esa-sceva/satcom-chunk-collection huggingface dataset:
# QoS-Aware Resource Management in 5G and 6G Cloud-Based Architectures with Priorities
## 3 Transmission Waiting Time FIFO Queue
### Packet Segmentation Analysis
MAC retransmissions are created (up to a maximum configured number known as maxHARQretransmit) when errors are present and detected in the CRC error detection decoding procedure in the received packets and the total resulting BER is higher than a threshold based on the QoS profile of the supported service [14]. In this analysis the corrupted packets are considered to be uncorrelated (a very good and well justified approximation considering radio channels with short time and frequency coherency response), hence if one received MAC transport block packet is found to be faulty based on BLER and retransmission is requested, the next received MAC transport block packet could either be found faulty or not, without any prior memory of the previously transmitted MAC packet. Assuming that each MAC packet can be retransmitted v times at most, and the average number of retransmissions is nmac, TCP/IP packet transmission delay time is recalculated as [24]  
\[T_{delay}^{retr}=\frac{(1+n_{mac})M_{I}+(1+n_{mac})\left\lceil\frac{M_{I}}{M _{mac}}\right\rceil\cdot M_{over}}{M\cdot N\cdot n_{TTI}}T_{s}+\overline{\pi}T _{s} \tag{12}\]where the average number of retransmissions \(\mathrm{n}\mathrm{m}\mathrm{c}\) is a function of the MAC packet error rate and it is calculated as [24]  
\[ \begin{split}& n_{mac}=\frac{1-\left(1-\left(1-p_{b}\right)^{M_{mac} }\right)^{n}}{\left(1-p_{b}\right)^{M_{mac}}}=\\ &=\frac{1-\left(1-\left(1-1-p_{b}\right)^{M_{mac}}\right)^{ \frac{n_{mac}-nT_{b}}{t_{s}}}}{\left(1-p_{b}\right)^{M_{mac}}}\end{split}  \tag{13}\]  
where \(p_{b}\) is the average bit error probability of the MAC bits which further depends on the channel conditions (i.e., CQI reports and SINR planned area) and is necessary for the computation of the average number of retransmissions \(n_{mac}\).
###### Abstract  
Geomorphological surveys and terrain analysis are essential for geomorphology, hydrology, and geographic information systems (GIS). Terrain characterization parameters are fundamental for comprehending geomorphological processes, delineating landforms, and evaluating geohazards. Hence, this study relies on data from the Gaofen-2 satellite (GF-2) and the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM). Focusing on the ring tectonics landform of Dinosaur Valley, the research utilizes visual interpretation, GIS terrain parameter extraction techniques, and visual mapping to identify, measure, and analyze terrain features. The results indicate that ALOS DEM can offer accurate geomorphic feature information. The optimal threshold for extracting the surface water system network is 150, and the optimal analysis window for extracting terrain feature parameters is 16 \(\times\) 16 (0.04 km\({}^{2}\)), determined through statistical methods. This study reveals distinct double-ring linear features and prominent ring-shaped terrain characteristics by extracting terrain feature parameters and utilizing mathematical and statistical analysis. The topography gradually ascends from the center to the ridgeline and then descends to the valley line, resembling a bowl shape. This study highlights the potential application of ALOS DEM for accurately identifying topographic features in the intricate mountains of the Yunnan Plateau. It establishes a practical research framework for high-precision topographic datasets and geomorphological characterization, facilitating further analysis of the evolution of topographic development and the assessment of tectonic activity.  
ring tectonics; feature parameters; GF-2 remote sensing images; ALOS DEM Article
###### Abstract  
In this paper, we present a new stereo vision-based system and its efficient hardware implementation for real-time underwater environments exploration throughout 3D sparse reconstruction based on a number of feature points. The proposed underwater 3D shape reconstruction algorithm details are presented. The main concepts and advantages are discussed and comparison with existing systems is performed. In order to achieve real-time video constraints, a hardware implementation of the algorithm is performed using Xilinx System Generator. The pipelined stereo vision system has been implemented using Field Programmable Gate Arrays (FPGA) technology. Both timing constraints and mathematical operations precision have been evaluated in order to validate the proposed hardware implementation of our system. Experimental results show that the proposed system presents high accuracy and execution time performances.  
Keywords:sterevision; underwater; 3D reconstruction; real-time; FPGA; System Generator +
Footnote †: journal: Applied Sciences
###### Abstract  
Monitoring volumetric glacier change requires cost-effective and accessible methods to generate multi-temporal digital elevation models (DEMs). We present Planet4 Stereo, an open-source photogrammetry pipeline developed to generate DEMs from low-cost PlanetScope images, exploiting the high temporal repetition rate of the constellation for stereo reconstruction. Our approach enables multi-temporal 3D change detection using the freely available NASA Ames Stereo Pipeline (ASP), making the pipeline particularly valuable for geoscientists. We applied Planet4 Stereo in two case studies: the Shisper glacier (Karakoram, Pakistan) for surge investigation and the Boverbren glacier (Smorstabb Massif, Norway) for change detection. The results from Shisper are in good agreement with previous studies using the same images but proprietary methods. The accuracy of the DEM of Boverbrenan was evaluated using high-precision LiDAR data, revealing varying deviations across terrain types, with higher errors in steep shadowed areas. Additionally, the change detection analysis confirmed the expected glacier retreat. Our results show that Planet4 Stereo produces DEMs with comparable accuracy to commercial software and is freely accessible and easy to use. As both ASP and the PlanetScope satellites evolve, future work could refine the pipeline's stereo-matching capabilities and evaluate its performance with next-generation satellite data.  
Keywords:digital elevation model (DEM); stereo reconstruction; glacier monitoring; PlanetScope; 3D change detection; NASA Ames Stereo Pipeline (ASP); multi-temporal analysis; glacier surge; satellite remote sensing Article
# Utilizing Multi-Dimensional MmWave MIMO Channel Features for Location Verification
## 2 Problem Formulation and System Model
### Channel Model
Although the channel-based method essentially authenticates the practical geographic location related to the inherent properties of channel features, it is an effective authentication to achieve legitimacy validation of the source identity. Therefore, this paper aims to design a new location verification scheme for validating data frame origin to resist against the impersonation attack.
# Analytical Electromechanical Modeling of Nanoscale Flexoelectric Energy Harvesting
## 1 Introduction
[36] have investigated the flexoelectric effect in piezoelectric nanobeams with three different electrical boundary conditions. The induced electric potential due to the flexoelectric effect has been obtained under the open circuit conditions, which may be important for sensing or energy harvesting applications. For the flexoelectric energy harvesting applications, [PERSON] et al. [37] discussed the flexoelectric energy harvester in the nanoscale. Based on the internal energy density, they obtained governing equations and solved the frequency response functions using the assumed-modes method. [PERSON] and [PERSON] [38] applied the distributed-parameter method to discuss the flexoelectric energy harvesters in elastic dielectrics. Considering the surface effects, [PERSON] [39] analyzed the flexoelectric energy harvest by use of the assumed-modes method. [PERSON] et al. [40] developed flexoelectric-piezoelectric energy harvesters based on the Timoshenko laminated beams model. The three-layered energy harvesters in parallel and series configurations have been discussed in detail. However, the closed-form analytical solution for the flexoelectric-piezoelectric energy harvesting has not been reported. In this paper, we focus on the flexoelectric energy harvesting system, which is a traditional cantilever piezoelectric beam model with a tip mass, by the coupled distributed-parameter model. Based on the electric Gibbs free energy [36] and generalized Hamilton's variational principle, the electromechanically-coupled dynamic and electrical circuit equations with the flexoelectric effect are derived. The closed-form analytical solution is presented for the flexoelectric energy harvesting under base excitations. The electromechanical frequency response functions (FRFs) that relate the voltage output, power density, and mechanical vibration response are derived for harmonic excitations in closed-form. Based on the multi-mode solutions, the performance of the flexoelectric energy harvesting is analyzed in detail.
## 2 Materials and Methods
### Loss Function  
The loss function of YOLOv5 consists of three parts: class loss, objectness loss, and location loss. The loss function of YOLOv5x6 can be denoted as:  
\[L(all)=L(obj)+L(x,y,w,h)+L(class) \tag{6}\]  
The total loss can be calculated as the sum of the three parts or their weighted sum. The authors used the cross-entropy function to calculate classes and location loss. YOLOv5 also determines the CIoU to calculate objectness loss, which can be denoted as:  
\[CIoU=1-IoU+\frac{\rho^{2}(\mathbf{b},\mathbf{b}_{\text{gt}})}{c^{2}}+\alpha\
u \tag{7}\]  
where IoU refers to the intersection and union ratio of A and B; C represents the smallest box surrounding A and B; \(\mathbf{b}\) and \(\mathbf{b}_{\text{gt}}\) are the center point of the prediction frame and the ground truth frame, respectively; \(\rho^{2}(\cdot)\) denotes the Euclidean distance; and \(c\) represents the diagonal length of the smallest rectangle containing these two frames.  
In order to solve the problem of unbalanced positive and negative samples, focal loss is implemented to further improve cross-entropy loss:  
\[FL(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}\log(p_{t}) \tag{8}\]  
where \(p_{t}\) is the predicted probability, and \(\alpha_{t}\) and \(\gamma\) are the focusing parameters, which reduce the weight of the samples that are easy to classify so that the model can pay more attention to the samples that are difficult to classify. In YOLOv5, the focal loss is integrated into the code.  
In order to further solve the imbalance between foreground and background in dense object detection, [42] proposed varifocal loss (VFL), which obtains SOTA results using COCO dataset. The formula is as follows:  
\[VFL(p_{t},q)=\left\{\begin{array}{rl}-q(q\log(p_{t}))+(1-q)\log(1-p_{t}))&q>0 \\ -\alpha_{t}p_{t}{}^{\gamma}\log(1-p_{t})&q=0\end{array}\right. \tag{9}\]  
Figure 6: Visualization of some problems. (**a**) The baby carriage (red box) is severely occluded by the billboard, and the person on the left of the right image is unlabeled. (**b**) Detection of baby carriage with a baby. (**c**) Detection of baby carriage without a baby.  
where \(q\) is the label, and when it is a negative sample, \(q\) is 0. VarifocalNet only reduces the loss contribution of negative samples and preserves the learning information of positive samples. In this paper, we embedded VFL into YOLOv5 and conducted experiments on the PANDA dataset. The experimental results showed that VFL worked well in gigapixel-level object detection.
## 3 Parameterization Schemes for Fiber Orientation
### Feature-Based Parameterization
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameterization** & **Design Freedom** & **Advantages** & **Applicability** & **Drawbacks** \\ \hline \multirow{4}{*}{Continuous} & Fully relaxed material orientation space. & Spatially varying fiber path both in 2D and 3D. & Adopted scheme for CF4 part design and verification. & Initial design dependency, significant variation in fiber angles, and \(\pi\) ambiguity result in poor local minima. \\ \hline \multirow{4}{*}{Discrete} & Most restrictive material orientation space. & Most effective gradient-based method for discrete settings 1, e.g., multi-phase TO. & Numerous studies on designing multi-layered composite laminates. & Several design variables, fiber convergence, and material discontinuity lead to ambiguous design. \\ \hline \multirow{4}{*}{Discrete-continuous} & Continuous & & & Only a few works are available, and an efficient optimization formulation is needed to tackle the general setting. \\ \cline{2-5}  & Continuous & General framework for both continuous and discrete settings. & Promising framework to withstand various FRC manufacturing units. & Topology is restricted 3  \\ \cline{2-5}  & Continuous & Continuous & & Only a few works are available, and an efficient optimization formulation is needed to tackle the general setting. \\ \hline \multirow{4}{*}{Discrete-continuous} & Continuous & Least number of optimization variables, easy-to-control feature size, and ready-to-manufacture design. & Favorable for large-scale application and industrial manufacturing units due to its simple topology. \\ \cline{2-5}  & & Allows spatially varying fiber path and volume fraction, complex loading, integration of failure criteria, damage model, etc. & Fully exploit the capabilities of CF4 with a design that can follow the response of the actual part. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Various attributes and comparison of enumerated parameterization techniques. Figure 5: The example demonstrates the design of MBB using the geometry projection method (adapted from [121]). The top of the left column shows the MBB beam design region, support, and unit load, whereas the bottom depicts colored penalized element densities for the optimal MBB beam designs. Note that the color denotes the orientation of the rest of the changes. In the right column, iteration histories of an objective function indicate the attained compliance value \(f^{(i)}\) at an iteration \(i\).
## 3 Methods
### Best-Worst Method (BWM)
Determine the optimal weights: \((w_{1}^{*},w_{2}^{*},\cdots,w_{n}^{*})\)  
The optimal weights of the RPN element are determined such that the maximum absolute differences \(\left|w_{B}-a_{Bj}\times w_{j}\right|\) and \(\left|w_{j}-a_{jW}\times w_{W}\right|\) for all \(j\) are minimized, which is translated to the following min-max model:  
\[\text{min}_{j}\text{max}\big{\{}\left|w_{B}-a_{Bj}\times w_{j}\right|,\left|w _{j}-a_{jW}\times w_{W}\right|\big{\}}\]  
Subject to  
\[\sum\limits_{j}w_{j}=1,\ w_{j}\geq 0,\ \text{for all}\ j \tag{1}\]  
This can be converted into the following linear programming formulation to solve: \begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Linguistic Variables** & **Codes** & **Interval Value** \\ \hline
1 & Equally important & E & [1, 2] \\
2 & Moderately important & M & [2, 4] \\
3 & Strongly important & S & [4, 6] \\
4 & Very strongly & VS & [6, 8] \\
5 & Extremely important & EX & [8, 9] \\ \hline \end{tabular}
\end{table} 
Table 2: Linguistic terms for pairwise comparison of RPN elements [19]. \[ \begin{array}{l}\min\,Z;\\ \text{Subject to}\\ \left|w_{B}-a_{Bj}\times w_{j}\right|\leq Z,\text{ for all }j\\ \left|w_{j}-a_{jw}\times w_{w}\right|\leq Z,\text{ for all }j\\ \sum\limits_{j}w_{j}=1,\ w_{j}\geq 0,\text{ for all }j\end{array}  \tag{2}\]  
After the expert answers the pairwise comparisons, we can obtain the BO and OW with a total of two grey vectors, which are illustrated as follows:  
\[\widetilde{A}_{B}=(\widetilde{a}_{B1},\widetilde{a}_{B2},\cdots,\widetilde{a }_{Bn})\] \[\widetilde{A}_{W}=(\widetilde{a}_{1W},\widetilde{a}_{2W},\cdots, \widetilde{a}_{nW})\]  
Therefore, the constrained optimization problem for determining the optimal interval weights \(\widetilde{w}_{1}^{*}=\left(\widetilde{w}_{1}^{*},\widetilde{w}_{2}^{*}, \cdots,\widetilde{w}_{n}^{*}\right)\) can be obtained as follows:  
\[ \begin{array}{l}\left|\left(w_{B}^{l},w_{B}^{u}\right)-\left(w_{j^{\prime}} ^{l},w_{j}^{u}\right)\times\left(w_{Bj^{\prime}}^{l},w_{Bj}^{u}\right)\right| \leq(Z^{*},Z^{*}),\text{ for all }j\\ \left|\left(w_{j^{\prime}}^{l},w_{j}^{u}\right)-\left(w_{WW}^{l},w_{W}^{u} \right)\times\left(w_{jW}^{l},w_{W}^{u}\right)\right|\leq(Z^{*},Z^{*}),\text { for all }j\\ \sum\limits_{j=1}^{n}R\left(\widetilde{w}_{j}\right)=1;\,w_{j}^{l}\leq w_{j} ^{u};\,w_{j}^{l}\geq 0,\text{ for all }j\end{array}  \tag{3}\]  
where \(\widetilde{w}_{B}=\left(w_{B}^{l},w_{B}^{u}\right)\), \(\widetilde{w}_{j}=\left(w_{j^{\prime}}^{l},w_{j}^{u}\right)\), \(\widetilde{w}_{W}=\left(w_{W^{\prime}}^{l},w_{W}^{u}\right)\), \(\widetilde{a}_{Bj}=\left(a_{Bj^{\prime}}^{l},a_{Bj}^{u}\right)\), \(\widetilde{a}_{jW}=\left(a_{jW}^{l},a_{jW}^{u}\right)\) and suppose \(\widetilde{Z}^{*}=(Z^{*},Z^{*})\). They are the interval values that replace the crisp value. An application of the calculation by this article is in Section 4.
## 2 Minimally Invasive Therapies for HCC and Their Tissular Effects
### Non-Thermal Ablative Therapies
#### 2.2.4 Yttrium-90 Radioembolization  
Yttrium-90 (90Y) transarterial radioembolization (TARE) is performed by injecting the microspheres (20-60 \(\upmu\)m in diameter) loaded with the isotope through an endovascular arterial approach [92]. The feeding vessel is not occluded in comparison to cTACE and DEB-TACE; after the injection, the microspheres are taken over by the tumor tissue and emit fatal \(\beta\)-radiation for up to 2 weeks but with a small penetrance (smaller than 3 mm), therefore limiting the negative effects on the healthy hepatocytes [92; 93; 94]. 90Y radioembolization has shown objective response rates of up to 88.3% and up to 86.6% overall survival at 3 years [95]. Moreover, 90Y radioembolization appears to be better tolerated than cTACE and yields a longer time to progression of the disease than cTACE [96].  
After TARE, the treated nodule may be stationary in size, or show either a decrease or increase in diameter; tumoral size increase usually occurs within the first month and is related to the associated edema and hemorrhage [97]. Therapeutic success is defined as the appearance of coagulative necrosis and usually occurs within the first 4 months [98]. However, transient necrotic areas with a patchy pattern may be seen early after the procedure. Also, a thin and smooth granulation tissue forms around the necrotic area and usually resolves after 4-5 months [97]. Capsular retraction may be seen in nodules closer to the liver surface and is considered a consequence of the fibrosis secondary to the radiation [97; 99].  
A summary of the advantages, limitations, and performance of the thermal and non-thermal ablative therapies is provided in Table 1.  
Figure 8: MRI scan of a patient 1 month after DEB-TACE for HCC in liver segments VIII–VII. **(a)**—T2 WI with fat saturation; **(b)**—T1 WI with fat saturation; **(c)**—T1 WI with fat saturation after intravenous contrast. The treated nodule (arrow) is completely necrotic (non-enhancing, **(c)**), with high signal intensity on T2 **(a)** and T1 **(b)**, demonstrating both coagulative and liquefactive necrosis.
## 3 Results and Discussion
### Identification of Flavonoids in Fig Leaves  
The UPLC chromatography of flavonoid extraction by UEAATPE was detected at 254 nm, as shown in Figure 4. The identification and structure elucidation of the compounds from the leaves was completed by UPLC-QTOF-MS/MS in negative ion mode. Approximately 11 peaks were separated in the extract. Subsequently, the ESI-MS\({}^{1}\) and MS\({}^{2}\) were used to identify and characterize the flavonoids from the fig leaves. The mass spectra and fragmentation pathways can be seen in Figures S1-S11 (see the Supplementary Material). The identification of peaks was performed using reference data such as retention time and mass spectrum, as shown in Table 5. According to the structural characteristics, these compounds are all flavonoids. The chemical structure compounds identified are shown in Figure 5.  
Figure 4: The UPLC chromatogram of flavonoid extraction from fig leaves detected at 254 nm. Figure 3: Effects of different methods on the yield of total flavonoids. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Peak No. ** & \(t_{\text{R}}\) & **MS** & **MS/MS** & \begin{tabular}{c} **Molecular** \\ **Weight** \\ \end{tabular}  & \ \begin{tabular}{c} **Molecular** \\ **Formula** \\ \end{tabular}  & \begin{tabular}{c} **Identification** \\ \end{tabular}
## 3 Design of Mobile Unit (MU)
### GNSS (GPS) Subsystem Design
#### 3.2.3 Time-to-Fix and Start-Up Times  
An additional dynamic parameter indirectly related to navigation accuracy is the time-to-fix, which is the time from power-on (cold start) or power-sleep (hot start) to the first valid position returned. The latency is important and has a great influence on the robustness of the system. It should be noted that _all receivers did not use A-GPS (Assisted GPS) technology in all tests_. Table 10 shows the cold start latency for the selected GPS modules with respect to different antennas, sky conditions, and number of visible satellites (rightmost column). A dash \"-\" indicates that the GPS module did not report the number of visible satellites.  
It can be observed that the common value for cold start is 2-3 min (both cases of built-in antennas). There is a weak relationship between time-to-fix and the number of visible satellites. At the same time, there is a significant relationship between time-to-fix and the strength of the received RF signal (compare cases with internal, microstrip, or active antennae). However, it can be observed that even in the best real-world measurement conditions, the value of time-to-fix (cold start) is worse than indicated in the data sheet (<40 s). This leads to the conclusion that the value of this parameter provided in the manufacturer's documentation was measured in the best possible working conditions (i.e., open and cloudless sky, no obstacles, and no sources of interference).  
Figure 10: Cross-tracking error.  
The current consumption of all tested modules (default settings) does not exceed 40 mA, while for low-power modules it is about half of this value. However, the low-power modules, such as mod_2, tend to perform worse (due to longer time-to-fix and less precise accuracy).