esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Validation
### Selected Results
#### 4.2.1 Ray Roberts, Lake
In order to demonstrate the improvement of our new approach, we compute the squared correlation coefficient between initial surface areas with data gaps and water levels from the in situ station first. The squared correlation coefficient \(R^{2}\) between initial surface areas and water levels from gauge is 0.777 by using 273 months of data. \(R^{2}\) increases up to 0.934 between the final filled surface areas and the water levels from gauge. By filling the data gaps, the quality improved by about 20%. An additional comparison between final filled surface areas and water levels from satellite altimetry yields a \(R^{2}\) of 0.910 which is similar to the in situ data. One can clearly see that our new approach leads to an improved surface area time series. In addition to in situ data, satellite altimetry is an alternative data source for validation of ungauged water bodies. The validation of surface areas is a challenging task as there are no in situ data available as for water levels. In order to provide not only correlations but also RMS errors for quality assessment, we carry out an additional cross-validation based on surface areas computed from the given water levels using a fixed stage-area relationship. For this purpose, we randomly selected 10% of points were surface area and water levels are available in order to compute a hyposometry using two functions (linear and 2 nd order polynom). This is shown in Figure 15 for Lake Ray Roberts. Then, the heights of the remaining 90% of points are used to predict the surface areas by using both fitted functions derived from the hyposometry. Based on them, RMS errors with respect to the final surface areas are computed. Figure 14: Validation of surface area time series for Lake Ray Roberts using water levels from in situ stations and satellite altimetry. The improvement of our new approach is shown by comparing initial surface areas with data gaps (**left**) and final filled surface areas (**middle**, **right**). For Lake Ray Roberts, the cross-validation using the linear fit yields an RMS of 3.04 km\({}^{2}\) which is 2.21% of the maximum area. By using the polynomial fit, the resulting RMS is 2.85 km\({}^{2}\) which is 2.07% of the maximum area. Both results perform similarly, but one can clearly see for Lake Ray Roberts that the commonly used linear and polynomial function for the calculation of the hypsometry do not clearly describe the dependency between surface areas and water levels as the influence of the surrounding topography is unpredictable.
# MuseStudio: Brain Activity Data Management Library for Low-Cost EEG Devices
## 2 Background
### Real-Time Remote View  
Performing neural experiments usually requires real-time visualization of the brain signals captured. With time-based graphs, it is possible to detect how good the data received from the electrodes are, due to the fact that they may not have full contact with the skin and produce extra noise. Muse, with four electrodes, is especially vulnerable to this issue because one bad sensor can invalidate a full recording. The current available solution [30] only shows real-time visualization for one device at a time. Moreover, it only works on the same computer to which Muse is connected. This problem narrows down the flexibility when researchers want to perform experiments with multiple devices at once. The MuseStudio library provides access to real-time graphs no matter the number of devices attached. Additionally, it shows when the contact of the sensors with the skin is good for each of them independently.  
Globalization has broken many barriers, and healthcare is one of them. Telemedicine [36] is increasingly being adopted for receiving medical treatment at a distance. In fact, patients who receive palliative care by telemedicine are very satisfied with the results. For this reason, we want everyone to be able to access neuroevaluations anywhere in the world without need to travel long distances to reach experts.  
Instead of creating a local instance of a program, we created a web server with an IP address and a port that users can access through a web browser. This allows many users to be connected to the same endpoint, even if they are located outside the local area network. However, as a prerequisite, the server port must be connected to the Internet for external access. The implementation can be used straight away without authentication, and it is modular, so it can be integrated with other Python environments without adaptation, such as a website with a log-in required. The web browser must have JavaScript enabled to show the graphs. Finally, the complete set of options added is: sensor selection, update interval (from 200 ms to 5 s), play/pause, zoom in/out, and expand graphs.
# Sound Localization Framework for Construction Site Monitoring
## 5 Experiments  
Before verifying the system's performance on an actual construction site, preliminary tests were conducted in a restricted experimental environment by using several microphones to examine the relationship between the number of microphones included in the array and the estimation accuracy. Furthermore, tests were performed with varying microphone arrangements to determine the most suitable arrangement for obtaining optimal performances in both 2D and 3D spaces. In this study, we conducted experiments with three or more microphones as the two-microphone arrangement exhibited a significantly larger error range in location estimation and was limited in determining a particular position in a 3D space [37].
# Characterizing the Tensile Behaviour of Woven and Composite Fabrics under UV Exposure
## 2 Materials and Methods
### Photolysis and Photo-Oxidation Degradation  
Photolysis degradation involves the energy from incoming solar radiation, which causes adverse changes in the molecular structure and bonding of polymers [20]. Theoretical explanations for the photolysis degradation of engineered fabrics can be found in the quantum mechanics of electromagnetic excitation [11]. Light photons from incoming solar radiation interact with individual electrons of the polymer bonds. Higher energy photons than the polymer's bonding strength allow the electron to dislodge from its orbit. The high-energy photons simulate a series of chemical reactions to produce free radicals (unstable atoms  
Figure 1: Global UV index map from 1996 to 2002.  
with high reactivity) responsible for chain scission (i.e., bond breaking) and crosslinking (i.e., bond formation) [21]. Unstable engineered fabric is characterized by chain scission dominance over crosslinking. Again, the energy required for the polymer chain scission is related to [PERSON]'s radiation law, where the energy is directly (inversely) proportional to its frequency (wavelength). [PERSON]'s radiation law is expressed in Equation (1), where the terms h, c, f, and w represent [PERSON]'s constant, speed of light, frequency of the light spectrum, and wavelength of light, respectively. Engineered fabrics under a freely-exposed condition with greater oxygen availability further simulate photo-oxidation degradation. The photo-oxidation reaction of polymers, specifically for PE and PP, can be described by the chemical reaction expressed in Equation (2); where RH denotes the polymer chain, \(O_{2}\) denotes oxygen, and \(R\), \(ROO\), \(RO\), and \(OH\) denote free radicals [22].  
\[E=hf=h\cdot\left(\frac{c}{w}\right) \tag{1}\]  
\[RH+O_{2}+hf\ \rightarrow\ R\cdot+RO\cdot+ROO\cdot+OH \tag{2}\]
## 4 Discussion  
The POD strategy of BDS is in the research stage. It can be seen that BDS satellite precise orbit products still face some challenges from the view of SLR validation. Firstly, the BDS processing strategies and force models of various ACs are still inconsistent at present, and there is a discrepancy of about 10 mm between the precise orbit products among different ACs. Secondly, the orbit accuracy of different BDS constellations varies significantly. As an example, the difference in the RMS of SLR residuals between the orbits of BDS-3 CAST and BDS-3 SECM released by GFZ, WU, and ESA is about \(-13\), \(-10\), and 14 mm, respectively. This suggests that there are significant differences in the model adaptability of different BDS constellations, which may be the main challenge in improving BDS orbit accuracy. Furthermore, the pure empirical SRP models including ECOM and ECOM2 are not entirely applicable to BDS, whereas the estimated PBW model used by ESA can reduce the RMS of SLR residuals by 20 mm with regard to other ACs. Based on the results presented above, it is evident that there is still room for improvement in the non-conservative force modeling of BDS satellites, including but not limited to the accurate calibration of optical parameters of BDS satellites, the application of the modeling of ERP and AT for all BDS satellites, and considering some unmodeled non-conservative forces such as thermal radiation pressure. Fortunately, with the increasing number of observations tracked by ILRS and the continuous research of SLR technology for BDS, the modeling effect of BDS can be better evaluated and the accuracy of BDS orbit products can be further improved.
###### Abstract  
The present work describes the effect of Ce\({}^{3+}\) ion doping on the structural, morphological, and magnetic properties of spinel manganese ferrite (MnFe\({}_{2}\)O\({}_{4}\)) nanocrystallites (NCs) using various instrument techniques. Rare earth element (REE) Cerium (Ce\({}^{3+}\)) doped MnFe\({}_{2}\)O\({}_{4}\) NCs were prepared by a simple microwave combustion technique. In the present scenario, ferrites are widely used for photocatalytic dye degradation and antibacterial applications. Aiming to achieve this, we prepared Ce\({}^{3+}\) doped MnFe\({}_{2}\)O\({}_{4}\) NCs by microwave combustion method and urea as burning agent and the obtained powder samples were characterized by powder X-ray diffraction (XRD), Fourier transform infrared (FT-IR), high resolution scanning electron microscope (HR-SEM), high resolution transmission electron microscope (HR-TEM) and vibration sample magnetometer (VSM) techniques. The pure spinel phase formation was confirmed by XRD analysis. FTIR spectra show two prominent absorption bands under 1000 cm\({}^{-1}\), which confirms the formation of the spinel structure. HR-SEM and HR-TEM pictures demonstrated a sphere-shaped morphology and also expose the combination and agglomeration of grains, which are mostly due to the magnetic characteristics of the samples. The magnetic properties of the synthesized MnCe\({}_{x}\)Fe\({}_{2-x}\)O\({}_{4}\) (x = 0.0, 0.1, 0.3, and 0.5) NCs were studied by VSM analysis at room temperature (RT) shows ferromagnetic behavior. The photodegradation results showed that MnFe\({}_{2}\)O\({}_{4}\) and Ce doped MnFe\({}_{2}\)O\({}_{4}\) NCs have a higher potential to degrade methylene blue (MB) and the sample MnFe\({}_{0.3}\)Fe\({}_{1.7}\)O\({}_{4}\) NCs showed superb photocatalytic performance (91.53%) compared to other samples. The antibacterial activities of Gram-positive _S. aureus_, _B. subtilis_ and Gram-negative _K. pneumoniae_ and _E. coli_ were investigated using pure and Ce\({}^{3+}\) substituted MnFe\({}_{2}\)O\({}_{4}\) NCs and a higher activity for MnCe\({}_{0.3}\)Fe\({}_{1.7}\)O\({}_{4}\) NCs than other samples was observed, which indicated that they can be used in biomedical applications.  
Keywords:Ce\({}^{3+}\) substituted MnFe\({}_{2}\)O\({}_{4}\); magnetic nanoparticles; organic dyes; photocatalysis; efficiency +
Footnote †: journal: Journal of Scientific Research
###### Abstract  
Flexible strain sensors have attracted significant attention due to their critical applications in wearable devices, biological detection, and artificial intelligence. However, achieving both a wide strain range and high sensitivity remains a major challenge in current research. This study aims to develop a novel composite material with a synergistic conductive network to construct high-performance flexible strain sensors. Thermoplastic polyurethane (TPU) nanofiber membranes were first prepared using electrospinning technology, and their surface was modified with polydopamine (PDA) via in-situ polymerization, which significantly enhanced the fibers' adsorption capacity for conductive materials. Subsequently, carbon nanotubes (CNTs) and carbon black (CB) were coated onto the PDA-modified TPU fibers through ultrasonic anchoring, forming a CB/CNT/PDA/TPU composite with a synergistic conductive network. The results demonstrated that the flexible strain sensor fabricated from this composite material (with a CB-to-CNT mass ratio of 7:3) achieved ultrahigh sensitivity (gauge factor, GF, up to 1063) over a wide strain range (up to 300%), along with a low detection limit (1% strain), fast response and recovery times (137 ms), and exceptional stability and durability. Further evaluations confirmed that this sensor reliably captured biological signals from various joint movements, highlighting its broad application potential in human motion monitoring, human-machine interaction, and soft robotics.  
elecrospun fiber membranes; synergistic conductive network; flexible strain sensing; carbon nanotubes; carbon black Article
## 2 Original SIFT Algorithm
### Direction Determination  
After the key points were found, considering that they are in the scale space, it was necessary to use local image features to assign a reference direction to all key points so that the descriptor had rotation invariance. Therefore, the image gradient method was used to obtain the stable direction of the local structure. The magnitude and direction of the gradient are shown in Equations (6) and (7):  
\[m(x,y)=\sqrt{(L(x+1,y)-L(x-1,y))^{2}+(L(x,y+1)-L(x,y-1))^{2}} \tag{6}\]  
\[\theta(x,y)=tan^{-1}(((L(x,y+1)-L(x,y-1))/(L(x+1,y)-L(x-1,y)))) \tag{7}\]  
where \(m(x,y)\) is the gradient value; \(L\) is the scale space value where the key point is located; and \(\theta\) is the gradient direction. The histogram was used to count the gradient directions of the key point in a certain neighborhood, find the direction corresponding to the highest histogram peak and then use it as the main direction of the key point.
## 3 VLC System Model
### MIMO VLC Channel Model  
The presented system model is intended for outdoor, long-range, point-to-point communication scenarios. Figure 2 shows the MIMO-VLC channel model with \(N_{t}\) LASER sources and \(N_{t}\) photodiodes at the receiver. A DC bias is typically applied alongside the AC signal for each light source. This ensures that the transmitted data is positive and real valued. As a result, the input to each LASER consists of a combination of the DC bias and the AC signal.  
Let \(\mathbf{x}=[x_{1},\;x_{2},\ldots,\;x_{N_{t}}]^{T}\) be the transmitted signal vector, \(\mathbf{H}\) is the \(N_{r}\times N_{t}\) MIMO channel matrix gain. The received signal vector \(\mathbf{y}=[y_{1},\;y_{2},\ldots,\;y_{N_{t}}]^{T}\) can be given by [2]:  
\[\mathbf{y}=\mathbf{H}\mathbf{x}+\mathbf{n} \tag{1}\]  
where \(\mathbf{n}=[n_{1},\;n_{2},\ldots,\;n_{N_{t}}]^{T}\) represents the noise vector, typically modeled as additive white Gaussian noise (AWGN), which is commonly used in analytical modeling of VLC systems. In this work, the noise is modeled as a combination of thermal noise and optical shot noise induced by ambient light sources, specifically fluorescent lamps and dimmable white LEDs, and is assumed to have zero mean with a variance given by [2]:  
\[\delta^{2}=\delta^{2}_{thermal}+\delta^{2}_{shot} \tag{2}\]  
Figure 1: Block diagram of a 2 \(\times\) 2 MIMO-based VLC system.  
where \(\delta^{2}_{thermal},\delta^{2}_{shot}\) are the thermal noise variance and shot noise variance, respectively. The shot noise is due to sunlight. Adaptive differential equalization, such as presented in [15; 25], has been employed to mitigate the impact of optical interference and shot noise resulting from ambient light. In this work, we considered a deterministic channel model with a line-of-sight (LOS) component.
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 4 Integration of Artificial Intelligence in LPBF Defect Sensing
### Integrated ML Techniques and Acoustic/Ultrasonic Sensors  
Acoustic and ultrasonic sensors collect the vibrational waves propagating on or through the surface of a material. Regarding LPBF defect detection, acoustic sensing techniques have been used to monitor the quality of the LPBF printing process. The recorded sound from the laser hitting the powder substrate during printing can be processed to hint towards porosities and undesired anomalies. The graphical diagram showing how AE signals can capture melt pool behavior in the LPBF process is provided in Figure 14. The acoustic sensor types used to train ML algorithms to recognize defects include acoustic emission (AE) sensors, fiber Bragg grating (FBG) sensors, and microphones. A compiled list of acoustic/ultrasonic sensors integrated with ML methods for detecting LPBF defects is provided in Table 12.  
An ML algorithm was utilized to recognize various LPBF defects, including porosities, delamination, and crack propagation [200]. The training data were produced using an airborne resonant acoustic emissions sensor. Two CNN algorithms were trained based on variational auto-encoder and general adversarial network models. Acoustic signals utilizing a fiber Bragg grating sensor were collected and taught by a CNN-based classifier to separate the acoustic signals of dissimilar feature quality [161]. The dissimilar acoustic features can hint at information about the locations of individual defects, including the formation of deep keyhole channels, tubular defects, and LOF defects. A novel method for defect detection within LPBF-printed parts using acoustic emission signals and deep belief networks (DBNs) was developed [159]. The DBNs achieved high defect detection rates, differentiating between five melted states without signal preprocessing. Figure 13: Graphical description of melt pool data extraction using in situ dual-wavelength imaging pyrometer and an SVM machine learning approach [197]. Acoustic signal and image features were used to train DBN and CNN models to recognize different melting states in the LPBF printing process of SS304L [201]. An SVM algorithm was used to identify and classify defect patterns, including underheating and overheating states. Acoustic emission (AE) signals and various ML methods were integrated to develop an in situ crack detection system for LPBF-printed parts. The investigated ML methods included logistic regression, SVM, RF, and Gaussian process classifier models [202]. Using a CNN, an acoustic monitoring system was utilized to classify three processing regimes: LOF pores, conduction modes, and keyhole pores. An ultrasound microphone collected the acoustic emission signals [203]. A supervised ML technique was used to analyze XCT imagery for defects. Pre- and post-laser scan layerwise imagery, acoustic and multi-spectral emissions, and scan vector trajectory-derived information also contributed to the process footprint [204].
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 5 Mror: Mobile Reliable Opportunistic Routing for CRSN
### Route Selection  
Once the first RREQ packet arrives at the sink, the sink sets a timer. The sink keeps accepting new route requests until the timer times out; after which, any arriving route request packets are declared out-of-date and subsequently discarded. It is inferred that the late arrival of such requests indicates the inferior quality of the corresponding paths. Two metrics are extremely considered in the final route selection; the channel switching and hop counts. Both are worked into the route cost-function \(\chi_{rut}\). This function favors routes with low channel switching by declaring half the weight of the hop count metric for the channel switch metric.  
\[\chi_{rut}=k_{h}\cdot W_{h}+\frac{k_{s}}{2}\cdot W_{s} \tag{27}\]  
where \(k_{h}\) is the assigned weight for the hop count and \(k_{s}\) is the assigned weight for hop count channel switch count, along the path. \(W_{h}\) and \(W_{s}\) are, respectively, the path's hop count and the channel switch count. The path having the least cost is the final one, and all nodes on the path are subsequently referred to as the path's local minimum resolution (LMR) nodes.
## 3 Multi-Objective Optimization  
Generally, the MOO problem can be defined as an optimization problem with \(m=1,\ldots,M\) objective functions that are simultaneously minimized. Most of the practical engineering optimization problems have some limitations regarding the range of the decision variables, and therefore, the optimization problems usually are further expanded with several type of constraints to ensure the physical feasibility of the solution. These problems, are referred to as constrained multi-objective optimization problems (CMOPs), which can be formulated in the mathematical form as follows\[\min f_{m}(\mathbf{x}_{i})\quad m=1,\ldots,M\wedge i=1,\ldots N_{P}\] \[s.t.\] \[g_{k}(\mathbf{x}_{i})\geq 0,\quad k=1,\ldots,K \tag{36}\] \[h_{l}(\mathbf{x}_{i})=0,\quad l=1,\ldots,L\] \[x_{i,j}\in\Big{[}x_{j}^{Lower},x_{j}^{Upper}\Big{]},\quad j=1, \ldots,n\]  
where \(M\) denotes the total number of objective functions to be optimized, \(g_{k}(\mathbf{x}_{i})\) is \(k\)-th inequality constraint, \(K\) is the total number of inequality constraints, \(h_{l}(\mathbf{x}_{i})\) is \(l\)th equality constraint, \(L\) is the total number of equality constraints, \(x_{j}^{Lower}\)and \(x_{j}^{Upper}\)denote the lower and upper bounds of the decision variables in the search space, respectively. The set \(\mathbb{F}\), which satisfies all the constraints, is called feasible domain \(\mathbb{F}=\Big{\{}\,\mathbf{x}\mid g_{k}(\mathbf{x}_{i})\geq 0\wedge h_{l}( \mathbf{x}_{i})=0\wedge x_{i,j}\in\Big{[}x_{j}^{Lower},x_{j}^{Upper}\Big{]} \Big{\}}\).  
Definition 1: Let \(\mathbf{x}\) be the point in the feasible solution space, \(\mathbf{x}\in\mathbb{F}\). The point \(\mathbf{x}\) is said to be Pareto optimal if and only if in the feasible domain there does not exist another point \(\mathbf{y}\in\mathbb{F}\) for which \(f(\mathbf{y})\leq f(\mathbf{x})\), \(f(\mathbf{y})\
ot=\!\!f(\mathbf{x})\).  
Definition 2: Dominated solution: The solution \(\mathbf{x}\) dominates the solution \(\mathbf{y}\) (denoted as \(\mathbf{x}\prec\mathbf{y}\)) if and only if \(f_{i}(\mathbf{x})\leq f_{i}(\mathbf{y})\)\(\forall i\in\{1,2,\ldots,m\}\) and \(\exists j\in\{1,2,\ldots,m\}\) such that \(f_{j}(\mathbf{x})<f_{j}(\mathbf{y})\).  
Definition 3: Non-dominated solution: The solution \(\mathbf{x}\) and solution \(\mathbf{y}\) are non-dominate with each other if and only if \(\exists j\in\{1,2,\ldots,m\}\) for which \(f_{j}(\mathbf{x})<f_{j}(\mathbf{y})\) and \(\
ot\exists i\in\{1,2,\ldots,m\}\) such that \(f_{i}(\mathbf{x})<f_{i}(\mathbf{y})\).  
Definition 4: Pareto front: The set, which is composed of non-dominated Pareto optimal solutions, is called Pareto front (PF), denoted as \(X=\{\,\mathbf{x}\in\mathbb{F}\,|\,\exists\mathbf{y}\in\mathbb{F}\) such that \(f(\mathbf{y})>f(\mathbf{x})\,\}\).  
Definition 5: Ideal point: the point \(\mathbf{x}_{ideal}\) is the point, which has the best objective function values in Pareto front \(\mathbf{x}_{ideal}=\min\mathbf{f}(\mathbf{x})=\min\{f_{1}(\mathbf{x}),\ldots, f_{i}(\mathbf{x}),\ldots,f_{m}(\mathbf{x})\}\).