esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Extraction Results of Building Information
#### 4.1.5 Extraction Results of Building Structure Type  
The structural types of buildings are divided into masonry structures, brick and concrete structures, reinforcement concrete structures, and others according to the seismic performance of buildings. The structure types of industrial buildings are divided into single-story industrial buildings and multi-story industrial buildings according to the story number. Su found that the seven-story brick and concrete structures in Tangshan City had completely disappeared in 2015, and in residential and office buildings, single-story buildings can be considered brick and masonry structures, 2-6-story buildings can be considered brick and concrete structures, and 7-story and above buildings are reinforced concrete structures; in residential buildings in rural areas of Tangshan, 66% are brick and masonry structures, 17% are reinforced concrete structures, and 17% are brick and masonry structures, and the public building structure is better than that of residential buildings [16]. The relationship between the building function and story number was analyzed based on  
Figure 14. Multi-temporal construction land display in Tangshan urban area.  
Figure 13. Distribution map of building story numbers in Tangshan urban area.  
the statistics of the deep network data. Figure 16 shows the percentage of reinforcement concrete and masonry structures of buildings in urban areas of Tangshan with different construction ages and building story numbers. Since the percentage of other structures is relatively small, it is not presented here. From the figure, it can be seen that buildings of 1-3 stories before 2001 are mainly masonry structures, while buildings of 1-3 stories after 2000 are mostly reinforcement concrete structures. The buildings of 4-6 stories after 2010 are mostly reinforced concrete structures, while the structure types of buildings before 2011 are brick and concrete structures. As a result, relationships were established among the story number, construction age, structure type, and function of buildings in the urban areas of Tangshan City, as shown in Table 3. The distribution of building structure types is derived from the correspondence, as shown in Figure 17, from which it can be seen that brick and steel structures account for more, which is consistent with the results of Su's study.  
Figure 16: The proportion of various structures with different construction ages and story numbers of buildings in the urban area of Tangshan City. **(a)** The proportion of reinforced concrete structures with different construction ages and story numbers of buildings in the urban area of Tangshan City. **(b)** The proportion of masonry structures with different construction ages and story numbers of buildings in the urban area of Tangshan City. **(c)** The proportion of brick and concrete structures with different construction ages and story numbers of buildings in the urban area of Tangshan City.  
Figure 15: Distribution map construction age of buildings in Tangshan urban area.
## 2 Description of the FTN Signaling System
Also, this algorithm must provide a minimum number of computational procedures when determining the choice of maximum numbers in the sequence. The choice of this optimality criterion is due to the fact that the minimum possible number of addition and multiplication operations is required in the function of selecting the maximum numbers in the sequence to implement the proposed algorithm. Proposed Algorithm with Decision Feedback with Estimation of Subsequent Symbols in Observation Interval by Maximum Likelihood Ratio  
Let us present the process at the input of the receiver in the following form:  
\[x(t)=\mu s_{r}^{(n)}(t)+\mu y_{-}(t,i)+\mu y_{+}(t,q)+n(t), \tag{3}\]  
where \(\mu\) is a channel transfer coefficient; \(y_{-}(t,i)\) and \(y_{+}(t,q)\) are the sequences of previous and subsequent signals. Note that \(y_{-}(t,i)\) and \(y_{+}(t,q)\) depend on \(R=1/\xi T\). The indexes \(i\) and \(q\) denote the numbers of specific combinations of previous \(I\) and subsequent \(Q\) signals. For \(M=2\) and \(R=1/T\), \(i=1,2,...,2^{l}\) and \(q=1,2,...,2^{Q}\). The normalized symbol rate \(R_{n}\) is equal to \(RT=1/\xi\). In this case, the number of previous signals in the observation interval \(T_{s}\) is equal to \(I=\left\lceil LR_{n}-1\right\rceil\). The number of subsequent signals is equal to \(Q=\left\lceil LR_{n}-1\right\rceil\). Then, for \(n=0\) in (1),  
\[y_{-}(t,i)=\sum_{p=-1}^{-\left\lceil LR_{n}-1\right\rceil}d_{ri}^{(p)}a(t-p \xi T)\cos(2\pi f_{0}t), \tag{4}\]  
\[y_{+}(t,q)=\sum_{p=+1}^{\left\lceil LR_{n}-1\right\rceil}d_{rq}^{(p)}a(t-p \xi T)\cos(2\pi f_{0}t), \tag{5}\]  
where \(d_{ri}^{(p)}\) and \(d_{rq}^{(p)}\) are the channel symbols of previous (\(i\)) and subsequent (\(q\)) sequences of signals; \(p\) is the ordinal number of the symbol in the sequence of signals. For example, values of \(I\) and \(Q\) are equal to \(7\) for \(R_{n}=1/\xi  
The analytical notation of the algorithm with decision feedback with the evaluation of subsequent symbols in accordance with the maximum likelihood ratio criterion has the following form for \(M=2\):  
\[2\int_{0}^{LT}A_{xc}(t)a(t)dt\mathop{\gtr{s_{1}(t)}}_{s_{2}(t)}\delta(i,q),\]  
\[\delta(i,q)=\max_{(q)}\biggl{(}\int_{0}^{LT}A_{xc}(t)\sum_{(p=1)}^{\left\lfloor LR _{n}-1\right\rfloor}d_{rq}^{(p)}a(t-p\xi T)dt\]  
\[-\mu\int_{0}^{LT}a(t)\sum_{p=-\left\lfloor LR_{n}-1\right\rfloor}^{(-1)}d_{rq}^ {*(p)}a(t-p\xi T)dt\]  
\[-\mu\int_{0}^{LT}a(t)\sum_{p=1}^{\left\lfloor LR_{n}-1\right\rfloor}d_{rq}^{(p) }a(t-p\xi T)dt\biggr{)} \tag{9}\]  
\[-\max_{(q)}\biggl{(}\int_{0}^{LT}A_{xc}(t)\sum_{(p=1)}^{\left\lfloor LR_{n}-1 \right\rfloor}d_{rq}^{(p)}a(t-p\xi T)dt\]  
Figure 1:0  
where \(d_{ri}^{*(p)}\) is the evaluation of the \(p\)-th received symbol. This algorithm is characterized, as will be shown later, by limited computational complexity with energy costs close to the MLSE algorithm (see Table 1).
## 3 Knee FEA Workflow
### Pre-Processing
#### 3.1.3 Material Constitutive Models
Regarding material models for meniscal tissue and meniscal attachments, similarly to cartilage, a variety of constitutive models have been proposed, from isotropic elastic to biphasic elastic reinforced with fibrils [96; 97; 98; 99; 100; 101]. [PERSON] et al. [99] compared two biphasic fibril-reinforced tissue models to justify using a complex or simpler distribution of meniscus constituents. They compared depth-dependent and homogenized distributions for collagen, water, and PGs in an axisymmetric model of the knee joint, including cartilage and meniscus. Results showed no significant differences in the cartilage fibril strain between the models. Thus, the authors suggested that using a homogenous distribution of meniscus constituents yields trends in stresses and strains similar to those found when using a depth-dependent one. Ligament and tendon. Similarly, the ligament tissue can be described as a complex material and different constitutive models have been used to model it [37; 63; 102; 103; 104; 105]. With respect to this tissue, [PERSON] et al. [106] studied the impacts of using different geometrical representations and formulations of ligaments on knee joint mechanics. They simulated the gait cycle of a 3D knee joint, including the anterior and posterior cruciate ligaments (ACL, PCL), lateral and medial collateral ligaments (LCL, MCL), quadriceps, patellar tendons, menisci, and patellar, femoral and tibial cartilage. They formulated ligaments as springs, elastic, hyperelastic, poro-hyperelastic, and fibril-reinforced poro-hyperelastic. Results suggested that, at certain time points of functional activities, simpler material formulations could mimic the mechanical response obtained with more complex models. This approach saves time in the pre-processing stage, by skipping the segmentation of ligament geometry, and in the processing stage, since solving models using springs is faster than using complex fibril-reinforced poro-hyperelastic formulations. Bone. Different authors model bone tissue as a homogenous and solid elastic material even though its heterogeneous and biphasic nature is known [28; 107; 108; 109; 110]. Moreover, the boundary between bone and cartilage is not abrupt. In this region we find transition zones between cartilage, calcified cartilage, subchondral bone, and trabecular bone. [PERSON] et al. [28; 108] identified the importance of these transition zones when modeling OA in bone and explained the mechanical crosstalk between the different tissues. Nevertheless, when the interest is focused on cartilage only, bones have been often modeled as rigid bodies in many studies. Through this assumption, researchers avoid including the specific geometry of bones and only consider their constraining effect as boundary conditions such as rigid and impermeable regions [24; 111].
###### Abstract
The field data collected focus on a five-day period within the vicinity of the LIMAK Cement Factory, providing a detailed snapshot of traffic dynamics in a specific area, serving as a foundation for our model's development and validation. While this dataset offers valuable insights, further studies incorporating broader datasets across different areas and longer periods are essential to fully capture the complexities of Ankara's urban traffic. The development and application of UTMOM involve a meticulous examination of daily traffic dynamics, focusing on reducing discrepancies and highlighting variations in traffic intensity, particularly during peak times. Our findings reveal pivotal insights into the differences across datasets, contributing significantly to traffic management and urban planning. UTMOM delves into the intricacies of traffic flow variations, emphasizing the critical importance of understanding fluctuations in traffic volume across diverse times and locations. By incorporating detailed graphical representations and statistical validations, including ANOVA analysis, our study delivers a comprehensive evaluation of UTMOM's precision in reflecting real-world traffic scenarios. These insights affirm the value of data-informed strategies in optimizing traffic flow and alleviating congestion. Positioned as a valuable asset for traffic engineers, data scientists, and urban planners, UTMOM advocates for advanced modeling techniques to improve urban mobility. Beyond enriching academic discourse on traffic analysis, UTMOM offers actionable intelligence for enhancing the efficiency and sustainability of urban transportation systems. Through this in-depth investigation, our aim is to catalyze the development of innovative solutions to traffic challenges, steering towards smoother and more sustainable urban environments. The following sections will detail the research methodology underpinning UTMOM, focusing on its mathematical programming aspects. From data preprocessing and feature extraction to cluster analyses and predictive modeling, each component of UTMOM is designed to uncover hidden traffic patterns and provide solutions that could redefine urban transportation in Ankara. Ultimately, this study aims to serve as a vital guide for urban planners, transportation authorities, and policymakers in Ankara. The insights derived from UTMOM are intended not only to light the way for data-informed decision-making but also to lay the groundwork for sustainable urban mobility strategies. An in-depth examination of our methodology, especially the mathematical programming model, and a thorough analysis of our findings will map out strategies for optimizing Ankara's transportation infrastructure.
## 3 Methodology  
In this study, the positioning performance of the GNSS receiver was evaluated in both simulated and real signal-jamming environments. A continuous wave (CW) jamming signal was applied on the L1 frequency (1575.42 MHz) at low- and high-power levels for both jamming conditions. For the simulated jamming, the jamming signal power level was 46 dB compared to the reference signal power (\(-\)130 dBm) for the low-power level. On the other hand, the power level of the jamming signal was set to 60 dB for the high-power level. To exert the low and high-power jamming levels for the actual jamming condition, a reference signal power of \(-\)130 dBm was used. After that, signal attenuations of 36 dB and 20 dB were applied to provide the low and high-power jamming levels, respectively. The jamming signal lasted for two minutes, and it was applied during a portion of the trajectory where the platform was experiencing low dynamics to analyze the effect of the jamming only. The GNSS receiver positioning performance was assessed in three consecutive steps. First, the contribution of using GPS multi-frequency measurements to the resulting positioning solution under both low and high jamming was evaluated. The benefit of GPS multi-frequency measurements was explored by enabling a dual-frequency (DF) signal at a time and then enabling GPS triple-frequency (TF) signals. Second, the benefit of multi-constellation to the GNSS receiver performance is assessed by enabling GPS TF signals and Galileo single-frequency (SF) signals at a time, then enabling GPS TF signals and Galileo TF signals all at once. Lastly, the GNSS receiver was assessed by enabling GPS TF, Galileo TF, and GLONASS SF signals at a time and then enabling GPS TF signals, Galileo TF signals, and GLONASS DF signals all at once. This, in turn, has the capability of providing improved performance due to the high number of visible Galileo and GLONASS satellites when there is no availability of GPS satellites. The used GNSS signal combinations are presented in detail in Table 1.
# RoseCliff Algorithm: Making Passwords Dynamic
## 2 Encryption
### Symmetric Encryption  
Symmetric encryption, also referred to as secret key or private key encryption, utilizes a single shared key for both encrypting and decrypting data. This shared secret key is known to both the sender and receiver and is employed to transform plaintext into ciphertext (encryption) and vice versa (decryption). One prominent example of symmetric encryption is the Advanced Encryption Standard (AES), which was endorsed by the National Institute of Standards and Technology (NIST) in 2001. The AES, replacing the older Data Encryption Standard (DES) and Triple DES (3 DES), operates on 128-bit data blocks, thus employing transformation rounds like SubBytes, ShiftRows, MixColumns, and AddRoundKey. The number of rounds varies based on the key size (AES-128, AES-192, or AES-256), thereby offering differing levels of security [20]. Notable characteristics of symmetric encryption include:  
1. **Shared Key--**Both the sender and receiver possess and safeguard the same secret key, thus ensuring its confidentiality.
2. **Efficiency--**Symmetric encryption typically outperforms asymmetric encryption (public key encryption) in terms of computational efficiency due to its simpler mathematical operations.
3. **Confidentiality--**Symmetric encryption ensures that unauthorized parties cannot decipher encrypted data unless they possess the secret key.  
4. **Data Integrity--**While symmetric encryption primarily focuses on confidentiality, additional techniques like message authentication codes (MACs) or digital signatures are used to guarantee data integrity and authentication.  
The AES is recognized for its high security and has been adopted globally as an encryption standard, thereby serving various industries and countries to secure data and communications. It is a foundational component of modern cryptography, thereby offering robust security and versatility in preserving data confidentiality and integrity across numerous applications [17; 20; 21].
# Calculations of Cross-Sections for Positron Scattering on Benzene
## 2 Theory and Computational Details
### Model for Direct Ionization of Molecules by Positron Impact
\[\sigma_{\rm elec}=\sum_{Y}\sigma_{0Y}^{\rm elec}. \tag{7}\]  
The cross-section for each electronically excited state is calculated using the binary-encounter-scaled Born cross-section ([PERSON], 1966)  
\[\sigma_{0Y}^{\rm elec}=\frac{E}{E+B_{j}+\Delta E_{0Y}}\sigma_{0Y}^{\rm Born}. \tag{8}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Orbital** & \(\mathbf{B_{i}/\text{eV}}\) & \(\mathbf{U_{i}/\text{eV}}\) & \(\mathbf{N_{i}}\) \\ \hline \(1e_{1g}\) & 9.26  & 27.81 & 4 \\ \(3e_{2g}\) & 12.16  & 37.81 & 4 \\ \(1a_{2u}\) & 12.45  & 23.70 & 2 \\ \(3e_{1u}\) & 14.47  & 32.46 & 4 \\ \(1b_{2u}\) & 14.90  & 39.91 & 2 \\ \(2b_{1u}\) & 15.82  & 34.21 & 2 \\ \(3a_{1g}\) & 17.40  & 25.33 & 2 \\ \(2e_{2g}\) & 22.44  & 39.03 & 4 \\ \(2e_{1u}\) & 27.64  & 42.36 & 4 \\ \(2a_{1g}\) & 31.36  & 39.36 & 2 \\ \(1b_{1u}\) & 305.62  & 436.29 & 2 \\ \(1e_{1u}\) & 305.64  & 436.11 & 4 \\ \(1e_{2g}\) & 305.68  & 435.74 & 4 \\ \(1a_{1g}\) & 305.69  & 435.59 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Computed binding energies \(B_{i}\), expectation values of the kinetic energies \(U_{i}\), and occupation numbers \(N_{i}\) of the molecular orbitals of benzene. Here, \(\Delta E_{0Y}\) is the excitation energy from the electronic ground state to the excited state \(Y\) and \(\sigma_{0Y}^{\text{Born}}\) is the cross-section for electronic excitation within the first Born approximation [44]. \[\sigma_{0Y}^{\text{Born}}=\frac{8\pi}{3k_{0}^{2}}D_{0Y}^{2}\ln\Big{|}\frac{k_{0 }+k_{Y}}{k_{0}-k_{Y}}\Big{|}. \tag{9}\]  
Here, \(D_{0Y}\) is the transition dipole moment between the electronic ground state and the electronically excited state \(Y\) of the molecule. The wavenumbers of the electron before (\(k_{0}\)) and after (\(k_{Y}\)) collision are given by  
\[k_{0}=\frac{1}{\hbar}\sqrt{2m_{e}E}\quad\text{and}\quad k_{Y}=\frac{1}{\hbar} \sqrt{2m_{e}(E-\Delta E_{0Y})}\, \tag{10}\]  
For the calculation of the electronically inelastic cross-section, we used the energies and oscillator strength from [PERSON] et al. [45], obtained with the symmetry-adapted cluster-configuration interaction (SAC-CI) method. In Table 2, we show the data for all dipole-allowed transitions obtained by [PERSON] et al. [45]. All three electronic excitations are transitions of one electron from the highest occupied molecular orbital (here denoted as \(\pi\)) to a virtual orbital. The transition dipole moment and the oscillator strength are connected through the equation  
\[f_{0Y}=\frac{2}{3}\frac{m_{e}\Delta E_{0Y}}{\hbar^{2}}|D_{0Y}|^{2}. \tag{11}\]  
For the binary-encounter scaling, we used the binding energy \(B_{i}=9.26\) eV for the \(1e_{1g}\) orbital from Table 1.
## 5 Numerical Results and Performance Analysis  
In this section, we first describe evaluation scenarios followed by a discussion of numerical results to validate the theoretical analysis of the previous sections. We use the average transport network latency, transport network traffic distribution, average substrate link load [25] and serviceability to evaluate the performance of our proposed NS resource allocation scheme.  
Except for minimizing the transport network latency in (P1), other performance metrics include the transport network traffic distribution \(R_{T}\) as defined in (15), the average substrate link load of 5G networks \(R_{L}\) as defined in (16), and the average serviceability \(R_{S}\) as defined in (17) for a given set of NSIs. In our scheme, we consider a three-layer network structure for the backhaul transport network,consisting of the access layer network, aggregation layer network and core layer network. Transport network traffic distribution is the slice traffic ratio distributed over three-layer networks (i.e., the access, aggregation and core layer networks) respectively, which can explain the resource allocation principle of our proposed model. Substrate link load is defined as the slice traffic and mapped physical link capacity ratio. By the comparison with other existing policies, we can observe intuitively whether our approach has a considerable impact on the load balance of three-layer networks. Furthermore, serviceability is the ratio of the number of NSIs which have been successfully mapped and the total number, evaluating the ability of a network to serve the concurrent NS requests [47]. As only a few deployment algorithms of E2E slices can be found in current studies, our algorithms are compared with NSDP and RFPA schemes. NSDP is an ILP problem for slice resource allocation, aiming at minimizing the total bandwidth consumption. The RFPA is compared with our proposed algorithm to evaluate whether our approach has a considerable impact on the load balance, or simply deploying VNFs into the preferred substrate node is enough.  
In (15), the total traffic flow of an NS \(s\) is denoted by \(f^{s}\). In (15) and (16), \(L_{N}^{p}\) stands for the substrate links in different layer networks, including access layer network, aggregation layer network and core layer network. The total number of substrate network links in different layer networks is denoted by \(|L_{N}^{p}|\). In (17), the total number of NSIs which requires resource allocation is denoted by \(NUM_{sum}\), and the number of NSIs that can be served is denoted by \(NUM_{available}\).  
\[R_{T}=\sum_{(u,v)\in L_{N}^{p}}\frac{f_{(u,v)}^{(s,k)(s,l)}}{f^{s}}, \tag{15}\]  
\[R_{L}=\frac{1}{|L_{N}^{p}|}\sum_{(u,v)\in L^{p}}\frac{f_{(u,v)}^{(s,k)(s,l)}}{ C_{(u,v)}}, \tag{16}\]  
\[R_{S}=\frac{NUM_{available}}{NUM_{sum}}. \tag{17}\]
## 3 Results and Discussion
### Simulation Results  
The simulation process of the material is as follows. The tool is first placed at the bottom of the material. The simulation process adopts the loading method of fixing the specimen and moving the tool, and taking pictures at an interval of 60 mm. The deformation process is shown in Figure 7a. Comparing the simulation (Figure 7b) and experiment test results, it can be found that the deformation of the stress-strain curves of the simulation and the test are relatively similar under the same loading conditions with the same specifications and parameters.  
In the range of the deformation amount of 300 mm, the stress of the material has four distinct stages. These are the initial elastic stage, the first plateau stage, the stress rising stage and the second plateau stage. The initial elastic stage occurs at the beginning of the material. This part is an oblique line until the plastic strain of the material is reached; after that, the material shows a small stress drop, the material begins to fail, and then the material deforms with a relatively stable stress. The continuous range of this stage is about the length of \(S_{1}\); after reaching the starting point of \(S_{2}\), \(S_{2}\) is a material fracture length with a variable slope, so the stress increases according to a certain slope, and the magnitude of the increase is related to the slope of the material thickness. When the material enters \(S_{3}\), the thickness of \(S_{3}\) is two times that of \(S_{1}\), and it is also a cuboid region from the side, and its stress is stable, at about two times that of \(S_{1}\).  
Figure 5: The shape of specimen. (**a**) Physical map; (**b**) sketch map.  
Figure 6: Force-displacement and energy-displacement curves. (**a**) Cushioning force-stroke curve; (**b**) energy-stroke curve.  
Observing the morphology of the material after cutting, it can be found that the material on both sides is cut off, and the sides are straight, as shown by the red line (Figure 7a).  
From the deformation process diagram, it can be found that there are two obvious inflection points in the material, that is, the curling of the material changes significantly (Figure 7a), these occur at 100 mm and 200 mm of the material's deformation.