esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Carbon Credits in Bamboo Projects
[107] found that project-related income and persistence in and perception of a project can largely influence a farmer's overall commitment to a bamboo project. Although [PERSON] et al. [108] broadly reviewed critical challenges in forestry carbon projects, there is minimal research on comprehensively analyzing the barriers and issues of developing bamboo projects and the potential solutions. Nevertheless, numerous studies have assessed the great potential of future bamboo carbon projects. For instance, [PERSON] et al. [56] demonstrated that three bamboo species in the Indian Eastern Himalaya, _Bambusa nutans, Dendrocalamus giganteus_, and _Melocamma buccifera_, have huge ecosystem carbon stocks (44.46-163.28 t/ha), indicating the considerable potential for developing CDM and REDD+ projects. Similarly, in Northeast India, village bamboo in the traditional home garden scheme showed a total carbon storage of 50.1 t ha\({}^{-1}\), providing the local small-hold farmers the opportunity to earn carbon credits under CDM [30]. Recently, new forms of bamboo carbon credits trading have emerged. For example, the government-led Liangshan Cooperative in Anji County, Zhejiang Province, has gained increasing public attention in China. The forest rights of farmers are transferred to the professional village cooperatives for unified management; after verification, a carbon project package is formed, and the transactions are carried out through the Liangshan Cooperative [109]. In addition, 80% of the net income from the bamboo project transaction are returned to the village collectives and townships [109]. The future global potential of bamboo forests in developing carbon projects regarding the area and emission reduction amount remains unclear, especially for undeveloped bamboo forests, which could turn into projects in all kinds of platforms, including compliance markets, voluntary markets, cooperatives, and banks.
# Performance Analysis and Parameter Optimization of the Optimal Fixed-Point Quantum Search
## 1 Introduction  
Grover search [1; 2] provides a quadratic speedup over classical search algorithms, and has been proven optimal [3; 4; 5; 6]. However, there still exists the souffle problem [7], i.e., the success probability will decline if the algorithm iterates too many times. Therefore, the Grover algorithm can only apply to the case where the optimal number of iterations [8] can be determined.  
Based on the quantum amplitude amplification [9; 10; 11; 12] and phase-matching methods [13; 14; 15; 16], a fixed-point quantum search algorithm [17] has been proposed, where the final state of the algorithm converges to the target states and the success probability increases as the number of iterations grows. This algorithm applies to the case where the lower bound (denoted by \(\lambda_{0}\)) of the fraction of target items (denoted by \(\lambda\)) is known. However, the advantage of quadratic speedup is lost [18; 19].  
Fortunately, [PERSON] et al. developed the optimal fixed-point quantum search (OFPQS) algorithm [20], which achieves both the fixed-point property and optimal query complexity, where the success probability for any unknown \(\lambda\geq\lambda_{0}\) can be always no less than a given value (denoted by \(1-\delta^{2}\)) between 0 and 1, as long as the given condition on the number of iterations is satisfied. However, this condition is approximate and not tight, i.e., the required number of iterations might be further reduced.  
In addition, the lower bound of success probability \(1-\delta^{2}\) of the OFPQS algorithm is lower than 100%, because setting \(\delta=0\), the algorithm will change back to the original fixed-pointalgorithm [17] and thus lose the quadratic speedup [20]. However, in practical search problems, it is often desired to find a target item eventually, rather than just succeed with probability above a lower bound. For this, a natural strategy is to make multiple trials of the OFPQS algorithm with \(\delta>0\) until success. This brings up a new problem, that is, how to choose the optimal parameters to find a target item as soon as possible.  
In this paper, we expect to give the minimum feasible number of iterations, analyze the extreme and minimum properties of the success probability, and further derive the optimal parameter of the OFPQS algorithm to find a target item with the minimum query complexity.  
The paper is organized as follows: In Section 2, we briefly introduce the OFPQS algorithm. In Section 3, we derive the optimal number of iterations and analyze the properties of the success probability of OFPQS algorithm. In Section 4, we give the selection method of optimal parameter \(\delta\). Section 5 comprises of discussions about the effects of the optimal number of iterations and the optimal \(\delta\), as well as the upper bound of expected queries of applying the OFPQS algorithm to find a target item certainly. Finally, a brief conclusion is given in Section 6.
## 3 Short-Reach Systems Classification and Enabling Technologies
### Data Center Network Requirements and Challenges
#### 3.2.1 Resilience  
Network resilience is one of the main concerns in DCNs. For example, optical interconnects that employ centralized architectures in which a single ultra-high-capacity optical switch is supporting a significant amount of servers are highly susceptible to single-point-of-failure issues. Consequently, robust architectures are required to address the issues. A notable way of attending to this is network redundancy. For instance, each network element (i.e., aggregate switch) can be connected to multiple core switches with multiple links. Apart from the fact that this approach can offer improved connectivity, it also ensures better resilience in the event of an interface or link failure [74].
# Low-Cost Full Correlated-Power-Noise Generator to Counteract Side-Channel Attacks
## 4 Hiding Countermeasure for SCA Attacks
### Area Overhead and Frecuency Degradation  
Table 2 summarizes the degradation characteristics in terms of timing and resource occupation resulting from the inclusion of the hiding countermeasure in comparison to the unprotected AES. The AES designs are implemented in a Spartan-6 Xilinx FPGA. As was mentioned in Section 3, the unprotected AES128-256 uses 3616 LUTS while the protected CPNG AES uses 4666 LUTS. The proposal has an area overhead of 29% when the LUTs are considered and a 7.38% in slices. Regarding maximum operating frequency, the proposal present no frequency degradation.
# Applying Game Theory to Teach the PERT Scheduling Method
## 5 Analysis of Results
### Discussion of Results  
The main findings from the qualitative and quantitative analyses carried out in this research are summarized below.  
From a qualitative point of view, the evaluation to assess the students' perceptions of the experience showed that they highlighted the clarity of the content taught, the professor's preparation, and the rapid understanding of the subjects covered. They also mentioned that Game Theory was a powerful tool for decision-making, highlighting its ease of use. However, they considered that its application to more complex problems present in real-world contexts could make its application difficult. Finally, when asked about the use of Game Theory to learn a scheduling method such as PERT, the answers were unanimous in that it was a handy teaching tool, significantly improving their understanding of decision-making, a cornerstone of Game Theory, but also very valuable to address the problem of uncertainty and variability present in construction projects and which PERT addresses.  
Based on the quantitative analysis performed, the results obtained showed that in the four categories evaluated (\"Identification of Activities\", \"Duration of Activities\", \"Critical Path\", and \"Variability and Decision\"), there were statistically significant differences between the pre- and post-test, confirming an evolution in the understanding of the contents after having used Game Theory to learn PERT. An interesting result is that although the differences between the pre- and post-test were significant in all categories, the smallest difference was in \"Identification of Activities\", which could be related to the fact that this skill is required by students in any other scheduling method (i.e., not only in the PERT method). As such, students acquire it previously in the same Construction Planning and Scheduling course and related courses in their study plan by studying other scheduling techniques, such as Gantt Chart, CPM (Critical Path Method), and others. On the other hand, the most significant differences in the results were found in the \"Variability and Decision\" category, which corresponded precisely to all those questions where Game Theory and PERT play a much more critical role. Similarly, the detailed question-by-question analysis corroborated this finding, where the questions that showed the most significant differences were also those related to the \"Variability and Decision\" category.  
Figure 4: Spiderweb plot for questions with statistically significant differences.
## 4 Design of TLQoS
### Parameter Estimation
#### 4.9.2 Delay Estimation  
The Delay Estimator module of a node \(n_{i}\) estimates the average node delay, \(D_{n_{i}}^{avg}\) as follows:  
\[D_{n_{i}}^{avg}=\overline{D_{n_{i}}^{DCQ}}+\overline{D^{tr}}+D_{n_{i}}^{proc }+D^{prop} \tag{22}\]  
Here, \(\overline{D_{n_{i}}^{DCQ}}\) denotes the average queuing delay of DCQ at node \(n_{i}\), \(\overline{D^{tr}}\) is the average transmission delay, \(D_{n_{i}}^{proc}\) is the processing delay at node \(n_{i}\), and \(D^{prop}\) is the propagation delay. Among these four types, \(\overline{D_{n_{i}}^{DCQ}}\) and \(\overline{D^{tr}}\) dominates the total latency. \(D_{n_{i}}^{proc}\) is trivial and assumed to be same for all packets and \(D^{prop}\) is the light speed propagation delay, hence these delays are ignored.  
In node delay estimation, TLQoS considers only delay sensitive packets that include Cr and Dc traffic, since the delay parameter is considered for the route selection of delay sensitive packets only as discussed in Section 4.5.  
Queuing delay for DCQ, \(D_{n_{i}}^{DCQ}\) is usually the interval between the time when a delay sensitive packet enters into the queue and the time it is ready for transmission. The running average of queuing delay, \(\overline{D_{n_{i}}^{DCQ}}\) is estimated using the Exponentially Weighted Moving Average (EWMA) formula [22] as follows:  
\[\overline{D_{n_{i}}^{DCQ}}=(1-\beta)\overline{D_{n_{i}}^{DCQ}}+\beta D_{n_{i}} ^{DCQ}(curr) \tag{23}\]  
Here, \(D_{n_{i}}^{DCQ}(curr)\) is the current observation of queuing delay at \(n_{i}\), \(\beta\) is a weighting factor which satisfies \(0<\beta\leq 1\). The value of \(\beta\) can be empirically chosen. We use the value \(\beta=0.2\) in our simulation.  
The transmission delay, \(D^{tr}\) is defined as the duration from the time a packet begins to be served by the MAC layer to the time when the acknowledgment of the packet is received. The transmission delay is also interpreted as service time of the MAC layer. Let \(t_{0}\) be time when a packet arrives at the head of the queue and ready for transmission, \(t_{ack}\) is the time when the acknowledgment of packet is received, \(L_{ack}\) is the length of acknowledgment packet and \(bw\) is the bandwidth. Hence, \(D^{tr}\) is estimated as  
\[D^{tr}=t_{ack}-\frac{L_{ack}}{bw}-t_{0} \tag{24}\]  
The running average of transmission delay, \(\overline{D^{tr}}\) is estimated used EWMA formula as follows  
\[\overline{D^{tr}}=(1-\beta)\overline{D^{tr}}+\beta D^{tr}(curr) \tag{25}\]  
Here, \(D^{tr}(curr)\) denotes the current observation of transmission delay and the \(\beta\) is similar to Equation (23).
## 2 UAV-Based Data Collections and Processing
### Post Processing Methodology  
Figure 2 shows the block diagram of the post-processing steps. First, we processed the digital samples of the direct RHCP signal using a GNSS software receiver. Here we extracted the UAV trajectory. We also retrieved the list of satellites in view during the data collection campaign, as shown in the upper chain of Figure 2. Then, we calculated the lines of specular points on the ground for all the visible satellites for the whole flight duration, utilizing the receiver trajectory extracted in the previous step, and the known satellites positions, as shown in the middle chain of Figure 2. We calculated the specular points using the algorithms in [16; 28].  
We processed samples of the LHCP signal to measure the power reflected by the surfaces, as shown in the bottom chain of Figure 2. We estimated the reflected power for all the visible satellites, by evaluating the cross-ambiguity function (CAF) [54] over a reduced search space [55]. The CAF is generally computed and compared to a threshold to detect the presence of a GNSS satellite signal, and in our case the detection of a GNSS signal reflected by a ground surface. The CAF was evaluated over a set of Doppler-delay values that define the search space where the reflected signal was cross-correlated with a local replica of the code. Figure 3a shows an example of the CAF of a visible satellite using the direct signal, and Figure 3b shows the CAF for the same satellite at the same time using the reflected signal. It can be noticed that the direct signal as expected has a peak that is well separated from the noise floor, while the CAF associated to the reflected signal shows a weaker peak but it is still visible.  
We choose the peak-to-noise-floor separation \(\alpha_{mean}\)[56] as a measure of the reflected signal strength:  
\[\alpha_{mean}=\frac{R_{P}}{M_{c}}\]  
where \(R_{P}\) is the correlation peak value, and \(M_{c}\) is the mean value of the correlation noise. \(M_{c}\) was calculated from the peak values of the search space obtained cross-correlating the received signal with an orthogonal code not used by the constellation for the whole flight duration. The non-coherent integration time was fixed to 10 ms, and the coherent integration time was 1 ms i.e., we averaged 10 consecutive coherent integrations of 1 ms long. We evaluated the CAF at various rates: 1, 10 and 20 Hz, which corresponded to different levels of resolution along the lines of specular points. In this paper we are interested in understanding if this peak corresponds to reflection from a water surface. As will be shown later, when the signal is reflected from water, \(\alpha_{mean}\) will generally be above the threshold we used to discriminate the presence of reflections.  
Figure 2: Block diagram of the post-processing steps.
# Synthetic Image Generation Using Conditional GAN-Provided Single-Sample Face Image
## 3 Proposed Model  
The overall approach is described in Figure 1. We form training image pairs by utilizing 80% of the dataset images, defining a training direction from the neutral image A to the expression image B to learn individual expressions on the neutral face. During training, the generator receives the neutral image and a noise vector as input and produces a synthetic expression image. Contrarily, the discriminator, which is provided with the original expression images, aims to distinguish these synthetic images from the real expression images. The generator's objective is to create highly realistic synthetic expressions that can deceive the discriminator. We train six separate Conditional GANs, each conditioned on the neutral input image, to generate one of the six target expressions.  
Figure 1: Proposed CGAN architecture for synthetic image generation.  
For testing, we utilize the remaining 20% images, pairing each neutral face image (candidate) with an expression reference image from any individual. Each trained CGAN then generates the corresponding expression. To strictly evaluate the accuracy of our approach, we tested it using several pre-trained facial recognition models (VGG-Face, ResNet-50, FaceNet, and DeepFace) as well as a custom CNN model we developed. We formed a training set with the synthetic facial expression images generated by our CGANs and used this dataset to train the CNN models. Subsequently, we tested these CNN models on the original real images of the respective individuals and calculated the softmax scores to assess the realism of the generated expressions.  
The CGAN [34] model comprises two networks; the generator and the discriminator, as described earlier. However, CGAN is slightly different from a standard GAN as it takes input in pairs. In CGAN, the generator processes an input image, through various convolutional filters to produce an output image. Conversely, the discriminator evaluates pairs of images, one pairing the input image with the target original image and another pairing the same input image with the output image from the generator. It compares both paired images and distinguishes between the real and generated images. Based on this comparison, the weights of the generator are adjusted during training to enhance the quality of the new image that it produces in the next iteration. These iterations continue until the generator creates a synthetic image so realistic that the discriminator can no longer differentiate it from the real image.
## 5 Results and Discussion
### Sensor Characteristics Using Developed Interface Electronics
Up to a certain range, a linear frequency shift of approximately 122.1 Hz was observed for every 1 uW/cm\({}^{2}\) increase in UV-C intensity, with the standard deviation remaining within 40-60 Hz, indicating stable and reliable performance. Notably, the sensor exhibited a frequency shift of approximately 500 Hz at a very low measurable power of around 2 uW/cm\({}^{2}\), as measured in our lab using a commercially available UV-C sensor. This result highlights the good sensitivity of the sensor, even under extremely low applied \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Device Type**} & **Sensitive** & **Sensitivity** & **Res/Rec** \\  & & **Material** & **(ppm(mW/cm\({}^{2}\))–1)** & **Time (s)** \\ \hline
[19] & SAW & AgNW/ZnO & 310.49 & 8/23 \\
[21] & SAW & Ta\({}_{2}\)O\({}_{5}\) & 1621.1 & 3.7/19.8 \\
[27] & SAW & ZnO film & 678 & 30/20 \\
[28] & SAW & ZnO nanorods & 31.3 & 450/1787 \\
[18] & SAW & 2D MoS\({}_{2}\) & 2.05 & - \\
[20] & SAW & In\({}_{2}\)O\({}_{3}\)/Ta\({}_{2}\)O\({}_{5}\) & 368.3 & 11/16 \\ This study & SAW & HfO\({}_{2}\) & 563 & 2.8/4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of response/recovery time and sensitivity in SAW-based UV sensors from previous studies. power. Figure 10f presents the calibration plot, derived using the least squares method, to evaluate the sensitivity of the sensor across the UV-C intensity range of 2-20 \(\upmu\)W/cm\({}^{2}\). The method used for sensitivity calculation is detailed in our previous reports [18]. From the analysis, the sensor demonstrated a sensitivity of 563 ppm/(mW/cm\({}^{2}\)). Figure 10g highlights the selective responsivity of the sensor to different UV wavelengths. A significant frequency shift, ranging from 2 to 2.5 kHz, was observed for UV-C wavelengths (254 nm and 220 nm), while negligible responses, below 60 Hz, were recorded for UV-A (365 nm) and UV-B (312 nm). These results confirm the sensor's high selectivity and sensitivity specifically for the solar blind corona discharge detection UV-C range. Figure 10h presents the results of long-term reliability testing. Over the course of 1 day, 1 week, and 1 month in a room-temperature environment, the sensor's response remained consistent, demonstrating the structural stability and durability of the HfO\({}_{2}\) thin film. However, a slight increase in recovery time was observed, which is likely attributed to the gradual accumulation of oxygen vacancies and surface defects caused by prolonged environmental exposure. These results demonstrate that the developed sensor is highly suitable for a wide range of applications, offering high selectivity for UV-C wavelengths, rapid response times, and reliable long-term stability and reliability.
# Fooling in Membrane Distillation, Osmotic Distillation and Osmotic Membrane Distillation
## 4 Characterization Techniques for Fouling Analysis
### Microscopy Techniques
#### 4.2.2 Transmission Electron Microscopy (TEM)  
This microscopy technique may be considered complementary to SEM mainly when the fouling structures are too small to be detected by SEM [97; 98]. It is also adequate to study the presence of fouling particles inside the membrane pores. In this technique, a sufficiently accelerated electron beam collides with a thin sample (i.e., about ten nanometers). Depending on the sample thickness and the type of atoms forming it, some electrons cross the sample directly and others pass through it but are diverted. After passing the sample the electrons are collected and focused by a lens to form a final image on a CCD camera with a high definition. If the image is formed from the transmitted beam, which has not undergone scattering, then the image of the object is dark on a bright background. On the other hand, if the scattered electrons are used in this case, the image appears bright on a dark background. Therefore, these two techniques are called image formation in a clear field and in a dark field, respectively; the first one is the most used. More details on this technique can be found in [99].  
Among the followed procedures to prepare transparent specimens to electrons, the most important and most used one is based on a mechanical thinning of the material in a very controlled way. This produces a flat specimen of a few microns thick with a flawless surface, which is then subjected to ionic surface polishing at low angle and low energy to achieve extremely thin areas ready for TEM observation. Ultramicrotomy is another technique used to produce TEM specimens, usually reserved for soft materials. The procedure consists of cutting sample slices using diamond blade. Polymeric samples of about 50 nm thickness can be prepared. Samples can be cut in a temperature range between \(-180\)\({}^{\circ}\)C and ambient temperature depending on the characteristics of the material.  
It is worth noting that TEM is not applied yet in MD, OD and OMD fouling analysis due to the necessity to prepare extremely thin samples to be transparent to electrons affecting considerably the fouling layer.
# Multi-Source Information Fusion Diagnosis Method for Aero Engine
## 2 Methodology
### Information Fusion Algorithm
#### 2.2.2 D-S Evidence Theory  
D-S evidence theory was proposed in 1967 to deal with the relations between propositions and sets [28]. Compared with probabilistic methods, D-S evidence theory does not require precise prior probabilities, making it especially suitable for applications in which reliable prior knowledge is difficult to obtain or incomplete--such as in real-world aero engine monitoring scenarios.  
In D-S evidence theory, the discrete value range of the research object is called the frame of discernment, which is defined as \(\Omega=[\omega_{1},\omega_{2},\cdots,\omega_{n}]\). For a specific fault diagnosis problem, \(\omega\) corresponds to the possible failure modes, and the identification framework corresponds to the union of all possible failure modes.  
As for a frame of discernment \(\Omega\), the basic probability assignment function \(m\) can be defined as a mapping of power-set \(2^{\Omega}\) to closed interval [0, 1]. A is a non-empty subset of \(\forall A\subseteq\Omega\) and satisfies the following conditions:  
\[\left\{\begin{array}{c}m\Big{(}\emptyset\Big{)}=0\\ \sum\limits_{A\subseteq\Omega}m(A)=1\end{array}\right. \tag{3}\]  
where \(m\Big{(}\emptyset\Big{)}=0\) indicates that if A is a null set, no belief will be assigned. \(\sum\limits_{A\subseteq\Omega}m(A)=1\) means that the total probability for non-empty subset \(A\) must equal 1.  
Composition rules are the key to evidence theory. Let frame of discernment \(\Omega\)