade-offs, the inference time can be further reduced to 14 ms. Additionally, we tested the inference time for different modules; our global matching operation is very low (0.35 ms, 2.5% and 4.5 ms, 15%), and even the feature enhancement component, which is the most time-consuming (Transformer blocks), takes significantly less time than RAFT's 16 iterations (56 ms). When training on an NVIDIA A100 80 GB card with image sizes of 256 \(\times\) 256 and a batch size of 12 and RAFT iterations of 16, FTGM requires slightly less GPU memory than the RAFT network (7.11 G vs. 7.16 G), yet it achieves performance surpassing RAFT; the network with one refinement requires 20.62 G of memory, mainly due to the quadratic increase in Transformer structure with the size of the input feature sequence. However, the performance improvement gained from one refinement is significant, and its inference performance remains attractive.
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 4 Applications with Numerical Examples  
The proposed technique has been used for the study of a couple of rather academic numerical examples and their solutions have been implemented in a Java open source framework for calculations [39]. The first one is related to an one-dimensional beam. A two dimensional frame structure, which is a common structure in various applications, is  
Figure 3: Spatial distribution of energy densities (J/m) from the backward step of time reversal at the time step corresponding to original source excitation. Here, the time reversal (TR) approach is for the full boundary conditions switch described in Section 2.2.2.  
Figure 2: Spatial distribution of energies densities (J/m) from the backward step of switching time reversal (Section 2.2.1) at the time step corresponding to the original source excitation. Plot (**a**) is by considering a receiver at about 0.2 \(L\) while plot (**b**) corresponds to similar results adding another receiver at the location about 0.3 \(L\).  
studied in the second example. It should be emphasized that the present feasibility study considers academic examples including the material properties, which do not represent specific real structures or materials. In particular, a standard approach in acoustics considering unit values for both the elasticity modulus and density is followed here, resulting in unit wave propagation velocity for axial waves.
# An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution
## 2 Materials and Methods
### Mathematical Model of GNSS Positioning
\, \tag{4}\]  
where \(\hat{a}\) and \(\hat{b}\) denote the ambiguity and baseline vector parameters of the real-valued resolution, respectively; and \(\hat{Q}_{\hat{a}\hat{a}}\) and \(\hat{Q}_{\hat{b}\hat{b}}\) are the corresponding VC matrix of \(\hat{a}\) and \(\hat{b}\), respectively. In the second step, we use the estimated float AR to compute the corresponding integer resolution. Numerous methods, such as the popular LAMBDA algorithm, can be adopted in this step. The Z-transformation is usually conducted for ambiguity decorrelation given the strong correlation between ambiguity parameters [24; 25]. \[ \begin{array}{l}\hat{Z}=T\hat{a}\\ Q_{zz}^{zz}=TQ_{\hat{a}\hat{b}}T^{T}\end{array} \, \tag{5}\]where \(T\) denotes the nonsingular transformation matrix; \(\hat{Z}\) is the ambiguity parameter vectors after decorrelation; and \(Q_{\pm a}\) is the corresponding VC matrix with \(Z\)-transformation for \(Q_{\pm a}\). Then, we can search for the optimal integer ambiguity parameters (\(\overline{Z}\)) by applying a certain method, such as LS or PSO. The search criteria can be described as  
\[f(\overline{Z})=(\hat{Z}-\overline{Z})^{T}Q_{\pm a}^{-1}(\hat{Z}-\overline{Z}) =\text{min}. \tag{6}\]  
Through inverse transformation, we can obtain the fixed ambiguity parameter resolution. \[\overline{a}=T^{-1}\overline{Z}. \tag{7}\]  
In the third step, the remaining real-valued parameters solved in the first step are updated by using the fixed integer parameters:  
\[\overline{b}=\hat{b}-Q_{\hat{b}\hat{a}}Q_{\pm a}(\hat{a}-\overline{a}). \tag{8}\]
## 4 Results and Discussion
### Open-Ended User Responses Analysis  
Responses about familiarity with obfuscation techniques revealed that over 85% of participants had limited knowledge of these methods. Nonetheless, some participants mentioned specific techniques like inpainting [36]--a common approach in visual data obfuscation--indicating recognition of its applications. One participant noted image swirling as an obfuscation method, while privacy-preserving photo sharing (P3) [37] and techniques like halftone and image quantization were also mentioned, reflecting some awareness of privacy-focused approaches among participants.  
The analysis of open-ended responses about barriers to the adoption of visual data obfuscation techniques revealed various perceived obstacles. Respondents noted technical and implementation challenges, usability concerns, and security issues. For example, some highlighted the \"technical complexity\" required to obfuscate visual data accurately without losing precision or causing high computational costs, which could impact device performance.  
Usability was another concern. Participants expressed reluctance toward functionalities that add complexity to image-sharing processes, suggesting \"convenience\" as an essential factor. Some suggested integrating optional obfuscation features within social media and photo-sharing platforms to reduce friction and improve user experience.  
Finally, concerns about the effectiveness and security of obfuscation methods were raised, with some participants worried about the potential for reversed obfuscation techniques. This emphasizes that trust in the security of these methods is crucial for widespread adoption.
## 2 Background and Related Technologies
### LoRa  
LoRa (Long Range) is an unlicensed LPWAN communication technology developed by Semtech, utilizing a proprietary Chirp Spread Spectrum (CSS) modulation scheme. This modulation technique enables long-range communication with minimal power consumption, making it particularly effective for large-scale IoT networks. The LoRaWAN protocol further enhances LoRa's efficiency by adopting a star topology, where each nodedirectly transmits data to a gateway. The gateway, powered by a direct energy source, connects to the core network infrastructure and can simultaneously receive and decode up to 50 concurrent transmissions, ensuring efficient large-scale deployments [13]. (The LoRaWAN protocol stack is defined in the official specification (v1.0.4) issued by the LoRa Alliance [14]).  
Field-scale measurements report an average latency of 2-6 s, an energy consumption of \(\approx\)0.993 mJ bit\({}^{-1}\), and \(\approx\)1200 simultaneous connections per 125 kHz channel for LoRaWAN nodes [15].  
LoRa's transmission characteristics are defined by several key parameters. It operates in the sub-1 GHz ISM (Industrial, Scientific, and Medical) band, with frequency allocations varying by region, such as 868 MHz for Europe, 915 MHz for the US, and 923 MHz for Asia. The spreading factor (SF) ranges from SF7 to SF12, controlling the ratio between the symbol rate and chip rate, thereby balancing sensitivity and the data rate. A higher SF increases sensitivity and allows for longer transmission ranges but reduces the data rate, while a lower SF improves the data rate at the cost of reduced sensitivity. The bandwidth (BW) parameter defines the frequency range used for data transmission. A wider bandwidth results in faster data rates and shorter transmission times but reduces receiver sensitivity, whereas a narrower bandwidth increases sensitivity while limiting the data rate. Finally, the coding rate (CR) determines the level of forward error correction applied during transmission, typically set between 4/5 and 4/8, balancing error resilience and data throughput.  
These flexible transmission parameters allow LoRa to be adapted for a wide range of deployment scenarios, providing robust data transmission and efficient spectrum utilization across diverse environments.
# Topological Phase Diagram of BiTeX-Graphene Hybrid Structures
## 2 Results
In the case of the Te terminated BiTeBr and BiTeCl, the work function is around 4.7-4.5 eV as it was calculated by [PERSON] et al. [24]. This is close to the theoretically calculated value for graphene (4.6 eV [PERSON] et al. [25]). The work function of Te terminated BiTeI is larger by almost 0.5 eV. The altered Dirac cones of graphene can be distinguished in the band structure of the hybrid systems because the cones are placed in the band gap of the BiTeX systems due to the similar work functions. As we stated in our previous work, the hybrid structure consisting graphene and X terminated BiTeX does not provide this nature [22], which is in agreement with the significantly larger work function of X terminated BiTeCl and BiTeBr (6.2-6.0 eV [24]). In the case of the BiTeI-graphene structure the altered Dirac cones are placed closer to the occupied bands of BiTeI due to the larger value of the work function. As we apply pressure those bands populate the band gap at a higher rate as in the case of BiTeBr or BiTeCl, resulting in a metallic phase. Based on the presented results we conclude that both type of mechanical distortions have a striking effect on the band gap, however they favor different topological phases. Out-of-plane strain opens a trivial band gap, while in-plane strain drives the system into the topological phase. We would like to point out that the largest compressive strain we applied during our calculations would correspond to a nominal pressure of 20 GPa. We estimated this value as the derivation of the total energy per unit area over the reduced distance. In modern experimental setups these mechanical stresses can be routinely achieved [26; 27; 28; 29; 30]. Figure 1: Top (**a**) and side (**b**) view of the BiTeX–graphene (X = I, Br, Cl) heterostructure. The black dashed line indicates the unit cell (color online).
# An Efficient Imaging Method for Medium-Earth-Orbit Multichannel SAR-GMTI Systems
## 6 Discussion  
In Section 5, the results show that the proposed imaging method can correct the RCMs of the five targets simultaneously, and thus the static target can be finely focused and the moving targets can be focused with defocusing only in the azimuth direction. Moreover, the results presented in Table 3 show the measured azimuth and range displacements in the SAR image and ATI phases. It is seen that the measured values are very close to the theoretical ones and thus verify the theoretical analysis of the target's properties in the SAR image. In this section, we will further present discussions on the computational complexity of the proposed imaging method, moving target refocusing, and link-budget determination.
## 4 Implementation
### Modelling DDoS Attack Traffic  
The approach used for the simulation of the traffic generated by the DDoS attacks is described in the present subsection. One C&C server \(h_{c}\) is chosen randomly out of set H (\(h_{c}\in H\)) to initiate out the DDoS attack. Host \(h_{c}\) controls a bot-net B, that consists of multiple infected IoT devices belonging to set D, (\(\forall b_{i}\in B,b_{i}\in D\)). Additionally a single victim host \(h_{v}\) is randomly selected out of set H (\(h_{v}\in H\)). All devices belonging to B, receive commands from host \(h_{c}\), and consequently they send chunks of traffic to host \(h_{v}\), which eventually sum up to unbearable traffic rates for the victim host \(h_{v}\).  
The traffic flows that are produced in the simulation are two folded. Short (few packets) connections between host \(h_{c}\) and hosts \(b_{i}\in B\) are established in the start of the DDoS attack to send the required commands to the bots. Larger continuous traffic flows from hosts \(b_{i}\in B\) to the victim host \(h_{v}\) are then generated and correspond to the actual traffic of the attack. Those start early in the attack time frame, while they last until the execution of the attack is completed or interrupted. The volume of packets of the flow from a specific host's \(b_{i}\in B\) to the victim host \(h_{v}\) is relatively low. These flows have as common destination the victim host \(h_{v}\) and eventually sum up to a large volume of traffic that concurrently reaches to the victim and the latter becomes unable to serve.
# Broadband DOA Estimation by Exploiting DFT Extrapolation
###### Abstract  
This study proposes broadband direction (DOA) estimation through discrete Fourier transform (DFT) extrapolation. We used DFT extrapolation in the lower band and extended the sampled data to reduce the beam width in the spectral domain and improved the accuracy of the estimated DOA. The sampled data with a length of 12 were extrapolated to 36 by the addition of 12-element virtual arrays to 12 real arrays on both sides. The average RMSEs of the estimated DOAs were measured throughout the wide frequency band. To verify the validity of the proposed algorithm, we demonstrated that the RMSE obtained from the broadband DOA estimation for multiple signals of interest (SOIs) was reduced in the extrapolated array. It was demonstrated that the proposed algorithm can broaden the frequency band at which a fixed number of array can estimate the DOA accurately.  
DOA estimation; broadband beamformer; DFT extrapolation +
Footnote †: journal: Medical Imaging  
1
Footnote 1: email: _Applied_  
Footnote †: journal: Medical Imaging
# MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model
## 1 Introduction
A multi-label classification sigmoid function is designed to independently output a probability for each category, with each category's output being independent, making it highly suitable for multi-label classification tasks. The paper makes significant contributions in the following areas:  
1. Through meticulous analysis and comparison of existing remote sensing image classification methods, it adopts a multispectral multi-label classification strategy to efficiently extract features from high-resolution remote sensing images;
2. It proposes a novel dual-number residual structure and multi-label classification module that can better learn and capture the details and semantic information of the input images, enabling the network to better adapt to the complex task of classifying remote sensing imagery applications;
3. It introduces a multispectral stacking module that effectively integrates information from bands of varying resolutions, thereby enriching the surface information available. Based on these contributions, the paper presents MMDL-Net, a powerful model aimed at effectively solving the task of application classification for high-resolution remote sensing imagery. The organization of the work is as follows: Section 2 provides an overview of the structure of the MMDL-Net model and introduces the MMDL-Net model along with all its internal components. Section 3 describes the experimental setup and the analysis of results. Discussions are presented in Section 4. Conclusions are drawn in Section 5.
## 3 Bioactivity of _Chlorella vulgaris_ Compounds
### Anti-Inflammatory Effects  
_C. vulgaris_ exhibits significant anti-inflammatory properties due to its bioactive compounds, particularly carotenoids and polysaccharides. These compounds help suppress inflammatory pathways by reducing the production of pro-inflammatory cytokines such as Tumor Necrosis Factor-alpha (TNF-\(\alpha\)) and Interleukin-6 (IL-6) while also inhibiting the activity of inflammatory enzymes like Cyclooxygenase-2 (COX-2). Studies have shown that extracts from _C. vulgaris_ can downregulate the expression of these cytokines in various cell models, suggesting its potential for therapeutic application in inflammatory diseases [65; 66]. Additionally, the bioactive compounds in _C. vulgaris_ can inhibit COX-2 expression and activity, thereby reducing the synthesis of inflammatory mediators like prostaglandandins [20; 65; 67; 68], making _C. vulgaris_ beneficial in managing chronic inflammatory conditions such as arthritis and cardiovascular diseases. The COX pathway represents a pivotal enzymatic cascade in the regulation of inflammatory processes. It is mediated by the COX enzyme, which catalyzes the conversion of arachidonic acid into bioactive lipid mediators, specifically prostaglandandins and thromboxane [68; 69]. Prostaglandins serve as key modulators in the pathophysiology of inflammation, mediating vasodilation, pyrexia, and nociception, while thromboxane plays a crucial role in platelet aggregation and hemostasis. This pathway is a major regulator of the inflammatory response, with both homeostatic and pathogenic outcomes depending on the extent and regulation of the inflammatory stimulus.  
_Chlorella_-11 peptide, a bioactive compound from _C. vulgaris_ (Val-Glu-Cys-Tyr-Gly-Pro-Asn-Arg-Pro-Gln-Phe) [67; 70], inhibits pro-inflammatory cytokines such as TNF-\(\alpha\) and reduces the production of prostaglandandins via the COX pathway. Additionally, it suppresses another key mediator of inflammation, nitric oxide (NO), which is derived from L-arginine through the action of inducible NOS (iNOS) [71].  
Inflammatory activity of _C. vulgaris_ extracts has been reported in some in vitro and in vivo studies [65; 70; 72]. Sibi et al. [72] examined the in vitro anti-inflammatory effects of _C. vulgaris_ and found that treatment with these microcalga extracts decreased the production of inflammatory mediators (PGE2, and IL6) and NO in LPS-activated RAW 264.7 cells, and suppressed inflammation. _Chlorella_-11 peptide, a component isolated from _C. vulgaris_ (Val-Glu-Cys-Tyr-Gly-Pro-Asn-Arg-Pro-Gln-Phe), has also been studied in vitro and in vivo. _C. vulgaris_ also modulates the (NF-kB) pathway, helping to reduce inflammation at a molecular level [65; 67; 73]. NF-kB is a key regulator of inflammatory processes and plays a crucial role in the pathogenesis of various chronic inflammatory diseases [68; 74]. Additionally, it promotes the production of anti-inflammatory cytokines like IL-10, further aiding in tissue repair and healing [66]. This makes _C. vulgaris_ a promising agent for addressing both acute and chronic inflammation in various conditions.