esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Pulse Period Normalization  
For pulse-like earthquakes, the pulse period (T\({}_{\text{p}}\)) is a crucial parameter that greatly affects structural response. As defined by [PERSON] [60], the pulse period is the pseudo-period of the largest wavelet coefficient returned by wavelet transform of velocity motion. The pulse periods, T\({}_{\text{p}}\), of the considered near-fault pulse-like data set obtained by wavelet analysis are presented in Table 1. In dynamic time history analyses, the normalized structural period by pulse period of ground motion (T/T\({}_{\text{p}}\) or \(\widetilde{\text{T}}\)/T\({}_{\text{p}}\)) is used for earthquake records both for fixed-base and interacting cases. Variations of investigated structural response parameters with normalized periods are presented. Figures 4 and 5 illustrate the variation of the inelastic displacement ratio across the period range for both fixed-base and interacting scenarios, respectively. The graphs on the left present the results of analyses according to the structural period (T) and the graphs on the right present those according to the normalized structural period by pulse period of the ground motion (T/T\({}_{\text{p}}\) or \(\widetilde{\text{T}}\)/T\({}_{\text{p}}\)). Results are presented for \(\beta\) = 0 and 0.5 and \(\alpha\) = 5%. It can be perceived from the graphs that, when the inelastic displacement ratio is computed with respect to the vibration period, C\({}_{\text{u}}\) values vary with ductility for the analyzed period range. However, when C\({}_{\text{u}}\) spectra are obtained as a function of T/T\({}_{\text{p}}\), inelastic displacement values decrease and approximately equal to unity, which known as the equal displacement rule. This statement is valid for both fixed-base and interacting case solutions.  
The effect of pulse period-based normalization on residual displacement is presented in Figures 6 and 7. Residual displacement spectra are significantly affected by pulse period not only in terms of residual displacement values, but also in terms of trends in spectral distribution. Record-to-record variability is more evident, especially for the \(\beta\) = 0.5 case for both fixed-base and interacting cases. Since the inelastic displacement ratio is a dimensionless variable, normalization collapses C\({}_{\text{u}}\) to unity. However, residual displacement not being dimensionless is affected more significantly from the record-to-record variation.  
Figure 4: Mean inelastic displacement ratios computed as a function of T and T/T\({}_{\rm p}\) for fixed-base systems.  
Figure 5: Cont.  
Figure 5: Mean inelastic displacement ratios computed as a function of T and T/T\({}_{\rm p}\) for interacting systems.  
Figure 6: Mean residual displacement demands computed as a function of T and T/T\({}_{\rm p}\) for fixed-base systems.
## 6 Evaluation
### Results and Analysis
#### 6.2.2 Effect of Deployment on Non-Functional Goals  
In-depth performance analysis is presented in Figure 11, while cost estimation is depicted in Figure 12. Performance analysis shows that depending on expected network delays, different deployment choices may have relevant performance consequences. Expressing the behavior in a deployment-independent way along with the availability of an evaluation tool enables for predicting the system's performance across several different possibilities, thus allowing for more informed deployment choices. Should conditions change and a different deployment be required to meet performance metrics, application-level logic would not get impacted in any way.  
Figure 11: System performance (mean communication delay) for different propagation delays (one per chart) and different deployments, obtained by changing the number of smart thermostats whose code is executed locally (\(P_{l}\)) and the deployment location of the broker (surface color). Lower values indicate better performance. The pulverized system does not require any adjustment to the business logic to tackle the diverse deployment schemes: the designer can thus use the following analysis to decide which deployment configuration to pick depending on how important it is to maximize performance. Changes in edge-to-edge delays (top left) impact performance minimally, as most of the accumulated delay is elsewhere; thus, they provide a good baseline to compare other configurations to. Changing the propagation delay between edge and cloud (top right) highlights that the worse the network performance, the more is important for the broker to be close to where the computation components of the logical system are concretely deployed. Performance of the household connection (bottom left) have higher impact on the overall performance of the CPS with the count of devices being executed locally, regardless of the broker location. This suggests that offloading the business logic to the cloud, thus sending fewer data to end devices, should be considered when the network is expected to experience high load. Finally, a difference in the delay in cloud-to-cloud communications (bottom right) is influential only for cases in which the broker is located there. If there is little or no guarantee on where the cloud components will get allocated (and hence, little knowledge about their communication performance), then adopting an edge-located broker can provide a less volatile performance.
# Design and Evaluation of an Acoustic Modern
## 2 System Design
### Hardware Implementation of Analog-To-Digital (AD) Sampling Rate Adjustment  
The acoustic modem designed in this paper is based on the STM32F4 series processor of the Coetex-M4 core, which has the advantages of short cycle times and low power consumption under a clock speed of up to 168 MHz. As the Doppler is directly compensated by hardware resampling, the proposed system is capable of greatly reducing the complexity of the real-time calculation in the processor, thus facilitating hardware implementation and engineering integration in a small AUV platform [12; 13]. To further improve the communication performance, convolutional coding is also adopted with a convolutional coding rate of 1/2. The detailed implementation of DSSS demodulation at the receiver is shown in Figure 4.  
For the hardware Doppler correction method by tuning the AD sampling rate, the resolution of the AD sampling rate adjustment will determine compensation performance. In the proposed system, the internal ADC (Analog-to-Digital Converter) of the STM32F4 processor is directly used under the timer interruption mode; thus, the instant sampling rate \(f_{\mathrm{as}}\) is calculated by dividing the clock frequency \(f_{\mathrm{c}}\) with a timer parameter. With an \(f_{\mathrm{c}}\) = 84 MHz, the timer parameter is set to generate the instant sampling rate \(f_{\mathrm{as}}\) corresponding to the calculated Doppler according to the following equation:  
\[f_{\mathrm{as}}=\frac{f_{\mathrm{c}}}{1+\Delta}. \tag{3}\]Specifically, implementation of the adjustable AD sampling rate is achieved by software to set the timer parameter of the processor according to the calculated Doppler. As there is a protection interval between the second LFM signal and the data segment, the calculation of Doppler as well as the adjustment of the AD sampling rate can be conducted during this period to ensure real-time implementation.
## 3 Results and Discussion
### Tortuosity-Average Coordination Number Relationship
This seemingly counterintuitive trend indicates that tortuosity derived from flow simulations is influenced not only by geometric connectivity in carbonate systems but also by localized features such as constrictions, dead ends, and preferential high-velocity channels. These characteristics, typical of carbonate pore structures, increase flow complexity despite well-connected networks and reflect the fundamental pore-structure differences between sandstones and carbonates. The strong inverse correlation observed in sandstones supports their relatively uniform granular framework, where the average coordination number (\(Z_{avg}\)), but also reliably correlates with tortuosity across different estimation methods. Conversely, in carbonates, the greater pore-type diversity and spatial heterogeneity disrupt the direct relationship between coordination number and flow behavior, limiting the effectiveness of empirical or geometric Z metrics in capturing flow complexity. These results highlight the necessity of considering both pore connectivity and dynamic flow processes when assessing tortuosity in heterogeneous media. While \(Z_{avg}\) remains a valuable structural parameter, its predictive power for transport properties varies with rock type and the method of tortuosity estimation. Therefore, integrating multiple characterization approaches is essential for accurately modeling fluid transport in complex porous systems.
## 1 Introduction
Typical cooling methods include an air-cooled type that cools the motor housing or the inside of the motor through forced convection by using air [7], a water-cooled type that cools by flowing coolant to the motor housing or internal parts [8], and oil cooling by directly spraying oil to the heat source inside the motor, considering oil insulation and cooling characteristics [9]. Recently, studies on an oil cooling method using oil with good insulation characteristics have been actively conducted to satisfy the limitations of size and weight in high-power motors. [PERSON] et al. [10] compared a motor cooling system using oil by considering a simplified computational fluid model that used periodic conditions and a one-dimensional parameter model. [PERSON] et al. [11] investigated the cooling effect according to the spraying speed by conducting an experiment varying the spraying speed of the oil. Moreover, [PERSON] et al. [12] explained the cooling effect due to oil through a computational fluid model that incorporated a one-dimensional heat transfer model. [PERSON] et al. [13] performed a thermal performance evaluation under continuous rated conditions using a three-dimensional flow analysis model for an in-wheel motor to which an oil cooling method was applied. The analysis results were then reflected in the design of the motor. However, a few studies have been conducted regarding cooling the motor using the rotational force of the motor itself without an external power source, among the studies on the cooling analysis of the motor to which the oil cooling method was applied. The range of application of the UAV will be further expanded if such a method is introduced to a UAV, because flight duration can be increased by reducing the weight of the aircraft, and the motor performance can be increased through a low heat generation design. Therefore, in this study, a hermetic IPM motor was designed and analyzed using the 3D finite element method. A self-oil cooling structure was designed and proposed to develop a motor housing with a low heat generation structure. A prototype was fabricatedaccording to the design, and experiments regarding the heat dissipation characteristics experiment were conducted to compare and analyze the prototype and the conventional air-cooled motor. The optimized self-oil cooling structure and shape were determined using a detailed shape design. The results of this study are expected to improve the flight duration and performance of UAVs and contribute to the efficiency of the design of the UAV drive motor.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Computational Resource Management Based on RRH Clustering Techniques
#### 3.2.4 QoS-aware RRH Clustering
Meanwhile, the traffic load in each cell was predicted using the Markov model, and the optimal solution for BBU-RRH mapping was determined using the GA. The objective was to maximize the QoS by minimizing the connection blocking and handover failure with a balanced load. The QoS was determined by two KPIs, which were represented as the inverse of the blocked calls in the network and handovers. During the prediction phase, the Markov model considered the location of the user and developed groups of cells by acquiring the neighbor cells to form a Markov state, based on the user behavior. This was performed to reduce complexity. After predicting the number of users in each cell, the GA was executed in advance according to the predicted load to determine the optimal BBU-RRH mapping and obtain the appropriate configuration for minimizing the number of blocked users with the minimum execution time delay. The QoS criteria of the existing mapping were analyzed and compared with the predicted mapping. The new BBU-RRH mapping was attained if the number of predicted users and the number of users in that time match.
###### Abstract  
Millimeter-wave channel modeling is the basis of fifth-generation (5G) communication network design and applications. In urban microcellular environments, the roughness of wall surfaces can be comparable to the wavelengths of millimeter waves, resulting in walls that cannot be considered as smooth surfaces. Therefore, channel modeling methods based on only traditional three-dimensional ray tracing (RT) or the three-dimensional parabolic equation (PE) result in the limited computational accuracy of millimeter-wave channel models for urban environments. Based on the scattering theory of a rough surface and the typical scattering characteristics of a millimeter wave, the end field of the three-dimensional vector PE is regarded as the initial field of three-dimensional RT. Moreover, the number of scattered rays and scattering angles are introduced. Finally, a three-dimensional fully polarized millimeter-wave hybrid propagation channel model (3 DFPHPCM) is proposed. The proposed model improves the computational accuracy of a single deterministic model. Millimeter-wave channel measurements in non-line-of-sight (NLOS) environments were carried out to verify and optimize the proposed 3 DFPHPCM. The results show that the root mean square error (RMSE) and mean absolute error (MAE) of the proposed 3 DFPHPCM are both minimized when compared to three-dimensional RT or the three-dimensional PE, which indicates that the proposed 3 DFPHPCM has higher computational accuracy. Moreover, its runtime is the shortest among the methods. The results presented herein provide technical support for the layout of base stations.  
3 DFPHPCM; millimeter wave; urban microcellular; three-dimensional RT; three-dimensional vector PE +
Footnote †: journal: Electronics
## 3 Results and Discussions
### Dynamic Compression  
As shown in Figures 9 and 10, it can be seen that in dynamic compression, the aluminum foam has obvious 3-stage characteristics: elastic section, stress platform section, and no densification stage is found, which is due to the fact that the forward bar of SHPB equipment gives impact force to the incoming bar, and the incoming bar strikes the aluminum foam specimen at a certain speed, and the specimen absorbs the impact energy brought by the incoming bar in the compression and denaturation process, and the impact energy of incoming bar gradually decreases with the compression of aluminum  
Figure 8: Quasi-static compressive deformation process of closed-cell aluminum foam (**a**–**f**).  
Figure 7: Stress-strain response curves obtained from quasi-static compression experiments.  
foam, which leads to the smaller impact energy of incoming bar, the lower strain rate, and the lower strain rate, and no densification stage occurs. The impact energy of the incident rod decreases gradually with the compression of the aluminum foam, which leads to the smaller impact energy of the incident rod, the lower strain rate, the smaller strain of the aluminum foam, and the stage of densification does not occur. Based on the compression process of the aluminum foam, it is reasonable to infer that if there is a continuous impact energy, the aluminum foam will eventually develop a densification stage.  
From the change of yield point stress of aluminum foam under different strain rates, it can be seen that different strain rates have a certain effect on the yield point stress of aluminum foam in compression, and with the increase of strain rate, the yield stress gradually increases and the elastic strain gradually decreases. The stress in the platform section has a small enhancement with the increase of strain rate, and the trend of stress change is basically the same, indicating that there is a slight strain rate effect on the yield stress and platform stress in aluminum foam.  
Figure 10: Dynamic compressive deformation process of closed-cell aluminum foam (**a–f**).  
Figure 9: Stress-strain curves of foam samples under different strain-rates.
## 8 Conclusions  
In this paper, insertion loss reduction techniques reduce the insertion loss of 6-bit DSA by 50%; series inductive compensation and small bit compensation structures are innovatively proposed to greatly broaden the bandwidth of the attenuator and effectively improve the return loss. To the best of our knowledge, the fabricated DSA shows the lowest insertion loss, optimal impedance matching, the highest attenuation accuracy and linearity, and the best FOM using the 0.25 \(\upmu\)m GaAs process and is suitable for wideband phased array systems.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & [33] & [34] & [35] & [6] & **This Work** \\ \hline
**Technology** & GaAs & GaAs & GaAs & CMOS & GaAs \\
**BW (GHz)** & 6–18 & 5–18 & DC–23 & DC–20 & 2–22 \\
**Range (dB)** & 31.75 & 31.5 & 31.5 & 31.5 & 31.5 \\
**II. (dB)** & 9 & 6.2 & 6.2 & 7.4 & 2.6 \\
**RMS Amp error (dB)** & 0.6 & 1.1 & 0.35 & 0.37 & 0.18 \\
**RMS Phase error (\({}^{\circ}\))** & 5 & 2 & 5 & 4 & 7 \\
**RL (dB)** & 12 & 8 & 10 & 12 & 17 \\
**IP1 dB (dBm)** & N/A & 24 & N/A & 10 & 29 \\
**Area (mm\({}^{2}\))** & 5.4 & 3.75 & 0.47 & 0.98\({}^{\star}\)0.14 & 0.51 \\
**FOM \({}^{\sharp}\)** & 169 & 240 & 668 & 690 & 3269 \\ \hline \hline \end{tabular}  
* with pads and non-active space. \(\sharp\) FOM = \(\frac{\text{BW}\times\text{Range}\times\text{RL}}{\text{IL}\times\text{RMS Ampular}\times\text{RMS Phaseff}}\).  
\end{table}
Table 2: Comparison of The Prior-Art Attenuators.  
Figure 14: (**a**) Measured relative attenuation. (**b**) Simulated and measured RMS amplitude and phase errors. (**c**) Simulated and measured insertion loss. (**d**) Measured input return loss. (**e**) Measured output return loss. (**f**) Simulated and measured IP1 dB at 12 GHz.  
**Author Contributions:** Conceptualization, Q.L., K.W., X.W., Y.Y. and X.L.; methodology, Q.L., K.W., Y.Y. and X.L.; software, Q.L. and X.W.; validation, Q.L., K.W. and X.W.; data curation, Q.L. and X.W.; writing\(-\)original draft preparation, Q.L. and K.W.; writing\(-\)review and editing, Q.L., Y.Y. and X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data can be obtained from the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Dataset Annotation
### Data Pre-Processing  
The preliminary step of the data pre-processing for intent annotation was tokenization. Urdu text is written in perso-arabic script that includes two types of Unicode characters; joiners and non-joiners. If the last character of the Urdu word is a joiner, a space is necessary; however, if the word ends with a non-joiner character, a space character may not be inserted to mark the word boundary or form the required word shape [35]. Thus, traditional tokenization techniques, such as separating words on whitespaces, may lead to two possible types of word tokenization errors in Urdu: a space insertion and a space deletion. A space insertion between morphemes in the context of affixation, compounding, reduplication, foreign words and abbreviations is erroneous. In the Urdu web queries dataset, a Zero Width Non-Joiner (ZWNJ) character was used to resolve the space insertion characters manually. Similarly, white space was added between words where the ending character of the word was a non-joiner, and therefore, space characters were omitted.  
After the tokenization, similar queries were removed systematically in two iterations. Firstly, the queries were trimmed for trailing spaces at the beginning and end duplicate queries were removed. The resulting dataset was normalized through diacritics removal, systematic whitespace normalization, the removal of punctuations or accents and the removal of English alphabets. After normalization, duplicate queries and records resulting in null queries were removed again. The resultant dataset had 8519 queries.
## 3 Preliminaries
### System Architecture  
Here, we consider a scenario where the WSN is formed by stationary sensors in a two-dimension \(\mathbf{R}^{2}\) sensing field. In order to better control congestion of local network, there are several main parameters to be set. The whole of \(\mathbf{R}^{2}\) space is divided into the grids number, \(K\), and each grid is deployed in a square with the length of side, \(h\). So the grid number \(K=(H\times H)/(h\times h)\), the value of \(k\) increases in turn from left to right, and the grid value of the lower level is often bigger than that of the upper level (as shown in the Figure1). Nodes are intensive enough and randomly distributed. The initial energy of each node is homogeneous and can not be complemented. The communication radius of the node is \(r\), and the aim of \(r\geq\sqrt{2}\)\(h\) is to ensure each node in the same grid can normally communicate. Each node knows its initial position. Sensors periodically report the information about the monitored events to BS. For each node, any other node in its communication range could become its neighbor.  
In order to further to demonstrate the node and packets of the network, it is necessary to demonstrate the node \(i\). The coordinates of \(i\) are \(v(X_{i},\,Y_{i})\), so according to the initial position, the grid value of the node \(i\) is \(k_{i}=[Y_{i}/h]\times(H/h-1)+[X/h]\), and the number of \(i\)'s neighboring node is \(n_{con}^{i}\). The length of the data packet is \(L_{Data}^{i}\). The packet header structure of node \(i\) in grid \(k\) is: \(\textit{PH}_{i}\left(t\right)\!=\!\textit{PH}_{i}\left(t\right)\!=\!\left\{ \left(k_{i},\textit{ParentID}_{S_{i}},\textit{ChildID}_{S_{i}},\lambda_{i}^{k} \left(t\right)\!,\mu_{i}^{i}\left(t\right)\!,\lambda_{\textit{avg}}^{i}\left(t \right)\!,\mu_{\  
Second, the period of each grid is described. The A-type node has been set to promiscuous mode to monitor all nodes in the grid. Once the A-type node has monitored that all nodes in the grid have already sent data one time, then it broadcasts to the other nodes in the grid that this period is finished and notices which node becomes a new A-type node in the next period. Concurrently, a new period begins and the new A-type node starts to monitor the data flow of the grid under promiscuous mode in the new period.  
Third, it is illustrated what to do if the A-type node fails. The period of each grid has its own maximum time _Max(t)_ (_Max(t)_ is described in subsection 5.1). Hence, each node maintains a counter to reflect the broadcast signal of the A-type node in the grid. If the node doesn't receive the broadcast signal from the A-type before the maximum time of its own grid, then the node believes that the A-type node has failed. The data sending sequence of the node which ranks first place in the grid (there will be illumination about the data sending sequence of the nodes in Section 4.2) will temporarily be used as the A-type node. Concurrently, the temporary A-type node broadcasts itself to all nodes in the grid. In the next period, the A-type node is still chosen to be the node which has the maximum residual energy in the grid.