esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.3 Classification Model
\begin{table}
\begin{tabular}{l l l} \hline
**2D-CNN 1** & **2D-CNN 2** & **RNN** \\ \hline
**Architecture** & **Architecture** & **Architecture** \\ Input: \(n\) patches size 32 \(\times\) 32 & Input: \(n\) patches size 32 \(\times\) 32 & Input: \(n\) patches size 8 \(\times\) 8 \\ Conv1: 32 filters size 2 \(\times\) 2 & Conv1: 60 filters size 5 \(\times\) 5 & Memory layer: 32 neurons \\ Conv2: 64 filters size 2 \(\times\) 2 & MaxPooling: size 2 \(\times\) 2 & Hidden layer 1: 64 neurons \\ Conv3: 128 filters size 2 \(\times\) 2 & Conv2: 70 filters size 3 \(\times\) 3 & Hidden layer 2: 100 neurons \\ MaxPooling: size 8 \(\times\) 8 & MaxPooling: size 2 \(\times\) 2 & Output layer: \(m\) classes \\ Hidden layer: 100 neurons & Conv3: 80 filters size 1 \(\times\) 1 & \\ Output layer: \(m\) classes & Hidden layer: 100 neurons & \\  & Output layer: \(m\) classes & \\
**Loss function** & **Loss function** & **Loss function** \\ Cross entropy & Cross entropy & Cross entropy \\
**Activation** & **Activation** & **Activation** \\ Softmax in the output layer & Softmax in the output layer & Softmax in the output layer \\ Relu in the other layers & Relu in the other layers & Relu in the other layers \\
**Weight initialization** & **Weight initialization** & **Weight initialization** \\ Randomly & Randomly & Randomly \\
**Drop out layer** & **Drop out layer** & **Drop out layer** \\ Dropout 25\% after pooling & Dropout 50\% after hidden layer & Dropout 50\% after first hidden layer \\ Dropout 50\% after hidden layer & & \\ \hline \end{tabular}
\end{table} 
Table 2: DL architectures settings. On the other hand, the 2D-CNN 2 is composed of three convolutional layers, each of which is followed by a max-pooling of \(2\times 2\). In this architecture, a \(1\times 1\) convolutional layer is applied in order to extract more features of the images without losing information. Then, dropout layers are applied to deactivate a percentage of the neurons and prevent overfitting. Figure 5 shows an explaining diagram of the architecture 2D-CNN 2. Figure 4: A schematic view of the proposed 2D-CNN 1. Figure 5: A schematic view of the proposed 2D-CNN 2. An RNN model for image classification analyzes the image as a single sequence of pixel vectors. RNN has neurons in its architecture known as memory cells, which create a notion of temporality [36]. Contrary to the 2D-CNN, before feeding the network, the multi-spectral images are reshaped as spectral signatures since there are only hidden layers in the RNN. For this work, its architecture is composed of one memory layer of 32 neurons followed by two hidden layers (one with 64, and another one with 100 neurons).
# Correlation Plenoptic Imaging: An Overview
###### Abstract  
Plenoptic imaging (PI) enables refocusing, depth-of-field (DOF) extension and 3D visualization, thanks to its ability to reconstruct the path of light rays from the lens to the image. However, in state-of-the-art plenoptic devices, these advantages come at the expenses of the image resolution, which is always well above the diffraction limit defined by the lens numerical aperture (NA). To overcome this limitation, we have proposed exploiting the spatio-temporal correlations of light, and to modify the ghost imaging scheme by endowing it with plenoptic properties. This approach, named Correlation Plenoptic Imaging (CPI), enables pushing both resolution and DOF to the fundamental limit imposed by wave-optics. In this paper, we review the methods to perform CPI both with chaotic light and with entangled photon pairs. Both simulations and a proof-of-principle experimental demonstration of CPI will be presented.  
Keywords:quantum imaging; quantum entanglement; speckle phenomena; image restoration +
Footnote †: journal: Applied Physics Letters
## 5 Experiments and Results  
### Routing Protocol Test  
Two WSN groups (each group has 18 nodes) with the same surroundings were classified to carry out the monitoring experiment simultaneously in black pepper farmland to test energy consumption of ESPIN and SPIN routing protocol. All the nodes in the network were programmed with default sampling interval (15 min). The sensor nodes transmit the data to sink node for forwarding. Then the data collected at the nodes is transmitted to the base station for further processing.  
The main consideration is the energy usage of the network. In order to study the performance of network with respect to battery charge consumption with ESPIN protocols, The number of ADV packet transmitted and received and the battery value are also transmitted to base station along with sensor data and are logged in database. Figure 5 shows the result of total number of ADV packets generated for an event at each hop level to reach the destination node while the network consists of 18 sensor nodes. In comparison with SPIN, ESPIN generates lower number of ADV messages, and thus saves the total energy of the WSN. In this way it can prolong the lifetime of the WSN.  
The battery power of all nodes in the network was adjusted to 3.3 volt at the beginning of experiment. Figure 6 shows the average battery charge gets exhausted gradually and it is obvious that the battery voltage of ESPIN group and SPIN group decrease to 1.2 V and 0.4 V respectively during one growth period of black pepper, it means energy consumption of ESPIN is 20% less than SPIN. Consequently, it is concluded that use of distance discovery of ESPIN actually increases the energy efficiency and network lifetime. This is because it discards packet transmission to the opposite direction of sink node.  
Figure 7 shows the average packet loss rate of two groups. The reason that packet loss rate had a sharp increase from the 3 rd month is black pepper came into the growing stage and ripening stage and intervene caused by the high density of the leaves has great influence to the signal propagation. We can see that ESPIN group drops about 1.2%, 4.5% and 8% packets in sowing stage, growing stage and ripening stage respectively, the SPIN group loses about 1.9%, 6% and 11% packets respectively. It is obvious that ESPIN outperforms SPIN in data transmission success ratio from Figure 5 because ESPIN decreases the hops that data packets sending to sink node and adds the CA to enhance the fault-tolerant ability. Hence, ESPIN is also more reliable in terms of data transmission.  
Figure 5: Number of ADV packets transferred to reach the sink node for an event.  
Figure 6: Average power voltage.
# Proactive Production Scheduling Approach for Off-Site Construction with Due Date Uncertainty
## 2 Relevant Studies on PC Production Scheduling Under Uncertain Environments
### Proactive Approach  
In contrast, the proactive approach has undergone comparatively limited exploration in PC production scheduling. [PERSON] et al. [17] developed the simulation-GA hybrid model for PC production incorporating stochastic processing time, reflecting uncertain environments where the processing times follow a distribution. Their approach involved generating multiple near-optimal schedules to minimize delivery penalties through repeated GA runs in a deterministic environment. Subsequently, they conducted 100 simulations with stochastic processing times and selected the schedule that minimized the delivery penalties and production costs from the set of near-optimal schedules.  
However, the approach of not considering uncertainty in the first stage when generating the schedule set can lead to several issues. Since the initial schedules are created in a deterministic environment, they may not accurately represent the impact of uncertainties. This results in schedules that are less resilient to fluctuations and unanticipated disruptions. While second-stage simulations aim to evaluate the performance of these schedules under uncertainty, post hoc corrections are often less effective because the initial schedules may deviate significantly from optimality when subjected to stochastic variations.  
To address these limitations, this study introduces a proactive PC production scheduling model that explicitly incorporates due date uncertainty. By considering uncertainty at the initial scheduling stage, the proposed model could improve robustness and overall efficiency in handling dynamic and unpredictable environments.
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 4 Results and Discussion
### Distances Calculation
#### 4.1.1 Maneuver AB  
Maneuver AB is performed when the CubeSat changes its orientation from the initial position A to the final position B. This maneuver represents two different scenarios: for 2U CubeSat from 2-unit drag face to 1-unit drag face and vice versa, or for 6U CubeSat from 6-unit drag face to 3-unit drag face and vice versa. Figure 6 shows the results of HDD and VDD for the three different orbits under study.  
The three CubeSats show similar evolutions during the two days regarding HDD and VDD, but with different effects. SAT-L, the one with the lowest orbit, shows the highest distance in each instant, and SAT-H, the one with the highest orbit, shows the lowest distance in each instant. SAT-M shows intermediate distances, between SAT-L and SAT-H. In all cases, the VDD varies proportionally with time and HDD shows an exponential behavior. The maximum VDD achieved by the maneuver corresponds to  
Figure 6: Results for two days after Maneuver AB for all satellites under study.  
SAT-L, with 159.67 m, an intermediate value of VDD is reached by SAT-M with 85.36 m, and the minimum VDD is 42.33 m for SAT-H. On the other hand, the maximum HDD achieved by the maneuver corresponds to SAT-L, with 7.84 km, an intermediate value of HDD is reached by SAT-M with 4.16 km, and the minimum HDD is 2.06 km for SAT-H.
## 1 Introduction
The synergy of deterministic classification, probabilistic GPs, and deep learning frameworks creates a robust and adaptable system for handling diverse objects in unstructured environments. Grasp force adaptation is crucial for handling objects of varying stiffness and fragility. Prior studies [28] explored compliance-based grasping, including variable stiffness control [29] and isotropic compliance [30] These methods rely on predefined mechanical properties, limiting adaptability in unstructured environments. While compliance-based mechanisms offer some flexibility, they require precise tuning and additional mechanical complexity. Instead of relying on predefined stiffness models, our approach directly utilises force sensors embedded in a dexterous robotic hand to measure applied forces and adjust grasping configurations dynamically. Unlike vision-based tactile reconstruction techniques [31] which estimates force fields using indirect measurements, our system processes real-time force sensor data to enable adaptive grasping. Gaussian Processes (GPs) were initially explored for force estimation but struggled to adjust dynamically in response to rapid force variations. Our experiments demonstrate that Transformers outperform GPs in force prediction, achieving higher success rates in dynamic grasping scenarios. This study provides new insights into integrating deep learning with sensor-driven grasping strategies to enhance force modulation in robotic grasping tasks. Our prior work [32] introduced a system that combines YOLOv8 with Long Short-Term Memory (LSTM) networks for adaptive grasping. YOLOv8 efficiently localized objects and estimated their poses, offering precise 3D coordinates for the robotic hand to approach. LSTM networks, trained on teleoperated demonstrations, adjusted grasping forces dynamically based on object feedback. This framework demonstrated effectiveness in handling diverse objects under controlled environments and served as a baseline for evaluating Gaussian Process and Transformer models for grasp force modulation. The framework of the proposed approach is shown in Figure 1.  
The key objectives include:  
* Implementing YOLOv8 for robust real-time object detection and grasp configuration prediction;
* Comparing LSTM, Gaussian Process (GP), and Transformer models for grasp force prediction and analysing their efficiency;
* Evaluating the system's performance across multiple grasp configurations (two-, three-, and five-finger grasps) and object categories to assess stability and adaptability. However, certain limitations persist in our framework. The five-finger grasp configuration exhibited instability when handling objects with complex shapes, particularly those with non-uniform stiffness, leading to inconsistent force distribution. While the vision model demonstrated strong performance in object detection, misclassification was observed for objects with visually similar characteristics (e.g., a tomato classified as a ball). Future  
Figure 1: The framework of the proposed approach. work will address these concerns by refining grasp configuration strategies and improving dataset diversity.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 2 Fundamentals of FSO Signal Transmission
### Pointing Error
\[f_{I_{p}}\big{(}I_{p}\big{)} = \frac{\xi^{2}}{A_{0}^{\xi^{2}}}I_{p}^{\xi^{2}-1},\qquad 0\leq I_{p} \leq A_{0} \tag{16}\]  
where \(A_{0}\) is the fraction of the collected power at zero displacement and \(\xi\) defines the ratio between the equivalent beam radius and the pointing error displacement standard deviation (i.e., \(\sigma_{s}\)). The second momentum of the pointing error coefficient can be calculated as \(E\big{[}I_{p}^{2}\big{]}=\xi^{2}A_{0}^{2}/\big{(}\xi^{2}+2\big{)}\). Therefore, the average change in received power due to pointing error is given by  
\[L_{\text{PE}}=10\log_{10}\left(\frac{\xi^{2}A_{0}^{2}}{\xi^{2}+2}\right). \tag{17}\]  
In Figure 10, we present the power loss experienced by an FSO link due to pointing error, based on Equation (17). It can be observed that this loss can vary significantly, ranging from tens of decibels to small values that can be neglected. This variability is primarily because of two key factors, namely \(A_{0}\) (i.e., the fraction of the collected power at zero displacement) and \(\xi\) (i.e., the ratio between the equivalent beam radius and the pointing error displacement standard deviation). In particular, when \(\xi^{2}\) surpasses a value of 2, the power loss is mainly due to \(A_{0}\), because the displacement standard deviation in this case is much smaller than the equivalent beam radius. Conversely, as \(\xi^{2}\) decreases, it can become the main contribution to the power loss. For example, for \(A_{0}=0.1\), the power loss reaches 33 dB for \(\xi^{2}\)= 0.1. It decreases to 24.77 dB, 23.01 dB, 22.22 dB, 21.76 dB, and 21.46 dB, respectively, for \(\xi^{2}\)=1,2,3,4, and 5. Now assume \(\xi^{2}=1\); the power losses are 24.77 dB, 10.79 dB, and 4.77 dB, respectively, for \(A_{0}=0.1,\,0.5,\,\text{ and }1\). Figure 10: Losses due to pointing error.
# Wind Lidar and Radiosonde Measurements of Low-Level Jets in Coastal Areas of the German Bight
## 3 Study Sites and Data Base
### FINO1/Alpha-Ventus  
The research platform FINO1 was constructed in 2003 to investigate, among other parameters, wind and wave fields in the North Sea. The platform is located approximately 45 km north of the German island Borkum. At the time of the measurements (2017-2018), FINO1 was surrounded by the wind farms Alpha-Ventus to the east, Borkum Riffgrund 1 and 2 to the south, and Trianel Windpark Borkum 1 to the west. The wind farm Merkur which is located next to FINO1 to its west and north was under construction in 2017. Due to the installed wind farms, FINO1 has only a small sector of open wind field to the north-northwest which is, however, facing away from the coast.  
A Windcube v2 lidar was installed on the platform FINO1, located 24 m above sea level from 1 April 2017 to 31 March 2018. Another Windcube v2 lidar was installed at the electrical substation of the wind farm Alpha-Ventus. The transformer station is located southeast of the wind farm and thus in the immediate vicinity of FINO1. The wind farms Borkum Riffgrund 2 (south), Borkum Riffgrund 1 (southeast), Trianel Windpark Borkum 1 (west) and Alpha-Ventus (west to north) surround the measuring station mostly. The wind farm Nordsee One at the east-southeast of the measurement point was under construction from December 2015 to September 2017. Individual wind turbines were tested since December 2017, and the complete wind farm started operation in December 2017. In conclusion, this means that only the sectors of 0\({}^{\circ}\) to 60\({}^{\circ}\) and 135\({}^{\circ}\) to 180\({}^{\circ}\) are facing an open wind field. Both platforms are 24 m and 29 m above mean sea level (MSL), respectively. The sites will be referred to as FI/AV, respectively.
## 3 Results  
The results of the microbial loads (MLs) are presented in Figure 2.  
Some samples had ML values below the LOD, in particular LA samples (Table 1).  
The ACC counts at T2 reveal lower average values for LA than the other groups, and at T48 the highest values were detected for CTR, followed by AV and LA samples. The PCC values were lower for LA than CTR in both of the times considered, without differences with AV. Regarding EC, the LA group differed from the other two groups both at T2 and T48. Similar results were detected for SC and LABC, with the main difference between CTR and LA groups at T2 and CTR and LA and AV at T48. In general, the treatment effect was always statistically significant, while the time effect was not detected. Only ACC increased in CTR samples during the storage time.  
Regarding the results of the same-different test, the calculated \(\chi^{2}\) statistic was below 3.84, indicating that no significant differences between the compared two surfaces (CTR versus AV and CTR versus LA, respectively) were recorded by assessors.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Treatments**} & \multicolumn{2}{c}{**CTR**} & \multicolumn{2}{c}{**LA**} & \multicolumn{2}{c}{**AV**} \\  & **Negative/Total Samples** & \multicolumn{2}{c}{**Negative/Total Samples**} & \multicolumn{2}{c}{**Negative/Total Samples**} \\ \cline{2-7}  & **T2** & **T48** & **T2** & **T48** & **T2** & **T48** \\ \hline ACC & 8/40 & 5/40 & 15/40 & 18/40 & 5/40 & 6/40 \\ PCC & 15/40 & 19/40 & 33/40 & 29/40 & 26/40 & 21/40 \\ EC & 27/40 & 29/40 & 31/40 & 35/40 & 33/40 & 29/40 \\ SC & 15/40 & 14/40 & 32/40 & 35/40 & 25/40 & 26/40 \\ LABC & 17/40 & 20/40 & 29/40 & 34/40 & 19/40 & 29/40 \\ \hline \end{tabular}  
* ACC = aerobic colony count; PCC = psychrophilic colony count; EC = Entrobacteriaceae count; SC = Staphylococcus count; LABC = Lactobacillus count; LA = 2% of lactic acid solution; AV = aromatic vinegar; CTR = control samples; T2 = 2 h; T48 = 48 h, SEM = standard error of the mean; negative sample = MLs \(<\) LOD.  
\end{table}
Table 1: Number of samples below the limit of detection for the microbial load considered in treated and untreated surfaces of wild boar carcasses at different storage times.
# An Audio Watermarking Algorithm Based on Adversarial Perturbation +
## 3 The Proposed Methodology
### Watermark Embedding and Attack Simulation Layer
#### 3.4.2 Loss Function and Attack Simulation Layer
\(\mathcal{L}_{a}\) provides a more accurate evaluation of imperceptibility than the mean squared error (MSE) loss or signal-to-noise ratio (SNR) metrics because it adaptively weights different frequency bands. Consequently, the loss function of watermark embedding is as follows:  
\[\mathcal{L}=\mathcal{L}_{w}+\lambda\mathcal{L}_{a} \tag{15}\]  
where the term \(\mathcal{L}_{w}\) pushes \(\mathbf{o}_{wl}\) to the designated point (watermark) and ensures that the watermark can be extracted after various attacks; furthermore, the term \(\mathcal{L}_{a}\) keeps the watermark imperceptible. The \(\mathcal{L}_{w}\) and \(\mathcal{L}_{a}\) are mutually constrained; when \(\mathcal{L}_{a}\) reaches negative infinity, it can be assumed that there is no change in the host audio, while \(\mathcal{L}_{w}\) will be large; conversely, if the watermarked audio changes very drastically, the watermark can be extracted more accurately, yet \(\mathcal{L}_{a}\) will be very large. Therefore, the parameter \(\lambda\) can control the trade-off between imperceptibility and robustness.
# Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios
## 2 Thermal Modeling
### Detection  
For the detection case, the camera does not need to be calibrated; thus, the output DN is taken as directly proportional to the heat flux received by the camera \(q_{fc}\). Therefore, the formulation is the following:  
\[q_{fc}\to DN=a\cdot q_{fc}+b\to DN_{noisy}=DN+Noise\,. \tag{17}\]  
As stated before, the goal is just to obtain the actual DN produced by the camera. Using this method, the \(q_{fc}\) can be translated into just a DN field, and the scale of the image can be set in order to have the best contrast between the target and background. Commercially available thermal cameras such as FLIR-Tau2 ([[https://www.flir.it/products/tau-2/](https://www.flir.it/products/tau-2/)]([https://www.flir.it/products/tau-2/](https://www.flir.it/products/tau-2/)), accessed on 21 March 2024) are sold, indeed, also without calibration, and they have image processing algorithms that can set the scale in non-uniform ways around the image to increase the target contrast. The interested reader can refer to the user guide of FLIR-Tau2 ([[https://www.flir.com/support/products/tau-2/?pn=Tau+2&vn=46336100H#Documents](https://www.flir.com/support/products/tau-2/?pn=Tau+2&vn=46336100H#Documents)]([https://www.flir.com/support/products/tau-2/?pn=Tau+2&vn=46336100H#Documents](https://www.flir.com/support/products/tau-2/?pn=Tau+2&vn=46336100H#Documents)), accessed on 21 March 2024) for more details on such algorithms. As a recap, the workflow of the method is presented in Figure 6, where the first part of the pipeline is identical to the one presented in Figure 5, and that is to associate a \(q_{fc}\) value to each pixel of the image. Starting from the image produced in Blender, since the maximum and minimum heat flux values \(q_{fc}\) are known, they are associated with the maximum and minimum DN values of the pixels in the image; in this way, the scale of the image is converted from DN values into \(q_{fc}\) values, and each pixel now corresponds to a \(q_{fc}\) value. With such values, each pixel \(q_{fc}\) value can be converted into the actual DN value produced by the given thermal camera based on the sensor model of Equation (17). In this conversion, the respective noise model can be added (17), and therefore the actual \(DN_{noisy}\) value produced by the given thermal camera is computed for each pixel, reproducing the actual output of the thermal camera.
## 2 Requirements Specifications and Measurements Gaps  
This study focuses on the identification of the EO measurements gaps in the time frame from 2020-2030, to complement the Copernicus space infrastructure, based on the top 10 use cases [1]. These use cases are not satisfied by the existing Copernicus infrastructure, and they were generated through a quantitative methodology that involved the prioritization of 38 EO data needs (the complete list of the identified needs with their description is presented in Table 1), 96 products across the six Copernicus services, 63 stakeholders, 131 measurements and 48 uses cases, which were scored. The top 10 use cases were defined as (1) marine weather forecast, (2) sea ice monitoring (extent and thickness), (3) fishing pressure and fish stock assessment, (4) land for infrastructure status assessment, (5) agriculture and forestry (hydric stress), (6) land for mapping (risk assessment), (7) sea ice melting emissions, (8) atmosphere for weather forecast, (9) climate for ozone layer and UV and (10) natural habitat and protected species monitoring. Figure 1: Methodology applied to define the end-user requirements and measurement gaps.