esa-sceva/satcom-chunk-collection huggingface dataset:
# Transactive Energy to Thwart Load Altering Attacks on Power Distribution Systems
## 5 Guarding against Load Altering Attacks  
The recently adopted reliability standard PRC-024-1 mandates that a generator's protective relays should tolerate frequency disturbances that fall within the interval of (7). According to the same reliability standard, the protective relays can disconnect the generators from the grid whenever the frequency stays out of the interval in (7) for a specific minimum duration. In such cases, the power system operator must resort to emergency actions to bring the frequency back to the safe interval in (7) and prevent disconnection of generators and a subsequent blackout. For instance, when the frequency falls below 59.5 Hz, as a last resort the power system operator must trigger the under-frequency load shedding (UFLS) and curtail the total power consumption in the power grid ([23], p. 13). Although the emergency load curtailment is effective at correcting the frequency disturbances, it causes a large number of electric loads to suffer a disruption in their power supply.  
Instead of resorting to the emergency load curtailment, the frequency disturbances can be corrected by the reaction of electric loads with flexible power consumption. In this approach, a group of flexible loads respond to the frequency disturbances long before the power grid reaches a point where emergency load curtailment becomes inevitable. This approach and its role in thwarting load altering attacks are discussed in the next section.
## 1 Introduction
The determination of the absorption and scattering coefficients can be performed with a measurement setup using frequency domain near-infrared spectroscopy [18], a frequency domain imaging system [19] or a broadband spectroscopy setup [20]. Goniometric methods [21] or the utilization of integrating spheres [22; 23; 24] are also commonly used. Goniometric techniques allow for the establishment of the directional distribution of scattered radiation. For materials of a substantial thickness, however, the directional characteristic exhibits a near-Lambertian distribution due to the blurring of directional properties. Moreover, obtaining the total scattered flux requires numerical integration over the entire hemisphere, i.e., employing the measured directional distribution of scattered radiation. Measurements using integrating spheres can be performed in configuration with one or two spheres. When using two spheres, advanced algorithms have to be used to eliminate the influence of the spheres on one another (especially during measurements with wide sample ports) [9; 25]. This problem can be solved by using only one sphere. In this case, the sample is examined for two modes of operation--transmissive and reflective. The absorption coefficient and reduced scattering coefficient can be calculated based on the Kubelka-Munk model [26]. Scattering parameters are mainly dependent on the wavelength, therefore selecting the right wavelength for a measurement is crucial. The spectrometer measurements require a broadband light source, which must be properly collimated--which is even challenging in some commercial solutions. Furthermore, the power of such a beam is highly limited, which, when combined with popular miniature spectrometers (with low sensing ability), causes low spectral power density. Therefore, detection setup noise influences the results. In this paper, a measurement system for the quasi-spectral determination of absorption and scattering parameters of tissue phantoms for veterinary applications is presented. This solution is characterized by a relatively simple configuration and data analysis. Moreover, an acceptable alternative for spectral measurements is provided, allowing for the determination of the absorption and scattering parameters for a set of selected wavelengths which are used in veterinary medicine.
# A Signaling-Free Underwater Code Division Multiple Access Scheme
## 2 An Overview of the Characteristics of the Underwater Channel and System Model  
### The Characteristics of the Underwater Channel  
In this paper, we consider [PERSON]'s underwater channel model [1], the most widely used underwater channel model, which derives the signal attenuation depending on the frequency and the distance. It also derives the underwater ambient noise power spectral density depending on the frequency. Frequency and the distance between transmitter and receiver mainly determine the attenuation of an acoustic wave. Hence, the overall attenuation can be expressed as a function of the frequency and distance, which is given by  
\[A(l,f)=A_{0}l^{k}a(f)^{l} \tag{1}\]  
where \(l\) is a distance between the transmitter and the receiver (km),\(f\) is signal frequency (kHz), \(A_{0}\) is the normalizing constant, and \(k\) is the spreading factor. The spreading factor has a value between one and two depending on the depth. \(k=1\) indicates a cylindrical spreading that characterizes shallow water communication, and \(k=2\) indicates a spherical spreading that characterizes deep water communication. In general, \(k=1.5\) is often considered as a practical spreading value. Therefore, we assume the spreading factor as 1.5 and the normalizing constant as 30 dB. We note that these values reflect the characteristics of a quiet, deep sea [20]. \(a(f)\) is the absorption coefficient and is expressed in dB/km for \(f\) in kHz:  
\[10\log a(f)=0.11\frac{f^{2}}{1+f^{2}}+44\frac{f^{2}}{4100+f^{2}}+2.75\times 10^ {-4}f^{2}+0.003 \tag{2}\]  
This equation is generally valid for frequencies above a few hundred Hz. For lower frequencies, the following formula may be used:  
\[10\log a(f)=0.002+0.11\frac{f^{2}}{1+f^{2}}+0.011f^{2} \tag{3}\]
## 3 Results and Discussion
### Adsorption Efficiency of LPD Biomass
#### 3.2.7 Adsorption Isotherms  
In order to describe the adsorption equilibrium, the remaining heavy metal ion concentrations (\(C_{e}\), mg/g) in the solution and the adsorption capacity (\(q_{e}\), mg/g) were measured and correlated to each other. Langmuir and Freundlich adsorption models (Equations (3) and (4)) were used to fit the isotherm data obtained at different initial ion concentrations (Section 3.2.4) [23; 36]. The analysis results are shown in Figure 5a,b, while the fitted parameters are listed in Table 5. According to the results, the Freundlich isotherm fits the data better than the Langmuir model, with correlation coefficient (\(R^{2}\)) values of 0.989 and 0.884, respectively. The maximum adsorption capacity, \(q_{m}\), calculated from Langmuir isotherm (\(K_{L}\) 0.0074 L/mg), corresponding to the monolayer coverage, was 91.32 mg/g, however,the \(K_{F}\) value indicates higher affinity for Pb\({}^{2+}\) adsorption, compared to \(K_{L}\). The equilibrium factor \(R_{L}\) (dimensionless), Equation (11), is one essential feature of the Langmuir equation, describing the adsorption shape and, therefore, the degree to which the adsorption process is favored: \(R_{L}>1\), unfavorable monolayer process; \(R_{L}=1\), linear; \(0<R_{L}<1\), favorable; and \(R_{L}=0\), irreversible [41].  
\[R_{L}=\frac{1}{1+K_{L}C_{0}} \tag{11}\]  
The obtained \(R_{L}\) values (Table 5) are between zero and the unity, thus indicating a favorable adsorption process. The Freundlich model is also characterized by the heterogeneity factor, \(1/n\), with value of 0.662 indicating good adsorption of Pb\({}^{2+}\) ions by the adsorbent LPD. Generally, the adsorption is a more suitable fit to Freundlich, and both the separation factor (\(R_{L}\)) and the heterogeneity factor (\(1/n\)) indicate a favorable adsorption process on a heterogeneous surface with a tendency toward chemisorption mechanism.
## 1 Introduction  
In the modern world, switching from analog to digital television offers the user several advantages, such as high-definition (HD) video, six-channel surround audio (5.1), and data-broadcasting services. To make full use of the new audiovisual experience, the reception antenna must fulfill some requirements. Therefore, the design is very challenging for printed channel-bandwidth receiving antennas operating in the ultrahigh frequency band (UHF) [1].  
Another well-known and important aspect of this challenge is that wireless communications suffer from interference that affects performance in terms of network capacity. This interference may arise, for example, from frequencies assigned to mobile networks (GSM, 3G, LTE, etc.) amplified by the input low-noise amplifier following the antenna. The upper limit of the digital television (DTV) band is next to either the GSM or LTE band [2]. Unlike other existing broadband antenna solutions, our novel proposed antenna avoids that interference because it has bandwidth close to a UHF IV and V band TV channel.  
Printed antennas have several advantages, such as ease of manufacturing, reduced cost, and low volume. They are also thin in one dimension, making them suitable to operate in indoor environments and placed on the wall like a photograph or art frame. This makes such antennas a promising choice for DTV reception. However, a better solution in this application is to design an antenna with adjacent-channel rejection. Thus, the goal is to have the necessary bandwidth centered on the reception frequency while rejecting adjacent signals. In this framework, we designed an antenna that is intended for digital-terrestrial-television (DTT) reception.  
The next section presents the state of the art. In Section 3, the problem of bandwidth is addressed. In Section 4, the antenna design is discussed, and its structure and dimensions are presented. In Section 5, antenna performance is substantively discussed, making use of several simulation results and practical measurements. Lastly, Section 6 concludes the paper.
## 3 Results and Discussion
### Results at Alkaline pH  
At alkaline pH, DMPO is degraded faster than at near-neutral pH, both alone and with phenanthroline (same intensity, see Figure S11), and the three-line signal attesting the DMPO degradation is observed even in the absence of copper or hydrogen peroxide. This is consistent with easier nucleophilic addition at alkaline pH.  
With CuSO4 (exp 17), the signal exhibits mostly DMPO degradation products, whereas with Cu-Phen (exp 18), the DMPO-OH signal can be clearly observed (Figure S12). Therefore, the Cu-Phen solution (the main species being CuPhen(OH)2 in this case) was analyzed in the presence of the sodium formate scavenger (exp 19, Figure S13), leading to the generation of the typical DMPO-COO\({}^{-}\) signal, and thus confirming that hydroxyl radicals are generated under such conditions. The generation of HO\({}^{\bullet}\) in the absence of hydrogen peroxide can be rationalized by the catalytic reduction of soluble oxygen by Cu-Phen under alkaline conditions [39; 40; 41].  
As previously examined under near-neutral conditions, the influence of DMPO on the electronic structure of the Cu\({}^{\text{II}}\) ion was tested at alkaline pH. The difference in the 100 K-EPR spectra of Cu-Phen recorded at both pHs (see Figures S19 and S23) is consistent with the binding of a hydroxo ligand at the Cu site [36]. When DMPO is added, a new EPR spectrum with well-resolved hyperfine couplings is observed, distinct from that obtained with CuSO4 at alkaline pH. This agrees with the fact that DMPO can coordinate copper at both alkaline and near-neutral pHs (Figure 8).  
When H2O2 is added to the Cu-Phen system (exp 20), a DMPO-OH signal appears (Figure 9). HO\({}^{\bullet}\) scavenging with DMSO (exp 21) leads to the disappearance of the DMPO-OH signal and the apparition of DMPO-CH3, thus confirming that the DMPO-OH signal is only due to the presence of hydroxyl radicals (Figure 9a). As a confirmation, the formation of DMPO-COO\({}^{-}\) is observed in the presence of sodium formate (exp 22, Figure 9b). However, the generation of HO\({}^{\bullet}\) is less intense (Figure 10) and slower (Figure S15) than at lower pH. Note that as observed at near-neutral pH, the hydroxyl radical is not produced in the alkaline H2O2/CuSO4 system (Figure S16).  
Figure 8: X-band EPR spectra at 100 K of DMPO + Cu–Phen with and without NaOH-[Cu] = 0.15 mM, [DMPO] = 60 mM.
## 2 Materials and Methods
### Feature Description
#### 2.2.1 Cooling Analysis  
As a first step, each frame has been split into 20 adjacent regions (region's size is equal to 20 \(\times\) 15 pixels) to study the local cooling dynamic along heat excitation sliding direction over time. Successively, the average temperature among the region's pixels has been computed for each region and for each time frame. The presented cooling analysis has been independently conducted for each region and considers the study of the one-dimensional curve composed of the average temperature values over time. The first value of this curve is the average temperature of the time frame whose average value was greater than 50% with respect to the previous time frame, whereas the last value corresponds to the average value of the last time frame. The cooling analysis section in Figure 2 aids understanding the process presented above.  
During the welding process, a huge quantity of heat is stored locally to the joint and is released to the surrounding environment in the next phase. The way this heat is cumulated and exchanged is deeply related to the welding's penetration and internal structure. Considering the [PERSON]'s cooling law, the derivative of the heat exchange \(Q\) (Joule) transfer versus time, or in other words, the cooling rate, is set proportional to the temperature (\({}^{\circ}\)C or K) gradient between the specimen \(T\) and the environment \(T_{a}\) through heat transfer coefficient \(\alpha\) (W/m\({}^{2}\)) and the exposed surface \(A\) (m\({}^{2}\)).  
\[\frac{\partial(Q)}{dt}=-\alpha A(T-T_{a}) \tag{1}\]  
In Equation (1), which is applicable only for one-dimension heat transfer between body and environment and represents a good simplification of the studied process, the cooling transient in time is defined as amplitude peak and time extension ratio. The segmented signal was described by a sum of two exponentially decaying signals \((a\cdot e^{-b\cdot t}+c\cdot e^{-d\cdot t})\) by means of four parameters, i.e., \(a\), \(b\), \(c\), \(d\), that accounted for curve amplitude (temperature, \((a\), \(c)\)) and velocity damping scale (\(b\), \(d\)). Temperature curve dynamic was derived by minimizing the fitting error of the real curve, selecting the best parameters with least square difference regression. To characterize the shape of the cooling curve, following the heat excitation, the following features were considered:  
1. Multiplicative \((a,c)\) and dumping factors \((b,d)\) for exponential signals.
2. Triangle area and triangle side ratio defined considering peak temperature (triangle height) and time interval between maximum temperature and minimum temperature reaching (triangle base). See part \(A\) of Figure 2 for the graphical representation of the aforementioned features.
3. Root mean squared error in fitting real curve with least square algorithm, which weights previously extracted features.
## 3 Results and Discussion
### HPLC-PDA Method Validation
#### 3.2.4 Analyte Recoveries  
The recovery results for L7 GC, CDR, C7 dGlu, and DSM using the standard addition method were as follows. For C7 GC, the recoveries from the 5.00, 50.00, and 125.00 ug/mL solutions were 106.04%, 99.24%, and 105.62%, respectively; the CDR recoveries from the 6.25, 62.50, and 156.25 ug/mL solutions were 113.18%, 99.55%, and 104.80%, respectively; the C7 dGlu recoveries from the 5.00, 50.00, and 125.00 ug/mL solutions were 119.35%, 96.83%, and 104.01%, respectively; and the DSM recoveries from the 6.25, 62.50, and 156.25 ug/mL solutions were 127.07%, 103.07%, and 109.68%, respectively. The %RSD values for recovery ranged from 0.14 to 1.73% (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Compound 1 **} & \multirow{2}{*}{**Recursion Equation 2 **} & \multirow{2}{*}{**\(r^{2}\)**} & \multirow{2}{*}{**LOD **} & & & **LOQ** \\  & **(min)** & **(ug/mL)** & **(ug/mL)** & **(ug/mL)** & **(ug/mL)** \\ \hline L7 GC & 7.445 \(\pm\) 0.02 & 3.00–360.00 & y = 11,715.29x \(-\) 5916.88 & 1.0000 & 2.152 & 6.521 \\ CDR & 9.445 \(\pm\) 0.01 & 5.00–600.00 & y = 5316.01x \(-\) 10,240.04 & 0.9999 & 1.927 & 5.840 \\ C7 dGlu & 10.121 \(\pm\) 0.02 & 3.00–360.00 & y = 5487.39x \(-\) 15,317.53 & 0.9996 & 0.379 & 1.147 \\ DSM & 15.034 \(\pm\) 0.03 & 5.00–600.00 & y = 1783.97x \(-\) 12,666.06 & 0.9991 & 0.901 & 2.732 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Retention times, linear ranges, regression equations, coefficients of determination (\(r^{2}\)), limits of detection (LODs), and limits of quantitation (LOQs) of L7 GC, CDR, C7 dGlu, and DSM for the developed HPLC-PDA method (\(n=6\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Compound 3 **} & \multirow{2}{*}{**Conc. (ug/mL)**} & \multirow{2}{*}{**Intra-Day (\(n=5\))**} & \multirow{2}{*}{**Inter-Day (\(n=5\))**} \\ \cline{3-4}  & & **Measured Conc. (\(\upmu\)g/mL)** & **RSD (\%)** & **Measured Conc. (\(\upmu\)g/mL)** & **RSD (\%)** \\ \hline \multirow{3}{*}{L7 GC} & 10.00 & 8.26 & 0.42 & 8.19 & 1.31 \\  & 100.00 & 100.46 & 0.20 & 97.95 & 1.96 \\  & 250.00 & 244.08 & 0.18 & 245.49 & 0.87 \\  & 12.50 & 12.28 & 0.56 & 12.32 & 1.69 \\ CDR & 125.00 & 119.56 & 0.54 & 118.95 & 0.77 \\  & 312.50 & 300.17 & 0.11 & 297.71 & 0.63 \\  & 5.00 & 5.06 & 0.92 & 5.08 & 1.29 \\ C7 dGlu & 50.00 & 48.74 & 0.13 & 49.27 & 1.41 \\  & 125.00 & 122.60 & 0.32 & 123.86 & 1.65 \\  & 12.50 & 11.33 & 1.93 & 11.35 & 1.41 \\ DSM & 125.00 & 123.97 & 0.24 & 125.73 & 1.17 \\  & 312.50 & 305.70 & 1.02 & 301.60 & 1.95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Inter- and intra-day precision of L7 GC, CDR, C7 dGlu, and DSM at low, medium, and high concentrations for the developed HPLC-PDA method.
## 2 Results and Discussion
### Effect of Composition of Copolymer Catalysts on Activity in Aerobic Oxidation of PMT
For the copolymer catalysts with lower cross-linking degrees, where the distance between the NHPI moieties is relatively long, the measured spectra show features typical of \(N\)-hydroxyphthalimide. In the XPS N 1s spectrum the peak at binding energy (E\({}_{\text{b}}\)) of 401.6 eV corresponds to the photoelectron emission from N atoms in the N-OH species [47]. On the other hand, the oxygen atoms present in the NHPI functionalities give two characteristic peaks in the O 1s spectrum. The photoemission at E\({}_{\text{b}}\) = 532.1 eV comes from O atoms in the C=O groups, while at E\({}_{\text{b}}\) = 533.9 eV--in the N-OH groups. The quantitative proportion of individual oxygen species is roughly maintained in relation to that resulting from the NHPI structure. The anomaly is found for the catalyst with the higher cross-linking degree (60%NHPI_7%DVB), in which self-condensation of adjacent NHPI groups occurred. In this  
Figure 4: XPS N 1s and O 1s spectra collected for the fresh 60%NHPI_3%DVB, 60%NHPI_5%DVB and 60%NHPI_7%DVB copolymer catalysts. case, the additional component appears in the N 1s spectrum at E\({}_{\rm b}\) = 402.7 eV that can be assigned to N atoms in a more oxidized environment (e.g., in N=O). Such clear features of this groups cannot be identified in the O 1s spectrum, because the photoemission of electrons from O atoms in N=O overlaps with the photoemission from O atoms in two other types of oxygen species present in the copolymer structure. In conclusion, in order to achieve the highest activity of the developed copolymer catalysts, the contents of functional monomer and cross-linking monomer should be properly chosen so as to ensure the optimal concentration of active groups and their exposure during the contact of the catalyst with the solvent, while protecting them against the undesirable process of self-decomposition. The most active catalysts containing NHPI in a fairly wide range, from 1.66 (30%NHPI_9%DVB) to 3.78 mmol/g catalyst (60%NHPI_5%DVB), were able to convert 3.47-3.81 mmol O\({}_{2}\) within 6 h of the reaction at 80 \({}^{\circ}\)C in the presence of Co\({}^{2+}\) and AIBN. Under the same reaction conditions, NHPI anchored to the surface of copolymer of 2-hydroxyethylacrylate (HEA) and divinylbenzene (NHPI loading = 2.06 mmol/g catalyst), (aminomethyl)polystyrene (NHPI loading = 1.12 mmol/g catalyst), and (chloromethyl)polystyrene (NHPI loading = 1.65 mmol/g catalyst) exhibited significantly lower O\({}_{2}\) consumption of 0.17 mmol, 0.41 mmol, and 0.38 mmol, respectively [20; 47]. The developed catalysts are, therefore, considerably more active compared to those described previously in the literature.
# Congestion-Aware Geocast Routing in Vehicular Delay-Tolerant Networks
## 4 V-GRADIENT Design  
The major points of improvement attained by benchmarking the different geocast strategies relate not only with a moderated replication process applied outside the messages' regions of interest but also with a dropping policy that can dynamically estimate if it is beneficial to keep messages being carried by nodes located outside the ROI. With this in mind, on one hand, with regard to the replication process, the V-GRADIENT algorithm incorporates the principles of the Spray-and-Wait protocol [9] in a geocast context and, during the spray phase, the methodology adopted by the GSAF protocol [11]. On the other hand, the deployed strategy follows the idea behind the Floating Content algorithm [13], i.e., defines a buffer zone whose range varies according to the network conditions and the ROI radius.
## 2 The Theory of the Smooth Local Subspace Projection Noise Reduction and PE
### SOD and Data Projection
\begin{table}
\begin{tabular}{l l} \hline \hline (1) & _Embedding of delay continues_ \\ \hline \(\bullet\) & Compute the appropriate delay time \(\tau\) and the minimum embedding dimension \(D\). \\ \(\bullet\) & Determine the local dimension by \(k\leq D\) and the global dimension by \(d\geq D\). Then use the global embedding coordinate \((\tau,d)\) to embed the time series into the high dimensional, reconstructed phase space trajectory \(\mathbf{Y}\). \\ \(\bullet\) & Establish a \(k\)-tree of the partitioned embedded points in \(\mathbf{Y}\). \\ \(\circ\) & _SD processing_ \\ \(\circ\) & Find appropriate strands for edge points by padding the end points of \(\mathbf{Y}\), and construct a \((2l+1)\) trajectory strand. Then find the corresponding nearest neighbor strands for the same points. \\ \(\circ\) & Conduct smooth orthogonal decomposition to each strand above, in order to obtain the corresponding \(k\) local dimensional smooth approximations. \\ \(\circ\) & _Shijing and repeating_ \\ \(\circ\) & Replace the points at the center of each strand of its approximation, in order to form new strands. Then average every \(k\) smooth approximation to estimate the filtered points. \\ \(\circ\) & Repeat all of the above steps to get the smoothed out data, namely the denoised time series. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The main steps of the smooth local subspace projection method.