esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evaluation of Collision Avoidance Techniques for Collaborative Robots
## 3 Implementation
### Test Cases  
A particular configuration of the manipulator was chosen to carry out experimental tests representing a collaborative task between human and robot: the manipulator was fixed in reversed mounting configuration to an aluminum frame positioned at the height of 1.90 m so that the encumbrance of the base is avoided and the human can physically enter the workspace of the robot (Figure 3). In addition, a tool with a length of 205 mm has been used to simulate the encumbrance of a generic end-effector. In all test cases, a common set of parameters is used for both the simulation and the real case; in this way, it is possible to compare the results obtained since the conditions in which the algorithm is implemented are the same. Common parameters are summarized in Table 1.  
Figure 3: Simulated (**left**) and real (**right**) test bench.  
Figure 2: Software/hardware architecture: standard PC connected to a UR5e robot with a socket communication cable (compliant with TCP/IP standards); the PC receives in input the robot’s joint rotations and sends out the joint velocities reference signal. Software is realized by Python and Matlab development environments for communication and data processing.  
As mentioned, the position of the obstacle is simulated to avoid possible errors in the object detection system. In the proposed examples, a general spherical obstacle is considered. Regarding the safety volume around the links of the robot, the radius \(r\) is set as a linear function of the velocity \(v\) of the obstacle, with lower and upper bounds (\(r=r_{inf}\) for \(v\leq v_{inf}\), \(r=r_{sup}\) for \(v\geq v_{inf}\)). The \(\theta_{max}\) value is introduced to saturate the angular velocity of the joints if the algorithm generates a speed excessively high. This safety limit is imposed at all joints, and the consequent positioning error is recovered in subsequent movements thanks to the proportional corrective term.  
Other parameters must be changed in the real system because the simulation operates in an ideal context that does not consider the actual reaction of the robot's motors, the communication latency, or other implementation and hardware limits. Gains parameters, in particular, such as the repulsive speed \(v_{rep}\) and the proportional gain \(k_{e}\) of Equation (7), resulted very sensible to variations and have been tuned by a trial and error procedure. Final values are collected and discussed in the next section.
# View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes
## 3 Materials and Method  
### Data Collection  
Four types of structured datasets were utilized in the experiments, covering typical tasks such as user behavior rating, online activity logging, synthetic sparse-labeled samples, and financial transaction records. The objective was to comprehensively evaluate the effectiveness and generalizability of the proposed self-supervised representation learning method across both real-world and simulated scenarios. All data were obtained from publicly accessible and reputable online platforms. The user behavior rating data were collected from two well-known recommendation system platforms, MovieLens and Book-Crossing, comprising approximately 170,000 records. Each sample contained features such as user ID, item ID, rating value, timestamp, gender, age, and item category, resulting in a total feature dimension of 50. Missing values primarily occurred in user attribute fields. Operational log data were sourced from the Kaggle Web User Logs Dataset, which encompasses user interactions on e-commerce and social media platforms. Features include click behavior, dwell time, page depth, and search queries, with a dimensionality of approximately 60. A total of 200,000 records were collected, with missingness mainly caused by irregular behavioral tracking frequencies. Synthetic sparse-labeled data were generated using the make_classification interface from the Scikit-learn library, with feature dimensions ranging from 100 to 200. Randomized missing mechanisms and sparse label settings were introduced to simulate low-response or weak-supervision modeling scenarios, resulting in 100,000 synthesized samples. Financial transaction data were collected from the IEEE-CIS Fraud Detection and UCI Credit Card Default datasets, totaling 180,000 records. Features included transaction time, amount, payment method, billing period, default status, and risk level, with a feature dimension of up to 120. Missing entries in this dataset were typically due to privacy-protected fields. Prior to processing, all data were uniformly encoded in UTF-8. Continuous variables were treated with statistical imputation and Gaussian-based missing simulation, while categorical variables were processed using embeddingmapping. Standard normalization was applied to ensure consistency in data format and model input structure. During model training, 80% of the data were used for training and validation, with the remaining 20% held out for testing to ensure the reproducibility and generalizability of the results. The structural statistics of the datasets are summarized in Tables 1 and 2.
###### Abstract  
In this work, a combination of molecularly imprinted polymer (MIP) technology with micro solid-phase extraction in a hollow fibre device is described. MIP microspheres were synthesized and packed into polypropylene hollow fibre (HF) segments. The proposed device was used directly for trace enrichment and clean-up of triazines in soil sample extracts by hollow-fibre membrane-protected molecularly imprinted micro solid-phase extraction (HFM-protected-MI-MSPE). Analytes were extracted from soil by ultrasonic assisted extraction, evaporated to dryness and reconstituted in toluene. The proposed device was immersed in the toluene extracts, and migrations of analytes through the walls of the hollow fibre was achieved with the help of external agitation. Then, selective recognition of the target analytes by MIP-microspheres took place in the inner part of the HF. All parameters affecting the extraction were optimized. Under optimum conditions, quantitative recoveries were obtained for simazine, cyanazine, atrazine, propazine and terbutylazine in soil samples, with relative standard deviations lower than 11%. The detection limits (LODs) were lower than 5 ng g\({}^{-1}\) in all cases.  
Keywords:triazines; imprinted polymers; micro-solid phase extraction; miniaturization; hollow fibre membrane +
Footnote †: journal:
# A Comprehensive Review of Micro UAV Charging Techniques
## 4 Wireless Power Transfer (WPT)
### Laser Power Transfer (LPT)
#### 4.1.2 Laser Selection  
The American National Standard for Safe Use of Lasers (ANSI Z136.1) [94] gives guidance for the installation, services, maintenance and safe use of lasers for optical communication systems. It serves as the basis for laser selection and safety programs throughout the USA, including academic laboratoriesand the government and market sectors. This document includes laser selection, working, standard operating process and safety features to protect users from any possible hazards. Several types of laser are available on the market nowadays. Proper selection of the laser is crucial for OWPT systems. In principle, laser selection should comply with various constraints such as: (1) OWPT through the atmosphere or different environment; and (2) the maximum required transfer energy for the OWPT system. One constraint is related to laser selection with proper wavelength. As the atmosphere contains several gases which fluctuate according to environment conditions, so th atmosphere can absorb laser light with specific wavelength. For the efficient performance of an OWPT system, lasers should have the capability to operate with maximum energy transfer. [PERSON] reported laser-based component selection in which the 780 nm and 1100 nm regions are suitable for commercially available laser technologies [95]. The spectral window of 800, 900 and 1500 nm is useful for power beaming to UAVs. Other important factors are laser weight, size, laser safety, component efficiency, working under harsh conditions, laser divergence and maturity of laser technology. The key features of lasers include:  
1. _Directionality_: typically, the laser beam gives a low divergence.
2. _Coherence_: The emitted photons are coherent and carry a constant phase relationship.
3. _Monochromaticity_: laser beams contain a narrow range of wavelengths.
# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 2 Materials and Methods
### Image Classification Methods
#### 2.3.3 NDVI Mapping  
To further delve into the vegetation monitoring tools, vegetation change assessment was carried out separately using NDVI. NDVI serves a specific purpose of monitoring vegetation density and health status.  
Gelephu, a scenic town in the southern part of Bhutan, is known for its harmonious natural landscapes and rich vegetation. The vegetation change dynamics is a critical component for effective environmental monitoring and conservation for sustainable land management. In this study, the author assessed the NDVI to identify vegetation change dynamics in Gelephu using the same Sentinel-2 satellite images for 2016 and 2023, taking into account the consistency of spatiotemporal and spectral resolution. The NDVI, derived from the remote sensing satellite imagery serves as a valuable indicator of vegetation health and density. Using the QGIS software 2.28.6, the NDVI is calculated as the ratio of the difference between the near-infrared (NIR) and red spectral bands normalized by their sum. This index efficiently captures vegetation density and vitality, providing insights into changes in plant health and growth. By applying the NDVI formula to band 8 (NIR) and band 4 (red), we obtained NDVI values for each pixel defined in Equation (1), which were then mapped to visualize the spatial distribution of vegetation in Gelephu.  
\[\text{NDVI}=(\text{NIR}-\text{red})/(\text{NIR}+\text{red}) \tag{1}\]  
To assess the changes between the 2016 and 2023 NDVI maps, a reclassification process was performed to categorize the NDVI values into different vegetation classes. The reclassification aimed to provide a more meaningful representation of the vegetation density and the dynamics of change.
# Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications
## 2 Network Setup and Architecture
### Ground Nodes  
As illustrated in Figure 2, the GNs are equipped with devices that can aggregate data coming from various sensors and transmit it using a LoRa module implementing the LoRaWAN protocol. LoRa is a proprietary modulation technique based on the chirp spread spectrum originally developed by Cycleo and then acquired by Semtech in 2012. Like many other LPWAN technologies, LoRa uses unlicensed sub-GHz bands, therefore taking advantage of the good propagation properties of the spectrum. Since the LoRa modulation is proprietary, no official comprehensive description of the modulation exists as of today, but some attempts to reverse engineer the protocol have been made. The description of the LoRa physical layer given in the present work is partially based on the little information given in the official LoRaWAN standard and on Semtech technical documents.  
Figure 1: Illustration of UAV-enabled LoRa architecture in the backdrop of a wildfire.
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 2 Data  
### Sample Preparation  
The variety of apples used in the experiment was \"Qin Guan\", harvested in October 2015 from an orchard in Baishui County, Shaanxi Province, China. Over 200 apples were selected, ensuring they were uniform in size and free from diseases. During the apple sampling, based on the farmer's experience with the annual pattern of apple core browning, 265 apples showing signs of potential disease were selected and harvested from the orchard. A total of 265 apples were collected from the orchard and transported back to the laboratory. Upon arrival at the laboratory, the apples were allowed to naturally emit field heat at a room temperature of 20 \({}^{\circ}\)C for 24 h. Subsequently, they were placed in a cold storage room for testing. Before each test, the apples were taken out of the cold storage and left at room temperature overnight to reach thermal equilibrium. High-resolution spectral imaging and dielectric feature data collection were conducted once the apples reached temperature equilibrium. Finally, manual fruit dissection was performed to confirm the presence of apple core browning in 17 of the collected apples. Source codes and datasets are available at [[https://github.com/hejinrong/Apple_HSI_Dielectric](https://github.com/hejinrong/Apple_HSI_Dielectric)]([https://github.com/hejinrong/Apple_HSI_Dielectric](https://github.com/hejinrong/Apple_HSI_Dielectric)) (accessed on 1 October 2024).
## 4 Proposed Q-Learning Method for Smart Grid Price Forecasting
### Q-Learning-Based Electricity Price Forecasting  
This paper features a hierarchical electricity market consisting of the grid operator (GO), service provider (SP) and customer (CU). The service provider monitors the energy demand and dissatisfaction level of customers. Monitored data are then reflected by adjusting the dynamic pricing strategy. Wholesale electricity prices provided by grid operators are also monitored for energy consumption. In this system, the service provider works as an agent who determines a retail price to serve the customers. Here, customers serve as the environment. Time is segmented on an hourly basis, and each time slot reward is generated from customers as an electric bill. Customer's energy demand and dissatisfactory factors will influence the state of dynamic pricing, and it will be determined with the help of \(Q\)-learning.  
Here, the implementation of \(Q\)-learning can provide significant advantages. As RL is model-free, it enables the determination of price actions without any model environment. The trial-and-error process of RL comes into play as customers and service providers dynamically set up the price and profit. Secondly, the adaptability of \(Q\)-learning is a key factor. The electricity market is changing massively day by day with the demand-supply, price factor and other factors such as customer dissatisfaction. \(Q\)-learning is adaptive to coping with the changes through its ongoing learning process. Thus, the flexibility of the dynamic energy market is kept in check.  
The proposed system will support a pricing strategy that will be applied in a hierarchical electricity market. The hierarchical framework of this pricing is formulated with RL as a Markov decision process (MDP). Finally, this decision-making of dynamic retail pricing is solved with \(Q\)-learning. Being model-free, the system will learn gradually about the flexibility and uncertainties of the demand change and system requirements over time through the online learning process. Customer dissatisfaction levels are also taken as a key factor in changing customers' demand usage through the online learning process.  
As previously mentioned, the proposed system can be structured in an RL method where service providers will serve as an agent, customers will serve as the environment, the action will be the retail price that the customers are provided by the service providers, the state will be represented by the energy demand, consumption and dissatisfaction levels,and finally, both the SPs' profit as well as CUs' minimized cost can be seen as a reward. Here, the Markov decision process (MDP) is used to formulate retail pricing, and then, using \(Q\)-learning, the dynamic pricing algorithm is formulated.
## 1 Introduction
### Research Background, Research Position, Research Purpose, and Method  
In this study, the STA (Line Segment Tracking Method) algorithm is optimized and improved [16; 17]. The improved algorithm is used to extract the lineaments of the remote sensing image of the Gaosong ore field in the Geju mining area and automatically connects the line elements. Then, the extraction lineaments are compared with the ones based on the traditional Canny algorithm [18; 19; 20; 21] and field measurement data. When comparing the results, we think that the improved algorithm is not only faster than the traditional lineaments extraction method but is also more accurate and closer to the field data than the traditional method. We believe that the improved method has great significance for the analysis of tectonic genesis, and also for the stability evaluation of geological engineering and the prediction of geological prospecting.
# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
## 5 Data Offloading and MEC Server Selection (DO-MECS) Algorithm  
DO-MECS Algorithm  
Step 1 (Initialization):At the first time slot \(t=0\), set the initial MEC server selection probability vector as \(\mathbf{Pr_{u}}(\mathbf{t=0})\), where \(\Pr_{u,s}(t=0)=\frac{1}{5},\forall u\in U,s\in S\).  
Step 2 (MEC Server Selection):At the beginning of each time slot (\(t>0\)), each end-user chooses a MEC server to offload its data based on its action probability vector \(\mathbf{Pr_{u}}(\mathbf{t})\). If \(\Pr_{u,s}(t)\geq 0.999\) for all the MEC servers \(s,s\in S\), then stop. Otherwise, set \(i=0\), where \(i\) denotes the iteration of the second part of the algorithm.  
Step 3 (Optimal Data Offloading): Each end-user has been associated with a MEC server and all the MEC servers announce their prices. Each end-user determines its optimal data offloading based on Equation (12).  
Step 4 (Optimal Pricing): Given the end-users' offloading data, each MEC server determines the optimal pricing of its computing services based on Equation (14).  
Step 5 (Convergence): If \(|b_{u,s}^{(t)}|_{i+1}-b_{u,s}^{(t)*}|_{i}|\leq e_{1}\) and \(|p_{s}^{(t)*}|_{i+1}-p_{s}^{(t)*}|_{i}|\leq e_{2},\forall s\in S,u\in U\), where \(e_{1},e_{2}\) (small positive constants) are the convergence control parameters, then stop. Otherwise, go to Step 3.  
Step 6 (Update): Update the end-users' action probabilities based on Equations (9a) and (9b) and return to Step 2.
## 2 Novel Contributions of the Current Paper
### Research Motivation  
Cybersecurity at the edge presents significant challenges due to the heterogeneous nature of computing environments. The availability of infrastructure varies across applications, whether in smart cities with well-established networks or smart villages with resource constraints. A resource-constrained environment has unique security and operational challenges that differ significantly from those in a well-developed smart city infrastructure.  
In such environments, lightweight yet robust cybersecurity solutions are essential. This research focuses on developing secure authentication protocols for collaborative computing systems that enable seamless processing at the resource-constrained edge. By leveraging PUFs and Machine Learning (ML), this work aims to design a secure, energy-efficient, lightweight, low-power, and low-latency authentication protocol with an emphasis on efficient bandwidth usage.  
Security and privacy are critical in cybersecurity, particularly in systems utilizing PUF-based Challenge-Response Pairs (CRPs) for authentication. Secure storage and communication of CRP data are of paramount importance, as any breach, such as fault injection, data manipulation, or data theft, could compromise the entire system. To mitigate these risks, this research introduces a novel feature-based authentication protocol that prevents the direct transmission of CRP data, enhancing data security and resilience against attacks.  
Additionally, bandwidth constraints in edge environments pose a significant challenge. Traditional cryptographic protocols are often computationally intensive and require large bandwidth, making them impractical for low-power edge devices. Cryptographic protocols, notably Rivest-Shamir-Adleman (RSA) and Advanced Encryption Standard (AES), are renowned for their robust security features. However, their computational and bandwidth demands could be challenging for resource-constrained environments. To achieve high-level security, RSA requires large key sizes; a 2048-bit RSA key is considered secure for most applications. The use of large keys increases computational overhead and bandwidth requirements. AES is more efficient than RSA in terms of computational requirements;however, the performance of AES depends on the underlying hardware and affects the performance throughput, making it unsuitable for real-time applications [10]. Power consumption of the security protocols needs to be considered while employing them in less resourceful environments [11]. This research aims to develop an efficient security mechanism that minimizes bandwidth consumption while maintaining high security, low latency, and adaptability to resource-limited environments.
## 3 Results
### Calibration Results  
The raw Bayer pattern data was reconstructed into three-band data, as shown in Figure 10. The reconstructed three-band raw data more visually and clearly demonstrates the inconsistent radiometric response between the detectors within a single band, indicating that the reconstructed virtual linear push-broom sensor has the same characteristics as the linear push-broom remote sensing sensors.  
The method described in Section 2.3 was used to solve the non-linear calibration parameter for each imaging detector in the RGB band based on the reconstructed sample data. The calibration parameters characterize the mapping between the raw DNs and calibrated DNs for each detector over the quantization range (Figure 11a). The mapping relationship characterizes the radiometric response of a single detector at specific incident radiances and the inconsistency of the radiometric response of different detectors at the same incident radiance. Figure 11b shows the radiometric response curves of each detector at low, medium, and high DNs, indicating that there is a serious non-linear radiometric response at the edges of the CMOS (shown as circles in the figure), while the radiometric response is relatively stable at non-edges. This inconsistency in the radiometric response  
Figure 9: Calibration and validation zone data.  
model between separate imaging detectors indicates that there is some instability in the on-orbit radiometric response of the OVS sensor, which is a major factor affecting the calibration accuracy and poses a challenge for radiometric calibration.