esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology of Construction Schedule Robustness Measure  
### Composite Robustness Criterion  
For real-life construction settings, activities and logic relationships among activities are two key components of a schedule, and construction duration is an important objective value of a schedule. Thus, it is meaningful to establish the robustness criterion from those three perspectives. Therefore, the composite robustness criterion includes start time deviation \(r_{s}\) for measuring the schedule solution robustness via deviation between the actual start time and planned start time of each activity, structural deviation \(r_{p}\) for measuring the schedule solution robustness via deviation between the actual construction order and planned construction order of each activity, and completion probability \(r_{c}\) for measuring the schedule quality robustness via deviation of the construction duration, as shown in Equation (1):  
\[r=\rho_{s}r_{s}+\rho_{p}r_{p}+\rho_{c}r_{c} \tag{1}\]  
where \(r_{s}\) is the absolute value of the weighted summation of the deviations between the activity's actual start time and planned start time, i.e.,  
\[r_{s}=\sum_{i=1}^{n}\omega_{i}(|S_{i}-s_{i}|) \tag{2}\]  
where \(i\in N+\) is the number of activities, \(\omega_{i}\) denotes the weight of activity \(i\) and is the normalized value of the additional cost corresponding to the activity \(i\), \(S_{i}\) represents the actual start time of activity \(i\) and \(s_{i}\) represents the planned start time of activity \(i\), the unit of which is days.  
Figure 1: Research framework.  
In Equation (1), \(r_{p}\) is the absolute value of the weighted summation of the deviations between the activity's actual construction order and planned construction order, i.e.,  
\[r_{p}=\sum_{i=1}^{n}\omega_{i}\Big{(}\Big{|}P_{i}-p_{i}\Big{|}\Big{)} \tag{3}\]  
where \(P_{i}\) and \(p_{i}\) are the actual and planned order of activity \(i\) respectively, which are dimensionless. \(P_{i}\) is determined by discrete event simulation [1], \(p_{i}\) is determined according to the design of the construction manager.  
In Equation (1), \(r_{c}\) is the contrary number of a schedule's completion probability, i.e.,  
\[r_{c}=-P(T\leq T\prime) \tag{4}\]  
where \(T\) and \(T^{\prime}\) are the actual and planned construction duration, the unit of which is days. \(P\) represents probability. A schedule's completion probability is determined as \(P(T\leq T^{\prime})\)[10]. A bigger completion probability indicates better quality robustness of a construction schedule. Nevertheless, smaller \(r_{s}\) and \(r_{p}\) indicate better solution robustness of a construction schedule. Since it is necessary to keep the monotonicity of the \(r_{s}\), \(r_{p}\), and \(r_{c}\) consistent, \(r_{c}\) should be the contrary number of \(P(T\leq T^{\prime})\), as shown in Equation (4).  
In Equation (1), \(\rho_{s}\), \(\rho_{p}\) and \(\rho_{c}\) are the weights of \(r_{s}\), \(r_{p}\), and \(r_{c}\), whose detailed calculation methods are illustrated in Sections 4.2-4.4.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 4 Discussion
### Decadal Declines in Coastal and Inter-Andean GWS  
Our analysis revealed significant decadal reductions in GWS along the Peruvian coast and inter-Andean valleys, particularly during dry periods associated with ENSO phases. These results align with findings in other arid, agriculturally intensive regions. For example, [PERSON] and [PERSON] (2020) [48] documented persistent GWS depletion in Pakistan's Indus Basin using GRACE and GLDAS data between 2005 and 2015. Similarly [49] reported strong correlations (R \(\approx\) 0.73) between GRACE-derived GWS anomalies and in situ well data in the semiarid High Plains aquifer of the USA. Likewise, [50] analyzed California's Central Valley and found a strong correlation (R \(\approx\) 0.81) between GRACE anomalies and field water table data, confirming the validity of the satellite-based approach for assessing groundwater storage in semi-arid agricultural regions. The authors of [51] determined that GRACE provides robust and consistent estimates of groundwater storage, validating them against California Department of Water Resources well data. These parallels reinforce the notion of widespread aquifer stress in irrigated, water-scarce zones.
## 5 Computational Experiments
### Experimental Experiments and Comparative Analysis  
To evaluate the effectiveness of the HACO algorithm, we compare it with the Gurobi solver, BR-GRASP, SA, GA, and GA-GSK. The experiments are organized into three parts: The first part examines HACO's solution accuracy and computational efficiency against Gurobi. The second part demonstrates HACO's ability to generate higher-quality solutions than other baseline algorithms under scenarios with unlimited CI capacity. The third part extends the evaluation to limited CI capacity conditions, where HACO, enhanced by a dynamic rescheduling mechanism for conflict resolution, exhibits superior robustness and adaptability compared to BR-GRASP, SA, GA, and GA-GSK.  
For smaller instances (C15 and C25), the HACO algorithm terminates after 300 iterations or 30 consecutive iterations without improvements. For larger-scale instances (C50  
\begin{table}
\begin{tabular}{l c c} \hline
**Symbol** & **Explanation** & **Quantity** \\ \hline \(q_{\text{b}}\) & On-board battery capacity & 32 kWh \\ \(g\) & Charging power & 0.94 min/kWh \\ \(c_{\text{cs}}\) & Unit energy replenishment cost for charging & 1.45 CNV/kWh \\ \(c_{\text{bs}}\) & Battery swapping cost & 100 CNV/swap \\ \(t_{\text{bs}}\) & Duration of battery swapping & 5 min \\ \(c_{f}\) & The cost of operation per vehicle & 197.20 CNV/vehicle \\ \(c_{\text{vp}}\) & Variable cost per unit distance & 1 CNV/km \\ \(v\) & Vehicle speed & 40 km/h \\ \(h\) & Energy consumption factor & 0.2 kWh/km \\ \(t_{max}\) & Maximum duration of one route & 420 min \\ \(s_{i}\) & Service time of customer \(i\) & 30 min \\ \hline \end{tabular}
\end{table}
Table 1: Parameterization settings for the instances.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters** & \multicolumn{4}{c}{**Parameter Values and Results**} \\ \hline \(\beta\) & 2 & 3 & 4 & 5 \\  & 9127.64 & 8893.62 & 9207.57 & **8648.33** \\ \(\rho\) & 0.1 & 0.2 & 0.3 & 0.4 \\  & 9002.25 & **8866.73** & 9003.82 & 8971.00 \\ \(q_{0}\) & 0.5 & 0.6 & 0.7 & 0.8 \\  & 8825.921 & **8764.76** & 9115.67 & 8992.04 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental results for the HACO algorithm’s parameters.  
and C100), it terminates after 100 iterations or 10 consecutive iterations without improvements. To simplify the presentation, the following terms will be abbreviated in subsequent figures, tables, and texts: number of vehicles (#Veh), times of battery swapping (#BS), charging cost (CC), total cost (TC), and running time (t).
## 5 Experiments and Results
### Transferability  
An adversarial example for one model can often serve as an adversarial example for another model, even if the two models were trained on different datasets, using different techniques; this is known as transferability [54]. White-box attacks may overfit on the source model, as evidenced by the fact that black-box success rates for an attack are almost always lower than those of white-box attacks [7; 13; 55; 56]. Herein, we checked transferability of our proposed black-box attack on 200 correctly classified ImageNet images by both the source model and the target model, using different \(\epsilon\) values. The results, summarized in Table 5, show a positive correlation between the \(\epsilon\) values and the transferability success rate. We noted that attacks are better transferred between ResNet-50 to VGG-16-BN models and surmise this is due to the fact that ResNets models were mostly inspired by the philosophy of VGG models, wherein they use relatively small \(3\times 3\) convolutional layers.
# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
## 5 Real-World Application
### Scalability of Deployment  
Scalability is a key advantage of our proposed architecture. Since agents operate autonomously and communicate via standardized offers and responses, new nodes (e.g., warehouse providers) can be integrated with minimal configuration. In addition,  
* LLM agents do not require centralized control or a global system state.
* The protocol layer and capacity pooling mechanisms are inherently decentralized and support horizontal scalability across networks of varying size and complexity.
* Simulation results show that LLM agents maintain consistent decision quality even in resource-constrained constellations. For the system to succeed in a real-world setting, it must handle increased loads effectively. We propose enhancing scalability through a distributed architecture (deploying multiple LLM instances in parallel to manage different segments of the warehouse network) and load balancing (implementing efficient resource allocation to maintain performance under heavy demand).  
These strategies will enable the system to scale seamlessly as operational needs grow and make the architecture adaptable for both SMEs and large enterprises.
## 3 Quality of Service
### Evaluation of the Selected Specifications  
Real-time Support  
The selected specifications are mainly based on the IEEE 802.15.4 data link layer, except for the WISA specification relying on the IEEE 802.15.1 standard. As 802.15.4e FA MAC Layer and WISA only address the specifications of the physical and data link layers, the comparisons of the selected technologies are mainly based on the features of their two first layers. To complete this comparison, the 802.15.4 FA MAC needs to be evaluated in the context of a whole system. However, this aspect is outside the scope of this survey.
## 4 Results and Discussion
### Model Fitting
#### 4.3.2 Fluorite Purity Grade (FPG)  
The influence of reagent on FPG is shown in Figure 5. It is observed that PS and QT reached a maximum close to their central point. On the other hand, WD and SS are the reagents whose variation influenced the FPG the least. The fact that WD did not have a considerable effect was due to the effect of PS [15; 16]. PS, being a carbonate flocculant, inhibits the effect of WD as a dispersant due to the concentrations of PS used compared to WD.  
Figure 4: 3D surface graphs of FMR showing the effect of PS, QT, SS, OA, and WD.  
As the oleic acid dose increased, the FPG decreases, reaching a minimum close to its upper level. This fact is due to the fact that high doses of OA promote the flotation of the rest of minerals present in the flotation cell. Therefore, it is necessary to determine the optimum value of OA dosage based on the desired FMR since it is not economically viable to obtain a very high FPG if the FMR is extremely low.  
If graphs of Figure 5 are compared with the \(p\)-value of Table 6 for Y\({}_{2}\), it can be seen that all of the factors except SS (X\({}_{5}\)) have an important effect on FPG. Some interactions between factors also are statistically significant for FPG. It is the case of PS and QT (X\({}_{1}\)X\({}_{2}\)), PS and SS (X\({}_{1}\)X\({}_{5}\)), QT and OA (X\({}_{2}\)X\({}_{4}\)), QT and SS (X\({}_{2}\)X\({}_{5}\)), OA and SS (X\({}_{4}\)X\({}_{5}\)). Finally, some quadratic effects show influence in FPG as PS\({}^{2}\), QT\({}^{2}\), and OA\({}^{2}\).
## 4 Discussion
This small difference falls within the range of typical segmentation or measurement variability, making the classification highly sensitive to minor inaccuracies. Additionally, the reliance on a percentage increase disproportionately penalizes smaller hematomas, where slight volumetric changes represent a higher relative change, compared to larger hematomas where substantial volume increases might still fall below the threshold. Thiscreates a systematic bias and potential misclassification, highlighting the limitations of using relative volume thresholds to define HE in a consistent and clinically meaningful manner. Segmentation of small hematomas can be particularly challenging for deep learning models due to their size and limited features. To address this, some research groups have implemented model design modifications, such as a multi-scale learning-to-rank framework within a U-Net architecture [59], to improve the segmentation of tiny hematomas [8]. In our study, however, we found no significant association between hematoma volume and segmentation Dice scores, indicating no systematic errors related to hematoma size. Furthermore, there were no instances of HE misclassification for baseline hematomas with volumes <1 mL. This outcome is likely due to our use of clinically relevant absolute HE definitions, starting at a threshold of \(\geq\)3 mL, which minimizes the influence of inaccuracies in segmenting very small hematomas. Differences in patient population characteristics, scanning parameters, and inter-reviewer variability in hematoma segmentation can potentially hamper the trans-institutional generalizability of our pipeline. However, our use of the multicentric ATACH-2 cohort for internal testing and for establishing sensitivity and specificity thresholds, combined with the validation of the final pipeline on an unseen external cohort from Charite--where hematomas on head CTs were delineated by an independent research team--suggest that our pipeline and thresholds are likely to be generalizable. Our results were limited by a sizable proportion of subjects that may need expert review of automated segmentations. The differences in cross-validation/training, internal test, and external validation cohorts might have contributed to maintaining sensitivity and specificity thresholds. The datasets used for external validation were obtained from ICH patients who presented to the respective centers prior to 2019. This timeline accounts for the inevitable delays associated with data collection, quality control, image processing, and hematoma segmentation. Finally, it should be noted that even human segmentations are inevitably prone to error and inter-reviewer variability.
## 5 Experiment Verification and Analysis
### Dataset Experiment of Worm Gear Reducer  
#### 5.2.1 Experimental Description  
The dataset of a worm gear reducer is from the experimental database of the Shanghai University of Science and Technology [48]; the type of worm gear reducer is WPA40, the deceleration ratio is 1:10, the worm head number is 2, the worm gear tooth number is 20, the worm gear indexing circle diameter is 30 mm, the modulus is 2 m, the lead angle is \(9^{\circ}28^{\prime}\), and the pressure angle is \(20^{\circ}\). Two AC servo motors are used to load and drive respectively, and the load size is 0 Nm or 6 Nm. Artificial faults are added to the worm gear, which are pitting, spalling, and breaking, respectively. The sensor uses a three-axis accelerometer, which can collect vibration signals in three orthogonal directions at the same time, which is installed on the gearbox body with a sampling frequency of 12.8 kH, and the fault experimental platform is shown in Figure 13. In this experiment, the vibration signal in the X-axis direction, which is most sensitive to the vibration caused by the worm gear fault, is selected as the analysis signal source. The servo motor speed is 1000 rpm, the mixed data with a load of 0 Nm and 6 Nm are selected, and four types of fault modes including normal state are selected, which are normal (NOR), pitting (PIT), spalling (SPA), and breaking (BRO). The length of each sample is 2048, the number of samples for each type of fault is 1000, and the training set, verification set, and test set are divided according to 6:1:3. For example, Table 3 shows the details of the experimental dataset. Figure 14 shows the original waveform of the vibration signal measured in the four states of the worm gear.
# Development of Compact and Robust Physical System for Strontium Optical Lattice Clock
## 5 Conclusions  
In summary, we have developed a compact, lightweight, and robust physical system for a strontium optical lattice clock, making it well suited for operation outside laboratory conditions. With dimensions of 465 mm \(\times\) 588 mm \(\times\) 415 mm, a mass of 66.6 kg, and a total power consumption of less than 60 W (40 W for the physics package and 20 W for the wavelength control system), it stands as one of the most compact physical systems of its kind reported to date. The system operates automatically without requiring manual assistance, typically locking all lasers within 5 min. Using this system, we successfully loaded 1.4 \(\times\) 10\({}^{4}\) 87 Sr atoms into 1D optical lattices with an estimated lifetime of 3.6 s.  
Additionally, we conducted numerous environmental tests. Despite undergoing multiple intercity relocations (including road, rail, and sea transportation), prolonged power outage tests, mechanical tests, thermal tests, and a series of harsh conditions, the performance of the physical system remained stable. The use of the magnetic shielding box allowed the physical system to rotate on the ground while maintaining atom capture and detection.  
Through various engineering designs, the physical system proved to be suitable for conditions outside the laboratory due to its size, power consumption, quality, and reliability. This advancement can accelerate the development of transportable optical clocks, and even space optical clocks, facilitating their use for experiments in outdoor environments or outer space. It bears significant implications for fundamental physics research, navigation systems, quantum science, and related fields [40].  
Conceptualization, Y.C. and C.Z.; methodology, Y.C., W.T. and C.Z.; software, J.X., G.Z. and F.G.; validation, Y.C., C.Z., W.T. and J.M.; formal analysis, Y.C., W.T. and C.Z.; writing--original draft preparation, Y.C. and C.Z.; writing--review and editing, C.Z. and H.C.; supervision, H.C.; project administration, H.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Evaluation of the Implicit Gradient-Enhanced Regularization of a Damage-Plasticity Rock Model
## 1 Introduction
In particular, many gradient-enhanced models have been developed explicitly for concrete [13; 16; 17; 21], and special attention has been paid to the proper representation of cracking under predominantly tensile stress states [17; 22]. However, while cracking in tension (cf. Figure 1 left) is an important failure mode for concrete structures, considerably less attention was paid to shear failure (cf. Figure 1 right), i.e., pure mode II failure, or mixed failure under confined stress states. In fact, many of the available models have been validated based on examples of mode I failure, and often it is tacitly assumed that they perform equally well for more complex failure modes. The apparent gap in the literature on the assessment of gradient-enhanced models for describing such complex failure modes motivates a systematic investigation of a gradient-enhanced constitutive model applied to different types of material failure. To this end, in the present contribution, a gradient-enhanced damage-plasticity model for rock is proposed and evaluated. This model is considered as a representative for a wide class of gradient-enhanced damage-plasticity models. The model is based on the damage-plasticity model by [PERSON] et al. [24], and its damage formulation is extended following the implicit gradient-enhanced approach proposed by [PERSON] and [PERSON] [17]. Based on numerical examples involving complex failure modes, the capability of the model to capture different types of material failure in a realistic and objective manner in finite element simulations will be demonstrated. Figure 1: Schematic illustration of two characteristic failure modes (according to [23]): tensile failure (opening mode, **left**) and shear failure (sliding mode, **right**). The remainder of the paper is organized as follows: In Section 2, the original damage-plasticity model for intact rock and rock mass, proposed in [24; 25], is briefly summarized. In Section 3, the implicit gradient-enhancement of the rock model is presented, and the numerical implementation into a finite element framework is discussed. Section 4 covers numerical simulations of laboratory tests including wedge splitting tests as well as triaxial compression tests and triaxial extension tests with various levels of confining pressure. Additionally, a benchmark example of tunnel excavation will be presented, and the representation of complex failure modes in an objective manner with respect to the employed finite element mesh will be demonstrated. Finally, in Section 5, the paper is closed with a summary and a discussion on recommended future research activities.
## Project Background
### Design of the Support Structure and Exavation  
The reinforced bored piles are 1.2 m in diameter, 1.4 m in pile spacing, and 20.4 m to 23.0 m in length. A reinforcement mesh of 20 cm in length and width is suspended between the piles, with 8 mm diameter reinforcement in the mesh, and a 10 cm thick C25 shotcrete surface layer. The slope angle of foundation pit excavation is 45\({}^{\circ}\). The angle between soil nailing and the horizontal direction is 15\({}^{\circ}\), the embedding length is 10 m, and the vertical spacing between soil nailing is 1.5 m. There are six lines of prestressed anchor cables, set every 3 m from top to bottom. The reinforced concrete top beam with 1.2 m in width and 1.0 m in height secures the first anchor cable. The waist beam also secures other five anchor cables. The anchor cable is adopted secondary pressure grouting, with water-cement ratio  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Soil Layer** & **Depth** & \(\gamma\) & \(c\) & \(\varphi\) & \(E_{\mathbf{s}}\) & \(E_{\mathbf{50}}\)ref & \(E_{\mathbf{ood}}\)ref & \(E_{\mathbf{ur}}\)ref & \(K_{\mathbf{0}}\) \\  & **(m)** & **(kN\(\cdot\)m\({}^{-3}\))** & **(kPa)** & **(\({}^{\circ}\))** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline Miscellaneous fill & 0–10 & 18.5 & 14 & 12 & 5.0 & 7.5 & 7.5 & 22.5 & 0.79 \\ Totally weathered red sandstone & 10–10.7 & 19.5 & 17.5 & 16.5 & 11.0 & 22 & 22 & 66 & 0.71 \\ Strongly weathered red sandstone & 10.7–15.7 & 23.5 & 35 & 22 & 30.0 & 60 & 60 & 180 & 0.63 \\ Moderately weathered red sandstone & \textgreater{}15.7 & 26.6 & 300 & 25.7 & — & 150 & 150 & 450 & 0.57 \\ \hline \end{tabular} \(\gamma\) = soil unit weight; \(c\) = cohesion; \(\varphi\) = internal friction angle; \(E_{\mathbf{s}}\) = modulus of compression; \(E_{\mathbf{50}}\)ref = reference secant stiffness in triaxial test; \(E_{\mathbf{ood}}\)ref = reference tangent stiffness for oedometer loading; \(E_{\mathbf{ur}}\)ref = reference unloading/reloading stiffness; \(K_{\mathbf{0}}\) = lateral coefficient of the earth pressure.  
\end{table}
Table 1: Mechanical parameters of soils.