esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Beamline Design
Exposing the system to 15 W of pump light resulted in pump clipping of 335 mW, or 2.2% of the total power, a considerable improvement on the 21% clipping observed previously in the conventional beamline. The clipped pump light is weak enough to be rejected using one or two sheets of paper (standard printing paper of \(\simeq\)0.11 mm thickness), removing the need for FZSi filters although introducing increasing losses at the higher frequencies (see, e.g., [36]). In addition, the reduced size of the rejection hole and increased THz beam size result in a reduced loss of THz radiation through the hole. We also note that, in our setup, the chopper was placed before the THz rectification process to reduce the thermal load on the generation crystal. However, in this way, the pump scattering is amplitude-modulated by the chopper, and it can, therefore, contribute to the detection noise. The rest of the setup is comparable to the conventional scheme: a second parabolic mirror placed after the flat THz mirror focuses the THz beam into a sampling spot, a third parabolic mirror recollimates the beam, and a final fourth parabolic mirror refocuses into the detection plane. This final mirror, as in the conventional setup has a hole in the center for injection of the probe beam. A knife-edge measurement of the THz beam after collimation resulted in a \(1/e^{2}\) beam diameter of 45.22 mm. The beam fills the aperture of the mirrors almost entirely, resulting in minimal losses from the pump rejection hole whilst also avoiding clipping of the THz beam on the outer edge of the mirror. To calculate the THz losses through the pump rejection hole, the THz beam was modelled as a Gaussian beam. Our design is expected to introduce a loss of \(<\)1%, compared to \(\simeq\)6% from the conventional beamline design.
## 3 Results and Discussion
### Microstructure and Reinforcement Dispersion
To quantitatively evaluate the effect of DC in reinforcement-dispersion improvement, an automated image-processing program with a statistical approach was applied. First, the program automatically studied the characteristics of reinforcement-particle images. Then, based on the learned reinforcement characteristics, the program extracted only reinforcement particles from the given microscopic image. Finally, the dispersion of extracted reinforcements was analyzed. The nearest neighbor (NN) distances between each reinforcement followed a Weibull distribution with a scale parameter \(\lambda\) and shape parameter k. This is an N-body problem in macroscopic volume. \(\lambda\) corresponds to the Wigner-Seitz radius [32] or the reinforcement content of the composite; k indicates the dimensions of the image (2D or 3D). The NN distances were calculated from the processed image, and distributions \(W_{a}(\lambda_{a},k_{a})\) were compared with the ideal distribution \(W_{i}(\lambda_{i},k_{i})\). Finally, the dispersion index (DI) was defined as the distribution parameter difference between \(W_{a}\) and \(W_{i}\), as follows (a smaller DI implies better reinforcement dispersion):  
\[0\leq\text{DI}=\sqrt{\left|\left(1-\frac{\lambda_{a}}{\lambda_{e}}\right) \left(1-\frac{k_{i}}{k_{i}}\right)\right|}\leq 1\]  
Figure 4: Results of the electron backscatter diffraction (EBSD) analysis of 9 vol.% SiC/ AA2024: (**a**) stir-casting (SC) process, (**b**) die-casting (DC) process. Figures 5 and 6 present calculated DI values of AMCs after SC and DC processes. In each case, a grayscale microscopic image of an AMC sample has been processed into a blue matrix and yellow reinforcements. The calculated NN distances between each reinforcement are presented as a histogram and fitted as a Weibull distribution (blue dashed line). This distribution is compared with the ideal distribution (red solid line) to evaluate the DI. The DI was decreased at the same volume fraction after the DC process, from 0.2564 to 0.1085 at 3 vol%, 0.2303 to 0.0537 at 6 vol%, and 0.1671 to 0.0464 at 9 vol% SiC content. A decreased DI implies that the distribution parameter of the actual NN-distance distribution is closer to the ideal distribution, corresponding to a homogeneous reinforcement dispersion. Thus, decreased DI values in the die-cast samples indicate the effectiveness of the DC process in improving the reinforcement dispersion. Furthermore, these results match well with qualitative dispersion enhancements observed in the original microscopic images and EBSD analyses. Figure 5: Degree of particle dispersion by the SC process: (**a**) 3 vol.%, (**b**) 6 vol.%, and (**c**) 9 vol.%.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 6 The Soft-Decision Helper Data Scheme
### New SD Strategy for Binary Enrollment Observations  
In practice, the one-probabilities \(\theta^{n}\) are non-observable and therefore, they must be estimated based on the binary observation vectors \(\big{(}x_{1}^{n},x_{2}^{n},\ldots,x_{t}^{n}\big{)}\) before the SD scheme can be applied. Instead of applying a pre-processing step for estimation, we propose to adjust the SD scheme s.t. the dominant values and error probabilities are directly estimated based on \(t\) binary observations. The dominant value of an SRAM cell is then estimated as  
\[\hat{u}_{t}\stackrel{{\Delta}}{{=}}\begin{cases}0&\text{if }w_{H}(x_{1}, \ldots,x_{t})\leq t/2,\\ 1&\text{otherwise.}\end{cases} \tag{34}\]  
And furthermore, the error probability (regarding the dominant value) is  
\[\hat{\psi}_{t}\stackrel{{\Delta}}{{=}}\frac{\pi_{t+1}(1+m_{t}^{ \text{\tiny SD}})}{\pi_{t}(m_{t}^{\text{\tiny SD}})}, \tag{35}\]  
with  
\[\begin{array}{rcl}m_{t}^{\text{\tiny SD}}&\stackrel{{\Delta}}{{= }}&\min(w_{H}(x_{1},\ldots,x_{t}),t-w_{H}(x_{1},\ldots,x_{t}))\\ &=&\begin{cases}w_{H}(x_{1},\ldots,x_{t})&\text{if }w_{H}(x_{1},\ldots,x_{t}) \leq t/2,\\ t-w_{H}(x_{1},\ldots,x_{t})&\text{otherwise.}\end{cases}\end{array} \tag{36}\]  
In Appendix A.3 we show that (36) indeed is equal to the error probability (regarding the dominant value) of a cell given the \(t\) binary enrollment observations.  
The helper data and noisy codeword are constructed as before, so \(w^{n}=c^{n}\oplus\hat{u}_{t}^{n}\) and \(r^{n}=c^{n}\oplus(\hat{u}_{t}^{n}\oplus y^{n})\). The decoder reconstructs the key \(\hat{s}\) based on the received noisy codeword \(r^{n}\) and the error probabilities of the cells \(\hat{\psi}_{t}^{n}\).
## 2 Compressed Sensing
It turns out that optimization based on the \(l_{1}\) norm is able to exactly recover \(S\)-sparse signals with a high probability using only \(M\geq cSlog(N/S)\) i.i.d. Gaussian measurements [5,6]. The convex optimization problem is given by:  
\[\hat{s}=argmin\left\|s^{\prime}\right\|_{1}\text{ such that }\Theta s^{ \prime}=y \tag{5}\]which can be reduced to a linear program. Algorithms based on basis pursuit (BP) [15] can be used to solve this problem with a computational complexity of \(O(N^{3})\)[5]. BP is a quadratically constrained \(l_{1}\)-minimization problem:  
\[\min_{x}\parallel x\parallel_{l_{1}}\text{ subject to }\parallel y-\theta x \parallel_{l_{2}}\leq\epsilon \tag{6}\]  
where \(y\) is the noisy CS measurements with noise \(\epsilon\). This is the preferred CS reconstruction formulation, as the estimate of noise \(\epsilon\) may be known or can be computed. A second approach to solving the CS reconstruction problem is to formulate the BP problem as a second order cone program:  
\[\min_{x}\frac{1}{2}||y-\theta x||_{l_{2}}^{2}+\lambda||x||_{l_{1}} \tag{7}\]  
This formulation is also known as basis pursuit denoising (BPDN) [16]. It is tractable due to its bounded convex optimization nature. The term \(\lambda||x||_{l_{1}}\), which is also known as regularization, can be interpreted as a maximum _a posteriori_ estimate in a Bayesian setting. BPDN is very popular in signal and image processing applications. The third approach is known as the least absolute shrinkage and selection operator (Lasso) [17]. It involves the minimization of an \(l_{2}\) norm subject to \(l_{1}\) norm constraints:  
\[\min_{x}||y-\theta x||_{l_{2}}\text{ subject to }||x||_{l_{1}}\leq\tau \tag{8}\]  
A number of different algorithms that have been developed to solve these three CS reconstruction problems. They include linear programming (LP) techniques [5,6], greedy algorithms [18, 19, 20, 21], gradient-based algorithms [22, 23] and iterative shrinkage algorithms [24, 25].
## 3 QoS Aware MAC Protocols for WMSNs
### Amph  
AMPH [44] by [PERSON] is similar to IH-MAC [43]. The major difference is that IH-MAC is mainly CSMA/CA based while AMPH is mainly TDMA based. Communication time in AMPH is divided into slots and within a two-hop range each node is assigned a distinct slot. Nevertheless, a node is allowed to transmit in any slot even if it is not the owner. This is done by allowing non-owner nodes to contend for slots using the backoff mechanism similar to CSMA/CA. By separating real-time traffic from best-effort traffic and judging whether a node is an owner or non-owner of a slot, the contending nodes are divided into the following four groups, as listed in an order of decreasing priority: Owner node with real-time traffic, non-owner node with real-time traffic, owner node with best-effort traffic, and non-owner node with best-effort traffic. Higher priority nodes are granted with earlier medium access with the non-overlapping contention window technique. To be precise, each node chooses a random backoff timer from the respective window at the beginning of a slot. When the backoff timer expires, nodes use clear channel assessment (CCA) to check the channel status. The node that wins channel contention sends data using the message passing feature, while nodes fail channel contention wait until the next slot. To be fair, AMPH allows best-effort traffic to have higher priority over real-timetraffic in a few slots during each cycle. At the end of each slot, nodes enter a waiting state during which the radio can be switched off to conserve energy.  
Pros and Cons. AMPH achieves high channel utilization and good adaptability to varying traffic loads by allowing nodes to use any slot for communication. The dynamic priority scheme also provides QoS support and good fairness among heterogeneous traffic types. However, the energy efficiency design in AMPH is inferior since it requires a node to keep listening to the medium until the waiting state even if it does not involve in any data communications. The simple differentiation between real-time and best-effort traffic may be inefficient for WMSNs with more types of coexisting traffic.
## 3 Results and Discussion
### Answering RQ3 to RQ5
Thus, the first factor that should be considered for the long-term benefits of using AR technology for the quality sector is solving the challenge of integrating intelligent agents into each AR-based solution at the beginning. In order to achieve that, a methodology adopted from [196], combined with the findings in this SLR based on AR architecture layer framework, is created to systematically consider all elements and factors that contribute to the development of AR-based assisted applications. Thus, the further enhancement and integration of AI elements to improve the intelligent capabilities of the AR systems in the long term are also benefited. This methodology is depicted in the following Figure 16. On the left side of the model is the systematic flow for the design and development of AR-based applications. Through each stage (Design, Development, Implementation, Evaluation, Improvement) and each step (Mockup design, Client validation, etc.) of the development flow, several valuable findings in this SLR (reference tables) and tools provided on the right-hand side of the model could be adapted as well as utilized to systematically create an AR-based application with a human-centered approach following several standards. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Classification Criteria**} & **References** \\ \hline \multirow{10}{*}{Evaluation} & \multirow{3}{*}{Usability} & [53, 57, 92, 99, 139, 141, 145, 151, 157] \\  & & [167, 187, 194, 196] \\ \cline{2-3}  & & [11, 52, 62, 64, 72, 73, 98, 107, 129, 138] \\  & & [142, 147, 159, 162, 164, 166, 169, 183] \\  & & [186, 189, 192, 201, 204, 206] \\ \cline{2-3}  & & [30, 63, 110, 113, 122, 140, 146, 148] \\ \cline{2-3}  & & [150, 163, 165, 170, 171, 173, 175, 176] \\ \cline{2-3}  & & [178, 182, 184, 188, 195, 198, 199, 211] \\ \cline{2-3}  & & [220, 222, 223] \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Articles contributing for Layer 3 Evaluation from 2010–2021. The second crucial factor for the long-term implementation of AR-based solutions in the quality sector is to employ the ubiquitous computing system or boost the fusion of manufacturing information systems with AR systems [37, 221], not only to save the information resource and boost the performance speed of AR applications but also to slowly transform to data-driven AR solutions and achieve the real-time feature for AR systems [102].
# A Distributed Trustable Framework for AI-Aided Anomaly Detection +
## 3 Distributed Trustable Engine Architecture
### Federated DTE Architecture  
The use of AI/ML-aided solutions is highly attractive in non-convex problems, as well as anomaly detection and response scenarios. To support the deployment of AI/ML algorithms, data must be collected from different network endpoints for training ML models. In real-world settings, data collection and processing at a single endpoint might not be feasible, as the computation burden might be excessive, and there is always the possibility of a single point of failure. Thus, distributed and decentralized ML approaches have been extensively investigated recently [32; 33]. In greater detail, FL has been proposed as a computationally efficient technique to support decentralized ML operation [14; 34]. To perform training in FL, multiple client nodes are required to periodically send updated model parameters to a master server. In each training round, various participating nodes are involved, assuming that the individual datasets are uncorrelated. As a result, each client node trains a local ML model based on locally obtained data. Then, at pre-determined time intervals, training results, in the form of model parameters, are transmitted to the master node for aggregation. Subsequently, an updated model is sent to the client nodes. Through this process, exchanged data between the client nodes and the master model server do not contain sensitive information since only trained model parameters are sent. This is the reason that FL is considered a privacy-preserving method that is adopted in the context of DTE, as mobile user data is kept locally, thus minimizing possible cases of personal information leakage.  
Based on the above, in Figure 3, a high-level architecture is presented for decentralized data collection and decentralized/distributed ML model training using multiple DTEs. In this case, there are multiple DTE instances per subgroup of nodes, where each one trains the corresponding models locally with the available datasets. Afterwards, the master DTE model with the NWDAF aggregator is responsible for updating the global parameters and informing the individual nodes of their updated values. In greater detail, the master FL server communicates with a transfer learning database, where all the generated ML models are stored and retrieved on demand. In the same context, local FL servers communicate with a meta-learning server, where model updates are inferred to all DTEs.  
Figure 2: The internal DTE components.
###### Abstract  
The prefabricated recyclable supporting structure (PRSS) is an innovative support system that integrates a steel skeleton with polymer waterproof technology. Earth berms are extensively adopted to support the PRSS, but there is limited understanding on the factors influencing their behavior in circular excavations. In this paper, a numerical model is first validated with a case history in Henan, China. Afterwards, the geometric parameters of the earth berms, including the height (\(H\)), the top width (\(B_{1}\)), and the bottom width (\(B_{2}\)), on the behavior of the PRSS, are investigated. It is shown that, by increasing the height, top width and bottom width of earth berms, the lateral deflections, and bending moments of supporting piles, as well as the ground surface settlements, tend to decrease. However, the reduction effect of these parameters diminishes as well. Moreover, the raised effective formation level considering the effect of the earth berms on stability and deformation analyses is discussed. The factor of the safety of the excavation is almost doubled when axisymmetric conditions are considered compared to plane strain conditions. In deformation analysis, the raised effective formation level increases with the height of the earth berms until a steady value is reached.  
supportive structure; circular excavation; earth berm; numerical simulation; deformation Article
# Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines
## 3 Implementing Collaborative UVs for the OWT IMR Operations
### Collaborative Communication Strategies
A UUV as an inspection-class ROV can be piloted wirelessly from a control station up to a range of a few tens of metres and can perform semi-autonomous operation up to a range of a few hundreds of metres. However, without direct data transmission between the control station and the UUV as a result of transmitting data via the satellite communication system through the USV, noticeable delays and poor quality of data transmission can be expected. As an alternative for the USV-UUV collaboration, a UUV may also collaborate directly with a UAV for various reasons such as to achieve high-speed mobility in tracking and updating a UUV [59] or for guiding a UUV directly from above [58]. There are very few applications of direct collaboration between a UAV and a UUV due to the communication barrier, as radio frequencies are heavily attenuated through a water medium. Usually in such a collaboration, a UUV is the one that requires cooperation from a UAV to determine its own localization with respect to the GNSS position. For such a collaboration, normally a water-air communication relay system is needed which can be installed on a vessel, buoy, or even on a USV. Previously, a UAV could only communicate with a UUV through a control station [58]. However, with recent advancements in UAV technology, UUVs are now able to communicate directly with a UAV that is equipped with sonar for underwater communication [59]. These UAVs are capable of landing smoothly and floating on a water surface to establish water-air communication. Nonetheless, the benefits of deploying a USV as the intermediary between a UUV and a GCS are greater when compared to direct UAV-UUV collaboration. In a USV-UUV collaboration, the USV not only solves the communication issue for the UUV but can also be used as a recovery and docking station, guidance for underwater localization and navigation, an energy provider, or a charging station. To ease the computational burden while processing large amounts of inspection data, the USV can also be deployed as a data processing and storage hub.
## 3 Results and Discussion
### Soot Production
In the case of HVO, its aromatic free composition suggests that less polycyclic aromatic hydrocarbons (PAH) are formed [39]. This could explain the lower soot production for HVO compared to diesel, as they can be considered as the building blocks for particulates in flames [40]. Figure 12: Accumulated KL map at the operating condition with the greatest tendency to soot formation (1000 K of temperature, 500 bar of injection pressure and 15% of O\({}_{2}\)). The total soot mass production was calculated from instantaneous KL, following the procedure described in Section 2.3.3. Figure 3 depicts the total soot mass for diesel, dodecane and HVO at different operating conditions with the aim of showing the effect of decreasing temperature, increasing injection pressure and increasing the ambient oxygen content. The shown temperatures are 1000 K and 900 K, from top to bottom; the injection pressures are 500 bar and 1000 bar, from left to right; and 15% and 21% of oxygen have been represented for 900 K and 500 bar at the bottom of the figure. It can be seen that when decreasing the temperature from 1000 K to 900 K (from top to bottom, at the left side of the Figure 13), the maximum value of soot production is reduced 40% for diesel and 30% for dodecane and HVO. When the injection pressure is increased from 500 bar to 1000 bar (from left to right at the top of figure), the soot reduction is around 30% for all fuels. When the oxygen concentration is increased from 15% to 21% (from left to right at the bottom of figure) the total average soot production is reduced 30% for diesel, 40% for HVO and 50% for dodecane. Results for all conditions show that the parametric variation in terms of soot tendency always holds, with diesel showing the largest soot production, while HVO and dodecane present similar soot production. In fact, considering all results presented in this work regarding combustion and soot formation characteristics, it can be concluded that n-dodecane is a good surrogate to model the HVO due to its similarities. Figure 13: Influence of the variation of injection pressure, temperature and oxygen concentration on the total soot mass.