esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Sanitization Algorithm
### SReB_GCA  
In the preceding subsection, the GGS method initially requires sorting. However, directly sorting the original histogram \(H\) would violate \(\epsilon\)-differential privacy. Consequently, a fraction of the total privacy budget, designated as \(\epsilon_{1}\), is allocated to perform the sorting of \(H\) in ascending order in compliance with \(\epsilon_{1}\)-DP. Subsequently, the SReB_GCA histogram publishing algorithm is designed, incorporating the grouping principles discussed, along with the approximate greedy optimization strategy outlined in GGS. Detailed descriptions of the algorithm can be found in Algorithms 1 and 2.  
```
0: Histogram \(H=\{H_{1},H_{2},\cdots,H_{n}\}\) and privacy budget \(\epsilon\)
0: Sanitized histogram \(\widehat{H}\)
1:\(\epsilon=\epsilon_{1}+\epsilon_{2}\), \(\lambda_{1}=\frac{1}{\epsilon_{1}}\) and \(\lambda_{2}=\frac{1}{\epsilon_{2}}\);
2:\(\widehat{H}=H+\langle Lap(\lambda_{1})\rangle^{n}\);
3:\(\widehat{H}\)=Ascendings\({}_{sort}(\widehat{H})\);
4:\(S=GGS(\widehat{H},\lambda_{2})\); // \(S\) stands for a group strategy, i.e., a set of segmentation points
5:\(C=Grouping(H,S)\);
6:for\(i=1\) to \(|C|\)do
7:\(\overline{C_{i}}=\sum_{H_{j}\in C_{i}C_{i}\in C}\frac{H_{j}}{|C_{i}|}\)
8:endfor
9:for each \(H_{j}\in H\)do
10:\(\widetilde{H}_{j}=\overline{C}_{i}+\frac{Lap(\lambda_{2})}{|C_{i}|}\), where \(H_{j}\in C_{i}\);
11:endfor
12:return\(\widehat{H}=\{\widehat{H}_{1},\widetilde{H}_{2},\cdots,\widetilde{H}_{n}\}\)
```  
**Algorithm 1** SReB_GCA  
Moreover, we can obtain two corollaries, Corollaries 1 and 2, related to SReB_GCA from the perspective of privacy and relative error respectively.  
**Corollary 1**.: _SReB_GCA in Algorithm 1 satisfies \(\epsilon\)-DP._  
Proof.: SReB_GCA in Algorithm 1 divides the privacy budget \(\epsilon\) into \(\epsilon_{1}\) and \(\epsilon_{2}\), where \(\epsilon_{1}\) is used to realize the perturbation of the original histogram in Line 2, and \(\epsilon_{2}\) is used to perturb the mean value of a group in Lines 9-11. According to Property 2, SReB_GCA satisfies \(\epsilon_{1}+\epsilon_{2}=\epsilon\)-DP.  
**Corollary 2**.: _Given the group \(C_{i}\) obtained by SReB_GCA in Algorithm 1, the mean value is \(\overline{C}_{i}\), the number of bins is \(|C_{i}|\), \(\widehat{H}_{j}\in C_{i}\) is a bin in the group, and \(\widehat{H}_{j}\) is the sanitization of \(\widehat{H}_{j}\); then, the relative error \(E[err(\widehat{H}_{j})]\) is approximately \(\frac{1}{\widehat{H}_{j}}(|\widehat{H}_{j}-\overline{C}_{i}|+\frac{\lambda_{ 2}}{|C_{i}|})\), where \(\frac{|\widehat{H}_{j}-\overline{C}_{i}|}{\widehat{H}_{j}}\) is RRE and \(\frac{\lambda_{2}}{\widehat{H}_{j}|C_{i}|}\) is RNE with \(\lambda_{2}=\frac{1}{\epsilon_{2}}\)._  
Proof.: From Theorem 1, we can obtain the result when \(\lambda=\lambda_{2}\).
## 3 Test Results and Analysis
### The Description of the Macroscopic Damage Characteristics of the Structures
From the perspective of the concrete falling area, the damage on the left side is relatively slight, while the straight wall on the right side is seriously damaged, resulting in a large stripping and crushing zone. There is a main crack with a width of 3 mm through the floor of test section B in the middle of the structure. A crack with a length of tens of centimeters appears at both the ends of the crack, which is caused by the foundation restraint reaction force. In addition, there is a transverse crack in the middle of the left side, which is connected with the damage area of the left straight wall, as shown in Figure 11a. Figure 10: The damage states of the left arch spring and the right one: (**a**) the left arch spring; (**b**) the right arch spring. The macroscopic damage of the composite test section of the C30 reinforced concrete with the foamed concrete backfill layer is shown in Figure 9. The tiny cracks appear at the left and right arch spring, the longitudinal full-length cracks appear at the left arch spring, and the length of the right arch spring is about 90 cm from the inner end face. There is no obvious damage to the floor, as shown in Figure 11b. The test section C is slightly damaged. Simultaneously, in order to determine the damage grades of the three structures based on the previous classification criteria of the damage grades that are classified as total damage, severe damage, moderate damage and slight damage, and combining with the macroscopic damage phenomenon and damage characteristics of the three structures, the damage grade and damage characteristics of the three different types of structures are given, as shown in Table 6. \begin{table}
\begin{tabular}{c c c c c} \hline
**Test Section** & **Structural Material** & **Damage Characteristics** & **Damage Mechanism** & **Damage Grade** \\ \hline A & High-performance reinforced concrete & The maximum width of the vault is about 5 mm. Cracks along the thickness direction appear at the left and right arch spring, and a tiny crack appears in the floor. & Tensile failure Compression shear damage & \multirow{2}{*}{Slight damage} \\ \hline B & C30 reinforced concrete & There are no obvious cracks in the vault. The concrete of the hance is stripped, resulting in the through cracks and shear damage. The arch spring has an extrusion fracture zone, part of the concrete falls off. A through crack with a width of about 3 mm appears on the floor. & \multirow{2}{*}{
\begin{tabular}{c} Part of compression shear damage \\ Splitting tensile \\ \end{tabular} } \\ \hline C & C30 reinforced concrete with foam concrete & Tiny cracks appear at the left and right arch spring. & Part of compression shear damage & \multirow{2}{*}{Slight damage} \\ \hline \end{tabular}
\end{table} 
Table 6: The damage grades and damage characteristics of the test sections. Figure 11: The damage state of the floor: (**a**) test section B; (**b**) test section C.
## 5 Discussion
### Tectonic Controls on Depositional Systems
#### 5.1.2 Foreland Tectonic Controls on Syn-Triassic Deposition  
Previous research on the Kuqa Foreland Basin indicates that the primary factors governing the depositional systems during the Triassic were basement paleogeomorphology (itself shaped by tectonic activity), sediment supply, and paleoclimate [70]. Climatic conditions influenced the types of sedimentary facies developed, while the interplay between tectonic subsidence and sediment influx directly controlled the spatial configuration of these systems. The evolution of the paleotectonic framework constrained sedimentary evolution, with parameters including thrust loading, sedimentary loading, crustal rheological properties, and intra-crustal forces directly governing lithospheric flexural subsidence. During deposition of the Triassic Ehuboulake Formation to Huangshanjie Formation, sustained compressional forces from the South Tianshan orogenic belt intensified lithospheric flexural deformation, driving basin morphological evolution from a broad-shallow to narrow-deep configuration. This lithospheric flexural variation dictated vertical sedimentary infilling patterns. Within the study area, the vertical succession from Ehuboulake to Huangshanjie formations exhibits an upward transition from braided-river-delta facies to lacustrine facies, reflecting progressive intensification of compressional stress in the foreland basin.
# Enhancing QoS in Opportunistic Networks Through Direct Communication for Dynamic Routing Challenges
## 4 Simulation Environment and Results
### Simulation Setup  
To evaluate the proposed methodology, the ONE simulator [22] is utilized, incorporating mobile nodes that operate at varying speeds. Some nodes share the same home location, while others are initialized with different starting points. Each node is capable of communicating within a range of 10 m. The nodes generate messages with sizes ranging from 500 KB to 1 MB at intervals of 25 to 35 s. They are organized into six distinct communities, each with its own home location, where every node begins its operation. The mobility of a node to its home location, as well as to other locations, can be characterized by the probabilities \(p\) and \(1-p\), respectively. In the simulation setup, a threshold is varied to assess the number of delivered messages and the overhead ratio.  
The performance metrics employed in this study are outlined as follows.  
* Number of delivered messages: It is measured as the total number of successful messages delivered to the destination.
* Number of dead nodes: If the remaining energy of the node is less than a certain value, it is referred to as a dead node.
* Overhead ratio: It is used to measure the number of average copies forwarded per message.
## 6 Advantages of Including 3D Printing in the Manufacture of Prostheses
### Rapid and Accessible Prototype Development  
Three-dimensional printing has allowed researchers and designers to experiment with new concepts more quickly and cheaply, which has accelerated the development cycle of robotic prosthetics. Researchers no longer need to wait weeks to receive test prototypes. They can produce them in their own labs or workshops in a matter of hours, allowing for a rapid iteration of designs. This has not only improved the quality of the prostheses but has also allowed for further innovation in terms of functionality and design [7].  
The ease with which prototypes can be modified and tweaked has significantly reduced the time between conceiving an idea and creating a viable final product. This immediate access to 3D-printed prototypes has been a crucial factor in the evolution of robotic prosthetics, where the needs of users vary greatly from person to person.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 3 Study Case and Results
### Bus Faults
#### 3.1.4 ROCOV Algorithm  
This algorithm computes the voltage's derivative concerning the sampling period, as indicated in Section 2.4. Subsequently, the computed value is compared against a predefined threshold. In this study, the selected threshold value for the ROCOV algorithm is set at 200 kV/ms, in absolute terms. This threshold is set based on the expected voltage fluctuations during fault conditions. The value reflects the typical rate at which voltage changes occur during faults, especially for pole-to-pole or pole-to-ground faults, and ensures prompt detection of the sudden voltage variations that are characteristic of faults. This threshold is selected after considering the system's operational voltage range, its fault resistance, and the dynamics of voltage fluctuations in the MVDC grid.  
In the depicted scenario, illustrated in Figure 6, pole-to-pole and pole-to-ground faults are shown. The voltages' rate of change is negative due to the negative slope of the voltage variation in the case of fault occurrence.  
The selected threshold value fulfills the requirements to protect the DC grid. It can be observed in Table 4 that the fault detection time does not change for the simulated fault resistances. This implies that the ROCOV algorithm is robust under different types of faults and fault resistances. As has been stated before, and comparing it to the ROCOC algorithm, the ROCOV has better performance when the fault resistance increases due to the fast drop of the voltage when a fault occurs.
# An Updated Review of Smac Mimetics, LCL161, Birinapant, and GDC-0152 in Cancer Treatment
## 8 Combination Therapy with Birinapant for Cancer Treatment  
Overexpression of IAPs induces chemotherapeutic drug resistance in many cancer types. Targeting IAPs sensitizes drug-resistant cancer cells to chemotherapeutic drugs. Birinapant as an IAP antagonist has been combined with many chemotherapeutic drugs for cancer treatment. Birinapant has been combined with norcanthanidin, docetaxel, or TNF-related apoptosis-inducing ligand (TRAIL) for breast cancer treatment [23; 91; 92]. In addition, birinapant has been combined with paclitaxel, demethylating agent (5-AZ), TNF-\(\alpha\), Fas ligand (FasL), gemcitabine, 5-fluorouracilc (5-FU), and oxaliplatin for different types of cancer treatment, such as pancreatic cancer, head and neck squamous cell carcinoma, melanoma, acute myeloid leukemia, and colon cancer [79; 80; 84; 86; 88; 93; 94; 95; 96; 97]. Birinapant improves the anti-cancer effects of radiotherapy for treating Fas-associated protein with death domain (FADD) and cLAP1-overexsed head and neck cancers [88]. [PERSON] et al. demonstrated that birinapant activates CD8 T-cell and natural killer cells, resulting in cancer cell death [90]. The anti-cancer effects of chimeric antigen receptor (CAR) T-cell therapy correlate directly with the level of TNF. To improve the anti-cancer potency of CAR T cells, [PERSON] et al. combined CAR T-cell therapy with birinapant for cancer treatment. Results showed that this combination approach significantly reduced cancer growth [98].
# Fuzzy-PID-Based Atmosphere Packaging Gas Distribution System for Fresh Food
## 4 Conclusions  
This paper addresses the technical challenges associated with the automation and remote monitoring of gas distribution by integrating modified atmosphere preservation technology, intelligent control, and network communication technology. The progress made in this work includes the design of a logical system structure that meets the requirements for modified atmosphere preservation and remote automation of gas distribution. It also provides a study on preparation and flow control, data management and communication, and gas distribution optimization control models and regulation mechanisms. The resulting system is capable of automatic monitoring and integration of fresh food's modified atmosphere preservation in a gas distribution system. The gas distribution system utilizes a fuzzy PID control algorithm and a mass flow controller to control the air path flow. Remote data transmission is facilitated by utilizing a 4G network, and remote monitoring is achieved through a mobile phone application software. The system demonstrates a mixing gas concentration error of \(\pm\)0.25% and an increased gas distribution speed of approximately 50%.  
The system still has room for further optimization, such as improving the automatic gas distribution control process for modified atmosphere preservation, and enhancing the expert knowledge base of fuzzy rules in the design of the fuzzy PID controller. Additionally, the design of fuzzy rules, the acquisition of scale factors and quantization factors, and the consideration of more actual scene characteristics are areas that could be further explored. The proposed system has potential application in many kinds of food preservation using MAP gas distribution to improve the precision, quality, and safety of fresh food in storage or in supply chain.  
Conceptualization: H.Z.; Methodology: X.Z.; Software: B.W.; Validation: X.Z. and J.F.; Investigation: B.S., B.W. and J.F.; Writing--original draft: H.Z.; Writing--review and editing: X.X.; Visualization: B.S.; Supervision: X.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research is supported by the 2115 talent development program of China Agricultural University.
# Study on the Hydrodynamic Performance of Typical Underwater Bionic Foils with Spanwise Flexibility
## 1 Introduction
Study of the hydrodynamic mechanism of bionic foils would help to reveal the hydrodynamic mechanism of biological prototypes [23; 24; 25]. Additionally, bionic foils can easily be applied to the existing underwater propellers. Related research has been conducted aiming at the problem. However, the foils were usually modeled in two dimensions or simplified to simple three-dimensional shapes [26; 27; 28]. [PERSON] et al. mainly studied the wake topology of forked and unforked fish-like plate with numerical method [10]. [PERSON] et al. studied experimentally the effect of adding an in-line oscillatory motion to the oscillatory heaving and pitching motion of flapping foils that use a power down stroke [19]. In addition, the foils in previous studies were usually regarded as rigid bodies. As is widely known, the locomotive organs of animals are flexible. The flexibility would influence the propulsion performance. Especially, spanwise flexibility merits more attention and further study because of the high aspect ratio of the bionic foil. As is shown in [29], a water tunnel study of the effect of spanwise flexibility on the thrust, lift and propulsive efficiency of a rectangular foil oscillating in pure heave was performed. [PERSON] et al. numerically examined the performance of a thin foil reinforced by embedded rays resembling the caudal fins of fish [30]. The numerical studies of spanwise flexibility on complex shaped foils were few. In [31], the influences of the motion parameters on the propulsion performance of a bionic caudal fin were analyzed by extracting performance parameters. In this paper, a more in-depth analysis is conducted aiming to further reveal the influences of spanwise flexibility by extracting the vortex patterns in the wake. To investigate the propulsion mechanism of bionic foils, this paper establishes a numerical model of propulsion of bionic foils and proposes a corresponding numerical method. First, according to the biological observations, a geometric model and a kinematics model of bionic foils are established. Next, the proposed numerical method is used to accomplish numerical simulations and study the propulsion mechanism of a NACA foil and a crescent-shaped foil. Additionally, a self-propulsion model of a thunniform bio-inspired model is adopted to verify the numerical model and solution approach and to further analyze the hydrodynamic mechanism of the thunniform mode. After that, the effects of spanwise flexibility on the propulsion performance are studied. By extracting the flow field characteristics in the wake and hydrodynamic forces acting on the foil, the hydrodynamics mechanism of these effects is analyzed. To verify the reliability of the numerical results, verification experiments are conducted.
## 3 Efficiency and Accuracy of the Proposed Simulation Method
### Wind Tunnel Test Performed by [PERSON] and [PERSON]
#### 3.1.3 Comparison of Efficiency  
(1) Modeling efficiency  
Powered by the FDSGenerator and the easy-to-use feature for mesh generation in the FDS, the authors constructed the above FDS model in half an hour through the proposed modeling method. It is worth noting that the proposed modeling method does not require engineers to be proficient in the FDS. Thus, for people who are not familiar with the FDS, the proposed modeling method enables such individuals to build the FDS model in a short time. By contrast, it took approximately 2 h for the authors to build the corresponding Fluent model, given that the mesh generation and parameter setting in the Fluent were more complicated. For engineers who are not familiar with Fluent, it is highly possible that they would have to spend more time on the modeling phase.  
(2) Computation efficiency  
In this study, 32- and 64-core cloud computing units were purchased and utilized, respectively, for the CFD computation of these two models. The processor employed was an AMD EPYC 7452 CPU with a clock rate of 2.35 GHz, loaded with the Linux operating system. The differences between the computation efficiencies of these two models are shown in Figure 9. With 32 cores, the FDS model required 1.2 h for the computation work, while 3.9 h were needed by the Fluent model. With 64 cores, the computation time of the FDS model decreased to 0.9 h, while that of the Fluent model was 2.8 h. It can be seen that the computation time of the Fluent model is more than three times that of the FDS model, regardless of whether 32 or 64 cores are used. Provided that the price of the cloud computing platform is 0.05 dollar per core hour, the Fluent model requires an additional 4.32 (32 cores) and 6.08 (64 cores) dollars in cloud computing resources compared to the FDS model.  
It should be noted that the cost of the license for the Fluent is generally high, and various cases are required to be simulated in an actual project. As a result, the Fluent-based simulation is not an efficient and low-cost solution to the problem being dealt with in this study. By comparison, the proposed high-efficiency simulation method is capable of saving a considerable amount of time and money.
## 1 Introduction
It is worth noting that the model presented in this paper, improved geometric optics with topography (IGOT), was used in the comparison rather than the GOT model of [11]. Another GNSS-R model is the Signals of opportunity Coherent Bistatic scattering model for Vegetated terrains (SCoBi-Veg), which simulates reflectivity from flat vegetated surfaces [18]. The complex received field estimated by the SCoBi-Veg comprises three main contributions: (1) a direct term, which represents the direct signal from the transmitter to the receiver; (2) a specular term, which accounts for the signal reflected from the surface in the specular direction; and (3) a diffuse term, which accounts for the incoherent scattered signal that arrives at the receiver. The diffuse term is calculated through a Monte Carlo simulation. As the effect of topography cannot be neglected in spaceborne GNSS-R modeling, SCoBi-Veg is more suited for airborne systems. In this paper, we expand the GOT model to include the effects of small-scale roughness of the ground and low-to-moderate vegetation cover. This model, the IGOT model, divides the surface roughness into three regions: (1) large-scale roughness (topography), (2) intermediate-scale roughness, and (3) small-scale roughness, which is on the order of a few electromagnetic wavelengths. The large-scale roughness captures large topographical features, such as hill slopes and plateaus. The intermediate-scale roughness covers the region between the large-scale and the small-scale roughness. Both the intermediate-scale and the small-scale roughness are assumed to be random, while the large-scale roughness is determined and derived from a DEM. The IGOT model is validated against CYGNSS delay-Doppler maps (DDMs) using in situ soil moisture and surface roughness measurements. The validation areas are Walnut Gulch, AZ, USA, and Jornada Experimental Range, NM, USA. These areas contain SoilSCAPE CYGNSS land calibration and validation sites. The rest of the paper is organized as follows: Section 2 describes the integration of the three-scale roughness concept into the original GOT model. The handling of vegetation in the model is explained in Section 3. An overview of the two validation sites, CYGNSS data, and ancillary data for the model is given in Section 3. The simulation and validation results are presented in Section 4, including four CYGNSS tracks and three individual DDMs. Discussions of the results and future directions are in Section 5. Lastly, conclusions and a summary of findings are given in Section 6.