esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Based on the principle and definition of ultra-tight integration, this paper analyzes the performance improvements of the receiver tracking loops with Doppler aid from an INS. The models of every error source in the standard receiver and the ultra-tightly integrated GNSS/INS receiver are established and compared, respectively. By comparison of the tracking performances and the error distributions, we can conclude that the ultra-tight integration can improve the tracking performance of the receiver by compensating the receiver antenna's dynamics and reducing the dynamic stress noise. Moreover, the performance comparisons of the PLLs aided by different grade IMUs demonstrate that the quality of the IMU has an effect on the tracking performances in the ultra-tightly integrated GNSS/INS receiver.
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 2 Related Work
### Beamforming  
Recently, beamforming technology has been widely discussed and applied [26; 27; 28; 29]. [PERSON] et al. mentioned that beamforming is composed of multiple beams from the antenna array with four or eight antennas [26]. The antenna array can adjust the transmission direction by combining beams between antennas. [PERSON] et al. adopted the beamforming for transmitting information and energy to improve the performance of the NOMA multiuser systems [27]. [PERSON] et al. proposed an online energy consumption minimization algorithm with latency constraints in order to minimize the energy consumption of multiple devices [28]. [PERSON] et al. considered a full-duplex wireless-powered communication network, in which the AP is equipped with an antenna array to transmit energy to devices and receive information from devices [29]. They proposed a wireless energy allocation scheme based on space division to optimize energy allocation between users under the max-min user fairness constraint. In summary, we attempt to combine the energy harvest with beamforming to improve the energy efficiency of wireless sensor devices.
# Evaluation of the Nutritional Quality and Shelf Life of
## 4 Discussion
### pH Analysis over Shelf Life  
After the fermentation and maturation of the processed meats, the pH tends to drop because of the production of lactic acid by starter cultures inoculated with probiotics that help that decrease, especially in the first days post-maturation [23].  
The results of day 0 between S2 and S3 did not show significant difference; however, both were different from S1. It was noticeable over time that assay S2 showed the lower statistic variation when it came to pH values.  
The results referring to the first 28 days of storage are coherent with the values obtained in the acidity analysis. The higher acidity value was found in assay S2, according to the lowest pH value found in the same period. Similar pH values (between 5.36 and 5.5 in the first days of storage) were found in a study with salami inoculated with _L. plantarum_[23]. That pH follow-up is extremely important to assess product stability, since its decrease can provoke water loss and a reduction in the meat's water holding capacity, which makes the product dry easier and reduces water activity levels [35].  
pH values were compared at the end of the 84-day storage time, the end of the maturation period, and a significant difference in the three assays was observed. Formulations from assays S1 and S3 had the highest values, which gradually increased over time because of the decay of lactic acid production since the first month of storage. That indicates that _Lactobacillus paracasei_ (present in assay S3) did not help fermentative activity in an efficient way for over 28 days. The same thing happened in another study using fermented processed meat which had their pH increasing over the storage period of 120 days [29]. The increase in pH can be assigned to the appearance of basic compounds from the degradation of proteins [36].
# Dirac Equation-Based Formulation for the Quantum Conductivity in 2D-Nanomaterials
## 4 Full Quantum-Mechanical Calculation  
Returning now to a ballistic regime with flow in the \(x\)-direction, uniform in \(y\), the distance \(x\) in (10) is to be replaced by \(L_{x}\). Moreover, for a band of energies, the classical electron density \(N\) is to be replaced by a density of states, weighted over allowed energies, by the occupation probability and integrated from the thermal energy level, \(k_{B}T\) at room temperature, up to the chemical potential \(\mu\). This means:  
\[J=-2|e|v_{F}\frac{1}{L_{x}L_{y}}\exp-\frac{2x}{L_{F}}\int_{k_{B}T}^{\mu}D(E^{ \prime})f(E^{\prime})\left[\frac{1+\frac{e}{E^{\prime}+|e|V_{0}}}{1+\frac{e}{ 4E_{F}+E^{\prime}+|e|V_{0}}}\right]^{\frac{1}{2}}dE^{\prime} \tag{12}\]  
The pre-factor 2 in the above expression is due to two electrons of opposite spins finding place in a state.  
Since we are considering a ribbon configuration, we assume the density of states of a two-dimensional system with parabolic \(E-k\) characteristic first. The particular case of graphene will be considered later. In (12)  
\[N_{S}=\frac{\pi k^{2}}{\frac{(2\pi)^{2}}{L_{x}L_{y}}}\quad\text{is the number of states in $k$-space, }E^{\prime}=\frac{(\hbar k)^{2}}{2m_{e}}\]  
\[D(E^{\prime})=\frac{dN_{S}}{dk}\frac{dk}{dE^{\prime}}=\frac{k}{2\pi}L_{x}L_{y} \frac{m_{e}}{\hbar^{2}k}=L_{x}L_{y}\frac{m_{e}}{2\pi\hbar^{2}}=const.\]  
The probability of occupation, shifted for the presence of bias, is [32]:  
\[f=\frac{1}{1+\exp\frac{E^{\prime}-(\mu-\mu_{0})}{k_{B}T}}\]  
where it is recalled \(E_{V}=|e|(V_{0}+\overline{E}\frac{L_{x}}{2})\), and \(\mu=\mu_{0}+E_{V}\). Again in the limit of weak fields, we find from (12):  
\[J=-|e|\frac{2E_{F}}{\pi\hbar^{2}v_{F}}\exp-\frac{2L_{x}}{L_{F}}\int_{k_{B}T}^ {\mu}f(E^{\prime})\left(1+\frac{1}{4}\frac{|e|\overline{E}L_{x}}{E^{\prime}+| e|V_{0}}\right)dE^{\prime},\]  
and the conductivity is:  
\[\sigma=-\frac{dJ}{d\overline{E}}=\frac{e^{2}E_{F}\tau^{\prime}}{2\pi\hbar^{2}} \exp-\frac{2L_{x}}{L_{F}}\int_{k_{B}T}^{\mu}f(E^{\prime})\frac{1}{E^{\prime}+| e|V_{0}}dE^{\prime}\ \Omega^{-1}, \tag{13}\]where \(\tau^{\prime}\equiv\frac{L_{x}}{v_{F}}\) is the \"transit\" time through the sample. At low temperatures, \(\mu\to E_{F}\), \(f(E^{\prime})\to 1\); considering \(L_{x}\ll L_{F}\), the exponential is close to unity and (13) reduces to:  
\[\sigma=\frac{e^{2}E_{F}\tau^{\prime}}{2\pi\hbar^{2}}\int_{k_{B}T}^{\mu}\frac{1 }{E^{\prime}+|e|V_{0}}dE^{\prime}. \tag{14}\]  
Approximating the integral by the average value of its argument \((E^{\prime}=E_{F})\times\) the interval, we get  
\[\sigma=\frac{e^{2}\tau^{\prime}\mu}{2\pi\hbar^{2}}\frac{E_{F}}{E_{F}+|e|V_{0}}. \tag{15}\]  
If, moreover, \(|e|V_{0}\ll E_{F}\), that is usually the case, (15) reduces to Equation (18) of ref. [36]. Equation (13) above, however, provides a more realistic ballistic model under more general conditions of room temperature, non negligible bias and finite lifetime in the band for parabolic \(E-k\) characteristics.
# Quaternion Signal Analysis for Detection of Broken Rotor Fault Degrees in Induction Motors
## 2 Methodology  
As BRB faults in induction motors develop gradually, establishing an effective model to predict future behavior with straightforward calculations becomes crucial. This approach not only helps prevent severe damage but also ensures timely maintenance, safeguarding your equipment and investment. By modeling quaternion-based statistical features, the proposed method characterizes BRB through predictive wear functions from a healthy motor state to three BRBs. Unlike traditional approaches that provide only discrete fault levels, this model enables a continuous fault severity assessment. The algorithm comprises two main stages: (1) the Quaternion Signal Analysis (QSA) method and (2) the fitting and evaluation procedures. Figure 1 illustrates the method's flowchart for detecting BRB severity, utilizing four vibration signals as input. Each stage of the method, as shown in Figure 1, is detailed in the following subsections.
## 2 A General Overview on SSNs
### SSNs Architecture
#### 2.4.2 SSNs Network Architecture
According to the previous proposals for the concept of sharing different applications by a single hardware solution, Figure 4 shows our SSN's proposed architecture. It consists of the traditional WSN infrastructure layer that is concerned with sensor deployment and in the maintenance and sensing of the desired phenomena. It is followed by the network hypervisor layer which abstracts the infrastructure of the lower layer using an abstraction manager, and then deals with it as a global resource. The hypervisor uses multiple managers, such as infrastructure manager, which contains information about the whole infrastructure, (sensor, geographical area, hardware types,..), resource discovery, scheduling, and allocation managers to discover and allocate the available resources, and then schedule them to be dedicated to a certain VSN for a certain duration, which is controlled by a resource hosting manager that contains information regarding hosted applications and users. VSN manager is concerned with the creation and removal of VSN hosted by the network. A security manager is responsible for user authentication and privileges on the network. Finally, spectrum manager manages access to the available spectrum, organizes them based on the requirements of served VSNs, and then provides a controlled isolation between the produced VSNs [28]. Thus, the network hypervisor provides global management and monitoring for the SSN through infrastructure, spectrum, and RF managers. Simply, the network hypervisor can be defined as a software that is responsible for separation between the deployed infrastructure and the offered services over the network, which is done by converting the deployed infrastructure into multiple logical units, called VSNs, while providing the isolation between them to keep the security and privacy. By this way, users can access it as if he/she were directly using the physical resource, and get the optimal infrastructure utilization by serving multiple concurrent applications and services. Thus, it handles the deployment of new applications over the network (without interrupting the current ones), information sharing, and data transmission among them, with an optimal routing cost and better energy conservation.
# Reversible Data Hiding in Encrypted Images Based on the Mixed Multi-Bit Layer Embedding Strategy
## 3 Proposed Scheme
### Data Embedding
\begin{array}{rl}(l_{1}-1)\times\log_{2}k-n_{f}&>0\\ l_{1}&>\frac{\log_{2}k+n_{f}}{\log_{2}k}\\ l_{1}&>n_{f}+1\end{array}  \tag{13}\]  
Formula (13) gives the judgment conditions for selecting two strategies. The derivation here is based on \(l_{1}\), which applies to all AHBs. The results show that the block length affects the selection of the embedding strategy in the case of given \(T\). When the block length is greater than \(n_{f}+1\), the pure capacity of the hidden block can be guaranteed to increase. However, when the block length does not meet this condition, it is also acceptable. The capacity increase caused by the small difference front can offset the cost of the large difference and have a balance, which can also realize the overall capacity increase. However, this situation depends on the gray values distribution. The more uniform the distribution, the lower the requirements on the block length and the easier it is to hide data. Thus, with this step, a mixed embedding strategy with a condition is created and used to generate encrypted transmission image containing secret data.
# Quantum PSO-Based Optimization of Secured IRS-Assisted Wireless-Powered IoT Networks
## 4 Results and Discussion
Figure 7 depicts the relationship between sum secrecy throughput and the number of IRS elements \(N\) for the four different optimization strategies: the proposed QPSO and bisection method, QPSO (phase-optimal), standard PSO, and fixed time, random phase. The figure demonstrates that secrecy throughput increases as the number of IRS elements increases, showcasing the impact of the IRS on improving network performance. For \(N=35\), the proposed method achieves 19.4 bps/Hz, which is 58% higher than phase-optimal (12.3 bps/Hz) and 85% higher than standard PSO (10.5 bps/Hz). The QPSO and bisection method achieved the highest throughput, indicating that optimizing the phase shift and time allocation yields the best results. The QPSO (phase-optimal) and standard PSO scenarios also saw improved throughput, although to a lesser degree than the optimal method. The fixed time, random phase strategy resulted in the lowest performance, reinforcing the significance of optimization in both phase configuration and time allocation. Figure 8 illustrates the variation in sum secrecy throughput as the maximum available transmit power \(P_{max}\) at the H-AP increases, measured in decibel milliwatts (dBm). As observed, increasing the transmit power leads to a corresponding increase in sum secrecy throughput across all scenarios. The proposed method achieves 17.9 bps/Hz at \(P_{max}=30\) dBm, which is 24% higher than phase-optimal (14.4 bps/Hz), 63% higher than standard PSO (11 bps/Hz), and 83% higher than fixed time, random phase (9.8 bps/Hz). This improvement is attributed to greater available power, which provides more freedom for the optimization algorithms to converge towards better solutions. Among the scenarios, the QPSO and bisection method yielded the highest throughput, followed by QPSO (phase-optimal), and then PSO. Again, the fixed time, random phase strategy performed the worst. The proposed method, which jointly optimizes both time allocation and phase shift, significantly outperformed the other scenarios, demonstrating the benefits of comprehensive optimization in maximizing network secrecy rates. Figure 6: Sum secrecy throughput versus the number of users \(K\).
## 6 Natural Compounds Inhibiting Protein Expression of Metabolic Enzymes
### Inhibition of FASN through Suppressing SREBPP1  
As upstream regulators in lipid metabolism signaling pathways, sterol regulatory element-binding proteins (SREBPs) play a key role in FASN gene expression [113]. Quercetin is a natural molecule produced in apples, onions, teas and berries, and possesses antihistamine and anti-inflammatory activities. [PERSON] et al. reported that quercetin could remarkably reduce SREBPP1 and FASN expression at both mRNA and protein levels in mouse stromal cells [114]. Similarly, platyphylloside, a diarylheptanol isolated from _Betula platyphyllma_, also inhibited the expression of SREBPP1, and its downstream targets FASN and SCD-1, leading to attenuated adipocyte differentiation in mouse embryonic fibroblasts (preadipocytes) [44]. Furthermore, the lipid portion extracted from a special blue-green alga could also decrease FASN and SCD-1 expression through targeting SREBPP1 [115].
# Electro-Optic Sensor for Measuring Electrostatic Fields in the Frequency Domain
## 2 Method and Experimental Results
### Possible Sources of Measurement Error
#### 2.6.1 Space Charge Build-Up  
Space charge on the boundaries of an EO crystal increases exponentially due to a DC or quasi-DC electric field acting on the crystal. The macroscopic internal electric field, which is also responsible for the Electro-Optic effect, follows Equation [18,22]:  
\[E_{in}=\frac{E_{ext}}{\epsilon_{r}}e^{\frac{-t}{\tau}} \tag{12}\]  
where \(E_{in}\) is the internal electric field, \(E_{ext}\) is the DC external field, \(\epsilon_{r}\) is the dielectric permittivity of the electro-optic material and \(\tau=\frac{\epsilon_{ext}}{\epsilon_{r}}\) is the _relaxation time constant_ that depends on the dielectric permittivity of the material and its conductivity.  
Figure 16: Comparison of the experimental and analytical results.  
The movement of the free charge carriers to the boundaries affects both of the sensor's crystals. The charge build-up in the bias crystal is due to the applied DC bias voltage. The variation of the internal electric field can be compensated by tuning the sensor and varying the DC component of the bias voltage.  
The space charge effects in the sensing crystal can not be compensated through the proposed scheme and it is difficult to quantify their impact on the measurement accuracy since they coincide with other parasitic effects such as temperature-related drifts. However, the sensor was designed to minimize the build-up of the charge through several means:  
* Following Equation (12), the charge build-up and decay depend on the relaxation time of the material and, therefore, on its conductivity. Lithium Niobate exhibits the highest time constant among common EO materials (\(10^{5}\) s) which in principle allows steady EO conditions in a large time window relative to the measurement time. From Equation (12), the internal electric field in a LiNbO\({}_{3}\) crystal decays by 1% after almost 17 min.
* Since the build-up of space charge is caused by free charge carriers, it is important to avoid effects which could increase the number of free carriers. The _photorefractive effect_ excites electrons to the conduction band and also leads to a variation of the material's refractive indices. The rate of excitation depends on the wavelength of the laser beam passing through the crystal. Figure 17 shows the absorption coefficient of \(LiNbO_{3}\) doped with manganese at different concentration levels.  
While some peaks are caused by the used dopant, the remaining characteristic is due to the properties of \(LiNbO_{3}\), showing an almost zero absorption coefficient at 1550 nm. The crystals used in the developed sensor are doped with magnesium oxide and not manganese. However, the lack of literature regarding the energy levels of defects introduced through magnesium oxide doping at wavelengths out of the visible light range suggests that the defects are located only in that range. Therefore, the wavelength of the laser beam chosen for the presented sensor is 1550 nm.
## 3 Related Works
### Routing Protocols  
The routing protocols determine the path from a source node to a destination node. In [15], a robust location based routing scheme was proposed. To achieve the robustness and energy efficiency, a novel routing protocol called Hop by Hop Vector Based Forwarding (HH-VBF) was suggested. HH-VBF overcomes the limitations of the existing VBF such as a small data delivery ratio and high throughput fluctuations through a self-adaption procedure. That means the decision of forwarding a packet to the next node is based on hop-by-hop.  
Energy Aware Routing for Real-Time and Reliable Communication (EARQ) [16] provides real-time and reliable delivery of packets by considering the energy awareness of the next path. It is one of the proactive routing protocols that maintain a routing table on each node. To achieve real-time delivery, only paths that may deliver a packet in time are selected. EARQ may send a redundant packet through an alternate path to improve the reliability.  
These routing protocols aimed to improve reliability of the packet transmission at the network layer by selecting the next proper node. These studies tried to modify routing algorithms of TWSNs considering to features of UWSNs. We designed our checkpointing scheme focused on clustering based routing algorithms, because the most typical UWSN architecture is based on a hierarchical structure.
## 4 Routing in 3D Sensor Network without Internal Holes
### Distributed Mapping Algorithm
#### 4.2.2 Distributed Volumetric Harmonic Map  
By now, we have arrived at a spherical harmonic mapping, which maps the network boundary one-to-one to a sphere. Next, we apply a volumetric harmonic map by minimizing the volumetric harmonic energy under the computed spherical boundary condition. More specifically, if a node is on the boundary, it simply keeps it current virtual coordinates obtained above. On the other hand, a non-boundary node, e.g., Node \(i\), determines its 3D virtual coordinates via the volumetric harmonic map. Similar to the spherical harmonic mapping discussed above, Node \(i\) is associated with a three-vector metric, _i.e._, \(u_{i}\), which represents its volumetric virtual coordinates. Node \(i\) iteratively calculates \(E_{i}\) and \(u_{i}\) according to Equations (6) and (7). However, note that the edge weight (_i.e._, \(k_{ij}\)) is now computed according to Definition 10, instead of Equation (5). When \(E_{i}\) differs by less than a small constant, \(\delta\), between two iterations for all nodes in the network, the volumetric harmonic mapping algorithm terminates, yielding the virtual coordinates for every internal node. An example of the result after volumetric harmonic mapping is given in Figure 6k-m. Again, the algorithm is distributed, where a node only needs to communicate with its one-hop neighbors in each iteration. The proof of its convergence can be found in the Appendix.
## 4 Hydrocarbon Potential of formations in Malaysia
### Organic Geochemical Characteristics of Rock Formations in Sabah Region
#### 4.2.5 Hydrocarbon Generation Potential of Rock Formations in Sabah  
The Sabah region has many formations that can generate hydrocarbons, probably commercially. The Pinangah, Tanjong, Sandakan, Kapilit, and Ganduman formations can all generate hydrocarbons in commercial quantities. These formations had very high TOC values, high hydrocarbon yield (S2), and very good GP. The Pinangah and Tanjong formations generate type I kerogen, whilst Sandakan, Kapilit and Ganduman formations generate type III kerogen. The Temburong and Setap formations had the slightest chance of generating hydrocarbons in the Sabah region, due to the low TOC nature, poor GP, and the matured nature of the organic matter in the formations. The other formations, such as Sebahat, Beliat, Meligan, West Crocker, and Kalabakan formations, had a moderate to low chance of generating hydrocarbons, due to the relatively average TOC and matured nature of the formations and with some having poor GP.