esa-sceva/satcom-chunk-collection huggingface dataset:
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
###### Abstract  
Infrared and visible light image fusion combines infrared and visible light images by extracting the main information from each image and fusing it together to provide a more comprehensive image with more features from the two photos. Infrared and visible image fusion has gained popularity in recent years and is increasingly being employed in sectors such as target recognition and tracking, night vision, scene segmentation, and others. In order to provide a concise overview of infrared and visible picture fusion, this paper first explores its historical context before outlining current domestic and international research efforts. Then, conventional approaches for infrared and visible picture fusion, such as the multi-scale decomposition method and the sparse representation method, are thoroughly introduced. The advancement of deep learning in recent years has greatly aided the field of picture fusion. The outcomes of the fusion have a wide range of potential applications due to the neural networks' strong feature extraction and reconstruction skills. As a result, this research also evaluates deep learning techniques. After that, some common objective evaluation indexes are provided, and the performance evaluation of infrared and visible image fusion is introduced. The common datasets in the areas of infrared and visible image fusion are also sorted out at the same time. Datasets play a significant role in the advancement of infrared and visible image fusion and are an essential component of infrared and visible image fusion testing. The application of infrared and visible image fusion in many domains is then simply studied with practical examples, particularly in developing fields, used to show its application. Finally, the prospect of the current infrared and visible image fusion field is presented, and the full text is summarized.  
infrared and visible image fusion; image fusion; multi-scale decomposition; compressed sensing; sparse representation; deep learning; performance evaluation
## 2 Fatigue Constitutive Model of Materials
### Fatigue Constitutive Model of Concrete
#### 2.1.2 Fatigue Residual Strength  
The maximum stress that concrete can still withstand after a certain number of fatigue loadings is called concrete fatigue residual strength. Its value can be solved using its residual strength envelope equation, which can be used to represent the softened section of the uniaxial stress-strain curve of concrete, as shown in Figure 1[11]. The fatigue residual strength of concrete under compression and tension after an arbitrary number of fatigue loading proposed by [PERSON] [11] is shown in Equations (2)-(4):  
\[\left\{\begin{array}{cc}\frac{\sigma_{\text{r,c}}(N)}{f_{\text{c}}}=\frac{X( N)}{\alpha_{\text{c}}[X(N)-X(1)]^{2}+X(N)}&X(N)\geq 1\\ \frac{\sigma_{\text{r,t}}(N)}{f_{\text{t}}}=\frac{X(N)}{\alpha_{\text{t}}[X(N) -X(1)]^{2}+X(N)}&X(N)\geq 1\end{array}\right. \tag{2}\]  
\[X(N)=\left\{\begin{array}{cc}1&N=1\\ \frac{\text{lg}N}{\text{lg}N_{\text{f}}}[X(N_{\text{f}})-X(1)]+1&1\leq N\leq N _{\text{f}}\end{array}\right. \tag{3}\]  
\(X(N_{\text{f}})\) when under pressure is:  
\[X(N_{\text{f}})=\frac{2\alpha_{\text{c}}\sigma_{\text{max}}+f_{\text{c}}- \sigma_{\text{max}}\pm\sqrt{\left(f_{\text{c}}-\sigma_{\text{max}}\right)^{2} +4\alpha_{\text{c}}\sigma_{\text{max}}(f_{\text{c}}-\sigma_{\text{max}})}}{2 \alpha_{\text{c}}\sigma_{\text{max}}} \tag{4}\]  
where \(\sigma_{\text{r,c}}(N)\) and \(\sigma_{\text{r,t}}(N)\) are the residual compression and tension strengths of concrete after \(N\) cycles of fatigue loading, respectively. \(f_{\text{c}}\) and \(f_{\text{t}}\) are the compressive and tensile strength of concrete under static load, respectively. \(X(N)\) is a function related to the number of fatigue load cycles \(N\), which can be calculated according to Equation (3). \(\alpha_{\text{c}}\) and \(\alpha_{\text{t}}\) are the coefficients corresponding to the uniaxial compressive and tensile stress-strain curves of concrete, which can be taken with reference to the Code of GB50010-2010 [13]. \(\sigma_{\text{c,max}}\) is the stress of concrete under fatigue upper-limit loading.
###### Abstract  
The aim of the present study was to assess a new reactive strength index (RSI RUN) based on contact time and stride length measured in sprint running and then to correlate this index with sprint performance, muscle architecture and echo intensity of the vastus lateralis. Participants included ten elite and sub-elite sprinters (age \(24.4\pm 3.1\) years, height \(177.5\pm 7.7\) cm, mass \(69.8\pm 11.7\) kg) who were tested with a vertical drop jump (VDJ) and a horizontal drop jump (HDJ) from a 30 cm high box, a 20 m straight-leg running drill (SLR) and a 60 m sprint. A nearly perfect correlation (r = from \(-0.90\) to \(-0.96\), \(p<0.01\)) was detected between RSI RUN and sprint performance (30 m, 60 m and 100 m sprint time), and a very large correlation (r = from \(-0.72\) to \(-0.77\), \(p<0.05\)) was found between the traditional RSI from vertical drop jump (RSIJDV) and sprint performance. In addition, the RSI RUN was more correlated to sprint performance than other RSI indices studied in previous research. The echo intensity of the vastus lateralis (VLEI) was largely correlated with maximum running speed (r = from 0.76 to 0.87, \(p<0.05\)) and the RSI RUN (r = \(-0.80\), \(p<0.05\)). No significant correlations were noted between echo intensity and other RSIs. In conclusion, the RSI RUN and VLEI seem to be good predictors for track and field sprinting performance.  
reactive strength index; sprint; eco intensity; performance +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON]; [PERSON] A New Method for Evaluating the Reactive Strength Index in Track and Field Sprinting: Relationships with Muscle Architecture. _Appl. Sci._**2024**, 14, 3232. [[https://doi.org/10.3390/app14083232](https://doi.org/10.3390/app14083232)]([https://doi.org/10.3390/app14083232](https://doi.org/10.3390/app14083232))
# Steering Pull Model and Its Sensitivity Analysis
## 3 Steering System Model  
A steering system generally consists of a steering column, a universal joint, and a rack and pinion gear. When a wheel is turned by external forces, such as driver steering force and tire pulling force, it turns with respect to the kingpin axis which is produced by the upper and lower body linkages, and thus these linkages are also considered in this study. In many studies, each component of a steering system is represented by a mass-spring-damper system. However, the friction forces generated within the components are also important for a steering pull model because they substantially affect steering pull by attenuating the tire pulling force.
# Building Survivable Software Systems by Automatically Adapting to Sensor Changes
## 3 Approach
### Formulation  
We follow the notations in Section 2. We refer to sensor values before the sensor change as the source domain and to sensor values after the sensor change as the target domain. Specifically, we aim to learn a reconstruction function \(\mathbf{f_{\Theta}(z)}\) that maps sensor values after the sensor change to values before the sensor change, where \(\mathbf{\Theta}\) denotes the parameters of the function. Note that the output of \(\mathbf{f_{\Theta}(z)}\) is a matrix when there are more than one replaced sensor. In our implementation, we use the form  
\[\mathbf{f_{\Theta}(z)}=\mathbf{\Theta}^{\mathrm{ T}}\mathbf{h}(z) \tag{1}\]  
where \(\mathbf{h}()\) is a nonlinear feature mapping, e.g., a quadratic function.  
We are interested in \(\mathbf{f_{\Theta}(z)}\) such that distributions of sensor values are similar across domains after the reconstruction. This motivates us to seek \(\mathbf{f_{\Theta}(z)}\) such that the two sets of samples \(\{\mathbf{x}_{s}\}\) and \(\{[x_{t,1:K^{\prime}};\mathbf{f_{\Theta}(z_{t})}]\}\) (i.e., reconstructed samples in the target domain) are \"mixed\" as much as possible. We use the notation \(1:K^{\prime}\) to denote a set of indices from \(1\) to \(K^{\prime}\). When this happens, each source-domain sample \(\mathbf{x}_{s}\) becomes close to its \(k\)-nearest neighbors in the target domain, and vice versa. Therefore, we propose the following objective function to minimize the cross-domain \(k\)-nearest neighbor distances  
\[\min_{\mathbf{\Theta}}\sum_{s=1}^{S}\sum_{t\in\mathcal{N}_{T}^{k }(s)}\mathcal{D}(\mathbf{x}_{s},[\mathbf{z}_{t,1:K^{\prime}};\mathbf{f_{\Theta }(z_{t})}])\] \[+\sum_{t=1}^{T}\sum_{s\in\mathcal{N}_{S}^{k}(t)}\mathcal{D}([ \mathbf{z}_{t,1:K^{\prime}};\mathbf{f_{\Theta}(z_{t})}],\mathbf{x}_{s})+ \lambda\|\mathbf{\Theta}\|_{2}^{2} \tag{2}\]  
where \(\mathcal{D}(\cdot,\cdot)\) is the distance function defined in the space \(\mathbf{x}\in\mathcal{R}^{K}\). \(\mathcal{N}_{\mathcal{T}}^{k}(s)\) denotes the set of indices corresponding to \(\mathbf{x}_{s}\)'s \(k\)-nearest neighbors in the target domain, and \(\mathcal{N}_{\mathcal{S}}^{k}(t)\) denotes the set of indices corresponding to \([\mathbf{z}_{t,1:K^{\prime}};\mathbf{f_{\Theta}(z_{t})}]\)'s \(k\)-nearest neighbors in the source domain. Here, nearest neighbors are determined based on the distance function \(\mathcal{D}\). \(\|\mathbf{\Theta}\|_{2}^{2}\) is the regularization term on \(\mathbf{\Theta}\) with \(\lambda\geq 0\) as the regularization parameter.  
For simplicity, we set \(\mathcal{D}\) to be the squared Euclidean distance. In our implementation, each dimension is normalized into the same scale. We assume that sensors are generally informative and that there aren't many noisy sensors.  
Figure 3: Illustration of the intuition behind ASC.  
The above procedure decreases the value of the objective function in Equation (7) in each alternating step, and converges to a local minimum of Equation (4). Empirically, the procedure converges quickly (usually within 50 iterations).
# A High-Conversion Ratio Multiphase Converter Realized with Generic Modular Cells
## 3 Steady State Analysis  
### Conversion Ratio  
The current ripple of inductors \(L_{1}\), \(L_{2}\), and \(L_{3}\) during \"on\" and \"off\" intervals can be expressed as follows:  
\[\Delta i_{L1}=\bigg{|}\frac{-V_{\rm o}}{L_{1}}\bigg{|}(1-D_{1})=\bigg{|}\frac {(V_{\rm in}-V_{\rm C1})-V_{\rm o}}{L_{1}}\bigg{|}D_{1}, \tag{2}\]  
Figure 4: Operation states I-VI. (**a**) State I: phase 1 is ON, and phases 2 and 3 are OFF. (**b**) States II, IV and V: all phases are OFF. (**c**) States III: phase 2 is ON, phases 1 and 3 are OFF. (**d**) State V: phase 3 is ON, phases 1 and 2 are OFF.  
\[\Delta i_{L2}=\left|\frac{-V_{\rm o}}{L_{2}}\right|(1-D_{2})=\left|\frac{(V_{\rm C 1}-V_{\rm C2})-V_{\rm o}}{L_{2}}\right|D_{2}, \tag{3}\]  
\[\Delta i_{L3}=\left|\frac{-V_{\rm o}}{L_{3}}\right|(1-D_{3})=\left|\frac{(V_{\rm C 2}-V_{\rm C3})-V_{\rm o}}{L_{3}}\right|D_{3}, \tag{4}\]  
where \(L_{1}\) is discharged during states II, III, IV, V, and VI, and charged during state I. Inductor \(L_{2}\) is discharged during states I, II, IV, V, and VI, and charged during state III. Similarly, inductor \(L_{3}\) is discharged during states I, II, III, IV, and VI, and charged during state V.  
Without losing the generality, and assuming equal inductance values, substituting (1), into (3), the following voltage relationship can be derived from (2)-(4):  
\[\left\{\begin{array}{l}V_{\rm o}=D_{1}(V_{\rm in}-V_{\rm C1})\\ V_{\rm o}=D_{2}V_{\rm C3}\\ V_{\rm o}=D_{3}(V_{\rm C2}-V_{\rm C3})\end{array}\right., \tag{5}\]  
where \(D_{1}=T_{\rm on1}/T_{\rm s}\), \(D_{2}=T_{\rm on2}/T_{\rm s}\), and \(D_{3}=T_{\rm on3}/T_{\rm s}\). Solving (5) for the average capacitors' voltages, yields the following:  
\[\begin{array}{l}V_{\rm C1}=\left[\frac{2}{D_{2}}+\frac{1}{D_{3}}\right]V_{ \rm o},\\ V_{\rm C2}=\left[\frac{1}{D_{2}}+\frac{1}{D_{3}}\right]V_{\rm o},\\ V_{\rm C3}=\frac{1}{D_{2}}V_{\rm o}.\end{array} \tag{6}\]  
Combining (5) and (6), converter voltage gain can be expressed as follows:  
\[M=\frac{V_{\rm o}}{V_{\rm in}}=1/\left(\frac{1}{D_{1}}+\frac{2}{D_{2}}+\frac {1}{D_{3}}\right), \tag{7}\]  
For the private case of equal duty ratios for all three phases, \(D_{1}=D_{2}=D_{3}=D\), average capacitors voltages are converged to \(V_{\rm C1}=3V_{\rm in}/4\), \(V_{\rm C2}=2V_{\rm in}/4\), and \(V_{\rm C3}=V_{\rm in}/4\), and the expression for converter voltage gain is reduced to the following:  
\[M=\frac{V_{\rm o}}{V_{\rm in}}=\frac{D}{4}. \tag{8}\]  
The effect of the series-capacitor stacking can be analyzed using (8), which implies that the conversion ratio of the new topology is four times higher than a typical buck converter.
# Power Management Strategy for an Autonomous DC Microgrid
## 4 Experimental Tests
### Test 3  
Figure 10a presents the profiles from test 3. Visibly, it is a perfect sunny day with almost no fluctuations: Most of the time, irradiance is greater than 700 W/m\({}^{2}\). Therefore, PV power is greater than load demand most of the time, as depicted in Figure 10b.  
During the first interval (\(\theta:00\leq t<10:20\)), Figure 11a shows the electrochemical battery bank and PV supply power (case 8). During the second interval (\(10:20\leq t<10:46\)), the SC is recharging with excess PV power. This corresponds to case 4 given in Figure 3.  
At around \(10:46\), the SC is full. Afterwards, in the third interval (\(10:46\leq t<13:41\)), the electrochemical battery bank begins to absorb power. This corresponds to case 3 given in Figure 3. During this interval, around \(11:50\) and \(12:58\), the SC is again recharged when \(SOC_{\text{SC}}\) is below the value \(SOC_{\text{SC}\_MAX\_MIN}\) and when there is excess PV power. This corresponds to case 4 given in  
Figure 10: (a) Solar irradiance and PV cell temperature and (b) Load power demand and PV MPPT power.  
Figure 9: Experimental results of test 2: (a) DC microgrid power evolutions, (b) SOC evolution of battery bank, (c) SOC evolution of the SC, and (d) DC bus voltage.  
Figure 3. In the next interval (\(13:41\leq t<15:42\)), after the electrochemical battery bank is fully charged, the PV power limited control is executed to supply the load and the SC when \(SOC_{SC}\) falls below \(SOC_{SC\_MAX\_MIN}\) at around \(14:03\) and \(15:13\). In the last interval (\(15:42\leq t\leq 18:00\)), the electrochemical battery bank supplies the deficient power. This corresponds to the case 8 given in Figure 3.  
Figure 11b,c show that \(SOC_{B}\) and \(SOC_{SC}\) evolutions always remain within the constrained range. The DC bus voltage shown in Figure 11d is quite stable within a \(\pm 1.5\%\) fluctuation amplitude of the rated DC bus voltage. The different DG operation modes are not compared in this test, because there is no need for DG power on this day.
# Visual Object Detection with DETR to Support Video-Diagnosis Using Conference Tools
## 2 Materials and Methods
### Algorithm Logic  
As it has been mentioned by [PERSON] et al. \"DETR model directly predicts (in parallel) the final set of detections by combining a common CNN with a transformer architecture\" [17]. In a generic way, the Transformer contains five major parts: (1) _Input transformation for Encoder_, (2) _Input Transformation for Decoder_, (3) _Encoder_--to extract the contextual features from input sequence through multi-head self-attention mechanism, (4) _Decoder_--from encoder output to generate the next prediction, and the (5) _Final Layer_ to map decoder's output to the object probability space [19].  
As is shown in Figure 1, the logic of the algorithm is structured as follows: (a) the backbone ResNet50 predicts image features from the image, (b) the algorithm calculates the spatial positional encoding for each block of the predicted image resized to the size range of 800 to 1333 pixels, (c) run Transformer module on the blocks with the belonging positional encoding, (d) each Transformer head predicts the bounding box and the object within it, and finally (e) the Transformer predicts the number of objects in the image [19].  
There are two pillars of the direct set predictions in detection algorithm as shown in Figure 1: (1) a set prediction loss, which enables unique matching between predicted and ground truth boxes, as well as (2) a dedicated architecture which enables the prediction of a set of objects and models the relations in one step [17].  
DETR's transformer uses positional encodings at every attention layer, as shown in Figure 2. From the CNN backbone, the image features are passed through the transformer encoder, in parallel with spatial positional encoding. These features are then added to queries and keys within the'multi-head self-attention' layers. After that, the decoder gets the queries, output (i) positional encoding, and (ii) encoder memory. Finally there are generated the final set of 'predicted class labels' and the 'textual bounding boxes', through the'multiple multihead self-attention' and 'decoder-encoder' attention [17].  
Figure 2: Detailed DETR transformer architecture as presented in [17].
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was conducted using TIBCO Statistica 13.3 (StatSoft, Krakow, Polska). One-way analysis of variance (ANOVA) was used to compare data and identify means with significant differences (\(p<0.05\)).  
For analyses using cell lines, first, the Shapiro-Wilk test was used to check whether the data obtained during analyses met the assumption of normality and were normally distributed. Then, statistical significance was assessed using a two-way analysis of variance (ANOVA) and [PERSON]'s post-hoc test for group comparisons. Statistical significance was indicated by \(\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\  
popular yarrow variety with white flowers is the best-researched, but in the present study, we determined the content of these compounds in water-ethanol extracts of the herb with red flowers (Table 2). Spectrophotometric methods were used to compare the content of bioactive compounds in two types of extract, obtained by ultrasound-assisted extraction (UAE) and supercritical fluid extraction (SFE).  
Polyphenic compounds are divided into several groups due to their diverse structure, including flavonoids, phenolic acids, and tannins. Extracts obtained by UAE had significantly higher total polyphenol content (50.06 mg GAE/mL) and total flavonoid content (14.58 mg CE/mL) than those obtained by SFE (2.44 mg GAE/mL and 0.041 mg CE/mL, respectively). The UAE also contributed to a better extraction of phenolic acids and tannins. Total phenolic acid content was 3.31 mg CAE/mL for UAE and 0.91 mg CAE/mL for SFE, while condensed tannin content was 4.93 mg DpE/mL for UAE and 0.79 mg DpE/mL for SFE, respectively. Significant differences between the two types of extracts were also observed for antioxidant activity determined by the DPPH method (70.00% for UAE and 20.04% for SFE) and the FRAP method (1.53 mmol TE/L for UAE and 0.77 mmol TE/L for SFE) (Table 2). A similar relationship for extracts from _A. millelfolium_ flowers and leaves was obtained by [PERSON] et al. [24], who reported a total polyphenonic content of 106 mg GAE/g extract for 50% EtOH extracts obtained by ultrasound-assisted extraction, but only 14 mg GAE/g for extracts obtained by supercritical fluid extraction. In the same study, antioxidant activity determined by the ABTS method was 0.52 mmol TE/g extract for UAE and 0.08 mmol TE/g extract for SFE, while antioxidant activity determined by the ORAC method was 2.16 and 0.73 mmol TE/g extract, respectively. The results confirm that ultrasound-assisted extraction is useful for the extraction of polar compounds, while supercritical carbon dioxide extraction is better suited for the extraction of nonpolar compounds. The water-ethanol extract from yarrow herb obtained by UAE, with a higher content of free radical scavenging compounds and with better antioxidant potential, was used for further analysis.
###### Abstract  
In light of the increasing demand for laurel, driven by renewed interest in natural products and traditional medicinal usage of this plant, our study aimed to investigate the _in vitro_ antimicrobial activity of essential oils from leaves and fruits of laurel (EOL and EOF, respectively) collected in the National Park Skadar Lake, Montenegro, as it related to their chemical composition, assessing the possibility of their usage in cosmetics and pharmaceuticals. Also, fatty oil from the remaining laurel fruit after EOF isolation was investigated as a possible source of bioactive compounds. The most abundant components in EOL and EOF were 1,8-cineol (35.1% and 33.3%, respectively) and \(\alpha\)-terpinyl acetate (10.4% and 7.0%, respectively). Linalool (7.6%) was found in EOL, while \(\alpha\)-pinene (5.8%) and \(\beta\)-elemene (5.7%) were present in significant amounts in EOF. Antibacterial and antifungal properties of EOL and EOF showed strong antibacterial activity against _Staphylococcus aureus_, _Enterococcus faecalis_, and _Bacillus subtilis_, and potent antifungal effects against _Candida albicans_, opening the door for their application as antimicrobial agents. Chemical analysis of fatty oil unexpectedly revealed prominent content of sesquiterpene lactone dehydrodocostunolide and phenylpropanol derivative (E)-2-hexyl-cinnamaldehyde (21% and 5%, respectively), suggesting further investigations of this waste material as the source of valuable compounds with proven health benefits.  
Laurus noblilis L.; laurel leaves and fruits essential oil; chemical profile; antimicrobial activity; Montenegro; fatty oil; fatty acids +
Footnote †: journal: Applied sciences