esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization of the Study Case  
A typical low-income house, widely replicated in all regions of Brazil by the Brazilian government under the social housing program named \"My House My Life\", was chosen for this study [26] (Figure 2a). This choice is based on the fact that this population is the most vulnerable to the impact of climate change, especially in developing countries. This typology is handed out to the users without any concern about climatic adaptation to the implantation region, with a poor thermal performance [9; 12; 13], compromising its indoor thermal comfort conditions as well as increasing energy consumption for cooling [13; 27]. Due to these facts and how this research focuses on the thermal mass strategy as a climate-responsive measure, the original standard building project was redesign to attend Brazilian RTQ-R level 'A' of efficiency [25], thereafter named as \"LIHe\". It is characterized by a one-story detached house in contact with the ground. LIHe presents 39.18 m\({}^{2}\) of total area and 34.54 m\({}^{2}\) of internal floor area, distributed in four designated areas, namely: living room/kitchen (17.44 m\({}^{2}\)), bedroom 1 (7.78 m\({}^{2}\)), bedroom 2 (7.57 m\({}^{2}\)), and bathroom (1.75 m\({}^{2}\)) (Figure 2a-c). The roof construction is dual pitched with an overhang of 0.30 m depth. The rooms' ceiling height is 3.00 m.  
Regarding openings, living room and bedroom spaces present metallic sliding windows with dimensions of 1.50 \(\times\) 1.00 m and 1.20 \(\times\) 1.00 m, respectively, composed of four panels, two of them being single glass fixed panels and the other two sliding metallic Venetian panels. The kitchen's window is a metallic tilting type of 1.00 \(\times\) 1.00 m. The external doors are made of metallic sheets, while interior doors are wood-made. The thermal resistance of the external walls and roof were improved to provide building efficient level \"A\". The external walls consist of ceramic bricks (six-hole type) coated on the outside with expanded polystyrene sheets (EPS) (0.04 m) and plaster (0.025 m) in the outermost and innermost layers. The internal partitions consist of ceramic brick (six-hole type) coated on both sides with plaster (0.015 m). The external walls absorptance was reduced from 0.3 to 0.15 by changing the painting to white color. The roof system is composed of ceramic tile, aluminum foil for thermal insulation, an air gap layer, and ceiling panels of polyvinyl chloride (PVC) (0.01 m). The outermost layer of the tiles was painted with white color (ranging the absorptance of the roof from 0.8 to 0.15). Regarding the roof air gap, this layer presents 0.61 m\({}^{2}\)K/W thermal resistance (R-value), with a thickness greater than 0.05 m and low thermal emissivity due to the installation of aluminum foil. The Brazilian Association of Technical Standards NBR 15.220 [16] was used to redesign the building envelope and to determine the thermal properties of the building system envelope of the efficient low-income house (LIHe), which are presented in Table 2.
## 3 Results and Discussion
### Mesh Sensitivity Analysis: Simulations 1-5  
In order to assure that the solution of the simulation is not heavily dependent on the mesh, a mesh sensitivity analysis is performed. Five different element sizes are used, namely 1e-3 m, 8e-4 m, 7.5e-4 m, 6e-4 m and 5e-4 m. This last simulation is the previously discussed benchmark simulation. [PERSON] et al. (2018) investigated the mesh independency of the primary LPBF process and showed that the method implemented in ANSYS Academic Research Mechanical, release 2019 R2, becomes independent for the smallest element size chosen. However, this work will evaluate the same for the two post-processing steps. The metric chosen to evaluate the mesh sensitivity is the stress in the left support at the top of the cantilever.  
Table 2 shows the values of the aforementioned metric for all five simulations for process sequence A. It shows that the stress increases for a finer mesh, indicating that the longitudinal normal stress is overestimated at the top of the cantilever for a too coarse mesh. However, the values show a clear convergent behaviour, indication that a finer mesh results in a mesh-independent solution. In this work, to ensure that the simulations remain computationally inexpensive, the mesh size of the part is kept at 5e-4 m, which is the finest mesh used for the presented simulations.
## 4 Results
### Ground Motion  
Ground motion presents a very high spatial and temporal variability (Table 2 and Figure 7). The EGMS data do not entirely cover the study areas; the maximum coverage is 59% (Follonica), but it is smaller in other regions (e.g., Pontina Plain, where it is about 25%). Therefore, we used accurate interpolations to fill the gaps.  
The middle panel in Figure 7 shows, for each of the study areas, the average values of ground motion in the time interval 2016-2021. These values are negative (subsidence) and decreasing in an almost linear way, indicating that it may be reasonable to project the observed trends in the next decades. The upper and lower panels of the figure show the minimum (positive) and maximum (negative) values of the ground motion over the study areas. The latter are much higher than the average values (up to about 15 cm in the Rome site; see also Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{6}{c}{Minimum Values (mm)} \\ \hline Study Area & 2016 & 2017 & 2018 & 2019 & 2020 & 2021 \\ \hline Piombino-Follonica & 18.80 & 17.20 & 21.00 & 27.00 & 31.60 & 36.50 \\ Marina di Campo & 17.30 & 14.50 & 13.00 & 11.60 & 14.60 & 15.50 \\ Alghero-Fertilia & 17.40 & 2240 & 2190 & 23.50 & 24.90 & 28.30 \\ Rome & 20.40 & 23.00 & 15.10 & 14.60 & 17.90 & 14.50 \\ Latina-Sabaudia & 16.00 & 14.10 & 13.00 & 13.00 & 23.20 & 33.20 \\ \hline  & \multicolumn{6}{c}{Medium Values (mm)} \\ \hline Study Area & 2016 & 2017 & 2018 & 2019 & 2020 & 2021 \\ Piombino-Follonica & \(-0.80\) & \(-2.00\) & \(-3.20\) & \(-4.50\) & \(-6.20\) & \(-8.40\) \\ Marina di Campo & \(-0.80\) & \(-2.50\) & \(-4.20\) & \(-6.20\) & \(-8.50\) & \(-10.90\) \\ Alghero-Fertilia & \(-0.80\) & \(-2.30\) & \(-3.50\) & \(-4.70\) & \(-6.00\) & \(-7.30\) \\ Rome & \(-1.00\) & \(-3.00\) & \(-4.90\) & \(-6.90\) & \(-9.40\) & \(-12.10\) \\ Latina-Sabaudia & \(-1.10\) & \(-3.20\) & \(-5.50\) & \(-7.30\) & \(-8.70\) & \(-9.60\) \\ \hline  & \multicolumn{6}{c}{Maximum Values (mm)} \\ \hline Study Area & 2016 & 2017 & 2018 & 2019 & 2020 & 2021 \\ Piombino-Follonica & \(-22.70\) & \(-33.10\) & \(-52.30\) & \(-67.40\) & \(-82.30\) & \(-95.70\) \\ Marina di Campo & \(-20.80\) & \(-24.90\) & \(-34.90\) & \(-37.10\) & \(-41.60\) & \(-51.50\) \\ Alghero-Fertilia & \(-19.70\) & \(-27.20\) & \(-43.80\) & \(-60.60\) & \(-72.50\) & \(-89.10\) \\ Rome & \(-15.40\) & \(-60.70\) & \(-78.60\) & \(-102.30\) & \(-125.70\) & \(-148.00\) \\ Latina-Sabaudia & \(-20.00\) & \(-31.00\) & \(-38.60\) & \(-44.20\) & \(-54.90\) & \(-61.80\) \\ \hline \end{tabular}
\end{table}
Table 3: Minimum, medium, and maximum displacement (mm) released by the EGMS platform within the studied areas in the time interval 2016–2021. Note that upward displacements have a positive sign (\(+\)), while negative displacements have a negative sign (\(-\)).  
Figure 7: Ground motion values from EGMS Copernicus. Values represent minimum, mean, and maximum variations measured in the time interval 2016–2021 within each of the five study areas of Piombino-Follonica, Marina di Campo, Alghero-Fertilia, Fiumcino-Osta, and Latina-Sabaudia.
## 5 Post-Evaluation Analysis
### Query-by-Example Spoken Term Detection
#### 5.2.1 System Analysis for In-Language and Out-of-Language Queries  
An analysis based on INL and OOL queries was carried out for the QBe STD systems and results are presented in Tables 23-25 for MAVIR, RTVE and SPARL20 data, respectively. These results show that OOL query detection obtains, in general and particularly for the systems that do not use language adaptation, better results than INL query detection, except for the RTVE data. This could be due to the fact that the QBe STD systems are built with English language, which matches the language of many OOL queries, particularly in MAVIR and SPARL20 databases.
## 4 Metal-Oxide Nanoparticles (MONPs) in Water Treatment
### Titanium Oxide Nanoparticles  
Titanium oxide (TiO\({}_{2}\)) nanoparticles have been studied in recent years because of their remarkable photocatalytic activity, chemical and biological stability, strong photostability, and reasonable cost [36]. TiO\({}_{2}\) NPs are a great catalyst for the breakdown of numerous pollutants, including pesticides, organic compounds, heavy metals, arsenic, and polycyclic aromatic hydrocarbons, present in wastewater [150]. TiO\({}_{2}\) NPs have an amazing ability to release reactive oxygen species (ROS) when exposed to UV light, which starts the breakdown of pollutants. Interestingly, TiO\({}_{2}\) NPs produce hydroxyl radicals when exposed to UV radiation (\(\lambda<400\) nm), which increases their capacity to destroy pollutants by interfering with the structures and operations of cells [151]. TiO\({}_{2}\) NPs commonly exist in different crystalline forms, such as anatase, rutile, and brookite, each exhibiting distinct photocatalytic activities (Figure 8) [152, 153].  
[PERSON] et al. [149] achieved a combined dye removal effect (CR dye 87%) and a strong antibiotic degradation effect (SMX 82% and CIP 94.6%) from non-doped simple TiO\({}_{2}\) NPs. Several recent studies reported the ability of TiO\({}_{2}\) NPs in wastewater treatment by photocatalytic degradation and adsorption, which are summarized in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
Figure 8: Different crystalline structures of TiO\({}_{2}\) nanomaterials: anatase, rutile, and brookite. The red ball represents the Ti\({}^{2+}\) ion, and the white ball is O\({}_{2}\)\({}^{-}\). Source: [154].
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
### Congestion Control Algorithms in TCP Networks
#### 1.4.2 Multipath TCP CC Algorithms  
With the advent of MPTCP, several algorithms have been introduced to address the challenges of managing multiple network paths. Possibly, the most representative ones are:  
* **LIA** (Linked Increases Algorithm)--one of the first MPTCP CC algorithms--ensures that the sum of the congestion windows of all the paths does not exceed that of a single-path one. LIA aims to be friendly to the regular TCP traffic, making sure that the MPTCP flows do not monopolize the network resources. This balances the congestion windows of different paths to provide efficient utilization of all the available paths [5; 33].
* **OLIA** (Opportunistic Linked-Increases Algorithm)--an improvement of LIA--was designed to achieve better throughput and fairness across multiple paths. OLIA dynamically adjusts the congestion windows of different paths based on the current network conditions toward optimal utilization of each path. This results in better overall performance and improved user experience [33; 34].  
* **BALIA** (Balanced Linked Adaptation) focuses on balancing the congestion windows of different paths. By doing so, BALIA ensures that no single path is overly congested, leading to better overall network performance. BALIA is particularly effective in scenarios where the available paths have diverse and variable characteristics [35].
* **wVegas**--a delay-based MPTCP algorithm--, monitors the delay on each path and adjusts the congestion window accordingly, unlike the traditional loss-based ones. By focusing on delay as the primary metric, wVegas can achieve better performance in networks where delay is a critical factor. wVegas seems particularly well-suited for real-time applications where low latency is crucial. However, as the conducted tests reveal, it is not always the case in a multipath traffic scenario.  
Each MPTCP CC algorithm reflects a unique methodology for managing data flow across multiple network paths. Their distinct design principles, combined with their strengths and potential challenges, make them apt for diverse scenarios and applications. As the landscape of digital services evolves and the demand for seamless, multipath connectivity intensifies, the selection of an appropriate MPTCP algorithm becomes a critical factor. Their strengths and weaknesses have been summarized in Table 1.  
While successfully addressing many aspects of Internet connectivity [14; 22; 36], the standard MPTCP algorithms discussed above were not designed with streaming traffic in mind. Their popularity, however, leads to the question we will try to answer in this work: Which one handles such delay-sensitive content most efficiently for a good user experience?
## 4 Proposed Suppression System Model
### Overview of Adaptive Digital Filters Structures  
Digital filters are the most widely used technique in the communications, radar, biomedical, and other related fields to solve problems of interference. The adaptive filter can be applied to a variety of structures and realizations. NF passes through all frequencies, except for those in a stop or rejection band centered on a central frequency. NF is defined by two parameters: bandwidth, and central frequency [49]. The bandwidth of an ideal NF should be zero, and the magnitude of the pass band should be one [50]. The Finite Impulse Response (FIR) and the IIR are the two most common types of Adaptive Digital NF (ADNF). FIR filters are also known as Moving Average (MA) filters because they implement a zero-based transfer function. While the Autoregression (AR) and the MA, or ARMA, are frequently used to implement an IIR filter that implements all poles and zero-based transfer functions, the IIR requires fewer filter coefficients to achieve the same notch bandwidth as the FIR. Furthermore, the IIR is more widely used and advantageous than FIR, because it requires less computation. According to [51], an IIR filter can model a pole-zero system more accurately than an FIR filter. In [52], the IIR NF could efficiently remove the CWI or NBI. According to [53], the IIR NF achieves a frequency response that is closer to the ideal NF than an FIR NF of equal length.  
Figure 4: Block diagram of the proposed suppression system model.  
Figure 5: The suppression system model of the MANF block diagram.
# 1D Barcode Detection via Integrated Deep-Learning and Geometric Approach
## 7 Conclusions  
In this work, we propose a novel two-stage method of 1D barcode detection. In the first stage, we use the YOLO object detection network to locate the position of the 1D barcode and detect all the line segments with the LSD algorithm in the bounding box region, where we predict the rotation angle using clustering line angles. In the second stage, we process the images cut from the bounding boxes and propose a selecting range for the line segments using the region estimation network. Finally, the line segments selected are used to generate a convex hull from the endpoints. We achieved state-of-the-art performance both in terms of accuracy and speed by combining the advantages of both the deep-learning model and geometric method. With no need to manually tune the parameters, this approach enjoys practicability, accuracy, and speed. We believe that our approach would be easily applied in practice and is applicable to all types of 1D barcodes.  
Conceptualization, Y.X. and Z.M.; methodology, Y.X.; software, Y.X.; validation, Z.M.; formal analysis, Z.M.; investigation, Y.X.; resources, Y.X.; data curation, Y.X.; writing-original draft preparation, Y.X.; writing-review and editing, Z.M.; supervision, Z.M.; project administration, Y.X.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
YOLOYou Only Look Once object detection network LSDLine segment detection algorithm MLPMulti Layer Perceptron neural network MSER Maximally Stable Extremal Regions mAPmean Average Precision ReLURectified Linear Units
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## 4 Performance Metrics
### Vardalis's Fairness Index (\(\gamma_{\text{Vardalis}}\))  
This fairness index has been introduced in Jain (2008). It ranges between \(0\) to \(1\) for any number of flows. The index is \(1\) when all flows are receiving equal allocation. If out of \(n\) flows, \(k\) are equally sharing all the bandwidth and the rest \(n-k\) are idle, the fairness index is \((k-1)/(n-1)\):  
\[\gamma_{\text{Vardalis}}=1-\frac{\sum_{[s,t]\in K}|\varphi^{st}-Avg|}{2(|K|-1) Avg}. \tag{30}\]  
The numerator of the fraction in Equation (30) is the sum of the absolute value of the difference of each flows' allocation from the average allocation. The system is more unfair if the sum is more. There are differences between [PERSON]'s fairness index (Equation (29)) and [PERSON]'s fairness index:  
* [PERSON]'s fairness index ranges from \(1/n\) to \(1\), whereas [PERSON]'s fairness index ranges from \(0\) to \(1\) for \(n\) being the number of flows. For example, if there are two flows, one with a certain allocation and other with zero allocation, then [PERSON]'s fairness index will be \(0.5\). On the other hand, the value of the fairness index-II will be \(0\).
* [PERSON]'s fairness index is more sensitive to changes, especially when the number of flows is small. For instance, in case of two flows where one flow achieves \(50\%\) more bandwidth than the other, [PERSON]'s fairness index will be \(0.96\) where [PERSON]'s fairness index will be \(0.8\). When one flow receives exactly twice as much bandwidth as the other, [PERSON]'s fairness index will be \(0.9\) while [PERSON]'s fairness index will be \(0.66\).
# Precise Orbit Determination of FY-3C with Calibration of Orbit Biases in BeiDou GEO Satellites
## 3 FY-3C POD Results and Analysis
### Distribution of FY-3C BeiDou Observations
Figure 5: Total number of observed times at 30 s interval for BeiDou satellites and types during July 2017, left subgraph represents for each BeiDou satellite and right subgraph represents for GEO, IGSO, and MEO, respectively. Figure 6: Sky view of BeiDou satellites in antenna reference frame observed by FY-3C GNOS receiver with 30s sampling interval on 14 July 2017. Top with blue points, middle with red points, and bottom with yellow points in the figure are prepared for GEO (C01, C03, C05), IGSO (C06, C08, C10), and MEO (C11, C12, C14), respectively.
## 2 Problem Definition
### Problem Modeling
#### 2.2.3 Visible Time Window (VTW)  
In this study, the concept of a Visible Time Window (VTW) is introduced to define the feasible opportunities for imaging a target with a specific satellite based on the user request and satellite information data. Figure 4 illustrates a scenario where a single satellite aims to capture images of 1000 targets over a 7-day period. Derived using the STK 11, these VTWs serve as crucial input parameters for optimization algorithms, which are tasked with formulating the most efficient imaging scheduling strategy. The analysis reveals that, on average, approximately 1000 VTWs are generated each day, culminating in nearly 7000 VTWs over the course of a week. These data suggest a proportional increase in VTWs with more satellites, a greater number of targets, and longer scheduling periods. Consequently, the effective application of optimization algorithms becomes increasingly essential to establish efficient imaging schedules, highlighting their critical role in managing the growing number of VTWs.  
Figure 3: Target priority in Eisenhower matrix framework.
## 3 Problem Formulation  
The spheroid was divided into 50 slices (\(z_{0---49}\)) for each ratio (Figure 2). The width of the whole spheroid was 300 \(\upmu\)m, which means that each slice had 6 \(\upmu\)m thickness. Therefore, the whole _in vitro_ 3D cell spheroid was partitioned into two regions: (1) the core region (from approx. slice nos. \(z_{16---32}\)) and (2) the peripheral region (from approx. slice nos. \(z_{0---15}and_{33---49}\)). The densest region was where the concentrations of the HCT-8 cells in the _in vitro_ 3D cell spheroid were the maximum between the two regions. The estimation of the densest region of the colon-cancer cells in the _in vitro_ 3D cell spheroid was  
Figure 1: Sample images of each ratio for horizontal cross-section of 3D cell spheroid: (**a**) 20.5; (**b**) 2:1; (c) 2:2; (**d**) 2:4.  
Figure 2: Partitioning of _in vitro_ 3D cell spheroid into regions.  
required for further analysis and treatment planning. With this, the technology of ultralow attachment surfaces of _in vitro_ techniques is of the recent trend [14; 15] recently became popular [16; 17] in reducing the burden animal model. These microwell technologies have been used for the past few decades [18], and several cancer tumor models were constructed _in vitro_, which may provide a better environment to build _in vitro_ 3D culturing of patient derived xenografts (PDX). Further improving image processing techniques may advance our understanding of stromal cell distribution in real tumor tissue _in vitro_, which helps in personalized medicine. Cells have different proliferation rates, but the environment depends on initial cell seeding density in the spheroid. We are currently experimenting on the aspect of the proliferation rate and monitoring them at different time points.  
In order to understand the regional distribution of _in vitro_ 3D cell spheroids, the total number \(N_{b}\) of HCT-8 cells and \(N_{g}\) NIH3T3 cells present in the _in vitro_ 3D cell spheroid was considered. The cells were considered to be circular. The counts of HCT-8 and NIH3T3 cells were evaluated for horizontal cross sectional images of the _in vitro_ 3D cell spheroid for each slice (slice nos. \(z_{0\ldots 49}\)). The identification of the densest region depends on the maximal concentrations of the HCT-8 cells in two different regions i.e., core \((B_{c})\) and periphery \((B_{p})\). Therefore, it was formulated as  
\[R_{dense}=\left\{\begin{array}{l}CR\,,\,ifCount(B_{c})>Count(G_{p})\\ ||Count(B_{c})>Count(B_{p})\\ PR\,,\,ifCount(B_{p})>Count(G_{c})\\ ||Count(B_{p})>Count(B_{c}))\end{array}\right. \tag{1}\]  
where \(G_{c}\) and \(G_{p}\) are the count of NIH3T3 cells for the core and peripheral regions, respectively; \(B_{c}\) and \(B_{p}\) are the count of HCT-8 cells for the core and peripheral regions, respectively. The proposed region estimation algorithm finds the densest region \(R_{dense}\) of the HCT-8 cells and gives the distribution patterns of the HCT-8 and NIH3T3 cells from the core to the peripheral region over the _in vitro_ 3D cell spheroid.