esa-sceva/satcom-chunk-collection huggingface dataset:
# Configurable Hardware Core for IoT Object Detection
## 2 Background and Related Work
### Tiny YOLOv3  
Object detectors locate and classify objects present in an image. Typically, the detectors locate objects and draw a bounding box around it and then classify the objects as belonging to a predefined set of classes. YOLO and all its versions [21] are one-stage object detectors with a common model topology based on convolutional neural networks. YOLOv3 has three different versions for different image sizes: 320 \(\times\) 320, 416 \(\times\) 416, or 608 \(\times\) 608. The YOLO detector extracts features using a CNN and returns candidate bounding boxes from those features for three different scales: 52 \(\times\) 52, 26 \(\times\) 26, and 13 \(\times\) 13. The set of candidate bounding boxes are filtered based on a score that depends on each class. Multiple detections of the same object are then removed using non-maximum suppression. The final bounding box and the classification of each object are presented over the original image. The CNN model of YOLO has convolutional, shortcut, YOLO, upsample, and route layers. The YOLO layer is specific for this type of detector. It applies the logistic activation to the predictions of each bounding box. Max-pooling is not used in YOLO. Instead, it considers convolutional layers with stride two. Batch-normalization is applied to all convolutional layers, and all layers use the Leaky ReLU activation function, except the layers before YOLO layers that uses a linear activation function. YOLO is able to detect objects of different sizes using three different scales: 52 \(\times\) 52 to detect small objects, 26 \(\times\) 26 to detect medium objects, and 13 \(\times\) 13 to detect large objects. Consequently, multiple bounding boxes of the same object might be found. To reduce multiple detections of an object to a single one, the non-maximum suppression algorithm is used [22]. The work proposed in this article targets tiny versions of YOLO that replace convolutions with a stride of two by convolutions with max-pooling and does not use shortcut layers. Tests were made with Tiny-YOLOv3 (see Figure 1). Table 1 details the sequence of layers with regards to the input, output, and kernel sizes and the activation function used in each convolutional layer. Most of the convolutional layers perform feature extraction. This network uses pooling layers to reduce the feature map resolution. This network uses two cell grid scales: (\(13\times 13\)) and (\(26\times 26\)). The indicated resolutions are specific to the tiny YOLOv3-416 version. The first part of the network is composed of a series of convolutional and maxpool layers. Maxpool layers reduce the FMs by a factor of four along the way. Note that layer 12 performs pooling with stride 1, so the input and output resolution is the same. In this network implementation, the convolutions use zero padding around the input FMs, so the size is maintained in the output FMs. This part of the network is responsible for the feature extraction from the input image.
## Data and Methods
### Ionosphere Data  
Electron density profiles, provided by the FORMOSAT-3/COSMIC, were used to investigate the F2 peak electron density (Nmf2), vertical structure oscillation (RO profiles), and ionosphere variations in multiple heights before the Concepcion shock. Over 1500 ionospheric occultation profiles available were acquired daily ([[https://www.cosmic.ucar.edu](https://www.cosmic.ucar.edu)]([https://www.cosmic.ucar.edu](https://www.cosmic.ucar.edu)), accessed on 1 June 2021). In previous studies, comparisons of COSMIC radio occultation with ground-based ionospheric observations have been made. The verification results showed that the electron density profiles were consistent with the measurements from ionosondes and incoherent scatter radars (ISR) [26; 27].  
In terms of ground-based systems, the TEC from signal station receivers and global ionospheric maps (GIMs) are commonly used ionospheric disturbance analysis tools. The former is dominated by GNSS or ionosonde networks (e.g., CORS observations and the madrigal database), covering the continental regions of the earth. By comparison, the GIMs from International GNSS Service (IGS) were obtained by weighing the products for each Ionosphere Analysis Centers with different solution strategies. The GIM model takes data from more than 400 stations and then interpolates and smooths them to produce ionospheric TEC maps on one- or two-hour increments ([[ftp://cddis.gsfc.nasa.gov/gnss/products/ionex/](ftp://cddis.gsfc.nasa.gov/gnss/products/ionex/)]([ftp://cddis.gsfc.nasa.gov/gnss/products/ionex/](ftp://cddis.gsfc.nasa.gov/gnss/products/ionex/)), accessed on 1 June 2021). Covering \(\pm\)180 degrees of longitude and \(\pm\)87.5 degrees of latitude with spatial resolutions of 5 and 2.5 degrees, respectively, its performance in marine areas can also be guarded [28]. Due to the Concepcion quake located in the coastal area, both the COSMIC RO ionospheric responses over the sea and land all need to be verified. Thus, the GIM TEC was further used to make a feasibility analysis of multi-dimensional and -channel seismo-ionosphere coupling by COSMIC RO profiles.
# Mo(VI) Complexes Immobilized on SBA-15 as an Efficient Catalyst for 1-Octene Epoxidation
## 3 Materials and Methods
### Catalytic Tests  
The catalytic activity of the molybdenum-containing hybrid SBA-15 material was assessed in the epoxidation of 1-octene (Scheme 3) under different conditions to evaluate the effect of three reaction variables: the amount of molybdenum catalyst (changing Mo/olefin molar ratio from 1:500 to 1:2000), the type of solvent (tetrahydrofuran, methanol, isopropanol, isopropylbenzene, toluene, and n-decane) and the type of hydroperoxide used as oxidant (tert-butyl hydroperoxide, ethylbenzene hydroperoxide and isopropylbenzene hydroperoxide). All the experiments were carried out in a magnetically stirred round-bottomed flask under nitrogen temperature, according to the procedure previously reported [15]. Control of the reaction temperature was performed by immersion of the flask in an oil bath kept at 75 \({}^{\circ}\)C. Olefin/oxidant and olefin/solvent molar ratios were 1 and 4, respectively, for all the cases.  
The conversion of the hydroperoxide used as oxidant was calculated from iodometric titration. Regarding the substrate conversion and the epoxide yield, these were quantified by gas chromatography. All these parameters were used to quantify the selectivity of the catalysts in the consumption of the oxidant to form the epoxide, which can be understood as the selectivity of the hydroperoxide towards the oxirane.  
The stability of the molybdenum species was assessed by estimating the leached metal fraction, by direct measurement of the molybdenum content in the used catalyst. Finally, reutilization tests were carried out with intermediate washing with the reaction solvent to avoid interferences between consecutive runs.
# Multiple-Mode Orthogonal Time Frequency Space with Index Modulation
## 3 System Performance Analysis
### The Complexity of the Receiver  
The complexity of the receiver signal detection algorithm consists of two parts. The first part is the complexity \(\mathcal{O}(MN\log_{2}(MN))\) of the MMSE algorithm for equalizing received signals [15]. The second part is the complexity of recovering index bits and modulation bits from each subblocks. According to the description of the signal detection algorithm in Section 2, the computational complexity of constellation demodulation is \(\mathcal{O}(M_{mod})\)and the computational complexity of the double layer nested loop is \(\mathcal{O}(m^{2}n^{2}M_{mod})\). The \(\textbf{Hungarian}\{\cdot\}\) algorithm has a computational complexity of \(\mathcal{O}(m^{3}n^{3})\)[16]. Therefore, the computational complexity of each subblock of the signal detection algorithm is \(\mathcal{O}(mn\log_{2}(MN)+m^{2}n^{2}M_{mod}+m^{3}n^{3})\). If the maximum likelihood (ML) detection algorithm is adopted, the computational complexity of each subblock is \(\mathcal{O}(M_{mod}{}^{(mn)}(mn)!)\). Compared with the ML detection algorithm, the computational complexity of the proposed signal detection algorithm is effectively reduced, and it is a low complexity signal detection algorithm.
## 5 Investigation of the MIMO-Antenna Diversity Performance  
When a single antenna is designed, one looks to some of its individual parameters, like the S11, input impedance, radiation pattern, efficiency, and gain, to measure its performance. In the case of the MIMO antenna in which several single elements are working together, other parameters are used to measure its performance, namely, the envelope correlation coefficient (ECC), the diversity gain (DG), the total active reflection coefficient (TARC),the channel capacity loss (CCL), and the mean effective gain (MEG). The calculation of these parameters is usually called the evaluation of the diversity performance of the MIMO system.
## 2 Measuring System, Materials and Methods
### Measuring System and Samples
#### 2.2.3 Measuring System  
One of the most significant advantages of magnetic non-destructive testing systems is their straightforward design. The developed measurement system comprised four distinct subsystems: an excitation subsystem, a positioning robot, a magnetic field sensor (HMC5883L, Honeywell, Morris Plains, NJ, USA) [81], and a data acquisition subsystem. The block diagram of the system is shown in Figure 11.  
Figure 10: Magnetization methods: (**a**) NoM, (**b**) OPM, and (**c**) SPM.  
Figure 9: The basic idea of the MFL transducer.  
The excitation subsystem consisted of two stationary neodymium magnets. These magnets were placed on the surface of the specimen, directly above the reinforcing bar under investigation (Figure 10c). The positioning subsystem (XYZ scanner) was utilized to move the magnetic sensor within the measurement area on the surface of the specimen. During the measurement process, the sensor captured the spatial components of the magnetic induction. The data obtained in this manner were transmitted to a computer (data acquisition subsystem). Figure 10c illustrates the placement of selected measurement system components on the specimen.  
The selection of the sensor was informed by several significant parameters, including the following:  
* The capacity to measure orthogonal components of static magnetic fields;
* \(\mathrm{Frange}\pm 8\) gauss;
* A small size (facilitating the construction of sensor arrays).  
These specifications can be fulfilled by Hall effect sensors, Anisotropic Magnetoresistance (AMR) sensors, Giant Magnetoresistance (GMR) sensors, and Magneto-Optical (MO) sensors. Finally, the AMR sensor (HMC5883L) was selected. This kind of sensor not only meets the essential requirements but also offers the capability to measure the spatial components of magnetic induction (\(B_{\mathrm{x}}\), \(B_{\mathrm{y}}\), and \(B_{\mathrm{z}}\)), possesses the ability to detect negative values (facilitating polarization recognition), reduces the risk of damage in the event of exceeding the measurement range, ensures simplicity in the measurement system's design, and cost-effectiveness. Moreover, it possesses high sensitivity and resolution.
## 3 Proposed Method
### Evaluation Method: AP  
In the realm of object detection, average precision (AP) is a commonly accepted metric for assessing accuracy. AP quantifies the average precision over all recall values ranging from 0 to 1 at different Intersection Over Union (IOU) thresholds. IOU is the ratio of the overlapping area between the predicted bounding box and the ground truth bounding box to the total area occupied by both boxes. AP is determined using the formula in Equation (9), where \(n\) is the total number of detected bounding boxes, \(i\) is the rank of a particular detected bounding box, \(p(i)\) is the precision value from the first to the \(i\)-th detection, and \(\Delta r(i)\) is the change in recall values between the \((i-1)\)-th and \(i\)-th detection [28].  
\[\text{AP}=\sum_{i=1}^{n}p(i)\Delta r(i) \tag{9}\]  
AP50 measures the model's ability to detect non-PPE items when the predicted bounding box overlaps with the ground truth by at least 50% (IOU \(\geq\) 0.5). This threshold ensures that objects are recognized correctly, even if the bounding box alignment is not precise. By contrast, AP50:95 averages the model's performance across IoU thresholds ranging from 0.50 to 0.95 in increments of 0.05, capturing increasingly strict localization requirements. In densely packed scenarios where non-PPE items are close together, tighter bounding boxes help differentiate individual objects and avoid misclassifications. Both metrics are used in this study to provide a comprehensive evaluation of model performance.
## 3 Experimental Techniques to Check a Possible Degradation of PCM
### Thermogravimetric Analysis (\"TGA\")  
Another way to study PCM mass variation and possible degradation reactions is performing thermogravimetric analysis (TGA). TGA results can be compared with the results of sample mass monitoring to obtain more information about PCM stability. TGA is a technique that monitors the weight of a sample within a furnace to study the mass changes within a temperature range. This technique is suitable for different thermal analysis, such as decomposition temperature, oxidation, or loss of volatiles. A material sample of a few mg is placed inside the TGA apparatus usually exposed to air or nitrogen atmosphere, which is able to heat it at a controlled rate or maintain it at a defined temperature. The mass of the sample is recorded and can be plotted against temperature or time by means of a thermogravimetric curve (thermogram).  
In the case of PCM, TGA can be used to determine its maximum working temperature, usually defined as the temperature at which the sample loosed 1.5 wt % of its initial mass or after the dehydration process, and also the final degradation temperature, which can be extracted from the thermogram. Moreover, if TGA measurements are performed at various heating rates, degradation kinetics could be obtained as well and hence an estimation of the PCM lifetime [52].
# Persistent Homology Approach for Human Presence Detection from 60 GHz OTFS Transmissions
## 3 Target Detection Approaches
### Persistent Homology  
The problem of target identification from delay-Doppler images is equivalent to searching for local maxima in the 2D grayscale image. Persistent homology [22] belongs to the family of topological data analysis (TDA) methods, providing insights into the shape of the data [28] in a noisy environment. In principle, it is very close to the so-called watershed transform from image processing, with the name referring to a drainage divide that separates the adjacent drainage basins in a 2D grayscale image, see [29]. A tutorial on persistent homology can be found on the webpage [30], and a detailed mathematical explanation in [22].  
Pictorial Description (without Math)  
Consider the delay-Doppler map as a graph of a 2D function that assigns to each pixel the height of the absolute value of the delay-Doppler bin, see the left-hand side of Figure 4. Let us now consider a fictitious water level that starts at the global maximum and continuously decreases to lower levels. Islands will form at local maxima (this action is called birth). At saddle points, two islands merge into one. We consider the lower island as merged with the higher one (death of the lower one). The so-called persistence diagram, see Figure 4 right-hand side, shows the death versus birth values (water levels) of all islands. The persistence of an island is then the difference between the birth and death levels. The further an island is from the main diagonal, the more importance (=persistence) it has.  
Now the islands (peaks) can be ordered by decreasing persistence. In the delay-Doppler map, one can draw the locations of the births of the islands, see the center of Figure 4. This method not only provides the local maxima, but also quantifies their \"significance\" using the persistence mentioned above. If necessary, one can filter out islands with too little persistence.  
Figure 3: CFAR detector principle.
###### Abstract  
Recent years have seen an increase in the acceptance of green chemistry as a viable method for producing nanoparticles. The aim of this work was to investigate the cytotoxicity, antioxidant effect, and hemocompatibility of zinc oxide nanoparticles (ZnO NPs) produced utilizing _Capparis spinosa_ L. (_C. spinosa_ L.) fruit extract as a powerful reducing agent. The reaction rate and ZnO NPs' shape concerning extract concentration, contact time, pH, and temperature were investigated. The production and characterization of ZnO-NPs were confirmed by ultraviolet spectroscopy (U-V), Dynamic Light Scattering (DLS), zeta potential, Field Emission Scanning Electron Microscope (FE-SEM), X-ray Diffraction (XRD), and Fourier Transform Infrared Spectroscopy (FTIR). The antioxidant activity of ZnO NPs was performed by DPPH assay; hemocompatibility of the erythrocytes and cell-cytotoxic properties were investigated using MTT assay. XRD confirmed the production of highly pure, crystalline ZnO-NPs, while FESEM analyses revealed that these particles were agglomerated and spherical, with an average size of about 37.49 nm. Moreover, UV-Vis absorption was revealed at 366 nm, and tests of ZnO NPs' \(\zeta\) potential and DLS revealed values of \(-\)44.76 mV and 116.80 nm, respectively. ZnO NPs exhibited good biocompatibility with RBCs in hemolytic activity experiments and did not exhibit any hemolytic reaction at doses ranging from 7.5 to 120 ug/mL. The study concluded that greenly generated ZnO NPs had good antioxidant activity and little cytotoxicity and can thus possibly be developed as a useful therapeutic option.  
zinc oxide; green synthesis; biocompatibility; antioxidant activities; _Capparis spinosa_ L.
## 2 Basic Handling Stability Control Strategy of ESP
### Construction of Direct Yaw Moment Controller
#### 2.1.2 Control Target: Ideal Yaw Rate and Sideslip Angle of Mass Center  
When no DYC control is added, the ideal vehicle should always be in a steady driving state. When the vehicle is in a steady state, the lateral acceleration and yaw angular acceleration are zero,  
Figure 1: Monorail model with two degrees of freedom. The two wheels on the axle indicated by the blue dotted line are replaced by a monorail equivalent to the black solid line in this model.  
and the differential term on the left side of Equations (9) and (10) disappears and becomes a solvable equation. The steady-state yaw rate gain and centroid sideslip angle gain can be obtained as  
\[\frac{r_{d}^{*}}{\delta_{1}}\bigg{)}_{s}=\frac{u/(L_{1}+L_{2})}{1+\frac{m_{t}}{( L_{1}+L_{2})^{2}}\bigg{(}\frac{L_{1}}{K_{\alpha 2}}-\frac{L_{2}}{K_{\alpha 1}}u^{2}}= \frac{u/(L_{1}+L_{2})}{1+Du^{2}} \tag{12}\]  
\[\frac{\beta_{d}^{*}}{\delta_{1}}\bigg{)}_{s}=\frac{1}{(L_{1}+L_{2})}\frac{L_{ 1}K_{\alpha 1}(L_{1}+L_{2})\big{(}1+Du^{2}\big{)}-\big{(}L_{1}^{2}K_{\alpha 1}+L_{2}^{2 }K_{\alpha 2}\big{)}}{(1+Du^{2})(L_{1}K_{\alpha 1}-L_{2}K_{\alpha 2})} \tag{13}\]  
where coefficient \(D\) is a stability factor, which is only related to the structure of the vehicle but is independent of the vehicle motion state. It is an important parameter in the study of vehicle steady-state response, and it can be expressed as  
\[D=\frac{m_{t}}{(L_{1}+L_{2})^{2}}\bigg{(}\frac{L_{1}}{K_{\alpha 2}}-\frac{L_{2}}{K_ {\alpha 1}}\bigg{)} \tag{14}\]  
When the sideslip angle is very small, the ground adhesion constraint condition can be expressed as follows:  
\[\big{|}u\
u_{d}^{\prime}\big{|}{\approx}\big{|}a_{y}\big{|}\leq\mu g \tag{15}\]  
\[\beta_{d}^{\prime}\leq\mu g\frac{L_{1}K_{\alpha 1}u^{2}\delta_{1}-\big{(}L_{1}^{2 }K_{\alpha 1}+L_{2}^{2}K_{\alpha 2}\big{)}}{u^{2}(L_{1}K_{\alpha 1}-L_{2}K_{ \alpha 2})} \tag{16}\]  
where \(a_{y}\) is the lateral acceleration of the vehicle; \(\mu\) is the friction coefficient of the road; \(g\) is the gravity acceleration.  
The ideal yaw rate and sideslip angle control targets with consideration of steady-state driving and adhesion conditions can be written as:  
\[r_{d}=\min\{\big{|}r_{d}^{*}\big{|},\big{|}r_{d}^{\prime}\big{|}\}=\min\{ \bigg{|}\frac{u/(L_{1}+L_{2})}{1+Du^{2}}\delta_{1}\bigg{|},\bigg{|}\frac{\mu g }{u}\bigg{|}\bigg{\}} \tag{17}\]  
\[=\min\{\bigg{|}\frac{1}{(L_{1}+L_{2})}\frac{L_{1}K_{\alpha 1}(L_{1}+L_{2}) \big{(}1+Du^{2}\big{)}-\big{(}L_{1}^{2}K_{\alpha 1}+L_{2}^{2}K_{\alpha 2})}{(1+Du^{2})( L_{1}K_{\alpha 1}-L_{2}K_{\alpha 2})}\delta_{1}\bigg{|},\bigg{|}\mu g\frac{L_{1}K_{ \alpha 1}u^{2}\delta_{1}-\big{(}L_{1}^{2}K_{\alpha 1}+L_{2}^{2}K_{\alpha 2} \big{)}}{u^{2}(L_{1}K_{\alpha 1}-L_{2}K_{\alpha 2})}\bigg{|}\} \tag{18}\]
## 2 Materials and Methods
### Mathematical Model  
With reference to TES geometry (Figure 1), the reactor has been divided into identical portions (nodes), all having the same shape and connected in series. For each _i-th_ node, a one-dimensional lumped parameter model has been implemented, in which the trends of the physical variables depend on time and longitudinal spatial dimension. Every node was then modelled through four sub-components, interacting with each other through energy and mass exchanges, represented by: the HTF, the finned heat-exchanger, the bed of zeolite and the water vapour enveloping the zeolite bed.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 5 Evaluation  
To validate the operation of the proposed adaptive energy management model, in this section, we present an optimal service scheduling algorithmic strategy denoted as OptTsNS. The objective of OptTsNS is to minimize power consumption from non-renewable sources considering finite energy provisioning and the management strategies described in Section 3.2. Concerning the optimization of power consumption, the task of OptTsNS is to find the best actions (strategies) for each service in such a way that the processed services (i.e., the combination of services that demands \(P_{D}\)) enable the efficient use of the available energy. The proposed algorithmic strategy, described in Section 5.2, bases its operation on an exhaustive search method. In this brute-force method, all possible combinations of \(N\) services, executed simultaneously, are explored considering all possible values of degradation and time shifting in service execution. To choose the best combination of services (i.e., the optimal distribution of services in time, with a \(P_{D}\) (\(P_{Dcomb}\)) that produces minimization of \(P_{NR}\) and, consequently, the optimal use of \(P_{R}\)), the cost functions defined in Section 4.2.3 are used. Specifically, the optimal solution delivered by OptTsNS corresponds to the combination of services that produces the minimum cost function, as shown in Equation (22). To quantitatively verify the improvements obtained with OptTsNS, the performance metrics defined in Section 5.1 are used and a numerical analysis is performed on a particular case study in Section 5.3. In this context, an analysis in different scenarios, as well as the online version of OptTsNS and the development of more sophisticated and efficient methods (that use as the optimal solutions as upper bounds), will be addressed in future work.
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 2 Related Work  
The related work concerning visual localization using satellite data can be separated into three different branches: visual localization without deep learning, global visual localization with deep learning, and local visual localization with deep learning. We first  
Figure 1: **Model overview.** Input consists of top-level and ground-level imagery. AlignNet matches the projected top-level image with the ground-level image and iteratively aligns towards a single output position. The output consists of a lateral, longitudinal, and angular position.  
summarize the three different branches and then look at work related to creating datasets for these tasks.