esa-sceva/satcom-chunk-collection huggingface dataset:
# Static and Moving Target Imaging Using Harmonic Radar
## 4 Synthetic Aperture Radar (SAR) Imaging
### SAR Imaging with Fixed 16-Channel Receiver
#### 4.3.2 Description of Targets  
SAR data were collected from five different nonlinear targets. Three of the targets were hand held push-to-talk radios, and the other two targets were commercial RF amplifiers. A description of the targets is provided in Table 3.  
Since the radio antennas did not operate over the 0.8-1 GHz or the 1.6-2 GHz frequency bands, custom made dual band dipoles were attached to the antenna port of the radios. The RF amplifiers were also outfitted with the custom antennas. The results would not change too much if the target's antennas operated at the desired frequency range, except that the received power would be higher. As long as the antennas have some gain at the second harmonic frequency and are able to capture the power in this range, the results would be similar. In applications involving detection of RF electronics, there is no effort made to have dual band antennas operating at both the fundamental and the harmonic, since the RF electronics operator wants to avoid detection. On the other hand, applications involving insect tracking do have dual band or ultrawideband antennas operating at both the fundamental and the harmonic since they want to exploit harmonic scattering.
## 2 Methodology
### Position and Elevation Error
#### 2.3.2 Elevation Error  
Recording seismic data at sites with rough terrain or big elevation differences (Figure 2) is challenging. The big difference in elevation affects the data results, particularly regarding accurate travel time picking. To overcome this problem, we first calculate the elevation error, then remove it from the recorded data. Equation (9) is used to calculate the elevation error of the \(n\)th receiver:  
\[e_{2n}=Z_{n}-Z_{D}, \tag{9}\]  
Figure 1: Illustration of error in receiver positions. Black and gray triangles indicate ideal and actual positions of the \(n\)th receiver, respectively.  
where \(Z_{n}\) and \(Z_{D}\) are the elevations of the \(n\)th receiver and the datum (reference plane), respectively, from mean sea level, and are available from the D-GPS measurements.
## 4 Discussions
### AE Evolution Process and Spatial Distribution Characteristics  
AE energy release is an important indicator reflecting the evolution of microcrack damage. Figure 11 shows the overall spatial distribution characteristics of AE signals in the Brazilian test. As can be seen from Figure 11, most AE signals are distributed near the fracture surface, which is consistent with the fracture path. AE signals gradually deviate from the center of the disc with the increase of layer orientation, and the number of signals has an increasing trend, which is especially prominent when \(\theta=60^{\circ}\). When \(\theta=0^{\circ}\), the disc is mainly subjected to compressive stress, which induces fewer microcracks in the lamination, sparse AE signals, and, therefore, fewer secondary cracks. On the contrary, when \(\theta=90^{\circ}\), the weaker lamination plane is subjected to horizontal tensile stress, which is more likely to induce micro-cracks in the lamination, and the AE signals are distributed in a crescent-shape on the side deviating from the meridian of the disc. When \(\theta=30^{\circ}\)-\(60^{\circ}\), the layer orientations lead to a more complex stress transfer path and a larger deviation of the fracture path from the disc meridian. AE signals also show complex spatial distribution characteristics and are generated around and on the edge of the meridian of the disc. More AE signals mean more fracture points and more micro-cracks initiation, so the fracture path is more complicated.  
Figure 10: The relationship between stress and strength at different layer orientations.
# Recovery/Reuse of Heterogeneous Supported Spent Catalysts
## 3 Methods for Recycling Heterogenous Catalysts
### Magnetic Separation
#### 3.2.2 Magnetically Recoverable Transition Metal Ferrite Nanoparticles  
In the last few years, magnetic spinel ferrite nanoparticles with a general molecular formula MFe\({}_{2}\)O\({}_{4}\) (M is transition metal, such as Fe, Cu, Zn, and Mn) have attracted significant interest on the field of heterogeneous catalysis. They offer several advantages such as chemical and thermal stability [63], and high surface area and mechanical hardness. Ferrite nanocatalysts not only are environmentally benign and compatible with green chemistry aspects but also can be simply recovered from reaction mixture and recycled up to several times almost without significant loss of their catalytic activity [64].  
[PERSON] et al. studied catalytic performance of cuprospinel CuFe\({}_{2}\)O\({}_{4}\) nanoparticles in the biodiesel production via the waste frying oil (WFO) transesterification process. This catalyst, synthesized by a combination of co-precipitation and hydrothermal methods, showed high activity and reactivity, simplicity, easy reusability, and excellent stability under different reaction conditions. After the reaction time, the catalyst was recovered by an external magnetic field, washed with acetone to remove the adhered oil and glycerol, and dried and reused again without any leaching or any catalytic efficiency reduction even for five reaction cycles [65].
## 4 Control Algorithm
### Fault-Coping Algorithm
studies observed through experiments that, in situations where foreign objects, such as ropes or seaweed, were entangled in the thruster, the current value increased by more than 4-50% compared with the input value, and the RPM value consistently decreased. Therefore, the threshold criterion in the thruster status block of this study was set to 1.5 times the input value. Figure 8 represents the platform position error block. The platform position error block assesses faults based on data such as the current position, heading angle, and bearing angle of the USV. During a malfunction, such as due to interference in the thruster from foreign objects, the distance error between the USV and the target location may not decrease or the USV may deviate from the intended trajectory,  
Figure 8: Platform position error block. Figure 7: Thruster status block. leading to a divergence in the error values between the current position, heading angle, and target location. These outcomes were detected using sensors and used to determine the occurrence of a fault. In this block, the error used to determine faults is the difference between the target cross-track error (CTE) value and the computed actual CTE value, as well as the difference between the target and actual heading angles detected by the sensors on the USV. Similar with the thruster status block, if the error value did not exceed a threshold, the system was considered normal. If the error value exceeded the threshold, it was considered a fault. To prevent the misinterpretation of transient errors caused by disturbances as faults, the threshold must persist for more than 3 s to be considered a fault. The threshold value in this block was set empirically with a CTE greater than 2 m and a heading angle greater than 10\({}^{\circ}\) as the fault criteria.
## 4 Proposed Method
### Node and Edge Representation  
The relevant parameters of nodes and edges are shown in Table 4. Each node \(i\in N\) is represented by a 7-dim feature vector:  
\[x_{i}=(x,y,d_{i},L_{i},U_{i},L_{i,\text{min}},U_{i,\text{max}}), \tag{17}\]  
where:  
* \(x,y\): Spatial coordinates.
* \(d_{i}\): Demand at node \(i\).
* \([L_{i},U_{i}]\): Ideal delivery time window.
* \([L_{i,\text{min}},U_{i,\text{max}}]\): Tolerance window for customer satisfaction. Edge weights between nodes \(i\) and \(j\) are defined by: \[w_{ij}=d_{ij}\cdot\alpha\bigg{(}\frac{L_{i}-t_{i}}{L_{i}-L_{i,\text{min}}} \bigg{)}^{\beta},\] (18)  
where:  
* \(d_{ij}\): Euclidean distance between nodes.
* \(t_{ij}=d_{ij}/v_{\text{max}}\): Travel time, with \(v_{\text{max}}=5\,\text{m}/\text{s}\).
* \(\alpha=0.8\), \(\beta=1.316\): Penalty coefficients.
## 4 Proposed Scheme
### Priority-Based Medium Access
The scores for the sensors are given by \(y_{1}=0.9\), \(y_{2}=0.5\) and \(y_{3}=0.1\). From Equation (2), ECW size for Sensor 1 is \(m\), thus it fully utilizes the contention phase period. Sensor 2 has the score 0.5, thus from Equation (2), its ECW size is given by 6. Sensor 2 picks a minislot randomly from minislot index 5, 6,..., 10. Thus the probability of selecting a minislot within ECW is 1/6. Sensor 3 has a low score of \(y_{3}=0.1\), thus gets ECW size of 2. The purpose of the parameter \(\beta\) is to set a lower bound on the size of ECW. Since \(\beta\geq 1\), the lower bound on \(w\) is at least 1. System Parameters  
The parameter \(\gamma\) is called _warping factor_. As the name suggests scores are nonlinearly distorted by \(\gamma\) in determining the ECW. By controlling \(\gamma\) one can achieve varying degrees of _aggressiveness_ in giving priority to nodes with high scores. To facilitate understanding the role of \(\gamma\), let us consider an illustrated example in Figure 3. The function \(y^{\gamma}\) has been plotted for \(y\in[0,1]\) with varying \(\gamma\). Consider four score values \(y_{1},y_{2},z_{1}\) and \(z_{2}\). The pair of high scores \(y_{1}\) and \(y_{2}\) are close to 1, and the pair of low scores \(z_{1}\) and \(z_{2}\) are close to 0. Figure 3: A plot of \(y^{\gamma}\) for \(y\in[0,1]\) with warping factors \(\gamma=1,2,3,\frac{1}{2},\frac{1}{3}\) and 0. Figure 2: Examples of contention window. Consider the case \(\gamma>1\). For high score pairs \(y_{1}\) and \(y_{2}\), a small difference between \(y_{1}\) and \(y_{2}\) will result in a large difference in the ECW size. Also consider a pair of 'low' scores \(z_{1}\) and \(z_{2}\), the difference between the ECW size is small relative to the difference between \(z_{1}\) and \(z_{2}\). Since \(w\) is monotonically decreasing function in \(\gamma\), the ECW shrinks with increasing \(\gamma\) for any fixed score. Thus by increasing \(\gamma\) one increases overall chance of collision, however also increases the probability of high score sensors winning the frame. The case where \(0<\gamma<1\) has the opposite effect. With decreasing \(\gamma\), the sensitivity of the ECW size decreases (resp. increases) for high score (resp. low score) sensors. Furthermore decreasing \(\gamma\) has an effect of increasing ECW for all the sensors, thus the probability of collision will decrease. Roughly speaking, small \(\gamma\) implies that the sensors are roughly fair in terms of the likelihood of channel access regardless of the scores. In the limiting case where \(\gamma=0\) all the sensors gets the maximum window size of \(m\) minislots. We refer to a baseline contention scheme such that every node selects a minislot randomly and uniformly from minislot indices \(1,2,\ldots,m\) as UNIFORM scheme. Thus when \(\gamma=0\), the proposed scheme reduces to UNIFORM scheme. Thus \(\gamma\) quantifies the aggressiveness in prioritizing high score sensors in channel access. Larger \(\gamma\) corresponds to higher aggressiveness. The choice of \(\gamma\) will be discussed later.
# Singing Voice Detection in Electronic Music with a Long-Term Recurrent Convolutional Network
## 3 Experiments
### Performance across Features  
In the first experiment, features were individually selected to perform the SVD task on the separated vocal signal of the Jamendo and Electrobyte datasets. Based on their \(F1\)-measure performance, they were selected or dropped for the next phase of the experiment. To determine the best feature combination, based of their performance of a single feature, each was added into the combined feature vector one by one. Once all of the best combinations were evaluated, the one with the best \(F1\)-measure was selected as the input for the LRCN.  
The performance of the features on the separated vocal signal evaluated individually on the Jamendo dataset is shown in Figure 4, and on the Electrobyte dataset in Figure 5.  
From the results in Figures 4 and 5, the chroma feature exhibited the poorest performance for SVD task, while the SSF achieved the best one. The feature sets were concatenated in each frame and extracted with the convolution layer of the LRCN. For the feature combination phase, the first feature added to the vector was the SSF given its highest \(F1\)-measure, and the rest were incrementally added to evaluate all possible combinations. The results of the combined features are shown in Figure 6 for the Jamendo dataset and Figure 7 for the Electrobyte dataset.  
Figure 4: Performance of each audio feature in the Jamendo dataset individually. Circles represent atypical values within the evaluation metrics.  
From the results on the Jamendo dataset shown in Figure 6, SSF, MFCC, LPCC, and PLP was the feature combination that achieved the best performance. For the Electrobyte dataset in Figure 7, the best feature combination was SSF and PLP. With the observations from both datasets, the combined features were set as the SSF and PLP.  
Figure 5: Performance of each audio feature in the Electrobyte dataset individually.  
Figure 6: Performance of the best possible feature combinations in the Jamendo dataset.
## 3 Overview of the Solution
### Dataset Generator Mode  
The simulation can be configured to run in the data generation mode. A perception [42] library is used to capture the corresponding RGB and semantic segmentation frames with the information of the bounding boxes. Additionally, the simulation uses a simulated depth camera aligned with the left camera of the rover and saves depth information.  
The semantic segmentation ground truth capture is configurable and can contain rocks, terrain, and rover parts masks. However, the bounding boxes are captured for rocks only. Both functions can be easily extended to process other objects of interest. All cameras simulate physical devices with configurable parameters. For the depth image, the clipping planes' settings are exposed, to enable the selection of depth range. This, however, can also be achieved when postprocessing the data. Another settable parameter is the registration frequency.  
Moreover, the simulator's versatility is further enhanced by the incorporation of additional camera configurations, allowing it to generate essential visual ground-truth masks for intricate tasks, like bird's-eye-view (BEV) analysis. The capabilities of the simulator are showcased in Figure 4, in which a bird's eye view of the rover, with automatically generated semantic segmentation masks for the rocks and the rover, is presented. BEV algorithms for perception tasks are trending and drawing extensive attention both from industry and academia [43].  
The data acquired using the simulator are synchronised and can later be used to train various machine learning algorithms. These include semantic scene segmentation, rock detection, monocular and stereo depth estimation, and optical flow or interest point location estimation. Additionally, we publish sample datasets, ready to use for the aforementioned tasks.
## 2 Background on Physical-Layer Security in LoRaWAN and WSNs
### Gaps in Existing PLA Approaches  
Despite advancements in PLA schemes, several challenges remain in ensuring their real-world applicability. Single-attribute methods, such as RSSI-based PLA, are prone to environmental interference and dynamic network conditions, leading to false positives and reduced reliability [15]. While multi-attribute schemes address some of these limitations, many rely on static or context-specific attributes that may not generalize well across diverse IoT environments. Dynamic environments, characterized by factors such as multipath fading and interference, significantly affect the accuracy of attributes like RFF and signal timing [14]. Moreover, existing approaches often overlook dynamic features that evolve naturally with device behaviour, such as battery usage or physical displacement. Emerging approaches aim to fill these gaps by using lightweight and hybrid models that balance computational efficiency and accuracy. For instance, Ref. [19] demonstrated the effectiveness of SVMs in constrained environments, while Ref. [20] highlighted hybrid deep-learning solutions, such as CNN-LSTM models, which adapt to dynamic IoT conditions. These advancements underline the ongoing efforts to develop scalable and robust PLA schemes for real-world deployments. Building on these insights, our proposed multi-attribute PLA scheme integrates both dynamic and static features, including RSSI, BL, and altitude. BL introduces a time-evolving attribute that reflects device behaviour, enhancing resilience against replication attacks. Altitude ensures spatial consistency, mitigating the risk of spoofing through unauthorized device relocation. By combining these attributes, our scheme adapts effectively to dynamic environments, offering a robust solution for securing LoRaWAN-based WSNs in challenging scenarios, such as forest fire detection systems.
# Establishment of a Pressure Variation Model for the State Estimation of an Underwater Vehicle
## 1 Introduction
Additionally, dynamic pressures observed during 3D motions, such as straight, turning, gliding, and spiral motions, are measured using pressure sensors arranged on the top, bottom, left, and right sides of arbitrary-shaped underwater vehicles. After determining the coefficients through regression analysis based on the acquired data, the PVM can estimate the velocity, direction, and localization of underwater vehicles.
# Improving the Efficiency of Energy Consumption in Buildings: Simulation of Alternative EnPC Models
## 4 Application to a Case Study
### Sensitivity Analysis
At the end of the contract, the client receives 100% of the savings, whilst also receiving the maintenance costs. An analysis of the tables in question shows that the client, by not having any expenses, always guarantees a positive NPV, whatever the energy savings or the contract period. However, it is of interest to the client that the contract duration be short because it is through such contracts that they can optimize their benefits. For example, in a 5-year contract with the most pessimistic savings scenario (15%), the client can guarantee an NPV of EUR 37,346.04, whereas for the most optimistic scenario, they can guarantee an NPV of EUR 60,946.61, for the same contract duration. By increasing the duration of the contract, this NPVs fall to EUR 16,854.64 and EUR 28,963.39, respectively, thus proving that shorter contracts represent a greater benefit for the client. On the other hand, short contract durations are extremely detrimental to ESCOs for this type of contract. The analysis shows that, for a contract duration of 5 years, regardless of an optimistic or pessimistic scenario for energy savings, the company always obtains negative NPVs and IRRs, as it does not have enough time to recover its investment. Increasing the contract duration to 10 years, if one considers an energy-saving scenario of 20%, the medium level between the pessimistic (15%) and the optimistic (25%) scenarios, the NPV and IRR become positive, with the NPV amounting to EUR 3055.39. In comparison to the NPVs obtained by the client in this type of contract, the NPV obtained by the company is much smaller, and when one adds this aspect to the fact that the ESCO must assume the financial and energy risks, this type of contract again transpires to be attractive for companies. However, when one increases the duration of the contract to 15 years, it can be observed that the NPV values for the company and for the client begin to come closer to each other, especially in the most optimistic scenario, which is associated with an annual energy saving of 25%, whereby the client's NPV is EUR 28,963.39 and the company's NPV is EUR 28,191.37. Another aspect that reveals itself as an indicator of which is the most favorable energy efficiency contract for both parties is that for 15 years, considering the intermediate energy savings scenario (20%), the client and the company achieve NPVs that are quite advantageous for both sides, with the client's NPV being EUR 22,909.02 and the company's NPV being EUR 15,353.1. Tables 5 and 6 summarize the main results obtained in this study.
# Numerical Simulation of Thermo-Hydro-Mechanical Coupling of Model Test for Nuclear Waste Disposal
###### Abstract  
This article presents a simulation of a long-term retardation performance Mock-up test of the multi-field coupling of buffer materials, with the aim to study the thermo-hydro-mechanical (THM) processes occurring in the engineered barrier system of a high-level waste (HLW) repository. In view of the theory of mixtures and mechanics of continuous media, the coupled THM mathematical model of unsaturated buffer materials is established, considering heat transport and multiphase fluid flow. Using the buffer material Mock-up test of multi-field coupling as a model, the partial differential equation (PDE) module in the general finite element software COMSOL Multiphysics was developed by a second development stage. The dynamic response process of buffer material under the condition of THM coupling was numerically simulated, and the spatial distribution and variation law of suction, porosity, horizontal displacement, temperature and swelling pressure in the engineered barrier were investigated. The porosity of the buffer material under THM coupling was influenced by the swelling pressure and the suction. The welling pressure evolution of the buffer material may be influenced by the thermal expansion induced by high temperature and the swelling pressure generated by buffer material saturation. The evolution of the horizontal displacement of the heater used to simulate a container with radioactive waste was validated. This paper provides technical reference for the design and safety evaluation of underground laboratory barrier engineering in China.  
thermo-hydro-mechanical coupling; buffer material; high-level radioactive waste; numerical simulation Article
# Concentrating Solar Collectors for a Trigeneration System--A Comparative Study
## 1 Introduction
The objective of this investigation is the comparison of different solar concentrating technologies in a promising and highly efficient trigeneration system. Parabolic trough solar collectors (PTC), linear Fresnel solar reflectors (LFR) and solar dish collectors (SD) are the studied solar technologies that are applied for the weather data of Athens in Greece. The results are evaluated in energetic and economic terms. More specifically, this work studies, in dynamic conditions, the three most usual solar concentrating collectors for medium- and high-temperature applications (300-400 \({}^{\circ}\)C). The results are evaluated in energy, exergy and financial terms in order to perform a multilateral work. According to the literature review of this work, the studies usually select one kind of solar collector and investigate it. To our knowledge, there are no studies that examine and compare different solar concentrating technologies in trigeneration systems in order to determine the most efficient and viable configurations. So, the present work aims to give an answer to the question about the most efficient and most viable scenario by comparing three solar concentrating systems. The selected trigeneration \"block\" includes an ORC coupled with an absorption chiller. This design is the most efficient, according to the results of reference [18]. The thermodynamic analysis of the unit is conducted by a homemade thermodynamic program in Engineering Equation Solver (EES) [28] and a homemade dynamic code that is developed in the programming language FORTRAN.