esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Latest Developed Biosensors for COVID-19
### Electrochemical Biosensor  
Electrochemical biosensors are known for their small size, cost-effectiveness, ease of use and fast response. These types of sensors are extensively used in the development of point of care devices for diagnosing viral infections [68; 69]. Among the electrochemical techniques, screen-printed electrode (SPE) technology has aided the development of portable sensors considerably as it provides miniaturised but robust and user-friendly electrodes at low production costs [70; 71]. Figure 4 shows a typical configuration of an SPE-based electrochemical sensor employing a three-electrode system: a reference electrode, a counter electrode and a working electrode transduction element for biochemical reaction. The working electrode surface of SPE can be easily modified to immobilise specific biorecognition elements, such as an antibody to target the analyte. The antibody-antigen interaction on the electrode surface is transduced into a measurable electrical quantity.  
Alternatively, the catalytic reaction of a signal probe (e.g., enzyme), which labels the detection antibody, forms an electroactive product that releases electrons, which are transduced as a measurable electrochemical signal. The electrochemical measurement can be one of the following: (1) potentiometric-based, (2) amperometric-based, (3) conductometric or impedimetric-based [72; 73] to determine the concentration of analyte in a sample. The SPE-based sensor can be integrated with a portable instrument (e.g., potentiostat) or reader (e.g., computer or a smartphone) [74] for digital processing.  
Compared to conventional electrode materials (e.g., glassy carbon or carbon paste electrodes), SPEs have the advantage of cost, disposability, size and do not require electrode polishing prior to electrochemical detection. Because of its compact size, SPE only requires a small reagent volume for assay, as low as a few \(\upmu\)L. This makes the SPE-based electrochemical biosensor a potential tool for on-site measurement in remote regions with limited resources. Moreover, the electroanalytical performance and sensitivity of the sensor can be enhanced by modifying the SPE working electrode surface with nanomaterials such as gold nanoparticles, graphene and carbon nanotubes to increase the electroactive area of the electrode for further immobilisation of biomolecules such as antibodies, protein or nucleic acid [75; 76; 77]. Graphene are two-dimensional carbon nanomaterials, in which the carbon atoms are positioned in a hexagonal honeycomb lattice [78], whereas carbon nanotubes are an allotropic form of carbon that can be rolled up into cylindrical tubes (e.g., single-walled or multi-walled) [79]. They possess characteristic properties of large surface area and excellent electrical conductivity. Hence, they are used as an electro modifier to promote electron transfer between a target analyte and electrode and thereby achieve high detection sensitivity [80; 81].
## 2 Materials and Methods
### Measurement Systems Overview  
In order to measure the received signal strength for a real FSO system, a 2958-m propagation path was established over Piraeus port entrance at the Hellenic Naval Academy (HNA), Piraeus, Greece. A commercial FSO system was used to take RSSI measurements, to compare with the model predictions. Specifically, the FSO system used in the experiments is a MRV TS500/155 transceiver, operating at 850 nm with a maximum output power of 150-mW and data rate of 155-Mbps. It utilizes three laser sources with a beam divergence of 2 mrad each and a single receiver with a diameter of 20-cm, a sensitivity of \(-\)46 dBm and an avalanche photodetector (APD). It also uses an open protocol to automatically identify and lock on the current data rate and clock. The first terminal was located on the roof of a building at HNA, whereas the second was located on the Psytalia Island lighthouse. The horizontal propagation path was approximately 35 m above the surface of the sea, and over 95% of it was over water. Tidal changes in that area are minimal; therefore altitude changes of the propagation path were ignored. A diagram of the MRV, WS-2000 weather station and the surrounding area are shown in Figure 1. In addition to collecting measured readings from the FSO system, a WS-2000 weather station near the MRV was deployed to measure macroscopic meteorological parameters, including ambient temperature, air pressure, relative and absolute humidity, dew point, solar flux, ultra-violet index, rainfall rate, wind speed and direction. An online weather statistics database, [41] was also exploited to take sea temperature measurements from which the air-sea temperature difference was estimated. Measurements from both sensors were taken every minute, twenty-four hours per day.
# A New Multi-Objective Unit Commitment Model
## 3 Methodology
### Stop Criterion
Furthermore, for any tie-line updated based on rule 1, the convergence could be guaranteed. Theorem 2: _The iterative process converges to the local optimum._Proof. : The MOUC cost of the whole system could be written as a function of tie-line. \[\min F^{\prime}=\sum_{t}\sum_{i}f^{\prime}_{i}\left(D_{i,t}+\sum_{j} p^{tie}_{i,j,t}\right) \tag{30}\] \[s.t.\] (31) \[p^{tie}_{i,j,t}=-p^{tie}_{j,i,t},i,j\in\{1,...,M\},t\in\{1,...,T\}\] (32) \[p^{tie}_{i,j,t}=0,i\in\{1,...,M\},t\in\{1,...,T\}\] (33) \[|p^{tie}_{i,j,t}|\leq P^{max}_{i,j,t},i,j\in\{1,...,M\},t\in\{1,...,T\} \tag{34}\]  
\(f^{\prime}_{i}(x)\) is the function of MOUC cost for subsystem \(i\). Adding the Lagrange multiplier, using the constraint (33), and setting derivative with respect to \(p^{tie}_{i,j,t}\) equal to zero (if there exists tie-line between sybsystem \(i\) and \(j\)), we get:  
\[\frac{\partial f^{\prime}_{i}\left(D_{i,t}+\sum_{k}p^{tie}_{i,k,t }\right)}{\partial p^{tie}_{i,j,t}}-\frac{\partial f^{\prime}_{j}\left(D_{j,t} +\sum_{k}p^{tie}_{j,k,t}\right)}{\partial p^{tie}_{i,j,t}} \tag{35}\] \[=\lambda^{max}_{i,j}-\lambda^{min}_{i,j}+\lambda^{max}_{j,t}- \lambda^{min}_{j,i}\]  
Where \(\lambda^{max}_{i,j}\) is the Lagrangian multiplier of inequality constraint \(p^{tie}_{i,j,t}\leq P^{max}_{i,j,t}\), and \(\lambda^{min}_{i,j}\) is for \(p^{tie}_{i,j,t}\geq p^{min}_{i,j,t}\). When rule 1 is met and rule 2, 3 have not been met, constraint (34) is satisfied, \(\lambda\) in (35) is zero. Then, for subsystem \(i\), \(j\), the derivative of MOUC cost with respect to attached tie-line power flow, or, \(MP\) of subsystems, are equal to each other. which is the same solution as our algorithm. If \(\lambda\
eq 0\), the boundary of constraint (34) has been met, then we still get the same solution as our algorithm considering rule 2. Furthermore, we have considered the feasibility of local MOUC. Please note that units parameters in each subsystem are not required to be uploaded. For each subsystem, only local \(MP\) and the cost for each objective (fuel cost and gas emission in this article) are required, which protects the privacy of generating companies.
# Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
## 3 Results
### Performance Metrics
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Model** & **Description** & **Training Method** & **Application** \\ \hline [PERSON] et al. [62] & GoogleNet with ensemble learning & Dataset contains a total of 7408 US breast images, with 657 used as the training set and 829 as the test set. The accuracy reached was 90.21\%. & The CNN was trained with 10-fold cross-validation. Data augmentation was carrying out with the Caffe method & Data augmentation, detection, and classification of breast lesions in US \\ \hline \multirow{4}{*}{[PERSON] et al. [178]} & \multirow{4}{*}{LeNet for CNN models in cascade R-CNN} & INbreast dataset was used, with 115 cases and 410 images from MLO and CC views. The results showed that the DL-CAD system is able to detect 90\% of masses, with a segmentation accuracy of 85\% and the classification reached a sensitivity of 0.98 and a specificity of 0.7. & DL detection: Fast R-CNN, multiscale-DBN, and random forest; DL segmentation: CRF; DL classification: regression method. & Detection, segmentation, and masses in DM \\ \hline \multirow{4}{*}{[PERSON] et al. [165]} & \multirow{4}{*}{GAN} & The Mendeley database [179] was used, which contains 150 malignant and 100 benign tumors. The performance metrics achieved scores of dice = 93.76\% and IoU = 88.82\%. & Segmentation with GAN learning. & Segmentation and classification of US images \\ \hline \multirow{4}{*}{[PERSON] [37]} & \multirow{4}{*}{SDAE based CADx} & The method was carried out on a private database, with 520 breast sonograms (275 benign and 245 malignant lesions). The AUC performance reached 0.80\%. & An SDAE (OverFeat) model was used to classify with the ensemble method. & Breast lesion/nodules diagnosis and classification of US images \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Furthermore, Table 8 gives a brief overview of the new DL-CAD systems' approaches and the traditional ML-CAD systems.
# Review of Image Forensic Techniques Based on Deep Learning
## 2 Image Forensic Techniques
### Passive Forensics
#### 2.1.1 Basic Framework of Image Forgery Detection  
With the development of deep learning techniques, certain advantages have been achieved in areas such as image classification and segmentation. Deep learning is also increasingly being applied to image forgery detection. A basic framework of image forgery detection based on deep learning is shown in Figure 2. First, the tamper detection network is built, and feature extraction, feature classification, and localization are performed by the network model. The weights of the network are saved by learning the parameters within the network through big data training to obtain the weights when optimal. The image to be detected is input to the network and tampering detection is performed using the saved network model.
## 3 Experiments and Analysis  
### Data Selection and Usage  
To filter out the features from the feature space that play a determinative role in the classification results, reducing the number of dimensions in the feature space not only reduces the time required to build the classification model effectively, but can also help avoid dimensional explosion and prevent model overfitting.  
By using two types of parameters, the Bhattacharyya distance (BD) [32] and the feature separability index (SI) [33], we could provide a reference for feature selection. The feature separability index is determined by considering the point at which the mean distance between two or more types of features is greater than the standard deviation between these features, and a larger value indicates better feature separability. To determine the optimal size of the extraction window for the gray-level concurrence matrix (GLCM) texture features [34], we considered the two categories of thin ice and multi-year ice, and each texture feature was calculated using a window size of 3 \(\times\) 3 to 61 \(\times\) 61. As shown in Figure 9, with a sliding window size of thirty, the two types of index features reached their maximum values and showed good separability.  
In dealing with the EW mode surface distance data within the SAR images, 2-dimensional backscattering joint coefficients, 6-dimensional band calculation features, and 18-dimensional image texture features were extracted after data pre-processing. Using the same method, the separability of the extracted 26-dimensional features was assessed, and the BD and SI parameters were calculated for these data with reference to one-year ice and multi-year ice; the comparison results are shown in Figure 10, and values with more detail can be found in Table 1. The bands with the best differentiability for both thin ice and multi-year ice were found to be \(\sigma^{0}_{HH}\), \(\sigma^{0}_{H\
u}\), HHMeans, HVMeans, \(\sigma^{0}_{H\
u}+\sigma^{0}_{HH}\), and \(\sigma^{0}_{HH}-\sigma^{0}_{HV}\). Therefore, for the completion of the deep learning open ocean extraction task, we chose the \(\sigma^{0}_{HH}\), \(\sigma^{0}_{H\
u}\), \(\sigma^{0}_{H\
u}+\sigma^{0}_{HH}\), and \(\sigma^{0}_{HH}-\sigma^{0}_{HV}\) features (applying PCA for dimensionality reduction) to obtain the four-dimensional features that would be used as the training input. The processed features are shown in Figure 3d.  
Figure 10: Feature optimization operation based on the Bhattacharyya distance and separability index.  
Figure 9: The optimal texture feature extraction window found based on the Bhattacharyya distance (BD) and separability index (SI). **(a)** HV texture separability calculated using the BD index; **(b)** HV texture separability calculated using the SI index; **(c)** HH texture separability calculated using the BD index; and **(d)** HH texture separability calculated using the SI index.
## Appendix A Intervention Analysis Results  
### Region 1 (Pacific Ocean) Results  
According to the BinSeg segmentation and HMM analysis results in Sections 4.4 and 4.5, respectively, three different change-points were detected for the Total Ozone Column for Region 1, day 59, 79 and 139 (Figure A1).  
Based on the highest Adjusted R-squared value and lowest MASE and AIC scores (Adjust \(R^{2}\) = 0.88, AIC = 2356.18, MASE = 0.85), Model R1 is selected as the best model (Table A1). It can be seen that the fitted model (Figure A2) was able to capture the behavior of the series quite successfully with small residuals. In summary, in this model all the following components involving mean ozone level (intercept) and ozone's lagged dependence of 1 and 5 days were highly significant, as were the negative trend lag1 effects at the first and second intervention points at 59 and 79 days, respectively; likewise, the sustained non-lagged negative effect at the third change-point at 139 days. Aerosol levels lagging at 1 day were not shown to be significant.
## 3 The Motion State Estimation Method
### Motion State Estimation  
Although the moving objects were segmented in the previous section, for consecutive frames, the same moving object cannot be well located. Therefore, the moving objects in different frames are transformed to the same coordinate system according to their ego-motion parameters. Then, the moving objects at different times are correlated and clustered by spatio-temporal constraints, and the motion state of each moving object is estimated with point matching. The structure of the motion state estimation is shown in Figure 7.
# On the Spectral Efficiency for Distributed Massive MIMO Systems
## 3 Power Unbalance on Sub-6 Ghz D-MIMO Systems
### Measurement Scenario  
The first subset of measurements is taken in a reverberation chamber. These measurements are characterized by the rich scattering scenario generated inside the chamber. The reflection of the waves on the shielded walls creates several multipath components (MPCs) that reach the UE in different time instants, which increase the diversity of the scenario. Seven different scenarios are measured inside the reverberation chamber, where the BS has twelve transmitter antennas and UE has three receiver antennas. In order to generate diversity on the propagation channel, two mechanical stirters are inserted inside the reverbera  
Figure 2: Scheme of a D-MIMO consisting of a set of MIMO subsystems acquired in a semi-anechoic and semi-reverberation chamber.  
tion chamber. Since stirrers can adopt 50 different positions, together with 201 frequency points (250 kHz frequency spacing) from 1775 GHz to 1825 GHz, 10,050 channel propagation are measured for each TX-RX pair on each scenario. The main difference between the seven scenarios is found in the load of the reverberation chamber, which is changed by introducing several absorbers in different positions. A deep explanation of the measuring process can be found in [24; 25].  
Once the acquisition process is carried out, seven MIMO systems are achieved and saved on seven \(\mathbf{H}_{i}\). The full D-MIMO system is formed by the matrix \(\mathbf{H}=~{}\begin{bmatrix}\mathbf{H}_{1}^{T},\mathbf{H}_{2}^{T},~{}\ldots,\mathbf{H}_{7}^{T }\end{bmatrix}^{T}\) whose dimensions are 84 TX \(\times\) 3 RX.
# Influence of Solvent Selection in the Electrospraying Process of Polycaprolactone
## 3 Selection of Solvent Systems Used in PCL Electrospraying
### Solution Properties for Cone-Jet in PCL Electrospraying
#### 3.2.2 Surface Tension  
The surface tension of the working solution affects the formation of the Taylor cone at the tip of the nozzle during the electrospraying process. According to [PERSON] and [PERSON] [69; 70], the value of surface tension of the electrosprayed solution cannot exceed 50 mN/m, even if some papers report  
Figure 3: The formation process of cone jet in coaxial electrospraying for inner and outer solutions with different electrical conductivities. (**a**) inner solution has higher electrical conductivity (**b**) outer solution has higher electrical conductivity.  
the use of glycerine (63 mN/m) or water (72.8 mN/m) to achieve it [132; 133]. Thus, the low surface tension of the PCL solutions, ranging from 22.5 to 32.8 mN/m (Table 3), allows Taylor cone formation. Furthermore, liquids with a high surface tension trigger also a corona discharge at the tip of the nozzle, which change the stable cone-jet into an irregular spraying and an asymmetrical mode.  
In the coaxial mode, when the surface tension of the inner phase is higher than the outer one, the formation of a stable Taylor cone is possible, which leads to the formation of core-shell particles as described by [PERSON] et al. [85]. The relations between the surface and interfacial tensions to promote the formation of a cone-jet mode have been studied in details by [PERSON] et al. [134] (Figure 4), with the use of the spreading coefficients (\(S_{i}\)) (Equation (10)).  
\[S_{i}=\gamma_{jk}-(\gamma_{jk}+\gamma_{ij}) \tag{10}\]  
where 1, 2, and 3 represent core liquid, air, and shell in the liquid state, respectively. \(\gamma_{23}\), \(\gamma_{12}\) and \(\gamma_{13}\) are the surface tension of shell and core liquid, and \(\gamma_{13}\) the interfacial tension between core liquid and shell liquid phases, respectively.  
The thermodynamic conditions of the system required for the formation of a complete engulfing are S\({}_{3}>0\), S\({}_{1}<0\) and S\({}_{2}<0\). In other cases, partial or non-engulfing are obtained, due to the presence of unstable cone-jet. Therefore, the core substance or inner phase needs to have a high surface tension value to promote the formation of cone-jet, and after that, a core-shell structure via a coaxial electrospray process [135; 136; 137]. To succeed in the design of the electrospray methods with three-needle or four-needle coaxial, the authors preconize the choice of solvents having similar surface tensions, or close to the dispersed active substance one [93; 129; 138]. Surfactants or protective colloid may also be added to the various phases until to reach the desired surface properties of the working solutions [139; 140].
## 2 System Model
The transmitted symbol with precoding and AN is  
\[\mathbf{x}_{l}^{m}=\zeta\mathbf{P}\mathbf{s}_{l}^{m}+\mathbf{V}^{0}\mathbf{n} _{a}, \tag{3}\]  
where \(\mathbf{P}\) is a precoding matrix with power constrain \(\text{tr}\left(\mathbf{P}\mathbf{P}^{H}\right)=P_{t}\), \(\zeta\) is the power-control factor, and \(\mathbf{V}^{0}\mathbf{n}_{a}\) is random AN. Consider designing the AN in the null space of [PERSON]'s channel, ensuring that it does not affect the signal received by [PERSON] but interferes with the signal received by [PERSON]. Thus, \(\mathbf{V}^{0}\) can be obtained by performing singular value decomposition on \(\mathbf{H}(\Theta_{b})\), such that it satisfies \(\mathbf{H}=\mathbf{U}[\mathbf{D},\mathbf{0}]\left[\mathbf{V}^{1},\mathbf{V}^ {0}\right]^{H}\). \(\mathbf{n}_{a}\) is complex Gaussian white noise with a mean of \(0\) and element-wise variance \(\sigma_{a}^{2}=\frac{\left(1-\zeta^{2}\right)P_{t}}{N_{t}-N_{t}}\). The received signals for [PERSON] and [PERSON] are, respectively,  
\[\mathbf{y}_{b}=\zeta\mathbf{H}(\Theta_{b})\mathbf{P}\mathbf{s}_{l}^{m}+ \underbrace{\mathbf{H}(\Theta_{b})\mathbf{V}^{0}\mathbf{n}_{a}}_{\triangleq \mathbf{n}}+\mathbf{n}_{b}=\zeta\mathbf{H}(\Theta_{b})\mathbf{P}\mathbf{s}_{l }^{m}+\mathbf{n}_{b}, \tag{4}\]  
\[\mathbf{y}_{e}=\zeta\mathbf{H}(\Theta_{e})\mathbf{P}\mathbf{s}_{l}^{m}+ \underbrace{\mathbf{H}(\Theta_{e})\mathbf{V}^{0}\mathbf{n}_{a}+\mathbf{n}_{e}} _{\triangleq\mathbf{n}}, \tag{5}\]  
where \(\mathbf{n}_{r}\) and \(\mathbf{n}_{e}\) are zero-mean complex Gaussian white noise with element-wise variances of \(\sigma_{b}^{2}\) and \(\sigma_{e}^{2}\), respectively. \(\mathbf{n}\) consists of AN-caused interference and thermal noise with variance \(\mathbf{\Sigma}=\sigma_{a}^{2}\mathbf{H}(\Theta_{e})\mathbf{V}^{0}\left( \mathbf{V}^{0}\right)^{H}\mathbf{H}^{H}(\Theta_{e})+\sigma_{e}^{2}\mathbf{I}_{ N_{e}}\). It is noted that \(0\leq\zeta\leq 1\), when \(\zeta=1\), with the above two models representing the case without AN. Due to the inherent mobility of the UAV, the relative positions between [PERSON], [PERSON], and [PERSON] are continuously changing. This dynamic movement leads to variations in the angles of arrival (AoAs) of the transmitted signals at both the legitimate receiver and the eavesdropper. Consequently, the fluctuations in these AoAs introduce challenges in maintaining a secure communication link, as the UAV must adapt its transmission strategy in real time to mitigate eavesdropping risks. To address this, it becomes essential to optimize the precoding matrix and power-control factor based on the constantly changing arrival angles. By doing so, the system can effectively manage the interference on [PERSON] introduced by the AN while simultaneously ensuring the reliable reception of data by the legitimate receiver, thus enhancing the overall transmission security.
## 7 Applications of HAR in Healthcare
### Assistive Healthcare for Elderly People
Generally, the adoption of new technology is not always easy for the elderly; using HAR systems that rely less on them operating it might be the best course of action. Also with the increase in more comfortable wearable systems, some of which are integrated into smartwatches, adoption by the elderly population should also increase. Smart homes with cameras or other ambient sensors, like those modelled in the ARAS and CASAS datasets, can be used by caregivers and healthcare practitioners to monitor the health status of the elderly and detect abnormalities in their pattern of activities. This can be used to monitor adherence to medication schedule and even in the early detection of cognitive decline (Alzheimer's and dementia). This method would eradicate having to depend on the elderly to wear the sensors; however, there is still the issue of privacy and data protection. With the HAR system monitoring their every move, it could be considered invasive, and some might feel like their independence is being taken away. For independent elderly people, quick intervention is important in the event of an accident, and this is dependent on quick reporting or the detection of issues, and HAR-based systems can help achieve this. However, the adoption of HAR in the assistive healthcare of the elderly depends on issues such as privacy and data protection being adequately handled.