esa-sceva/satcom-chunk-collection huggingface dataset:
# Fine-Tuning Large Language Models for Kazakh Text Simplification
## 4 Experiments
### Evaluation Metrics  
To evaluate simplification quality, we use the following standard automatic metrics: BLEU, ROUGE-L, SARI, Bertscore. Each metric captures different aspects of output quality, including lexical overlap, structural alignment, and simplification-specific edits.  
BLEU measures n-gram overlap between the system output and the reference. While originally developed for machine translation, it is widely used in simplification.  
Figure 3: The training loss of trajectories of KazSim fine-tuned on three large language models.  
Figure 2: Training loss comparison between small and large Seq2 Seq models.  
ROUGE-L computes the longest common subsequence between the prediction and the reference. Compared to BLEU, it is less sensitive to exact token matches and better reflects sentence-level alignment and fluency.  
SARI is designed specifically for simplification. It evaluates the system output against both the reference and the original complex sentence, and scores three operations: addition, deletion, and retention.  
BERTScore [22] leverages contextual embeddings from pretrained language models to compute the similarity between candidate and reference sentences. It reports precision, recall, and F1 based on semantic alignment, and is particularly useful for capturing meaning preservation even when surface-level tokens differ.
## 3 Results and Discussion
### Effect of CO\({}_{2}\) Pressure on Mechanical and Physical Behavior  
The effects of applied CO\({}_{2}\) pressure on carbonation are shown in Figure 6. When pressure was increased from 1 to 3 bar at 28 days, MOR increased from 11.3 to 13.1 MPa and MOE increased from 10,053 to 12,342 MPa for 9 h curing time. On the other hand, the pressure increase caused a negative impact on the toughness, which was reduced from 3.48 to 1.71 kJ/m\({}^{2}\) for 9 h curing period and a smaller reduction for a shorter curing period. In the case of 1 bar, the carbonation was very limited when cured for less than 5 h because of low pressure. Furthermore, when the samples were retrieved from the chamber after carbonation, heat loss occurred. Therefore, the curing temperature was less than for the case of non-carbonated samples, which compensates the slight MOR gain because of carbonation. Therefore we observed similar MOR for both carbonated and non-carbonated samples. However, the results showed that there were some differences in mechanical properties of 5 and 9 h carbonation and if we increase the carbonation time up to 1 day or more, the improvement of MOR would be clearer as shown in [42].  
Higher CO\({}_{2}\) pressure also improved the physical properties as shown in Figure 7. AVV decreased from 34.9% to 33.9% when CO\({}_{2}\) pressure was increased from 1 to 3 bar in the case of 9 h carbonation curing. The same observation was made for the WA, whose value decreased from 21.82% to 20.81%, while the BD increased from 1.60 to 1.64 g/cm\({}^{3}\).  
Figure 5: Temperature profile in carbonation chamber at 3 bar CO\({}_{2}\) pressure.  
Figure 6: Average values of mechanical properties at different CO\({}_{2}\) pressures (\(\times\) 1 and \(\square\) 3 bar CO\({}_{2}\) pressure): (**a**) Modulus of rupture (MOR); (**b**) modulus of elasticity (MOE) and (**c**) toughness.  
Figure 7: _Cont._  
Higher CO\({}_{2}\) pressure increases CO\({}_{2}\) penetration into the samples [40] resulting in improved physical and mechanical properties. This is because the dissolved CO\({}_{2}\) concentration is related to the gas (CO\({}_{2}\)) pressure by [PERSON]'s law [43], which means that higher pressure increases the dissolution rate of CO\({}_{2}\) in the pore water of the samples. Furthermore, as long as the sample is not fully saturated, at higher applied pressure, the advective transport (together with advection induced by capillary pressure) contributes to a faster penetration of CO\({}_{2}\) into the samples, and thereby a higher carbonation rate.
# WIPP: Wi-Fi Compass for Indoor Passive Positioning with Decimeter Accuracy
## 4 Experimental Results
### Target Location Estimation  
**Testbed construction:** The testbed is a lab with the dimensions of 15 m by 10 m, as shown in Figure 8.  
**System process:** Three Wi-Fi NICs act as the APs with known locations and height, while the location of the target equipped with Wi-Fi module is unknown. Each AP collects the CSI from the target, and then sends it to the server. The server conducts the joint estimation of AOA and TOA measurements to identify the direct signal path with respect to each AP, and finally estimates the target locations.  
**Performance comparison:** The WIPP is compared with two existing indoor passive positioning systems, the RADAR system and the conventional system using the AOA solely. For better comparison with the most well-known RADAR system, we add three more APs [42]--AP4, AP5, and AP6--in the target environment. Furthermore, the Reference Points (RPs) [43] (with black dots) are uniformly calibrated with the interval of 0.6 m and the test points (with red triangles) are randomly selected, as shown in Figure 16. In addition, the purpose of choosing the conventional system using the AOA solely the comparison is due to the reason that it is effective and also independent of hardware modification.  
Figure 17 shows the CDFs of errors in target location estimation by the WIPP, RADAR system, and the conventional system using the AOA solely. As can be seen from Table 1, the WIPP achieves the Root Mean Square Error (RMSE) 0.7 m and 90% errors within 2 m, which performs much better than the RADAR system and the conventional system using the AOA solely.
# Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
## 6 Results and Evaluation
### Cost Evaluation
#### 6.4.3 Maintenance and Reliability
* **Mean time between failures (MTBF)**: The MTBF is the average time between system or component failures. To calculate this, we divide the system's total operating time by the number of downtime incidents that occur. Both GNS3 and the SEED Internet Emulator offer software-based appliances, eliminating the risk of specific device failures that were common in physical appliances. However, since both platforms rely on a single host system for emulation, there is a potential single point of failure. In the event of a server breakdown, whether due to natural causes or a cyber-attack, the entire emulation system could become unavailable. To mitigate this risk, it is crucial to have proper backup and redundancy measures in place to ensure the continuity of operations and minimize downtime. * **Mean Time To Repair (MTTR)**: This is the measurement of how long it will take for a failed or disabled system component to return to operational. This is calculated by the time period of system downtime and dividing it by the number of downtime incidents. Both GNS3 and the SEED Internet Emulator can suffer from having a particular device failing despite it being in logical rather than physical format; in the case of a broken node, this can be simply fixed by restarting the node virtually or deleting the node and dragging in a new one. One of the significant benefits of doing this is that the logistical element is removed as there is no need to wait for a new device to be delivered; this can be performed in a matter of seconds. * **Mean Time To Failure (MTTF)**: The MTTF represents the average lifetime period of a system or component that cannot be repaired. It is calculated by adding the total operating time before failure and dividing that by the quantity of these assets in use. Both GNS3 and the SEED Internet Emulator do not face this concern with their network nodes, as more nodes can be easily added to the network, ready to take over from a failing node. However, it is worth noting that the host server itself can be susceptible to this issue.
## 2 Materials and Methods
### Data Analysis  
#### 2.4.1 Spectral Data Extraction and Preprocessing  
Because of the sophistication of the hyperspectral raw image data, it is essential to perform a sequence of processes on the hyperspectral raw image to ultimately extract the spectral data. As shown in Figure 2, the steps to extract the spectral data are as follows: (1) extracting edges of the white shrimp in the corrected hyperspectral images by using the Sobel operator; (2) binarizing the raw image to generate a mask, then separating the shrimp and the black standard plate to eliminate the black backdrop; (3) extracting the entire area of each white shrimp on the hyperspectral image as the region of interest (ROI), and then calculating the average reflectance of all pixels in the selected region as the spectral value of each white shrimp. In this process, the image segmentation was carried out on Matlab 2019a (The Math Works, Natick, MA, USA), while the computation of the average spectral values of all pixels in the ROI was performed on ENVI 5.3 (ITT Visual Information Solutions, Boulder, UT, USA).  
Noise may be introduced during the data acquisition process due to interfering factors in some experimental conditions, it is essential to preprocess the raw spectral data to improve the modeling accuracy. In this research, three spectral preprocessing methods (Savitzky-Golay first derivative (SG1), multivariate scatter correction (MSC), and standard normal variate (SNV)) were employed to preprocess the raw spectral data, respectively.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 4 Commercial UWB Modules and Chips for IPSs
### Microchip ATA8350/ATA8352 UWB Transceivers  
The Microchip ATA8350 UWB transceiver (Microchip Technology Inc., Chandler, AZ, USA) is designed for measuring hardware-controlled sequential distance for localization. It is compliant with UWB regulations and supports a frequency range of 6.2-7.8 GHz with a data rate of 246 Kbps. It can be hosted by any processor with an SPI interface of up to 20 MHz and can measure the ToF with a resolution of \(\pm\)15 cm. The ATA8352 transceiver has a wider frequency range of 6.2-8.3 GHz, transfers higher data rates of up to 1 Mbps, has SPI control with up to 24 MHz, and supports the time difference of arrival (TDoA) approach. It has a more accurate ToF measurement resolution of \(\pm\)4.5 cm, which reflects better system accuracy [37].
## 3 System Model and Problem Formulation
### Constraints  
Next, we describe the constraints of the problem.  
For a given network topology, the number of controllers to place is not known in advance. Therefore, when it is determined that the number of controllers to be placed is \(k,k\) switches should be selected as control nodes in the network. Constraint (14) ensures that \(k\) controllers are placed in the entire network when solving the controller placement problem.  
\[\sum_{c=1}^{N}y_{c}=k, \tag{14}\]  
Constraint (15) ensures that each switch is managed by only one controller.  
\[\sum_{c=1}^{C}x_{nc}=1,\forall n\in N,\ x_{nc}\leq y_{c} \tag{15}\]  
The processing capability of the controller in the real network environment is affected by the performance of its software and hardware. Therefore, to simulate reality, the amount of flow requests handled by the controller to the switch does not exceed the capacity of the controller, which is defined by Constraint (16).  
\[l_{c}=\sum_{n_{i}\in A_{c}}\lambda_{i}<Ca,\ \forall n_{i}\in A_{c},\forall c\in C \tag{16}\]  
Link bandwidth is limited in the network. Constraint (17) ensures that the link consumption through which the switch-controller communication and the inter-controller synchronization pass does not exceed the bandwidth threshold of the link itself.  
\[z_{e_{ij}}p_{ne}\leq B_{e},\ z_{e_{ij}}\rho_{ce}\leq B_{e},\ \forall n\in N, \forall c\in C,\forall e_{ij}\in P_{ij} \tag{17}\]  
Constraints (18), (19), and (20) are binary variables. \(y_{c}\) represents whether the controller is placed on switch \(c\), \(x_{nc}\) represents whether switch \(n\) is within the control range of controller \(c\), and \(z_{e_{ij}}\) represents whether the control path \(e_{ij}\) is in the set of shortest control paths \(P_{ij}\).  
\[y_{c}=\left\{\begin{array}{ll}0,if\ \text{the controller is not placed on node }c\\ 1,if\ \text{the controller is placed on node }c\end{array}\right.,\forall c\in N, \tag{18}\]  
\[x_{nc}=\left\{\begin{array}{ll}0,if\ \text{switch }n\ \text{is managed by controller }c\\ 1,if\ \text{switch }n\ \text{is not managed by controller }c\end{array}\right.,\forall n\in N, \forall c\in C, \tag{19}\]  
\[z_{e_{ij}}=\left\{\begin{array}{ll}0,if\ \text{the link is not in the set}\\ 1,if\ \text{the link is in the set}\end{array}\right.,\forall e_{ij}\in P_{ij}, \forall n_{i},n_{j}\in N, \tag{20}\]
# Smart Parking: A Literature Review from the Technological Perspective
## 3 Smart Parking Solutions
### Software Solutions
#### 3.2.1 Information Management  
One of the most sought after solutions in the parking systems is the way information is handled, either for data storage processing to improve the operation of the system itself, or to decide how and what information will be presented to the user.  
In [15], a basic Parking Guidance and Information (PGI) structure is proposed together with a Driver Request Processing Center and a Smart Parking Allocation Center. These are the systems responsible for the collection of driver requests and real-time information, such as the location of the vehicle and the allocation of parking spaces according to previously stored requests, respectively. It interacts with the Parking Resource Management Center, which is in charge of real-time information retrieval and exposes, either through Internet, Variable Message Signs (VMS), mobile, or applications, similarly to the solution presented in [46], where services are handled and presented only through a web page. In both cases, the communication of the sensors with applications and user interactions is considered, so as to make reservations or check the availability of parking services.  
Many of the reviewed solutions handle three states for a parking slot: free, busy, and reserved. Also, there are cases in which intermediate states are handled to facilitate user guidance. An application of the management of the information network of parking spaces facilitates the monitoring of illegal parking actions, as in [76], where the authorities control vehicles that exceed the authorized parking time limit or that have not paid for the space, and also use it to change the parking costs depending on the area in which it is located, the time, or the day they are being used.  
There are also three modeled architectures as in [54]. In the first one, the availability of parking spaces is detected. The second, handles queries of availability performed by the client according to its location and the need thereof. The last, shows the parking information in real-time to the user. Other approaches, such as that in [77], include control by the authorities plus a user application which is used to verify the location of the parking space. It also includes a secure storage system wherepayment information is saved and an application for the authorities responsible of road control to verify inappropriate use of parking spaces or identify spaces that have not been paid.  
Many of the solutions found, emphasize that a cloud storage system is required either to centralize information in case that there are many different parking lot service providers, as described in [78]. It will allow to connect several service providers to a common request cloud so that users can consume information from all of them. On the other hand, this schema could be used only to store parking data such as parking time, user information and locations as shown in [42].
## 5 Conclusions  
In this study, the process of designing the high-performance generator was described. The generator resulted from the idea of upgrading the output power (\(P_{out}\)) and improving the THD of the most qualified AFPMG in [10] and is also because of the rapid development of power electronic devices. By increasing the number of stator cores installed on the upper and lower sides of the original generator, \(P_{out}\) of the original generator can be upgraded from 413,91 watts to 3.77 kW. By varying the pole width to pole pitch ratio (\(\alpha_{i}\)) of the arc-shaped permanent magnet and varying the slot opening width (\(S_{o}\)), an optimal rotor configuration was achieved, which resulted in THD of 1.55%. Changing the dimension of the air gap length, changing the offset angle of the stator teeth on the upper side to the stator teeth on the lower side, and changing the stator yoke height are the control factors in the Taguchi method and were validated by the Artificial Neural Network to achieve the goal of generating the lowest THD, which resulted in THD of 0.66% being realized. The improvement of 94.72% in ripple torque and the improvement of 88.78% in the ripple of the rectified voltage were reached for the proposed generator when compared to the original generator. The improvements in the ripple torque and in the ripple of the rectified voltage are proof of generating the lowest THD and adopting the five-phase system on the proposed generator. From a mechanical point of view, the geometry of the proposed generator is not too complicated. In pace with innovation within manufacturing technology, and in an effort to utilize the energy from the wind and to produce a high-performance generator, this proposed generator can be a reference for small-scale wind power generation to help societies in remote areas.  
Conceptualization, C.-Y.H. and K.W.; methodology, C.-Y.H. and K.W.; software, K.W.; validation, C.-Y.H., K.W. and N.-C.Y.; formal analysis, C.-Y.H. and K.W.; investigation, C.-Y.H., K.W. and N.-C.Y.; resources, C.-Y.H.; data curation, C.-Y.H. and K.W.; writing--original draft preparation, C.-Y.H. and K.W.; writing--review and editing, C.-Y.H., K.W. and N.-C.Y.; visualization, C.-Y.H. and K.W.; supervision, C.-Y.H. and N.-C.Y.; project administration, C.-Y.H., K.W. and N.-C.Y. All authors have read and agreed to the published version of the manuscript.  
The research has been supported by the Ministry of Science and Technology Taiwan (MOST 109-2221-E-011-051-MY2, MOST 108-2637-E-011-002).  
Not applicable.  
**Data Availability Statement:** All data generated or analyzed to support the findings of the present study are included this article. The raw data can be obtained from the authors, upon reasonable request.  
Figure 24: Exploded geometry of the proposed generator.  
**Acknowledgments:** The authors would like to thank the Editor-in-Chief, Editor, and Reviewers for their valuable reviews.
## 2 Intelligent Vehicle Dynamics Model
### Vehicle Dynamics Model
#### 2.1.2 Seven-Degrees-of-Freedom Vehicle Model  
This article relates to the design of the lateral stability control system of a distributed drive electric vehicle. The design of the model-based control system needs to be based on a vehicle model that is closer to reality and fully reflects the lateral, nonlinear, dynamic characteristics of the vehicle. Therefore, in this section, a seven-degrees-of-freedom vehicle model including the longitudinal, lateral, yaw, and four-wheel rotation of the vehicle is established, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Dimension** & **Value** \\ \hline \(m\) & kg & 1296 \\ \(I_{z}\) & kg-m\({}^{2}\) & 1750 \\ \(h_{g}\) & m & 0.54 \\ \(a\) & m & 1.25 \\ \(b\) & m & 1.32 \\ \(B\) & m & 1.405 \\ \(r\) & m & 0.286 \\ \(J_{w}\) & kg-m\({}^{2}\) & 0.8 \\ \(k_{1}\) & N/rad & 100,000 \\ \(k_{2}\) & N/rad & 100,000 \\ \(i\) & - & 20.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle parameters.  
Figure 2: Seven-degrees-of-freedom vehicle model.  
In the same way, make the same assumptions as the three-degrees-of-freedom vehicle model, and the coordinate axis regulations are also the same.  
Longitudinal movement:  
\[m(\dot{v}_{x}-v_{y}\gamma)=(F_{xfl}+F_{xff})\cos\delta-(F_{yfl}+F_{yfr})\sin \delta+F_{xrl}+F_{xrr} \tag{8}\]  
Lateral movement:  
\[m(\dot{v}_{y}+v_{x}\gamma)=(F_{xfl}+F_{xfr})\sin\delta+(F_{yfl}+F_{yfr})\cos \delta+F_{yrl}+F_{yrr} \tag{9}\]  
Yaw movement:  
\[I_{z}\dot{\gamma}=\frac{B}{2}(F_{yfl}-F_{yfr})\sin\delta+a(F_{yfl}+F_{yfr})\cos \delta-b(F_{yfl}+F_{yrr})+M_{z} \tag{10}\]  
Among them, \(M_{z}\) is the direct yaw moment, which is produced by the combined action of the longitudinal forces of the four-wheel tires, as follows:  
\[M_{z}=a(F_{xfl}+F_{xfr})\sin\delta-\frac{B}{2}(F_{xfl}-F_{xfr})\cos\delta- \frac{B}{2}(F_{xrl}-F_{xrr}) \tag{11}\]  
Rotation of the wheels:  
\[J_{w}\dot{w}_{ij}=T_{ij}-F_{xij}r \tag{12}\]  
In Formulas (8)-(12), \(r\) represents the tire radius. \(J_{w}\) represents the moment of inertia of the wheel assembly. \(T_{ij}\) is the output torque of the in-wheel motor; \(F_{xij}\) and \(F_{yji}\) are the tire longitudinal and lateral forces, respectively; \(i=f\) or \(i=r\) are the front or rear wheel respectively, \(j=f\) or \(j=r\) are the left or right wheel; \(w\) is the wheel rotation angular velocity. In the designed distributed-drive electric vehicle lateral stability control system, the optimal longitudinal force distribution algorithm in the lower controller decides the four-wheel longitudinal force distribution, which can be converted through the wheel model of the four-wheel torque distribution.
###### Abstract  
The evolution of fiber optic technology in the past few decades has led to significant advancements in various fields, including high-speed and long-distance communication, big data transport, optical imaging, and sensing. However, relatively few studies have examined the use of fiber optic sensors (FOSs) as point and distributed sensors in geophysics. Distributed Acoustic Sensing (DAS) is a widely used method for subsurface imaging and monitoring in wells, specifically in Vertical Seismic Profiling (VSP) surveys. This method allows for detailed analysis of subsurface structures and properties of reservoirs. Four different strategies for deploying FOS cables in DAS VSP are evaluated and compared: cementing behind casing, cable behind inflatable liner, strapping to production tubing, and wireline deployment. Cementing the fiber behind casing is considered the most effective method for coupling with the formation. However, the other methods also have their own advantages and limitations. The fiber cable behind inflatable liner, for example, allows for accessibility to the fiber without affecting the acoustic signal, while strapping the fiber to production tubing can still record DAS signals; tubing noise and signal attenuation from the annular fluid, however, can make it difficult to differentiate from the seismic signal. Nonetheless, this method has the benefit of being simpler to deploy and replace in case of failure. Wireline deployment can pick up some acoustic signals in regions where the cable touches the well wall, but in vertical sections where the cable is not in contact with the wall, the signal is attenuated. Results from pilot tests in a field in Canada are discussed and evaluated, and suggestions for improving the VSP signal are provided.  
Distributed Acoustic Sensing; Vertical Seismic Profiling; DAS deployment 2023