esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Services and Performance Indicators
#### 2.3.3 Service 3: Day-Ahead Power-Commitment with the Utility Grid (GC)  
This service can be thought as the ability of the SB to issue -beforehand-, an hourly-averaged power profile for the upcoming day. The added value of this service lies on the capacity of the MG + EMS of dealing internally with the uncertainty, which is associated with the renewable production and consumption, by using the energy storage and the curtailment of excess PV energy. The associated performance indicator used to quantify this grid-commitment (GC) service is computed as the absolute difference between the hourly scheduled and real grid energy profiles. This indicator is normalized with respect to the total-absolute amount of energy scheduled to be exchanged with the grid during the test period. GC is expressed as a percentage, where 100% means a perfect match between scheduled and forecasted energy and 0% represents a deviation equivalent to the scheduled energy to be exchanged with the grid during the test period. This can be more clearly observed in Equation (7).  
\[GC=100\cdot\left\{1-\frac{1}{\sum_{h=1}^{H}\left\|\Delta t\cdot P_{grid}^{sch_{ h}}\right\|}\sum_{h=1}^{H}\Delta t\cdot\left\|P_{grid}^{real_{h}}-P_{grid}^{sch_{ h}}\right\|\right\} \tag{7}\]  
where \(P_{grid}^{real_{h}}\) and \(P_{grid}^{real_{h}}\) are the average-hourly real and scheduled power values exchanged with the grid. Both sums are performed over the total number of hours \(H\) of the test period.
## 3 Materials and Methods
### Lstm  
Photovoltaic (PV) power data are a set of time series data with the characteristic that later data points are related to previous ones. In comparison to traditional recurrent neural networks (RNNs), LSTM neural networks [32] offer specific advantages. The gated mechanism in LSTM allows for controlled information flow, enabling the selective transmission and forgetting of information across different time steps. This helps to overcome the vanishing gradient problem and enables the network to better learn and retain long-term memories. Additionally, LSTM's memory cell state allows for long-term information retention, reducing the issue of information loss and facilitating the capture of important features within sequences. The structure of an LSTM network is depicted in Figure 4.  
\[\mathrm{f_{t}} =\sigma(\mathrm{W_{f}}\cdot[\mathrm{h_{t-1}},\mathrm{x_{t}}]+ \mathrm{b_{f}}) \tag{7}\] \[\mathrm{i_{t}} =\sigma(\mathrm{W_{i}}\cdot[\mathrm{h_{t-1}},\mathrm{x_{t}}]+ \mathrm{b_{i}})\] (8) \[\mathrm{o_{t}} =\sigma(\mathrm{W_{o}}\cdot[\mathrm{h_{t-1}},\mathrm{x_{t}}]+ \mathrm{b_{o}})\] (9) \[\mathrm{\widetilde{c}_{t}} =\tanh(\mathrm{W_{c}}\cdot[\mathrm{h_{t-1}},\mathrm{x_{t}}]+ \mathrm{b_{c}})\] (10) \[\mathrm{c_{t}} =\mathrm{f_{t}}*\mathrm{c_{t-1}}+\mathrm{i_{t}}*\mathrm{ \widetilde{c}_{t}}\] (11) \[\mathrm{h_{t}} =\mathrm{o_{t}}*\tanh(\mathrm{c_{t}}) \tag{12}\]  
In the above equations, \(\mathrm{f_{t}}\) represents the forget gate, \(\mathrm{i_{t}}\) represents the input gate, \(\mathrm{o_{t}}\) represents the output gate, \(\mathrm{c_{t}}\) represents the cell state, \(\mathrm{\widetilde{c}_{t}}\) represents the cell state candidate value, and \(\mathrm{h_{t}}\) represents the hidden state value. W and b are the weight and bias parameters, respectively. \(\sigma\) denotes the sigmoid activation function. After obtaining the outputs of the three gates using Equations (7)-(9), the cell state \(\mathrm{c_{t}}\) and the final output \(\mathrm{h_{t}}\) of the cell can be further computed using Equations (10) and (11).
# Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks
## 5 Results
### Scenario II: LTE at 3.5 GHz with Carrier Aggregation  
The previous scenario shows that the network was saturated due to the full usage of resource blocks. To overcome this situation, carrier aggregation (CA) the land-based system severely where five contiguous component carriers of 20 MHz each were aggregated for a total of 100 MHz and 500 RB. Figure 5a compares the backhaul network capacity without (Scenario I) and with CA (Scenario II). The first scenario only used 100 RB while the second 500 RB. All the network resources were allocated at about 190 users assigning nearly 175 Mbps, and the capacity usage was independent of the type of drones. Moreover, when the available RB five-folds, the used capacity only incremented 3.7 times (47.6 Mbps to 173.1 Mbps). This is because our algorithm organized the best suitable UABS with higher SNR and served them first. Hence, after the first 100 RBs were allocated, the best UABSs were served and now UABSs with lower SNR and worst MCS were served, decreasing the overall efficiency of the backhaul link. Similarly, the number of covered users incremented 3.5 times (from 54 users to 189 users), but after 200 requested users the number of served ones was constant due to RB saturation in the backhaul as shown in Figure 5b. Likewise, the number of UAV used increased only 1.4 times (from 18.5 T2 drones without CA to 26.5 T2 drones with CA) leading to an optimization of the backhaul and an improvement of the mean served users by each UABS when using CA.  
Figure 5: Evaluation results for Scenario II. (**a**) Used capacity of the backhaul for 80 m flight height. (**b**) Provisioned users. (**c**) Network coverage for 260 users at 80 m flight height Type 2 drone in Scenario I. (**d**) Network coverage for 260 users at 80 m flight height Type 2 drone in Scenario II (carrier aggregation). Facility location (dark green square); users covered (green star); uncovered Uuers (red cross); active UABS (orange triangle); covered area (yellow circle).  
In Figure 5c,d, we presented the results of the coverage in the real scenario in the city of Ghent. Green stars are the served users, while the red crosses are the uncovered users. Figure 5c shows that even when a user could have a viable connection in path loss terms, it was not served due to backhaul capacity restrictions. When CA was used in Figure 5d, the number of users served was 354.3% higher and the average distance of UABS locations increased leading to more distant UABSs from the central facility as seen by the orange triangles.
# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
## 2 Methods
### Factor Graphs and Message Passing-Based Inference  
As described in Section 2.1, the source modeling, source separation and soundscaping tasks can be framed respectively as inference tasks for computing \(p(\theta^{k}\mid\hat{s}^{k})\), \(p(s_{n}\mid x_{1:n},\;\hat{s})\) and \(p(y_{n}\mid x_{1:n},\;\hat{s})\) on the generative model. Before describing how inference is realized in our generative model, we present a brief review of factor graphs and message passing algorithms. We use message passing in a factor graph as our probabilistic inference approach of choice, due to of its efficiency, automatability, scalability and modularity [25; 32]. Factor graphs allow us both to visualize factorized probabilistic models as graphs and to execute inference by automatable message passing algorithms on these graphs.
## 2 NDN Background and Related Work
### Related Work  
Several survey studies [22; 23; 24; 25] have been conducted to categorize the producer mobility support strategies into different approaches based on the technique used and their functions and features. However, these strategies can be generally classified into two broad distinct categories: Anchor-less and Anchor-based approaches.
## 1 Introduction
The works in [21; 22; 23; 24] estimate the IFO together during the PSSS match process, which is accomplished by evaluating a frequency-shifted version of the received PSSS and requires a large amount of computational complexity because of using correlation banks for a large number of hypotheses. Maximum likelihood (ML) detection has been efficiently adopted to detect the IFO and PSSS in [25; 26], which are based upon the reduced-rank approximation of the channel and the PSSS matrix, respectively. Although ML detection can achieve optimal performance, it requires a computationally intensive exhaustive search. The differential correlation based method with low complexity has been presented in [27], wherein the joint detection problem of the IFO and SI is decoupled by utilizing the central-symmetric feature of the PSSS. A drawback of the sequential method is its sensitivity to residual STO. Furthermore, the performance of differential correlation based PSSS detection schemes is severely degraded because of a poor autocorrelation property of the differential PSSS. For the D2D UE's simple operation, therefore, it is of crucial importance to develop a reduced-complexity and robust joint synchronization scheme in the LTE-D2D system. To address this issue, a robust scheme for the IFO recovery and SI detection is proposed in the LTE-D2D system. Unlike the existing methods, two received PSSS and SSSS sequences are used to sequentially detect the IFO and SI. The intrinsic properties associated with the PSSS and SSSS facilitate sequential detection without priori information on two synchronization signals. Simulation results demonstrate that the proposed sequential detection method offers strong resistance to symbol timing error and achieves a reduced complexity, compared to the conventional joint detection schemes. The remainder of this paper is structured as follows. In Section 2, the OFDM system model and synchronization signal are introduced. Section 3 highlights the existing joint detection schemes as benchmark. In Section 4, the joint detection method is proposed in the LTE-D2D system. The effectiveness of the presented method is verified through computer simulations in Section 5, and a summary is given in Section 6.
## 4 Motivations and the Main Contributions
Another important problem is anomaly trace detection. We assume that anomalies are known and traces arrive as either normal or abnormal. This is an interesting problem, as anomalies have different sources, and ML approaches can help to identify unusual or abnormal traces that deviate from the \"norm\". The next important problem is detecting the change in the dynamic flow of traces. Appropriate actions or decisions in dynamic trace analysis scenarios may be required. In some scenarios, when modeling and forecasting trace data over time, the capture of temporal dependencies can be an important problem. In general, diversifying our focus to include other ML methods could provide valuable insights into their effectiveness within our sampling strategy. While our primary emphasis has been on rule induction methods, driven by their interpretability features, there is potential value in exploring alternative ML approaches and assessing how sampling strategies influence their performance. Furthermore, investigating how different sampling techniques impact these methods can offer a deeper understanding of their behavior and potential for optimization. In instances where interpretability may be compromised, supplementary techniques such as SHAP [72] or LIME [73] could be employed to mitigate this limitation and enhance the overall interpretability of the results.
## 3 Methodology
### Atmospheric Model Setting  
The atmospheric model primarily simulates the interplay among wind speed, temperature, humidity, atmospheric turbulence, and long-short wave radiation. Parameters adjustable within the model encompass wind speed, wind direction, surface roughness, initial temperature, relative humidity, and absolute temperature at a 10 m height. For the simulation, environmental parameters are derived from climate data corresponding to July, the warmest month, in Xitun District, Taichung City. Specifically, data from the four hottest days in July 2022--July 22 nd, July 23 rd, and July 24 th--are utilized. Average values of temperature, humidity, wind direction, and wind speed from July 25 th serve as input parameters. The simulation is conducted at 13:00, a peak hot time conducive to outdoor activities, particularly during breaks on the Tunghai University campus, as shown in Figure 2.
## 3 Discussion
### KLDC7B Gene Silencing Inhibits HCV Replication  
Regarding _KLHDC7B_, not normally expressed in human liver cells, one of our new findings was that it was highly upregulated (9 to 19-fold) during acute HCV infection. Moreover, siRNA silencing of _KLHDC7B_ in cell cultures inhibited HCV RNA replication and the production of infectious virus. _KLHDC7B_ contains six kelch motifs that are found in a number of proteins, some with roles during other infectious conditions [21]. Proteins with kelch-repeat domains bind other proteins, notably actin filaments [21].  
Mining the GEO database [73] we found that _KLHDC7B_ mRNA was increased under a number of biological conditions with most of them due to infectious agents. _KLHDC7B_ was upregulated during vulvar intraepithelial neoplasia due to human papillomavirus [74] and in dendritic cells infected with Chlamydia pneumonia [75]. A role for _KLHDC7B_ during acute infections is further suggested by its upregulation in dendritic cells infected with Aspergillus fumigatus [76] and bronchial epithelial cells during acute RSV infection [77]. _KLHDC7B_ expression is also induced by interferon gamma, TNF-\(\alpha\) and IL-4 [78]. Although the function of _KLHDC7B_ remains unknown, our results provide the first evidence that changes in _KLHDC7B_ expression have significant effects on acute HCV infection. In view of the proposed role of _KLHDC7B_ in actin binding [79], we speculate that it has a role in cytoskeleton and membrane rearrangements required for efficient HCV RNA replication and possibly secretion of infectious virus. The specific function of increases in _KLHDC7B_ during acute HCV infection will require further investigation.
# Adaptive Emergency Call Service for Disaster Management
## 5 Simulation Results
### Test Environment
#### 5.1.1 Propagation Pathloss Model and RSSI  
In 4G/5G networks, operational gNBs periodically broadcast control signals, PBCH, over predefined resource blocks. The RSSI of such signals is computed using (2), where \(P_{l}(d,f)\) represents the path loss of the signal due to environmental factors, i.e., buildings, mountains, etc. To compute the \(P_{l}(d,f)\) in (2), the Cost231 propagation model for semi-urban areas, given in [42], is used. \(P_{l}(d,f)\) of a signal transmitted by a working gNB varies according to the signal transmission frequency of the working gNB, \(f\), and the distance between the working gNB and receiver UE, \(d\), as given in (8). Further, \(h_{g}\) and \(h_{m}\) represent, respectively, the height of the gNB and receiver UE. \(C_{m}\) is a correction offset associated with the semi-urban environment. For the simulations, \(f\) is set to 885 MHz. \(P_{l}(d,f)\) varies according to the used frequency, \(f\), and the distance between a functioning gNB and a receiver UE, \(d\), is modeled using (8) and (9). Further, the measured values of RSSI are handled in Watts.  
\[\begin{split} P_{l}(d,f)&=46.3+33.9\log_{10}(f)-13.8 2\log_{10}(h_{g})-a(h_{m})\\ &+(44.9-6.55\log_{10}(h_{m}))\times\log_{10}(d)+C_{m},\end{split} \tag{8}\]  
with:  
\[a(h_{m})=3.2\times log(11.75h_{m})^{2}-4.97. \tag{9}\]
## 2 Materials and Methods
### Adsorption Experiments
#### 2.4.2 Experimental Validation of Polymers Towards MB  
In order to study the effect of pH and initial concentration of MB, 30 mg of polymers were introduced in a Syncore glass tube with 50 mL of MB solution. The same treatment was carried out on these samples. For the pH study, 0.1 N NaOH and 0.1 N HCl were used to adjust the pH of the desired solution. The effect of the adsorbent amount was studied using 50 to 400 mg of the polymer. For each assay, the adsorption experiments were performed in triplicate to ensure the reproducibility. The amount of MB adsorbed, \(q_{t}\) (mg/g), was determined using the following Equation (1):  
\[q_{t}=\frac{(C_{0}-C_{r})}{w}*V \tag{1}\]where \(C_{0}\) and \(C_{r}\) (mg/L) are the initial and residual MB concentrations, respectively; \(V\) (L) is the volume of the solution; and \(w\) (g) is the mass of the polymer.
## 2 Methods
### Rover IFCB Correction Method
#### 2.2.2 Correcting the Fractional Part  
We first process daily static PPP using the WL and NL UPD products generated from the reference stations. The estimated WL and NL ambiguities have been corrected with the satellite UPD products. The P1 and P2 IFCB corrections have also been applied to the code observations. Therefore, considering the IFCB residuals, the WL ambiguity can be expressed as  
\[\widetilde{N}_{w,i}^{k}=N_{w,i}^{k}+\phi_{w,i}-\widetilde{H}_{w,i}^{k}, \tag{10}\]  
where \(\widetilde{H}_{w,i}^{k}\) represents the residual WL IFCB. Not that, the integer part is not separable from the integer ambiguity, thus we only have to further estimate the fractional part of the residual WL IFCB.  
Assuming that m GLONASS satellites can be observed and there are two continuous tracking arcs for each satellite, and we have fixed all WL ambiguities in Equation (10), we then get  
\[\left[\begin{array}{c}\widetilde{N}_{w,i}^{1}-N_{w,i}^{1}\\ \widetilde{N}_{w,i}^{2}-N_{w,i}^{2}\\ \vdots\\ \widetilde{N}_{w,i}^{m}-N_{w,i}^{m}\\ \widetilde{N}_{w,i}^{1}-N_{w,i}^{1}\\ \widetilde{N}_{w,i}^{2}-N_{w,i}^{2}\\ \vdots\\ \widetilde{N}_{w,i}^{m^{\prime}}-N_{w,i}^{m^{\prime}}\end{array}\right]= \left[\begin{array}{cccc}1&-1&0&\cdots&0\\ 1&0&-1&\cdots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 1&0&0&\cdots&-1\\ 1&-1&0&\cdots&0\\ 1&0&-1&\cdots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 1&0&0&\cdots&-1\end{array}\right]\left[\begin{array}{c}\phi_{w,i}\\ \widetilde{H}_{w,i}^{1}\\ \widetilde{H}_{w,i}^{2}\\ \vdots\\ \widetilde{H}_{w,i}^{m}\end{array}\right], \tag{11}\]  
where \(\widetilde{N}_{w,i}^{m}\) and \(\widetilde{N}_{w,i}^{m^{\prime}}\) represent the float WL ambiguities for the two arcs of satellite m, and \(N_{w,i}^{m}\) and \(N_{w,i}^{m^{\prime}}\) are for the fixed integers. Given Equation (11), the WL IFCB residuals can be estimated in a least-squares manner. The WL ambiguity fixing procedure is described as follows.  
1. Select the ambiguity with the longest tracking arc, assume the WL IFCB residual is zero for the satellite, and fix the WL and NL ambiguities by rounding. The receiver UPD can thus be separated from all WL and NL float ambiguities.
2. Select another WL and NL ambiguity pair. First round down the WL ambiguity and if the corresponding NL ambiguity can be fixed, fix the WL ambiguity as the rounded-down value. If not, round up the WL ambiguity and if the corresponding NL ambiguity can be fixed, fix the WL ambiguity as the rounded-up value.
3. Repeat (b) for all remaining WL and NL ambiguity pairs.  
After obtaining the accurate fractional part of the WL IFCB correction, we can get the NL IFCB correction with (6), which can be used to correct the initial value obtained via (8) and get the final P1 and P2 IFCB estimates using (9). Although we only estimate the fractional part of the residual WL IFCB, Equation (9) reveals that the P1 and P2 IFCB estimates both satisfy the WL ambiguity resolution and the high-precision of IF code observation. Thus, the proposed IFCB calibration method will not be affected by the ionospheric residuals, so we do not need a dense reference network.
###### Abstract  
An accurate understanding of the tensile mechanical behavior of shale rock is essential for optimizing shale gas drilling and hydraulic fracturing operations. However, the mechanical behavior of shale is significantly influenced by its anisotropy. Therefore, this study investigated the tensile mechanical behavior of layered shale by combining acoustic emission (AE) monitoring with two testing methods: the Brazilian splitting test (BST) and a novel direct tensile test (DTT). The impact of anisotropy on the tensile mechanical behavior and failure modes of layered shale under different test methods was evaluated. Additionally, seven anisotropic tensile strength criteria were compared and validated using the experimental results. The results show that: (1) As the loading angle (\(\beta\)) increased, the tensile strength measured by both BST and DTT increased. Both methods exhibited maximum tensile strength at \(\beta=90^{\circ}\) and minimum tensile strength at \(\beta=0^{\circ}\). The anisotropy ratios for BST and DTT were 1.52 and 2.36, respectively, indicating the significant influence of the loading angle on tensile strength. (2) The AE results indicated that both DTT and BST specimens exhibited brittle failure characteristics. However, the DTT specimens demonstrated more pronounced progressive failure behavior, with failure modes categorized into four types: tensile failure across the bedding plane, shear failure along the bedding plane, and two types of tensile-shear mixed failure. In contrast, the BST specimens primarily exhibited tensile-shear mixed failure, except for tensile failure along the bedding plane at \(\beta=0^{\circ}\) and tensile failure across the bedding plane at \(\beta=90^{\circ}\). (3) Neither of the two test methods could fully eliminate the influence of anisotropy, but three anisotropic tensile criteria, the Lee-Pietruszczak criterion, the critical plane approach criterion, and the anisotropic mode I fracture toughness criterion based on the stress-strain transformation rule demonstrated high accuracy in predicting tensile strength. Furthermore, in alignment with previous studies, the indirect tensile strength of various rock types was found to range between one and three times the direct tensile strength, and a linear correlation between the two variables was established, with a coefficient of approximately 1.11.  
Keywords:anisotropy; layered shale; direct tensile test; indirect tensile test; tensile strength Isc: 2025, 15, 2669