esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Literature Review
Overall, the existing works mainly focused on the designing of low interference beamforming methods. UTs are assumed to be served by all satellites or specified satellites where selection is not involved. In addition, the advantages or disadvantages of accessing schemes have not yet been deeply analyzed.
## 3 Results
### Comparison of Sentinel-1 and Sentinel-2 Combinations  
#### 3.2.1 Quantitative Evaluation  
In Tables 11-13 the accuracy, loss, F1 score, and IOU are shown for the proposed model when trained with Sentinel-1, Sentinel-2, and each fusion-configuration of the two satellite image types. It is evident from these tables that in most cases, the fusion of Sentinel-1 and Sentinel-2 bands improved the performance over Sentinel-1 alone but were not able to surpass the performance of Sentinel-2 alone. During training, the only exception to this was where Sentinel-1 was fused with the RGB bands of Sentinel-2, where the performance was weaker than for Sentinel-1 alone. During validation and testing, the fusion of Sentinel-1 with both NDVI and MNDWI also underperformed in comparison to Sentinel-1 alone, which given their superior performance in training suggests potential overfitting and lack of ability to generalize to unseen data. Moreover, the fusion of Sentinel-1 with the RGB, NIR, and SWIR bands of Sentinel-2 provided performance comparable to that of Sentinel-2 alone, and in some cases, such as the training loss and testing IOU was able to outperform Sentinel-2.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Module Removed** & **Accuracy** & **Loss** & **F1 Score** & **IOU** \\ \hline Dense Skip Connections & 0.9219 & 0.2269 & 0.6565 & 0.5361 \\ Deep Supervision & 0.9406 & 0.2731 & 0.7053 & 0.5714 \\ Atrous Convolution & 0.9323 & 0.2255 & 0.6882 & 0.5480 \\ Spatial Pyramid Pooling & 0.9226 & 0.2348 & 0.6632 & 0.5291 \\ Weighting & 0.9348 & 0.2317 & 0.6924 & 0.5706 \\ \hline Proposed Model & 0.9432 & 0.2237 & 0.7176 & 0.5862 \\ \hline \end{tabular}
\end{table}
Table 10: Ablation study demonstrating the impact of each module on the performance of the proposed model.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Accuracy** & **Loss** & **F1 Score** & **IOU** \\ \hline Sentinel-1 (VV + VH) & 0.9478 & 0.1342 & 0.7425 & 0.5997 \\ Sentinel-2 (All Bands) & 0.9789 & 0.0883 & 0.8396 & 0.7307 \\ \hline VV + VH + Sentinel-2 & 0.9607 & 0.0953 & 0.7882 & 0.6651 \\ VV + VH + NIR & 0.9551 & 0.1146 & 0.7722 & 0.6395 \\ VV + VH + SWIR & 0.9531 & 0.1157 & 0.7602 & 0.6253 \\ VV + VH + NIR + SWIR & 0.9614 & 0.0934 & 0.7925 & 0.6662 \\ VV + VH + RGB & 0.9373 & 0.1687 & 0.6965 & 0.5473 \\ VV + VH + RGB + NIR & 0.9611 & 0.0955 & 0.7898 & 0.6640 \\ VV + VH + RGB + SWIR & 0.9569 & 0.1072 & 0.7701 & 0.6421 \\ \hline \end{tabular}
\end{table}
Table 11: Training scores for each combination.
## 2 Theoretical Background  
### Diffusion of Innovations Theory (DOI)  
The Diffusion of Innovations Theory (DIO) is put forward by [PERSON], who defines innovation as an idea, technology, or thing that is considered novel by individuals or other adopters [30]. The collaborative robot is regarded as an innovation by scholars [24; 29; 31] as a new and safe human-assisted robot designed to reduce the cost of traditional industrial robots [32].  
The adaptation of innovation by individuals or other organizations is determined by many factors, one of which is the cognitive attributes of innovation (including comparative advantage, compatibility, complexity, observability, and trialability) that directly affect the degree of innovation adoption [30; 33]. The cognitive attributes of collaborative robot innovation are characterized by safety, low cost, and easy use (plug and play can be achieved in most workplaces) [9; 10; 11]. Compared with traditional industrial robots, collaborative robots not only have the comparative advantages of being safer, more economical, and more flexible [34; 35], but also are easier to install and use, and are far less complex than traditional industrial robots [5; 11; 36]. In terms of compatibility, collaborative robots and traditional industrial robots are complementary today and can be compatible with the existing production lines of enterprises [29]. In previous research on innovation adoption, scholars generally believed that comparative advantage, compatibility, and complexity were applicable to explain innovation adoption, while observability and trialability were not extensive in research on innovation adoption [37; 38; 39; 40]. Collaborative robots are small, lightweight, and plug-and-play in most workplaces [23; 41; 42], which facilitates collaborative robot manufacturers (or vendors) to display and are convenient for enterprises with intentions to conduct on-site trials.
## 5 System Architecture and Technology
### Architectural Details and Confirmation Process Example  
In this section, we describe an exemplary system walkthrough of the confirmation process during the execution phase of a project, as shown in Figure 6. Consider the following scenario: A contractor finished their construction task, and the client wants to confirm the completion, eventually triggering a payment to the contractor. The contractor has already uploaded a detailed work documentation (text documents, photos) in the mobile or web app. These files are stored in a CDE (Figure 6, 1). The client uploads a review documentation and confirms the completion, thereby sending the review details to the Management Backend (Figure 6, 2). Additional technical information, such as the target smart contract address or the client's digital identity,is added and sent to the Blockchain Communication Backend (Figure 6, 3). The Blockchain Communication Backend crafts a transaction to trigger the review function in the smart contract and sends this transaction to the Signing Backend (Figure 6, 4). In step 5, the client's permission is  
Figure 7: General process for creating and signing transactions.  
validated and, in the case of acceptance, the transaction is digitally signed by the client's company. The signed transaction is sent to the blockchain, where the task review results in a smart contract state transition that emits a payout command (Figure 6, 6). Finally, in step 7, the Management Backend is informed about this command and forwards it to the Payment Provider, leading to a bank transfer.  
As mentioned above, process steps that are necessary for payment are virtually represented as state machines. To be able to track the progress of payments as well as the progress of a construction task, both levels of detail are mapped onto separate state machines. Nevertheless, both machines are interconnected, and a change on the task level can automatically trigger a transition on the payment level. Hence, the actual payment state is derived from the underlying task state(s).  
As visualized in Figure 8, billing unit items can either start as _Open_ if they are mandatory or in the _Not Requested_ state in case of a contingent item. Ideally, a contractor claims completion and the client confirm this, which results in a full payment confirmed by the contractor. In the worst case, both parties agree on the cancellation of the billing unit item. If clients find the work faulty or only a partial payment shall be made, both parties must agree thereupon. The result might be full payment, a discounted payment, or even a task cancellation.  
Figure 9 shows the related billing unit state machine and its possible transitions. Every unit starts as _Unpaid_, and payment confirmations of inferior items can trigger a transition into the _Partial Payment_ state. As long as at least one item has not reached a final state, the billing unit remains in the partial payment state. Based on the final states of the items, billing units can either end at _Full Payment_ or _Discounted Payment_.  
Figure 8: State machine billing unit item.
## 2 Materials and Methods
### Quantitative RT PCR (qRT-PCR)  
RNA was collected from patient-derived organoid cultures using the RNeasy Mini Kit (Qiagen). cDNA was generated from the extracted RNA, and then pre-amplified using TaqMan PreAmp Master Mix (Thermo Fisher Scientific 391128). The primers used were human-specific GAPDH (Thermo Fisher Scientific, Applied Biosystems Hs02786624_g1), NR5A1 (SF1) (Thermo Fisher Scientific, Hs00610436_m1), PIT1 (Thermo Fisher Scientific, Hs00230821_m1), TPit (Thermo Fisher Scientific, Hs00193027), and POMC (Thermo Fisher Scientific, Hs01596743_m1). Each PCR reaction was performed using a final volume of 20 \(\mu\)L, composed of 20X TaqMan Expression Assay primers, 2X TaqMan Universal Master Mix (Applied Biosystems, TaqMan(r) Gene Expression Systems), and a cDNA template. Amplification of each PCR reaction was conducted in a StepOne(tm) Real-Time PCR System (Applied Biosystems, Foster City, CA, USA), using the following PCR conditions: 2 min at 50 \({}^{\circ}\)C, 10 min at 95 \({}^{\circ}\)C, denaturing for 15 s at 95 \({}^{\circ}\)C, and annealing/extending for 1 min at 60 \({}^{\circ}\)C, for a total of 40 cycles. Relative fold change was calculated using the \(2-\Delta\Delta\)Ct method [32], where CT = threshold cycle. Results were analyzed as the average fold change in gene expression compared to the control, and GAPDH served as an internal control.
###### Abstract  
The satellite-mediated continuous-variable quantum key distribution (CV-QKD) protocol, which relies on off-the-shelf telecommunication components, has the potential for a global quantum communication network with all-day operation. However, the transmittance fluctuation of satellite-mediated links leads to the arriving quantum state showing non-Gaussian property, introducing extra fading noise in security analysis and limiting the secret key rate of the protocol. Here, we consider the clusterization method for data post-processing to suppress the fading noise in both downlink and uplink scenarios, where the measurement data are divided into several clusters, and we perform security analysis separately. In particular, we set the optimal upper and lower bounds of each cluster in terms of the probability distribution of transmittance (PDT), while finding an optimal cluster number for the trade-off between fading noise and the composable finite-size effect. Numerical analysis shows that the proposed method can improve the composable finite-size rate when the fading noise is large enough, even with only two clusters. Moreover, a high-speed CV-QKD system with a higher frequency of signal preparation and detection can extend the proposed method to work in the case of lower fading noise.  
clusterization; continuous-variable quantum key distribution; fading noise; satellite 1
## 5 Case Study
### Scenario 2: Fast Approaching Vehicle Passed by During Obstacle Avoidance  
Scenario 2 is an extension of scenario 1, and the setting of this scenario is shown in Figure 11. After the mobility of the vehicle is negatively impacted by the unanticipated obstacle vehicle, the compliance-oriented potential field in the cost function switches to the violation cost potential field for obstacle avoidance motion planning. The ego vehicle is about to cross the solid white line to overtake the obstacle vehicle. At this moment, there  
Figure 10: Vehicle states in algorithm testing for scenario 1. (**a**) Paths of ego and obstacle vehicles; (**b**) ego vehicle speed; (**c**) ego vehicle steering angle; (**d**) value of PVCI.  
is another vehicle approaching at a speed of 30 km/h from the adjacent lane in the same direction. The results can be observed in Figure 12. Due to the predicted collision risk, the PVCI for the solid white line and the car following increases rapidly, and the violation cost potential field is elevated, leading the ego vehicle to halt its evasive maneuver. After the vehicle from the adjacent lane passes and is safe, the ego vehicle continues its obstacle avoidance. The maximum occupancy of the adjacent lane is 1.83 m. The obstructed delay time is 151.35 s. The detailed PVCI of the ego vehicle is shown in Figure 12d.  
Figure 11: Schematic diagram of scenario 2.  
Figure 12: Vehicle states in algorithm testing for scenario 2. **(a)** Paths of ego and obstacle vehicles; **(b)** ego vehicle speed; **(c)** ego vehicle steering angle; **(d)** value of PVCI.
# Reducing Model Complexity in Neural Networks by Using Pyramid Training Approaches
## 2 Related Work
Matrix factorization techniques have also been widely used in recommender systems to improve performance by reducing the dimensionality of data while retaining essential information [31; 32]. However, low-rank approximations can sometimes oversimplify the data representation, leading to a loss in model performance. Dynamic network surgery (DNS), an advanced form of model optimization, combines network pruning and growth strategies to dynamically adjust network architecture during training. This method, pioneered by [PERSON] et al., allows for adaptive model refinement, enhancing performance while maintaining or even reducing computational requirements [17]. It has been effectively applied in medical image segmentation, demonstrating its versatility across different domains [6]. Despite its advantages, DNS can be complex to implement and requires careful tuning of parameters to balance pruning and regrowth effectively. Additionally, it can introduce instability during training if not managed properly. The existing methods, while effective in various scenarios, often require trade-offs between model size, accuracy, and computational efficiency. Additionally, many techniques depend heavily on the initial model's quality or involve complex retraining processes. Our study addresses these gaps by introducing a novel pyramid training methodology coupled with a feature size reduction strategy that leverages similarity comparison. This approach dynamically adjusts model complexity during the training process, reduces redundancy in feature maps, and maintains essential information, thereby achieving significant size reduction without substantial loss in accuracy. Furthermore, our method is complementary and can be combined with other techniques such as quantization and pruning to further enhance model efficiency. Our contributions are particularly relevant for environments with limited hardware resources, making advanced machine learning models more accessible and practical for broader applications.
## 3 Methods
### Model Constraint Method
#### 3.1.3 Simulation Parameters  
To demonstrate the effectiveness of this method, a numerical test was conducted using a model dataset for Kirchhoff integral pre-stack depth migration imaging. The designed two-dimensional model is shown in Figure 2. The model dimensions are 1005 m \(\times\) 615 m, consisting of five layers of media and four reflection interfaces from top to bottom. The parameters of each layer are listed in Table 1. It is noteworthy to focus exclusively on the influence of velocity heterogeneity, so a uniform density of 2.25 g/cm\({}^{3}\)--representative of average sedimentary formations--is assumed, thereby minimizing the confounding effects of density variations on migration imaging.  
The elastic wave finite difference forward modeling method was used, and the observation system was designed as follows: The trace spacing is 10 m, with one shot, and each shot has 100 traces. The shot point is located at x = 500 m. The sampling interval is 0.5 ms, and the number of sampling points is 2001. The Kirchhoff integral pre-stack depth migration imaging calculations of the subsequent synthesis models in this paper were completed on an Intel (R) Core (TM) i7-8700 K laptop with a CPU main frequency of 3.70 GHz with 48 GB RAM, and the calculation time of the model is only 206.75 s. The excitation method adopts P28M28 controllable seismic source excitation, using single-point excitation. The wireless node seismic acquisition system adopts SmartSolo IGU-BD3C-5 broadband integrated 3 component seismograph reception. The detailed parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Layer Number** & **Medium Velocity (m/s)** & **Density (g/cm\({}^{3}\))** & **Computing Time (s)** \\ \hline
1 & 1000 & 2.250 & \\
2 & 1200 & 2.250 & \\
3 & 1500 & 2.250 & 206.75 \\
4 & 2000 & 2.250 & \\
5 & 3000 & 2.250 & \\ \hline \end{tabular}
\end{table}
Table 1: Model medium parameters.  
\begin{table}
\begin{tabular}{c c} \hline
**SmartSolo IGU-BD3C-5** \\ \hline
**Physical Specs** & **Channel Performance** \\ \hline  & Maximum Input Signal: \(\pm\)2.5 Vpeak @ Gain \\ Seismic data channel(s): 3 & 0 dB \\ ADC resolution: 32 bits & Common Mode Rejection: \(\geq\)100 dB \\ Sample intervals: 0.25, 0.5, 1, 2, 4, 8, 10, 20 ms & Gain Accuracy: \(<\)1\% \\ Preamplifier gain: 0 dB to 36 dB in 6 dB steps & GPS Time Standard: 1 ppm \\ Instrument noise floor: Whole frequency & Timing Accuracy: \(\pm\)10 μs GPS Disciplined \\ band lower than the NHNM curve, 5 s\(\sim\)1 Hz & Cross-Feed: \(<\)\(-\)110 dB \\ lower than the NLNM curve & Inter-channel Phase Offset: \(<\)0.1 ms \\ Operating temperature: \(-\)40 \({}^{\circ}\)C\(\rightarrow\)70 \({}^{\circ}\)C & Transverse Vibration Rejection: Better \\ Waterproof: IP68 & than 0.1\% \\ Data harvesting: USB 3.0 & Inter-channel Amplitude Coherence: 5\% \\  & System Dynamic Range: 140 dB \\  & Frequency Response: 0\(-\)1652 Hz \\ \hline \end{tabular}
\end{table}
Table 2: SmartSolo IGU-BD3C-5 parameter table.  
Figure 2: Synthetic velocity model.
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 4 Adaptive GLAN Using Reinforcement Learning
### AGLAN Environment Setting  
Since the UAVs are clustered, it is necessary that they maintain a specific topology. The topology used in this system is in the form of a 2 \(\times\) 2 cube, as in Figure 5. The UAV swarm topology can be set to a sphere, pyramid, etc. In addition, there is no difficulty in applying various cluster topologies that are different from the topology discussed in this paper, as long as the location information can be easily obtained. We proceeded in the form of a cube topology that is easy to understand spatially. In Figure 5, the blue UAV is the source and the red UAV is the destination, and the rest of the UAVs are intermediate nodes. We applied the simulation environment containing geographic location information and data transfer information as an RL environment. We considered UAV mobility that varied according to the environment by adding mobility errors within a certain range defined for each UAV model to the current location. AGLAN considers possible mobility errors for each UAV and blocks possible variables by reflecting the information in advance to the RL environment. In each episode, all UAVs in the swarm were set to randomly change positions within the mobility error range at their respective locations.  
If a mobility error is applied in the environment, as shown in Figure 6, there are a couple of network changes, such as disconnecting links or creating new network links. For example, with node A, it can move out of the network range of the previously connected node, disconnect the existing connection, and form a new network with another node that is within the coverage range, or a new link can be created, such as node B. In this system, the situation in which the mobility error occurs at a fixed location is implemented, and then the best result is found through learning.  
Figure 5: \(2\times 2\) cube topology setting in RL environment.  
Figure 6: Mobility error considered.
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 5 Proposed Work Overview
### Improved Transfer Confirmation (ITC)  
The improved transfer confirmation (ITC) is a fundamental mechanism within the CrossDeFi framework, designed to ensure secure and verifiable asset transfers from a source DeFi system (DeFi X) to a destination system (DeFi Y). By tailoring the ITC, CrossDeFi enables faster and more reliable confirmations for asset transfers, significantly reducing the risk of fraudulent behaviors like double-spending, where a user spends the same asset more than once. Activated after the successful MBS process, ITC ensures that the asset ownership is singular and authenticated before initiating a transfer. Upon confirming that DeFi X possesses undisputed ownership of an asset Ass, characterized by the asset being uniquely owned at any given time and identified as Ass \(\in m_{i=1}\)Ass, the ITC mechanism is initiated.  
As depicted in Figure 5, miner BMiY plays a critical role in communicating the successful confirmation of asset transfers to both users and miner BMiX. To secure the confidentiality and authenticity of the asset transfer between DeFi X and DeFi Y, CrossDeFi integrates a hybrid cryptographic strategy. This strategy employs elliptic curve cryptography (ECC) and secure hash algorithm (SHA-256), with the cryptographic key pairs designated as SKFX for DeFi X and SKFY for DeFi Y.  
The transfer process begins when a user from DeFi X initiates the transfer by sending the asset to DeFi X's blockchain. The asset is then placed into the asset transaction pool of X (ATP-X) after being recorded in the latest block by miners. Miner BMiX facilitates the transfer of the asset from ATP-X to ATP-Y using the selected bridge B(X-Y), as outlined in the MBS guidelines. Upon receipt, the blockchain of DeFi Y processes the asset from ATP-Y, with miner BMiY ensuring its integration into the DeFi Y network. If the asset is verified as legitimate, DeFi Y sends a transfer confirmation back to both BMiX and BMiY, culminating in user X receiving this confirmation. To support asset traceability, BMiX records the transaction within the latest block of blockchain X, accessible only to authorized users, ensuring data integrity and the traceability of the asset's origin.
## 4 Results
### Analysis of Experimental Examples  
The YOLO v4 model was used to analyze the images when DPAC was enabled and disabled. Figure 8 contains two original images from the brainwash dataset. There are 18 people in Figure 8a and 16 people in Figure 8b. The yellow boxes in the images are marks for easy comparison.  
The confidence threshold was set to be higher than 0.5. The image detection results obtained when DPAC was not used are shown in Figure 9. There are 17 heads detected in Figure 9a, with a precision rate of 100% and recall rate of 94.4%, and there are 17 heads detected in Figure 9b, with a precision rate of 94.1% and a recall rate of 100%.  
Figure 10 shows the detection results when the confidence threshold was set greater than 0.5 and DPAC was used to analyze the images.  
One TP detection target was added in Figure 10a, the label of which is white. There are 18 heads detected in Figure 10a, and compared to Figure 9a, the precision remains unchanged, and the recall rate is increased by 5.6%.  
One FP detection target is reduced in Figure 10b, the label of which is green. There are 16 heads detected in Figure 10b, and compared to Figure 9a, the recall remains unchanged, and the precision is increased by 5.9%.  
The experiments show that detection performance is improved by increasing TP and reducing FP through DPAC.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DPAC** & **Speed of First Test** &
\begin{tabular}{c} **Speed of** \\ **Second Test** \\ \end{tabular} & **Speed of Third Test** & **Average Speed** \\ \hline Disable & 4.31 & 4.35 & 4.23 & 4.30 \\ Enable & 4.24 & 4.26 & 4.33 & 4.28 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Speed comparison results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**DPAC** & **Errors** & **Correct** & **Detection** & **GT Number** & **AP** & **Recall** & **Precision** \\  & **Number** & **Number** & **Number** & **Confidence 0.5** & **Recall** & **Precision** \\ \hline None & 241 & 4265 & 4506 & 4989 & 84.44\% & 85.49\% & 94.65\% \\ \(f_{1}(x)\) & 255 & 4284 & 4539 & 4989 & 84.80\% & 85.87\% & 94.38\% \\ \(f_{2}(x)\) & 239 &