esa-sceva/satcom-chunk-collection huggingface dataset:
# Rate of Penetration Estimation with Parameter Correction
## 2 Well Depth Correction
### Dynamic Elongation
#### 2.3.1 Drill String Dynamic Model  
The drill string dynamic model considers both axial and torsional dynamics of the drill string, which are coupled through the interaction between the drill bit and the formation. The axial and torsional dynamic model from [18] is expressed below:  
\[M\mathbb{z}(t)+C_{a}\dot{z}(t)+K_{a}(z(t)-V_{0}t)=W_{s}-W(t) \tag{16}\]  
where \(M\) is the equivalent translation inertia, \(C_{a}\) is the axial damping coefficient, \(K_{a}\) is the axial stiffness, \(z(t)\) is the axial displacement, \(V_{0}\) is the constant axial speed, \(W_{s}\) is the submerged weight of the drill string, and \(W(t)\) is the dynamic force due to bit-rock interactions. For torsional dynamics, the model is formulated as  
\[I\dot{\phi}(t)+C_{t}\dot{\phi}(t)+K_{t}(\phi(t)-\Omega_{0}t)=-T(t) \tag{17}\]  
where \(I\) is the rotary inertia, \(C_{t}\) is the torsional damping coefficient, \(K_{t}\) is the torsional stiffness, \(\phi(t)\) is the angular displacement, \(\Omega_{0}\) is the constant rotational speed, and \(T(t)\) is the Torque on Bit. The dynamic force \(W(t)\) acting on the drill string is the sum of the cutting force (\(W_{c}(t)\)) and the frictional force (\(W_{f}(t)\)), or  
\[W(t)=W_{c}(t)+W_{f}(t). \tag{18}\]  
Both of them vary with time due to the changing position and motion of the drill bit. For \(W_{c}(t)\), it is cutting force, which can be calculated by  
\[W_{c}(t)=e\dot{a}\zeta R(d(t))H(\dot{\phi}(t)) \tag{19}\]  
where \(e\) is the intrinsic specific energy of the rock, \(a\) is the radius of the drill bit, \(\zeta\) is the cutter face inclination, \(R(d(t))\) is the ramp function describing the cutting depth, and \(H(\dot{\phi}(t))\) is the Heaviside function representing the direction of rotation. For the friction force \(W_{f}(t)\), it can be estimated by  
\[W_{f}(t)=\sigma aH(d(t))H(\dot{z}(t)) \tag{20}\]  
where \(\sigma\) is the contact strength, \(l\) is the wearflat length of the drill bit, and \(H(d(t))\) and \(H(\dot{z}(t))\) are Heaviside functions representing the cutting depth and axial motion, respectively. \(d(t)\) is the instantaneous cutting depth as  
\[d(t)=z(t)-z(t-\tau(t)) \tag{21}\]  
where \(\tau(t)\) is the state-dependent delay representing the time it takes for the drill bit to rotate and re-engage with the formation.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 3 Methodology
### Transformer Grouping and Fault Types
#### 3.1.2 Group Y Transformers  
Group Y includes operational transformers prone to faults. Faults are identified by analyzing FRA traces before physically inspecting the transformers. The frequency response data provide insights into potential faults within the RLC circuitry, enabling pre-emptive fault identification.
# Investigation of Visual Stimulus Signals Using Hue Change for SSVEP
## 4 Discussion
In visual display terminal (VDT) work, it has been reported that the degree of fatigue felt by the user changes depending on the display color, and this study confirmed that the visual stimulus of SSVEP also affects the degree of fatigue in accordance with the changing of the display color. Further, it was confirmed that the fatigue level of the flash stimulus generated by the hue change was less than that of the conventional visual stimulus generated by the brightness change. However, because there were some color combinations for which the flash stimulus generated by color could not be experienced and which reduced the algorithm accuracy rate of SSVEP, it is necessary to be aware of them. The present study revealed that it is possible to acquire SSVEPs with color signals. Therefore, it is possible to add color to the interface by using color stimuli as well as the conventional black-and-white flash stimuli. By doing so, we believe that it is possible to make BCI/BMI operate intuitively. In the future, it will be necessary to investigate the algorithm accuracy rate and intuitive operability by mixing multiple color signals.
## 3 Data Explanation  
### The Results of Pull-Out Tests  
Pull-out tests were conducted on rock bolts using a 100 kN tensile testing machine at a 1 mm/min rate. The machine recorded load and displacement data, which were then used to generate load-displacement curves. These tests were repeated for samples cured for 7, 14, 21, and 28 days, with internal diameters of 23 mm and 50 mm.  
Figure 8 presents the peak load values for 60 pull-out tests on samples with a diameter of 23 mm, 1% FA content, three different W/G, and five varying CTs. As shown in Figure 8a, the axial load bearing capacity of fully grouted rock bolts is significantly influenced by CT, W/G ratio, and DC. As expected, bolts with larger diameters exhibited greater strength and maximum peak load values. Visual inspection of the failed bolts revealed radial and tangential cracks and some grout displacement, as illustrated in Figure 8b,c.
## 2 Related Works
### Use Cases of xBD Dataset  
The xBD dataset, developed jointly by Maxar Technologies and DARPA, is a publicly available large-scale satellite imagery dataset designed for multi-disaster building damage analysis. It includes over 850,000 annotated building footprints across 15 countries and eight disaster types, including earthquakes, floods, hurricanes, and wildfires [21; 22; 23; 38; 39; 40].  
[PERSON] and [PERSON] [24] proposed a multi-temporal fusion CNN using xBD image pairs, and [PERSON] et al. [38] combined a semi-Siamese network with a pyramid pooling module to enhance classification performance, achieving an F1-score of up to 0.90. [PERSON] et al. [39] applied self-supervised learning to the unlabeled portion of xBD images, obtaining better performance comparable to fully supervised models. [PERSON] et al. [25] introduced a state-space model architecture using Visual Mamba for building damage assessment, outperforming CNN- and Transformer-based methods. [PERSON] et al. [41] and [PERSON] et al. [42] developed extended and augmented versions of xBD to assess damage to not only buildings but also electrical infrastructure.  
Despite these research efforts, there remain inherent limitations. While most of these studies focus on building-level damage assessment, applications to linear infrastructure (i.e., roads) are still rare. Therefore, this study repurposes the temporally aligned satellite imagery in xBD for road-specific training, presenting a novel use case of the dataset for road damage detection.
# Recent Advances in Tunable External Cavity Diode Lasers
## 3 Research Progress of ECDL  
### External Cavity Quantum Well Laser  
Quantum well semiconductor lasers offer several advantages, including narrow linewidth, high conversion efficiency, and excellent stability. When utilized as a gain chip in external cavity semiconductor lasers, quantum well lasers can be integrated into various external cavity structures. By employing different semiconductor structures as gain chips, it is possible to design ECDLs capable of operating across a broad wavelength spectrum, ranging from ultraviolet to far-infrared [23; 24; 25; 26].
# Development Status and Service Performance Preliminary Analysis for BDSBAS
## 4 BDSBAS Service Performance Evaluation
### SF Service Integrity and Availability
Considering both the accuracy and the integrity performance, the service availabilities of all monitoring receivers are analyzed and shown in Table 4. The results shown that the average value of the service availabilities of the 30 monitoring receivers is about 98%. Some of the receivers located at the service marginal areas have lower service availabilities as their vertical position errors exceed vertical accuracy threshold (20 m), mainly related to the low accuracy of nearby grid corrections as well as the poor dilution of precision (DOP). The accuracy and availability of grid ionospheric corrections at the service marginal areas are low for very few monitoring stations distributed at these areas, which leads to a smaller number of augmented satellites which could be observed in the areas at some time, and leads to the challenge of the service availability. Figure 9: Time series of position errors and protection levels at the Beijing station.
## 3 Analysis and Discussion of Results
### Thermal Conductivity
Figure 12 shows that a relatively weak linear regression between these two properties is obtained (R\({}^{2}\) = 0.57). In turn, Figure 13 shows, as reported by several authors, that there is a reasonable linear association between the thermal conductivity and the amount of water present in the concrete mixes (R\({}^{2}\) = 0.60). These results are in agreement with the observations made by [PERSON] et al. [15]. These authors also observed a moderate linear relation between these two properties, concluding that, compared with other factors, the influence of the effective w/c ratio on thermal conductivity is not significant. Figure 12: Variation of the thermal conductivity with the air content. Figure 13: Variation of the thermal conductivity with the effective w/c ratio. Figure 14 shows that there is a clearly linear relationship between the thermal conductivity and the compressive strength at 28 days, as evidenced by the high value of R\({}^{2}\) = 0.94. The strong correlation that exists between these two properties is justified by the fact that they are influenced by the same factors. As previously stated, the nature of the various RA and the quality of the interfacial transition zone (ITZ) between the RA and the cement paste were the factors that most influenced the compressive strength. On the other hand, the thermal conductivity is also strongly influenced by the above mentioned factors, since the composition of the RA and the ITZ between the aggregates and the cement paste are the main aspects that influence the porosity of concrete, which, in turn, is a fundamental parameter for thermal conductivity [15].
## 2 Materials and Methods
### Artificial Neural Network Modeling
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Input Variable**} & \multicolumn{4}{c}{**Output Variable**} \\ \cline{3-14} \multirow{2}{*}{**\#***} & **Origin of** & **Temp.** & **Dosage** & \multicolumn{2}{c}{**wt. \% of Mineral for Energy Source**} & \multirow{2}{*}{**pH**} & \multirow{2}{*}{**Eh**} & \multicolumn{2}{c}{**Concentration at Time \(t\) (mg/L)**} \\  & **Bacteria** & & & & & & & & & & & & & & & \\ \cline{1-1} \cline{6-14}  & Mine & & & & & & & & & & & & & & & & \\ \cline{1-1} \cline{6-14}  & drainage & 32 & 1 & 0 & 45.64 & 50.34 & 0.03 & 0.01 & O & O & O & X & O & O \\ \hline \multirow{3}{*}{2} & Hot spring & 42 & & & & & & & & & O & X & O & \(\triangle\) & O \\  & water & 52 & 2 & 3.42 & 0.07 & 9.20 & 87.10 & 0.18 & O & O & X & \(\triangle\) & O & O \\ \cline{1-1} \cline{6-14}  & & 62 & & & & & & & & O & X & O & O & O \\ \hline \multirow{3}{*}{3} & Hot spring & & & & & & & & & & & & & & & & \\ \cline{1-1}  & water & 32 & 10 & 13.05 & 0.20 & 39.70 & 46.78 & 0.28 & O & O & X & O & O & O \\ \hline \multirow{3}{*}{4} & Mine & & & & & & & & & & O & X & O & X & O & X \\  & drainage & 42 & 40 & 0 & 45.64 & 50.34 & 0.03 & 0.01 & O & \(\triangle\) & X & X & O & X \\ \cline{1-1} \cline{6-14}  & & 160 & & & & & & & & O & X & O & X & O & X \\ \hline \multirow{3}{*}{5} & Hot spring & & & & & & & & & & & & & & & & \\ \cline{1-1}  & water & 32 & 10 & 13.05 & 0.20 & 39.70 & 46.78 & 0.28 & O & X & O & O & O & O \\ \hline \multirow{3}{*}{6} & Hot spring & & & & & & & & & & & & & & & & & \\ \cline{1-1}  & water & 42 & 10 & 0 & 45.64 & 50.34 & 0.03 & 0.01 & X & X & X & X & O & O \\ \hline \multirow{3}{*}{7} & Mine & & & & & & & & & & & & & & & & & \\ \cline{1-1}  & drainage & 32 & 1.50 & 0 & 61.3 & 0 & 28.51 & 10.16 & O & X & O & X & O & \(\triangle\) \\ \cline{1-1} \cline{6-14}  & & 3.75 & & & & & & & & & & & & & & & & \\ \hline \multirow{3}{*}{8} & Mine & & & & & & & & & & & & & & & & & & \\ \cline{1-1}  & drainage & 32 & 1.33 & 14.67 & 44.86 & 0 & 6.55 & 33.82 & O & X & \(\triangle\) & X & X & O \\ \cline{1-1} \cline{6-14}  & & 1.33 & 0 & 61.3 & 0 & 28.51 & 10.16 & \(\triangle\) & X & O & X & X & O \\ \hline \hline \end{tabular}  
* the same number as Table 1, O: data exist, used for ANN model training, validating, and testing; \(\triangle\): data exist, only used for ANN model further verification; X: no data  
\end{table} 
Table 2: Input and Output variables comparison from previous studies.
## 3 Materials and Methods
### Accuracy Assessment  
As mentioned earlier, field measurements were made at eighteen fixed points in the study area using GNSS/RTK before the flight and imaging procedure. One of these points was monitored for approximately 5 h as it served as a reference point. Then, the remaining points were monitored and measured relative to this base point. Five of these points were used as ground control points (GCPs) in indirect georeferencing, and the remaining twelve served as checkpoints (CPs) to verify the accuracy of the digital elevation models and the resulting orthophotos.  
The checkpoints were used to calculate the positional accuracy of the DEMs and orthophotos produced. The positional accuracy was determined from the value of the calculated errors between the observed coordinates of the CPs using GNSS/RTK measurements and the predicted coordinates of the CPs from the geospatial data products (DEMs and orthophotos). In addition, the root mean square errors (RMSEs) were calculated for the east (X), north (Y), and vertical (Z) coordinates, as well as the total RMSE.  
Figure 6: Distribution of ground control points and checkpoints.
## 4 Results
### Ship Target Tracking Experiment  
In the ship target tracking experiments, to validate the tracking performance of the algorithm used in this study under complex scenarios, six scenes with varying resolutions, noise levels, brightness, and contrast were selected from the FLShip dataset for extensive tracking experiments. Four state-of-the-art trackers, i.e., StrongSORT [53], OC-SORT, Byte Track [54], and BOT-SORT [55], are selected for the comparative tracking experiments in this paper. The performance of the trackers is evaluated using metrics such as Multi-Object Tracking Accuracy (MOTA), Identification F1 (IDF1), and Identity Switches (IDs). Additionally, we evaluated the speed of the tracker in terms of frames per second (FPS). Although the runtime may vary significantly with different hardware, the specific details are shown in Table 5. MOTA is calculated based on false positives (FPs), false negatives (FNs), and identity switches (IDs), emphasizing detection performance. In contrast, IDF1 better measures the consistency of ID matching, and IDs measure the performance of the multi-object tracking model in handling target identity switches. An upward arrow \(\uparrow\) indicates that a higher value of the metric corresponds to better performance, while a downward arrow \(\downarrow\) indicates that a lower value of the metric corresponds to better performance. From Table 5, it can be observed that, under the same detector, OC-SORT demonstrates higher localization accuracy and tracking performance across different scenarios and complex environments. Compared to StrongSORT, ByteTrack, and BOT-SORT, OC-SORT maintains MOTA and IDF1 scores above 90%, even in conditions of severe noise interference or insufficient lighting, accompanied by faster inference speeds, allowing it to perform real-time perception updates at a higher frame rate. This implies that OC-SORT can handle input images more frequently and provide real-time and accurate target tracking information, thus providing reliable perception and decision support for subsequent applications. Figures 15-17 present the tracking results of four different trackers in Scene-2, Scene-4 with noise interference, and Scene-6 with low lighting conditions. It can be observed that the StrongSORT and BOT-SORT trackers experience multiple ID switches in Scene-2, Scene-4, and Scene-6. Additionally, ByteTrack exhibits lower IOU at several time points (e.g., T1 and T3 in Scene-4, and T4 in Scene-6), resulting in relatively lower bounding box localization accuracy. Comparative experiments show that OC-SORT achieves more stable tracking of the target ship while ensuring real-time performance, striking a good balance between real-time processing and stability. The combination of the Bi-YOLO detector and the OC-SORT tracker used in this study maintains high accuracy and stability in multi-ship tracking tasks, providing a solid foundation for precise and stable target localization in subsequent experiments.
# A Study of an EOQ Model of Growing Items with Parabolic Dense Fuzzy Lock Demand Rate
### Literature Review on Inventory Models  
[PERSON] [14] first proposed the EOQ inventory model. In this model, he minimized the total inventory cost, including the ordering cost and holding cost. After that, several researchers produced many research articles on inventory problems. All of them considered the demand rate as a deterministic one. For the first time, [PERSON] [15] found uncertainty in one-stage inventory problems. Today, modern researchers have shown interest in solving inventory problems in uncertain environments. [PERSON] [16] studied an EOQ model considering the daytime nonrandom fuzzy demand rate. [PERSON] et al. [17] used a step order fuzzy approach for solving an EOQ model. He regarded it as uncertain daytime demand, where demand in the backorder period depends on time. [PERSON] et al. [18] developed an EOQ model with selling price and promotional efforts depending on demand rate. They optimized the model using the intuitionistic fuzzy technique. [PERSON] et al. [19] introduced the learning effect concept for solving an EOQ model for imperfect quality items. In 2016, [PERSON] and [PERSON] [20] studied an EPQ model with uncertain lead time and demand rate. They considered all parameters of the intuitionistic fuzzy number and used the intuitionistic fuzzy aggregation Bonferroni mean for defuzzification of the model. [PERSON] et al. [21, 22] considered an EPQ model and used the dense fuzzy lock set concept to reduce pollution by reusing waste items of the sponge iron industry. [PERSON] et al. [23] developed an EOQ model with dense fuzzy demand rate where two decision makers make a single decision to optimize the model. [PERSON] et al. [24] studied an EOQ model under daytime uncertain demand rate. They introduced a computer-based algorithm and flowchart to optimize the model by updating key vectors automatically. [PERSON] and [PERSON] [25] considered and EOQ model of imperfect-quality items with considerable discounts. They optimized the model using cloudy fuzzy numbers. [PERSON] and [PERSON] [26] presented an integrated decision support model for customers to buy their desired products online. [PERSON] et al. [27] studied a generalized EOQ model for growing items where the size and weight of the items increase over time. He also gave a case study of a poultry farm. [PERSON] [28] developed an EOQ model for growing items for fish farming and poultry industries. In these inventories, the items increase during the shortage period. [PERSON] et al. [29] introduced an inventory model of growing items and added a constraint of carbon emissions. [PERSON] et al. [30] incorporated the carbon emission issue into an EOQ model of deteriorating items under a volumetric fuzzy system. He developed a methodology for optimizing the inventory profit function. [PERSON] et al. [31] studied a three-echelon supply chain model in an imperfect production system. In this model, they considered inspection error, learning effect, and return policy under a fuzzy system.
# From Complex Modulus E* to Creep Compliance D(t): Experimental and Modeling Study
## 2 Methods and Materials  
### Methods  
The objective of this study is to predict the results of creep compliance in time domain D(t) directly from the results of complex modulus.  
The methodology is divided into two main parts: an experimental part and a modelling part. The experimental part consists of realization of complex modulus tests and creep compliance tests (Figure 2).  
Complex modulus tests were performed in traction-compression configuration according to Quebec's standard LC26-700. Seven test temperatures ranging from \(-20\)\({}^{\circ}\)C to \(40\)\({}^{\circ}\)C with a step of \(10\)\({}^{\circ}\)C and a frequency sweep from \(0.1\) Hz to \(10\) Hz were used. The imposed strain was \(\pm 50\)strain. Complex modulus results were fitted using 2S2P1D model, then two different methods were used to  
Figure 1: (**a**) The generalized Kelvin–Voigt model (GKV); (**b**) 2S2P1D model.  
Figure 2: The work plan.  
switch from the frequency domain to the time domain for creep compliance. First, 2S2P1D results were coupled with the approximation proposed by [PERSON] which is mathematically simpler than the other three equations proposed in [PERSON]'s et al. [14] study. Second, the GKV model was calibrated according to the minimization with 2S2P1D model proposed by [PERSON] et al. [19]. and the creep compliance in time domain is given by Equation (6).  
Creep tests were performed using three configurations namely: direct tensile configuration, direct compression configuration and indirect tensile configuration. Direct tensile and direct compression tests were achieved on cylindrical specimens with a diameter of 75 \(\pm\) 1 mm and a height of 135 \(\pm\) 2 mm. A static traction or compression stress load was applied on the samples for 300 s; it took 0.1 s to reach the imposed load, without impact, from a contact force of 50 N. The measurement of deformation was insured by three extensometers of 25 mm in length. Four test temperatures were used ranging from \(-\)20 \({}^{\circ}\)C to 10 \({}^{\circ}\)C with a step of 10 \({}^{\circ}\)C. The amplitude of the imposed stress was fixed by taking in to account complex modulus results. For each test temperature, before to start the creep test and at the end of it, a partial complex modulus test was done in order to check if the material was still in the viscoelastic behavior domain. Indirect tensile creep tests were performed on cylindrical samples with a diameter of 150 mm and a thickness of 38 mm. AASHTO T322-07 has not set a load application speed or a maximum amplitude, so, in order to have the same test parameters, the IDT loading was applied in 0.1 s and the amplitude was fixed manually in order to have a minimum 50 \(\upmu\)strain at 0.1 s.