esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Management of Virtual Power Plant Considering Distributed Generation Sizing and Pricing
## 2 Concept of Energy Management Problem  
The VPPs consist of a set of DGs and responsive loads in a microgrid equipped with smart grid technology. Responsive loads as energy applicants, such as an industrial estate, colleges of a university, or a residential area can change their energy consumption in response to change in energy prices. The energy supply unit is responsible for energy management and can provide the energy required by the main grid and solar stations as a source of uncertain DGs. Beside each of the solar stations, there is an energy storage system that enables the energy management system to store or produce energy at appropriate times. The set of responsive loads, DERs and storage systems in the form of VPP, have the ability to buy energy from the main grid during low-cost energy hours and sell them at expensive periods [26; 27]. It is assumed that the VPP does not play a role in determining market price. Energy management system is related to wholesale electricity market and energy price announced by the main network, is considered as market price. The VPP owns solar stations and does not pay for solar energy.  
Using smart grid technology, there is a bidirectional relation between time and reality of energy management and users in the microgrid. The energy management system will receive information about energy suppliers, including the energy price and the availability of obtainable power several minutes before the energy delivery date. Based on this information, the energy management system determines the optimal consumption rate, storage, and energy exchange for each time period and subsequent periods to send decisions a few minutes before the deadline for energy delivery to applicants and energy suppliers. These decisions are made in such that the social welfare of the VPP will include the difference in the cost of energy supply from the income of the applicants. Notice that information about the availability of network components including production resources and transmission lines is provided to the energy management system according to the scheduling period. If anything else happens, the energy management system will modify the decisions taken in the previous step to distribute power. The microgrid is also assumed to run at the beginning of the planning period.
# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 3 Methodology
### Audio Features
As thresholds increase, \(P(FC|SC)\) gradually declines, indicating a reduction in sensitivity as the system filters out smaller changes. Conversely, \(P(SC|FC)\) tends to rise as the threshold increases, indicating improved specificity. However, it does not reach unity (1) for any feature, meaning that even at high thresholds, the system does not perfectly align feature changes with speaker changes. However, no feature reaches unity (1) for \(P(SC|FC)\), suggesting that even at high thresholds, the perfect alignment between feature changes and speaker changes is hard to be observed. The intersection point of \(P(SC|FC)\) and \(P(FC|SC)\) can be observed in the Figure 2. These points represent the thresholds at which sensitivity and specificity are balanced, marking where the two probabilities are equal. Table 1 provides the crossing points and the maximum observed \(P(SC|FC)\) values for each feature. Crossing points where \(P(FC|SC)\) and \(P(SC|FC)\) intersect, and represent the thresholds at which sensitivity and specificity are balanced, marking where the two probabilities are equal. \(P(SC|FC)\) value reflects the peak specificity that each feature achieves at a particular threshold, giving insights into how closely feature changes align with speaker changes. The highest values of \(P(SC|FC)\) are observed for MFCC (0.534), chroma (0.515), pitch (0.40), and all the spectral related features have over 0.39 values. Other features, such as zero crossing rate and RMS energy, have lower maximum values around 0.32, indicating weaker alignment with speaker changes.
## 3 Laboratory Study
### Testing
#### 3.3.3 Fourier-Transform Infrared Spectroscopy (FTIR) Test  
The chemical composition of bitumen is complex and multifaceted due to the diverse origins of crude oil resources. The addition of modifiers to enhance the mechanical properties of bitumen results in further complexity in its chemical characteristics.  
Fourier-transform infrared spectroscopy (FTIR) is a commonly used and appropriate analytical technique for detecting chemical compounds and obtaining information on the chemical composition of materials at the molecular level. It has the potential to yield both qualitative and quantitative outcomes, which could be valuable for research studies. This study investigates the chemical interactions between bitumen and Kraft lignin. When two materials are in contact, their molecular vibrations can change due to chemical interactions. These changes can be detected by comparing the FTIR spectra of the individual materials with the FTIR spectra of the mixture. In this study, attenuated total reflectance ATR-FTIR was used to gather spectral information from Kraft lignin and bitumen samples. The Spectrum Two(tm) FTIR Perkin Elmer, which incorporates a Diamond crystal, was employed to obtain data within the wavenumber range of 4000 to 400 cm\({}^{-1}\) at a 4 cm\({}^{-1}\) resolution. Twenty (20) scans were recorded for each spectrum. Prior to each test, the ATR crystal was cleansed using limonene and isopropanol, and a background spectrum was captured.
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 4 Conclusions and Future Work
### Future Work  
Related to the learning approach, further research could examine the possibility of adaptive learning rates. One possibility for polar regions would be increasing the transmission attempt frequency (e.g., by decreasing the discount factor \(\lambda\)) during the summer when solar power is abundant, and lowering the transmission attempt frequency during the polar winters.  
There are two natural steps that could be taken to improve the simulation setup in future: (1) the simulations could be based on extracted real-world data for a given application, or (2) multiple sensors could be placed in the field for weeks or months. The first option is natural, as the risk of needing to adjust the algorithm is high, making tuning in simulations and testing on hardware only when confident in good results is the best route.
## 2 Instrument, Model, and Method
### RTDC Model  
The PSAC operation temperature differences between on-orbit and laboratory environments were compared to analyze the responsivity temperature dependence sources of the PSAC. With the instrument thermal controller, a sub-system of a spacecraft maintaining a stable spacecraft temperature in the harsh space environment, the temperatures of the PSAC structure and the optical assemblies were consistent in the two conditions. However, with the active-passive cooling scheme, the heat sink and the photosensitive surface faced a temperature difference of nearly 35 \({}^{\circ}\)C. The PSAC is equipped with a DC Restore (DCR) circuit [23], which adopts a direct current recovery loop circuit to realize the dynamic correction of signal drift of a front-end circuit output, a signal amplification and conditioning circuit, and a photoelectric detector. With the cooperation of the DCR function and dark reference, background radiation response in different heat sink temperatures is subtracted in each scan. We determined that the PST difference is the main source of PSAC responsivity change. Thus, research focusing on the responsivity temperature dependence of the detectors used in the PSAC 2250 nm channel was performed in this study. As for the detectors used in 1380 nm and 1610 nm channels, their temperature dependence is very small and can be ignored.  
The formula for performing RTDC is shown in Equation (2). G(T) is the RTDC model for the 2250 nm channel. T represents the PST. DN(T) and DN(Tref) are the Digital Number (DN) values of the PSAC when the PSTs are T and Tref, respectively. Tref represents the reference temperature. With G(T), PSAC response in different PSTs is corrected to that in Tref.  
\[\text{DN(Tref)}=\text{DN(T)/G(T)} \tag{2}\]  
In the measurement and calculation of G(T), DN(Tref) is used as a reference to normalize the DN values at other PSTs in the same radiation, and G(T) is obtained using cubic spline fitting normalization results. Tref is set to \(-30\)\({}^{\circ}\)C, which is the same as that in the laboratory-based radiometric test, and T is varied in the range of \(-65\)\({}^{\circ}\)C \(<\) T \(<\)\(-30\)\({}^{\circ}\)C at least to ensure the applicability of G(T) in on-orbit and ground environments.
# High Performance Graph Data Imputation on Multiple GPUs
## 4 Efficient Convolutional Imputation Algorithm on GPU
### Optimizations
#### 4.2.2 Optimizing SVD Computation  
On GPUs, CUDA streams can be used to overlap computations and communications because the hardware compute engine and copy engine are independent of each other. We utilize two streams for data transmission and computation, respectively, to overlap their execution and improve efficiency.  
The execution time of SVD computation (line 2 of Algorithm 2) accounts for a large proportion of the total time. In the baseline GPU implementation, for \(\widetilde{\mathcal{G}}^{\text{impute}}\) with a stack of \(N\) frontal slices of size \(m\times n\), we use the matrix SVD routine cuSOLVER library to  
Figure 4: Thread mapping to perform the diagonal matrix-matrix multiplication of a batch of matrices. The diagonal matrix in (**a**) is to the left of the operator, which enables coalesced memory access to device memory, and in (**b**) is to the right of the operator.  
compute SVD of these \(N\) frontal slices sequentially on GPU. This leads to low performance, primarily for two reasons. First, a matrix SVD on a single frontal slice is inadequate to utilize all thousands of computing cores on GPU, especially for graph-tensors with a small size of frontal slices. Second, kernel launch overhead is incurred every time the matrix SVD routine is called. This kernel launch overhead is especially high for graph-tensors with a large \(N\). In order to improve GPU utilization and the performance of SVD computation, we consider to pack these \(N\) matrix SVDs into a batch to compute them parallel using a single GPU kernel. Note that \(N\) can be large if GPU has enough device memory. This is suitable for the social network application in the experiment section, where the data matrix of vertex \(v_{k}\) is small, but the number of vertices is greater than thousands.  
The cuSOLVER library provides a batched method to calculate multiple SVD in parallel. However, the size of the matrix is limited to \(32\times 32\) or smaller. Therefore, we utilize the KBLAS library [18], which provides batched SVD routines for matrices with a maximum size of \(512\times 512\). Therefore are multiple batched SVD routines in the KBLAS library, but none of them returns a matrix composed of right-singular vectors. Let the singular value decomposition of a graph-tensor in spectral space be \(\vec{\mathcal{G}}=\mathcal{US}\mathcal{V}^{T}\) with size \(m\times n\times N\), the KBLAS library only provides \(\mathcal{U}\) of size \(m\times n\times N\) containing left-singular vectors, \(\mathcal{S}\) of size \(n\times n\times N\) containing singular values, and \(\mathcal{VS}\) of size \(n\times n\times N\). Here we set the rank of \(\vec{\mathcal{G}}\) to \(n\). Therefore, besides utilizing the batched SVD routine in the KBLAS library, we design a kernel to obtain the right-singular vectors in the batched method by computing \((\mathcal{VS})\mathcal{S}^{-1}\) in parallel. In this kernel, each GPU thread corresponds to one element of the tensor \(\mathcal{V}\), as shown in Figure 3(b).
## 4 Forecasting Methods
### CNN-Based Models  
DeepAnt [22] proposes an unsupervised anomaly detection method based on CNN, which does not require labeled training data. This method uses CNN to automatically learn the representation of time series data and identifies anomalies using reconstruction error. Compared to traditional CNNs, Temporal Convolutional Networks(TCN) can better handle the temporal characteristics of time series, TCN-ms [23] uses TCN for anomaly detection, emphasizing the importance of temporal information. By introducing causal convolution and dilated convolution in the convolution operation to process time series data, it helps to capture long distance dependencies. However, the aforementioned methods only consider the one-dimensional changes of the time series. To enhance the model's understanding of the structure of time series, TimesNet [24] extends the analysis of temporal changes to 2D space by innovatively incorporates Fast Fourier Transform, making it easy to model 2D changes with 2D kernels, and providing a new perspective for more complex time series analysis. It not only improves the accuracy of anomaly detection but also enhances the model's generalization ability of the model for time series data.
# ATLAS: Latest Advancements and First Observations
## 2 Materials and Methods
### Space Object Tracking in LEO: Error Sources and Observation Strategy
Passive calibration satellites, like STELLA and STARLETTE, were designed to have high reflectivity and spherical shapes, ensuring that their RCS remains independent of the viewing angle [22]. They play a critical role in fine-tuning RCS measurements obtained by the sensors, as they provide a reliable reference point for RCS values. In light of the foregoing discussion, the following are some key ideas that every SST ground station should be aware of:  
* Use the most recent TLE sets available to reduce propagation errors and unaccounted maneuvers;
* Perform regular calibration routines using celestial sources;
* Use well-behaved satellites that provide precise ephemeris information and/or constantly updated TLEs to refine the pointing system;  
Finally, the observation strategy used can also help mitigate some of the aforementioned errors. We suggest implementing a semi-surveillance approach. This approach entails positioning the antenna at a predicted location in the sky for the target satellite and illuminating it both before and after the expected time of the object's passage within the antenna's beamwidth. By adopting this method, the likelihood of successfully capturing the target increases, and the resulting echoes yield valuable insights into how to rectify discrepancies in orbit predictions and offsets of the system. By assuming a symmetric illumination time before and after the expected time of passage, it is expected that the echoes will appear centered around the middle of the illumination time window. By assuming a range acquisition window centered on the object's expected range, the echoes are expected to appear centered in the range window. Deviations from the center of the range-pulse map can be used to compensate for pointing, timing, and orbit prediction errors.
## 3 Results
### Primary Part: Individual Patient-Level Data for Patients Initially Diagnosed with Stage I-III NSCLC
#### 3.1.4 Patient Journey  
The most common reason for the 'visit that led to diagnosis' was 'incidental medical findings' (51.7%; 165/319), followed by'symptoms' (37.3%; 119/319), 'lung cancer screening' (14.7%; 47/319) and 'during admission in the emergency room' (2.5%; 8/319) (Table S2). Pulmonologists and primary care physicians were the most frequent specialties involved in this visit [53.6% (158/295) and 10.8% (32/295) of evaluable patients, respectively].  
Among patients who were not under the medical care of the study site during the 'visit that led to diagnosis', (N = 142), the majority were either under the care of another public peripheral hospital, or the physician's own public practice (Figure S3A). These patients first visited the study site after a median (IQR) of 28.5 (12.0-70.0) days (Supplementary Figure S3A).  
Imaging testing was performed a median (IQR) of 0.0 (\(-\)11.0 to 3.0) days from the 'visit that led to diagnosis' (Figure 3). In the overall population, i.e., including patients who did not undergo imaging testing (N = 319), NSCLC was histologically confirmed a median (IQR) of 25.0 (10.0-52.0) days from the 'visit that led to diagnosis.' Among treated patients (N = 307), ITS was initiated a median of 24.0 days after histological confirmation, for a total median time of 55.0 days from the 'visit that led to diagnosis' (Figure 3).  
Figure 3: Time from the visit that led to the suspicion of the disease or NSCLC diagnosis until start of initial therapeutic strategy for early-stage NSCLC, per reason that led to suspicion/diagnosis and  
healthcare setting of the medical visit that led to suspicion/diagnosis. Numbers inside arrows indicate median time for the overall population. Box-plots depict median with interquartile range. \({}^{\ddagger}\) Among patients who underwent diagnostic imaging testing. \({}^{\ddagger}\) Among patients that received any treatment as part of ITS. Abbreviations: ER, Emergency Room; ITS, Initial Therapeutic Strategy; N, number of patients with available data; NSCLC, Non-Small Cell Lung Cancer.  
In terms of treatment decision-making, 98.7% (312/316) of evaluable patients were presented in the corresponding multidisciplinary tumor board. In more than half of patients, patient care during ITS was followed-up by pulmonologists (Figure S3B).  
Timeliness of patient journey by reason and healthcare setting is presented in Figure 3, and by gender and health insurance in Table S3. Some differences were noted in a purely descriptive manner. Specifically, the time taken to start ITS was slightly longer for patients: with incidentally detected NSCLC; who were not under the care of the study site from the beginning of their NSCLC journey; and, who did not have additional private health insurance.
# Levy Flight and the Interpersonal Distance of a Pedestrian in a Crowd
## 5 Conclusions  
A smartphone was utilized to measure the trajectory of a pedestrian in a fairground corridor, enabling the acquisition of GPS data along their path. Multiple trajectories were measured with GPS data.  
Based on the recorded data, it appears that:  
Mean square displacement (MSD) as a function of time, calculated and plotted on a log-log scale, demonstrates a linear curve that could be fitted with a power law.  
The best-fitted coefficient, greater than 1, indicates that the trajectory is the result of Levy flight behavior. The MSD histogram is best fitted with a power law featuring an exponential cut-off and another power law.  
Under the bidirectional parameters of our experiment, pedestrians diffused along the \(x\) and \(y\) axes with an exponent greater than 1. Consequently, pedestrians walking in opposite or transversal directions (at a low level) relative to the pedestrian under test primarily created a super-diffusive effect in a direction perpendicular to the flow (\(\Delta y\)). Furthermore, the step-length distribution in the lateral direction followed a power law, signifying that pedestrians can deviate from their desired direction with a Levy walk process.  
The strategy observed in the pedestrian under test is also observed in a wide variety of organisms and animals. This movement pattern is often attributed to the success of random searches, particularly in uncertain or dynamic environments [48].  
The pedestrian behavior measured during this experiment with a heterogeneous crowd appears to be explained by the same reasoning [49].  
This study can be generalized to cases such as the movement of a walker who needs to find a friend in a crowd, the movement of a lost walker in search of a companion, or a child looking for family members. Finally, some works relate to crowd evacuation when considering rescue behavior or crowd evacuation behavior under terrorist attacks [50], but the present study can be used to model an attacker or a person fleeing in a crowd for different reasons.  
Moreover, if we consider the trajectory of an autonomous vehicle that moves by probing the space in front of it with ultrasonic proximity sensors or LiDAR, the vehicle's trajectory could follow the same behavior as that displayed by a pedestrian in a crowd. The study of a pedestrian in a crowd of heterogeneous people enables us to define a behavioral model that can be transposed to an autonomous vehicle, in order to anticipate its trajectory in a complex environment. Another important point is the use of search algorithms based on Levy flight to guide autonomous vehicles like surface or underwater vehicles toward their goals [51].  
This research received no external funding.
# Frequency Contour-Strip Method for Characterization of Damage in Structures under Noisy Conditions
## 3 Existing Frequency Contour-Line Method with Limitations in Portraying Damage
### Limitations in Frequency Contour-Line Method
#### 3.3.1. Inefficiency in Establishing Eigen-Frequency Panorama  
Establishment of the eigen-frequency panorama is the basic component of identifying a crack using the frequency contour-line method, where the _panorama_ implies that numerous eigen-frequencies need to be evaluated. Furthermore, one eigen-frequency of an eigen-frequency panorama is obtained by solving the frequency characteristic equation (Equation (8)). Notably, Equation (8a) involves complex transcendental functions with sinh- and cosh-functions as essential elements, incurring extreme nonlinearity and complexity. In the frequency contour-line method, the frequency characteristic equation is solved by numerical methods, typically the direct determinant evaluation (DDE) method. The key of the DDE is to evaluate the value of every independent element of the frequency determinant at a given value of \(\omega\). Accordingly, the frequency determinant is calculated by pure algebraic operations of the determination expansion. The solutions of eigen-frequencies could be determined from the zero-crossings of the profile of frequency determinant versus \(\omega\). It is obvious that it needs large amount of computation to produce one eigen-frequency value. To establish the eigen-frequency panorama, a large number of eigen-frequencies should be evaluated, resulting in huge computation cost and intractable manipulation. As a result, the inefficiency in establishing the eigen-frequency panorama largely influences the use of the frequency contour-line method to identify cracks in beams.
## 3 Evaluation Experiments
### Evaluation Experiment for Waveform-Based Method  
To confirm the effectiveness of the proposed waveform-based method, we conducted evaluation experiments using the observed speech, speech with only noise suppression, and that with the waveform-based method. Figure 13 shows the amplitude spectrum of each speech. The high-frequency components were restored with the waveform-based method and the noise in high-frequency bands was smaller than those in Figure 13c. As shown in Figure 13c, the noise-suppression DNN of the waveform-based method sufficiently enhanced speech in the low-frequency band. Figure 13b-d were evaluated in each frequency band using the criterion of log-spectral distance (LSD) [27]. The LSD is defined by the following equation, and the smaller the value, the less speech degradation.  
\[\text{LSD}=\sqrt{\frac{1}{f_{1}-f_{0}}\sum_{k=f_{0}}^{f_{1}}\left(10\text{log }_{10}\frac{P(k)}{\tilde{P}(k)}\right)^{2}} \tag{4}\]  
Here, \(f_{0}\) and \(f_{1}\) are the frequency band ranges, and \(P\) and \(\tilde{P}\) are the clean and estimated speech powers, respectively. Figure 14 shows the LSD in each frequency band. The LSD was about 0.3 dB smaller in the 4-8 kHz band where the high-frequency band was restored. In the 0-4 kHz band, the difference in the LSD between the presence and absence of high-frequency restoration was very small. Therefore, the effectiveness of the waveform-based method was confirmed.  
Figure 12: Cosine distance of reconstructed phases with each method.