esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Optical Configuration  
The configuration of DPMI is shown in Figure 1. The output beam of a stabilized He-Ne laser is adjusted into 45\({}^{\circ}\) linearly polarized light by an isolator (IO), followed by an incident beam on an electro-optic phase modulator (EOM). The beam is output from EOM and expanded by a beam expander (BE); then, it is split into two beams by a non-polarization beam splitting cube (NPBS). The transmitted (blue) beam serves as the source of the reference interferometer (RI), and the reflected (red) beam is reflected by a plane mirror (M) and serves as the source of the measuring interferometer (MI). In RI, the blue beam is split into two beams with orthogonal polarizations by a polarization beam splitting cube (PBS), and the reflected beam serves as the reference beam of RI and is reflected by a reference plane mirror (M\({}_{\text{R}}\)). The beam transmitted via PBS serves as the measuring beam of RI and is reflected by a fixed measuring plane mirror (M\({}_{1}\)). M\({}_{1}\) is fixed at a suitable position. As the reference beam and the measuring beam pass quarter-wave plates (QWP\({}_{1}\) and QWP\({}_{2}\)) twice, the polarization directions of the two beams become perpendicular to their original polarization directions. Then, the two beams return to PBS. After the use of a polarizer (P) with an angle of 45\({}^{\circ}\), the two beams interfere with each other in the same polarization direction, reaching a photodetector (PD). The measuring signal of PD is used to measure the displacement drift in M\({}_{1}\). Similarly, in MI, the reference beam is reflected by M\({}_{R}\), and the measuring beam is reflected by another measuring plane mirror (M\({}_{2}\)). M\({}_{2}\) is mounted on the measured object and moves with it. The reference and measuring beams interfere with each other and reach an integrated 2 \(\times\) 2 array photodetector (APD). The measuring signals of APD are used to measure the displacement and rotational angle of M\({}_{2}\) simultaneously.
# Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network
## 1 Introduction
Recent studies developed [18; 19] several neural network algorithms to mimic the computation performed inside the human brain as described by the neurological findings [20]. The Extreme Learning Machine (ELM) is an emerging algorithm in machine learning [21; 22; 23; 24; 25], which is biologically-inspired and which performs with accuracy similar to Support Vector Machines (SVM) [26] and is also relatively fast to train as compared to iterative training methods. Although ELM is getting popular, ELM-based classifiers have not been used for fabric texture classifications. Numerous implementations of multi-layered extreme learning machines have been developed [27; 28; 29], and it was found that the multi-layer implementation of extreme learning machine performed better than the conventional ELM in term of the recognition and classification performance. Recent works that were intended to develop neuromorphic implementations of Extreme Learning Machines [30; 31; 32] motivated this current work. Further details of neuromorphic implementations are described elsewhere [33]. One of the potential limitations is the simultaneous activation of the several hidden layers. The main focus of our study is to develop a deep machine learning algorithm based on extreme learning machine architecture, which requires fewer hidden layers needed for simultaneous activation, offering good classification accuracy, and trains relatively faster. In this paper, we have taken inspiration from the advances in the architectures of deep learning [34; 35; 36] and from neurological findings of deep learning architecture in the primate visual cortex. In order to improve the recognition accuracy of fabric weave pattern and yarn color recognition and classification, this paper bring together two biologically-inspired algorithms, namely, color HMAX and deep ELM (a multi-layer variant of the conventional Extreme Learning Machine), and insights to develop a novel biologically-inspired model for robust pattern recognition and classification. In the following section, first we describe the outline of the proposed method with a flowchart. Then we propose a novel approach to deep ELM (D-ELM) implementation as a weave pattern classifier, and describe the method briefly. In the later sub-sections we describe the weave pattern and yarn color feature descriptor in detail, followed by the brief description about the integration of the pattern feature descriptor with the weave pattern classifier.
## 4 Results and Discussion  
### Density  
The density of concrete prepared using all mixes given in Table 2 is presented in Figure 7, where it is noticed that the replacement of fine and coarse NAs with RAs caused a drop in the density of the resulting concrete. This is mainly attributed to the lower density of RBAs and RCAs compared to NAs given in Table 1. RAC containing both fine and coarse RBAs exhibited a density 23.6% lower than the control concrete; however, keeping the fine aggregates the same (i.e., RBAs) and replacing 100% coarse RBAs with RCAs resulted in an increase in density of 14.7%. Similarly, in comparison of F(RBA) + C(RBA) mix, keeping the fine aggregates the same (i.e., RBA) and gradually replacing 25%, 50%, 75%, and 100% coarse RBAs with coarse NAs resulted in an increase in concrete density of 3.5%, 11%, 14%, and 19%, respectively. However, their density remained less than that of control concrete. The results further showed that RAC containing both fine and coarse RCAs exhibited a density 11.4% lower than the control concrete; however, keeping the fine aggregates the same (i.e., RCAs) and replacing 100% coarse RCAs with RBAs resulted in a further decrease in density by 9.8% when compared to the density of the F(RCA) + C(RCA) mix. Similarly, in comparison to the F(RCA) + C(RCA) mix, keeping the fine aggregates the same (i.e., RCAs) and gradually replacing 25%, 50%, 75%, and 100% coarse RCAs with coarse NAs resulted in an increase in concrete density of 0.9%, 2.4%, 3.9%, and 6.9%, respectively. However, their density remained less than that of control concrete, as shown in Figure 7. Among the two RAs used in this study (i.e., RBAs and RCAs), the drop in density of hardened concrete was higher with RBAs, which was mainly because of their low bulk density, as given in Table 1.  
Figure 6: Test setup for the four-point bending test.
# Modeling of Vessel Traffic Flow for Waterway Design-Port of Swinoujscie Case Study
## 2 Methodology
### Analyzed Area  
Located in north-western Poland at the mouth of the Oder River, the universal ports of Szczecin and Swinoujscie are among the most extensive port facilities in the Baltic Sea region. Swinoujscie offers dense direct traffic to/from Sweden, thanks to the largest ferry terminal in Poland; the port also specializes in handling various bulk cargoes. The port areas are overseen by vessel traffic service systems, defined by lines marking the reporting obligation via VHF radio.  
Marine traffic in the approach to Swinoujscie consists of two main types: transit and cross-strait traffic. The first is the traffic of cargo and passengers ships transiting to the ports Swinoujscie and Szczecin. The second is the traffic of small passenger vessels and pleasure boats operating on the routes between the small ports. This analysis was based only on the latter type.  
The authors analysed the movement of ships in the main flows from Arkona, as shown in Figure 2a,b. The daily average of ship passages using these lanes (towards Swinoujscie) is about 25, including ferries, passenger ships, and fishing boats.  
Figure 2: Pomeranian Bayresearch area: (**a**) Established sections; (**b**) density plot.  
The traffic streams in the Pomeranian Bay were divided into 9 sections, called legs, as shown in Figure 2a. Section N1 is located on the north side of the northern approach fairway near buoy N1, while N9 is the section near the head of the Swinoujscie breakwater (buoy A). Section N7 represents the western approach to the port. The sections have been defined so that the internal navigation conditions within each section are constant (e.g., distance to isobaths, aids to navigation).
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Bandwidth
#### 4.3.2 Random Access-Based  
In this method, the node begins broadcasting as soon as it has a packet ready to send. The data packet can be successfully received if the receiver is not busy and there is no conflict in the generated acknowledgements [102]. Using random access technologies, multiple nodes in UWSNs can share the transmitting medium in a random manner [46]. In the section of ALOHA protocol, we will goover this concept in greater depth.
# Dynamic Optimization of Data Packet-based Communication for PLC Visual Monitoring
## 2 Dynamic Optimization of Data Packet-based Communication for PLC Visual Monitoring  
The communication effectiveness between a supervisory computer and PLCs in practice takes into account both the protocol's transmission efficiency [27] and the human visual response delay [13; 28; 29]. The communication timeline designs for PLC visual monitoring focus on two aspects, including transmission load and real-time response. A communication packet is associated with a communication request/response between a supervisor computer and field PLCs, and its data frame as illustrated in Figure 2 is normally composed of a preamble, start delimiter, destination and source addresses, data length, data and frame check sequence [11].  
It is commonly known that the larger the data packet the longer transmission time it takes, although the transmission efficiency is higher without transmitting the packet headers and cyclic redundancy check (CRC) many times. However, transmitting a large data packet can cause time delay, which may lead to cognitive confusion. For example, if the real-time data cannot be transmitted and visualized to the current monitoring screen within the human response time limit, the user can simply take the out-of-date data as the current status of the monitoring system. In addition, a large data packet is more likely to incur packet errors or losses during transmission and the packets with data errors or losses should be retransmitted again, which contrarily decreases the communication efficiency. Thus, it is important to optimally decide the packet size in terms of the retransmission rate, human visual response, communication efficiency, etc.  
Figure 2: A typical frame of communication data packets.
# Missed Detection of Entanglement in Two-Mode Squeezed States Based on the Inseparability Criterion
## 3 Experimental and Results
Notably, when the asymmetry exceeds 0.83, the inseparability value surpasses 1, signifying its inability to detect the entangled state. This underscores the intricacies of asymmetric losses in entanglement detection and emphasizes the significance of selecting an appropriate criterion. The close alignment between experimental measurements and theoretical predictions validates both the accuracy of our theoretical analysis and the reliability of the experimental procedure. In practical experimental setups, the presence of cavity losses and noise effects inherent to optical oscillators necessitates the preparation and manipulation of mixed states rather than pure states. Consequently, these states fail to uphold the minimum uncertainty principle, i.e., \(\Delta^{2}X_{a}^{+}\cdot\Delta^{2}X_{a}^{-}=\Delta^{2}X_{b}^{+}\cdot\Delta^{2}X_ {b}^{-}>1\), yielding a purity metric that falls short of the ideal unity value. To delve into the implications of input state purity on entanglement assessment criteria, we conducted an analysis as illustrated in Figure 6. By systematically varying the purity levels to 1.0, 0.40, 0.138, and 0.04, achieved by maintaining a constant squeezing parameter with \(r_{1}=0.69\) and adjusting the anti-squeezing parameter to 0.69, 1.15, 1.7, and 2.3, respectively, we examined their influence. Figure 6a,b explicitly depict the effects of purity on entanglement criteria under symmetric and asymmetric loss conditions, respectively. Intriguingly, under symmetric losses, the inseparability criterion and the PPT criterion remain invariant to changes in the purity of the two-mode squeezed state, once the initial squeezing factor is established. Conversely, in the presence of asymmetric losses, the PPT criterion remains relatively immune, whereas the inseparability criterion becomes acutely sensitive to state purity. As purity diminishes, the violation of the inseparability criterion intensifies markedly, attributed to the heightened total uncertainty that may hinder the fulfillment of its criteria, ultimately hindering entanglement detection. Furthermore, an overemphasis on squeezing a single quadrature component can potentially lead to erroneous entanglement assessments.
# Multi-Year Monitoring of Ecosystem Metabolism in Two Branches of a Cold-Water Stream
## 4 Discussion
Since the North Branch was found in a wetland, it may have received more DOM than the South Branch resulting in the greater dependence of ER on temperature in this branch. The negative relationship between temperature and ER was unexpected in the South Branch, but there was little variation in temperature in this branch (\(\sim\)13 \({}^{\circ}\)C). The R\({}^{2}\) for this relationship was only 0.09, indicating little impact of temperature on ER. The greater effect of temperature on both ER and GPP in the North Branch could be due to the greater temperature range experienced there (\(\sim\)23 \({}^{\circ}\)C). Generally, NEP increases with temperature due to the greater temperature sensitivity of ER relative to GPP [41; 78]. In this study, NEP increased with increasing temperature in the South Branch but not in the North Branch. In the South Branch, the negative relationship between ER and temperature drove NEP to be positively correlated with temperature (GPP increased while ER decreased), while in the North Branch, the similar impact of temperature on GPP and ER results in no change in NEP with temperature. Discharge had no effect on GPP in the North Branch and a small but negative impact in the South Branch. While some have found little correlation between discharge and GPP [45], others found that in large rivers increased discharge may have a negative relationship with GPP presumably due to light attenuation in these systems [75]. A positive correlation between discharge and ER, similar to that found in this study, has been noted [45; 75], likely due to the greater amounts of allochthonous material reaching streams and rivers resulting in higher ER. The impact of discharge on ER was greater in the North Branch than the South Branch (slope 7.3 versus 1.9 for the North and South Branches, respectively). The North Branch sampling location is in a wetland area, and greater discharge may bring in larger amounts of allochthonous materials or dissolved organic carbon (DOC) than in the South Branch resulting in increased ER [80]. In both branches, NEP decreased, likely due to the increased ER.
# Delegation-Based Personal Data Processing Request
## 2 Background and Related Work
### Blockchain  
BC is a technology that distributes and verifies data within peer-to-peer network nodes in the form of blocks having a chain-type link. It is a data forgery prevention technique wherein several blocks are connected similar to a chain such that the hash of the current block becomes a component of the subsequent block using data encryption technology [27; 28]. In the traditional transaction model, a central entity with authority functions as a gate and manages and guarantees the ledger data generated between nodes. In this centralized model, when a system with a central authority is incapacitated by internal or external intentional or unintentional attacks and failures, or when data are damaged or contaminated, the damage can spread throughout the entire network. In contrast, in the BC model, a copy of the ledger is distributed and stored to all nodesin the network, thereby reducing the risk and maintaining trust by removing the central authority. Owing to this structure, the BC has four key characteristics as follows [25]:  
* Decentralization: BC network transactions can be performed between two peers (P2P) without authentication from a central authority.
* Persistence: As each transaction spreading through the network must be verified and recorded in blocks distributed throughout the network, tampering is almost impossible.
* Anonymity: Owing to the absence of a central system to store the personal data of the user, each user can communicate with the BC network using the created address, thus minimizing identity exposure.
* Auditability: In the BC, each transaction can repeatedly trace the previous transaction. This improves the traceability and transparency of the stored data.  
Currently, the BC system can be divided into a public, private, and consortium BCs. Among these, private BC allows only authorized nodes to process consensus, can restrict read permission, and has high efficiency, so it is often used as a framework for corporate business processing [25].
# Antiference: New Concept for Evolutive Mitigation of RFI to GNSS +
## 1 Introduction  
The past decade has shown a growing awareness in the dangers of intentional interference in the area of GNSS signals. Jamming and Spoofing (the two main intentional and harmful radio frequency interference (RFI) types) have been receiving a lot of attention in terms of the availability and affordability of hardware to induce the attacks, and the resulting costs to the victims. Jamming is the broadcasting of a radio frequency (RF) signal with the goal of blocking the reception of authentic GNSS signals and it leads to the complete blockage of the service or to the significant degradation of the service quality. Victims of jamming usually know that they are experiencing a form of interference. However, newer jamming methods, such as low-power jamming, code/spectrum-matching jamming (or PRN jammers) [1], and systemic jamming [2] lead to the degradation or blocking of the service without the victim being completely aware of what is happening.  
Spoofing, on the other hand, is the targeted broadcasting of counterfeit GNSS signals so that the victim receiver will perceive them as the authentic signals and follow the counterfeit signals. In these types of attacks, the victim receivers are not aware of being attacked and can be steered to unknown locations or be prone to accidents.  
The Antiference project uses reconfigurable digital signal processing (DSP) methods and tools in the detection, classification, characterization, and mitigation of harmful RFI. While some forms of RFI signal mitigation do not rely on the detection and classification of the RFI (like Notch filters or narrow correlators for multipath mitigation), effective mitigation in most cases relies on knowing the parameters of the interfering signal and can be significantly improved if accurate knowledge about the type and parameters of the attack is known. Employing data-driven artificial intelligence (AI) and machine learning(ML) techniques to facilitate the detection and classification of RFI is the main focus of this project. However, like many other defensive procedures, the recording and sharing of previously encountered attacks (RFI incidents in this case) can save time in their detection and classification and facilitate effective mitigation measures. Since ML models can suffer from explicit knowledge representation and storing that can be shared between multiple equipment (multiple instances of the same model), a fingerprinting (FP) architecture is introduced to alleviate the possible shortcomings. The FPs of each incoming signal have been generated and matched to a database that contains all the previously encountered RFI types. This results in a database of fingerprints that can be shared between devices of the possible RFIs. To test and validate the implemented functionalities, a testbed has been developed that can run test scenarios and benchmark the results with state-of-the-art COTS receivers which include RFI mitigation capabilities.
## 3 Results and Discussion
### Centrifugation and Optical Spectroscopy  
To determine the cause of the destabilization of the colloid by monoclonal antibodies, Mab2H3C6 was first filtered through Amicon Ultra centrifugal filters with different transmittance. In each centrifugation, the antibodies were concentrated four times and then diluted with 10 mm Tris-HCl buffer, pH 8.5 to the original volume.  
It was found that filtering Mab2H3C6 through filters that pass molecules of less than 30 kDa reduces the aggregation ability of the preparation (Figure 3). For samples 1 and 2 (first and second centrifugation cycles) the aggregates having spectral peak at 580 nm, as it follows from [31], were formed and the colloidal solution became unstable and turbid. Sample 3 (third cycle) had an additional adsorption at  
Figure 2: Flocculation curves of GNP conjugates with antibodies. (**A**; red curve), after adding Mab 2H2F6 and 10% NaCl. (**B**; black curve), after adding Mab2H3C6.  
580 nm and the main peak at 525 nm. Spectra for samples 4 and 5 (fourth and fifth cycles) approaching a native colloidal solution of naked GNPs, which indicates a non-aggregated state.  
In the case of filters with a cutoff threshold of up to 10 kDa, the aggregating ability of the preparation was preserved even after six centrifugation cycles (Figure 4). In addition, the fraction of the solution that passed through the 30 kDa filters, when mixed with GNPs, caused their immediate aggregation (Figure 4, curve 3). From these data it follows that the reason for the aggregation was high molecular weight impurities with a mass of not less than 10 kDa and not more than 30 kDa. Note that the monoclonal antibody preparations were purified by affinity chromatography on a column of streptococcal protein G and should not have contained foreign macromolecular impurities.