esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic Emission/Seismicity at Depth Beneath an Artificial Lake after the 2011 Tohoku Earthquake
## 3 Tectonic Conditions before and after the Tohoku Earthquake  
On 15 September 1998, an earthquake of magnitude (M) 5.0 occurred at a 12.4 km depth, on the deepest part of the Nagamachi-Rifu fault [14]. The mechanism of this earthquake and its aftershocks was reverse faulting, and the P-axes of the events described a line oriented east-west to northwest-southeast; the maximum principal stress direction in the deep part of the active fault was northwest-southeast.  
On the basis of GPS data from an observation point about 6 km southeast of the dam, the Geospatial Information Authority of Japan (GSI) reported crustal movement of 2.55 m eastward and 16 cm ground subsidence during the Tohoku earthquake (e.g., [15]). The GSI also measured eastward crustal movements of 1.85 m in Tendo city, 30 km to the west, and 3.98 m in Higashi-Matsushima city, 70 km to the east (GSI, [[http://www.gsi.go.jp/index.html](http://www.gsi.go.jp/index.html)]([http://www.gsi.go.jp/index.html](http://www.gsi.go.jp/index.html)); Figure 1a). From these data plus the assumption of an elastic rock mass in the area, the strain change resulting from the Tohoku earthquake was estimated to be 2.13 \(\times\) 10\({}^{-5}\) in the study area. Eastward crustal movement around Sendai continued after the earthquake, reaching about 1 m during the following year (GSI; [[http://www.gsi.go.jp/cais/chikakuhendo40012.html](http://www.gsi.go.jp/cais/chikakuhendo40012.html)]([http://www.gsi.go.jp/cais/chikakuhendo40012.html](http://www.gsi.go.jp/cais/chikakuhendo40012.html))).  
It appears that the direction of maximum principal stress in the study area was roughly east-west both before and after the Tohoku earthquake [7]. However, at the Kamaishi mine, \(\approx\)170 km northeast of Sendai, horizontal displacement of 3.3 m was observed during the Tohoku earthquake [16]. In situ measurements of tectonic stress at the mine documented a large change in the stress field in the shallow crust in the two years after the Tohoku earthquake, with the principal stress decreasing in the east-west direction and increasing in the north-south direction [16].  
The evidence suggests that although the Tohoku earthquake caused large changes of the stress field in some parts of the shallow crust, changes in the orientation of the stress field at seismogenic depths were small in the study area, which implies that the stress field in the study area still favors reverse faulting.
## 5 Simulation Results
### Communication-System Performance Evaluation  
In this subsection, we compare the performance of the communication system of the UFMC scheme with OFDM over a multipath vehicular channel with different Doppler frequencies, in particular, the tapped delay line A (TDL-A) channel. The TDL-A channel model has a Doppler spectrum that is characterized by Jake's spectrum shape. The power delay profile (PDP) of the model is presented in [21], and the delay spread used to scale the normalised tap delays was \(D_{s}=26\) ns. This delay spread was chosen to correspond to a short delay profile in a UMi Street-canyon for a 70 GHz carrier frequency to match the carrier frequency of our RadCom system.  
Seven-symbol slots were considered in the transmission system, and pilot-aided channel estimation was used in our simulations. We chose to insert pilot symbols in the grid according to two different configurations. In the frequency domain, the positions of the pilots were determined by pilot spacing. In our case, we fixed the pilot spacing to 4; hence, 512 resource elements (RE) were allocated for pilots. For the time domain, we chose a low-density configuration where only the first symbol was allocated for pilots, and high-density configuration where the first and the fifth symbols were allocated for pilot symbols. For complexity and enhancement considerations, it is more interesting to use the MMSE channel estimator and ZF equalizer.  
While communicating, the system parameters are updated for optimization reasons. First, subcarrier spacing is reduced to 15 KHz for optimal spectral efficiency; hence, sampling frequency is also reduced. Moreover, the total number of transmitted symbols is no longer fixed at 175 symbols and becomes flexible depending on the requirements of the transmitter. No error-correction coding was deployed. For UFMC-CDMA, we added multiuser interference (MUI) with 3 different patterns, where 5, 6, and 7 users were superimposed at each subcarrier for each pattern, respectively.  
As presented in Figure 8, the high-density configuration slightly outperformed the low-density configuration; however, the overall performance of both OFDM and UFMC waveforms was highly degraded due to the high delays induced by the TDL-A channel. Comparing the previous figures shows that the gap between the pilot configurations is more significant over a higher maximal Doppler shift i.e., a higher speed. This is due to the time-domain enhancement of the pilot insertion. With regard to waveform comparison, UFMC and OFDM exhibited the same BER performance, while UFMC offered the great advantage of OOB reduction and increased the overall spectral efficiency.  
Regarding UFMC-CDMA, the TDL-A channel delays and patterns of interference between served users directly impacted the BER. System performance degraded as the number of users became larger, as shown in Figure 9. However, even for the long delays induced by channel and filter selectivity, UFMC-CDMA showed improved overall performance.
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
## 4 Performance Evaluation  
To evaluate the performance of the proposed CCAR scheme, we used the ns-2 simulator, which is a discrete event simulator widely used in network research. In the following experiments, all nodes are in the promiscuous mode and time synchronized. We adopted IEEE 802.11g in the MAC layer. In addition, we used the user datagram protocol (UDP) traffic source and constant bit rate (CBR) flows with a fixed packet size of 512 bytes. The packet loss ratio was set to 5%. The radio transmission range was 25 m. The flow rate was set to 100-400 kbps. The maximum length of the input and output queue and packet pool was uniformly set to 200 packets, which was long and large enough on each node. The packet in the packet pool would be purged when the packet pool was full. Moreover, each node can maintain a table of routing information and process a list of one-hop neighbors using periodical exchanging of the HELLO messages. Periodical exchanging of HELLO messages has been widely used by some CAR schemes, and it will not impose more message overhead [10; 14; 15]. Our simulation results were averaged over five randomly-generated simulations, and the topologies were created by the \"setdest\" tool in the ns-2 simulation. In addition, we chose two different topologies to evaluate the different performances: one is a grid topology, and the other is a random topology. It is worth noting that the time and energy consumed by the data process can be ignored because the XOR operation is simple and fast, and the time and energy consumed by data processing is less than that consumed by communicating the data.
# Analysis of Azure Zero Trust Architecture Implementation for Mid-Size Organizations
## 6 Analyzing Azure ZTA Implementation and Implications
### Semi-Hybrid Cloud Approach Case Study
Implementing ZTA in a hybrid context necessitates considerable initial investment in technology and expertise. IT teams must restructure current systems, including hybrid AD integration, to comply with ZTA criteria. This encompasses the incorporation of Microsoft Purview, Syntex, and ongoing access review procedures. In contrast, although less secure, conventional security configurations are typically easier to maintain and entail fewer initial expenses due to their familiarity with established perimeter-based security instruments;
* ZTAs stringent access constraints may adversely affect user experience due to frequent authentication requests or limitations imposed by real-time access regulations. It may result in annoyance if it is not meticulously matched with user productivity. Conventional solutions, typically dependent on single-sign-on (SSO) or network trust, generally result in fewer immediate user disturbances; however, this convenience compromises security;* Continuous Management Overhead: ZTA necessitates ongoing monitoring, policy revisions, and adaptive control mechanisms, including the management of JIT and the execution of periodic access reviews. Upholding this degree of supervision may elevate operational expenses. Conversely, a non-ZTA system often functions with static policies, necessitating fewer modifications but at the expense of security agility;
* Initial Learning Curve: Organizations unfamiliar with complex identity and access management systems may encounter a learning curve for both users and administrators when deploying technologies such as JIT. Conventional approaches, albeit providing diminished security, generally have more uncomplicated processes requiring fewer operational modifications for daily IT management. As we can see, even this more straightforward scenario means a lot of effort and disruption to the existing processes. This case study did not lead to many disagreements or problems with the staff; the critical discomfort was related to the introduction of MFA, as it requires a mobile phone and the Microsoft Authenticator app for almost every login. Employees complained about the lack of SSPR frequently, but this is a trade-off based on how IT handles security footprints, which was agreed upon beforehand. Let us now discuss the fully hybrid approach that is been quite popular in the past few years. The pandemic required as much remote work as possible, which led to people getting used to remote work much more than in the past.
# Integrated Replay Spoofing-Aware Text-Independent Speaker Verification
## 4 Experiments and Results
### Experimental Configurations  
We used PyTorch, a Python deep learning library, for all experiments. For all DNNs, we input 64-dimensional Mel-filterbank features, with utterance-level mean normalization following [22]. We applied weight decay with \(\lambda=1e^{-4}\), and optimized with an AMSGrad optimizer [24].  
Regarding our use of ASVspoof2017-v2, we found that relatively thin LCNN structures were helpful for performance improvement; this may have been a result of the small size of the dataset. In addition, we also found that minute changes to the DNN greatly influence the performance because of the small data scale; therefore, a relatively thin structure remained particularly helpful for performance improvement. To derive a value between 0 and 1 for the PAD task, we used a network architecture identical to that of [11] but replaced the angular margin softmax activation [25] with a sigmoid function. We also modified the architecture for the SV task based on [11]. Speaker embeddings had a dimensionality of 1024.
## 3 Transition Models For Piezoelectricity
### Basic Models of Linear Piezoelectricity  
The starting point for two basic models of piezoelectricity, based on the first-order and higher-order mechanical fields, is the model corresponding to the three-dimensional piezoelectricity. The matrix form of the constitutive relations of linear three-dimensional piezoelectricity, expressed by the experimentally obtainable constitutive constants, is presented in Reference [57]:  
\[\left\{\begin{array}{rcl}\mathbf{\varepsilon}&=&\mathbf{D}^{-1}\mathbf{\sigma}-\mathbf{c} \mathbf{E}\\ \mathbf{d}&=&-\mathbf{c}^{T}\mathbf{\sigma}+\delta\mathbf{E}.\end{array}\right. \tag{12}\]  
Above, \(\mathbf{D}^{-1}\), \(\delta\), and \(\mathbf{c}\) denote the isotropic inverse matrix of elasticities (or compliance matrix), the orthotropic matrix of dielectric constants under constant stress, and the coupling orthotropic matrix of piezoelectric constants under constant stress, respectively. The six-component column vectors \(\mathbf{\sigma}\), \(\mathbf{\varepsilon}\) represent the symmetric stress and strain tensors, \(\sigma_{ij}\) and \(\epsilon_{ij}\), \(i,j=1,2,3\), while \(\mathbf{d}\) and \(\mathbf{E}\) are the column three-component electric displacement and electric field vectors, with components \(d_{j}\), \(E_{j},j=1,2,3\). The mentioned tensors and vectors are defined locally, namely in the directions consistent with the axes of piezoelectric orthotropy. In the presentation below, the third local direction is the polarization direction of the piezoelectric. In the case of the thick- or thin-walled structures, this direction coincides with the transverse one.  
The standard definition of the isotropic elasticity matrix reads:  
\[\mathbf{D}=\left[\begin{array}{cccccc}D_{11}&D_{12}&D_{13}&0&0&0\\ D_{21}&D_{22}&D_{23}&0&0&0\\ D_{31}&D_{23}&D_{33}&0&0&0\\ 0&0&0&D_{44}&0&0\\ 0&0&0&0&D_{55}&0\\ 0&0&0&0&D_{66}\end{array}\right]=\left[\begin{array}{cccccc}1\!-\!\
u&\
u& \
u&0&0&0\\ \
u&1\!-\!\
u&\
u&0&0&0\\ \
u&0&1\!-\!\
u&0&0&0\\ 0&0&0&(1\!-\!2\
u)/2&0&0\\ 0&0&0&0&(1\!-\!2\
u)/2&0\\ 0&0&0&0&0&(1\!-\!2\
u)/2\end{array}\right], \tag{13}\]  
where \(D=E/[(1+\
u)(1-2\
u)]\). As it can be seen above, the non-zero terms of the matrix are expressed by [PERSON]'s modulus \(E\) and [PERSON]'s ratio \(\
u\).  
The dielectricity constant matrix under constant stress reads:  
\[\delta=\left[\begin{array}{ccc}\delta_{11}&0&0\\ 0&\delta_{22}&0\\ 0&0&\delta_{33}\end{array}\right], \tag{14}\]  
where \(\delta_{ii}\), \(i=1,2,3\) stand for the orthotropic permittivity constants.  
In the case of the piezoelectricity constant matrix under constant stress, one has  
\[\mathbf{c}=\left[\begin{array}{ccc}0&0&c_{13}\\ 0&0&c_{23}\\ 0&0&c_{33}\\ 0&0&0\\ 0&c_{52}&0\\ c_{61}&0&0\end{array}\right], \tag{15}\]  
where \(c_{13}\), \(c_{23}\), \(c_{33}\), \(c_{52}\), and \(c_{61}\) are non-zero piezoelectric constants within this matrix.
# Overview of FEM-Based Resistance Models for Local Buckling of Welded Steel Box Section Columns
## 3 Finite Element Model  
### General Details  
The numerical model was created using commercial finite element software Ansys 2023 R2 [37]. A full-shell model was developed using thin shell elements called SHELL181. This element type is suitable for analyzing the behavior of the thick plates, having the capability to work in a non-linear context. The applied element is a four-node thin element with 6 DOF's on each node, 3 translational and 3 rotational. The purpose of the numerical model is to determine the local buckling resistance of box section columns; therefore, the overall flexural buckling failure mode should be avoided in the calculation. To achieve this, the general geometry of the modeled section is set in a way that the length of the specimens is equal to three times the longer width of the section or to be equal as the given value of the corresponding research paper where the specimen is taken from.  
The buckling resistance was determined by using geometrical and material nonlinear analysis using equivalent geometric imperfections (GMNIA). A full Newton-Raphson approach was used in the nonlinear analysis with 0.1% convergence tolerance of the residual force-based Euclidian norm.
## 2 Materials and Methods
### Measurements and Statistical Analyses  
The lithium disilicate crown retrieval measurements included the first retrieval (LD1), the second retrieval (LD2: re-cemented without adjustment), and the third retrieval (LD3: re-cemented with abutment adjustment). Similarly, the zirconia crown retrieval measurements included the first retrieval (Z1), the second retrieval (Z2: re-cemented without abutment adjustment), and the third retrieval (Z3: re-cemented with abutment adjustment). Data analysis was performed only on the specimens that had gone through all three retrievals (n = 10 for each group). The crown retrieval time and the highest temperature from each group were statistically analyzed using analysis of variance (ANOVA, single factor) with level of significance, \(\alpha\) = 0.05. The null hypotheses were that there was no statistical difference among the group of different materials, lithium disilicate and zirconia, and with different retrieval attempts (1, 2, and 3). ANOVA with \(\alpha\) = 0.05 was used to examine the statistical  
Figure 2: **Laser-assisted crow removal.****(A)** Laser irradiation, (**B**) Radiograph showing temperature probe positioning, (**C**) Abutment after crown removal, and (**D**) crown and abutment specimens after crown removal.  
difference within the same material. \(T\)-test assuming unequal variances with \(\alpha\) = 0.05 was used to determine the difference between materials for each retrieval. To examine the effects of tooth alteration in the third retrieval, Pearson correlations were calculated for the abutment surface and retrieval time for the first and third retrieval, as well as the cement layer and retrieval time/temperature.
## 5 Results and Evaluation
### Study on the Protocol Performance  
We used previously described simulation setting and 120 sensor nodes randomly deployed within the network area. Firstly, we investigated the proposed enhancement for TEAR protocol, _i.e._, the minimum threshold for conductivity value mapped to the relative residual energy of the sensor node \(\xi_{E_{MIN}}\). We varied the \(\xi_{E_{MIN}}\) value from 0 to 0.5 by step 0.05. When \(\xi_{E_{MIN}}=0\), sensor node will not start own  
\begin{table}
\begin{tabular}{|l|l|} \hline Playground & 1,000 \(\times\) 1,000 m \\ \hline Number of sensor nodes & 80–240 \\ \hline Data sinks & 4 (in the corners) \\ \hline Mobility & Waypoint Mobility Model \\ \hline Speed & uniform(1,3) m/s \\ \hline Propagation model & Two Ray Ground Model \\ \hline Radio\&MAC & IEEE 802.15.4 \\ \hline Battery voltage & 3 V \\ \hline Battery capacity & 1,150 mAhr \\ \hline Battery capacity simulated & 115 mAhr (10\%) \\ \hline Sleep mode & 0.39 mA \\ \hline Active mode, radio off & 31 mA \\ \hline Active mode, Tx/Rx mode & 44 mA \\ \hline Data packet size & 512 bytes \\ \hline Packet interval & \(\mu\) = 2 s, \(\sigma=0.1\) \\ \hline Routing methods & TEAR, AODVjr, Epidemic \\ \hline \end{tabular}
\end{table}
Table 1: Simulation matrix.  
poison process. On the other hand, when \(\xi_{E_{MIN}}=0.5\), the sensor node will start the poison process when its relative residual energy drops below 50%. Figure 3(a) shows the performance of TEAR protocol.  
Clearly, the lifetime grows with increasing minimum thermal conductivity threshold \(\xi_{E_{MIN}}\), when mapped to the energy. The self-poisoning behavior makes sensor node behaving selfishly from the point that the poisoned sensor nodes cannot be used as a forwarder afterwards. Thus, after some time, many sensor nodes are poisoned and the network becomes disconnected at the network level. To model the behavior of the sensor nodes, we investigated the energetic cost of the protocol (shown in Figure 3(b)). In this case, we simulated every run until the first sensor node dissipates and calculated the ratio between spent energy by the system and delivered useful data. It can be seen that for large \(\xi_{E_{MIN}}\) the cost is very high and has growing tendency. This is because the amount of delivered data is decreasing when many nodes cannot be used as forwarders. The cost function decreases for low \(\xi_{E_{MIN}}\) and reaches minimum when \(\xi_{E_{MIN}}\approx 0.25\). This is because some sensor nodes started poison process and stopped sending periodical updates (beacons) to its neighbors to save energy while there are still alternative paths for data delivery.  
From the previous evaluations we found out that in order to increase lifetime of the sensor nodes, the minimum conductivity threshold value should be properly chosen. Based on our results, the sensor node should start poison process when it has remaining only 25 percent of the relative residual energy (\(\xi_{E_{MIN}}=0.25\)).
# Spatial Multiplexing Holography for Multi-User Visible Light Communication
## 5 Experimental Results
### Experimental Results for OAM Secure Communication  
In the OAM secure communication experiment depicted in Figure 10, we evaluate the ROP for each user when subjected to various OAM keys. Figure 10a illustrates the scenario under the specific condition of the APD's received aperture size. It is observed that utilizing keys that are proximate to the correct modes marginally reduces the diameter of the optical rings, thereby concentrating the power. Due to the compact size of OAM rings when decoding with closely matched OAM keys and the large photosurface of the VLC receiver, which directly receives optical signals in free space, the isolation rate between modes is relatively low compared to other couplings to fiber works [27; 28]. Nonetheless, the peak optical power is achieved using the correct OAM keys, confirming the anticipated trend.  
Figure 10b presents a bar chart illustrating the data rates achieved under different OAM keys for three users. The OAM modes encoded in the holograms for these users increase from 10 to 30, from User 1 to User 3, respectively. Correspondingly, the isolation rate between different modes increases. This phenomenon can be attributed to the physical characteristics of the light rings associated with higher OAM modes; specifically, the higher the mode, the larger the diameter of the resultant OAM rings. This results in the failure of the APD to adequately receive signals under incorrect keys due to the dispersion of optical power.  
Figure 10c shows images captured by a CCD demonstrating the effect of using no OAM keys versus the correct OAM keys. Without the correct OAM keys, the light forms large rings, leading to significant ROP dissipation. Additionally, these rings increase in size with the diffraction distance. In contrast, when the correct OAM keys are applied, the optical light spot contracts to a Gaussian-type distribution at the target distance, greatly enhancing the efficiency of signal reception.  
In Figure 10d, User 3 serves as the focal point for an experiment transmitting a 1080P, 10 bit, 60 FPS real-color video under three distinct OAM keys. When the correct OAM key is used, the video transmits and plays seamlessly, with a BER below the threshold required for post-FEC, ensuring clear and uninterrupted viewing. However, incorrect OAM keys lead to decreased ROP and subsequently lower data rates, resulting in an elevated BER, distorted video playback, and a degraded viewing experience.