esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Method of Applying Data Engine Technology to Realize Neural Network Control
## 3 Research on DRNN Hybrid Controller Based on DE
### Algorithm and Configuration Component Integration
#### 3.1.3 Learning Algorithm of the DRNC  
The input of the DRNC controller network is the reference input \(r(k)\), the output of the device at the previous moment \(y(k-1)\), and the control signal of the previous moment \(u(k-1)\). The output of the DRNC network is the control signal of the device \(u(k)\). After several training cycles by using the DBP algorithm to adjust the weight of the DRNC network, the error between the output \(y(k)\) and the desired output \(yr(k)\) can be reached to a small value. However, it needs the device's Jacobian information when training DRNC network, which is usually unknown, so DRNI is required to estimate the sensitivity information of unknown devices (Jacobian information) for controller DRNC.  
The performance index function of the DRNC network was defined as  
\[E_{\text{C}}=\frac{1}{2}(y_{r}(k)-y(k))^{2} \tag{6}\]  
The update formula of weight value in DRNC network can be rewritten as  
\[W_{\text{C}}(K+1)=W_{\text{C}}(K)+\delta_{\text{C}}e_{\text{C}}(k)y_{u}(k) \frac{\partial y_{u}(k)}{\partial W_{\text{C}}} \tag{7}\]  
where \(\delta_{\text{C}}\) is the learning rate of the DRNI network, \(y_{u}(k)\) represents the sensitivity of the device, for unknown controlled devices, it is difficult to obtain by direct calculation, but if DRNI network has better training, it can approximate it as  
\[y_{u}(k)\equiv\frac{\partial y(k)}{\partial u(k)}\approx\frac{\partial y_{m} (k)}{\partial u(k)}=\sum_{j}W_{j}^{o}\Big{\lceil}S_{j}(k)\Big{\rceil}W_{ij}^{l} \tag{8}\]  
Therefore, by training the DRNI and DRNC network continuously, can get the more effective weight of the DRNC network, which is the algorithm of the control system based on the diagonal recursive neural network.
# A Novel Deep Learning Method for Detecting Strawberry Fruit
## 2 Materials and Methods
### RFT-YOLO for Detecting Strawberry
#### 2.2.3 Loss Function  
In our field strawberry detection scenario, some instances were difficult to detect due to lighting variations, leaf occlusions, and similar colors between the foreground and  
Figure 6: Illustration of the QARepNeXt module, which adopts a QARepVgg block (multi-branch structure) during training and a convolution module (single-branch structure) during inference.  
Figure 7: Illustration of the triplet attention, which has three parallel branches.  
background. To address this, [PERSON] et al. [31] proposed the focal loss function, which can mitigate the foreground-background class imbalance problem and enhance the learning of challenging cases. This approach significantly improved the detection accuracy.  
The focal loss function reduces the weight assigned to well-classified instances of loss by reshaping the standard cross-entropy loss, which is defined as  
\[CE(p,y)=\left\{\begin{array}{ll}-log(p)&if~{}~{}y=1\\ -log(1-p)&otherwise,\end{array}\right. \tag{2}\]  
where \(y\) is a component of \(\{\pm 1\}\) that indicates the ground truth, and \(p\in[0,1]\) demonstrates the probability for the class labeled as \(y=1\). To simplify our notation, let us define \(p_{t}\) as follows:  
\[p_{t}=\left\{\begin{array}{ll}p&if~{}~{}y=1\\ 1-p&otherwise,\end{array}\right. \tag{3}\]  
and the CE loss can be rewritten as  
\[CE(p,y)=CE(p_{t})=-log(p_{t}). \tag{4}\]  
The class imbalance problem is typically addressed by introducing a weight parameter \(\alpha\) within the range \([0,1]\) for class \(1\) and \(1-\alpha\) for class \(-1\). With the definition of \(\alpha_{t}\) being the same as \(p_{t}\), the \(\alpha\)-balanced CE loss can be written as  
\[CE(p_{t})=-\alpha_{t}log(p_{t}). \tag{5}\]  
Although \(\alpha\) can balance the importance of positive and negative examples, it cannot differentiate between easy and hard examples. To address this, the focal loss function introduces a modulating factor \((1-p_{t})^{\gamma}\) to reduce the importance of easy examples and direct the training toward hard examples. The focal loss function is expressed as follows:  
\[FL(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}log(p_{t}), \tag{6}\]  
where the tunable focusing parameter \(\gamma\) > 0.  
The modulating factor effectively reduces the impact of loss caused by easy examples. For instance, if \(\gamma=2\), an example with a classification of \(p_{t}=0.95\) will demonstrate 400\(\times\) lower loss compared with CE. Consequently, there was an increased significance in rectifying misclassified examples.
## 2 Background
### Coherent Optical OFDM Network
The \(k\)th received information symbol can be expressed as follows [24; 38]:  
\[x_{1}(t)=\sum_{k=0}^{N_{\rm SC}-1}a(k)\exp{(j2\pi f_{k}t)}, \tag{3}\]2  
where \(\theta(0)\) refers to the dc component of the RF phase noise, which is known as CPE term, \(I(k)=\sum_{m=0,m\
eq k}^{N_{\text{SC}}-1}a(m)\theta(k-m)\) represents the ICI coupling coefficient between two subcarriers with distance of \(k\), and \(N^{*}(k)\) denotes AWGN on the \(k\)th subcarrier. \(\theta(m)\) is defined as follows [30]:  
\[x_{1}(t)=\sum_{k=0}^{N_{\rm SC}-1}a(k)\exp{(j2\pi f_{k}t)}, \tag{3}\]3  
with \(\phi_{RF}(n)\) being the discrete-time converted RF phase variation. In order to consider all subcarriers of the OFDM symbol, Equation (9) may be expressed in a compact matrix form as:  
\[ \begin{array}{c}\overline{A_{N_{\text{SC}}\times 1}}\\ \left[\begin{array}{c}\frac{a_{p}(0)}{a_{d}(1)}\\ \vdots\\ \overline{a_{d}(N_{\text{SC}})}\\ \overline{a_{p}(N_{\text{SC}}-1)}\end{array}\right]&=\\ \left[\begin{array}{c}\theta(0)\\ \overline{a_{d}(1)}\\ \vdots\\ \overline{a_{p}(N_{\text{SC}})}\\ \theta(N_{\text{SC}}-1)\end{array}\right]&=\\ \end{array} \left[ \begin{array}{c}\theta(0)\\ \theta(1)\\ \vdots\\ \theta(N_{\text{SC}})\\ \theta(N_{\text{SC}}-1)\end{array} \right]&=\\ \end{array}\left[ \begin{array}{c}\theta(0)\\ \theta(1)\\ \vdots\\ \theta(N_{\text{SC}})\\ \theta(N_{\text{SC}}-1)\end{array} \right]&+\left[ \begin{array}{c}N^{*}(0) \\ N^{*}(1)\\ \vdots\\ N^{*}(N_{\text{SC}})\\ N^{*}(N_{\text{SC}}-1)\end{array} \right].\end{array} \tag{10}\]  
A certain number of subcarriers across the OFDM spectrum is dedicated for the time-varying channel estimation. As mentioned in the Introduction, the channel frequency response for data subcarriers is usually estimated through linear interpolation from the values given by pilots [39], technique knows as PAE. This kind of equalization has demonstrated high performance and low complexity [4; 6; 7]. Here, the power of pilot subcarriers comes from the average power of the constellation while the quadrature component (the imaginary part of the symbol in the constellation diagram) of pilots is fixed to 0 for simplicity purposes [5]. In other words, all reference tones are located in a single point of the constellation in the PAE technique. Fortunately, at the same time, this equalizer reduces phase noise for small OFDM symbols [7; 40], especially when CPE dominates over ICI in terms of their relative powers.
## 6 Simulation Results
### Robustness to Geometric Attacks  
Geometric transformations such as circular shifting translation and amplitude scaling can be used by an attacker when they try to recognize the security keys of a security system. In this section, the ability of the proposed scheme to resist geometric attacks is investigated.
## Appendix B List of Acronyms  
AMS  
Accelerator Mass Spectrometry  
ASIC  
Application Specific Integrated Circuit  
CCD  
Cht  
Cultural Heritage  
CHNet  
CIL  
Commission Internationale de l'Eclairage  
CL  
Cathodoluminescence  
CMOS  
Complementary Metal-Oxide Semiconductor  
CZT  
Cadmium Zinc Telluride  
CMT  
Cadmium Manganese Telluride  
CVD  
Chemical vapour Deposition  
DE  
Detection Efficiency
DTGS  
Deuterated Triglycine Sulphate  
ED  
Energy Dispersive  
ER  
Energy Resolution  
FIR  
Far Infrared  
FT-IR  
Fourier-Transform InfraRed spectroscopy  
FDA  
Focal Plane Array  
FWHM  
Full Width Half Maximum  
GEM  
Gas Electron Multiplier  
GOS  
Gadolinium oxyulfide  
HPDCs  
Hybrid Photon Counting Detectors  
HPGe  
High Purity Germanium  
HR-ICP-MS  
High Resolution Inductively Coupled Plasma Mass Spectrometry  
IBA  
Ion Beam Analysis  
IL  
Ionoluminescence  
INFN  
Istituto Nazionale di Fisica Nucleare  
INFN-LABEC  
Laboratorio per l'Ambiente e i BEni Culturali  
INFN-LNF  
Laboratori Nazionali di Frascati  
INFN-LNGS  
Laboratori Nazionali del Gran Sasso  
INFN-LNL  
Laboratori Nazionali di Legnaro  
IR  
Infrared  
IRMS  
Isotope Ratio Mass Spectrometry  
JFET  
Junction gate Field-Effect Transistor  
KID  
Kinetic Inductance Detector  
LA  
Linear Array  
LN  
Liquid Nitrogen  
MA-XRF  
MAXRS  
Muonic Atom X-Rays Spectroscopy  
MCT  
Mercury Cadmium Telluride  
MIR  
Mid Infrared  
MS  
Mass Spectrometry  
NEP  
noise-equivalent power  
NIR  
Near Infrared  
OCR  
output count-rate  
PCB  
Printed Circuit Board  
PGNAA  
Prompt Gamma Neutron Activation Analysis  
PIN
P-type, Intrinsic, N-type  
PLE  
Particle Induced X-ray Emission  
PM  
Photomultiplier  
PMT  
Photomultiplier Tube  
PMMA  
Polymethylmethacrylate  
RBS  
Rutherford Back Scattering  
RX  
Radiography  
SD  
Superconductive Detectors  
SDDs  
Silicon Drift Detectors  
SEM  
Scanning Electron Microscope  
SEM  
Secondary Electron Multiplier  
SiPM  
Silicon Photomultiplier  
STJ  
Superconducting Tunnel Junction detector  
TES  
Transition-Edge-Sensor  
TIMS  
Thermal Ionization Mass Spectrometry  
ToF-SIMS  
Time of Flight-Secondary Ion Mass Spectrometry  
UV-vis-IR  
UltraViolet-visible-InfraRed  
XAS  
X-ray Absorption Spectroscopy  
XCT  
Computed X-ray Tomography  
XRD  
X-Ray Diffraction  
XRF  
X-Ray Fluorescence  
XRL  
X-ray luminescence
## 1 Introduction
### Literature Review
The simulation results proved the benefits of using the proposed algorithm in reducing operational costs and emissions. An equilibrium optimization problem has been addressed in [39] for optimum PV-storage system integration in a radial distribution network. Multi-objective functions have been addressed to minimize the cost of investment in PV and storage system installations, their cost of operation, the cost of energy not supplied, the power losses in the distribution lines, and the CO\({}_{2}\) emissions by the PV and the grid. The proposed method is compared with various techniques to prove its effectiveness. In [40], the authors have proposed an equilibrium algorithm to optimally find the lithium-ion battery parameters, formulated as a nonlinear optimization problem. The proposed method was compared with various recent techniques to prove its accuracy; also, it has proved its closeness to the experimental measurement. An artificial hummingbird optimization technique has been presented in [41] to find the unknown parameters of lithium-ion batteries used in EVs. The proposed method is compared with various recent techniques to prove its value and effectiveness. An experimental test was conducted, and the proposed technique had the highest degree of precision among the other techniques.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 3 Problem Description and System Model
### System Model
#### 3.2.2 5G Core Slice Request  
5G core slice requests arrive dynamically at the resource provisioning system. The \(i\)th slice request is represented by a triplet \(SR_{i}=(G^{S}_{i},t^{a}_{i},t^{I}_{i})\), where \(G^{S}_{i}\), \(t^{a}_{i}\), and \(t^{I}_{i}\) represent the topology of the \(i\)th slice, its arrival time and its lifetime, respectively. The slice topology is represented by a weighted undirected graph \(G^{S}=(V^{S},E^{S})\). Here, \(V^{S}\) is the set of slice nodes and \(E^{S}\) is the set of slice links. For each slice node \(v^{S}\in V^{S}\), the CPU capability required by it is \(c(v^{S})\). Its expected deployed location is \(loc(v^{S})=(x(v^{S}),y(v^{S}))\) and the maximum deployed deviation allowed is \(r(v^{S})\), that is, the slice node can be deployed at the location within a circle whose center is the expected location \(loc(v^{S})\) and the radius is \(r(v^{S})\). Each slice link \(e^{S}\in E^{S}\) is characterized by the amount of required bandwidth \(b(e^{S})\).
# Study on the Impact of Ventilation Methods on Droplet Nuclei Transmission in Subway Carriages
## 3 Results
### Spatiotemporal Distribution of Cough Droplets Under the SFRC System  
Compared to the MV system, the SFRC method has a stronger ventilation effect, with a more distinctive distribution of droplets. Figure 8 illustrates the droplet nucleus distribution in Case 1, where the air supply velocity of the SFRC system is 0.2 m/s. Notably, at t = 5 s, the droplets move more quickly towards the return air vents, with droplets larger than 30 \(\upmu\)m disappearing. Due to evaporation, most droplets shrink to nuclei smaller than 15 \(\upmu\)m, rising due to the combined effects of exhaled airflow and thermal plume, then spreading out upon nearing the ceiling. Under the influence of the SFRC system, the diffused droplet nuclei quickly gather near the return air vents. At t = 80 s, most droplets in the carriage have been cleared, and droplet nuclei are more uniform. This contrasts sharply with Case 4 and Case 6, demonstrating the superiority of ceiling return air in removing contaminants (as can also be observed from the diffusion range of the tracer gas).
## 3 Results and Discussion
### Microscopic Analysis
#### 3.4.2 Scanning Electron Microscopy  
To further elucidate the mechanism of crack self-healing in cement mortar, this study collected the healing products from N4.5-M27 cement mortar samples with the highest self-healing rate and employed scanning electron microscopy (SEM) to observe the morphology of the healing products. Figures 8-10 illustrate the microstructure of these products at the crack healing sites. Figure 8 displays the microstructure of hyphae within the cement mortar. These hyphae not only grow into and fill the cracks but also occupy space within them. This growth process facilitates the formation of calcium carbonate (CaCO\({}_{3}\)) and calcium silicate hydrate (C-S-H).  
Figure 9 shows the microstructure of calcium carbonate (CaCO\({}_{3}\)) generated at the crack. The CaCO\({}_{3}\) generated covers and fills the crack, thus helping to close the crack [50]. The generation of CaCO\({}_{3}\) is an important part of the self-healing process. CaCO\({}_{3}\) can effectively fill the crack and reduce the width and depth of the crack. In addition, the generated CaCO  
Figure 8: Microscopic image of fungal hyphae under SEM.  
microstructure shows that it has a certain density, which helps to improve the compressive strength of the crack.  
Figure 10 shows the microstructure of calcium silicate hydrate (C-S-H) generated at the crack. C-S-H is a major hydration product with high strength and adhesion, which helps to repair cracks. C-S-H gel is formed by the chemical reaction between silica added  
Figure 10: Microscopic structure of C-S-H self-healing product at crack site under SEM.  
Figure 9: Microscopic structure of CaCO\({}_{3}\) self-healing product at crack site under SEM.  
to concrete and calcium hydroxide in concrete. This result can be corroborated with the existing findings of [PERSON] et al. [51] During the self-healing process, the generation of C-S-H can further fill the cracks and improve the strength and durability of the repaired area.  
The self-healing of cement mortar is mainly dependent on mycelial growth and the production of metabolites (such as CaCO\({}_{3}\) and C-S-H). The addition of silica increases the production of CaCO\({}_{3}\) and C-S-H. The growth of hyphae in the cracks not only physically fills the cracks, but also generates substances with repair functions through chemical reactions. Under the combined action of CaCO\({}_{3}\) and C-S-H, the cracks can be effectively filled and repaired, improving the overall performance of cement mortar.
## Nomenclature  
* **Abbreviation**: **Description**:
* **VLC**: **Visible light communication**:
* **VLP**: **Visible light positioning**:
* **VLCP**: **visible light communication and positioning**:
* **6G**: **sixth-generation**:
* **IoT**: **Internet of Things**:
* **RF**: **radio frequency**:
* **TDM**: **time-division multiplexing**:
* **FDM**: **frequency-division multiplexing**:
* **CDM**: **code-division multiplexing**:
* **DDM**: **dual-domain multiplexing**:
* **RSS**: **receiver signal strength**:
* **PD**: **photodetector**:
* **OOK**: **on-off keying**:
* **LOS**: **light-of-sight**:
* **AWGN**: **additive white Gaussian noise**:
* **LSE**: **least squares estimation**:
* **SNR**: **signal to noise ratio**:
* **BER**: **bit-error rate**:
* **FEC**: **forward error correction**:
* **CDF**: **cumulative distribution function**:
* **ETD**: **error of transmission distance**:
# A Set of Tools and Data Management Framework for the IoT-Edge-Cloud Continuum
## 3 Data Management Framework
### Plugin Manager
\[Pr(M(x)\;\varepsilon\;S)\leq e^{\varepsilon}Pr(M(x^{\prime})\;\varepsilon\;S)+\delta \tag{1}\]  
If we look at the DP definition (see Equation (1)), the noise that is added to adjacent datasets \(x\) and \(x^{\prime}\) by a mechanism \(M\) is bounded by \(\epsilon\). The smaller the \(\varepsilon\), the greater the privacy, with an upper bound of \(\varepsilon<10\), so that the implementation can be deemed as \((\varepsilon,\delta)\)-differentially private. As for the relaxation parameter (\(\delta\)), the requirement is to have \(\delta<<\frac{1}{\mu}\), where n denotes the dataset size. By definition, it follows that an attacker is able to initiate \(\delta*n\) successful data retrieval attacks on the resulting dataset. With this, it is ensured that the count of successful attacks is strictly less than 1 [22, 24]. By following the implementation guidelines of \((\varepsilon,\delta)\)-DP found in [25], the plugin for adding differential privacy to data was developed. In order to use the DP plugin, the user has to use the YAML configuration file to configure the following parameters:  
* privacy_level--(Required): The chosen privacy level as a percentage.Here, 0 percent denotes the lowest privacy level whilst still being differentially private but with the least amount of privacy, and 100 percent denotes the highest privacy level as well as, potentially, the highest utility loss;
* mechanism--(Required): The chosen mechanism for noise addition. There are two possible choices, \"Gauss\" and \"Laplace\", from which the latter usually results in worse results because in the literature, it is described as the mechanism which generates more noise in comparison to the \"Gauss\" mechanism;
* data_size--(Required): The data size for the dataset to which noise needs to be added;
* sensitivity--(Required): The difference between the largest and the smallest value in the dataset;
* c_offset_amount--(Optional): The value for setting an offset that is required for the \"Gauss\" mechanism noise calculation. It is required to be > 0;
* delta_coefficient--(Optional): Parameter to calculate delta as strictly less than the inverse of the data size. The \"Required\" keyword denotes the parameters which do not have a default value; however, the \"Optional\" keyword denotes the parameters which do have a default value. Next, during the initialization of the plugin, noise will be generated for each dataset sample in relation to the specified parameters. After that, the user is expected to send the data per sample. The plugin will add the respective noise to the received sample and return the differentially private sample back. This will continue for the whole dataset until the plugin returns a message stating that the whole dataset has been processed. This message indicates that the iterator for the generated noise now is larger than the configured data size.
# GNSS Interference Monitoring and Detection (GIMAD) System +
## 4 Results
### I-SAM Test Results  
A real GNSS signal was combined with an RFI signal generated by a signal generator. Then, this signal was injected into a National Instruments USRP-2945 where the signal was digitalized. IQ samples were sent to the SPM, where detection algorithms run. As expected, the probability of detecting RFI is higher when the Interference-to-Noise Ratio (INR) is higher. The GIMAD system is capable of detecting a pulsed interference RFI of \(-\)75 dBm with a duty cycle of 14%, a pulse duration of 1 ms, and a pulse repetition of 7 ms, as shown in Figure 6.  
Figure 6 shows the techniques Excess Kurtosis FFT, 6 th Cumulant FFT, and PSD Peak. Power in red, which trigger an alarm. Other techniques (such as Power Level) cannot detect the RFI. This result is in line with the Receiver Operating Characteristic (ROC) curve analysis previously simulated, where algorithms working with signals in the frequency domain achieve a higher probability of detection (pd) at the same pfa (probability of a false alarm).
## 2 Materials and Methods
### Methods
#### 2.3.1 Image Preprocessing  
The latest version of Sen2 Cor V2.11, provided by ESA, allows for radiometric calibration and atmospheric correction of collected Sentinel-2 L1C class images. It adopts higher precision Digital Elevation Model (DEM) data (30 m), enhancing support for the improved radiometric calibration and atmospheric correction of Sentinel-2 images. The Sentinel-2 MSI images encompass 13 effective bands, each containing rich spectral information. Resample images from a total of 13 bands, B1 to B12, into images with a 10-m resolution. To accurately extract the water body boundaries, the Normalized Difference Water Index (NDWI) was applied within the study area [27,28]. NDWI is a normalized ratio index based on the green and near-infrared spectral bands. It is used to highlight water bodies within images. The formula for calculating NDWI is as follows:  
\[\mathrm{NDWI}= (\mathrm{Rrs}_{\mathrm{Green}}-\mathrm{Rrs}_{\mathrm{NIR}})/( \mathrm{Rrs}_{\mathrm{Green}}\ +\ \mathrm{Rrs}_{\mathrm{NIR}}) \tag{1}\]  
where \(\mathrm{Rrs}_{\mathrm{Green}}\) represents the reflectance values in the green spectral band; \(\mathrm{Rrs}_{\mathrm{NIR}}\) represents the reflectance values in the near-infrared spectral band; NDWI values typically range from \(-1\) to \(1\), with higher values indicating a higher likelihood of water presence.
# A New Propagation Channel Synthesizer for UAVs in the Presence of Tree Canopies
## 3 The Proposed Channel Synthesizer
### Coherent Scattered Field  
The coherent scattered field calculations follow the new combined PO/MST model described in Section 2.3. First of all, the canopies' surface samples visible from the UAV need to be identified. This can be done, for example, by determining the dot product between each surface sample normal  
Figure 3: The new combined PO/MST approach. The inner part of the canopy (green outlines) is supposed to be illuminated by the incident plane wave. The field propagating through the canopy \(\mathbf{E}_{\text{aper}}\) is accounted for by utilizing the complex propagation constant \(K\) inside the canopy, \(\mathbf{\hat{n}}=\mathbf{\hat{n}}_{\text{aper}}\) and path through the canopy towards \(\mathbf{r}\). The common PO blockage field \(\mathbf{E}_{\text{block}}\) uses \(\mathbf{\hat{n}}=\mathbf{\hat{n}}_{\text{block}}\) and \(k_{0}\).  
vector and the incident plane wave direction; if the dot product is negative, the corresponding surface sample is illuminated. Another option may be using the algorithm provided in [17] which may be suitable when the tree canopies are intersecting.  
Following Section 2.3, the blockage fields \(\mathbf{E}_{\text{block}}\) and \(\mathbf{H}_{\text{block}}\) are calculated first; see Equations (1) and (2). Then, the distances through the canopy towards individual illuminated surface samples are determined and the aperture fields \(\mathbf{E}_{\text{aper}}\) and \(\mathbf{H}_{\text{aper}}\) are obtained.  
Summing the blockage and aperture coherent contributions with the direct fields \(\mathbf{E}_{\text{dir}}\) and \(\mathbf{H}_{\text{dir}}\) then results in the coherent scattered fields.