esa-sceva/satcom-chunk-collection huggingface dataset:
# Trace Elements and Mineralogy of Upper Permian (Zechstein)
## 4 Materials and Methods
### Geochemical Analyses  
The following trace elements were analysed in the 308 rock samples: silver (Ag), arsenic (As), barium (Ba), beryllium (Be), Br, cerium (Ce), cadmium (Cd), Co, Cr, Cs, Cu, iodine (I), Fe, gallium (Ga), lanthanum (La), Li, Mn, molybdenum (Mo), nickel (Ni), lead (Pb), Rb, antimony (Sb), selenium (Se), tin (Sn), Sr, titanium (Ti), thallium (Tl), uranium (U), vanadium (V), and zinc (Zn). For several samples, a content of insoluble (in water) residuum was identified.  
282 samples were analysed in the certificated Central Chemical Laboratory of Polish Geological Institute--National Research Institute in Warsaw.  
A powdered rock sample (fraction < 1 mm) was dried and dissolved in HCl solution (1 g of sample to 50 g of solution). For Br and I detection the powdered sample was dissolved in heat water.  
The following analytical methods were applied for detecting:  
* Ba, Cr, Cu, Fe, Mn, Sr, Ti, V, and Zn--emission atomic spectrometry with activation in inductively coupled plasma (ICP-OES);
* Ag, As, Be, Ce, Cd, Co, Cs, Ga, La, Li, Mo, Ni, Pb, Rb, Sb, Se, Sn, Tl, and U--mass spectrometry with ionization in inductively coupled plasma (ICP-MS);
* Br and I--ion chromatography (IC).  
24 samples were analysed by the Hydrogeochemical Laboratory at the Hydrogeology and Engineering Geology Department of the AGH University of Science and Technology in Cracow.  
A powdered rock sample (fraction 0.1-1 mm) of ca. 0.3 g was dissolved in a mixed solution of HCl and HNO\({}_{3}\) (proportion 1:3) at 230 \({}^{\circ}\)C and pressure 35 atm. Chloride and bromide content was detected in a solution of dissolved sample (ca. 5 g sample and 100 mL H\({}_{2}\)O). Chlorides were detected with the argentometric method, and bromine, as well as other trace elements were detected with mass spectrometry, using the Spectrometer ICP MS \"Elan 6100\" (produced by Perkin-Elmer) with inductively activated plasma.
# Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics
## 3 Materials and Methods
### Field Tests  
As test scenarios, wherein diverse missions for victim identification will be executed to undertake the proposed comparison, indoor and outdoor environments with conditions akin to a post-disaster setting have been explicitly delineated. Within these environments, individuals simulating victims have been strategically positioned.  
Outdoor experiments were conducted in collaboration with the Spanish Military Emergency Unit (UME) during the \"XVI Jornadas Internacionales de la Universidad de Malaga sobre Seguridad, Emergencias y Catastrofes\". Together with the organization, and leveraging their expertise, we meticulously recreated environmental conditions, allowing for the extrapolation of results to realistic situations. Additionally, the standards for such experiments were dictated by the National Institute of Standards and Technology (NIST) [41], which was also considered for indoor tests, defining parameters such as the type of debris, ground obstacles, etc. The experiments were conducted repetitively under the same environmental conditions to minimise error variation for each evaluation aspect.  
In the first scenario, a series of victims were strategically placed in a tunnel, requiring identification by various participating robotic teams. Subsequently, these identifications were verified through specialized canine units. Figure 4a illustrates a panoramic view of the tunnel exit, featuring emergency response teams, a helicopter, and a vehicle positioned for other simulation exercises. On the other hand, Figure 4b depicts an indoor scenario within the Center for Automation and Robotics facilities, where primary phase system validation tests were conducted before outdoor experiments.
# Runway Pavement Structural Analysis Using Remote Laser Doppler Vibrometers
## 2 Remote Laser Vibration Measurement System
### Deflection Test Scheme Based on RLDV  
The scheme proposed in this study, based on RLDV for pavement deflection testing, is illustrated in Figure 2. RLDV is mounted on a fixed-end beam outside the runway sideline, positioned at an elevation of 1 m. The deflection measurement point is situated at the outer margin of the aircraft wheel paths, at a distance of 25-30 m from the laser, and encompasses a testing angle of 1.9-2.2\({}^{\circ}\). This approach is distinguished by its non-interference with regular airport runway traffic and its capacity for non-contact, long-term runway performance monitoring.  
Figure 1: Remote Laser Doppler Vibrometer and testing principle.  
Nonetheless, two significant issues persist within this system framework: (1) the imposition of the small angle installation leads to the measurement result reflecting the component of runway pavement vibration in the direction of the measurement angle, (2) the fixed-end beam influenced by aircraft load and natural factors vibrates, which results in the measurement data comprising two parts: the vibration of the runway pavement and the beam itself. Consequently, subsequent efforts focus on addressing these two aspects: compensation for angle and vibration of the fixed-end beam.
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 3 Results and Discussion
### Performance of Machine Learning Models
#### 3.1.2 Root Mean Squared Error (RMSE)  
Root Mean Squared Error (RMSE) is another measure of prediction error. It is the square root of the average squared differences between the predicted and actual values. The RMSE gives more weight to larger errors, thus providing a more sensitive measure of model performance when larger errors are particularly undesirable  
Similar to the MAE, the RF model exhibited the smallest RMSE among the three models, indicating its superior performance in controlling larger errors in prediction [44; 45].
# Dual Partial Reversible Data Hiding Using Enhanced Hamming Code
## 3 Proposed Scheme
### Data Embedding  
In this subsection, we describe the detailed procedure for embedding secret data into two cover images by applying the improved HC(7,4) scheme. The overall process is summarized in Algorithm 2.  
In HC(7,4) codes, coset leaders correspond to eight distinct patterns, which can be naturally represented by 3-bit binary numbers ranging from 000 to 111. Thus, in the proposed embedding scheme, each group of three secret bits is directly mapped to a target coset leader. This mapping forms the basis for aligning the virtual pixel structure with the intended secret message. For each pixel location, the corresponding values from \(\mathbf{I}_{1}\) and \(\mathbf{I}_{2}\) are accessed to reconstruct the virtual pixel \(y\) by selecting and concatenating specific bits, as defined in Equation (4).  
To embed secret data, a group of three consecutive secret bits \(m_{k}^{k+2}\) is selected. The three bits are interpreted as the target coset leader corresponding to the intended secret message. Thus, the _coset leader pattern_ is defined as a 3-bit sequence indicating the desired coset class for the constructed virtual pixel.  
Figure 3: Standard array of the HC(7,4) code for coset-based data embedding and extraction [2]: (a) binary representation and (b) decimal representation.  
Although the same error minimization formula presented in Equation (5) is employed, its purpose in this phase differs from that in cover image generation (Section 3.1). While the previous minimization aimed solely at ensuring codeword validity, here it is driven by the secret bits to align the virtual pixel with the target coset leader while minimizing visual distortion.  
If the current virtual pixel \(y\) does not belong to the desired coset (i.e., the syndrome of \(y\) does not match the target coset leader pattern), then the closest matching codeword \(h\) is selected from the lookup table (LUT) by minimizing the error \(\varepsilon\) as defined in Equation (5).  
After selecting the optimal codeword \(h\), the cover pixel bits are updated according to  
\[\begin{cases}x_{j}^{1}=(a_{8}\;a_{7}\;a_{6}\;a_{5}||h_{7}\;h_{6}\;h_{5}\;h_{4}),\\ x_{j}^{2}=(b_{8}\;b_{7}\;b_{6}\;b_{5}\;b_{4}||h_{3}\;h_{2}\;h_{1}).\end{cases} \tag{6}\]  
Finally, the modified pixels are stored into the stego images \(\Delta\mathbf{I}_{1}\) and \(\Delta\mathbf{I}_{2}\). The full embedding process is outlined in Algorithm 2.
# System Proposal for Mass Transit Service Quality Control Based on GPS Data
## 3 Preliminary
### Conceptual and Formal Model of the Public Transport Network and Activity
If for each pair of possible values for service lines \(l_{i}\), \(l_{i+1}\), the average and standard deviation of \(HR\) at each stop \(p\) on route \(r\) is calculated, then the HV metric is obtained as follows:  
\[HV^{p}=\frac{\sigma_{HR}^{p}}{\mu_{HR}^{p}} \tag{4}\]  
The EWT metric is an estimate of excess user waiting time due to failure to meet the scheduled frequency of two line services on route \(r\). Its value, at stop \(p\) on route \(r\), is obtained from the value \(HV^{p}\) using the following expression:  
\[EWT^{p}=\frac{\left(\sigma_{HR}^{p}\right)^{2}}{2x\mu_{HR}^{p}} \tag{5}\]
## 4 Example Verification
### Experiment Results and Analysis
#### 4.3.2 Experimental Data Analysis and Discussion  
The measured results of the pressure gauge and pressure sensor are shown in Figure 18b.  
After five experiments, the average value was taken to obtain the measured values. The measured values and the theoretical values for this example are illustrated in Table 3.  
According to the experimental results, the fastener's pressure force and clamping force are slightly different from the theoretical values. Because the theoretical analysis ignored  
\begin{table}
\begin{tabular}{c c c c} \hline
**Forces** & **Measured Value/N** & **Theoretical Value/N** & **Error Rate** \\ \hline Pressure force \(F\) & 53.00 & 48.75 & 8.01\% \\ \hline Clamping force \(F_{c}\) & 22.09 & 21.09 & 4.53\% \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of results.  
the gravity and friction of the parts, and because there are some inaccuracies in manual measurement, the actual measured values are greater than the theoretical ones.  
The experiment also showed that the fastener could achieve quick installation and removal, and a simple operation could achieve unilateral loading and unloading, which is conducive to automatic loading and unloading.  
The advantages of the fastener are demonstrated in Table 4.
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 5 Methodology
### Channel Simulation Environment with Full Transmitter-Receiver  
This section is focused on the simulations of the PHY layer of the full transmitter-receiver chain of PRIME version 1.3.6.  
The transmission and reception tests of the PHY layer are composed of the following three blocks: the transmitter block, the propagation channel model and the receiver block. The transmitter block contains the complete PHY layer as in Figure 1, and it is in charge of generating the PRIME 1.3.6 standard signal with some configurable parameters. The propagation channel model is different depending on the test to perform. The receiver is a complete PRIME 1.3.6 receiver as described in Figure 2, which tries to decode the signal distorted by the channel simulator in order to measure the FER.
## 5 Simulation Setup and Experiments
### Greedy Resource Allocation  
We proposed an additional algorithm based on the Greedy Approach grounded on the baseline graph-based resource allocation algorithm. Algorithm 2 shows our proposed greedy approach.  
Figure 6: Cumulative Distribution Function (CDF) of Instantaneous Signal\(-\)to\(-\)Interference-Plus-Noise Ratio (SINR) of V2V and V2I Communications.  
Figure 7: Sum of V2I Capacity with Varying Vehicle Speeds.  
The greedy resource allocation algorithm is a method used to allocate resources in vehicular networks, explicitly focusing on vehicular-to-vehicular (V2V) communication. The algorithm uses a greedy approach to allocate resources based on immediate local optimization criteria. The algorithm operates as follows:  
* Initialization: Begin with an initial set of available resources and an open allocation plan.
* Vehicle Selection: Select a vehicle from the pool of vehicles requiring resource allocation.
* Resource Evaluation: Evaluate the available resources and the requirements of the selected vehicle. This evaluation may include available bandwidth, signal quality, channel conditions, proximity to other vehicles, and communication demands.
* Resource Allocation: Allocate the resources to the selected vehicle based on the evaluation. The allocation decision is made greedily, considering the local optimization criteria. For example, the algorithm may prioritize allocating resources to vehicles with higher communication demands or vehicles experiencing poor signal quality.
* Update Resource Pool: Update the pool of available resources by subtracting the allocated resources from the total available resources.
* Repeat Steps 2-5: Repeat the vehicle selection process, resource evaluation, allocation, and resource pool update for the remaining vehicles until all vehicles have been allocated resources.  
The greedy resource allocation algorithm aims to maximize the immediate benefit by making locally optimal decisions at each step. However, it may not always result in the globally optimal allocation of resources. Nevertheless, the algorithm provides a simple and efficient solution for resource allocation in dynamic vehicular environments where real-time decision-making is crucial. It is important to note that the greedy resource allocation algorithm can be customized and adapted based on the specific requirements and constraints of the vehicular network, such as quality-of-service (QoS) guarantees, fairness considerations, or traffic prioritization. This algorithm can optimize resource utilization, enhancing V2V communication performance and network efficiency. Figure 8a,b shows the simulation results of the baseline greedy resource allocation algorithm:
# Socioeconomic Impact on Urban Resilience against Flood Damage
## 3 Results
### Relationship between Urban Resilience and Socioeconomic Cost
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**City**} & \multicolumn{3}{c}{**R**} & \multicolumn{3}{c}{**Socioeconomic**} & \multirow{2}{*}{**Flood Area Ratio**} \\ \cline{2-2} \cline{4-6}  & **Flood Before** & **Flood After** & **Difference (\%)** & **PL (USD)** & **TCC (USD)** & **(\%)** \\ \hline I & 0.762 & 0.761 & 0.131 & 104,104 & 0 & 0.883 \\ H & 0.763 & 0.548 & 28.178 & 633,955 & 0 & 1.342 \\ K & 0.761 & 0.533 & 29.961 & 126,357 & 0 & 0.496 \\ R & 0.759 & 0.515 & 32.148 & 314,373 & 0 & 2.527 \\ N & 0.761 & 0.502 & 34.034 & 33,417 & 0 & 0.825 \\ F & 0.764 & 0.501 & 34.424 & 45,292 & 7493 & 0.479 \\ P & 0.760 & 0.499 & 34.342 & 43,540 & 997 & 0.364 \\ Q & 0.760 & 0.498 & 34.474 & 41,174 & 0 & 1.145 \\ \hline M & 0.761 & 0.476 & 37.451 & 4533 & 169 & 0.647 \\ O & 0.760 & 0.469 & 38.289 & 636,302 & 4356 & 7.342 \\ D & 0.765 & 0.467 & 38.954 & 632,710 & 5928 & 4.759 \\ L & 0.761 & 0.409 & 46.255 & 57,805 & 9765 & 1.658 \\ C & 0.768 & 0.369 & 51.953 & 414,758 & 337 & 6.478 \\ \hline A & 0.780 & 0.352 & 54.871 & 2,961,942 & 2119 & 25.537 \\ E & 0.764 & 0.349 & 54.319 & 6,297,107 & 3125 & 25.869 \\ G & 0.764 & 0.321 & 57.984 & 1,812,906 & 5009 & 20.268 \\ J & 0.762 & 0.313 & 58.924 & 989,560 & 2491 & 12.105 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Urban resilience and socioeconomic factors of each city. Figure 7: Urban resilience and socioeconomic cost; in the case of City E, it was found that although it does not have the lowest resilience, it experienced the highest level of damage.
# Research on Bandwidth Improvement of Fine Tracking Control System in Space Laser Communication
###### Abstract  
Piezoelectric fast steering mirror (PZT FSM) is the core component of the fine tracking system for space laser communication, and its actuator is a piezoelectric ceramic. Consequently, there is a hysteretic nonlinear disturbance throughout the entire range of the FSM's steering. To enhance the fine tracking system's performance, this paper innovatively analyzes and verifies the effect of the PZT FSM hysteresis characteristics on the error suppression bandwidth of the fine tracking system. Firstly, the rate-dependent hysteresis model is established by serially connecting the Prandtl-Ishlinskii (P-I) model with the dynamic linear mode. The inverse model is designed as a feedforward controller, followed by the conduction of open-loop feedforward compensation experiments. Subsequently, we propose a compound control method based on the rate-dependent hysteresis mode and conduct a simulation analysis. Finally, the experimental platform for the fine tracking system is set up, and the optimization effect of compensating for hysteresis nonlinearity on the fine tracking system is verified. The experimental results show that the nonlinearity of the PZT FSM is improved by 30% in the middle- and high-frequency ranges, and the error suppression bandwidth of the fine tracking system is improved by 41.7%. This effectively enhances the fine tracking system's error suppression capability.  
Keywords:space laser communication; fine tracking control system; hysteresis; Prandtl-Ishlinskii; FSM; bandwidth +
Footnote †: journal: _Photonics_
## 4 Case Studies
### Probabilistic Forecasting of ALSC
#### 4.2.1 Modified IEEE 39-Bus System  
To evaluate the performance of the proposed methods, a modified IEEE 39-bus system is designed. The base power is 100 MVA. Bus 31, bus 35, bus 36, bus 37, bus 38 and bus 39 are selected as the slack buses, and bus 31 is assigned as the reference bus. A wind farm is connected at bus 34. The power of the wind farm is determined by 100 times of the predicted wind power. The photovoltaic power plant is connected at bus 30. The power of the photovoltaic power plant is determined by 70 times of the predicted photovoltaic power. The active power of loads is determined by integer multiples of the forecasting load power, as shown in Table 8. The reactive power of loads is determined by keeping the power factor of the load constant. In the RPF calculation, it is assumed that loads grow in accordance with the original power factor. There are a total of 23 uncertainty sources. The allowed bus voltage range is 0.94-1.06 p.u. The active power capacity of each branch is specified as in Table 9 and the maximum active power of each generator is set as shown in Table 10. The figure of modified IEEE 39-bus system is as shown in Figure 7.  
Correlations always exist among loads, among renewable generation, and between loads and renewable generation in the power system. The wind power data, photovoltaic power data and load power data are used to calculate the correlation amongst each type of the random input variables. The correlation coefficient between the wind power and photovoltaic power is calculated as \(-0.0419\); the correlation coefficient between the wind power and loads is calculated as \(-0.1203\); the correlation coefficient between the photovoltaic power and photovoltaic power is calculated as \(0.3755\); and the correlation coefficient among loads is set as \(0.7\) according to [3].  
\begin{table}
\begin{tabular}{c c} \hline
**Load of Bus** & **Multiple of Forecasting Load** \\ \hline Bus 9, 12, 31 & 1 \\ Bus 1, 18, 26 & 20 \\ Bus 7, 23, 25, 28 & 35 \\ Bus 3, 15, 16, 21, 24, 27, 29 & 45 \\ Bus 4, 8 & 85 \\ Bus 20 & 110 \\ Bus 39 & 185 \\ \hline \end{tabular}
\end{table}
Table 8: The active power of loads of each bus.
## 4 Experiment Validation
### Experimental Result Analysis
In this study, the input acceleration is only measured from two cross-sections; therefore, only the first two modes of vibrations can be considered. Correspondingly, the number of signal resources is designed as two. Furthermore, high-frequency components cannot be estimated accurately. Although Figure 20 illustrates the estimation results around 20 Hz, 30 Hz, 50 Hz, and 60 Hz, this does not necessarily imply that their amplitudes are accurate. This is because the final step (Step 4) of the proposed algorithm involves a linear combination that can indeed preserve these high-frequency components. However, since this study only considers the first two modes, the amplitudes of the higher modes might be incorrectly calculated using the inter-section transfer matrix intended for lower modes. To achieve a more precise estimation of vibrations involving higher modes, it is suggested that additional inputs from more cross-sections would be required. Correspondingly, the signal receiver number, the mixing matrix, and the other parameters involved in the proposed method should also change.
# Visual MAV Tracker with Adaptive Search Region
## 4 Results
### Optimization of SR Size  
The size of the SR must be optimized to improve the tracking performance of the proposed method. If SR is extremely large compared to the size of the target object, other distracting objects can also be contained in the SR. Subsequently, the tracker may track other objects in the SR. If the SR is extremely small, the tracker is likely to lose moving objects. A constant value is selected for the scale ratio of the object and SR. Then, the size of the SR can be determined by multiplying the scale ratio by the object size of the previous frame. Because the object size in the current frame can be estimated after the current SR is  
Figure 5: Shrinkage of SR in the CSR tracker. (**a–c**) are the SR images in the sequential frames. The white box is the bounding box estimated by the FCNN.  
Figure 6: Effects of SC on the truncation in SRs. (**a1–f1**) represent the SR of the CSR tracker; (**a2–f2**) show the SR when truncation preventive SR is applied.  
determined in the online tracking, the object size in the previous frame should be used to determine the size of the current SR.  
The effect of the scale ratio (of SR and target object) on the accuracy was investigated to determine the optimal size, as shown in Figure 7. These results were measured when truncation-preventive SR was applied. The tracking accuracy was at a maximum when the scale ratio is 1.4, 1.5, and 1.6. When the scale ratio was larger than 1.7, the accuracy considerably decreased because several other objects and large areas of the background were within the SR. Although truncation-preventive SR is applied, it cannot prevent all possibilities of truncation when the scale ratio is small. Thus, a rapidly moving object can be truncated in the SR when a small-scale ratio (i.e., 1.2) is used. Owing to this truncation, the accuracy decreases when the scale ratio is small. Inaccurate estimation of the target size results in an inappropriate SR size because the size of the SR is determined by the target size in the previous frame. Considering the accuracy, the optimal scale ratio is determined to be 1.6, which can vary depending on the target object because the value depends on the motion and shape features of the target object.