esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Investigation of Five Parametric Models
### Model Attribute  
From Table 2, a comparative study on different kinds of model attributes is further discussed based on the research conducted by previous scholars [6; 7; 8; 9; 10; 11; 12; 13]. M1-M4 are macroscopic models denoting the viscoelastic properties of MRE. These models can show the relationship between shear stress and shear strain, and are functions of magnetic field, strain amplitude and loading frequency. However, none of them can consider the effect of pre-strain, and only M4 can incorporate the Payne effect due to the consideration of the element of modified Kraus model.  
\begin{table}
\begin{tabular}{c  
Specifically, as shown in Figure 1(a), M5 reveals the relationship between magnetic-induced modulus \(\Delta G\) versus lateral shear strain \(\gamma\) and compressive pre-strain \(\varepsilon\). It is obviously found that \(\Delta G\) is positive with the shear strain \(\gamma\) increasing, while the gradually increasing compressive prestrain \(\varepsilon\) has a positive effect on the magnetic-dependent modulus. However, if the lateral shear effect is considerably large, such as seismic effect with large acceleration and random vibration, the dynamic strain will cause decreasing dynamic stiffness and growing energy dissipation. In this case, the elastomer has a large deformation and the breakdown of filler networks initiates. At the same time, the interaction of particles attenuates as the distances of particles within and between chains increase, which gives rise to a decreasing trend of both storage modulus and loss modulus.  
This phenomenon is more visually indicated in Figure 1(b). The magnetic-induced modulus \(\Delta G\) becomes zero and even negative in the large shear-deformation area (\(\gamma\) = 1.2 in the extreme case). Therefore, M5 is more suitable for comprehensively considering both Payne effect of shear loading and pre-stress in the micro scale when MRE is simultaneously subjected to external magnetic excitation, lateral shear and vertical compression.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 4 Techniques of VLC-Based Indoor Positioning Systems
### Channel Model
#### 4.1.1 Direct Light  
To consider the propagation paths of LEDs, our initial focus shall be solely dedicated to the directed light. The channel link gain is given by [48]:  
\[G_{d}=\left\{\begin{array}{cc}\frac{(m+1)A}{2\pi D_{d}{}^{2}}\cos^{m}(\phi)T( \theta)g(\theta)\cos(\theta),&\text{ if }\left|\frac{\theta}{FOV}\right|\leq 1,\\ 0,&\text{ if }\left|\frac{\theta}{FOV}\right|>1\end{array}\right. \tag{1}\]  
where the parameters are illustrated in Table 2.  
The received power is thus given by  
\[P_{rd}=G_{d}\cdot P_{t} \tag{2}\]  
where \(P_{rd}\) denotes the received power and \(P_{t}\) denotes the transmit power.
# RESTful API Testing Methodologies: Rationale, Challenges, and Solution Directions
## 2 Background and Related Work  
### Background  
HTTP has its own methods, which are also known as verbs, that facilitate client-server communication. Originally only GET, HEAD, and POST verbs were defined in the HTTP/1.0. By HTTP/1.1, however, the verbs were expanded to include PUT, DELETE, CONNECT, OPTIONS, and TRACE and, as recent as 2010, PATCH was proposed in RFC 5789.  
Representational state transfer (REST) was proposed by [PERSON] in 2000 in his Ph.D. dissertation [7]. The aim of this style was to improve performance, scalability,simplicity, modifiability, visibility of the communication, portability, and reliability. To achieve such properties, [PERSON] defined six controls as follows: architecture artifacts related to client and server, statelessness, the ability to cache data, multi-tier system, standard interface, and optionally on-demand code. As such, out of the HTTP verbs, only the following ones are used for RESTful API: DELETE, GET, POST, and PUT [8].  
Whenever a REST API endpoint is invoked, it can be done using one of the verbs described above. For example, if there is an endpoint that returns a list of products from an online shopping website, it can be invoked using the GET verb, which means that there are some data that we want to get as a list of instances or a single instance. The request is called a GET request, and it may have some parameters. Similarly, data can be inserted using a POST request, deleted using a DELETE request, and edited using a PUT request. Accessing any REST API endpoint involves preparing a URL with address, parameters if any, and marking it appropriately with one of the verbs. POST verb is used for sending data with the request as part of the request body instead of embedding it in the URL. This makes it invisible, which is better for sensitive data like credentials. For example, when a user logs in, the username and password are sent to a REST API endpoint as a POST request.
## 5 Results and Analysis
### Performance Using Different Shaping Pulses  
As mentioned in Section 2.1, both LFM and RRC were used as shaping pulses. In the scenario of using LFM as a shaping pulse, the peak-to-average power ratio (PAPR) is large [9], and it could result in lower power efficiency for a linear amplifier. With a constant transmission power, the source level is reduced. However, the advantage is that the received channel probe signal is immediately used for PPC processing. Using RRC as a shaping pulse, PAPR is reduced, and then the channel response is estimated using different methods.  
In terms of output signal-to-noise ratio (SNR), Figure 8 shows the performance of PPC-DFE using different shaping pulses, where the symbol rate is 1 kilo-symbol/s. Due to low input SNRs in the 10 th period of Figure 8(a) and the 9 th-11 th periods of Figure 8(b), the receiver structures fail to recover distorted information. For RRC shaping pulse, the LS method is used to estimate the CIR within 40 ms. The observed time variant performance of PPC-DFE may be caused by the channel variations which resulted in sometimes a low input SNR, as for example at the 10 th period. Generally the performance difference between LFM and RRC is small for BPSK, as shown in Figure 8(a). In Figure 8(b), there are small differences over the 9 periods, and large differences in other periods, in particular for 7 th and 13 th periods. Channel estimations obtained by the LS method are impacted by the noise in the scenarios of low input SNRs, and thus using a LFM as a shaping pulse has shown its advantage. There is also a spreading gain by using the LFM as the shaping pulse, since the bandwidth of the LFM of 2.2 kHz is larger than the signals bandwidth of 1 kHz.  
\begin{table}
\begin{tabular}{l l r} \hline
**Parameters** & **Description** & **Value** \\ \hline \(F_{s}\) & Sampling frequency at the receiver & 96 kHz \\ \(f_{c}\) & Carrier frequency & 12 kHz \\ \(R\) & Symbol rate & 1, 2 kilo-symbol/s \\ \(P\) & Number of taps in the MP processing & 4 \\ \(N\) & Over sampling factor & 2 \\ \(N^{l}_{\vec{\pi}}\) & Number of the feed-forward filter taps (McDFE) & 20 \\ \(N^{l}_{\vec{\pi}b}\) & Number of the feedback filter taps (McDFE) & 5 \\ \(N^{2}_{\vec{\pi}}\) & Number of the feed-forward filter taps (PPC-DFE) & 8 \\ \(N^{2}_{\vec{\pi}}\) & Number of the feedback filter taps (PPC-DFE) & 2 \\ \(N^{3}_{\vec{\pi}}\) & Number of the feed-forward filter taps (PPC-McDFE) & 8 \\ \(N^{3}_{\vec{\pi}b}\) & Number of the feedback filter taps (PPC-McDFE) & 2 \\ \(T_{block}\) & Time duration of each block & 1 s \\ \(\lambda\) & RLS forgetting factor & 0.999 \\ \(K\) & Number of receiving channels & 12 \\ \(K_{1}\) & Proportional tracking constant in DPLL & 0.01 \\ \(K_{2}\) & Integral tracking constant in DPLL & 0.001 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters used in the signal processing of the three receiver structures.
## 3 Results
### Standard Data Processing Results  
The mean RMT was 65% (64.50 \(\pm\) 8.64, mean \(\pm\) SD) of the stimulator output. The presented electromyogram in Figure 2 demonstrates from left to right: pre-TMS EMG activity, TMS artefact, MEP latency, MEP amplitude, silent period, and post-SP EMG activity.  
Data processing with two-way repeated measures ANOVA showed a significant effect of both factors, \"TMS intensity\" and \"motor condition task\". We presented the results for the subject group in Figure 3. MEP amplitudes at relaxed muscles were the lowest at all TMS intensities compared to both abduction and co-contraction active motor tasks, and amplitudes of MEPs at abduction were the highest. At relaxed condition, recruitment curve was very close to linear and analysis with Bonferroni post hoc test found that MEP amplitudes were significantly lower (\(p\leq 0.05\)) in comparison to those at abduction in all investigated TMS intensities (100%, 110%, 120%, 130%, 140% RMT) (Figure 3a). The results  
\begin{table}
\begin{tabular}{c c c c c c}  & & \(C_{1}\) & \(\dots\) & \(C_{k}\) & \(\dots\) & \(C_{n}\) \\ \hline \multirow{5}{*}{FIM =} & \(C_{1}\) & \(\langle 1,0\rangle\) & \(\dots\) & \(\mu_{C1,Ck},v_{C1,Ck}\) & \(\dots\) & \(\mu_{C1,Cn},v_{C1,Cn}\) \\  & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\  & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\  & \(C_{n}\) & \(\mu_{Cn,C1},v_{Cn,C1}\) & \(\dots\) & \(\mu_{Cn,Ck},v_{Cn,Ck}\) & \(\dots\) & \(\langle 1,0\rangle\) \\ \end{tabular}
\end{table}
Table 1: ICrA \(\mu\)-scale for consonance and dissonance.  
from comparison of MEP amplitudes at relaxed muscles and co-contraction showed that MEP amplitudes at relaxed condition task were not significantly lower (\(p\leq 0.05\)) only in 100% RMT (Figure 3a). The test did not find considerable differences between MEP amplitudes in abduction and co-contraction (Figure 3a). There were no considerable differences between silent periods in abduction and co-contraction (Figure 3b), and between MEP latencies in all muscle condition tasks and all investigated TMS intensities (Figure 3c). Also, tests did not find any considerable differences between the pairs 100-110%, 110-120%, 120-130%, and 130-140% RMT of MEPs amplitudes, and MEPs latencies in relaxed condition, abduction, and co-contraction, respectively and between the same pairs of silent periods in abduction and co-contraction, respectively. However, there is a stable trend for the growth of MEP amplitude sizes and silent period durations with the increase in TMS intensity (Figure 3a,b). MEP latencies did not show any considerable trend (Figure 3c).
## 2 Optimization Siting Model
### Revised Site Planning Model  
The risk classifications method was then applied to the traditional MCLP model. The objective function was defined as a weighted function of the protected area of fire stations under four different risk scenarios as follows:  
\[Maximize\sum_{r\in R}\sum_{i\in I}y_{i}^{(r)}\times\beta_{r}\times s_{i} \tag{2}\]  
In the equation, \(I\) is the set of protected units and \(R\) is the set of fire risk classifications:  
\[R=\{A,B,C,D\} \tag{3}\]  
\(i\) and \(r\) are the counts of the site, \(\beta\) represents the area correction factor, and \(s\) represents the area of the protected units.  
Each site \(y\) should meet  
\[y_{i}^{(r)}-\sum_{j\in I}a_{ij}x_{i}\leq 0(i\in I) \tag{4}\]  
\[y_{i}^{(r)}\in\{0,1\}(i\in I) \tag{5}\]  
\[y_{i}^{(r)}=\left\{\begin{array}{c}1,\text{ Minimum response time meets the requirement}\\ 0,\text{ Minimum response time does not meet the requirement}\end{array}\right. \tag{6}\]  
\(J\) is the set of candidate sites for fire stations proposed by the risk classifications method and \(j\) is the count of the site. \(x_{i}\) is the count function of the candidate point, as follows:  
\[x_{i}=\left\{\begin{array}{c}1,\text{ the supply point is candidate point }j\\ 0,\text{ the supply point is non }-\text{candidate point }j\end{array}\right. \tag{7}\]  
\(\alpha_{ij}\) is the count function of the covered demand point, as follows:  
\[\alpha_{ij}=\left\{\begin{array}{c}1,\text{ Demand point }i\text{ is not covered by candidate point }j\\ 0,\text{ Demand point }i\text{ is covered by candidate point }j\end{array}\right. \tag{8}\]  
In particular, \(\beta_{i}\) represents the area correction factor. Since the objective function of this model represented the weighted area of the protected regions, and the response time varied for different classifications of risk, the area that could be covered by fire stations also differed in regions with different risks, as shown in Figure 4. To achieve equal coverage by fire stations, it was necessary to adjust the area of high-risk protected regions. The values of the area correction coefficients for different classifications of risk were inversely proportional to the square of the response time, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Risk Classification** & **Travel Time (min)** & **Risk Classification (min)** \\ \hline A & 5 & 4 \\ B & 8 & 7 \\ C & 15 & 14 \\ D & 20 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Travel and response times for different risk classifications.
## 4 Results and Discussion
### Analysis of Metrics for the Implementation of V2G with Power Smoothing Methods
#### 4.4.1 Standard Deviation  
The standard deviation (see Figure 15), which measures the spread of data around the mean, showed a trend toward reduction with the application of smoothing methods. In particular, the ramp-rate method with a variable rate of change using fuzzy logic stood out by achieving a significant decrease in energy generation variability.
## 2 Baseline Multichannel Acoustic Score Computation of a CSC BLSTM AM
### Configuration of a Server-Client-Based ASR System
#### 2.1.2 Decoder Thread of the Online ASR Server  
Whenever a decoder thread is in idle or ready state, the thread checks whether the size of the speech features in \(\mathcal{R}_{feat}\) is larger than a pre-defined minibatch size, \(\mathcal{T}_{bat}\). \(\mathcal{T}_{bat}\) indicates the length of the audio samples to be decoded at a time, and is defined during the initialization stage of the ASR server. In this paper, we set \(\mathcal{T}_{bat}\) as 200 frames, each of which is 2-s long [14; 26; 36].  
If the decoder thread detects speech features that are larger than \(\mathcal{T}_{bat}\), the thread obtains the speech features with a length of (\(\mathcal{T}_{bat}\)+\(\mathcal{T}_{ext}\)), while those with a length of \(\mathcal{T}_{bat}\) are popped from \(\mathcal{R}_{feat}\). \(\mathcal{T}_{ext}\) is the length of the audio samples for left or right context information [30]. In this paper, we set \(\mathcal{T}_{ext}\) as 40 frames. The obtained features are used to calculate the acoustic scores of a CSC BLSTM AM where the multichannel parallel decoding is proposed in Section 3. The acoustic scores are used in two ways: (a) DNN-VAD and (b) Viterbi decoding. DNN-VAD aims to automatically send the decoded text to the client even though no end-of-audio message is received from the client, where DNN-VAD is proposed in Section 4. If DNN-VAD detects any short pause in an utterance, the thread finds optimal texts up to the time, sends the texts to the client, and resets the search space of Viterbi decoding. Viterbi decoding estimates the probabilities of possible paths using the acoustic scores and an \(n\)-gram LM. The processes are repeatedly performed until the decoder thread receives an end-of-audio or end-of-connection message.
# Infrastructure Elements for Smart Campuses: A Bibliometric Analysis
## 3 Results: Overview of Smart Campus Research  
### Trend in Smart Campus Research  
Out of the 285 documents reviewed and analysed in this study in the last ten years, less than ten articles were published on smart campus technologies and construction between 2011 and 2014, as illustrated in Figure 1. From the year 2015 to 2017, 11 to 35 articles were published. The massive growth of publications in smart campus studies started in 2018 with 64 publications in a year. This number declined in 2019 to 55 articles. The highest growth in publication was experienced in 2020, with 68 articles (although bibliometric analyses are traditionally conducted including entirely up to a specific year) [28; 29]. The publications from 2021 (up until June 2021) were included in the analysis because of the 25 recently published articles and their relevance.  
The trends in smart campus publications from 2017 indicate more awareness and desire to study smart campus applications. Publications from 2021 are still in the growth process, with 25 articles far greater than the combination of smart-campus-related articles from 2011 to 2014. The non-inclusion of these 25 articles in this analysis will prove a major deficit in discussing new technologies related to smart campuses. This result denotes a greater need for smart campus development. With the growth in smart campus research, the geographical distribution of the articles, as illustrated in Figure 2, indicated that 138 articles emanated from China. They are forming 48.4% of the total sample size. The key articles from China are [PERSON] [23]; [PERSON] [24]; [PERSON] [25]; and [PERSON] [26] that featured localised and regional studies on the construction of smart campuses using big data architecture, cloud computing, learning analytics and teaching systems.  
Figure 1: Annual trends in smart-campus-related publications.  
Other dominant studies emerged from the United States of America, Taiwan, Italy, Brazil, and Canada with 34, 27, 23, 18 and 14 publications respectively. The combination of these countries only accounts for a combined percentage of 89.12% of the total publications of smart campus research, while other countries account for only 10.88% publication in this field of research. The focus of the publication search was to identify smart campus technologies and procurement indicators. This preliminary search analysis provided a basis for categorising smart campus infrastructure for procurement purposes.
## 3 The Effects of the Second Phases on Properties
### \(\sigma\) Phase  
The accumulation of \(\sigma\) phase at grain boundaries in AISI 321 stainless steel can adversely impact the material's corrosion resistance and crack resistance [34]. This generation of the \(\sigma\) phase is primarily attributed to the transformation of \(\delta\)-ferrite into austenite, denoted as \(\delta\)\(\rightarrow\)\(\gamma\)\(+\)\(\sigma\). As observed from previous studies, a substantial amount of carbides are produced during the decomposition of \(\delta\)-ferrite, and the formation of these carbides is hypothesized to facilitate the emergence of the \(\sigma\) phase [35]. To elucidate the specific correlation between carbides and the \(\sigma\) phase, [PERSON], S.K utilized the Thermo-Calc software (Thermo-Calc 2019b) for predictive analysis, varying the content of carbon (C) and titanium (Ti) independently. The predictions revealed no definitive relationship between the \(\sigma\) phase and M\({}_{23}\)C\({}_{6}\), but rather an increasing trend with elevated Ti (C, N) content (Figure 8) [36].  
Figure 8: Phase diagram simulation, (**a**) thermodynamic property diagram; (**b**) C content of 0–0.1 wt.%; (c) Ti content of 0–0.5 wt.% [36].  
Figure 7: Stress–strain curves of AISI 321 stainless steels in different directions, (**a**) vertical; (**b**) horizontal [33].  
To assess the behavior of the \(\sigma\) phase, [PERSON], S.K also conducted impact experiments on AISI 321 stainless steel samples with varying \(\sigma\) phase contents (where longer sensitization times resulted in higher \(\sigma\) phase concentrations [37]). The impact fracture morphology transitioned from a fibrous ductile pattern to a mixed mode comprising ductile transgranular and brittle cleavage features, with broken parts evident within the toughness dimples (Figure 9). Following a sensitization period of 1500 h, the impact energy decreased from 264.3 J to 160 J. This decrement suggests that an excessive presence of carbon (C) and titanium (_Ti_) accelerates the aging process of the material, thereby diminishing its lifespan [36].
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 5 Preliminary Results and Validation of QKD Emulation with PUF
### Generating a Sample Key  
Finally, we tested a 75 trit long sample key with randomly generated bases and input trits. Table 4 shows the first 22 trits of the 75 trit long key. The sifted key is the final key after keeping only matching bases positions. We observed zero errors in our sifted key.  
This is a real world test, where nothing is hard-coded. Instead, bases and transmitted trits are generated at random. This tests different combinations of polarization, input, and preceding polarization and input. The preceding polarization and input is of interest because it demonstrates the system's ability to remain accurate regardless of its preceding configuration. The preceding polarization state can affect the error rate since the polarization rotator (half-wave plate) must rotate different amounts for different states.  
Again, we observe that matching bases give matching input-output, and mismatching bases do not about 50% of the time. Once mismatching bases are removed, and the sifted key remains, we observe zero error, meaning all inputs and outputs match when bases are the same.
## 3 Methods and Materials
### Gravity Data Characterizations
Despite the interpretations, the influence of the basement effect on the modeling of the gravity field had to be minimized. Although the study area is limited (Figure 5), additional analyses were carried on in order to include the gravity effects generated even by far distant zones (Figure 5A,B anomaly I) and, thus, to ensure proper boundary conditions for gravity modeling. This resulted from the fact that the gravity method is very sensitive to superposition effects [6; 35]. For this reason, the anomaly described in the literature as \"the Carpathian gravity depression\" [35] (Figure 5A,B anomaly I) strongly influenced the values of the Bouguer anomaly in the entire analyzed area. Particularly, it influenced the results of modeling in the marginal parts of the Carpathians where the boundary line between the Carpathian structures and their foreland [38] was most clearly marked by the presented distribution of residual values of Bouguer anomaly along the analyzed seismic traverse (Figure 5A,B anomaly II). To the NE of this line, the strikes of structures built of Miocene formations in the Carpathian Foredeep radically changed their directions in relation to the structural directions typical of the Polish Eastern Carpathians. The block structures developed within the sub-Moocene basement were separated by zones of discontinuous deformations [33; 35; 36]. Residual anomalies were characterized by symmetric shapes of transversal cross-sections and high-intensity changes within the main Carpathian gravity depression. However, these changes clearly decreased to the NE from the marginal zone of the Carpathian Overthrust [29; 38]. The central part of the profile was dominated by anomalous zones corresponding to structures of a smaller depth range whereas in the marginal zones, the structures of a greater depth range were more common. However, regardless of the type of transformation, the directions of their strikes and their positions on the profile were highly consistent. Figure 5: The Bouguer anomalies compared with the regional anomalies extracted for the described seismic traverse. (**A**) Comparison of regional anomalies calculated as differences between the Bouguer anomalies and the corresponding residual trends. (**B**) Comparison of residual anomalies calculated as differences between the Bouguer anomalies and the corresponding regional trends. The black rectangle represents the Raczyna deposit area where PreDSM migration tests were performed. Other explanations: I—Zone of Carpathian gravity depression, II—Zone of Carpathian Overthrust (Area of Interest (AoI), III—Zone of shallowing of crystalline basement (boundary of Carpathian Foredeep).