esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Structural Characteristics
#### 3.1.2 Relative Density  
The relative density, denoted as (\(\overline{\rho}\)), is determined as the ratio between the macroscopic density of a cellular solid (\(\rho\)) and the density of the base material (\(\rho_{\rm s}\)). These are crucial parameters for understanding the mechanical properties of lattice structures, especially in terms of stiffness and strength [2].  
\[\overline{\rho}=\frac{\rho}{\rho_{s}} \tag{3}\]  
Relative density is directly related to the porosity and the volume fraction of the material. As the relative density increases, the lattice structure generally becomes stiffer and stronger, but also heavier. On the contrary, as the relative density decreases, the structure becomes lighter, but can also become less stiff and weak. The values shown in Table 3 confirmed these descriptions. The measured relative densities for FGLSs are smaller for re-entrant structures than for other lattice designs. In addition, for re-entrant structures, the uniform configuration has been seen to have smaller relative densities than other configurations. The re-entrant structure has a lower relative density than the other structures, which is consistent with its low weight. The VG configuration of the chiral lattice structure has the highest relative density value. As seen in Equation (3), the relative density is directly related to the weight and volume of the structure. In this respect, it is consistent that the relative density is higher in the VG chiral lattice structure, which is a heavier structure than the other structures. The highest relative density among all specimens was found in the VG chiral lattice structure, followed by the VG rotation lattice structure.  
Among all of the configurations, chirals have higher relative density values than other structures. The lowest relative density values were obtained for re-entrant structures. Except for the HG rotation lattice structure, uniform configurations have lower relative densities than gradient lattice structures. Uniform configurations have thinner strut diameters than FGLSs and the support material dissolves better than FGLSs, resulting in a lower relative density. Less material will result in less resistance to the applied force [9]. The relative density differences, which reduces the strength of the FGLS specimens discussed in Section 3.3, is also reflected in the mechanical strength values of the specimens.
## 3 Problem Description and Working Assumptions  
This section presents a description of the Rail-Road PI-hub cross-docking process with its different functionalities and layout. The Rail-Road PI-hub is a type of PI-nodes that is used to transfer PI-containers from the train to the outgoing trucks. It is composed of mainly a PI-sorter, and two maneuvering areas in front of the train and the loading docks. The process of cross-docking starts by unloading the PI-containers from the wagons. Then, the PI-container are routed to the outbound docks, grouped by destination, and then loaded into the outgoing trucks (Figure 1).  
In the cross-dock scheduling context, objectives and constraints that are related to sustainability were not largely addressed in the literature. To the best of our knowledge, references [30; 50] are the only ones considering sustainability or energy consumption constraints in classical cross-docking terminals. Therefore, in the PI context there is still a need for research that considers sustainable objectives and constraints for the Rail-Road PI-hubs cross-docking terminals. For this study, in the context of Rail-Road PI-hub cross-dock truck scheduling, insuring the sustainability consists on minimizing the use of the energy consumed by the PI-conveyors to move PI-containers between the outbound trucks and the train's wagons in addition to the minimization of the cost of the trucks used to serve each destination. Therefore, the cost of using outgoing trucks must be minimized while, at the same time, minimizing the PI-conveyors energy by finding the shortest path from the wagons to the outgoing trucks. The energy is then calculated by finding the number of PI-conveying units swept by the PI-containers during the way from the wagons until arriving to the trucks. Those two objectives are conflicting. Indeed, minimizing the PI-conveying energy could lead to the use of multiple trucks for the same destination, which is not an optimal choice, since the cost of using additional trucks for the same destination is expensive compared to the PI-conveying energy. Moreover, many trucks will leave the PI-hub with empty spaces. To face this issue, Multi-Objective approaches are developed and detailed in the next two sections: a Multi-Objective mathematical MIP model (MO-MIP) and two Multi-Objective meta-heuristics (MO-VNSSA and MO-VNSTS).  
The following are the main assumptions considered for this problem:  
* There are two objective functions to minimize: \(F_{1}\) (the cost of using outbound trucks) and \(F_{2}\) (the energy consumption cost for the PI-conveyors).
* The two objective functions are arranged in some order defined by the decision maker: minimizing \(F_{1}\) and then \(F_{2}\) in this study for example.
* The train unloads PI-containers that have different lengths and destinations.
* Each outbound truck must load only PI-containers that have the same destination.
# Transient Friction Analysis of Pressure Waves Propagating in Power-Law Non-Newtonian Fluids
## 4 Conclusions  
In this study, a numerical model of transient pipe flow of power-law non-Newtonian fluids was established using the weighted residual collocation method. The apparent viscosity of the analytical solution was corrected using frequency and power-law index, resulting in more accurate results with the analytical model. Finally, the influencing factors of the wall shear stress were analyzed and the following conclusions were drawn:  
1. The apparent viscosity of power-law non-Newtonian fluids is not only related to shear rate, but also to the square root of the pulsation frequency;
2. For Newtonian fluids, the ratio of the amplitude, \(P_{p}\), to the average, \(P_{c}\), of the pressure pulsation will not affect the distribution of pulsating shear stress. However, for non-Newtonian fluids, the ratio of the amplitude to the average of the pressure pulsation has different effects on the wall shear stress. Moreover, even under the same ratio, for \(n<1\), an increase in the average pressure pulsation will reduce the wall shear stress;  
Figure 14: Relative pulsating shear stress at the wall varied with frequency.  
Figure 13: Variation of pulsating shear stress at the wall for different frequencies. (**a**) \(n=0.5\) and \(P_{p}/P_{c}=0.2\), (**b**) \(Q=0.03\) m\({}^{3}\)/s and \(P_{p}/P_{c}=0.2\).  
Because an increase in frequency leads to a decrease in velocity fluctuations, the pulsating shear stress at the wall decreases monotonically with the increase in frequency, regardless of how the flow rate and power-law index change.  
Conceptualization, X.Z.; methodology, H.L. and P.J.; software, P.J. and H.W.; validation, H.L., H.W. and X.Z.; investigation, C.R., Y.S. and X.Z.; resources, P.J. and H.W.; data curation, P.J.; writing--original draft preparation, H.L. and P.J.; writing--review and editing, Y.S., C.R.; supervision, C.R.; funding acquisition, C.R. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (No. 2019 YFC1509204), and \"the Fundamental Research Funds for the Central Universities (No. 24 CX03015A)\".  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company CNPC North China Petroleum Administration Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Empirical Study
### Datasets  
In this empirical investigation, 102 software defect datasets were utilized. These datasets were publicly available and provided by [PERSON] et al. [34], [PERSON] et al. [35], [PERSON] et al. [36], [PERSON] et al. [37], [PERSON] et al. [38], [PERSON] et al. [39] and [PERSON] et al. [40]. For each dataset, we calculated the number of events per variable (EPV) [41] and the defective rate. EPV is the ratio of the number of defective instances to the number of independent variables. Previous study has shown the effect of EPV on defect prediction models: higher EPV values produce more stable prediction models [42]. [PERSON] et al. [41] reported that roughly 10 EPV were essential for accurate estimation of machine learning models. Therefore, among the 102 datasets, we selected 25 datasets that had EPVs greater than 10. Moreover, we calculated the defective rate for each dataset, and we excluded the datasets with defective rates greater than 50%.  
The final set of datasets used in this paper is listed in Table 3. A total of 21 publicly available software defect datasets were used. To ensure variety and reduce bias, the selected datasets were collected by different research groups and from various domains. Each dataset was of a different size (i.e., number of instances) and granularity (i.e., instances represent a method, class or file). Each instance consisted of: (1) a single binary value indicating whether the instance was defective or non-defective (i.e., dependent variable) and (2) a set of software metrics (i.e., independent variables).
# Seismic Hazard Evaluation and Strain Dynamics in the
## 4 Discussion and Findings
### Strain Accumulation and Evaluation of \(a\) and \(b\) Parameters
[7] estimated an \(a\)-value of 6.82 and \(b\)-value of 1.0 in a study that focused on mostly Eskisehir, while [PERSON] [114] found \(a\)-values varying between 5.5 and 6.3 and \(b\)-values between 0.8 and 0.9. Also, [PERSON] et al. [130] found the same \(b\)-values around SFZ. [PERSON] et al. [131] and [PERSON] [132] estimated the \(b\)-value around 1.02 for the region including the Kutahya, Simav, and Zeytinadg-Bergama faults (KSZBF) together; for the same fault zones, [PERSON] et al. [133] found a \(b\)-value of 0.65. Similarly, [PERSON] and [PERSON] [134] calculated \(b\)-values between 0.52 and 0.66 and \(a\)-values between 3.85 and 4.78 for two separate sub-region containing different sections of the SFZ. In another study, where Western Anatolia was divided into 15 sub-regions, [PERSON] and [PERSON] [135] and [PERSON] et al. [136] calculated the \(a\)-value as 5.7 and the \(b\)-value as 0.94 for the region containing the Simav and Gediz-Dumlupnar Faults. In addition to \(b\)-values, some studies suggest that modal values (\(a/b\)) are compatible with tectonic environments and also reflect seismic activity in the region of interest better than \(a\)- and \(b\)-values alone. In this study, the modal value was calculated as 6.9, and this value points out that it is both in agreement with \(b\)-values and the general seismicity of the study area [90; 95; 108; 137; 138]. As a result of this study, when compared with previous studies conducted in the region, most of the studies are in concordance with our seismic parameters, except for those where the \(b\)-value was estimated to be considerably low. Most of the studies where the \(b\)-value was estimated to be low are those that examine Western Anatolia partially or completely, particularly without focusing on a specific fault zone. Therefore, it is believed that the low \(b\)-values obtained do not reflect the general tectonic regime of Western Anatolia, which releases energy through a large number of low-magnitude earthquakes. When considering the high \(a\)-value and low-average \(b\)-value obtained specifically for the SFZ in this study, these values suggest that the SFZ generally produces numerous low-to-moderate magnitude earthquakes. However, due to the associated subduction tectonics, it is capable of accumulating stress that could potentially generate large earthquakes, such as the Gediz earthquake (Mw = 7.2), despite being less frequent. A similar calculation was made by [PERSON] [132], who interpreted the low \(b\)-values obtained for the SFZ and its surroundings as an indication of relatively uniform structural characteristics and significant stress accumulation due to subduction tectonics. This, in turn, could lead to stress buildup over time, which is released by earthquakes that, although less frequent, are great in magnitude [139]. Also, the main location of the Izmit earthquake in 1999 (Mw = 7.4) is situated in a region characterized by a higher \(a\)-value and an average \(b\)-value [130; 141; 140].
# Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals
## 3 Rotordynamic Coefficient Identification Based on Orbit Decomposition Method  
The orbit decomposition method is established for the rapid identification of frequency-dependent rotordynamic coefficients for annular seals. This method includes three parts: elliptical orbit decomposition, fluid-induced force superposition, and rotordynamic coefficient identification. The detailed identification procedures of frequency-dependent rotordynamic coefficients are shown in Figure 3.  
Figure 3: Rotordynamic coefficient identification procedures of orbit decomposition method.
## 3 Protocols Analysis
### Protocols Comparison
It can be seen from the frame rate and the timing mode that GB/T 26865.2-2011 has higher requirements in terms of the time precision and communication rate. The advantage of this is that the data has a high real-time performance and precision, and the disadvantage is the waste of communication resources. Combining the frame type for analysis, taking the data frame as an example, the frame structure is fixed, and the data type of each data frame is the same, including phasor data, analog quantity, and digital quantity. From the perspective of data change frequency, many analog quantities are based on DC sampling, and the sampling rate is low and the change is slow. Generally, they change once in one second or several seconds, and digital quantities change more slowly. If such analog and digital quantities are transmitted at a high speed, like the transmission speed of phasor data, there will be a large amount of data duplication and redundancy, wasting communication bandwidth resources. From the perspective of application, whether all the information is needed in different application scenarios needs to be considered. For example, the master station needs all data to improve the accuracy of SCADA data [22; 33]. However, for the hierarchical-distributed protection and control of the active distribution network, control units of each area or layer only need to acquire the synchronized phasor data to start the corresponding protection, and do not need to obtain all the information [8; 34]. Excessive data can also cause problems, such as communication blocking and an increased data processing time. In terms of offline files, GB/T 26865.2-2011 only contains dynamic data files and transient recording files. In actual applications, continuous recording files will be added. IEC 60870-5-101/104 contains a wide variety of documents to meet the data recording needs of various distribution terminals. Based on the above analysis, GB/T 26865.2-2011 lacks definitions of remote control, files, data types, etc., required for power distribution terminals. Part of the data has a high redundancy, and frame types are fixed. The frame type, communication flow, and file type should be increased, so that the \(\upmu\)MPMU can perform data classification and transmission, add more power distribution terminal functions, increase file storage types, and reduce communication traffic and storage usage.
# Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning
## 2 Problem Description
### Task Scheduling Problem
#### 2.2.3 Optimization Objectives  
Models of observation satellite scheduling are always built as multiple objective optimization problems, and a scheduling algorithm aims to generate a compromise solution between objectives. [PERSON] et al. [39] implemented an indicator-based multi-objective local search method for the EOSSP, whose objectives were to maximize the total profit and simultaneously to ensure the fairness of resource allocation among multiple users. Sometimes, energy balance and fuel consumption are designed as optimization objectives [40; 41].  
In this paper, to maximize the total observation profit, more tasks and tasks with higher priority were scheduled. Hence, the objective function \(f\) was designed to maximize the total profit by the sum of priority associated with selected tasks.  
\[f=\text{max}\Bigg{(}\sum_{u=1}^{N}\frac{x_{u}}{N}prio_{u}\Bigg{)} \tag{13}\]  
This optimization objective function is subject to the constraint model mentioned above.
## 5 Conclusions  
This paper introduced an end-to-end zero-watermarking approach built on neural networks, which has practical applicability in scenarios such as image copyright registration, copyright authentication, and piracy detection. In contrast to traditional approaches that rely on handcrafted features, our methodology employs pure neural networks to learn robust features automatically. The structure of ZWNet consists of ConvNeXt and LK-PAN as the backbone and neck, respectively. Furthermore, we introduced the watermark block as the head component, transforming the challenge of enhancing robustness and discriminability into a multi-label classification task based on image identifiers. The experimental results clearly demonstrate that ZWNet effectively extracts resilient image features and generates zero-watermarks without the need for retraining. Moreover, ZWNet exhibits superior robustness, discriminability, and efficiency compared with existing methods. The results suggest that through the implementation of the proposed training strategy on image identifiers, the zero-watermark performance has been notably enhanced in terms of both robustness and discriminability simultaneously.  
Conceptualization, C.L. and D.T.; Data curation, S.C. and X.L.; Formal analysis, C.L. and D.T.; Funding acquisition, N.R. and D.T.; Investigation, X.L. and Y.Z.; Methodology, C.L. and H.S.; Project administration, D.T.; Resources, H.S. and N.R.; Software, C.L. and H.S.; Supervision, D.T.; Validation, C.W. and S.C.; Visualization, H.S. and C.W.; Writing--original draft, C.L., H.S. and C.W.; Writing--review and editing, N.R. and D.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Jiangsu Province, grant number BK20200839, the National Natural Science Foundation of China, grant numbers 42301484 and 42071362, and the Open Topic of Hunan Engineering Research Center of Geographic Information Security and Application, grant number HNGISA2023004.  
Not applicable.  
The dataset of mini-ImageNet is available at [[https://www.kaggle.com/datasets/arjunashok33/miniimagenet](https://www.kaggle.com/datasets/arjunashok33/miniimagenet)]([https://www.kaggle.com/datasets/arjunashok33/miniimagenet](https://www.kaggle.com/datasets/arjunashok33/miniimagenet)) (accessed on 30 July 2023).  
Author [PERSON] was employed by the company Nanjing Geomarking Information Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Experiments
### Practical Implications
This is particularly relevant for organizations facing persistent threats or targeted attacks, where the ability to adapt defense strategies over time is crucial for maintaining security posture. However, it is important to acknowledge certain practical considerations for deployment. Organizations implementing ARCS should ensure adequate monitoring and periodic validation of the system's decision-making process, particularly in early deployment phases. While our experiments show robust performance across different attack types, the framework's effectiveness may vary depending on the specific threat landscape and network architecture of the deploying organization. Regular model updates and fine tuning may be necessary to maintain optimal performance as new attack patterns emerge.
# Surface Subsidence Monitoring in Kunming City with Time-Series InSAR and GNSS
## 4 Results and Discussion  
Through data processing, the surface deformation rate of 2018-2020 time series in the study area is finally obtained, as shown in Figure 6. As can be seen from the figure, there is no large area of violent vertical movement in Kunming city area, and the subsidence rate ranges from \(-\)48 mm/year to 26 mm/year (Negative value indicates land subsidence, while positive value indicates uplift). The surface subsidence area is mainly distributed in the southwest, east and northeast of Dianchi Lake, and there is no continuous subsidence  
Figure 5: CORS sites time series. (**a**) Temporal analysis of geodetic height change at ANNI station; (**b**) Temporal analysis of geodetic height change at CHG0 station; (**c**) Temporal analysis of geodetic height change at KMRCH station.  
field. However, relevant departments should pay attention to it and prevent geological disasters such as surface subsidence in advance. In the Kunyang Phosphate Mine in the southwest of Dianchi Lake, the maximum deformation rate exceeds 35 mm/year due to the surface subsidence caused by underground mining.
## 7 The Case Study
### Kinetic Energy Harvesting Materials Assessment  
To appreciate the large space available in the Al-Haram Mosque to be utilized for kinetic-based energy harvesting, an illustration is provided in Figure 13. The illustration shows a simplified 3D-style top-down view of Masjid Al-Haram in Mecca, with the Kaaba located centrally within the Mataf area (the open white circular zone surrounding the Kaaba). The image highlights designated blank areas where smart mats can be placed. These areas include Main courtyards--open spaces outside the covered prayer halls, shown in light beige, typically used during peak prayer times or overflow situations. Terrace levels--upper-level flat zones along the mosque's structure, represented in lighter tones and labeled with arrows. These areas are accessible and provide extra capacity for smart mat placement. Peripheral open zones--labeled blank spaces along the outer mosque walls, commonly used by pilgrims, especially during non-peak hours. The colored arrows and labels clearly indicate mat-eligible zones while helping differentiate between the core sacred area (darker or highlighted central sections) and general-use prayer areas (lighter open spaces). The minarets and covered halls are shown in raised 3D shapes to illustrate the layout. The light color palette is intentionally used to keep the focus on open zones and maintain clarity for academic or instructional use.  
Figure 13: A 3D-Style Top-Down View of Al-Haram Mosque with Highlighted Mat Placement Areas.
## 4 Computational Complexity  
Assume that the image has height \(M\) and width \(N\) respectively. In the pre-processing phase (where a lossless compression is used for the location map; however, we can assume that it can be done in time linearly in the number of pixels if we do not require the space usage as small as possible), training-and-prediction phase, embedding-and-shifting phase and extraction and recovery phase, the computational complexity is basically \(O(MN)\) because there are \(O(MN)\) pixels to be scanned for a constant number of times. We remark here that though the structure of the MLP neural network is fixed so that this part contributes a constant factor in the complexity, such a constant factor hidden in the asymptotic notation can actually be huge. More specifically, for each input data point (i.e., a set of four pixels) fed to the input layer of the MLP neural network in one iteration, there are \(100\times 200\) multiplications required to compute the activation of all the neurons.