esa-sceva/satcom-chunk-collection huggingface dataset:
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 5 Simulations
### Moving Target over a Forward Scatter Receiver Array
#### 5.2.2 Linear Receiver Array Geometry  
Figure 7 depicts the basic geometry of a uniform linear array setup for FSR. Each receiver module is unique, but we observe the L1 C/A signal from only a single transmitter. Since the GPS satellites are at a sufficiently high altitude (20,200 km [23]), the incident signal at each receiver module arrives at approximately the same phase. It was shown in [41] that by applying a first-order Taylor series expansion to the Pythagorean theorem, we have \(h_{2}\approx h_{1}+\frac{1}{2}\left(\frac{d^{2}}{h_{1}}\right)\approx h_{1}\) where \(h_{1}\) is the vertical distance of the \(T_{x}\) above \(R_{x1}\), \(d\) is the distance to \(R_{x2}\), and \(h_{2}\) is the hypotenuse distance to \(T_{x}\).  
The major benefit of implementing an array is persistent Doppler information. When the target reaches the crossing point of the first receiver, \(R_{x1}\), the shift measurement at that time instance is 0 Hz. However, the other receivers will register a non-zero shift as \(\beta\) is close to but not equal to 180\({}^{\circ}\). Therefore, Doppler can be extracted and tracked for the entire duration of the target path visible to the array.  
Figure 5: (**a**) Self-mixing and FFT results with one target; (**b**) self-mixing and FFT results with two targets.  
The secondary benefit is some processing gain. An example scenario could be two receivers observing a single moving target. For a single snapshot, if the received signals were added and then filtered, the signal would be of the form in Equation (19).  
\[y(t)=A_{ref,1}A_{ref,2}\text{cos}((\omega_{D,1}-\omega_{D,2})t)+2A_{dir}\Big{[}A _{ref,1}\text{sin}(\omega_{D,1}t)+A_{sc2}\text{sin}(\omega_{sc2}t)\Big{]} \tag{19}\]  
This result is an improvement on Equation (13) as the magnitudes of the Doppler signals have doubled. Therefore, it can be expected that an \(N\)-element array will provide an \(N\)-fold increase in Doppler signal power. This behavior aligns with the \(N\)-fold improvement in SNR due to coherent integration [42].  
Figure 6: Bistatic geometry involving one target, and one receiver–transmitter pair.  
Figure 7: FS bistatic geometry for a linear receiver array.
# An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images
## 5 Experiments
### Ablation Study  
In this section, we analyze the validity of each component in the AFSF network. In order to clearly verify the effectiveness of each module or part proposed in this study, we add one module at a time and observe the changes in model performance. Note that we use U2 Net as the baseline network in this section.  
**The effect of attention-based residual block:** We start our ablation study by verifying the effectiveness of the attention module introduced in the residual block. As shown in Table 3, when introducing attention mechanisms into the basic modules of the U2 Net network (\(U2 Net+ARIU\)), we observed that the model precision, recall, and IoU were improved by 2.7%, 3.6%, and 3.2%, respectively, compared with the U2 Net. The improvement of various indicators indicates that introducing an attention mechanism in the feature extraction stage can effectively enhance the model's ability to extract semantic features, thereby enhancing the semantic segmentation effect of the model.  
**The effect of full-scale fusion with an attention module:** As can be seen in Table 3, after introducing full-scale fusion with an attention operation on the basis of \(U2 Net+ARIU\) (\(U2 Net+ARIU+FSFA\)), the precision, recall, and IoU of the model have been improved again by 4.0%, 2.4%, and 3.2%, respectively. These improvements confirm that there are differences in the information contained in multi-scale features, and sufficient multi-scale feature fusion is beneficial for semantic segmentation tasks.  
**The effect of roof discrimination:** As for the last part of our ablation study, we add a roof discrimination branch and apply a roof discrimination function to the output of the last encoder in \(U2 Net+ARIU+FSFA\) (AFSF in Table 3). Meanwhile, we introduce weakly labeled data during the network training phase. It can be observed that the performance of the model has been improved, with improvements of 0.3%, 2.2%, and 1.2% in precision, recall, and IoU, respectively. These results demonstrate the effectiveness of introducing weakly labeled data in improving the performance of semantic segmentation models.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Definition** \\ \hline TP & The number of positive pixels predicted as positive by the model \\ TN & The number of negative pixels predicted as negative by the model \\ FP & The number of negative pixels predicted as positive by the model \\ FN & The number of positive pixels predicted as negative by the model \\ \hline \hline \end{tabular}
\end{table}
Table 2: The definition of TP, TN, FP, and FN.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & **IoU (\%)** \\ \hline U2 Net & 76.3 & 80.7 & 78.4 \\ U2 Net+ARU & 79.0 & 84.3 & 81.6 \\ U2 Net+ARU+FSFA & 83.0 & 86.7 & 84.8 \\ AFSF & **83.3** & **88.9** & **86.0** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study results.
## 2 Methods and Available Data
### Convolutional Neural Network Model  
The ML model developed to predict the permeability of a fibrous GDL micro-structure is a CNN. It was developed using Tensorflow and Keras tools [36]. The particular hidden layers of the CNN are as follows:  
* Three pairs of three-dimensional convolutional (Conv3D) and average pooling layers;
* Two additional Conv3D layers;
* A flattened layer;
* As a second input, the compression level of the represented material was concatenated with the output of the previous layer;
* Two additional dense layers.  
The architecture is illustrated in Figure 2. The main input layer is shown on the left-hand side, with the hidden layers attached from left to right. Noticeable layers in Figure 2 include the three-dimensional convolutional layers, marked as Conv3D. The shape  
Figure 1: Data scheme. (**A**) Historical data from [PERSON] et al. [9]; (**B**) ML approach in the manuscript.  
of the structures is written on the right of the Conv3D boxes, after the keyword 'None'. The dimensions begin with 65 \(\times\) 512 \(\times\) 512 and one filter from the input layer, after which the dimensions are reduced stepwise down to 8 \(\times\) 8 \(\times\) 8, while the number of filters is doubled from one Conv3D layer to its successor. The functional concatenation with the compression level as a second input layer is also embedded in the bottom part of the visualization. The compression level is extended to an array size of 128 to allow matching with the output of the dense layer.  
Given the size of the stacks of input images of 65 \(\times\) 512 \(\times\) 512, the architecture shown in Figure 2 leads to 9,776,497 trainable parameters, i.e., the weights of the hidden layers. To evaluate the accuracy of the CNN, the mean squared error (MSE) was used:  
\[\text{MSE}(\mathbf{X},h)=\frac{1}{m}\sum_{i=1}^{m}(h(\vec{x}_{i})-y_{i})^{2} \tag{1}\]  
where \(\mathbf{X}\) is the set of input structures \(\vec{x}_{i}\); aside from these, the compression levels were also included. \(h\) is the function represented by the CNN, \(y_{i}\) the real permeability of the input structure \(\vec{x}_{i}\), and \(m\) is the number of structures \(\vec{x}_{i}\) in the set \(\mathbf{X}\). The MSE from Equation (1) is the loss function of the ML model represented by the CNN. In a similar manner, a percentage error can be defined:  
\[\text{MPE}(\mathbf{X},h)=\frac{1}{m}\sum_{i=1}^{m}|h(\vec{x}_{i})-y_{i})/y_{i}| \tag{2}\]  
The MPE is more convenient from the technical viewpoint of the application of this CNN, as it can be directly compared with the inaccuracies in experiments or modeling approaches.
## 3 Machine Learning-Based GNSS Residual Error Modeling Methodology
### Inflation Factor and Threshold Computation
Although the shapes of the center areas for the normalized HPF and LPF may not perfectly match that of the standard normal distribution, our goal is to over-bound the tails of these distributions in order to meet the false alarm statistics required for monitoring purposes, so only the total area under the probability density function (pdf) bounded by the tails is of interest. If the proportion of normalized data exceeding the threshold is greater than the desired false alarm rate at the tail of the normal distribution pdf, an inflation factor (IF)needs to be applied. To determine the inflation factor, we use Equation (19) to search for the two points at which the actual residual accumulation from the end satisfies half of the maximum allowable false alarm rate. \[\int_{-\infty}^{M_{L}}p_{true}(x)\ dx=\int_{M_{R}}^{\infty}p_{true}(x)\ dx=\int_{K}^{ \infty}p_{N(0,1)}(x)\ dx=\frac{1}{2}\ast P_{FA} \tag{19}\]  
where \(p_{true}(x)\) and \(p_{N(0,1)}(x)\) represent the pdfs of actual residuals and standard normal distribution, respectively. \(P_{FA}\) is the allowed probability of a false alarm and \(K\) is the coverage factor of the standard normal distribution, which is calculated using Equation (19) [44; 45; 46; 47; 48; 49; 50]. \(M_{L}\) and \(M_{R}\) refer to the points at both ends of the actual data distribution that satisfy half of the allowed probability of a false alarm (\(P_{FA}\)). If the \(M_{L}\) or \(M_{R}\) is larger than \(K\), it indicates that the estimated standard deviation needs to be inflated to meet the false alarm requirements. Therefore, the IF can be calculated using Equation (20). \[IF=\frac{max(M_{L},\ M_{R})}{K} \tag{20}\]  
After applying the inflation factors to the standard deviation, \(\sigma\), of the HPF and LPF, the variance of SBDD residuals should be calculated by summing the squares of the inflated HPF and LPF standard deviations. Then, we multiply the expected residuals by the coverage factor to derive the thresholds. The final equation for the threshold is shown in Equation (21). \[Threshold=K\sqrt{(IF_{HPF}\cdot\sigma_{HPF})^{2}+(IF_{LPF}\cdot\sigma_{LPF})^{2}} \tag{21}\]
## 5 Simulation Results  
A modified 22-bus 20-kV radial distribution test system [15] with two feeders and three dispatchable DGs is used as a case study to analyze and investigate the proposed method. The single line diagram of the distribution test system has been shown in Figure 6. The lines' length has been multiplied by 1.5 to create a severe voltage situation. The HV/MV transformer has 11 tap positions ([\(-5\), \(-4\), \(-3\), \(-2\), \(-1\), \(0\), \(1\), \(2\), \(3\), \(4\), \(5\)]) and each tap ratio is 0.01 p.u. It is capable of varying the bus voltage at substation between 0.95 and 1.05 pu. Capacitors one and two, with a capacity of 1000 kVAr, have five switching steps of 200 kVAr.  
Table 1 lists active power bids of DGs including the blocks of offered quantity and energy bid price. Additionally, the hourly wholesale market energy prices which are considered as active bid prices of Disco have been given in Table 2. Table 3 presents the reactive bid prices of DGs to participate in the joint active/reactive power market. The price of reactive power offered by Disco is $16/MVArh. The mandatory power factor (\(pf_{mund}\) ) has been taken to be 0.95.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DG** & **Hour** & **Block Number** & **Quantity (kW)** & **Price (\$/MWh)** \\ \hline \multirow{3}{*}{DG1} & \multirow{3}{*}{1-24} & 1 & 200 & 41 \\  & & 2 & 200 & 50 \\  & & 3 & 100 & 58 \\  & & 1 & 400 & 39 \\ \multirow{3}{*}{DG2} & \multirow{3}{*}{1-24} & 2 & 400 & 46 \\  & & 3 & 200 & 53 \\ \multirow{3}{*}{DG2} & \multirow{3}{*}{1-24} & 1 & 200 & 42 \\  & & 2 & 150 & 50 \\ \cline{1-1}  & & 3 & 150 & 67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Price-Quantity offer of DGs.  
Figure 5: Time horizon of proposed joint active/reactive power dispatch model.  
Figure 6: The single line diagram of 22-bus distribution test system.  
The initial active power dispatch which is needed to estimate LOC payment of DG units is depicted in Figure 7. The proposed joint active/reactive power market settlement model has been tested on two scenarios as follows:  
**Scenario 1:** Maximum available reactive power of Disco is 3 MVAr.  
**Scenario 2:** Maximum available reactive power of Disco is 2.2 MVAr.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hour** & **Wholesale Energy** & **Hour** & **Wholesale Energy** \\  & **Price (\$/MWh)** & **Hour** & **Price (\$/MWh)** \\ \hline
1 & 45 & 13 & 50 \\
2 & 45 & 14 & 55 \\
3 & 45 & 15 & 55 \\
4 & 45 & 16 & 58 \\
5 & 45 & 17 & 58 \\
6 & 45 & 18 & 59 \\
7 & 47 & 19 & 64 \\
8 & 47 & 20 & 64 \\
9 & 48 & 21 & 62 \\
10 & 48 & 22 & 57 \\
11 & 50 & 23 & 50 \\
12 & 50 & 24 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hourly wholesale market energy price.  
Figure 7: Initial active power dispatch obtained in the day-ahead energy-only market.
# The Role of the CFO of an Industrial Company: An Analysis of the Impact of Blockchain Technology
## 4 Analysis of the Results
#### Improvement of Treasury and Cash Management  
The strategic financial management of a CFO comprises the overall cash management, while heading the treasury department of a company [40; 41]. Again, the interviews conducted revealed a substantial influence on the CFO's key performance indicators in the realms of cash management and treasury.  
The blockchain-enabled automated M2M payments provide the ability to integrate machines into payment networks. Notably, the blockchain technology also facilitates the representation of traditional currencies as units of account. According to the experts, the representation of e.g., the Euro on the blockchain permits the association of the currency with functions by then being programmable. Hence, not only the programming of simple payment logics is conceivable, but also the depiction of predefinedand already existing entire business logics. In the industrial environment, leasing contracts, factoring processes or interest payments would be plausible.  
Accordingly, a possible application would be a revolutionary form of leasing of machinery and production equipment. As previously discussed, the blockchain technology creates a high degree of transparency and integrity associated with machine sensor data. As a result, these data could be used by the lessor to provide the lessee with a usage-based and flexible leasing rate. In accordance with the profit center logic, the lessee does not represent a specific company, but the machine itself, which also fully automatically pays the lease payments and keeps accounts.  
Similarly, a machine could conclude factoring contracts and decide for itself when an inflow of money should occur. In order to make this possible, a blockchain-based capital market that runs in the background would ensure the sufficient provision and funding of the factoring. Up until now, economically feasible asset-backed security transactions can only be carried out if the securitization is based on a sufficient volume of receivables. In contrast, due to its high degree of automation and transparency, the blockchain could prospectively securitize even relatively small receivables without the need for an intermediary. Additionally, the CFO can also use machine data to more accurately determine financing needs to prevent the over- or under-funding of dept capital.
# Nanomaterials for Healthcare Biosensing Applications
## 4 Organic Nanomaterial Applications for Healthcare Biosensing
### Hyperbranched Polymeric Nanoparticles
aeruginosa_ and its viability was tested in the presence of various antibiotics, such as ciprofloxacin, simvastatin, and kanamycin. The OD\({}_{600}\) LOD was 0.025 and the linear response range was between 0.025 and 0.5. Niu and co-workers reported an immunosensor for the label-free detection of \(\upalpha\) tetoprotein (AFP) using hyperbranched polyester with nitrite end groups in conjugation with a chitosan-AuNP functionalised GCE [266]. The modified electrode was then used as a surface for AFP antibody immobilisation and the free sites on the surface were blocked with BSA. The sensor displayed a linear response from 0.1 to 120 ng mL\({}^{-1}\) of AFP and a very low LOD of 55 pg mL\({}^{-1}\) was observed. In one study, hyperbranched ferrocene polymer was used for the detection of dihydronicotinamide adenine dinucleotide (NADH) in association with PtNPs. Polymethyldiundecenylsilane (PMDUS) and polydiallymethylsilane (PDAMS) were the hyperbranched polymers chosen to interact with ferrocene. Both the respective assemblies had a synergistic effect with PtNPs on the catalysis of the sensing and exhibited wider detection ranges in comparison to sensors without PtNPs. The LOD for PDAMS assembly was 4.78 \(\upmu\)M, which was lower than the 6.18 \(\upmu\)M limit observed in the case of PMDUS.
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 4 Experiments and Results
### Experiments on Simulated Data
#### 4.1.1 Experiment 1: The Output SINR Performance under Different Noise Conditions  
In this experiment, we have carried out 12 trials (numbered as trial 1 to trial 12) to evaluate the performance of the algorithms under different spatial and additive noise conditions following the experimental protocols in Ref. [25]. The details are given below:  
(1) The DOAs of target speech, the first speech interference (male speech) and the second speech interference (female speech) are at (\(\theta_{s}\),\(\phi_{s}\)) = (45\({}^{\circ}\),45\({}^{\circ}\)), (\(\theta_{1}\),\(\phi_{1}\)) = (90\({}^{\circ}\),135\({}^{\circ}\)), and (\(\theta_{2}\),\(\phi_{2}\)) = (45\({}^{\circ}\),120\({}^{\circ}\)), respectively. The background noise is chosen as babble noise \(n(t)\);  
(2) We evaluate the performance under three different conditions: (a) there exists only additive background noise: \(n(t)\
eq 0\) and \(s_{i}(t)=0\); (b) there exists only speech interferences: \(n(t)=0\) and \(s_{i}(t)\
eq 0\); (c) there exists both background noise and speech interferences: \(n(t)\
eq 0\) and \(s_{i}(t)\
eq 0\);  
(3) The input SINR (denoted as SINR-input) is set as \(-5\) dB, \(0\) dB, \(5\) dB, and \(10\) dB, respectively.  
Following the setting above, 12 different datasets are generated for this experiment.  
In addition, the parameters of algorithms are set as follows. (1) The sampling rate is 16 kHz, 1024-point FFT (Fast Fourier Transform), and 1024-point Hamming window with 50% overlapping are used. (2) For our proposed AVS-SMASK algorithm, we set \(\delta_{1}=\delta_{2}=25^{\circ}\), \(\rho=0.07\), and \(\xi=3\). (3) For comparing algorithm AVS-FMV: F = 32, M = 1.001 followed Ref. [17]. The experimental results are given in Table 3.  
As shown in Table 3, for all of the noise conditions (Trial 1 to Trial 12), our proposed AVS-SMASK algorithm outperforms AVS-FMV [17]. From Table 3, we can see that our proposed AVS-SMASK algorithm gives about 3.26 dB, 4.14 dB, and 2.25 dB improvement compared with that of AVS-FMV under three different experimental settings, respectively. We can conclude that our proposed AVS-SMASK is effective in suppressing the spatial interferences and background noise.
## 2 Theoretical Basis and Related Work
### The Framework of CS
#### 2.1.3 Reconstruction  
The most powerful method to recover the sparse signal is by solving the \(l_{0}\)-norm minimization problem. Then, sparse coefficient \(\theta\) can be reconstructed from \(\boldsymbol{Y}\) by:  
\[\min\|\boldsymbol{\theta}\|_{0}\ s.t\ \boldsymbol{Y}=\boldsymbol{\varphi} \cdot\boldsymbol{\psi}\cdot\boldsymbol{\theta}. \tag{4}\]  
The \(l_{0}\)-norm minimization makes results as sparse as possible. Nevertheless, to solve this optimization problem is NP-hard. Papers suggest that conversion of the \(l_{0}\)-norm minimization problem and the \(l_{1}\)-norm one can be reached under certain conditions. Thus, Equation (4) can be transformed into its convex approximation \(l_{1}\)-norm as follows:  
\[\min\|\boldsymbol{\theta}\|_{1}\ s.t\ \boldsymbol{Y}=\boldsymbol{\varphi} \cdot\boldsymbol{\psi}\cdot\boldsymbol{\theta}. \tag{5}\]  
However, solving this optimization still requires high computational complexity [26]. Currently, researchers mainly focus on how to construct low complexity approaches, such as greedy recovery algorithms.
## 1 Introduction
Motivated by this fact, we thoroughly investigate the performance of a multi-hop system by means of the average symbol error performance (ASEP) and the mean outage duration (MOD) per hour. To this end, we assume the freshly launched \(M\)(alaga) distribution for turbulence description, the appearance of non-zero boresight pointing errors, subcarrier intensity modulation, and phase noise effects. We employ decode-and-forward (DF) relays, where each relay decodes the signal after detection, performs modulation, and retransmits it to the next relay or the destination. It is to be emphasized that the generic \(M\)(alaga) distribution incorporates all of the well-known models describing weak-to-strong turbulence conditions. The remainder of the manuscript is organized as follows: Section 2 describes in brief the system and channel model. In Section 3, novel mathematical expressions related to specific performance metrics are derived. To this end, we first estimate the ASEP under the assumption of negligible phase noise effects by considering both \(M\)(alaga) turbulent and non-zero boresight misalignment fading conditions. Next, we consider the impact of phase noise on the error performance; then, we deduce an estimation of the MOD per hour. Proper numerical results validated by Monte Carlo simulations are provided in Section 4 to verify the accuracy of the extracted closed-form expressions. Finally, some concluding remarks are outlined in Section 5.
## 3 Results and Validation
### Validation  
Since the AVHRR, MODIS, and Landsat satellite images from 2001 were available, this paper compared the super-resolution reconstructed AVHRR data of 2001 with the actual MODIS and Landsat data using two image reconstruction metrics to evaluate the usability of the reconstructed data (Section 3.2.1). Due to the challenges in collecting annual validation samples from 1980 to 1990 within the study area, three independent validation datasets were utilized to assess our land cover products from spatial, temporal, and change dimensions (Section 3.2.2): First, in the spatial dimension, 431 validation samples collected in 1986 based on field sampling and prior knowledge were used to compute confusion matrix, allowing for a comparison of the accuracy between the products developed in this study and the CLUD-A, which is also an available reconstructed historical land cover from AVHRR data. Second, 1000 randomly distributed sample points within the study area were analyzed to compare the consistency of the two products over the entire time span from 1980 to 1990 from the perspective of temporal dimension. Finally, in the change dimension, some of the major changes were singled out to be compared to the historical Landsat imagery and visually interpret the actual year of change based on the reconstructed annual land cover maps.  
Figure 13: The expansion of built-up in the study area.