esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Steady State Equation of Motion of LSCs
From the \(\Delta\)OED in Figure 2, the length \(\overline{\text{OD}}\) or \(\overline{\text{OE}}\) should be identified to find \(\alpha^{\prime}\), and the length \(\overline{\text{ED}}\) is \(2 lsin\alpha\). Because \(\overline{\text{OB}}\) is perpendicular to the jetting plane \(\Delta\)OED before collapse, the following geometrical similarity relations can be made (Figure 5). Figure 4: Jetting plane and liner. Figure 5: Geometrical similarity of \(\Delta\)OCB and \(\Delta\)OFD (refer to Figure 1). \[\overline{OD}=l\sqrt{\left(\frac{l}{Dt_{m}}\right)^{2}+1} \tag{8}\] \[\mathrm{sin}\alpha^{\prime}=\frac{\mathrm{sin}\alpha}{\sqrt{\left( \frac{l}{Dt_{m}}\right)^{2}+1}} \tag{9}\]  
As a result, the collapse point angle \(\beta\) and the modified apex angle \(\alpha^{\prime}\) are,  
\[\beta=\mathrm{sin}^{-1}\left\{sin\alpha\sqrt{\left(\frac{l}{Dt_{m}} \right)^{2}+1}\right\} \tag{10}\] \[\mathrm{and}\;\alpha^{\prime}=sin^{-1}\left\{sin\alpha\big{/} \sqrt{\left(\frac{l}{Dt_{m}}\right)^{2}+1}\right\} \tag{11}\]  
Now, the collapse point angle \(\beta\) and the modified apex angle \(\alpha^{\prime}\) are determined from the three dimensional geometrical configuration of LSCs. The next step is to apply the results in the Birkhoff theory to achieve the jet and slug velocities. Because the finer collapse behavior in the jetting plane A'HE'D'I in Figures 1 and 2 is identical to the original Birkhoff theory derivation, the following equations can be used (Figure 6). Detailed derivation of the equations is omitted to simplify the report. The only difference in between the original Birkhoff and the modified Birkhoff theory is the direction of the detonation front movement. The original Birkhoff theory assumed the sweeping detonation effect from top to the bottom of the liner. The modified Birkhoff theory, however, assumes a diagonal direction of the detonation front movement along the LSCs liner because of the different jetting and detonation front plane. This effect will be further discussed in later section. Figure 6: Geometrical description of Birkhoff theory.
## 5 Conclusions  
Core-shell microparticles were generated using the PILP process to coat oil-in-water emulsion droplets (for hydrophobic active agents) and phospholipid bilayer liposomes (for both hydrophobic and hydrophilic active agents) with a smooth and continuous calcium carbonate shell. The microcapsules may be suitable for potential use in drug delivery and other commercial applications where encapsulation of an active agent can be done within the fluidic interior of the core-shell system. The PILP method of generating microcapsules presented here may be used as an alternative to, or in conjunction with, current liposome- or emulsion-coating technologies to bypass or eliminate many of the challenges and limitations they face, such as leakage, premature release, destruction through lyophilization, storage instability, and ease of packaging and transport. The smooth and continuous calcite coating that the PILP process generates is quite remarkable considering that calcite usually forms faceted crystals when generated in vitro by the conventional crystallization process. Further investigations not included here may be required to tailor the core-shell microcapsule product depending on the intended application and its associated chemical environment. These further investigations include adjustments to the synthesis protocol to yield control over the size and size distribution, drug loading, release, and encapsulation efficiencies, as well as studies to evaluate the biocompatibility/bioactivity of the resulting calcium carbonate-coated microcapsule.  
Conceptualization, L.B.G.; methodology, L.B.G., M.A.B., A.C.L.; investigation, M.A.B. and A.C.L.; writing--original draft preparation, M.A.B.; writing--review and editing, L.B.G., M.A.B., and A.C.L.; supervision, L.B.G.; project administration, L.B.G.; funding acquisition, L.B.G. All authors have read and agreed to the published version of the manuscript.  
This material is based upon work supported by the National Science Foundation under Grant No. DMR-0710605, and initial studies by the Particle Engineering Research Center (PERC) at the University of Florida, supported by the National Science Foundation (NSF) Grant No. EEC-9402989. Part of the research reported in this publication was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health (NIH) under Award Number R01 DK092311. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.  
We would also like to thank the staff at the Research Service Centers within the Herbert Wertheim College of Engineering for their training and guidance on the SEM and XRD instruments, and the staff at the McKnight Brain Institute for the training and assistance on the confocal fluorescence microscope.  
The authors have no conflict of interest to declare.
# A Data Processing and Distribution System Based on Apache NiFi
## 5 Research and Results
### Summary of the Results  
Figure 16 shows the total processing time of the input set as a function of the number of nodes in the cluster. In both the default and optimized configurations, the biggest gain was achieved by adding a second node to the cluster. That is, the processing was two times faster. Adding more instances did not bring such a large gain. This effect was caused by the increased overhead required for cluster management and load balancing. Interestingly, the proposed configuration using \(n\) nodes allowed the input set to be processed in an amount of time that was close to that required by a cluster of \(n+1\) nodes in the default configuration. It is also important to note the reduction in the difference between the optimized and default configurations after the third node was included in the cluster. This suggests that optimization is especially important for highly utilized clusters.  
Efficient cluster scaling is crucial in the case of 5G/6G networks. The increasing number of high-density optical devices incorporated with every additional site will require more computing power to ensure effective monitoring. On the other hand, it is also important not to oversize the cluster. Above some threshold, larger clusters will not bring any significant advantages to the processing speed. For example, based on Figure 16, we can use the Elbow method to conclude that the optimal number of nodes is 3 because there is a minimal gain when enabling the fourth and fifth nodes.  
Figure 15: Distribution of time required to extract a single cited article (five nodes). Panel (**a**) shows the result of the optimized configuration, whereas (**b**) depicts the default configuration.  
Despite the relatively small differences in the total processing times of the input set, the difference between the default and optimized configurations was even more significant for the time required to serve a single FlowFile. As shown in Figure 17, it reached an order of magnitude. This is the result of the long time for which a FlowFile waits in queues when the system is overloaded in the default configuration.  
Figure 16: Summary of the processing time for the entire collection.  
Figure 17: Average processing times for individual files.
# Modeling and Solving Method for Supporting Vehicle-to-Anything' EV Charging Mode
## 4 Simulation Cases
### Consideration for the Issue of Battery Degradation  
As modeled in Equations (6) and (10), the issue of battery degradation is quantified through the evaluation of EV charging/discharging cycles. The values of penalty coefficient for the charging cycles of EVs are therefore simulated to investigate the influence of battery degradation. For convenience, the values of \(\lambda_{i}\) are uniformly set for all EVs. The charging scenario of the EV charging station is analyzed with different \(\lambda_{i}\) and the simulation outcome including the total load and economic profit for the operator side is presented in Figure 14. The charging/discharging behaviors of EVs are given in Figure 15. Since no EV selects discharging under the case \(\lambda_{i}=0\), the details of EV behaviors for this scenario are therefore omitted here.  
Figure 14: Simulation results of the charging station with different \(\lambda_{i}\): the load profiles in the big frame demonstrate the load curve changes to be more fluctuate with the increase of \(\lambda_{i}\); the profit of the operator in the small frame indicates the profit can be improved with the increase of \(\lambda_{i}\).  
Figure 13: Outcome for EV charging of the charging station: basic charging station load refers to the charging station load without EV load profile; the optimized load profile refers to the basic charging station load plus the aggregated EV charging/discharging result based on the proposed model.  
As can be seen from Figure 15, with the increase of \(\lambda_{i}\), EVs choose to perform less discharging due to the increased penalty for battery charging/discharging cycles. The users smartly choose to perform charging or discharging at adjacent time slots when \(\lambda_{i}\) increases, or even give up discharging when \(\lambda_{i}=1\). Based on Figure 14, the load profile tends to fluctuate more when \(\lambda_{i}\) decreases, while the power consumption cost changes to be higher when the demand for the lifecycle of EV batteries changes is higher (i.e., with higher \(\lambda_{i}\)). The battery degradation is therefore an important concern that can affect the charging/discharging behavior of EVs and the operational costs of the power consumption entity.
## 5 Experiments
### Comparative Experiments on Bone Stick Preprocessing  
To systematically assess the role of image preprocessing in enhancing bone stick matching accuracy, a controlled comparative experiment was designed and conducted. The objective was to evaluate the performance variations in the matching algorithm under distinct image processing conditions. The experiment was divided into two groups: The first group served as the baseline, where raw bone stick images--without any preprocessing--were directly used as inputs, along with the corresponding insertion features and archeological attributes, and fed into the matching model proposed in this study. The associated test sets likewise consisted of original bone stick images accompanied by their respective textual and contextual attributes. In contrast, the second group introduced the image preprocessing pipeline developed in this study, which corresponds to the method adopted in this paper,applying standardization and enhancement operations to the bone stick images prior to input. These preprocessed images were then combined with the same inscription information and archeological features and fed into the same model for performance evaluation. The corresponding test sets were uniformly constructed using preprocessed bone stick images and their respective inscription and contextual features. As shown in Table 5, under the experimental setting involving no image preprocessing--i.e., using raw bone stick images--the proposed method achieved an average matching accuracy of 56.14% on Test Set 1 and 44.30% on Test Set 2. In comparison, when utilizing preprocessed bone stick images under identical model conditions, the average matching accuracy significantly improved to 74.13% and 68.88%, respectively--corresponding to increases of 17.99 and 24.58 percentage points. These findings clearly demonstrate the substantial effectiveness of image preprocessing in enhancing bone stick image matching accuracy and provide strong empirical evidence for the practical utility of the proposed preprocessing pipeline in optimizing feature representation and enhancing model performance.
## 3 Numerical Results and Discussion
### Without Sprung Eccentricity
From Figure 7a, it can be seen that as \(e_{2}\) increases from 0.1 to 1.0 mm, the corresponding peak response speed increases from 1600 to 2200 rad/s. By comparing Figure 6a,b, it can be seen that the peak response speed range shifts to 1800-2200 rad/s in Figure 6b, which is much larger than that shown in Figure 6a. 6. The jump phenomenon is closely related to the SFD and the amount of unbalance, as shown in Figures 6 and 7. From Figures 6c-h and 7b, it can be seen that the jump phenomenon occurs when \(e_{4}\geq 0.7\) mm. 7. The operating deflection shape of the rotor system under a continuous operating speed of 3663 rad/s is greatly influenced by the amount and the axial location of the unbalance, as shown in Figure 6a,c. Figure 6a, \(e_{2}=0.1\) mm, shows not only that the vibration level of support I is larger than the other parts of the rotor system around 3600 rad/s but also that the operating deflection shape coincides with the second mode shape presented in Figure 3b. However, when \(e_{4}=0.1\) mm, Figure 6c shows that the vibration level of support II is the largest around 3600 rad/s. By comparing Figure 6a,c, it can be seen that the vibration level of the rotor system at \(e_{4}=0.1\) mm  
Figure 5: Waterfall diagram of support I in 1000–4500 rad/s: (**a**) waterfall diagram \(-e_{2}=0.1\) mm; (**b**) waterfall diagram \(-e_{2}=1.0\) mm; (**c**) waterfall diagram \(-e_{4}=0.1\) mm; (**d**) waterfall diagram \(-e_{4}=1.0\) mm. Figure 6: _Cont._  
Figure 6: Horizontal response of the rotor in 1000\(-\)4500 rad/s: (**a**) \(e_{2}=0.1\) mm; (**b**) \(e_{2}=1.0\) mm; (**c**) \(e_{4}=0.1\) mm; (**d**) \(e_{4}=0.6\) mm; (**e**) \(e_{4}=0.7\) mm; (**f**) \(e_{4}=0.8\) mm; (**g**) \(e_{4}=0.9\) mm; (**h**) \(e_{4}=1.0\) mm.
## 5 Discussion
### Limitations and Urban Applicability  
Although our research provides an innovative model for self-service locker selection, there are some limitations. (1) Our model treats urban space as two-dimensional and uniform, omitting factors like road networks and building density. This may cause deviations in complex urban areas due to the model's disregard for the impact of building density on paths. For instance, in high-density urban areas, users may be forced to detour around buildings and choose different route, resulting in discrepancies between theoretical distance and actual travel time [42]. (2) The current model was validated on a limited sample of university students in Shenzhen, activity patterns exhibit strong regularity, which inherently restricts generalizability to broader populations (e.g., older adults' or professionals' behavior may be more dispersed), and the number of participants was relatively small, so the model's generalization capability requires further verification. (3) KDE bandwidth was fixed according to [PERSON]'s rule. The heterogeneity of user activity ranges in different urban environments was not considered, which may affect the accuracy of the estimation due to the different ranges of human activities or different-scale cities (e.g., small- and medium-sized cities vs. megacities).  
The applicability of this model varies by city type: (1) Highly planned scenarios: In areas with regular layouts such as campuses or industrial parks, user activity hotspots are concentrated and path regularity is strong, allowing the model to effectively reduce detours (see results in Section 4.1). (2) High-complexity scenarios: In historic old urban districts or megacity cores, where road networks are intricate and building density is high, extending 3D spatial modeling (e.g., integrating 3D-GIS data) is necessary to improve accuracy. (3) Multimodal transportation scenarios: In cities reliant on public transit (e.g., Tokyo, Singapore), user paths may form multicentric hotspots around subway stations, requiring the model to integrate transfer time costs to redefine spatiotemporal distance [5]. Future work could develop adaptive modules for these scenarios, such as dynamically adjusting KDE parameters via reinforcement learning or incorporating road network topology data to optimize path weights.
# Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
## 2 Signal Model
### Observable Calculation
#### 2.2.1 Observable Based on Time of Arrival
The computation process of RTT observables is slightly different from TDoA case. The \(n\)-th 5G RTT observable is formulated as  
\[\hat{\rho}_{\text{RTT,n}}=c\cdot\hat{\tau}_{\text{RTT,n}}=\parallel\mathbf{x}_ {\text{BS,n}}-\mathbf{x}\parallel+e_{\text{RTT,n}}, \tag{3}\]  
where \(\hat{\tau}_{\text{RTT,n}}\) is the two-way time-of-flight of the 5G signal, \(\mathbf{x}_{\text{BS,n}}=[x_{\text{BS,n}},y_{\text{BS,n}},z_{\text{BS,n}}]\) is the \(n\)-th BS position, \(\mathbf{x}=[x,y,z]\) is the receiver position, \(c\) is the speed of light and \(e_{\text{RTT,n}}\) is the RTT error, which is defined as \(e_{\text{RTT,n}}\sim N(0,{\sigma_{\text{RTT,n}}}^{2})\), where \(e_{\text{RTT,n}}\) is the RTT error variance from the \(n\)-th BS. This error variance includes the receiver-transmitter synchronization error, noise errors and multipath errors. Note that the position error with RTT follows approximately the same relation with the downlink (DL) and uplink (UL) position error as the corresponding observables do. Moreover, if the noise in UL is bigger than the noise in DL, then the position error with UTDoA should be worse than the position error with the OTDoA. The RTT error is calculated as:  
\[e_{\text{RTT,n}}=\frac{(e_{\text{DL-TDE,n}}+e_{\text{UL-TDE,n}})}{2}, \tag{4}\]  
therefore  
\[\sigma_{\text{RTT,n}}=\sqrt{\frac{\sigma_{\text{DL-TDE,n}}^{2}+\sigma_{\text{ UL-TDE,n}}^{2}}{4}}, \tag{5}\]  
where \(\sigma_{\text{RTT,n}}\), \(\sigma_{\text{DL-TDE,n}}\) and \(\sigma_{\text{UL-TDE,n}}\) are the variance of the position error of RTT, OTDoA and UTDoA, respectively. Thanks to the two-way transmission between receiver and transmitter, there is no UE clock offset present in the RTT observables, which relaxes the positioning problem.
# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion
## 4 Results and Discussion  
### Training Process and Results  
In this study, the TRNet model was utilised to predict the surrounding rock grades at the current tunnel face of the TBM. The model's input variable is constituted by a 12-dimensional feature vector, which is derived from three distinct data sources (comprising five columns of raw TBM data, three columns of rock fragmentation index, and three columns of spatial data, alongside one column of HSP data). The model's output variable is a two-dimensional vector representing the probabilities of two rock classes (III and IV). The final total dataset size was \(12\times 7751\). A three-segment segmentation method was applied to this dataset in order to prevent overfitting of the model and to ensure consistency with the actual tunnel excavation. The initial segment of the tunnel was designated as the training set, the medial segment as the validation set, and the terminal segment as the test set, with a ratio of 8:1:1. The batch size for each training iteration was set at 32. The algorithm was optimised by an early stopping algorithm, and the loss function was CrossEntropyLoss. In addition, a Bayesian optimisation algorithm was used to optimise the hyperparameters of the model [41]. The search range and optimal hyperparameters for each hyperparameter are presented in tabular form (Table 6).  
In order to verify the prediction performance of the TRNet model, the present study randomly conducted 10 model trainings and recorded the accuracy, AUC, Kappa, number of training rounds, and average training duration of each training on the test set. As demonstrated in Table 7, the mean prediction accuracy, AUC and Kappa of TRNet reached 92.15%, 95.90% and 74.56%, respectively. It is noteworthy that the average number of training rounds is only 13, with each training requiring an average of 6 s, which is a highly efficient computation time for deep learning models. This suggests that TRNet achieves both computational efficiency and high prediction accuracy.
## 3 Load Distribution and Progressive Decoding on Cell BE
### Synchronization on the Cell BE  
For an efficient decoding operation, we first distribute the computational region as shown in Figure 7. The Cell processor provided in Play Station 3, which is our experimental platform, is configured with two of the eight SPE cores disabled; therefore, we can only use seven programmable cores as one PPE core and six SPE cores. As the PPE has dual-threaded and dual issue hardware, it has two threads running simultaneously. Different from the PPE, the SPEs are able to manage only one thread per core. As indicated by the thread distribution method detailed in Figure 7, PPE _thread 1_ manages pivot column's elements and should transfer the elements to the other SPE threads before processing a newly received row. In addition, the Cell BE has a communication system called _mailbox_ which delivers 32 bit data between the cores [32; 33]. In fact, we use the mailbox system to synchronize the threads as well as to transfer the elements.  
The mailbox system is designed for each SPE and implemented with an asymmetric manner; both the _inbound_ and _outbound_ mailboxes are contained in each SPE and messages are transmitted to the MFC from the SPE via the EIB. The mailboxes have one outbound entry and four inbound entries. At each SPE, a 32 bit inbound mail is read by the SPE and an outbound mail is sent by the SPE. A reading operation from SPEs stalls when the inbound mailbox entry is empty. As soon as a new message becomes  
Figure 6: Parallelization algorithms of network coding on Homogeneous processor; (**a**) HP; (**b**) RRP; and (**c**) DVP.  
available, the reading operation resumes. This stalling is also caused for a writing operation when the outbound mailbox entry is full.  
Synchronization can be achieved by using the outbound mailbox entry in the following manner. Each SPE writes a mail in the outbound entry and continuously checks whether the PPE reads the mail and makes the outbound entry empty. At receiving all mails from the SPEs, the synchronization is achieved. This also implies that the PPE is responsible to wait until it receives all the mails. After the synchronization is guaranteed, each SPE waits a reply which contains a pivot element from the PPE.  
On the other hand, we also propose to use the inbound mailbox solely for synchronization, which provides better performance with simple implementation. The PPE transfers the pivot element to the inbound mailbox entry and the SPE continuously checks until the pivot element is completely transferred. In this way, we can simply eliminate the necessity of synchronization messages from the SPE side. This is possible due to the fact that any stalled reading operation with an empty inbound entry can be used for the synchronization purpose.