esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper has put into context the use cases given by the ITU-R M.2150-1 recommendation, including eMBB, URLLC, and mMTC, which require a combination of the spectrum allocated for the IMT-2020 system (known as 5G), high, mid, and low-frequency bands, to meet the objectives set for these 5G networks, such as large bandwidth, higher data rates, massive connectivity, low end-to-end latency, low cost, and consistent quality of service, among others. It has also been described how the mid-band spectrum has great potential for connecting IoT services and applications in 5G cellular networks.  
The values obtained for the PLE and their 95% confidence intervals are 2.21 (2.214-2.217) for the FI model and 2.252 (2.251-2.217) for the CI model at 850 MHz and 2.376 (2.374-2.378) for the FI model and 2.419 (2.418-2.420) for the CI model at 3.5 GHz, which are larger than the theoretical free space model (PLE = 2). This shows that the multipath effect causes a lack of constructive interference to the received power signal for this type of outdoor corridor scenario in which the measurements were collected. Also, a higher PLE was found at 3.5 GHz than at 850 MHz.  
Future lines of work include performing broadband measurement campaigns, with an automated georeferencing process for applications in the Industrial IoT (IIoT) and smart transportation scenarios to generate path loss and capacity models that enable the optimal deployment of device and sensor networks in 5G-IoT ecosystems.  
**Author Contributions:** Conceptualization, H.F., L.R. and V.M.R.P.; methodology, H.F., L.R. and V.M.R.P.; channel measurements, J.M., D.M. and H.F.; validation, H.F., L.R. and V.M.R.P.; formal analysis, J.M., D.M., H.F., L.R., V.M.R.P. and J.R.; investigation, J.M., D.M., H.F., L.R., V.M.R.P. and J.R.; resources, J.M., D.M. and H.F.; data curation, H.F., L.R. and V.M.R.P.; writing--original draft preparation, J.M., D.M., H.F., L.R., V.M.R.P. and J.R.; writing--review and editing, J.M., D.M., H.F., L.R., V.M.R.P. and J.R.; visualization, H.F., L.R., V.M.R.P. and D.M.; supervision, H.F., L.R. and V.M.R.P.; project administration, H.F., L.R. and V.M.R.P.; funding acquisition, H.F., J.M. and D.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been funded in part by the MCIN/AEI/10.13039/501100011033/ through the I+D+i Project under Grant PID2020-119173 RB-C21, by the Pedagogical and Technological University of Colombia (Project Number SGI 3159) and by the Bicentennial Doctoral Excellence Scholarship Minciencias-Program (Contract BPIN 2019000100041).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** The authors would like to thank [PERSON] for his support during the writing review.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Reducing Motion Blur in Ghost Imaging Via the Hessian Matrix
## 2 Materials and Methods
In our method, we first set the value of standard deviation \(\sigma\) of the Gaussian function empirically and obtained the convolution window \(w\). We then calculated the second-order partial derivative of the Gaussian function, which we think of as the Hessian matrix, then we used the matrix to process the data collected in the reference arm. The data processed by the Hessian matrix were then transferred to compute the correlation with the data from the signal arm instead of the data collected by the CMOS. To state how the Hessian matrix works, we demonstrated the change of the point spread function (PSF) between it being processed by Hessian matrix and not being processed by the Hessian matrix. The PSF of a reconstructed image with motion blur in GI and its projection in the x-z plane and y-z plane is shown in Figure 2. Due to motion blur, the shape of the projection in the x-y plane changed from a circle to an oval. Usually, the projection in the x-z plane and the y-z plane are called a sombrero function. As shown in Figure 3, when we put the sombrero functions into the same coordinates, the difference between the functions was clear. The full width at half maximum (FWHM) of the sombrero function in the x-z plane was larger than that in the y-z plane, which meant that the gradient of the intensity of the sombrero function in the y-z plane dropped faster, leading to a bigger absolute value of the gradient. When the Hessian matrix was introduced to process the data recorded in the reference arm, we actually used the gradient of the intensity instead of the intensity in the data to reconstruct the image. After being filtered by the Hessian matrix, the value of the sombrero function in the y-z plane was increased compared with the function in the z-x plane. This equivalently broadened the FWHM, making the projection in the x-y plane close to a circle; thus, the influence of motion blur was reduced.
## 1 Introduction
### Classification of the Received Signals
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Power Level** \\ \hline \(A_{0}^{2}\) & normalized to 0 dB \\ \(A_{1}^{2}\) & \(-40\) dB to 0 dB \\ \(B_{0}^{2}\) & \(-39\) dB to \(-15\) dB \\ \(B_{1}^{2}\) & \(-40\) dB to \(-3\) dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The possible relative power of the received LOS and MP signals by a dual-polarization antenna. Figure 2: The relative power of the one-bounce multipath received by the LHCP antenna when different types of materials are used. The AR of the MP signal before reflection is 1.8 dB, and \(R_{\text{L}}(\psi_{1},\varphi_{1})=4\) dB. The received MP signal power is normalized to that of the LOS signal.
# Isogeometric Schwarz Preconditioners with Generalized B-Splines for the Biharmonic Problem +
## 5 Numerical Results  
In this section, we provide numerical results to validate the convergence analysis of our 2-level overlapping Schwarz preconditioners (24) for solving the biharmonic Equation (1) over two-dimensional and three-dimensional domains.  
**Example 1**.: _The first example involves the biharmonic problem in the 2D unit square:_  
\[\Delta^{2}u = f\quad\text{ in }\widehat{\Omega}=(0,1)^{2},\] \[u = 0\quad\text{ on }\partial\widehat{\Omega},\] \[\frac{\partial u}{\partial  
Along with mesh size \(h\), polynomial degree \(p\), regularity \(k\) and the overlap index \(r\), isogeometric GB-splines discretize these 2D and 3D biharmonic problems by using the MATLAB isogeometric library GeoPDEs [49; 50] (see also [51] for a MATLAB library recently introduced for the construction of Tchebycheff B-splines). To run the tests, we used a Samsung Notebook 9 (900X3L) laptop with two Intel(R) Core(TM) i7-6500U @2.50 GHz processors and 8.00 CiB of RAM memory. As mentioned in Section 3.1, we recall that the geometrical domains \(\widehat{\Omega}\) and \(\Omega\) are decomposed into \(N\) overlapping subdomains of characteristic order \(H\) and overlap index \(r\).  
The biharmonic problem resulting from the IGA Galerkin discretization using GB-splines is tackled using the preconditioned conjugate gradient (PCG) technique with one-level (OAS(1)) and two-level (OAS(2)) overlapping additive Schwarz preconditioners (24), starting from an initial guess of zero and employing as stopping criterion a \(10^{-6}\) reduction of the relative residual.
# Reinforcement Learning Approach for Adaptive C-V2X Resource Management
## 5 Conclusions  
The impacts of the variable and adaptive MCS index in the C-V2X communication should be discussed among researchers. In this work, we propose the application of the RL algorithm, as we used the Q-learning approach to adaptively change the MCS index according to the current environmental states. We gathered some MCS index values used by related works as benchmark information, and we then used the MCS index values in our initial simulations. Our proposed RL approach design was implemented as an \"on-the-run\" learning process. The simulation results showed that our proposed RL approach outperformed the static MCS index for the total number of vehicles, being 100 vehicles and 200 vehicles. Our proposed method achieved an average of 10% and 21% PRR improvements for the \(n_{V}=100\) and \(n_{V}=200\) vehicles scenario, respectively. Our proposed method also showed that the learning mechanism reaches its stability at an acceptable number of events. Despite the promising results, this work only utilizes the PRR statistics, and it is possible to improve in the case of real-world parameter configuration. These real-world parameters will be carried out in our future works.  
Figure 8: Rewards evolution against number of events from the proposed RL approach; **(a)** The overall rewards evolution from the \(n_{V}=200\) vehicles scenario; **(b)** The detailed rewards evolution for the first 1000 events.  
Figure 7: PRR evolution against number of events from the proposed RL approach; **(a)** The overall PRR evolution from \(n_{V}=200\) vehicles scenario; **(b)** The detailed PRR evolution for the first 24,000 to 32,000 events.  
**Author Contributions:** Conceptualization, Y.-F.H. and J.-K.C.; Investigation, T.I.B. and Y.-F.H.; Methodology, T.I.B. and Y.-F.H.; Software, T.I.B. and J.-K.C.; Supervision, Y.-F.H. and J.-K.C.; Writing--Original Draft, T.I.B. and Y.-F.H.; Writing--Review and Editing, T.I.B. and Y.-F.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Science and Technology Council of Taiwan under grant NSTC-111-2221-E-324-018.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Risk Assessment  
#### 3.2.1 Ecological Risk Assessment (ERA)  
In the ecological risk analysis, the element contents of the filtrated water samples were used. Consequently, the ecological risk values were below the acceptable level of one, indicating no risk. The values of toxicity potential (TP) of roof runoff, calculated on the basis of available drinking water standards, with respect to all the analyzed elements, were \(<1\), revealing a lack of ecological risk (Table 4). To involve a higher number of elements in the ecological risk assessment, since the values for drinking water standards were not available for all elements, we used the values of contamination factor (CF) and contamination degree (Cdeg) in our study. The calculated values of CF, in the case of all analyzed elements, were \(<1\) (Table 4), indicating, according to the [PERSON] classification [63], a low contamination of roof runoff, with single elements. However, the value of Cdeg, calculated for all analyzed elements altogether, was equal to 9.44, indicating moderate contamination of roof runoff, analyzed in the 2019/2020 season, suggesting that negative impact on the environment might occur. The above also indicates that as the total risk is the sum partial risk values for investigated components the higher number of considered pollutants the higher total risk value, which further points the size of pollution. Thus, it reveals also that during comparison of total risk values the number of individual pollutant analyses should be considered.
## 3 Results
### Statistical Comparison against ISRs Data
In particular, during winter the enhancement of \(T_{\rm e}\) reaches the maximum expansion from dusk to about 08:00 MLT, especially in the Southern hemisphere. The explanation of this behavior is the simultaneous collapse of both electron and ion density in the ionospheric F layer, and the following collapse of the contribution of collisional cooling to \(T_{\rm e}\). Therefore, in the absence of mechanisms that reduce the energy of precipitating particles \(T_{\rm e}\) is free to increase due, e.g., to energy exchanges with the nightside magnetosphere. Not surprisingly, there is an anticorrelation between \(N_{\rm e}\) and \(T_{\rm e}\), and \(T_{\rm e}\) is significantly enhanced in regions of depleted \(N_{\rm e}\), such as between region R2 and the main trough in the nightside winter; while, in contrast, it decreases in regions of enhanced \(N_{\rm e}\) due to energy loss to the ions [19; 48; 49; 57; 63]. Some studies in the past tried to establish a link between \(N_{\rm e}\) and \(T_{\rm e}\), since \(N_{\rm e}\) is a major factor in electron energy loss due to the ionospheric plasma quasi-neutrality (density of ions is nearly equal to \(N_{\rm e}\)). Generally, an anti-correlation was found. [PERSON] and [PERSON] [20] established an interrelationship between \(N_{\rm e}\) and \(T_{\rm e}\) using Atmosphere Explorer C data. [PERSON] et al. [64] found a U shape in the \(T_{\rm e}\)-\(N_{\rm e}\) dependence using Hinotori satellite data. Their results suggest that an additional heat source has to be considered, even though the mechanism is unknown. [PERSON] et al. [65] dealt with the investigation of \(T_{\rm e}\)-\(N_{\rm e}\) correlation using data from the DEMETER satellite, by highlighting the variability of this correlation depending on the latitude, local time, and season. [PERSON] et al. [66] used \(N_{\rm e}\) measured from COSMIC as a driver for \(T_{\rm e}\) in their neural network \(T_{\rm e}\) model. Generally, they found a better agreement between their model and ISR data than between ISR data and IRI values. Thus, it is evident that the \(T_{\rm e}\)-\(N_{\rm e}\) relationship could help in the investigation of \(T_{\rm e}\) behavior in the ionosphere but it is a complex problem itself. This is why, in this study, we did not investigate the \(T_{\rm e}\)-\(N_{\rm e}\) correlation using the Swarm data and we focus only on \(T_{\rm e}\). Compared to the spatial and diurnal trends, the interpretation of the seasonal trends exhibited by Swarm data is more difficult and the differences with IRI are significant. Overall, the main seasonal differences concern the summer and winter seasons for specific MLT sectors. Nighttime \(T_{\mathrm{e}}\) values at high latitudes have been found to be deeply influenced by the solar illumination conditions [67]; in fact, high values are found in the summer hemisphere lit by the Sun. In contrast, low latitudes are not affected by seasonal changes at nighttime. \(T_{\mathrm{e}}\) at mid latitudes during local winter can be affected by strong heating due to conjugate photoelectrons if the opposite hemisphere is already sunlit.
## 1 Introduction
### Flood Management Plans  
Governments incur considerable expenditure in connection with plans for flood management. The intensity of flooding problems and resource availability govern the extent of resource allocation for flood projects. Flood management projects or plans are the practical steps of a flood management strategy consisting of one or more measures planned according to the nature of flood issues. Plans are transformed into projects using resources, while a combination of measures may be implemented throughout a strategy's lifetime [19; 20]. The following examples demonstrate different strategies:  
* **Flood control/flood mitigation** tends to limit flood distribution through structural measures.
* The adjustment or modification of human activities to minimize flood losses is obtained by **adaptation**.
* Flood management based on **resilience** is an approach that emphasizes structural floodplain operations to ensure that after a flood occurs, the system can recover.
* **Integrated flood management** ensures that flood management is appropriately linked to other river functions and floodplain operations. There is multidisciplinary knowledge involved.
* **Sustainable flood management** ensures that measures that will not cause serious complications in the future are chosen. A fair valuation of social, economic, and environmental assets is required for this approach.
* The **no adverse impact** strategy considers plans that do not shift or increase flood hazards to neighboring floodplains or areas.
* **Floodplain restoration** assumes that a natural floodplain has better flood-handling capacities.  
The modern flood management concepts above have been evaluated over time. A brief history of different flood management eras will shed light on the development of the different concepts over time.
# An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks
## 4 Performance Evaluation  
This section presents the simulation results obtained from the UDP process. The computer simulation was performed on a custom-made MATLAB(r) simulator executed on a computer consisting of Windows 10 64-bit operating system, Intel Core i7-8700 CPU, and 8 GB of RAM. For the environment where the whole transmission occurs, we consider an urban region of interest covered by three MBSs, as described in Section 2. Due to the start of an unexpected event, it is essential to integrate the available \(D\) UAV-BSs into the existing infrastructure to maximize the offloading of existing MBSs. In this way, we developa dynamic UAV-aided offloading scheme for the duration of the event. Regarding the users' handover procedure from MBSs to UAV-BSs, it is triggered for the users who receive a stronger signal from the UAV-BS. As a result, these users are stopped being served by the MBS and continue to receive their services from the UAV-BS.  
The users are placed in the coverage area according to the specifications of 3 GPP [25], creating H hotspots with an average of \(N/H\) users in each hotspot. For comparative purposes, we simulate fixed pico-base stations (PBS) deployments by either placing them randomly in the region of interest or through planned strategy in the center of each hotspot [25]. Hereinafter, we refer to these two schemes as picocell deployment process planned (PDPP) and picocell deployment process randomly (PDPR), respectively. Additionally, performance comparisons are made with another placement technique of UAV-BSs that utilizes the k-means unsupervised machine learning algorithm [23], from now on referred to as k-means deployment process (KDP). Also, for PDPP and PDPR we assume that both LOS and non line of sight (NLOS) channel conditions can exist; therefore, the proposed 3 GPP models [25] were used to evaluate the picocell deployment under both LOS and NLOS conditions. The rest of the selected parameters regarding the H-CRAN network configured in the event of an exponential increase in data traffic, are listed in Table 1.
## 3 Results and Discussion
### Taguchi Method
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiments**} & \multicolumn{3}{c}{**Factors**} & \multicolumn{3}{c}{**Weld Strength (Mpa)**} & \multirow{2}{*}{\(\sigma^{2}\)} & \multirow{2}{*}{S/N} \\ \cline{3-3} \cline{5-10}  & & **A** & **P** & **T** & **S** & **1** & **2** & **3** & \\ \hline \multirow{8}{*}{PA} & 1 & A1 & P1 & T1 & S1 & 76.42 & 85.43 & 76.00 & 5.32 & 37.94 \\ \cline{2-10}  & 2 & A1 & P2 & T2 & S2 & 88.15 & 82.93 & 68.11 & 10.39 & 37.87 \\ \cline{2-10}  & 3 & A1 & P3 & T3 & S3 & 85.03 & 82.29 & 77.64 & 3.73 & 38.22 \\ \cline{2-10}  & 4 & A2 & P1 & T2 & S3 & 73.86 & 63.24 & 63.52 & 6.05 & 36.43 \\ \cline{2-10}  & 5 & A2 & P2 & T3 & S1 & 85.86 & 77.30 & 80.43 & 4.33 & 38.16 \\ \cline{2-10}  & 6 & A2 & P3 & T1 & S2 & 76.65 & 78.74 & 78.57 & 1.16 & 37.83 \\ \cline{2-10}  & 7 & A3 & P1 & T3 & S2 & 78.07 & 62.42 & 63.92 & 8.65 & 36.54 \\ \cline{2-10}  & 8 & A3 & P2 & T1 & S3 & 72.65 & 78.61 & 85.63 & 6.49 & 37.89 \\ \cline{2-10}  & 9 & A3 & P3 & T2 & S1 & 67.83 & 69.46 & 73.77 & 3.06 & 36.92 \\ \hline \multirow{8}{*}{PP} & 1 & A1 & P1 & T1 & S1 & 107.72 & 68.75 & 85.24 & 19.56 & 38.38 \\ \cline{2-10}  & 2 & A1 & P2 & T2 & S2 & 90.60 & 98.71 & 72.41 & 13.46 & 38.58 \\ \cline{2-10}  & 3 & A1 & P3 & T3 & S3 & 68.60 & 98.35 & 83.72 & 14.87 & 38.15 \\ \cline{2-10}  & 4 & A2 & P1 & T2 & S3 & 83.93 & 69.08 & 71.26 & 8.01 & 37.37 \\ \cline{2-10}  & 5 & A2 & P2 & T3 & S1 & 72.11 & 70.52 & 85.30 & 8.11 & 37.52 \\ \cline{2-10}  & 6 & A2 & P3 & T1 & S2 & 66.65 & 76.47 & 92.28 & 12.93 & 37.66 \\ \cline{2-10}  & 7 & A3 & P1 & T3 & S2 & 114.63 & 88.40 & 97.43 & 13.32 & 39.86 \\ \cline{2-10}  & 8 & A3 & P2 & T1 & S3 & 76.90 & 66.75 & 61.16 & 7.97 & 36.57 \\ \cline{2-10}  & 9 & A3 & P3 & T2 & S1 & 104.91 & 81.38 & 83.23 & 13.08 & 38.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of tensile testing.
# An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent
## 1 Introduction  
### Motivation  
During software development, particularly within the C++ ecosystem, ensuring software quality is a critical task that heavily relies on effective testing. This is especially relevant for libraries that evolve intensively and are applied across diverse domains, from embedded systems to high-performance computing. As the size of the codebase and the complexity of such libraries' APIs grow, the associated test suites also expand significantly. In the context of continuous integration (CI) systems, the need to execute bulky and redundant test cases (TCs) after each codebase modification leads to the inefficient use of resources and hampers the timely detection of defects. An additional challenge arises from the fact that open-source libraries frequently lack well-documented test requirements and specifications for valid API usage. However, classical test suite optimization methods, suchas greedy minimization, static test slicing, or delta debugging, typically operate under restrictive assumptions. These include the atomicity of TCs, the availability of failure traces, or the presence of specifications which are rarely available in black-box testing or libraries with undocumented APIs. Moreover, as test suites scale with the growth of codebases and the combinatorial explosion of API usage patterns, such methods struggle to remain efficient, both in terms of computational cost and reduction quality. Consequently, there is a clear need for an adaptive, specification-agnostic method capable of fine-grained optimization at the API level, without compromising coverage or increasing computational burden.