esa-sceva/satcom-chunk-collection huggingface dataset:
# An Innovative Information Hiding Scheme Based on Block-Wise Pixel Reordering
## 3 Proposed Scheme  
To protect the privacy of original cover images, our novel scheme encrypts the cover image before data embedding using an encryption key. A content owner sends the encrypted image to a data hider to hide secret messages. The data hider sends the marked images, the encryption key and the data hiding key to designated receivers after hiding data. In order to be able to simulate the codeword table to manipulate index order as the codeword index reordering scheme in Section 2 Background of the Work, Section 3.2 Codeword Table Formation describes how codeword tables are generated.  
Figure 5 shows the framework of the proposed scheme. The content owner uses an encryption key \(k_{e}\) to activate a random number generator and uses the stream Cipher to encrypt an image and generate an encrypted image. The encrypted image is then divided into pixel-blocks and block-groups. In order to be reversible, the LSBs are used to record the pixel-block index in its block-group. The LSB-replaced encrypted image is then sent to the data hider. The data hider used the same method to divide pixel-blocks and block-groups to create codeword tables. The codeword table is sorted by using projected values to a line generated based on a data hiding key \(k_{d}\). After sorting, the table indices are used to hide secret messages. A marked image is generated and sent to receivers after secret messages are embedded. A marked image is divided into pixel-blocks and block-groups to form the codeword tables using the same method as the data hider after receiving. When a receiver owns a data hiding key \(k_{d}\), the codeword table is sorted using the key and the  
Figure 4: Data extraction of a codeword index reordering scheme.  
secret message can be extracted by finding indices of matched codewords. If a receiver has an encryption key \(k_{e}\), the encrypted image can be recovered partially. If a receiver obtains both keys, both the secret message and the encrypted image can be recovered with minor distortions.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 2 Related Literature
### Vulnerilities Exposed by Quantum Computing  
[PERSON]'s algorithm, presented in 1994 [8], poses a significant theoretical threat to traditional public-key cryptography, and is well known as a critical issue which must be addressed. Shor offers a 'polynomial-time quantum algorithm' for factorising large integers, a computationally hard problem for classical computers, and is the basis of many classical key exchange mechanisms. Although highly speculative, some industry estimates such as those from MITRE [27], suggest that attacks on RSA-2048 could become feasible by 2035. It is probable that 'Store Now, Decrypt Later' activities are already underway, anticipating quantum computing's potential. This highlights why the subject is receiving urgent attention in critical government, military, and commercial applications, where confidentiality time-frames must be maintained beyond the prospective availability of quantum computing techniques.  
Another significant quantum algorithm that threatens modern cryptography is [PERSON]'s algorithm: 'A fast quantum mechanical algorithm for database search' [28] (1996). [PERSON]'s algorithm provides a quadratic speedup for searching unsorted databases (such as the AES keyspace), effectively halving the key length of symmetric algorithms like AES [28], thus AES-256's effective security is reduced to AES-128. Whilst this still provides a sufficient-sized window for the foreseeable future, halving the effective number of bits weakens the encryption and leaves it more vulnerable should further advances be made in this area.  
Despite their theoretical nature, the increasing feasibility of quantum computing has driven significant attention to Post-Quantum Cryptography (PQC) at regulatory and governmental levels. While the extension of existing algorithms has been explored, with larger keys for example, the consensus is that practical RSA is fundamentally vulnerable to [PERSON]'s algorithm on a sufficiently powerful quantum computer. Simply increasing the key size is not a viable long-term solution, with [PERSON] et al. [29] suggesting impractical key sizes (e.g., 1 TB) would be needed for post-quantum safety. Section 3 of this paper demonstrates the exponentially increasing resource demands (time and energy) for generating larger RSA key pairs (e.g., 4096-bit). Whilst the impact of [PERSON] algorithm's is considered less catastrophic compared with the potential of [PERSON]'s, the cryptographic community is also actively exploring ways to reinforce symmetric encryption security and address other limitations of algorithms like AES [30] after its 20+ years of practical use.
# Fluid Net Models: From Behavioral Properties to Structural Objects
## 2 Basic Concepts and Notations
### Mono-T-Semiflow Nets and Performance Properties Relationships  
The equilibrium throughput, in the case of MTS systems, is proportional to the unique T-semiflow of the net. Thus, it can be expressed as:  
\[\mathbf{f}_{e}=\mathbf{\Lambda}\mathbf{\Pi}_{C}\mathbf{m}_{e}=\mathbf{\alpha}\mathbf{x},\]  
where \(\mathbf{x}\) is the T-semiflow of the net and \(\alpha(N,\mathbf{\Lambda},\mathbf{m}_{0})\) is a scalar function. In other words, the equilibrium throughput is of the form \(\mathbf{f}_{e}(N,\mathbf{\lambda},\mathbf{m}_{0})\).  
The dynamic behavior of a TCPN system is described by Equations (4) and (6). From them, it can be directly derived that, since the dynamic behavior of a TCPN system can be described by Equation (6)--the marking evolution of \(\langle N,\mathbf{\lambda},\mathbf{m}_{0}\rangle\) is the solution to that initial value problem--the following well known _homothecy_ properties are dynamically satisfied [6]:  
* If \(\mathbf{m}_{0}\) is multiplied by a constant \(k>0\), the reachable markings are multiplied by \(k\) and the firing flow will also be \(k\) times bigger (or smaller if \(k<1\)).
* If \(\mathbf{\lambda}\) is multiplied by a constant \(k>0\), then identical markings will be reached, but the system will evolve \(k\) times faster (or slower if \(k<1\)).  
We emphasize that in both cases, the whole \(\mathbf{m}_{0}\) and \(\mathbf{\lambda}\) are multiplied by \(k\). However, when the value of only one entry of these vectors varies, then the equilibrium throughput could exhibit paradoxical behaviors. This case is analyzed in this work.
###### Abstract  
We propose a new method for precisely fabricating a lensed fiber with a desired focal length by first cleaving a coreless silica fiber using an ultrafast femtosecond laser without thermal effects and subsequently shaping the radius of curvature at the optical-fiber end using a CO\({}_{2}\) laser. The precisely cleaved segment of the coreless silica fiber obtained with the femtosecond laser is attached to a long single-mode fiber. The beam-exposure time and laser power of the CO\({}_{2}\) laser are adjusted to melt the coreless-fiber end to yield a uniform, consistent, and precise radius of curvature, thereby realizing a lensed optical fiber. The precision of the radius of curvature in this case is greater than those obtained with the conventional arc discharge method with thermal treatment requiring fairly complex processes and yielding relatively low fabrication accuracy. In our study, we observe a difference between the measured and calculated focal lengths of the fabricated lens, possibly because the exact value of the mode field diameter is uncertain. On the other hand, the beam size measured using the knife-edge method matches closely with the theoretical size. Our findings confirm the feasibility of fabricating lensed optical fibers for fiber-based biosensing using CO\({}_{2}\) and femtosecond lasers.  
CO\({}_{2}\) laser; femtosecond laser; lensed fiber; fiber cleaving Article
## 1 Introduction
Several sensors for acetone vapor sensing operating in the microwave frequency band, which is directly related to the solution presented in this paper, have been published in the literature [22; 23; 24; 25]. The main drawback of the solutions published in [22; 24; 25] was that they were not contactless detection, but used direct measurement of the microwave transmission line to detect the response. Although the solution in [23] was contactless, the overall RCS response level was quite low (\(-30\) dBsm) and consequently, the structure of the reader antenna had to be placed in close proximity (within about 15 mm). In some situations, chipless RFID gas sensors may be subject to multiple factors that may affect their behavior or require a modification of their characteristics or a variation in frequency to adapt to their environment and achieve nominal results. In such situations, the effective solution is to incorporate the concept of agility in the sensing applications. Agility can be achieved either by the physical sensor itself when it is used as a control unit to sense variations in the surrounding environment [26; 27] or by RF elements including PIN diodes [28], microelectromechanical system (MEMS) switches [29], and varactor diodes [30]. The present paper introduces a proof of concept of frequency adjustment of a chipless radio frequency identification (RFID) sensor tag for detecting solvent vapors/gas using a wireless power transfer (WPT). The concept was based on the authors' previously published rectangular loop structure equipped with a U-folded dipole loaded with a glide-symmetrical interdigital capacitor coated with a thin layer of tetrasulfonated copper phthalocyanine deposited as a sensing layer to improve the sensing capability in the presence of acetone vapor. In order to further maximize the sensitivity of the designed structure to the desired solvent, a circuit for a central frequency adjustment using a radio frequency varactor diode biased with a wireless power transfer (WPT) was designed. In principle, different gases and sensitive layers can be differently sensitive at different frequencies. To find the appropriate frequency, the resonant dip detuning option can then be used. The aim of this paper was to present a custom transponder geometry that potentially offered this possibility. The paper is organized as follows. Section 2 is devoted to a detailed presentation of the static version of the proposed sensor and a description of a reconfigurability solution based on the WPT system. Section 3 covers an experimental validation of the sensor and describes the fabricated prototype, measurement setups, and measurement results along with their interpretation. Finally, Section 4 is reserved for conclusions.
## 4 Key Parameters of Cavitating Jet
### Standoff Distance  
As shown in Figure 9b, as cavitation is generated inside and/or outside of the nozzle, it becomes cloud cavitation and forms ring vortex cavitation on the impinging surface, then collapses, producing the impacts. In view of the practical applications of the cavitating jet, the working mechanism strongly depends on the standoff distance, which is the distance from the nozzle to the surface. Figure 12 illustrates the weight loss as a function of standoff distance at constant injection pressure, \(p_{1}\) = 120 bar (12 MPa) [77]. In Figure 12, the weight loss means a kind of aggressive intensity of the jet. Two peaks are observed at each condition in Figure 12. For convenience, the peak near the nozzle side and the peak at the further nozzle side are named the 1 st peak and 2 nd peak, respectively. The 1 st peak results from the impacts produced by water column collisions in the jet center. Even for the submerged water jet, the similar mechanism of water jet cutting is still active, whereas the affective region is limited near the nozzle. The 2 nd peak is generated by the cavitation impact, as shown in Figures 6 and 9b. As cavitation is developed and then collapsed, a certain distance from the nozzle is required. As shown in Figure 12, the optimum standoff distance \(s_{opt}\) of the 1 st peak is scarcely affected by cavitation number, and that of the 2 nd peak strongly depends on cavitation number. At \(p_{2}\) = 2.4 bar (0.24 MPa), the weight losses at 1 st peak and 2 nd peak are 250 and 450 mg, respectively. They are 250 and 220 mg for \(p_{2}\) = 3.0 bar (0.3 MPa) and 300 and 110 mg for \(p_{2}\) = 3.6 bar (0.36 MPa). When the maximum values of the 1 st peak and 2 nd peak are compared, the value of the 2 nd peak at \(p_{2}\) = 0.24 MPa is 1.5 times larger than that of the 1 st peak at \(p_{2}\) = 0.36 MPa. Namely, at optimum cavitating conditions, the aggressive intensity due to cavitation impact, i.e., the 2 nd peak, is larger than that of water column impact, i.e., 1 st peak. Note that water jet peening and cavitation peening use the 1 st peak and 2 nd peak, respectively.  
To avoid confusing cavitation peening and water jet peening, [PERSON] proposed a classification map for cavitation peening and water jet peening using standoff distance and cavitation number, as shown in Figure 13. Over 150 points were collected from references [39; 74; 77; 78; 79], and it was found that the line shown in Equation (8) distinguished between cavitation peening and water jet peening [90].  
\[\frac{s_{opt}}{d}=1.8\ \sigma^{-0.6} \tag{8}\]  
One easy way to confirm the 2 nd peak region, i.e., cavitating peening region, is as follows. Considering the aggressive intensity of the cavitating jet, the target materials are chosen. For example, in the case of the weak cavitating jet, a soft metal would be better. Then, the target is exposed to the jet. When a ring pattern is obtained, such as in Figure 8, it is a cavitation peening condition. The removal of paint can show the treatment area of cavitation peening [91]. A pressure-sensitive film also detects the treatment area of a cavitating jet [12].
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 3 System Model  
### Network Model  
In this paper, we consider a WBAN consisting of a sensor node and a sink node, which is located at the waist of the human body. The sensor node is mainly responsible for forwarding the collected data to the sink. The sink node is mainly responsible for forwarding the data.  
Model Assumptions and Features:  
1. All sensor nodes are placed at different locations of the human body to collect corresponding physiological information and have specific IDs.
2. All sensor nodes have the same initial energy and transmission range.
3. The sink node has strong information processing capability and receives data from sensors only, and the energy of the sink is not considered.  
The sensor nodes classify the sensed physiological data into emergency data and common data based on their different levels of importance and then prioritize the data, and the priority of emergency data is higher than that of common data.
## 5 Examples
### Altitude Feature Extraction  
Feature extraction was performed on the flight profile shown in Figure 1a, with a data sampling frequency of \(f_{\mathrm{s}}=1\) Hz, a sliding window width of \(k=60\), and a data compression interval of \(\Delta n=100\). Variable \(k\) denotes the fewest sampling points taken by a rapid climb or descent action and is usually closely related to the limit performance of an aircraft, which can be set using big flight data. Corresponding to \(k\), \(\Delta n\) denotes the fewest sampling points taken by one climb-descent process. There may be many rapid climb actions in the process because the altitude level can often change significantly in anticipation of some other training action. The results are shown in Figures 2 and 3.  
In Figure 2, due to the limitation of window width, the trend algorithm filters out short-term altitude jump signals that do not denote climb-descent flight training but preserves and smooths cases with a climb-descent pattern and then obtains the trend of the altitude data.  
Next, the data compression algorithm in Figure 3 resamples the trend signal according to the shortest time interval of the aircraft climb or descent, further filtering the relatively high-frequency signal in the trend data while retaining the climb-descent feature points. Based on the PV difference de-redundancy algorithm in Section 2.2, Figure 3 calculates the PV of the trend term after data compression and then uses a threshold to remove redundancies from the peak values, achieving the extraction of the FCDF. The number of feature points in Figure 3 indicates that the training frequency is 2.  
Due to the non-ergodic nature of the altitude curve in the flight profile, the altitude feature extraction results of other flight profiles are listed, as shown in Figure 6. Compared to the large-span time interval between the two training sessions in Figure 3, Figure 6a shows two consecutive climb-descent training sessions, one short-term session, and one climb-descent session in sequence; Figure 6b shows the process of five consecutive training sessions that are inserted into other short-term training sessions during their executed progress; Figure 6c shows the process of intermittent climbing or other maneuvers during the second climbing training, and Figure 6d shows the process of trapezoidal climb-descent training only. From the number of feature points in Figures 3 and 6, the altitude feature extraction method presented in this article is effective: for the same aircraft model with the same engine model and different flight times, the presented methods can correctly obtain the FCDF in each profile.
# ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis
## 2 Fundamentals  
### Color Theory  
Color theory is the science that explains how humans perceive color: this perception results from how light is absorbed, reflected, and transmitted by the objects around us. When light strikes an object, some wavelengths are absorbed while others are reflected: this reflection is what allows us to see the color of an object. Nevertheless, all humans have different cultural and contextual associations with color that vary our perceptions [12; 13; 14]. As [PERSON] said, \"Colour is a sensation in the viewers' mind\" [15].  
Three color-making attributes are used to communicate this subjective experience of color: hue; hue purity (expressed as colorfulness, chroma, saturation, or dullness); and lightness or darkness. In turn, hues are divided into primary, secondary, and tertiary. The primary hues are Red, Yellow, and Cyan. Blending two primary hues gives the secondary ones: Green, Orange, and Purple. Lastly, we obtain tertiary hues by mixing a primary hue with its nearest secondary. The six tertiary hues are Red-Orange, Yellow-Orange, Yellow-Green, Blue-Green, Blue-Violet, and Red-Violet. However, the nature of color is continuous; therefore, the best representation of colors is the color or hue wheel created by Sir [PERSON] in 1704 [16]. Figure 1A shows how this hue wheel can be understood as the progressive blending of the primary, secondary, and tertiary colors represented inside it. It is important to note that some colors are not represented in the color wheel, because the color wheel only shows pure or fully chromatic colors. Modifications to these pure hues create the rest of the possible colors. The hues can be modified by changing the color-making attributes of purity and lightness with achromatic or neutral colors--white, black, and gray--to obtain tints, shades, and tones, respectively. A wider variety of colors can be seen in the tints, shades, and tones images of Figure 1B. This difference between pure hues and modifications can be better understood if we single out certain shades and tones (Figure 1D). Triangle 5.t shows what can be referred to as \"forest green\"; however, this deep shade of green is usually described simply as \"dark green\". Circles 1 to 4 show increasingly dark (1-4.s) shades and duller tones (1-4.t) of orange: when observed separately, it is clear that these colors are what we usually refer to as \"brown.\" Brown is technically a near-neutral dark-valued orange; however, in contrast to dark green, almost nobody would refer to brown as dark orange: this is a crucial point to understand, because most research conducted to date on color detection and classification aims to find a unique space for brown as a pure hue instead of treating it as a modified hue, the same as dark green or dark blue [17; 18; 19]. This repeated inaccuracy in color research--which contradicts even [PERSON]'s and [PERSON]'s precise placement of brown as dark orange in their color wheels [20; 21]--likely stems from the complicated gap between color theory, color perception, and color naming [22].
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 3 Composition of ECMs from Various Tissues
### Dental Pulp ECM  
The dental pulp is a soft, vascular, connective tissue found in the centre of the tooth, which provides sensory function, nutrition [67] and reparative capacity [68]. The ECM formed by the dental pulp is highly hydrated and encompasses a range of cell types, including fibroblasts, mesenchymal stem cells (MSCs), macrophages and lymphocytes, along with the factors they secrete. Pulp fibroblasts secrete a complex ECM that varies significantly to other soft connective tissues, although it constitutes some of the most significant ECM proteins such as collagen, fibronectin, proteoglycans and glycosaminoglycans [68]. There are four main subtypes of collagen found within the dental pulp: type I (56%); type III (41%); type V (2%) and type VI (0.5%), where type I and III are most abundant [68]. Collagen fibrils (especially type I) usually form long, thick, parallel sheets throughout pulp tissue, which provides crucial structural support to the matrix [69]. The main non-collagenous protein found within the dental pulp is fibronectin, although its specific abundance in the dental pulp has not been reported. Despite this, it is known that fibronectin tends to form a complex with type III collagen in a 1:1 ratio [68]. Proteoglycans and glycosaminoglycans contribute to the dental pulp ECM by providing tissue turgor and facilitating cell-cell interactions [69]. The most common proteoglycans of the dental pulp are decorin, biglycan and version, with the following glycosaminoglycan modifications also being present: 4-and-6 chondro sulfate (60%); dermatan sulfate (34%); and keratan sulfate (2%) [68]. The protein component elastin has a structural role in the blood vessels of the dental pulp, but it does not form part of the ECM itself [69].
# A Conceptual Design of Deployable Antenna Mechanisms
The six-bar mechanism is employed, which acts like the power source, resulting in the deployment. By exploiting the conical frustum morphology, the 6-bar mechanism achieves both high rigidity and a high deployment ratio. Finally, a single pantograph mechanism is incorporated to ensure synchronized dynamic characteristics, allowing each unit frame of the deployment mechanism to extend with identical displacement. Ultimately, through multibody dynamic analysis, the proposed mechanism is demonstratedto evaluate its mechanical characteristics, including deployment ratio, area-to-weight ratio, and synchronized dynamic performance.
# Two-Level Congestion Control Mechanism (2 LCCM) for Information-Centric Networking
## 4 Implementation
### Congestion Control Algorithm
If the BBR has not sampled a smaller RTT value in the last 10 s, it enters the _PROBE_RTT_ state. During this state, _cwnd_ is reduced to the size of 4 packets to reduce the inflight data in the network thus an accurate RTT can be detected, and this _PROBE_RTT_ state usually lasts for 200 ms. The receiver-driven BBR congestion control algorithm is shown in Algorithm 2, and the time complexity of this algorithm is also O(1). _Inflight_ represents the amount of data that is requested but has not received the corresponding response message yet.