esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Feasibility of PAZ as an Opportunity Illuminator in a Passive Bistatic Radar System
### Incident Power Density and Required Sensitivity  
For the study of the power density reaching the desired targets, the following considerations have been taken:  
1. The incidence angle of the PAZ antenna beam, \(\alpha\), can vary in \([15^{\circ},65^{\circ}]\) (Figure 6). This parameter determines the baseline, the OI-target distances and the associated propagation losses.
2. The estimated values of Equivalent Isotropic Radiated Power (EIRP) calculated in Section 3.1 range from \(65.4\) dBm for \(\alpha=15^{\circ}\), to \(63.4\) dBm for \(\alpha=65^{\circ}\).
3. A receiver gain (\(G_{R}\)) of 23 dB was considered. Average bistatic RCS values were calculated for each incidence angle following the methodology explained in Section 2.5. Estimated values for the car model used in Section 2.5 are presented in Table 4.
4. The PR receiver was located at \(40^{\circ}30^{\prime}48.44^{\prime\prime}\) N and \(3^{\circ}20^{\prime}55.30^{\prime\prime}\) W.  
The available power at the PR antenna was calculated as a function of the target-to-PR distance, and it is presented in Figure 19 as a function of the OI incidence angle. For a coverage of 15 km, affordable power levels are obtained, proving the feasibility of PAZ as the OI from the point of view of this parameter.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**RCS** & \(\alpha=15^{\circ}\) & \(\alpha=25^{\circ}\) & \(\alpha=35^{\circ}\) & \(\alpha=45^{\circ}\) & \(\alpha=55^{\circ}\) & \(\alpha=65^{\circ}\) \\ \hline Maximum & 11.074 dBm & \(-0.046\) dBm & \(-5.539\) dBm & \(-7.631\) dBm & \(-8.385\) dBm & \(-6.293\) dBm \\ Minimum & \(-28.678\) dBm & \(-29.317\) dBm & \(-28.164\) dBm & \(-26.995\) dBm & \(-26.685\) dBm & \(-22.193\) dBm \\ Average & \(-15.522\) dBm & \(-15.912\) dBm & \(-15.897\) dBm & \(-16.729\) dBm & \(-16.132\) dBm & \(-14.658\) dBm \\ \hline \end{tabular}
\end{table}
Table 4: Simulated bistatic RCS for the car model used for calculating the received power in Figure 19.  
Figure 18: Footprint of the opportunity illuminator (OI) during the acquisition of a spotlight image.
# Extracellular Vesicles for Regenerative Medicine Applications
## 3 Pre-Clinical Applications of EVs in Tissue Engineering and Regenerative Medicine
### Renal System  
While EV research regarding the renal system has primarily focused on EV-based diagnostics, there have been a few recent examples of EV-based therapies in TERM. Specifically, there is evidence supporting the use of microvesicles from both endothelial progenitor cells and MSCs to protect kidney epithelial cells in models of acute kidney injury as well as an ischemia-reperfusion injury of the kidney [89; 90]. These microvesicles have great promise in aiding tissue regeneration and cellular survival of bio-engineered kidney devices.
## 3 The Relay Experiment and Parameters Analysis  
### The Relay Experiment  
When the relay is energized, the contacts are closed. The mechanical collision of dynamic and static contacts causes the vibration and bounce of the contacts, which indicates that the stable state of the contacts cannot be reached immediately. The contacts have a process of bounce. The dynamic contact and static contact have different dynamic contact characteristics. There are faults in electromagnetic relay and different kinds of failure mode. However, the failures caused by contact of relay account for more than 80% of relay failures.  
Thirteen HH52P small relays (Shanghai People Electric Co., Ltd, Shanghai, China) were used for life test. The performance parameters of the relay are shown in Table 1. The relays are divided into three groups to simulate the actual working conditions due to the certain difference in the working environment of the relay. The reliability tests are carried out at temperatures of -20, 20, and 55 \({}^{\circ}\)C in the incubator. A constant current of 5 A is applied to the relay to obtain a large dynamic contact voltage drop peak and a static contact pressure drop since the contact resistance between the contacts is small and difficult to measure.  
The bounce time of the contact, the duration of the dynamic contact resistance, the peak of the dynamic contact pressure drop, and the static contact pressure drop are tested. The measurement is performed every same time and the values of each parameter at different temperatures are recorded separately. The number of intermediate intervals is set to 10,000 times. If the measured values differ more or are unstable in a certain period, the number of measurements is appropriately increased. The test interval should be shortened and the number of test groups increased at the beginning of life since the relay life prediction is based on its vital state information. The life of the relay used in the test is generally about one million times. The test data of the first 10,000 times are used as the basis for judging the initial state of life.
# Quantum Circuit-Width Reduction through Parameterisation and Specialisation
## 4 Discussion of Key Concepts  
A key aspect of the quantum circuit transformations presented in this work involves reducing the number of qubits by redefining the circuit for specific states of one or more qubits. To illustrate this concept, consider the reduction of a 3-qubit modulo adder. Figure 5 illustrates the step-by-step specialisation of the ripple-carry 3-qubit modulo adder for the specific case \(|a2|a1|a0\rangle=|101\rangle\). In terms of 2's complement representation, this modulo adder can then be used to subtract 3 from a positive integer represented in binary by 2 qubits \(|b1|b0\rangle\). In the modulo adder, this input is set as \(|b2|b1|b0\rangle\) with \(|b2\rangle=|0\rangle\).  
In Figure 5, 'Step 0' shows the Cuccaro-type modulo adder [45] with the \(MAJ\) (Majority) and \(UMA\) (UnMajority and Add) blocks expanded. The 3-qubit modulo adder in its unmodified form employs three \(MAJ\) blocks on the left-hand side of the circuit shown, and three \(UMA\) blocks on the right-hand side. The steps shown in the figure are described here:  
Figure 5: Specialised modulo 3-qubit modulo adders. Example shown for \(|a2|a1|a0\rangle=|101\rangle\). Can be used to subtract 3 from integer defined by binary representation of \(|b2|b1|b0\rangle\).  
* **Step 1** : For the three most-significant qubits \(|a2|r2|a1\rangle\) at the top of the circuit, the gate operations in the neighbouring \(MA\)\(/\) and \(UMA\) blocks partially cancel out. Specifically, only two \(CNOT\)s remain. The \(CNOT\) with \(|a2\rangle\) as control and \(|b2\rangle\) target can then be reduced to a \(NOT\) on \(|b2\rangle\) since in the specialisation shown \(|a2\rangle=|1\rangle\).
* **Step 2**: \(|a1\rangle=|0\rangle\) is accounted for by removing the three \(CNOT\)s where \(|a1\rangle\) acts as control.
* **Step 3**: The actions on \(|a1\rangle\) can be removed by replacing the two Toffoli gates that conditionally change \(|a1\rangle=|0\rangle\) into state \(|1\rangle\) and the \(CNOT\) gate with \(|b2\rangle\) as target, by a single Toffoli gate with \(|b2\rangle\) as target and with the same control qubits as the replaced Toffoli gates.
* **Step 4**: \(|a0\rangle=|1\rangle\) is accounted for first by replacing three \(CNOT\)s by three \(NOT\)s.
* **Step 5**: The three \(NOT\)s resulting from the previous step are then accounted for by modifying the control in two Toffoli gates with \(|a0\rangle\) as target.
* **Step 6**: \(|c\rangle=|0\rangle\) is accounted for by removing \(|c\rangle\) as control from the two Toffoli gates with \(|b0\rangle\) as target and removing the \(CNOT\) with \(|b0\rangle\) as target.
* **Step 7**: The actions on \(|a0\rangle\) are removed by removing the remaining two \(CNOT\)s with \(|a0\rangle\) acting as target. The resulting circuit has no gate operations on the qubits used for specialisation and so this is the final reduced form of the circuit for these values of the specialisation qubits (\(|a2|a1|a0\rangle\)).
## 3 Experiments
### Evaluation Metrics  
To comprehensively evaluate the classification performance of HoRFNet on the BreakHis dataset, this study introduces six commonly used evaluation metrics including image level accuracy, patient level accuracy, precision, recall, F1-score, and confusion matrix. These metrics provide a systematic assessment of model performance from multiple perspectives, including recognition capability at the image and patient levels, as well as classification stability and generalization ability.  
Image level accuracy (ILA) measures the proportion of correctly classified images to the total number of images and is used to evaluate the model's recognition capability at the individual image level:  
\[ILA=\frac{N_{c}}{N_{t}} \tag{9}\]  
where \(N_{c}\) represents the number of correctly classified images and \(N_{t}\) denotes the total number of images.  
Patient level accuracy (PLA) is computed on a per-patient basis by aggregating the classification results of all associated images using a majority voting strategy. This metric reflects the model's consistency and stability in overall patient level classification:  
\[PLA=\frac{P_{c}}{P_{t}} \tag{10}\]  
where \(P_{c}\) represents the number of correctly classified patients and \(P_{t}\) denotes the total number of patients.  
A confusion matrix is an intuitive tool for evaluating the performance of a classification model. It summarizes the relationship between the model's predictions and the actual labels, illustrating how the model performs across different classes. It includes four fundamental cases: a true positive (TP) is a sample that is actually positive and correctly predicted as positive; a false positive (FP) is a sample that is actually negative but incorrectly predicted as positive; a true negative (TN) is a sample that is actually negative and correctly predicted as negative; and a false negative (FN) is a sample that is actually positive but incorrectly predicted as negative.  
Precision measures the proportion of true positive samples among all samples predicted as positive, reflecting the model's ability to reduce false positives. Recall measures the proportion of true positive samples among all actual positive samples, evaluating the model's ability to capture positive cases. The F1-score is the harmonic mean of precision and recall, used to comprehensively evaluate the model's ability to identify positive samples in classification tasks.
# Occurrence of Fungi on Duck Egg Shells and Drug Resistance Analysis of _Aspergillus_ spp. Isolates
###### Abstract  
Fungi are a typical part of the microbiome of poultry houses, but some of the genera can be pathogenic for poultry and humans. An investigation was conducted on 200 duck eggs from 10 flocks to determine total fungal contamination on the eggshells. The colony types were identified morphologically and microscopically, and a representative group was identified using PCR. The resistance profiles for all obtained _Aspergillus_ isolates were conducted. The dominating genera on eggshells were _Penicillium_, _Alternaria_ and _Aspergillus_ and the number of fungal colonies ranged from 0 to 7100. _Aspergillus_ was cultured from 9.5% eggshells, and all isolates were obtained from three flocks. The minimum inhibitory concentration (MIC) values for _A. fumigatus_ isolates ranged from 0.094-32 ug/mL for amphoteric B (MIC 50 1 mg/L and MIC 90 32 ug/mL), 0.125-32 ug/mL for caspfungin (MIC 50 0.38 ug/mL and MIC 90 32 ug/mL), 0.19-32 ug/mL for itraconazole (MIC 50 1.5 ug/mL and MIC 90 32 ug/mL), 0.047-12 ug/mL for posaconazole (MIC 50 0.5 ug/mL and MIC 90 8 ug/mL) and 0.023-32 ug/mL for voriconazole (MIC 50 0.19 ug/mL and MIC 90 32 ug/mL). A total of 73.7% of the isolates were resistant to posaconazole and 68.4% to itraconazole. Nearly half of the strains (47.4%) showed resistance to amphoteric B and 31.6% to voriconazole. Because of the lack of antifungals registered for poultry, hygiene and the regular disinfection of litter in particular are needed to prevent the contamination of the eggs by fungi for both animal and human health.  
egghell; fungi; _Aspergillus_; resistance; duck
## 3 Experimental Setup  
### Experimental Apparatus  
Figure 6 shows a schematic of the experimental setup and optical alignment. The pump beam, which was the heating source, was a continuous-wave, diode-pumped solid-state (DPSS) source with a 532 nm wavelength laser (Sprout-G, Lighthouse Photonics, San Jose, CA, USA) and Gaussian intensity distribution. The pump beam was modulated to a cosine waveform using a mechanical chopper (SRS 40, Stanford Research System, Sunnyvale, CA, USA). The measurement specimen was fixed to a rotate-stage with a resolution of 1/60\({}^{\circ}\) to measure thermal properties according to the crystal orientation. The rotate-stage (M481-1, Newport, Irvine, CA, USA) used the tilting-stage (M-36, Newport, Irvine, CA, USA) with a resolution of 1/60\({}^{\circ}\), and the measurement specimen was aligned perpendicular to the pump beam. The probe beam was a He-Ne laser (Newport 1123P, Newport, Irvine, CA, USA) with a 633 nm wavelength and a 5 mW intensity. The probe beam was aligned perpendicular to the measurement specimen using a dichroic mirror that selectively transmits the pump beam. The relative position between the probe beam and the pump beam was adjusted by a motorized-stage (IMS300 CCHA, Newport, Irvine, CA, USA) with a resolution of 1.25 um equipped with the probe beam. The periodic deflection angle of the probe beam was measured using a photoelectric position sensor (C10443-01, Hamamatsu Photonics, Hamamatsu City, Shizuoka Pref., Japan). Later, the signal of the position sensor passed through the amplifier, and it was synchronized with the modulation signal of the mechanical chopper in a lock-in amplifier (AMETEK 7270 DSP, AMETEK, Berwyn, PA, USA) to obtain the phase difference between the two signals.
## 4 Extraction of Information  
For each window \(t_{w}\), the Hilbert transform (Ht) is calculated first and then \(TFR\) of the PWV (Pseudo Wigner-Ville) type is calculated. Once the \(TFR\) is obtained, the contributions of frequency over 45 Hz are canceled, thereby eliminating both the network interference (50 Hz or 60 Hz) as well as the electromyogram (EMG). After this process, a Data Matrix (\(DM^{{}^{\prime}}\)) obtained from the \(TFR\), being \(45\times 150\) in size is obtained. This is useful since the signal of interest is found in values below 45 Hz (see Figure 3a,b).  
Once \(DM^{{}^{\prime}}\) is obtained from the \(TFR\) for each \(t_{w}\) window, this data matrix \(TFR\) is converted into an image \(TFRI_{L_{f}\times L_{t}}\) with size \(L_{f}\times L_{t}\) pixels being \(L_{f}=45\) and \(L_{t}=150\) converting the energy levels of the signal into a pixel intensity range from 0 to 255. These values correspond to different levels of grey in an image as they are shown in Figure 3c,d. Each \(TFRI\) image is then stored in a Data Matrix (\(DM^{{}^{\prime\prime}}\)) of size \(45\times 150\).  
Once \(DM^{{}^{\prime\prime}}\) data matrix is obtained, the data matrix is directly fed into the classifier. By doing this, all ECG signal information in the temporal and spectral domains is contained in the data matrix, providing the classifier with maximal data information. Note that this method requires a large number of inputs to the classifiers (\(45\times 150=6750\)) since each \(DM^{{}^{\prime\prime}}_{ij}\) data in a matrix coordinate corresponds to an input. It is important to note that there is no feature extraction from the data matrix \(DM^{{}^{\prime\prime}}\) as it contains the temporal and spectral information from the ECG signal.  
Figure 2: IIR bandpass filter applied to a ’Normal’ type ECG. The input temporal signal is plotted in blue and the filtered output signal is plotted in red. Its frequency response is shown below.
## 2 Materials and Methods
### Experimental Settings  
The work is implemented in Python using the Pytorch deep learning framework, accelerated with the NVIDIA A100 graphics processing unit (GPU), accessed through the cloud. An overview of the experimental process is shown in Figure 4.  
The 446 Sentinel-1 and Sentinel-2 images are split into training, validation, and testing sets with sample sizes of 251, 89, and 90, respectively. The data are loaded with a batch size of 16 to accommodate processing limitations, and are shuffled for each epoch, aiding in the reduction of the possibility of overfitting. The Cross Entropy Loss function is employed, which is a measure of the difference between the probability distributions of the true and predicted values, calculated as shown in Equation (5), where \(t_{i}\) is the true value and \(p_{i}\) is the predicted value, with \(i\) being the target class.  
\[\begin{split} L&=-\sum\limits_{i=1}^{2}{t_{i}\text{ log}(p_{i})}=-t_{1}log(p_{1})-t_{2}\text{log}(p_{2})\\ &=-[t_{1}log(p_{1})+(1-t_{1})\text{log}(1-p_{1})]\end{split} \tag{5}\]  
As in most flood image data, there is a significant imbalance of water to background pixels, with water making up the minority class. Weighting is employed within the loss function in order to overcome this, where the water class is given 8 times the importance of the background class.  
Adaptive Moment Estimation with Decoupled Weight Decay (AdamW) is utilized; this is an optimization technique based on stochastic gradient descent. It dynamically estimates first-order and second-order moments, offering adaptive adjustments during training. In AdamW the weight decay method is enhanced through decoupling it from the optimization steps [43]. The learning rate is set to 0.0005, scheduled with Cosine Annealing Warm Restarts, where the learning rate is decreased from a high value to a low value, and then restarts from the previously found 'good' value [44]. The model is trained and validated over 250 epochs, while being evaluated on the accuracy, loss, F1 score, and Intersection Over Union (IOU) every 10 epochs, which are calculated as follows:  
\[accuracy=\frac{correct\ predictions}{total\ predictions} \tag{6}\]  
\[F1\ score=2\cdot\frac{precision\cdot recall}{precision+recall} \tag{7}\]  
\[IOU=\frac{area\ of\ intersection}{area\ of\ union} \tag{8}\]  
IOU is typically considered to be the most valuable metric for evaluating semantic-segmentation models, as it is able to quantify how effectively the predicted mask overlaps with the ground truth. Therefore, the version of the model with the highest validation IOU is saved for testing.
# A Comparative Study of Ensemble Models for Predicting Road Traffic Congestion
###### Abstract
Ensemble methods have shown superior performance in the road traffic congestion domain recently. In the recent study [16], ensemble methods performed best compared to regression models and multi-layer perceptron. Ensemble methods were considered in this study to improve machine learning classifier performance. This study aimed to develop an ensemble learning model using a real-life road traffic flow dataset to evaluate the negative impacts of road traffic congestion, which, in turn, might lead to high numbers of road traffic collisions on the highways and delays, to name a few. The three main objectives of this study, which deals with a real-life road traffic flow dataset, are as follows:  
1. To handle missing values using the listwise deletion method;
2. To compare the performance of three traditional machine-learning methods and three ensemble methods;
3. To assess each model's performance using key evaluation metrics and a cost model incorporating cost to commuters, businesses, and the economy. The study's contribution is to present an RTC framework that inputs missing values and performs comprehensive analysis using ensemble and machine learning methods, computes classification cost, and evaluates the performance of the models. The entire paper is outlined as follows: Section 2 contains the RTC-related literature review, Section 3 describes the study methodology and methods used. Section 4 describes the experimental settings for the study, while Section 5 provides the experimental results and discussion of the findings. Finally, a conclusion is presented in Section 6.
## 3 Experiments
### Recognition Results on the Bosphorus  
In this section, we compare the proposed MFCT-3 DOFFNet method with the existing state-of-the-art deep learning methods. All methods are based on the Bosphorus database. We train each network model parameter using unobstructed faces and select the optimal model training results by using the validation set as the test parameter for facial recognition performance. The proposed MFCT-3 DOFFNet uses the multiple feature combinatorial thresholding technique to remove the occluded regions and the missing face data generation method to train the model. The results of our experiments are shown in Table 4. For the unobscured face test set (containing only pose changes and expression changes), all the deep techniques achieve more than 90% for Top-1 and Top-5 recognition accuracy, while our proposed MFCT-3 DOFFNet network achieves 100% accuracy in its recognition rate. The occlusion of faces will increase the intra-class aggregation and decrease the inter-class variability, as demonstrated by the experimental results in Table 4. Compared with the recognition of pose and expression faces, the recognition accuracy of each neural network for occluded faces decreases to different degrees. Moreover, comparing the recognition accuracy of each network for obscured and unobscured faces, the network that is better at recognizing unobscured faces is not necessarily better at recognizing obscured faces. However, the proposed MFCT-3 DOFFNet still maintains the highest Top-1 recognition rate for obscured faces. Moreover, for the average facial recognition rate under pose change, expression change, and occlusion change, the proposed network MFCT-3 DOFFNet has the highest Top-1 and Top-5 recognition rates, with 99.52% and 99.76%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Raw Training Data**} & \multicolumn{3}{c}{**Missing Face Data Generation Method for Training**} \\ \hline
**Depth-Based Method** & \multicolumn{3}{c}{**Multi-Feature Combined Threshold Method**} & \multicolumn{3}{c}{**Depth-Based**} & \multicolumn{3}{c}{**Multi-Feature Combined Threshold Method**} \\ \hline
**Top-1** & **Top-5** & **Top-1** & **Top-5** & **Top-1** & **Top-5** & **Top-1** & **Top-5** \\ \hline
97.09 & 97.89 & 97.07 & 98.67 & 97.89 & 98.15 & 98.94 & 99.47 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recognition accuracy (%) under occlusion based on the depth method and the multi-feature combined threshold method before and after using the training data generation method. The input size is 112 \(\times\) 112 and the loss is set to Focal-Arcface.