esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation  
### Time-Domain Pulses of MLS Phase-Modulated Lasers  
Based on the transient SBS three-wave coupling theory, we simulated the time-domain waveforms of the output laser and the backward Stokes light after the phase modulation of PRBS-9 at an \(f_{cr}\) of 1 GHz by using MATLAB, as shown in Figure 3; the positions of the first and second longest sequences of the PRBS are shown in this figure. We observed that the output laser produced a strong pulse and a second strong pulse of the backward Stokes light during each PRBS period, which corresponded to the first longest dwell time (\"11111111\") and the second longest dwell time (\"00000000\") in the PRBS.  
In reference to [23], we knew that the strong pulses in the output waveform were generated by the long dwell time in the PRBS and that the laser and Stokes light produced  
Figure 3: (**a**) PRBS signal; (**b**) output laser; (**c**) time waveform of backward Stokes light injected by 1 GHz, PRBS-9 phase-modulated laser.  
Figure 2: Sequence diagram of PRBS-9 with \(f_{cr}\) = 1 GHz.  
corresponding pulses during each PRBS period. This phenomenon was elucidated by establishing a time constant for SBS. The laser power at the passive fiber input is 6.4 W, which corresponded to a time constant of about 5.6 ns for SBS establishment. The duration of each bit of the PRBS-9 signal was T = \(f_{cr}\)/N = 1 ns; the first longest dwell time was nT = 9 ns, which exceeded the SBS establishment time constant; and the spike of Stokes intensity was gradually generated in the time window of the first longest dwell time. The second longest dwell time needed to run (n \(-\) 1)T = 8 ns, which also exceeded the SBS establishment time constant, but the Stokes intensity relaxation oscillation time decreased, and the pulses generated were relatively weak, and the forward output light and backward Stokes light produced second-intensity pulses. A deeper explanation revealed that when the dwell time was longer than the SBS establishment time constant, the laser was equivalent to an unmodulated state, which behaved as a single-frequency signal, which carried a power much higher than the threshold in the actual single-frequency case, resulting in a giant pulse. The third longest dwell time (\"1111111\" \"000000\") was (n \(-\) 2) T = 7 ns, which was close to the time constant of SBS establishment, and the pulses generated were smaller, and in other smaller dwell times, the pulses were not enough time to be established, and SBS could be better suppressed.
## 4 Results and Analysis
### Stand Density Effects
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable** & **Spruce, Fir** & **Larch, Pine** & **Other Coniferous** & **Becch** & **Oak** & **Other Broadleaf** & **All** \\ \hline Number of plots & 5809 & 1001 & 140 & 886 & 238 & 864 & 8939 \\ Age dominant trees & 81 & 95 & 122 & 95 & 80 & 51 & 82 \\ (a) & (15–175) & (15–175) & (15–175) & (15–175) & (15–165) & (15–175) & (15–175) \\ Elevation (m) & 1019 & 914 & 1084 & 741 & 377 & 544 & 880 \\  & (220–2110) & (245–2066) & (259–2212) & (244–1467) & (176–971) & (129–1685) & (129–2212) \\ Number of trees & 1029 & 859 & 741 & 890 & 692 & 1141 & 993 \\ (ha\({}^{1}\)) & (3–10084) & (4–6205) & (5–3544) & (8–9394) & (6–5934) & (1–8917) & (1–10084) \\ Stem volume & 361 & 304 & 268 & 329 & 220 & 200 & 331 \\ (m\({}^{3}\)/ha) & (2–1672) & (2–1205) & (13–726) & (2–1382) & (8–758) & (2–1281) & (2–1672) \\ Basal area & 36 & 33 & 35 & 33 & 25 & 25 & 34 \\ (m\({}^{3}\)/ha) & (1–124) & (1–104) & (4–79) & (1–100) & (2–76) & (1–107) & (1–124) \\ SDI & 738 & 665 & 679 & 655 & 512 & 553 & 697 \\  & (36–2618) & (38–2086) & (48–1705) & (36–2066) & (49–1523) & (36–2649) & (36–2649) \\  & 204 & 206 & 188 & 350 & 209 & 315 & 229 \\ CCF & (7–1308) & (10–969) & (7–697) & (21–1622) & (16–625) & (11–1699) & (7–1699) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary statistics of the inventory plots for period 2007–2009 by dominant tree species. Given is mean and in brackets the data range (minimum—maximum). _SDI_ the Stand Density Index [28], and _CCF_ the Crown Competition Factor [27]. land cover types represent different tree species groups [40] and, due to different coefficients in the BPLUT, affect the MODIS NPP results [8]. Thus, next we are interested if the observed stand density related trends (Figure 3) may differ to MODIS land cover types. Austria consists of a fragmented landscape, varying forest ownership and distinct historical management impacts. Consequently, many of our 3 \(\times\) 3 km areas (nine pixels) include several forest land cover classes. To avoid any impact of this \"mixture effect\" we select only plots which feature nine pixels with only one MODIS land cover type. The land cover types \" deciduous needleleaf forest\" and \"evergreen broadleaf forest\" are excluded as there are very few pixel patches that have this land cover type exclusively (one for deciduous needleleaf forest, two for evergreen broadleaf forest). The results for the \"deciduous broadleaf forest\" (not shown) are similar. To summarize, the results using plots with only one biome type exhibit the same trend when using all data (Figure 3): overestimation of terrestrial NPP by MODIS NPP at low stand density and underestimation at high stand density, which is consistent for both stand density measures (Figure 4). Figure 3: Difference between MODIS NPP and NFI NPP (MODIS minus NFI) grouped by stand density measures: SDI (**top**); CCF (**bottom**). Properties of illustration analogous to Figure 2.
# Feasibility of LED-Assisted CMOS Camera: Contrast Estimation for Laser Tattoo Treatment
## 2 Materials and Methods
The contrast values from each image were then calculated by estimating the differences in the measured intensity values between the non-tattooed (i.e., background) and the tattooed areas with Image J (National Institute of Health, Bethesda, MD, USA) as follows  
\[\text{Contrast}=(\text{I}_{\text{BGD}}-\text{I}_{\text{tattoo}})/\text{I}_{ \text{BGD}} \tag{1}\]  
where \(\text{I}_{\text{BGD}}\) is the intensity value measured from the non-tattooed area (i.e., initial mouse skin as background), and \(\text{I}_{\text{tattoo}}\) is the intensity value measured from the tattooed area. Zero contrast means that the tattoo is removed completely (i.e., \(\text{I}_{\text{attoo}}\) = \(\text{I}_{\text{BGD}}\)). The reduction in the tattoo contrast after the laser treatment was also quantified by normalizing the difference in the calculated contrast values between the pretreatment (\(\text{Contrast}_{\text{pre}}\)) and the post treatment (\(\text{Contrast}_{\text{post}}\)) by \(\text{Contrast}_{\text{pre}}\) as  
\[\text{Reduction}=(\text{Contrast}_{\text{pre}}-\text{Contrast}_{\text{ post}})/\text{Contrast}_{\text{pre}}\times 100\;(\%) \tag{2}\]  
Two weeks after the laser treatment, all mice were euthanized using CO\({}_{2}\) gas. The non-treated and the treated tattooed tissues were stored in 10% neutral-buffered formalin (VWR International, Westchester, PA, USA) for five days. To evaluate the tattoo distribution in the pre- and post-treated areas, all the tattooed tissues were cross sectioned. Subsequently, each cross-section was embedded in paraffin and was imaged to show an overall cross-sectional view of the tattoo distribution using a Huvitz stereo microscope (Meyer Instruments Inc., Houston, TX, USA). Image J was then used to measure the tattooed areas as well as the positions in the acquired images from the pre and the post \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pigment Color** & \begin{tabular}{c} **Wavelength** \\ **(nm)** \\ \end{tabular} & \begin{tabular}{c} **Spot Size** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Fluence** \\ **(J/cm\({}^{2}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Frequency** \\ **(Hz)** \\ \end{tabular} &
\begin{tabular}{c} **Number of Shots** \\ \end{tabular} \\ \hline Black & 1064 & 5 & 1.5 & 1 & 5 \\ Red & 532 & 5 & 0.5 & 1 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Laser parameters used for tattoo treatment. treatment. Thus, the reduction in each tattoo after the laser treatment was quantified by estimating the variations in the tattoo distribution between the two conditions. For a non-parametric statistical analysis, the Mann-Whitney U test was performed on the calculated contrasts and the reduced tattoos using the SPSS program (SPSS Inc., Chicago, IL, USA), and \(p<0.05\) represents the statistical significance.
# Beyond 100G: All-Optical Processor for High-Capacity Access\(\sim\)Networks
## 3 Overview of the Proposed All-Optical Processor
### Task 1: All-Optical Processor as an Equalizer  
The first task of the AOP involves all-optical equalization, as illustrated in Figure 2. This functionality is designed to mirror the inverse of the channel's frequency response as closely as possible. The input signal is first divided into four duplicates using cascaded couplers. Each duplicate is then sequentially delayed by a time increment \(\tau\), which approximates the duration of a single transmission symbol. The system's adaptability is enhanced through operational tuning, which allows adjustment of \(\tau\). After the delay, each signal is amplified and phase-shifted to ensure either constructive or destructive interference across the branches, thereby controlling the signal's gain. The overall output is a linear combination of each branch's input, as described by Equation (1):  
\[P_{\text{out}}(t)=\frac{1}{n}\sum_{i=1}^{n}g_{i}P_{\text{in}}(t-i\tau) \tag{1}\]  
Figure 1: Schematic of the all-optical processor.  
Figure 2: Operation of the AOP at 0 state and optical equalizer.  
The operational principle and schematic of this task highlight the processor's ability to act as an optical equalizer, providing an efficient solution to counteract signal impairments and mitigate bandwidth limitations without relying on conventional DSP techniques. This is particularly advantageous in PONs, which are often bandwidth limited to remain cost effective.
# Improvements of the xAAL Home Automation System
## 3 The xAAL Devices Behavior and xAAL Schemas
### Definition of a Schema, the Type of Device  
Each device is typed, i.e., described by a schema.  
An xAAL schema is a JSON object with a specific form that must validate given CDDL rules (_Concise Data Definition Language_, RFC 8610). See Appendix A.  
The schema provides a bit of semantics for a device and describes its capabilities: a list of attributes that describe the operation of the device in real time (the device announces change of values on the bus), a list of possible methods on this device (mechanism of request replies), a list of notifications that it emits spontaneously.  
* A list of _attributes_. If the value changes, this spontaneously generates a notification to the bus. Each attribute is defined by:
* A unique name that identifies it within the schema;
* A type name relating to the data model section.
* A list of _methods_. Each method is described by:
* A unique name that identifies it within the schema;
* A textual description;
* A list of \"\"in\" arguments to be filled by peers when invoking this method;
* A list of \"out\" arguments returned by the device to peers; Each argument is defined by:
* A unique name that identifies it within this method definition in the schema;
* A type name relating to the data model section.
* A list of related attributes that may be affected by the method (e.g., to be refreshed in an HMI after the method call)
* A list of _notifications_.  
A unique name which identifies it within the schema;
* A textual description;
* A list of \"out\" arguments included in the notification; each argument is defined by:
* A unique name that identifies it within this notification definition in the schema;
* A type name relating to the data model section.
* A _data model_ section, that is to say a list of _data types_ definitions. For each type name:
* A textual description;
* The unit, if any. This unit should be one of the IANA Sensor Measurement Lists (SenML) registry [26]. Note that, contrary to SenML, within the xAAL context the percent symbol \"%\" is used for values between 0 and 100, and not for values between 0 and 1. If none is relevant, use a unit of the International Bureau of Weights and Measures. A standard unit allows automatic processing, data computation, or at least a consistent way for rendering by HMIs;
* A type definition in the form of CDDL rules, for extra processing (to dynamically build software skeleton, a generic HMI, etc.).
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 7 Assessment of the Available Architectures
### Market Category  
This category of use cases requires signals from external sources such as the retailers' channels. Therefore, Architecture 1 is not suitable for such services. Using Architecture 2, market data can be obtained from the public network and, together with metering data coming from the HAN, used to feed services such Energy Management Systems (EMSs), dynamic pricing, or prepaid contracts.  
Architecture 3 generally provides a downlink channel, which can be used to receive data from external sources; additionally, the service provider can notify customers of events or provide real-time data using the customers' own devices such as smartphones. Given the loose requirements of such category, technologies suitable for Architecture 3 include cellular-based technologies as well as LPWAN.  
Architecture 4 also supports this category well, making it possible to provide the service to SMs that only support Architecture 1. Since the transmission frequency is low and the latency is not critical, it is unlikely that the SM to IHD-GW segment is congested even if shared medium technologies, such as PLC-C, are used.
## 2 Problem Statement and Contributions  
Authentication is an important issue of multimedia data protection; it makes possible to trace the author of the multimedia data and to allow the determination of whether an original multimedia data content was altered in any way from the time of its recording. The hash value is a compact abstract of the content. We can re-generate a hash value from the received content, and compare it with the original hash value. If they match, the content is considered as authentic. In the proposed algorithm, we aim to compute the common hashing function \(h_{k}(.)\) for image authentication work. Let \(D(.,.)\) indicate a decision making function for comparing two hash values. For given thresholds \(\tau\), the perceptual image hashing for tamper detection should satisfy the following criteria. If two images \(\mathbf{x}\) and \(\mathbf{y}\) are perceptually similar, their corresponding hashes need to be highly correlated, i.e., \(D(h_{k}(\mathbf{x}),h_{k}(\mathbf{y}))<\tau\). Otherwise, If \(z\) is the tampered image from \(x\), we should have \(D(h_{k}(\mathbf{x}),h_{k}(\mathbf{z}))>\tau\). The main contributions can be summarized as follows:  
1. We propose building the prior information set based on the help of multiple virtual prior attacks, which we did by applying virtual prior distortions and attacks to the original images. On the basis of said prior image set we aimed to infer the clustering centroids for reference hashing generation, which is used for a similarity measure.
2. We effectively exploited the semi-supervised information into the perceptual image hashing learning. Instead of determining metric distance on training results, we explored the hashing distance for thresholding by considering the effect on different images.
3. In order to account for variations in exacted features of different images, we took into account the pairwise variations among different originally-received image pairs. Those adaptive thresholding improvements maximally discriminate the malicious tampering from content-preserving operations, leading to an excellent tamper detection rate.
## 4 Optimization Process
### 2D Initial Geometry  
A 0D-based design of the mixer was done as a first step. After obtaining the average properties of the flow at the mixer input, different approaches for the design were evaluated. For every approach, the flow evolution was considered isentropic, the mass was conserved along the flow channels and no bleeding occurred. Secondly, several design strategies were proposed for the initial mixer in order to obtain an initial geometry that, even for the beginning, provided a promising solution:  
* **Isobaric mixer:** the objective was to design a mixing area such that it matches the static pressure of the core and bypass streams of the flow.
* **Isospeed mixer:** in this design, the mixing areas were imposed so the velocities of the flow streams coincided.
* **Iso-\(\rho\cdot v\) mixer:** in this design, the mixing areas were imposed so the product of velocity and local density of the flow streams coincided.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Coarse (25M Cells)** & **Medium (75M Cells)** & **Fine (155M Cells)** \\ \hline _in_ **(kg/s)** & 5.886 & 5.92 & 5.92 \\ \(BPR(-)\) & 2.61 & 2.55 & 2.55 \\ \(P_{t,outlet}\) **(Pa)** & 15,550 & 16,490 & 16,500 \\ \(P_{t,core}\) **(Pa)** & 16,601 & 17,200 & 17,160 \\ \(P_{t, bypass}\) **(Pa)** & 16,031 & 16,649 & 16,612 \\ \(\pi_{t-t}(-)\) & 1.29 & 1.37 & 1.37 \\ \(\pi_{core}(-)\) & 1.38 & 1.43 & 1.43 \\ \(\pi_{bypass}(-)\) & 1.33 & 1.385 & 1.385 \\ \(\eta_{t-t}(-)\) & 0.7 & 0.73 & 0.73 \\ \(\eta_{core}(-)\) & 0.75 & 0.8 & 0.8 \\ \(\eta_{bypass}(-)\) & 0.68 & 0.72 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Independence of the 3D mesh.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Coarse (9k Cells)** & **Medium (19k Cells)** & **Fine (40k Cells)** \\ \hline _in_ **(kg/s)** & 3.73 & 3.746 & 3.741 \\ \(BPR(-)\) & 2.61 & 2.55 & 2.55 \\ \(P_{t,outlet}\) **(Pa)** & 5730 & 5771 & 5778 \\ \(P_{t,core}\) **(Pa)** & 8380 & 8311 & 8240 \\ \(P_{t,bypass}\) **(Pa)** & 4738 & 4790 & 4810 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Independence of the 2D mesh.  
* **Isomomentum mixer:** this design guaranteed that the axial momentum of the bypass and core flow streams was the same by varying the mixing section areas.
* **Custom mixer:** For this design a custom weighted optimization process was performed to vary the mixing areas. The weights were applied to the stream velocity difference, stream static pressure difference and target outlet velocity. The relative respective weights applied were 1, 5, and 3.  
After evaluating each design (see Figure 7), it was decided to use the custom approach because, as it can be observed, it achieved a good compromise between the discharge pressure comparison--almost equal to the isobaric configuration--and the velocity difference between both branches.
## 2 Materials and Methods
### Net Photosynthetic Rate  
Autotrophic organisms like plants photosynthesize and assimilate CO2 using the energy of light. High levels of chlorophylls can be attributed to increased physiological activity and photosynthesis in plants. Enhanced leaf photosynthesis results in increased phloem sap movement and plant growth [53]. The photosynthetic rate is given by photosynthetic pigment content, but may decrease when plants experience a stress [54]. The environmental stress may cause reduction in the chlorophyll content. This reduction could be due to destruction of the chloroplast and instability of chlorophyll protein complex [55]. The plasma or PAW can potentially also induce stress or otherwise affect the  
Figure 4: Chlorophyll (a + b) content in above-ground parts of lettuce plants after 5 weeks of soil cultivation irrigated with the plasma activated water (PAW), solutions of various concentrations of H2O2 (0.4, 1, 10 mM), NO3– (0.85, 2, 20 mM), H2O2 + NO3– (0.4 mM + 0.85 mM, 10 mM + 20 mM), and control irrigated with tap water. The data are presented as mean values \(\pm\) standard deviation from 3 independent repetitions. The lowercase letters represent statistically significant difference at probability \(p<0.05\).  
plant metabolism. Therefore, we measured the photosynthetic rate of lettuce irrigated with the PAW and correlated it with the chlorophyll contents.  
The photosynthetic rate was positively affected by the PAW irrigation (Figure 5). The highest photosynthetic rate was observed for plants irrigated with solutions with the highest concentration of NO3\({}^{-}\) (20) and the solution of H2O2 (10) + NO3\({}^{-}\) (20). On the other hand, the highest concentration of H2O2 (10) had no effect on photosynthetic rate. This again confirms that NO3\({}^{-}\) plays a very important role in photosynthesis and pigment process compared to H2O2. However, under special conditions even H2O2 can promote photosynthesis (e.g., under the stress) [50]. A comparison of the effects of PAW and the chemically equivalent solution mimicking the PAW, i.e., H2O2 (.4) + NO3\({}^{-}\) (.85), showed slightly better results for the PAW. The result on net photosynthetic rate correlates with the results on photosynthetic pigment content. It indicates that the PAW had a positive effect on both the chlorophyll content and the photosynthetic rate.
# Mono Camera-Based Optical Vehicular Communication for an Advanced Driver Assistance System
## 4 Results
Moreover, \(Th_{L}\) was compared with every bit to detect the high-rate data stream, and \(Th_{H}\) was compared with the intensity level of every two consecutive data bits to detect the low-rate stream. Figure 11: Estimation of distance measurement accuracy. As the light intensity varies with the variation of distance, the data rate remarkably depends on the inter-vehicular distance. To illustrate the effect of transmitted power on data rate, data rate is plotted against distance at two different power levels (i.e., 2 and 5 dB) in Figure 13. The data rate of the high-rate stream of 2 dB power level falls below 200 bps whereas it is approximately 600 bps for 5 dB power level at 25 m distance. The difference in data rate between low-rate streams was also maintained at approximately 50 bps up to 30 m which is a crucial factor in determining link setup time in a high mobility environment. Additionally, the OCC channel capacity was strongly dependent on the camera frame rate, which could be determined from [PERSON]'s channel capacity formula [24]. \[C_{OCC}=F_{\mathrm{C}}(W\log_{2}(1+SINR)), \tag{17}\]  
where \(F_{\mathrm{C}}\), \(W\), and \(SINR\) represent the photo-receptor sampling rate in frames per second, number of attainable pixels per frame, and signal-to-interference-plus-noise ratio for every pixel, respectively. The optical power received depended on both the distance between the transmitter and receiver and the difference between the two threshold levels that played a vital role in the system performance. The bit-error-rate (BER) performance of the proposed system versus the difference between the two threshold levels while keeping the \(Th_{H}\) value constant is shown in Figure 14. When the difference between the two levels increased, the  
Figure 12: Received stripe patterns in the camera receiver. Figure 13: Variation of data-rate with distance at 2 dB and 5 dB transmitter power level. probability of bit corruption decreased, which resulted in a sharp fall of the BER. However, the selection of the optimum threshold value should consider the required illumination level and sensitivity of the IS. The BER of our proposed scheme was deter-mined with the following equation  
Figure 10:0  
where \(T_{d}\), \(S_{i}\), and \(N_{0}\) refers to the threshold value, received power, and noise power spectral density. \(b\) is equal to the transmitted bit value and \(l\) is the transmitted bit sequence length.