esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Polyphenolic Composition and Antioxidant Activity
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Phenolic Acid Content**} & \multicolumn{2}{c}{**A\({}_{\text{AR}}\)**} & \multicolumn{2}{c}{**P\({}_{\text{R}}\)**} \\  & & **(\(\text{\mu g g}^{-1}\) DM)** & & **(\(\text{\mu mol DPPH}\) & **(\(\text{\mu mol AAE}\)** \\  & & CA & CGA & _p_-CouA & & \\
40\% 1-prop/1.5\% SuAc & 772.25 \(\pm\) 54.30 & 1771.84 \(\pm\) 98.52 & 17.00 \(\pm\) 2.34 & 2561.00 & 148.66 \(\pm\) 10.32 & 29.94 \(\pm\) 1.85 \\
40\% 2-prop/1.36\% SuAc & 764.20 \(\pm\) 63.02 & 1181.80 \(\pm\) 84.61 & 24.04 \(\pm\) 1.87 & 1970.00 & 192.03 \(\pm\) 14.20 & 24.88 \(\pm\) 2.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Polypphenolic composition and antioxidant characteristics of the extracts obtained under optimized conditions. Values are given along with the standard deviation. Assignments: CA, caffeic acid; CGA, chlorogenic acid; _p_-CouA, _p_-coumaric acid.
# Targeting KRAS in PDAC: A New Way to Cure It?
## 4 KRAS Inhibitors for PDAC  
### \(\text{Kras}^{\text{G12C}}\) Inhibitors  
KRAS\({}^{\text{G12C}}\) inhibitors have shown excellent results in the treatment of non-small cell lung cancer, and studies on their efficacy for other solid tumors are still advancing [71]. A phase 1 trial (NCT03600883) evaluating the various aspects of sotorasib (AMG510) performance showed that sotorasib has good antitumor activity against solid tumors containing KRAS\({}^{\text{G12C}}\) mutations [49] (Figure 2). Another KRAS\({}^{\text{G12C}}\) inhibitor, MRTX849, validated its antitumor activity against KRAS\({}^{\text{G12C}}\) mutation-containing tumors in a mouse xenograft model [72]. However, none of the KRAS\({}^{\text{G12C}}\) inhibitors have been approved by the FDA as a treatment for pancreatic cancer. Although the frequency of KRAS\({}^{\text{G12C}}\) mutations in PDAC patients is abnormally high in some regions, for example, more than 60% in Japan [73], the frequency of KRAS\({}^{\text{G12C}}\) mutations in PDAC patients worldwide remains quite low, which leads to a limited prospect for the clinical treatment of PDAC using KRAS\({}^{\text{G12C}}\) inhibitors [11; 74].
# Microbial Growth Dynamics in Minced Meat Enriched with Plant Powders
## 3 Results
### Challenge Testing  
The initial total viable counts of the cooked minced pork samples ranged from 10 to <100 cfu/g and increased to a range of 20-250 cfu/g by the end of the storage period. The total number of bacteria remained very low in the cooked samples supplemented with garlic, onion, and rhubarb petioles (20-55 cfu/g).  
As shown in Table 4, all the cooked minced pork samples supported the growth of _L. monocytogenes_ as the growth potential was higher than 0.5 log cfu/g during the specified shelf-life. The highest growth potential (\(\delta\) = 7.92 log cfu/g) was found for the meat samples with onion, followed by garlic (\(\delta\) = 7.85 log cfu/g), and in the meat sample without additive (\(\delta\) = 7.3 log cfu/g). These samples had a higher pH (6.30-6.73) compared to the other samples (pH < 6).  
During the 12-day storage period, the lowest _L. monocytogenes_ growth potential was determined for RTE minced pork samples enriched with rhubarb (\(\delta\) = 2.74 log cfu/g). Moreover, the blends of Apple+On+BCber (\(\delta\) = 3.63 log cfu/g) and Apple+Tom+Rhub (\(\delta\) = 3.74 log cfu/g) showed higher growth inhibition of _L. monocytogenes_ compared to the other tested samples (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & \multicolumn{3}{c}{**Minced Meat 13\%**} & \multicolumn{3}{c}{**Minced Meat 27\%**} \\ \hline \multirow{2}{*}{**Plant Powder**} & **Total** & _Pseudomonas_ & **Yeasts and** & **Total** & _Pseudomonas_ & **Yeasts and** \\  & **Microbial** & **spp.** & **Moulds** & **Microbial** & **spp.** & **Moulds** \\ \hline Apple & 0.901 & 0.811 & 0.827 & ND & 0.030 ** & 0.142 \\ Apple+BCber & 0.892 & 1.000 & 0.421 & ND & 0.001 ** & 0.160 \\ Apple+Rber & 0.901 & 0.621 & 0.130 & ND & 0.001 ** & 0.160 \\ Apple+Tom & 0.586 & 0.630 & 0.190 & ND & 0.002 ** & 0.014 ** \\ Apple+On+BCber & 0.008 ** & 0.004 ** & 0.329 & ND & 0.002 ** & 0.142 \\ Apple+Ga+Tom & 0.315 & 0.053 & 0.421 & ND & 0.002 ** & 0.007 ** \\ Apple+CHber & 0.782 & 0.621 & 0.114 & ND & 0.002 ** & 0.067 \\ Apple+CHber+Rber & 0.964 & 1.000 & 0.087 & ND & 0.001 ** & 0.067 \\ Apple+Tom+Rhub & 0.915 & 0.004 ** & 0.022 ** & ND & 0.001 ** & 0.380 \\ \hline \hline \end{tabular}  
* Non-significant differences (Kruskal-Wallis rank sum test _p_-value = 0.099). ** Statistically significant differences (_p_-value < 0.05). Abbreviations: ND, multiple pairwise-comparison not performed; BCber, blackcurrant berry; Rber, rowanberry; Tom, tomato; On, onion; Ga, garlic; CHber, chokeberry; Rhub, rhubarb petioles.  
\end{table}
Table 3: _p_-values of differences in microbial counts between minced meat controls (13% and 27% fat content) and minced meat samples with added plant powders on 5, 7, 9, and 12 storage days combined.
## 2 Materials and Methods  
### Sample Preparation and Storage  
Gilthead seabream and seabass of cage-farming origin were obtained from AVRAMAR S.A. (Athens, Greece). Standard commercial procedures were used for scale and gut removal prior to the filleting process. Afterwards, samples of whole-gutted fish (300-400 g) and skin-on fillets (100-140 g) were packed with ice and shipped, under refrigeration, to the processing plant (SelectFish S.A., Athens, Greece), where they were immediately packaged under modified atmosphere, MAP (40% CO\({}_{2}\), 40% N\({}_{2}\), 20% O\({}_{2}\)). Packaging took place within 48 hours after slaughter, which is a customary time interval in the industry, and packaging conditions were the those commonly preferred by the fish production company in question. Half of the packages, hereafter named MAP-PAD, additionally contained a CO\({}_{2}\)-emitting pad (90 \(\times\) 255 mm, maximum absorbency: 100 mL, maximum CO\({}_{2}\) production: 128 mL for the whole-gutted fish, and 80 \(\times\) 130 mm, maximum absorbency: 35 mL, maximum CO\({}_{2}\) production: 120 mL for the fillets). Pads were kindly provided by McAirlaid's (Berlingerode, Germany). Thereafter, samples were immediately transported to the Hellenic Center for Marine Research (HCMR) under cold chain, and kept in refrigeration at 2.5 \({}^{\circ}\)C until used in the consumer test or analyzed. This temperature was chosen based on previous findings on optimum storage under MAP and MAP-PAD conditions for the same species [18].
# Rolling-Element Bearing Fault Diagnosis Using Advanced Machine Learning-Based Observer
## 4 Proposed Algorithm for Fault Diagnosis in Rolling-Element Bearing
### Fault Detection and Fault Identification Based on Decision Tree Technique  
#### 4.2.1 Residual Signal Characterization  
Once the residual signal is obtained based on Equation (32), this signal can be successfully used to perform the fault detection and diagnosis process. To apply the DT approach for the problem of fault diagnosis, the specific numerical feature parameter sensitive to the changing signal conditions is essential. Because of the fact that the residual signal itself represents an error between the original signal and that estimated by the proposed observer, this signal cannot be quantified by most of the  
Figure 6: Residual signal based on advanced SMO (ASMO).  
Figure 7: Residual signal based on advanced fuzzy SMO (AFSMO).  
conventional feature parameters that are used in fault diagnosis. However, the parameters that are sensitive to the amplitude can be applied to characterize the residual (i.e., the error) signal.  
To characterize the residual signal by a numerical parameter, first, we split the time sequence into windows of equal size [22]. Then, the feature parameter called the energy of the signal is extracted from these windows to deliver a numerical value that can be used to characterize \"the amount of error\" in the particular window. The formulation of the energy feature parameter is provided below:  
\[E=\sum_{i=1}^{N}{r_{x_{i}}}^{2}. \tag{33}\]  
Here, \(r_{x_{i}}\) is the \(i\)th sample of the residual signal and \(N\) is the total number of samples. When the different types of mechanical faults appear in REBs, the amplitudes of the vibration signals change drastically. This signal behavior strongly affects the residues as well. Thus, the use of the energy as a numerical feature for characterizing the windows of the residual signals is a reasonable choice because the value of the energy is closely related to the amplitude of the sequence being investigated. Moreover, since the residue represents the error between the two signals, the values of its samples can be both positive and negative. The application of the energy feature allows us to consider the error values located on both sides from zero because of the squared term presented in Equation (33). Figure 8 shows the residual signal characterization based on the energy for normal and faulty conditions. As can be seen from this figure, the extracted feature parameters corresponding to different system's conditions are well separable in 1D feature space which means that the proposed methodology can be efficiently applied for purposes of REB fault diagnosis.  
Figure 8: The energy of the residual signal based on the AFSMO: (**a**) original, (**b**) zoom frame.
## 2 Materials and Methods
### Device Fabrication and Characterization  
Electroluminescent (EL) devices, based on a multilayer structure have been fabricated onto patterned ITO coated glass substrates from Xin Yang Technology (90 nm thick and sheet resistance below 20 \(\Omega\)/sq). Prior to organic layer deposition, the ITO substrates were carefully cleaned. The organic materials (from Aldrich and Syntec) are deposited onto the ITO anode by sublimation under high vacuum (<10\({}^{-6}\) Torr) at a rate of 0.2-0.3 nm/s. The common structure of all the devices (**A-G**) is the following: 10 nm of CuPc/40 nm of \(\alpha\)-NPB/ 10 nm of TcTa/ 20 nm of emitting layer/50 nm of TPBi/1.2 nm of LiF and 100 nm of aluminum as the cathode. The copper phtalocyanine (CuPc) is used as the hole injection layer (HIL), \(N\),\(N^{\prime}\)-diphenyl-\(N\),\(N^{\prime}\)-bis(1-naphthyl)-1,1\({}^{\prime}\)-biphenyl-4,4\({}^{\prime}\)-diamine (\(\alpha\)-NPB) and Tris(4-carbazoyl-9-ylphenyl)amine (TcTa) as hole transporting layers (HTL) and 1,3,5-tris(\(N\)-phenylbenzimide-2-yl)benzene (TPBi) as the electron transport layer (ETL). The emitting layer is the pure compound (devices A and D) or a guest/host system (devices B, C and E-G) with the compound used as a dopant in 1,3-Bis(\(N\)-carbazolyl)benzene (mCP) matrix. The guest/host layer is obtained by co-evaporation of the two materials and the doping rate is controlled by tuning the evaporation rate of each material. In this study, the thicknesses of the different organic layers were kept constant for all the devices. The active area of the devices defined by the overlap between the ITO anode and the metallic cathode was 0.3 cm\({}^{2}\). The current-voltage-luminance (I-V-L) characteristics of the devices were measured with a regulated power supply (Laboratory Power Supply EA-PS 3032-10B) combined with a multimeter and a 1 cm\({}^{2}\) area silicon calibrated photodiode (Hamamatsu). The spectral emission was recorded with a SpectraScan PR650 spectrophotometer (Photo research, North Syracuse, NY, USA). All the measurements were performed at room temperature and at ambient atmosphere, with no further encapsulation of devices.
# A Semantic and Knowledge-Based Approach for Handover Management
## 4 Evaluation
### Performance Metrics and Traffic Generation  
We compared SIM-Know to SSF and AHP-TOPSIS in terms of the number of handovers, number of throughput drops, handover latency, and various well-known network performance metrics (throughput, delay, jitter and packet loss) [42]. The quantity of handovers is the number of transfers an end-device makes when it moves from one place to another [43]. The throughput drops represent the times that the number of bytes transmitted falls to zero because of a handover [28]. The handover latency is the time that elapses between the instant that the user-device sends the last link-going-down message to the serving AP and the moment at which the end-user device establishes the connection with the target AP [3].  
In the emulation experiments, scripts for generating traffic were developed by using the iPerf3 [44] and D-ITG [45] tools. We used D-ITG to generate VoIP flows with audio  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Wireless technology & 802.11n, 802.11 ac \\ \hline Emulation area & 180 \(\times\) 180 m \\ \hline Carrier frequency & 2 GHz \\ \hline Channel bandwidth & 20 MHz \\ \hline Transmission power of cells & 25/14 dBm \\ large-range/short-range & \\ \hline Path loss model from cells & Log-Distance Propagation Loss/ITU-R P1283 \\ \hline Emulation time for HighMobility & 30 s \\ \hline Emulation time for ModerateMobility & 80 s \\ \hline Emulation time for SlowMobility & 180 s \\ \hline TCP traffic & Flows with constant inter-departure time between packets (1000 pkts/s) and constant packets size (512 bytes) \\ \hline VoIP traffic & Flows with audio code (G.711.2-84 Kbps and 50 pkts/s) transmitted using real time protocol and voice activity detection \\ \hline \end{tabular}
\end{table}
Table 2: Experiment Setup.  
code (G.711.2 - 84 Kbps and 50 pkts/s) transmitted using real-time protocol and voice activity detection. We used iperf3 to generate TCP flows with constant inter-departure time between packets (1000 pkts/s) and constant packet size (512 bytes).
## 3 DTN Solutions for IoT
### Literature Review
#### 3.3.2 Routing-Based  
This category is about routing in DTN for IoT environments. In [34], the solution uses a delay-tolerant approach for data routing and carrier nodes selection to minimize delay across the network. In [35], the authors proposed a generic Prediction Assisted Single-Copy Routing (PASR) scheme for underwater DTNs; PASR utilizes the greedy algorithm ACPG (Aggressive Chronological Projected Graph) for mobility prediction using historical information about the network connectivity. The authors of [27] proposed a buffer management algorithm called SmartGap for DT-WSNs (Delay Tolerant-WSNs). SmartGap targets the Quality of Information (QoI); the value of a single packet is estimated by means of its contribution to the recreation of the original signal. Reliable Multicast Disruption Tolerant Networking (RMDTN) [36] is a routing protocol that supports multicast in DTN, a designated ferry is used as a data mule to achieve better resource utilization and data delivery.  
\"IoB-DTN\" [37] is a DTN multi-hop protocol designed for IoT applications running on urban bike sharing system based sensor network. It is based on DTN flooding strategies where knowledge on the network is mitigated by redundancy of packets. However, this redundancy costs energy as part of packet transmission. The authors of [38] introduced a new DTN routing protocol optimized for IoT called Scheduling-Probabilistic Routing Protocol using History of Encounters and Transitivity (Scheduling-PROPHET). The protocol's basic idea is using the history of encounters to calculate delivery predictability, in order to deduce if a carrier node will transmit a message or drop it. However, the protocol performs better with large buffer and Bluetooth as a transmission interface for all nodes, which is not the case in real IoT environments.
# Novel Composite Powders with Uniform TiB\({}_{2}\) Nano-Particle Distribution for 3D Printing
One of the advantages of these particles is that they have a well-documented crystallographic orientation relationship [5,6] which provides high coherency, thus (1) acting as a nucleus during the solidification of Al [5]; and (2) lowering the solid-particle interfacial energy to improve particle engulfment during solidification [6]. Besides, previous works have proved that the rapid solidification process and the decrease of the particle size can improve the particle engulfment during solidification [7, 8, 9, 10]. Here we show that a uniform distribution of a high fraction of TiB\({}_{2}\) nanoparticles in Al-based metal matrix powders was achieved by gas atomization solidification processing through the combined effects of coherency among the metal-diboride interface, supercooling and a nanoscale particle size. The resulting Al-based composite powders exhibited a fine grain structure, with uniformly dispersed TiB\({}_{2}\) nanoparticles, and thereby are promising candidates for nanocomposite synthesis. Since one of the major challenges in the laser-based additive manufacturing (AM) field (e.g., selective laser melting) is the severe limitation of powder materials with acceptable laser processability [11, 12, 13], the introduction of pre-embedded nanometer-sized TiB\({}_{2}\) into the metal matrix (e.g., Al-Cu-Mg in this study) would help to expand the powder materials' palette for AM due to the higher laser absorptivity of TiB\({}_{2}\) compared to the Al matrix [14]. Furthermore, since the nanometer-sized TiB\({}_{2}\) particles are embedded into spherical, micrometer-sized composite powders obtained by gas atomization, the powder flowability is not jeopardized.
## 3 PAI-Rife Algorithm  
### Redefined the Frequency Deviation Factor  
It is known that when the estimated frequency is in the center region of the two quantization frequency points, the estimation accuracy of the Rife algorithm is higher. Nevertheless, the estimation accuracy is lower when close to the quantized frequency point. To overcome the shortcomings of the Rife algorithm and reduce the effect of spectral leakage, we added a Rife-Vincent (I) window [29] to the raw sinusoidal signal and redefined the frequency deviation factor.  
The Rife-Vincent (I) window function is defined as  
\[h(t)=h_{\tau}(t)\frac{4^{M}(M!)^{2}}{(2M)!}\sin^{2M}\!\left(\frac{\pi t}{T} \right), \tag{1}\]  
where, \(0\leq M\ll N/2\) is the order of the window function, \(T=N/f_{\rm s}\) is the sampling period, \(h_{\tau}(t)\) is the rectangular window.  
Due to the symmetry of the DFT of the real sequence, only the positive frequency part of the spectrum is considered here. The spectrum of the windowed signal is expressed as  
\[F(k)=(-1)^{M}(M!)^{2}\frac{\Delta N}{2}\frac{\sin[2\pi(k-k_{0})]}{\pi\prod \limits_{n=-M}^{M}(k-k_{0}+n)}, \tag{2}\]  
where, \(A\) is the amplitude of the sinusoidal signal, \(N\) is the sampling number of DFT.  
Assuming that the maximum spectral value is \(F(k_{1})\), the corresponding quantization frequency point index is \(k_{1}\); the sub-maximum spectral value is \(F(k_{2})\), and the corresponding quantization frequency point index is \(k_{2}\); the quantization index corresponding to the actual frequency of the signal is \(k_{0}\). The actual frequency of the signal is always between \(k_{1}\) and \(k_{2}\), and the interval between these two indices is \(r=\pm 1\). It is known that the sampling frequency and the actual frequency do not satisfy the relationship of integer multiples in practice, so there is a deviation \(\delta_{0}\) between \(k_{1}\) and \(k_{0}\), i.e., the frequency deviation factor. It satisfied the relationship \(0\leq\delta_{0}\leq 1/2\). According to the relationship between the frequency spectra, the frequency deviation factor is obtained using the ratio of \(F(k_{1})\) to \(F(k_{2})\). These two spectral values are obtained using Formula (2).  
\[F(k_{1})=(-1)^{M}(M!)^{2}\frac{\Delta N}{2}\frac{\sin(\pi\delta_{0})}{\pi\prod \limits_{n=-M}^{M}(\delta_{0}+n)}, \tag{3}\]  
and  
\[F(k_{2})=(-1)^{M}(M!)^{2}\frac{\Delta N}{2}\frac{\sin[\pi(\delta_{0}-r)]}{\pi \prod\limits_{n=-M}^{M}(\delta_{0}+n-r)}. \tag{4}\]  
[PERSON] et al. gave the relevant parameters of the Rife-Vincent (I) window [15]. When \(M=0\), there is no tailing phenomenon in this window function, and the influence on the signal is minimal. Therefore, our redefined frequency deviation factor can be expressed as  
\[\delta_{0}=r\frac{F(k_{1})}{F(k_{1})+F(k_{2})}. \tag{5}\]
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
## 7 Implementation
### Package Format  
One of the premises that the scheme meets is that it is lightweight in terms of size and the speed of computation, so it fulfills the requirements of the devices of the IoT. Therefore, the size of the message or package has been reduced in the implementation to be as small as possible in order to use the smallest possible space in memory and in order to have fast communication between devices. Because of this, also the storage format of each of the elements that make up the message has been optimized. Thus, the following measures have been taken to serialize these elements:  
* Graphs: Graphs are serialized by denoting their adjacency matrices into integers, because this is their fastest implementation. This serialization has been also improved by using hexadecimal rather than integer numbers. In this case, the storage size is smaller, but the speed serialization is quite slow. Thus, a faster serialization has been chosen compared to a slight improvement in the package size, so graphs have been serialized through their adjacency matrices. For instance, given the adjacency matrix: \[\left(\begin{array}{ccccc}0&1&1&0&1\\ 1&0&1&0&0\\ 1&1&0&0&1\\ 0&0&0&0&1\\ 1&0&1&1&0\end{array}\right)\] it can be represented in one dimension as: \[01101101001100000110110\] This one-dimensional representation is serialized by converting it into an integer of eight digits, formed of five integers separated by the character \",\" \[13,20,25,1,22\]
* Solutions: Solutions have been serialized by using lists of integers separated by the character \",\" because both types of solutions, the Hamiltonian cycle and the isomorphism between graphs, can be represented as lists. For example, if the isomorphism between two graphs is \(1\to 2\), \(2\to 5\), \(3\to 1\), \(4\to 3\), \(5\to 4\), using array indexes as original nodes and the values of the array as the values of the new nodes, the isomorphism is represented as: \[2,5,1,3,4\]
* Segments: Each segment of the message is serialized in hexadecimal. Besides, all of the segments, but the first one, are encrypted as explained in previous sections. The last segment contains a secret encrypted by the key that can be obtained with the solution to the challenge of the previous segment. For instance, the aspect of a segment containing an isomorphic graph and challenge and response corresponding to the graph is the following, expressed with hexadecimal characters: \[A324D0E3F19\]
* Message or package: The complete message is the concatenation of all of the generated segments. To separate the segments, the character \"\(|\)\" is used. An example of a package with three segments is: \[A324D0E3F19\]
###### Abstract
The comparative results demonstrated that the RMS tracking errors of the ARISE controller were less than those of the SM controller across every tested scenario, which indicates that the ARISE controller is more robust than the SM controller for the considered switched system. Overall, the proposed ARISE controller can reduce chattering, handle model uncertainties, and account for dynamic parameter switching. Because of this, the controller can be used with a wide range of real-world engineering systems, including robotic manipulators, spaceflight systems with switched dynamics, mobile robots, and many more. To illustrate the practicality of ARISE control, an additional simulation was conducted on a soft robotic end-effector.