esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Changing Laws of BER Performance  
According to the analysis conducted above, in the case of a short-code system, when the interference frequency offset \(\Delta f\) is a multiple of \(R_{b}\), the resulting single-frequency interference remains constant after processing. However, it only affects the mean value of the receiver output decision statistics, increasing or decreasing it depending on whether the data bit is 0 or 1, without changing its variance. The variance of the decision statistic is still determined solely by the processing outcome of Gaussian noise. Therefore, this scenario is appropriate for assuming the standard Gaussian approximation (SGA). By solving the value of the signal component (including the interference component) in the decision statistics and the variance of the Gaussian noise, the bit error rate (BER) can be obtained using the error function. However, if the interference frequency offset \(\Delta f\) is not a multiple of \(R_{b}\), the interference component at the output of the correlation despreader fluctuates sinusoidally. As a result, the decision statistics no longer follow a Gaussian distribution, making the method based on the SGA hypothesis inadequate for analyzing the BER.
# Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems
## 5 Detailed System Analysis
### Behavioural Verification
#### 5.2.3 Social Affiliation  
The social affiliations between honest users is an additional behaviour which can be exploited to verify the rate that an \(ARC\) was created. When two users meet, they may exchange the sequence numbers \(N\) and hashes \(h(n)\) of their last blocks as well as their \(ARC\)-\(IDs\). The two users can then verify the date and time of the meeting by adding an Affiliation Block \(AB\) in their respective \(ARCs\) with each others' information. In order to be valid, the \(ABs\) which are added to the \(ARCs\) of both users need to have matching dates and times but this does not require a perfect synchronization between the two users. Mobile applications typically have a recommended refresh rate for adverts that is between 30 s to 120 s while malicious applications generate Ad-Reports at a higher rate. For the purpose of detecting fraud, the time difference between the two users can therefore be tolerant to a margin of a couple of minutes without seriously affecting the system. In the event that two \(ABs\) do not match because one of the users provided an unrealistically inaccurate date and time (either maliciously or accidentally), the Broker can simply ignore it while relying on other Tokens to validate the particular \(ARC\).  
Figure 8 illustrates an example where two users \(A\) and \(B\) have added each others' Affiliation Blocks within their respective \(ARCs\). The \(AB\) which was added by user \(B\), is shown in the diagram to contain a new sequence number \(n\), the hash of the previous block \(h(n-1)\), the **Date** of the meeting (as registered by \(B\)) and the information that was sent by \(A\) which includes her \(ARC\)-\(ID\)=\(ARC1\) as well as the sequence number \(m\) and digest \(h(m)\) of her last block. The date which is added by \(B\) works as a \(T\)-_Token_ which verifies the last block of \(ARC1\) at a particular time. Notice that \(ARC1\) and sequence number \(m\) are sent encrypted with the Broker's public key \(B_{Puk}\) while the \(h(m)\) is signed with \(A\)'s signing key \(sk1\) which is also used for the creation of the Integrity Hash \(IH\) as explained in Section 5.1.2. This ensures that \(B\) does not lean any information about \(ARC1\) and is not able to alter \(h(m)\).  
Through the exchange of \(ABs\), the Broker can infer that two \(ARCs\) were submitted by affiliated users but this does not compromise user privacy in any way. The \(ARCs\) are submitted anonymously and the Broker has no means of obtaining any information about a particular user's social network nor is he able to identify \(ARCs\) that were submitted by the same user. One limitation of this verification method lies on the fact that a dishonest user may exchange \(ABs\) between multiple fictitious \(ARCs\). Although plausible, this is prevented by combining all three verification methods as discussed in the following Section 5.2.4.  
Figure 7: Behavioural verification by checkpoint.
# Advanced Brain Tumor Segmentation Using SAM2-UNet
## 3 Materials and Methods
### Data Pre-Processing  
As SAM2-Unet is a 2-dimensional model [17] and BraTS 2020 is a 3-dimensional dataset, this study provides a method to transform a 3-dimensional dataset into 2-dimensional images. The FLAIR modality was chosen to be converted into a 2D image out of the rest of the modalities, i.e., T1, T1 ce, and T2, as FLAIR suppresses the fluid signals in the scans, which makes it easy to identify lesions. Lesions refer to any abnormal changes or damage in tissue, typically caused by injury, disease, or other conditions. It is a challenging task to identify these lesions as their appearance, location, and characteristics can vary depending on the underlying cause. Additionally, all types of lesions may look similar, making diagnosis dependent on a combination of clinical evaluation, imaging studies, laboratory tests, and sometimes biopsies. As the FLAIR modality contains 155 images, 2-dimensional matrices, or slices, we chose the 95 th slice as the ideal slice for all the scans of different patients, as this slice highlights lesions very clearly along with the whole MRI image of the brain from the top angle. This 95 th slice was then converted into a 2-dimensional black and white image of dimensions 352 \(\times\) 352 \(\times\) 3. We repeated the same procedure for the 3-dimensional segmentation mask, i.e., we converted the 95 th slice into a 2-dimensional image, as shown in the Figures 7 and 8.  
Figure 7: **(a)** The collection of all the modalities of data for a patient, containing the following modalities: T1, T1 ce, T2, and FLAIR. **(b)** The ground-truth segmentation mask related to the patient. **(c)** The FLAIR modality of the patient being passed into the algorithm that converts from 3D to 2D, where it converts the 3D data into a 2D image of type ‘png’ **(d)**. The same conversion with **(e)** to produce **(f)**.
## 3 Numerical Example  
### Bridge Model Description  
As shown in Figure 2, a three-span continuous girder bridge, which consists of three spans each of 30 m, was modeled by OpenSees software. The superstructure is a box girder with a cross-section area of 7.168 m\({}^{2}\) and Elastic modulus \(3.25*10^{4}\) MPa (C40 concrete). Since the stiffness of the superstructure is much larger than that of the substructure, the girder is not likely or expected to show nonlinear behaviour during seismic events. The girder is simplified using 15 linear elastic beam-column elements along the central axis, and the form and structural differences along the superstructure are ignored. Each pier of the bridge is 10 m high and 1.5 m in diameter. The concrete material is C30 with 0.06 m cover thickness. There are 36 longitudinal reinforcement bars arranged in the circumferential direction of the pier section. Along the vertical direction of the pier column, stirrups are arranged with 1.5% stirrup ratio. The reinforced concrete  
Figure 1: The flow chart of the proposed method.  
piers were each divided into four elements with five integrated points and modeled by force-based fiber-section beam-column elements for simulating the material nonlinearities and the force interaction. The material model \(Concrete02\) in OpenSees was employed for uniaxial constitutive behavior of both confined and unconfined concrete. Its constitutive model is characterized by four parameters: peak compressive strength (\(f_{c}\)), strain at the peak strength (\(\varepsilon_{c}\)), strain at the crushing strength (\(\varepsilon_{u}\)), ratio at uploading slope at \(\varepsilon_{u}\) and initial slope (\(\lambda_{c}\)). The modified Giuffre-Menegotto-Pinto (GMP) material constitutive law (_Steel_02 model in OpenSees [25]) was employed to model the uniaxial behavior of the steel fibers. The four main parameters of steel02 are the elastic Young's modulus (\(E_{s}\)), initial yield stress (\(f_{y}\)), strain hardening ratio (\(b_{s}\)) and a parameter (\(R_{0}\)) describing the curvature of the transition curve between the asymptotes of the elastic and plastic branches during the first loading. The bearings on the top of the four piers are all laminated rubber bearings (LRBs) and modeled by the zero-length element for transferring the loading from the superstructure to the piers. The _Steel_01 model was employed to simulate the bilinear hysteretic of LRB, which is defined by the initial elastic stiffness \(K_{eb}\), yield strength \(f_{yb}\) and strain hardening ratio \(b_{b}\). Additionally, the Rayleigh damping model with a critical damping ratio of 5% was assumed. The NTHA was carried out by OpenSees software to obtain the output response.  
The base excitation was at the bottom of the four piers. The El Centro ground motion with PGA of 0.8 g was excited in the longitudinal direction to generate the strong nonlinear behavior of the bridge. The uncertainty of the earthquake was not considered in this paper.
###### Abstract  
For multiple extended target tracking, the accuracy of measurement partitioning directly affects the target tracking performance, so the existing partitioning algorithms tend to use as many partitions as possible to obtain accurate estimates of target number and states. Unfortunately, this may create an intolerable computational burden. What is worse is that the measurement partitioning problem of closely spaced targets is still challenging and difficult to solve well. In view of this, a prediction-driven measurement sub-partitioning (PMS) algorithm is first proposed, in which target predictions are fully utilized to determine the clustering centers for obtaining accurate partitioning results. Due to its concise mathematical forms and favorable properties, redundant measurement partitions can be eliminated so that the computational burden is largely reduced. More importantly, the unreasonable target predictions may be marked and replaced by PMS for solving the so-called cardinality underestimation problem without adding extra measurement partitions. PMS is simple to implement, and based on it, an effective multiple closely spaced extended target tracking approach is easily obtained. Simulation results verify the benefit of what we proposed--it has a much faster tracking speed without degrading the performance compared with other approaches, especially in a closely spaced target tracking scenario.  
Keywords:multiple extended target tracking; prediction-driven measurements sub-partitioning; closely spaced targets; cardinality underestimation +
Footnote †: journal: Applied
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 3 The Causes of Thermal Hazards Associated with Lithium-Ion Batteries  
Generally, LIB possesses stable structure in which lithium ions transfer between the cathode and the anode during charging/discharging such that it can be regularly cycled considerable times. Whereas, the original stable-structure of the LIB will become damaged due to the effect of abusive factors, generating thermal hazard. The factors can be summarized as several main types: physical, electrical and thermal factors, as well as manufacturing defect and battery aging [14; 58; 59; 60; 61; 62].
## 4 Experiments and Results
### Experimental Setting  
The training of deep learning models was accelerated in this study by using Google Colab Pro+ in conjunction with a Tesla V100-SXM2-16 GB (Los Angeles, CA, USA) graphics processor module. The fundamental deep learning models, including CNN-ResBiGRU, were implemented using a Python 3.10.12 framework with TensorFlow and CUDA backends. The investigations specifically examined the Python packages listed below:  
* The Numpy and Pandas libraries were used for the purpose of data management throughout the retrieval, processing, and analysis of sensor data.
* The outcomes of data exploration and model assessment were charted and presented using Matplotlib and Seaborn.
* In the experiments, the Scikit-learn model was used for the purposes of sampling and data creation.
* TensorFlow was used to generate and train deep learning models.  
This study involved multiple experiments using the Myogym dataset to determine the best approach for recognizing gym exercises. To ensure the reliability and applicability of our findings, we employed a five-fold cross-validation technique. The experimental process was divided into five distinct scenarios, each focusing on different combinations of sensor data available in the Myogym dataset. In Scenario I, models were trained and tested using only accelerometer data, while Scenario II utilized only gyroscope data. Scenario III involved accelerometer and gyroscope data, referred to as IMU data, to assess the models' performance with a broader range of motion-related features. Scenario IV concentrated solely on EMG data, which captures muscle activation patterns during physical exercises. In the fifth scenario, IMU and EMG data were combined (IMU + EMG) to explore the potential benefits of integrating motion and muscle activity information for gym exercise recognition.
# A Dynamic Framework for Community-Facility Siting with Inter-Community Competition
## 2 Literature Review
### Community-Facility Location Problems
This indicates that the focus on equity has primarily been on spatial equity, which ensures equal spatial proximity between residents in different areas. The fairness of facility service distribution among different groups, which refers to social equity, has been neglected [45]. In terms of constraints, previous CFLP research commonly used facility capacity, the number of facilities, threshold distance, and demand point-facility assignment, which usually refers to a demand point being assigned to only one facility. However, in our review of CFLP studies, we found only one instance where the spatial relationship between proposed facilities and communities was considered. [PERSON], [PERSON], and [PERSON] addressed this by minimizing the maximum planned transportation distance from each community to promote a balanced distribution of community hospitals [35]. Nevertheless, this study did not consider the physical boundaries of communities or differences in community size, thus failing to ensure facilities were allocated to specific communities. We also noticed that, among the studies mentioned above, only two considered the issue of the multi-scenario application of models. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] developed nine scenarios based on different types of community facilities and allocation strategies and then used GIS to identify the ideal distribution of community facilities [46]. [PERSON] et al. [38] established three scenarios and corresponding strategies based on existing facilities; the proposed solutions balanced the equity, local accessibility, and efficiency of community-based health programs. Based on the above review, it is evident that research models for CFLPs remain limited and have yet to account for many practical requirements and constraints. For example, issues such as inter-community competition for facilities and the social equity of facility allocation are often overlooked. Moreover, the use of singular scenarios further restricts the applicability of these models.
# Towards Flexible Distribution Systems: Future Adaptive Management Schemes
## 4 Market Schemes for Adaptive Control and Management Methods Collaborative TSO-DSO Use  
During the evolution, flexibilities efficient use requires new enabling business models, market structures, and marketplaces [83] as well as regulation development [84]. In addition, quite recently, different types of flexibility aggregation platforms and marketplaces have been developed, e.g., Equigy [85] and NODES [86; 87], as well as proposed in different research projects such as FLEXIMAR [88; 89]. For example, in [90], the aggregation platform for flexibility (APF) is presented, which can be used to sell flexibility on multiple markets. In [91; 92], another platform (Distributed Energy Resource Management Systems, DERMS) has been presented, which is a centralized control system that enables DERs connected to the distribution network to provide services to the TSO in the U.K., in a coordinated and controlled manner by also taking into account DSO requirements and needs. In [92], it is stated that in the future, DERMS and ANM systems together will provide the possibility to manage the potential conflicts of services between TSOs and DSOs for both simple and complex networks.
###### Abstract  
In the engine room and stern adjacent to the main excitation force of the ship, there are many fuel and fresh water tank structures required for ship operation which are always exposed to vibrations. Therefore, it is necessary to review the anti-vibration design to prevent such vibration problems at the design stage, and for this reason, although commercial finite element analysis (FEA) programs are widely used, approximate analysis methods are still developed and used because of the limited time until modeling and analysis results are obtained. Until now, only known elastic boundary conditions have been used in many studies using approximate analysis methods used to calculate natural vibrations for beams or plates. However, many local structures, such as tank edges and equipment foundations, consist of connected structures and it is very difficult to find suitable elastic boundary conditions. Vibration analysis of many local structures in ships, such as tanks and supports for equipment, can be simplified by breaking them up into smaller subsystems which are related through geometrical conditions and natural conditions at junctions. In this study, polynomials for simple support and fixed support were proposed to represent each subsystem and a polynomial to be applied to the plate constituting the tank was proposed by combining them. Until now, there have been many studies on single beams or single plates for approximate analysis. However, there was no research on this to the extent that no reference material could be found for the connected structure. The proposed method has been applied to tanks which are bounded by bulkhead and a deck. The results of this study shows good agreements with those obtained by the FEA Software (Patran/Nastran).  
**Keywords:** assumed mode function; CMS (component mode synthesis); FEA (finite element analysis); DOF (degree of freedom)
## 2 Materials and Methods
### Test Sample Preparation and Testing Methods  
We performed the oedometer tests on compacted soil specimens prepared with the same method. The oven-dry soil with a known mass was sprayed with water to the appropriate moisture content. The compaction process was conducted with respect to the ASTM D1557-12e1 method. Molded soil was subsequently confined to a cylindrical steel confining ring with a diameter of 75 mm and 20 mm high and placed in a water bath. We subjected the soil samples to compressive stress by applying a vertical load that uniformly acted over the area of the sample. Compressive stress was equal to 12.5 kPa, 25.0 kPa, 50.0 kPa, 100.0 kPa, 200.0 kPa, 400.0 kPa, 800.0 kPa, 1600.0 kPa, and 3200.0 kPa.  
We performed the resonant column tests on compacted soil samples (with respect to ASTM 4015-15). The resonant column used for the test was [PERSON]'s fixed-free-type device. In this type of resonant column, the torsional type of vibration is created. The samples were compacted in the Proctor mold, and from the compacted sample, the resonant column sample was timed with the height \(h\) equal to 10 cm and diameter \(d\) equal to 5 cm. During the resonant column test, soil samples remained unsaturated. The confining pressure \(\sigma_{3}\), applied in the resonant column cell on the sample was equal to 30, 45, and 60 and on one sample to 150 kPa. After consolidation in each step, the torsional shear and damping tests were performed.  
The last test performed in the undrained triaxial apparatus aimed to establish the stress-dependent soil-water characteristic curve. We prepared the sample for this test by compaction in optimum moisture content. After the preparation of the sample, the soil was fully saturated and consolidated to \(\sigma^{\prime}_{3}\) equal to 100 kPa. After this phase, the unsaturated tests were conducted by using the axis translation technique. We also measured the \(G_{0}\) moduli with the use of the bender elements peak-to-peak technique.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(m\) (\%) & \(\rho_{d}\) (g:cm\({}^{-3}\)) & \(e\) & \(S_{r}\) & \(I_{D}\) \\ \hline
4.6 & 1.78 & 0.53 & 0.73 & 0.89 \\
6.1 & 1.79 & 0.53 & 0.76 & 0.90 \\
8.1 & 1.82 & 0.50 & 0.81 & 0.93 \\
9.4 & 1.85 & 0.48 & 0.85 & 0.96 \\
10.8 & 1.89 & 0.44 & 0.9 & 1.00 \\
12.9 & 1.89 & 0.44 & 0.94 & 1.00 \\
14.8 & 1.83 & 0.49 & 0.95 & 0.94 \\ \hline \end{tabular}
\end{table}
Table 1: Proctor’s compaction test data and physical properties of the compacted samples.  
Figure 2: Soil compaction curve of the tested soil.
## 3 Results
### Test 2: Comparison of Filter Paper and Water Potential Sensor  
Figure 6 shows data from 14 days of monitoring, in which the sensors (i.e., initially starting from wet and dry condition), starting from the same initial water content in the specimens, at the beginning of the first test, were put together with four filter papers, as already explained in the methodology (Figure 2c). The suction data logged with the sensor have been compared to those monitored by means of the FP method.  
Figure 5: Water content versus suction plots of (**a**) the soil studied with wet and dry sensors along with the WRC of the sensors at equilibrium. (**b**) Water content versus suction plots results for both wet and dry sensors of the soil sample at equilibrium after re-measuring the water content. (**c**) Water exchange between the sensor and the soil samples.  
The results of the TEROS 21 in terms of suction converged toward the same value, with a difference between 20 kPa and 2 MPa at equilibrium. When the data logged by the TEROS 21 probes are compared with those measured by the FP technique, some differences arise and can be highlighted. The dry sensors demonstrated the closest values to the FP technique at higher water content percentages, specifically when the soil water content was higher than 28%. In this range, the percentage difference from the FP method is approximately 20-25%. However, it is worth mentioning that the calibration curves used for the FP method are typically developed for higher suction ranges, and the sensitivity decreases at lower suctions [28]. As suction increased, with a water content value below 28%, there was a tendency to measure similar suction values, particularly from the wet probe, with similar percentage differences. The latter achieved similar values with a difference between 50 kPa and 200 kPa. Contrarily, the percentage difference from the FP method can reach up to 60-70% in the case of a dry sensor.  
It is worth noting that after 14 days, although the suction values tended to converge about the same value, differences persisted, which became more relevant as the suction value rose. This behavior can, once again, be attributed to the hysteretic effect of the sensor, as reflected in the soil specimens, and as already shown in the first test. Furthermore, the effect of temperature on the suction values was evident between 60 and 180 h after the test began, likely due to technical issues encountered in the laboratory.  
Figure 6: Measured suction and temperature over time for the soil samples in test 2.