esa-sceva/satcom-chunk-collection huggingface dataset:
# Chemical Compositional Changes in Over-Oxidized Fish Oils
###### Abstract  
A recent study has reported that the administration during gestation of a highly rancid hoki liver oil, obtained by oxidation through sustained exposure to oxygen gas and incident light for 30 days, causes newborn mortality in rats. This effect was attributed to lipid hydroperoxides formed in the omega-3 long-chain polyunsaturated fatty acid-rich oil, while other chemical changes in the damaged oil were overlooked. In the present study, the oxidation condition employed to damage the hoki liver oil was replicated, and the extreme rancidity was confirmed. A detailed analysis of temporal chemical changes resulting from the sustained oxidative challenge involved measures of eicosapentaenoic acid/docosahexaenoicenoic acid (EPA/DHA) omega-3 oil oxidative quality (peroxide value, _para_-anisidine value, total oxidation number, acid value, oligomers, antioxidant content, and induction time) as well as changes in fatty acid content, volatiles, isoprostanoids, and oxysterols. The chemical description was extended to refined anchovy oil, which is a more representative ingredient oil used in omega-3 finished products. The present study also analyzed the effects of a different oxidation method involving thermal exposure in the dark in contact with air, which is an oxidation condition that is more relevant to retail products. The two oils had different susceptibility to the oxidation conditions, resulting in distinct chemical oxidation signatures that were determined primarily by antioxidant protection as well as specific methodological aspects of the applied oxidative conditions. Unique isoprostanoids and oxysterols were formed in the over-oxidized fish oils, which are discussed in light of their potential biological activities.  
Keywords:dietary supplement; docosahexaenoicenoic acid; eicosapentaenoic acid; fatty acids; food analysis; fish oils; isoprostanoids; omega-3; oxidation condition; oxysterols; volatiles +
Footnote †: journal: _foods_
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 3 Method and Implementation
### Communication Across IPv6 Networks  
This requirement was evaluated in two ways:  
1. **Lab-based IPv6 testbed.** A Testbed was configured using consumer equipment at the University of St Andrews, including desktop workstations and a commercial IPv6 router. This gave an environment for reproducibility in making measurements of test traffic against the metric \(P_{f}\).
2. **Global IPv6 network.** With connectivity between the University lab and the Global IPv6 network at an Internet Engineering Task Force (IETF) Hackathon (IETF118). This test used the Global IPv6 Internet connectivity between the University of St Andrews (UK) and Prague (CZ).
# FBG-Based Monitoring of Geohazards: Current Status and Trends
## 2 Basics of Fiber Bragg Grating (FBG)
### Sensing Principle
Since then, FBG has been widely used in a variety of fields all over the world. By far, it is one of the most popular FOS technologies for health monitoring of civil infrastructures. A main reason for this popularity is that, with special configurations, FBG can monitor many physical parameters, such as temperature, strain, relative displacement, humidity, and pressure, offering excellent measuring resolution and range, absolute measurement, and modest cost per channel. As the sampling rate of FBG is up to 2 MHz, it is suited for both static and dynamic measurement. Furthermore, because FBGs are passive sensors, they can be either time- or wavelength-multiplexed, which allows for quasi-distributed sensing. Owing to these advantages, FBG is very attractive for geohazard monitoring.
# A Simplified Methodology to Evaluate the Design Specifications of Hydraulic Components
## 2 Pressure: Load History and Damage Factor
Damage is then summed for all cycles in the loading history. What should the damage be at failure? If damage were truly a linear process, the damage at failure would be equal to 1. In simple two step block loading, a sequence of high amplitude cycles followed by low amplitude cycles has a damage sum \(D\) < 1.0. Similarly, a sequence of low amplitude cycles followed by high amplitude cycles ha a damage sum \(D\) > 1.0. In our case, as the succession of peaks of low and high amplitude were randomly distributed, we considered that \(D\) \(\sim\) 1.0. From the pressure spectrum vs. percent cycles (Figure 1), the step damage for every pressure step of the spectrum(\(\varepsilon_{i}\)) can be defined as  
\[\varepsilon_{i}=\
u_{i}P_{i}^{m} \tag{2}\]  
where \(\varepsilon_{i}\) is the spectrum relative damage for the \(i\)th pressure step of a task, \(\
u_{i}\) is the portion of the task in unified percentage during the \(P_{i}\) pressure level, \(m\) is the material coeficient from Basquin equation (\(m\) = 3 for steel for hydraulic cylinders [10]), \(P_{i}\) is the pressure value assigned to the \(i\)th pressure step of a task, and \(i\) is the index that identifies the pressure step number. The damage factor statistical distribution for \(k=1\) (loading task) is  
\[D(\log\phi_{1})=N[4.49,\ 0.25] \tag{4}\]  
Following the same process, the statistical distributions of the other tasks were calculated. For the task \(k=2\) (transport task), the following statistical distribution was considered:  
\[D(\log\phi_{2})=N[3.0,\ 0.31] \tag{5}\]  
To estimate the equivalent damage produced in the cylinder during the full life, we worked with the following data supplied by the machine manufacturer (Table 2). The equivalent damage factor \(\phi_{eq}\) can be obtained as the sum of the damage factors for all the tasks weighted by a time factor \(\pi_{k}\) as follows:  
\[\phi_{eq}=\sum_{1}^{n}\phi_{k}\pi_{k} \tag{6}\]  
Figure 3 shows the statistical distributions of the damage factors of the two considered tasks, as well as the statistical distribution of the equivalent damage. This statistical distributio follows the log-normal type, given that the sum of two (or more) log-normal random variables approaches a log-normal distribution [16]. \[D\big{(}\log\phi_{eq}\big{)}=N\big{[}m_{\phi eq},\ s_{\phi eq}\big{]}=N\Bigg{[} \sum_{k}\pi_{k}m_{\phi k},\ \sum_{k}\pi_{k}\ s_{\phi k}\Bigg{]} \tag{7}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline \(k\) & **Task** & \(T\) **(Hour)** & \(\pi_{k}\) \\ \hline
1 & Loading & 3500 & 0.70 \\
2 & Transport & 1500 & 0.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cylinder data from the machine manufacturer. Figure 2: Statistical distribution of damage factor for \(k=1\) (loading task).
# Sum Rate Optimization Scheme of UAV-Assisted NOMA under Hardware Impairments
## 6 Conclusions  
This paper has investigated the downlink NOMA networks with an UAV relay. To maximize the sum rate of ground users, we decomposed the optimization problem into UAV location optimization with binary variable constraints and power allocation problems. We first converted the MIPP into a standard convex optimization problem with the SCA method. Then the quadratic transform method and Lagrangian dual transformation were used to obtain the power allocation. Simulation results reveal that our proposed algorithm outperforms other benchmark algorithms. As energy efficiency [34; 35; 36] is another crucial index for the future wireless network, we will investigate the energy efficient resource allocation for the UAV-assisted NOMA system in the future. In addition, as the channel between the UAV and users can be with both LoS and NLoS propagation [37; 38], we will jointly investigate optimizing the height of the UAV in our future work.  
Conceptualization, methodology, and software, X.W. and Z.W.; validation and investigation, X.Y. and B.D.; writing--original draft preparation, X.Y.; and writing--review and editing, Z.W. and Z.F. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Natural Science Foundation of China (No. 61701064), Chongqing Natural Science Foundation (No. cstc2019 cyj-msxmX0264), and The Sichuan Regional Innovation Cooperation Project (2022 YGQ0017).  
The authors declare no conflict of interest.
# NN-Based 8 FSK Demodulator for the Covert Channel
## 3 Imperfect SIC Operation  
The severe amplitude and phase fluctuations inherent to wireless channels significantly degrade the BER performance of the signal. Moreover, errors in the demodulation process come from every part of the transmitter and receiver, including antenna systems [20; 21; 22; 23] The typical receiver block diagram is depicted in Figure 8.  
The main sources of distortion considered in our paper are AGC and CE (channel estimator). Other errors can come from ADC (analog to digital converter) and FCO (frequency carrier oscillator) corrector. The impact of the channel gain error and hardware impartments for SIC were presented in [24; 25], whereas the mean-reverting signal strength was presented in [26]. AGC and CE errors lead to non-ideal SIC operation. As a result, SIC cancels only a part of the cover signal.  
The covert signal after non-deal SIC is provided by [15], as shown in Equation (4):  
\[\hat{s}_{2}(t)=\frac{\left(h-\hat{h}\right)s_{1}(t)}{\hat{h}}+\frac{h\cdot s_{ 2}(t)}{\hat{h}}, \tag{4}\]  
where \(\hat{h}\) is a channel gain estimator and represents in our paper AGC and CE errors.  
When the channel gain estimator is equal to zero, i.e., \(e_{x}=\left|h\right|-\left|\hat{h}\right|=0\), then \(\hat{s}_{2}(t)=s_{2}(t)\), that indicates perfect SIC. In the case of covert transmission, when \(\sigma_{2}^{2}<<\sigma_{1}^{2}\), even a small error \(e_{x}\) causes severe damage to the covert signal. In the subsequent discussion, the normalized channel gain estimator error will be used to evaluate the demodulator's performance, provided by [27], as shown in Equation (5):  
\[e_{abs}=\frac{\left|h\right|-\left|\hat{h}\right|}{\left|h\right|}\cdot 100\%. \tag{5}\]  
The example of the IQ constellation of the covert signal after perfect (\(e_{abs}=0\)) and imperfect SIC (\(e_{abs}=2\%\)) for 8 FSK (covert) and 16 QAM as a cover signal is depicted in  
Figure 8: Typical receiver block diagram.  
Figure 9. However, such a graphical presentation does not reflect the detection performance, so the detailed assessment and numerical evaluation must be performed via simulation.
## 3 On the Prospect of TinyML/DL
### Quantization  
The quantization technique refers to the process of reducing the precision of the model parameters, e.g., weights or gradients, from floating-point (e.g., 32-bit or 64-bit) to lower-bit-width representations (e.g., 8-bit integers). For instance, [PERSON] et al. [72] proposed a novel compression method; they combined both pruning and quantization methods and achieved a conversion from the 64-bit floating-point representation to almost 8 bits, while in [73], a 2-bit precision representation was proposed, when running a person detection task, losing approximately only 3% of its accuracy. A new software accelerator library, running on Arm Cortex - M processors, in which kernels are implemented in a way that supports both 8-bit and 16-bit data precision, is presented in [3] and used in [74] in order to smooth out the differences among data representations, which is a hard task for a CPU to run. An interesting quantization pipeline is proposed in [75], in which the weights are saved to an external L3 memory, and in every epoch, only the weights being used are downloaded to an L2 cache with a capacity of 512 KB.
## 2 Materials and Methods
### Electrochemical Measurements  
Electrocatalytic efficacy was rigorously evaluated utilizing an Autolab PGSTAT204 (Metrohm AG, Herisau, Switzerland) electrochemical workstation, structured in a classical three-electrode cell arrangement. This setup included a working electrode comprising a glassy carbon rotating disk electrode (RDE) with a diameter of 5 mm, a counter electrode fashioned from Pt foil, and a Hg/HgO reference electrode (referenced at 0.92 V vs. RHE). For preparatory steps, 5 mg of the catalyst was uniformly dispersed in a solvent blend comprising 300 \(\upmu\)L of isopropanol, 700 \(\upmu\)L of ultrapure water, and 30 \(\upmu\)L of a 5.0 wt% Nafion solution. This dispersion was subjected to a 30 min sonication to ensure a well-homogenized ink. Specifically for ORR processes, the catalyst ink was similarly prepared, though 1 mg of Vulcan XC-72R carbon powder replaced 20 wt% of the catalyst. Subsequently, 10 \(\upmu\)L of this catalyst ink was carefully applied onto the glassy carbon electrode, establishing a catalyst loading density of approximately 0.25 mg cm\({}^{-2}\). The working electrode thus prepared was utilized in the evaluations. Electrolyte solutions, saturated, respectively, with O\({}_{2}\) and N\({}_{2}\), were used to examine ORR and OER activities, with both solutions containing aqueous 0.1 M KOH. For typical measurement protocols, cyclic voltammetry (CV) tests were conducted at a scan rate of 5 mV s\({}^{-1}\), whereas linear sweep voltammetry (LSV) tests proceeded at a scan rate of 10 mV s\({}^{-1}\). The stability of the electrocatalysts under chronoamperometric conditions was ascertained at a rotating speed of 1600 rpm and a potential of 0.7 V over a duration of 10,000 s.
# Signature Split Method for a PQC-DSA Compliant with V2V Communication Standards
## 3 Proposed Simulation
### Application Plan for V2 Verifier
```
0: Param: - Basic-Safety Message: \(bsm\), - public key: \(pk\), - secret key: \(sk\), - signature: \(z[k]\), \(h\), and \(c\) (\(k\) has a value of 4, 6, or 8 depending on the security level, respectively), - payload: \(payload[k/2]\), - verification flag: \(flag\), \(\mathrm{Func}\): - unpack API of Dilithium: \(\mathbf{Dilithium}.\mathbf{Verify}.\mathbf{unpack}\_sig\), - align split signature: \(\mathbf{Align}\_sig\)
1:[run_remote] API
2:... (Generate \(bsm\))
3: z, \(h\), and \(c\leftarrow\mathbf{Dilithium}.\mathbf{Sign}(pk,\,bsm)\)
4:for\(i=0\) to \(k/2\)do
5:if i = 0 then
6: \(payload[0]\leftarrow(bsm||0b100||c||h||\mathbf{z}[0]||\mathbf{z}[1])\)
7:else
8: \(payload[i]\leftarrow(bsm||i||\mathbf{z}[2i]||\mathbf{z}[2i+1])\)
9:endif
10: Sending \(payload[i]\) to \"local car\"
11:endfor
12:...
13:[run_local] API
14:for\(j=0\) to \(k/2\)do
15:if there is no received \(payload\)then
16:return // waiting payload of next remote car
17:endif
18: Receiving \(payload[j]\) from \"remote car\" // random order
19:\((bsm||order||sig[j])\gets payload[j]\)
20:if order < 4 then
21:\(unpacked\_sig[j]\leftarrow\mathbf{Dilithium}.\mathbf{Verify}.\mathbf{unpack}\_sig \_for\_z(sig[j])\)
22:else
23:\(unpacked\_sig[j]\leftarrow\mathbf{Dilithium}.\mathbf{Verify}.\mathbf{unpack}\_ sig\_for\_zch(sig[j])\)
24:endif
25:endfor
26:\(merged\_unpack\_sig\gets Align\_sig(unpacked\_sig)\)
27:\(flag\leftarrow\mathbf{Dilithium}.\mathbf{Verify}(merged\_unpack\_sig)\) // Verify API without unpack
28: process
29:if \(flag\) == SUCCESS then
30: Decomposing \(bsm\)
31:else
32:return // waiting payload of next remote car
33:endif
34:...
```  
**Algorithm 3** Split Way for Crystals-Dilithium
## 1 Introduction  
On 11 September 2016 at 13:10 UTC time a strong, magnitude M\({}_{\rm L}\)5.3, earthquake occurred under the Macedonian capital Skopje. More than 100 people were injured and required medical assistance according to the MIA news agency. Using data from the local and regional seismic stations, the UKIM Seismological Observatory SKO estimated its location at 42.008\({}^{\circ}\) N and 21.488\({}^{\circ}\) E [1,2]. Global seismic records favored a shallow focal depth of about 10 km [3].  
Such tremors create panic among residents because the Skopje region has a history of strong and damaging earthquakes. In 1963, the city was hit by a shallow, magnitude 6.1 earthquake with an intensity rating of IX (Mercalli scale). The event caused heavy losses of life and property, killing more than 1000 people, injuring around 4000, and leaving more than 200,000 people homeless. About 80% of the city was destroyed and many public buildings, schools, hospitals, and historical monuments were badly damaged according to numerous reports [4]. Following the disastrous Skopje 1963 earthquake, a 1964 law was enacted that mandated earthquake resistant design rules for the former territory of Yugoslavia.  
On the morning of the 22 March 2020, an earthquake of magnitude M\({}_{\rm L}\)5.5 struck the Croatian capital city of Zagreb. The maximum felt intensity was VII-VIII (MSK scale), causing one death and injuring 27 people. It was the strongest earthquake in the Zagrebarea in the last 140 years and caused substantial damage in the historical town center. The event parameters were calculated by using data from the Croatian national seismological network and regional seismic stations. According to [5], the earthquake epicenter was located at 45.907\({}^{\circ}\) N and 15.970\({}^{\circ}\) E in the Medvednica Mountain at a focal depth of 10 km.  
Unreinforced masonry buildings from pre-1945 suffered substantially more damage than reinforced concrete (walls or infilled frames) or confined masonry buildings from the post-1964 period. Such extensive damage from a moderate sized earthquake can be attributed to other factors besides the construction date and building materials, possibly the source mechanism and focal distance in combination with local site effects, which can significantly amplify or de-amplify the shaking.  
Beside intensity maps [6], limited strong motion records of near field ground shaking during moderate earthquakes exist, and those that are available are quite important for testing the validity of the building standards. Here, we aim to analyze response spectra from the Skopje September 2016 and Zagreb March 2020 earthquakes recorded on the closest stations and then apply the Nakamura method and discuss the results in the context of the existing geological maps at each site.
# Microseismic Electronic Fencing for Monitoring of Transboundary Mining in Mines
## 2 Factors Affecting Localization Accuracy
### Analysis of Observing Systems
In addition, the nested arrangement with only 24 sensors has an average error of 0.736 m in the horizontal direction and 2.409 m in the depth direction, which is slightly better than the regular arrangement of 36 sensors, which reflects the advantage of the nested system in terms of the number of sensors, as will be discussed in detail in Section 2.4. The nested system after adding 12 in-well sensors has the highest localization accuracy, with average localization errors of 0.844 m and 0.884 m in the horizontal and depth directions, respectively. The analysis results indicate that in the observation systems arranged on the ground, the regular, circular, and nested arrangements demonstrate relatively high localization accuracy in the horizontal direction, but the errors are larger in the depth direction. And the nested arrangement adds some sensors in the well, the error in the depth direction gets smaller and the localization accuracy increases significantly. Overall, among the five observing systems mentioned, the nested observing system proposed in this paper has the smallest localization error for events at the boundaries of the observing area.
## 3 Results  
### Effect of Varying Optical Properties on Diffuse Reflectance  
Frequency-dependent diffuse reflectance predicted by the two-layered diffusion model for different optical properties of each layer is plotted in Figure 2; for each plot in the figure one parameter is varied while the other four are held constant. Generally, the reflectance decreased with the increased absorption coefficients, while it increased with the reduced scattering coefficients. The most distinct separation in diffuse reflectance for different values of \(\mu_{a1}\), \(\mu_{a2}\) and \(\mu_{s2}\)\({}^{\prime}\) was observed at relatively low frequencies. For varying \(\mu_{s1}\)\({}^{\prime}\) values, the five reflectance curves show distinct differences across the frequency range with the best separation occurring around 0.2 mm\({}^{-1}\) (Figure 2(a2)). Overall, Figure 2 demonstrates that there is good separation in diffuse reflectance over a large range of spatial frequencies for  
\begin{table}
\begin{tabular}{c c c c} \hline
**Curve Fitting Method** & **Estimated Parameter** & **Known Parameter** & **Fitted Model** \\ \hline Five-variable fit & \(\mu_{a1}\), \(\mu_{s1}\)\({}^{\prime}\), \(\mu_{a2}\)\({}^{\prime}\), \(d\) & – & Equation (2) \\ Four-variable fit & \(\mu_{a1}\), \(\mu_{s1}\)\({}^{\prime}\), \(\mu_{a2}\), \(\mu_{s2}\)\({}^{\prime}\) & \(d\) & Equation (2) \\ Two-variable fit & \(\mu_{a1}\), \(\mu_{s1}\)\({}^{\prime}\) or \(\mu_{a2}\), \(\mu_{s2}\)\({}^{\prime}\) & the other three & Equation (2) \\ One-variable fit & \(\mu_{a1}\) or \(\mu_{s1}\)\({}^{\prime}\) or \(\mu_{a2}\) or \(\mu_{s2}\)\({}^{\prime}\) & the other four & Equation (2) \\ One-layered model & \(\mu_{a1}\), \(\mu_{s1}\)\({}^{\prime}\) & – & Equation (1) \\ \hline \end{tabular}
\end{table}
Table 1: Different curve fitting methods for estimating optical properties of two-layered simulation samples.  
different \(\mu_{s1}\)\({}^{\prime}\) values, whereas less separation in diffuse reflectance for different \(\mu_{a1}\) values, and even less separation for different values of \(\mu_{a2}\) and \(\mu_{s2}\)\({}^{\prime}\), suggesting the difficulty of accurately estimating the bottom layer. That is because light must propagate through the top layer before interacting with the bottom layer tissue, in which case only a small number of energy packets reemitted from the bottom layer can be detected. In Figure 2(b1,b2), the value of _mfp_1 is equal to 0.5 mm, while the top-layer thickness is 2 mm, which implies that light would have travelled more than four steps before entering into the bottom layer. Hence, the separation in diffuse reflectance for different values of \(\mu_{a2}\) and \(\mu_{s2}\)\({}^{\prime}\) would increase with the decreased top-layer thickness due to the fact that the detected light have more interaction with the bottom layer. The results from the four plots in Figure 2 further suggest that better estimation of the four optical parameters (\(\mu_{a1}\), \(\mu_{s1}\)\({}^{\prime}\), \(\mu_{a2}\) and \(\mu_{s2}\)\({}^{\prime}\)) could be achieved when low spatial frequencies are used.