esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment  
The optimum design of the CFDM in Section 3 is fabricated by wire-EDM cutting from Al 7075-T6, and is shown in Figure 13. Figure 14 shows the experimental setup to test the prototype to assess its performance.  
In this experiment, a piezoelectric actuator P-225.10 (Physik Instrumente PI, Karlsruhe, Germany) drives the CFDM. This PZT actuator can provide a motion of up to 15 \(\upmu\)m, and the stiffness in the motion direction is 480 \(\mathrm{N}/\upmu\)m. It is suggested that the stiffness of the piezo actuator should be larger than the inner stiffness of the mechanism to ensure stable and improved operation. The optimization process in Section 3.2 has imposed this constraint in its inequality (46). A piezo controller E-470.20 (Piezo System, PI, Inc., Karlsruhe, Germany) amplifies the input voltage from a function generator. Two laser displacement sensors, LK-G30 (Keyence Corporation, Osaka, Japan), with a measuring range of \(\pm\)5 mm, a resolution of 0.05 \(\upmu\)m, and a maximum sampling frequency up to 50 kHz, are used to measure the input, output, and parasitic motion. The experiment uses two microtranslation stages to calibrate the focus of the laser sensors. An NI myRIO 1900 control card (National Instruments, Austin, TX, USA) generates an analog signal, and stores the reading from the displacement sensors with the sampling rate of 50 kHz. LabVIEW 2014 software performs the deterministic real-time control of the CFDM system. These devices are mounted on an antivibration table. It isolates external vibrations that  
Figure 12: Evaluation of the natural frequency: (**a**) simulation result; (**b**) experimental result.  
Figure 13: The fabricated CFDM prototype.  
could affect the accuracy of the measurement results. Figure 15 shows an actual experimental layout. For practical turning machining, the effect of the cutting force on the mechanism is dynamic and very significant. However, an ideal testing condition is assumed, to verify the theoretical and simulation model in this experimental setup. The RBDO problem has considered all variations in an authentic manufacturing environment.
# On the Impulse Response of Singular Discrete LTI Systems and Three Fourier Transform Pairs
## 4 Equivalence to the Unit Impulse Response Function
### Examples  
In our first example (\(N/L\) is even), input \(x[n]=\left\{\begin{array}{ll}1,\ 0\leq n\leq 2\\ 0,\ 3\leq n\leq N^{\prime}\end{array}\right.\) and output \(y[n]=\left\{\begin{array}{ll}1,\ 0\leq n\leq 4\\ 0,\ 5\leq n\leq N^{\prime}\end{array}\right.\), with \(N\) being 36. \(h_{1}[n]\) and \(h_{2}[n]\) are plotted in Figure 7A, while \(h_{3}[n]\) and \(h_{4}[n]\) are plotted along with the true impulse response function, \(h[n]\), in Figure 7B. As seen in Figure 7A, \(h_{1}[n]\) starts to be periodically linear at index \(3\) (i.e., \(L_{y}-L+1\)). \(h_{3}[n]\) is the same as \(h[n]=[1,\ 0,\ 0,\ 1,\ 0,\ -1\ldots]\) for the first \(N/2-int(Ly/L)L=15\) points and \(h_{4}[n]\) is the same as \(h[n]\) for the first \(N/2=18\) points in agreement with the above discussion. In the second example (\(N/L\) is odd), the input is \(x[n]=\left\{\begin{array}{ll}1,\ 0\leq n\leq 3\\ 0,\ 4\leq n\leq N^{\prime}\end{array}\right.\), and the output is \(y[n]=\left\{\begin{array}{ll}n-1,\ 0\leq n\leq 8\\ 0,\ 9\leq n\leq N\end{array}\right.\), with \(N=36\). The impulse response function for this system is \(h[n]=\left\{\begin{array}{ll}-1,\ 1,\ 1,\ 1,\ 0,\ 2,\ 2,\ 2,\ 1,\ -5,\ldots\ \right\}\), with the periodic oscillations starting after six data points. The periodic linearity for \(h[n]\) starts at index \(6\), as seen from Figure 8A. As indicated in Figure 8A, \(h_{3}[n]\) is the same as \(h[n]\) for the first \((N-L)/2-int(Ly/L)L=8\) data points. In this case, \(N\) is not large enough for \(h_{3}[n]\) to obtain all 10 independent points in \(h[n]\). \(h_{4}[n]\), which is computed from Equation (29), is the same as \(h[n]\) for the first \(\frac{N-L}{2}-1=15\) points. In Figure 9, we show an example of relatively large \(L\) (80) and \(L_{y}\) (149) with \(N=4L\). The output sequence, \(y[n]\), is an integer array consisting of randomized numbers from 1 to 149. \(h_{4}[n]\) is the same as \(h[n]\) for the first \(\frac{N}{2}=160\) points, while \(h_{4}[n]\) equals \(h[n]\) for the first \(\frac{N}{2}-L_{l}=80\) points.  
Figure 7: The upper panel is for \(h_{1}[n]\) and \(h_{2}[n]\) while the lower panel is for \(h_{3}[n]\) and \(h_{4}[n]\) and the true impulse response, \(h[n]\). In this example, \(N=36\), \(L=3\) and \(L_{y}=5\). Note that \(h[n]\) and \(h_{3}[n]\) are the same for \(n=0\):\(14\), while \(h[n]\) and \(h_{4}[n]\) are the same for \(n=0\):\(17\).
## 4 Experimental Results and Discussion
### Datasets  
The proposed network was trained and tested on two different datasets, namely: German Traffic Sign Detection Benchmark (GTSDB) and Swedish Traffic Sign (STS). GTSDB is a widely used data set, containing 600 training images and 300 testing images of size 1360 \(\times\) 800 pixels. The size of traffic signs in the images ranges from 16 \(\times\) 16 pixels to 128 \(\times\) 128 pixels. While the number of traffic signs in an image varies from 0 to 6. The dataset includes traffic signs of three superclasses: danger, mandatory, and prohibitive.  
STS is a more complex and bigger data set than GTSDB, with 20,000 images of size 1280 \(\times\) 960 pixels, among those 20% are annotated. The size of the traffic sign in an image varies from 12 \(\times\) 12 pixels to 156 \(\times\) 156 pixels. The dataset was gathered from Swedish highways and cities road with a 1.6-megapixel camera. The dataset can be divided into three superclasses: danger, mandatory, and prohibitive. In experiments, Part0 of Set1 is used as a training set and Part0 of Set2 is used as the test set, considering only visible traffic signs.
# Sustainable Organic Dyes from Winemaking Lees for Photoelectrochemical Dye-Sensitized Solar Cells
Winemaking is one of the most important activities of agri-food industries all over the world, with an approximate worldwide production of 250 million hectoliters (OIV, 2017). This kind of activity  
Figure 1: Three molecules commonly used in dye-sensitized solar cells (DSSCs) devices. From left: (**a**) Ruthenium complex N719, (**b**) anthocyanin, and (**c**) chlorophyll-a. produces 2-6% less on the total volume of wine produced [18; 19], therefore it generates a large quantity of waste that must be properly disposed, with considerable costs for wineries. Wine lees can be defined as 'the residue formed at the bottom of recipients containing wine, after fermentation, during storage or after authorized treatments, as well as the residue obtained following filtration or centrifugation', according to literature and EEC regulation No. 337/79 [20]. Therefore, at the end of the winemaking and clarification wine process, the so-called lees are the precipitate composed of many organic materials like yeast, bacteria, tartaric acid salts and vegetable residues. Furthermore, lees also contain a large amount of bentonite, a natural phyllosilicate clay that is used by wineries to reduce protein instability and precipitate suspended solids responsible for wine turbidity [21]. Usually winemaking lees are re-used for the recovery of tartaric acid [22], but, in recent years, studies showed that the concentration of anthocyanins in these lees are 10 times higher than grape skins [23], and this has increased the interest in these lees. Bentonite slightly bleaches white and red wines absorbing molecules responsible of wines color, such as anthocyanins, tannins, catechins, and polyphenols, which, if properly extracted, can be valorized and used as dyes in DSSCs. The current work aims to study a method to recover the main waste product of a winery, extracting the organic dyes contained in the lees, reintroducing them into the production cycle, and transforming them into a resource to produce renewable and sustainable electricity from organic photovoltaic solar cells. To the best of our knowledge, the use of winemaking lees extracts is being reported for the first time as sensitizers for TiO\({}_{2}\) based dye-sensitized solar cells (DSSCs).
## 1 Introduction  
Phase-locked loops (PLLs) are the integral building blocks of microprocessor, micro-controller and many wireless communication systems to generate a high-frequency and accurate clock signal from a reference frequency signal. Nowadays, the exponential growth in mobile communication devices also drives the demand for low-power integrated precise frequency synthesizers [1, 2, 3, 4].  
A classical PLL uses a single large frequency band for a large locking range, and it has a large voltage-controlled oscillator (VCO) gain. As a result, large jitter noise and dependency on process, voltage and temperature (PVT) variation are observed in VCOs Refs. [5; 6; 7; 8; 9]. Hence, small Kvco is desirable to reduce the VCO jitter noise and PVT variation, but a small Kvco causes the tuning range to be narrower Refs. [10; 11; 12; 13; 14]. Using both continuous and discrete frequency bands in the form of many overlapping sub-bands can maintain the desired frequency range with a low VCO gain [15; 16].  
The proposed architecture uses equally spaced, sufficiently overlapped discrete VCO sub-bands to overcome these issues and to obtain a wide frequency tuning range for the desired locking frequency. In this way, the tuning range is increased and VCO jitter noise is suppressed due to the small Kvco gain. Supply voltage scaling also renders the proposed architecture more attractive and vital. To select the required frequency band among multiband effectively, an automatic band selection circuit block is also proposed. The automatic band selection feature is also useful when the PLL acknowledges a change in the divider ratio. The VCO circuit of multiple overlapped discrete bands produces low jitter noise at a variable power supply. The self-calibration technique provides the required low Kvco. In Section 2, the PLL architecture and different blocks are described with an analysis of the loop stability. The design parameters and simulations are considered in Section 3. The measurement setup and measured results are discussed in Section 4, along with the conclusion in Section 5.
###### Abstract  
Experimental analysis on small-scale models is widely used to predict the dynamic responses of full-scale structures subjected to impact loads. However, due to manufacturing constraints, achieving a perfectly scaled model under a large scaling factor is challenging, leading to the use of distorted scaled models as a compromise. This paper introduces an improved similarity law that ensures distorted scaled models accurately replicate the dynamic responses of prototype stiffened under impact loads. The proposed similarity law meticulously considers both the distorted attached plate thickness and variations in stiffener configuration. Double input parameters involving the load case and geometry are formulated to govern the dynamic responses of integrated stiffeners. Additionally, an approximate method rooted in elastic-plastic theory is developed to assess the dominant behaviors of stiffened plates during impact. Consequently, the distorted stiffener configuration of scaled models is designed through a methodology primarily centered on capturing dominant behaviors. Comprehensive numerical simulations are conducted to evaluate the behavior of stiffened plates subjected to impact loads. The results compellingly demonstrate that the proposed similarity law adeptly compensates for geometric distortions, ensuring reliable predictions of dynamic responses in distorted scaled models.  
design criteria; distorted similarity; dynamic behaviors; scaled model; stiffened plates; stiffener configuration; structural impact +
Footnote †: journal: Applied Sciences
# Experimental Evaluation of Thermal Performance and Durability of Thermally-Enhanced Concretes
## 2 Materials and Methods
### Materials
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Density** & **Absorption** & **No. 140 \({}^{*}\)** & **Color** & **Thermal Conductivity** \\  & **(g/cm\({}^{3}\))** & **(\%)** & **(\% Retained)** & **(W/m-K)** \\ \hline diatomite powder & 1.1 & 0.5 & 0.5 & Light red & 0.014 \\ Hollow micro-sphere & 0.125 & 0.0 & - & white & 0.044 \\ \hline \hline \end{tabular} \({}^{*}\)Opening: 0.105 mm.  
\end{table} 
Table 2: Physical properties of the diatomite powder and hollow micro-spheres. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Items**} & \multicolumn{3}{c}{**Type of Aggregates**} \\ \cline{2-4}  & **Normalweight Fine** & **Normalweight Coarse** & **Lightweight Coarse** \\ \hline Source & Cleaned sea-sand & Crushed stone & Slate \\ Fineness modulus & 2.83 & 6.84 & - \\ Dry bulk density (kg/m\({}^{3}\)) & 1480 & 1680 & 800 \\ Specific gravity & 2.60 & 2.61 & 1.46 \\ Gmax \({}^{*}\)(mm) & - & 25.4 & 12.5 \\ Thermal conductivity (W/m\(\cdot\)K) & 3.5 & 2.75 & 0.106 \\ \hline \hline \end{tabular} \({}^{*}\)Gmax: Maximum size of coarse aggregate (mm).  
\end{table} 
Table 3: Physical properties of aggregates [24]. was set as 24 MPa at 28 days in this study. A foam agent tends to reduce the compressive strength of the concrete [26]. Thus, to achieve the designed 28 days compressive strength (24 MPa) of the foamed concrete, the cement contents of NM and LM were increased comparing to other specimens. The cylindrical specimens 100 mm in diameter and 200 mm in height were cast to measure the density and compressive strength for each mixture. The prism specimens with 100 \(\times\) 100 \(\times\) 400 mm were cast for the accelerated carbonation and cyclic freeze-thaw tests. Three 65 \(\times\) 114 \(\times\) 230 mm bricks for each mixture were prepared to measure the thermal conductivity. After 24 h of mixing, all specimens were de-molded and cured in water at 20 \(\pm\) 2 \({}^{\circ}\)C until the time of testing. Seven large-scale specimens with dimensions of 1500 \(\times\) 1500 \(\times\) 220 mm were built for the thermal transmission test. Unfortunately, there was an error for making a formwork, so specimen LN failed to install the calibrated hot box apparatus. These specimens were removed from the forms after seven days of curing at an average temperature of 23 \({}^{\circ}\)C and a relative humidity of 50%.
# Research on X-ray Diagnosis Model of Musculoskeletal Diseases Based on Deep Learning
## 2 Background Literature
### MURA Dataset and Data Processing  
The MURA dataset [24] is the largest publicly available collection of musculoskeletal radiographic images, encompassing multi-view images of fingers, hands, wrists, forearms, elbows, humeri, and shoulders in the upper extremity region. It comprises 40,561 musculoskeletal radiographic images from 14,656 studies of 12,173 patients, with each study containing one or more radiographic images manually annotated by radiologists. This dataset is collected and released by the Stanford ML group as part of the Bone X-Ray DL Competition [24]. The training and validation sets consist of 13,457 and 1199 images, respectively, with the total number of images for each study type in the training and validation sets illustrated in Figure 1a,b.  
MURA classification is a binary task with labels represented as a 0-1 variable, where 0 indicates normal and 1 indicates the presence of an abnormality. In this dataset, there are 1912 studies of the elbow, 2110 studies of the fingers, 2185 studies of the hand, 727 studies of the humerus, 1010 studies of the forearm, 3015 studies of the shoulder, and 3697 studies of the wrist. For the test set, the majority vote of three radiologists serves as the gold standard. The official team trained a baseline model using a 169-layer DenseNet to detect and localize  
Figure 1: (**a**) Training set statistics for all study types. (**b**) Valid set statistics for all study types.  
abnormalities, achieving an AUROC of 0.929, sensitivity of 0.815 at a working point of 0.815, and specificity of 0.887. When analyzing the dataset, we observed that each study had one or more images, with most studies having three images, as exemplified in Figure 2, an illustration of the MURA. dataset.  
When utilizing the MURA dataset for training and validating the feasibility and accuracy of the model, it is also necessary to convert image data into tensors. In computer vision, images are typically composed of pixel matrices, with each pixel containing values for the red, green, and blue channels. Converting images to tensors is carried out to facilitate computer processing and analysis of the images. Typically, the pixel matrix of an image is represented as a three-dimensional tensor, where the first dimension denotes the number of channels, the second dimension represents the height of the image, and the third dimension represents the width of the image. Specifically, if the image is in color, it is often represented as a three-dimensional tensor with a shape of [3, H, W], where H is the height of the image, and W is the width of the image. If the image is grayscale, it can be represented as a three-dimensional tensor with a shape of [1, H, W]. Converting images to tensors is a common data preprocessing method in deep learning, typically performed before feeding the images into neural networks.
# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
## 2 Credible Kalman Filter Model
### Credible Kalman Filter Model  
The state parameter of the CKF filter system based on the DDE/INS system is  
\[\mathbf{x}\texttt{=}[x,y,z,v_{x},v_{y},v_{z}]^{\text{T}} \tag{7}\]  
where \((x,y,z)\) is the navigation coordinate of the mobile terminal, while \((v_{x},v_{y},v_{z})\) is the velocity of the mobile terminal. Then, the state update model \(\mathbf{x}_{k}{}^{\prime}\) at time \(k\) of the DDE/INS system is  
\[\mathbf{x}_{k}{}^{\prime}=\left[\begin{array}{c}x_{k}\\ y_{k}\\ z_{k}\\ v_{x,k}\\ v_{y,k}\\ v_{z,k}\end{array}\right]=\left[\begin{array}{c}x_{k-1}+v_{x,k-1}\cdot \Delta t^{\text{D}}\\ y_{k-1}+v_{y,k-1}\cdot\Delta t^{\text{D}}\\ z_{k-1}+v_{z,k-1}\cdot\Delta t^{\text{D}}\\ v_{x,k-1}\\ v_{y,k-1}\\ v_{z,k-1}\end{array}\right]=\mathbf{x}_{k-1}{}^{\prime}+\Delta t^{\text{D}}.\left[ \begin{array}{c}v_{x,k-1}\\ v_{y,k-1}\\ v_{z,k-1}\\ 0\\ 0\end{array}\right] \tag{8}\]  
If the GNSS information is determined to be credible, the GNSS state parameter \(\mathbf{x}^{\text{G}}\) and the DDE/INS state parameters \(\mathbf{x}^{\text{DI}}\) are used as observations of the CKF filter system to generate a credible update model \(\mathbf{A}\). In the update process of the CKF filter system, the measurement vector \(\mathbf{Z}^{\text{A}}\) is expressed as  
\[\mathbf{Z}^{\text{A}}\texttt{=}[e_{k}^{\text{DI}},n_{k}^{\text{DI}},u_{k}^{\text{ DI}},yaw_{k}^{\text{I}},e_{k}^{\text{G}},n_{k}^{\text{G}},u_{k}^{\text{G}}]^{ \text{T}}. \tag{9}\]  
If the GNSS information is determined to be untrusted, the CKF filter system shields the GNSS state parameter \(\mathbf{x}^{\text{G}}\), and only uses the DDE/INS state parameters \(\mathbf{x}^{\text{DI}}\) to generate an auxiliary update model \(\mathbf{V}\). Then, it enters the CKF filter system update process, in which the measurement vector \(\mathbf{Z}^{\text{V}}\) is expressed as  
\[\mathbf{Z}^{\text{V}}\texttt{=}[e_{k}^{\text{DI}},n_{k}^{\text{DI}},u_{k}^{\text{ DI}},yaw_{k}^{\text{I}}]^{\text{T}}. \tag{10}\]
## 3 Results and Discussion
### Molecular Characterization of Stem from Saccharum officinarum  
Bagasse is rich with a lignocellulosic component. FT-IR analysis was carried out to show the presence of cellulose, hemicellulose, and lignin. Besides, FT-IR analysis was also carried out to identify the effectiveness of the extraction process using NaOH to degrade the hemicellulose and deform lignin. Figure 4 shows the FT-IR analysis while Table 4 explains various peaks of FT-IR analysis. Based on Figure 4a and Table 3, raw bagasse has O-H stretching at peak 3338.1 cm\({}^{-1}\) and C-H stretching at peak 1371.8 cm\({}^{-1}\) indicating intramolecular hydrogen bonds for cellulose and ionic carboxylic groups that are usually present in cellulose. Cellulose is a polysaccharide composed of a linear chain of $-1,4 linked d-glucose units [40]. Besides, there are four (4) peaks that show the presence of lignin and hemicellulose. Peak 1728.7 cm\({}^{-1}\) shows C-O stretching indicating the presence of acetyl and ester linkage in lignin and hemicellulose. Meanwhile, peaks 1243.4, 1160.3 and 1034.8 cm\({}^{-1}\) also show C-O stretching but indicate stretching of hemicellulose and lignin [41; 42]. Based on these peaks, it shows that raw bagasse consists of cellulose, hemicellulose and lignin that form lignocellulosic components. Therefore, the extraction process by using NaOH was carried out to degrade hemicellulose and deform lignin.  
Figure 4: FT-IR graph for: (**a**) Raw bagasse and (**b**) extracted bagasse.  
Figure 3: Polysaccharide extraction on different mixing duration.  
Figure 4b shows the FT-IR analysis after bagasse was extracted by using 8% NaOH at 120 \({}^{\circ}\)C for 2 h. Based on Figure 4b and Table 3, extracted bagasse contains two (2) peaks. The first peak at 3339.0 cm\({}^{-1}\) shows O-H stretching. This peak indicates the presence of intramolecular hydrogen bonds for cellulose. The second peak at 1638.0 cm\({}^{-1}\) shows C=C stretching indicating an aromatic ring present in lignin [41; 42]. As stated previously, extraction using alkali affects both lignin and hemicellulose. However, only hemicellulose will be degraded while lignin will be deformed thus, shows lignin content in the analysis. Raw bagasse shows more peaks as proof of the presence of more hemicellulose and lignin. However, after the extraction process, only the peak at 1649-1620 was identified as a source of lignin. Therefore, extraction by NaOH manages to degrade hemicellulose and deform lignin thus, results in high extracted polysaccharides.