esa-sceva/satcom-chunk-collection huggingface dataset:
# Hard and Soft EM in Bayesian Network Learning from Incomplete Data
## 2 Methods
### Missing Data Imputation  
A possible approach to handle missing data is to transform an incomplete data set into a complete one. The easiest way to achieve this is to just remove all the observations containing at least one missing value. However, this can markedly reduce the amount of data and it is widely documented to introduce bias in both learning and inference, see, for instance [2; 20]. A more principled approach is to perform _imputation_; that is, to predict missing values based on the observed ones. Two groups of imputation approaches have been explored in the literature: _single imputation_ and _multiple imputation_. We provide a quick overview of both below, and we refer the reader to [4] for a more comprehensive theoretical treatment.  
_Single imputation_ approaches impute one value for each missing value in the data set. As a result, they can potentially introduce bias in subsequent inference because the imputed values naturally have a smaller variability than the observed values. Furthermore, it is impossible to assess imputation uncertainty from that single value. Two examples of this type of approach are mean imputation (replacing each missing value with the sample mean or mode) or k-NN imputation [21; 22] (replacing each missing value with the most common value from similar cases identified via \(k\)-nearest neighbours).  
_Multiple imputation_ replaces each missing value by \(B\) possible values to create \(B\) complete data sets, usually with \(B\in[5,10]\). Standard complete-data probabilistic methods are then used to analyse each completed data set, and the \(B\) completed-data inferences are combined to form a single inference that properly reflects uncertainty due to missingness under that model [13]. A popular example is multiple imputation by chained equation [23], which has seen widespread use in the medical and life sciences fields.  
It is important to note that there are limits to the amount of missing data that can be effectively managed. While there is no common guideline on that, since each method and missing data mechanism are different in that respect, suggested limits found in the literature range from 5% [13] to 10% [24].
# Measurement Uncertainty and Compliance Evaluation Applied to Natural Gas Moisture
## 2 Methodology
### ASTM D1142 Test Method, Approach 1  
The ASTM D1142-95 (2021) [2] test method includes two correlations, the first of which (herein referred to as ASTM D1142 test method, approach 1) is a variation of Equation (1) that expresses water content in terms of the weight of saturated water vapor, Equation (2):  
\[y_{w}=\frac{P_{w}^{sat}}{P} \tag{1}\]  
where \(y_{w}\) is the mole fraction of water in the vapor phase, \(P\) is the total pressure, and \(P_{w}^{sat}\) is pure water's saturation (vapor) pressure at the dew point temperature \(T\).  
\[W=\left(w\times 10^{6}\right)\frac{P_{b}\times T}{P\times T_{b}} \tag{2}\]  
where \(W\) is the water content (pounds water/million standard cubic feet natural gas) at reference conditions, \(T_{b}\) (base gas measurement temperature, \(t_{b}+400\)), \(P_{b}\) (base gas measurement pressure, psia), \(w\) is the weight of saturated water vapor (lb/ft\({}^{3}\)), \(P\) is the pressure at which the dew point was determined (psia), and \(T\) is the observed Rankine dew point temperature (\(t+460\)). The reciprocal of \(w\), or the specific volume of saturated water vapor (ft\({}^{3}\)/lb), is listed as a function of temperature in Table 1 of ASTM D1142-95 (2021) for temperatures ranging from 0 \({}^{\circ}\)F to 100 \({}^{\circ}\)F. Although not explicit in temperature due to the temperature dependence of \(w\), given the water content, the corresponding dew point temperature can be solved iteratively.
## 3 Results
### Technologies and Implementation
#### 3.3.4 Traceability/Trustability  
In terms of traceability and trustability, the proposals stemming from the early investigation into the appropriate architecture to support a more generic framework of component interaction resulted in the use of a blockchain substrate. The ecosystem under evaluation required the integration of several key components including (a) an ERP to provide a customer interface and order placement, subsequently passed on to (b) the MES component to provide manufacturing control and (c) provide a programmatic allocation of hardware resources. The implementation was performed using Hyperledger Sawtooth ([[https://www.hyperledger.org/use/sawtooth](https://www.hyperledger.org/use/sawtooth)]([https://www.hyperledger.org/use/sawtooth](https://www.hyperledger.org/use/sawtooth)) (accessed on 7 September 2023)) since it was the one that after brief research seemed more appropriate. This technology has a loose coupling architecture, is more energy-efficient (than other blockchain implementations), has a Byzantine-fault tolerance, and implements parallel scheduling, the smart contracts can be developed in several programming languages like Python, C++, and JavaScript, among others, and it has two modes of operation, Permissioned and Permissionless.  
Since blockchain is a decentralized technology to be running in the cloud, four nodes were implemented inclusive of the required services to support the architecture integration requirements and support any future integrations, with real-world manufacturing environments. The following figure (Figure 23) outlines the network and exposes the services implemented.  
A transaction family under the Hyperledger Sawtooth blockchain framework consists of application-specific business logic. A transaction family smart contract aimed to alter the data stored in addresses under the platform namespace. All data under a namespace prefix follow a consistent address and data encoding/serialization scheme that is determined by the transaction family, which in turn defines the namespace.  
Figure 23: Hyperledger Sawtooth blockchain implementation.  
Figure 22: REST API services.  
Hyperledger Sawtooth represents the state of all transaction families in a single instance of a Merkle-Radix tree on each Sawtooth validator. Data are stored in leaf nodes, and each node is accessed using an addressing scheme represented as a 70-hex character combination. The address format contains a 6-hex character prefix defining the namespace and 64-hex characters describing schemes for subdividing further and distinguishing entities (agents, records, etc.).
# Investigation of the Scattering Noise in Underwater Optical Wireless Communications
## 2 System Model  
Figure 1 shows the schematic system diagram of UOWC. At the transmitter (Tx), a pseudorandom binary sequence (PRBS) of length \(2^{7}-1\) in a non-return to zero (NRZ)  
format is for intensity modulation of the light source (i.e., a laser diode in this case). The modulated light beam is transmitted over the free space channel via a telescope. At the Rx, the incoming optical signal is applied via a telescope to an optical Rx, which is composed of a photodetector and a trans-impedance amplifier, via a telescope. The regenerated electrical signal is passed through a low pass filter and a decision circuit to recover the transmitted binary sequence. The noise sources considered are shot noise, thermal noise, dark current noise, background noise, and scattering noise. For more information on the laser, photodiode model, etc., refer to [25].
## 4 Classification of MAC Protocols Based on Channel
### Distributed Multi-Channel MAC Protocols
#### 4.3.3 Tcg Mac  
The TDMA-based MAC with the collision alleviation protocol combines TDMA plus CSMA-based MAC for collision mitigation. The methodology of game theory is implemented when more nodes acquire the same slot as in the CSMA period for efficient slot allocation. This reduces access collision. Vehicles are equipped with GPS to know the position and direction. Synchronization among vehicles is performed by the 1 PPS signal administered by a GPS receiver.  
The nodes are divided into the direct neighbor set (DNS) belonging to a one-hop neighbor of the transmitting node and the indirect neighbor set (INS), those not a direct neighbor but within the communication range and other nodes that are not in the transmission range. Upon broadcasting the packet, the vehicle ID and slot reservation information of each node belonging to DNS are attached to the header; this enables the nodes to know the slot allocation information of its two-hop neighbors. The nodes update their neighbor set based on information from the DNS. Only collision nodes send reservation messages in CP when they hold a TP slot, and each node must acquire exactly a single-time slot in TP to broadcast messages. The transmission ranges of R and 2R are broadcast by nodes in TP and CP, respectively, as depicted in Figure 7. For all relative neighbor set (RNS) nodes to receive the slot reservation message transmitted by CP, it must have a longer transmission range. The strategy of game theory alleviates collision; this protocol shows high throughput with proper allocation of slots in different channel conditions. There is high power consumption due to the use of transmitters and receivers. There is high network overhead due to the game theory approach.
## 3 Results
### Comparative Analysis between Flapless Surgery and Open-Flap Surgery  
Each deviation was statistically compared between flapless surgery and open-flap surgery. The results of the data comparison are presented in Table 3. There were significant differences in apical (\(p<0.01\), r = 0.36; medium) and coronal (\(p<0.05\), r = 0.30; medium) lateral deviations, although there were no differences in angle and vertical deviations. These findings revealed that the implants were placed more accurately when using dynamic navigation in flapless surgery.
## 3 The Problem of Synthesis of Four-Bar Grashof-Type Mechanisms
### The General Problem of Dimensional Synthesis  
The general problem for the synthesis of four-bar Grashof-type mechanisms consists of finding the dimensional parameters in \(\vec{p}\) that minimize the differences between the trajectory that the mechanism develops with them and the desired path. Of course, a continuous trajectory is made up of an infinite number of points, and an exact comparison between two of them to determine their differences is unfeasible. In practice, the desired trajectory is described in terms of \(n\) representative precision points \(\dot{c}_{i},\ i=1,2,\ldots,n\), as suggested by the diagram in Figure 1. Thus, the cost function can be written as in (7) to quantify the differences between the \(n\) precision points of the desired trajectory and the \(n\) points generated by the mechanism with different values of the input \(q_{2}\). In (7), \(\|\dot{c}_{i}-c_{i}\|^{2}\) is the square norm of the difference between the \(i\)-th precision point \(\dot{c}_{i}\) from the desired trajectory and \(c_{i}\), which coincides with the point \(E=[x_{e},y_{e}]^{T}\) of the mechanism when it is subject to the input \(q_{2,i}\).  
\[J(\vec{p})=\sum_{i=1}^{n}\|\dot{c}_{i}-c_{i}\|^{2} \tag{7}\]  
On the other hand, the lengths of the bars in the four-bar mechanism must satisfy (2) so that at least one of the bars can rotate freely. In this sense, it is aimed that the bar \(l_{2}\) rotates completely with respect to the fixed bar \(l_{1}\), i.e., that the input \(q_{2}\) can take any value to generate closed trajectories with the point \(E\). To guarantee the above, the constraints are set to (8).  
\[\begin{split} g_{1}:l_{1}+l_{2}-l_{3}-l_{4}&\leq 0 \\ g_{2}:l_{2}+l_{4}-l_{1}-l_{3}&\leq 0\\ g_{3}:l_{1}+l_{3}-l_{1}-l_{4}&\leq 0\end{split} \tag{8}\]  
Finally, box constraints are used in the dimensional synthesis problem to limit the space occupied by the complete mechanism and to ensure that its manufacture is possible.  
On the other side, two conceptualizations of the mechanism synthesis problem can be found in the specialized literature, which are detailed below.
# Intelligent Network Service Optimization in the Context of 5G/NFV
## 5 Experimental Results
### QoE: Experimenting with Frame Rate, Bit Rate, and Packet Loss  
The MOS was evaluated as a metric of great importance; hence, a series of comparisons of MOS values against different combinations of packet loss, frame rate, and bit rate were considered essential. The model adopted was the one defined by ITU and described in Section 4.5.  
The plot in Figure 4 illustrates the impact the packet loss metric has on the MOS, with the settings for the frame rate and bit rate configured to 24 fps and approximately 8 Mbps, respectively. The observation that the computed MOS value declines to under the value of 1.5 when a loss rate of the order of 2% emerges validates the dominant role the packet loss plays in the context of this QoE model. Such a reduction is considered unacceptable, whereas a MOS value of at least 3.5 exceeds the acceptance threshold.  
Afterwards, the packet loss is set to a ratio of 0.05%, with a constant bit rate of approximately 8 Mbps. As expected, the higher the fps, the greater the increase in the MOS value, as Figure 5 suggests. It should be highlighted that a frame rate of at least 16 fps is considered to be a minimum threshold for acceptable video transmissions.  
Finally, in Figure 6, we set a constant packet loss of 0.05% and a frame rate of 24 fps. It can be discerned that the MOS fluctuates constantly at a satisfactory level, above the threshold value of 4.0, with a lower bound at around 7 Mbps.  
Figure 4: MOS with respect to packet loss (ITU-T Recommendation P.1070).  
Figure 5: MOS with respect to frame rate (ITU-T Recommendation P.1070).  
Figure 6: MOS with respect to bit rate (ITU-T Recommendation P.1070).
# Building Information Modeling Impact on Cost Overrun Risk Factors and Interrelationships
## 2 Literature Review
### Risk Factors Related to Construction Project Cost Overruns in BIM Context  
Numerous risk factors contribute significantly to construction project cost overruns. A vital issue is that of the frequent changes occurring during the construction phase, leading to schedule disruptions and inadequate monitoring, as observed in Indonesian construction projects [19]. These challenges are compounded by deficient financial control, inadequate supervision, and logistical hurdles, like shipping delays and equipment shortages [19]. In design and build (D and B) projects, continual design alterations stemming from incomplete initial drawings and on-site errors significantly contribute to cost overruns, aggravated by contractors' inefficient planning and scheduling [20]. BIM is vital in mitigating these risks through enhanced interoperability, clash detection, and digitalized quantity assessments, improving planning accuracy and reducing uncertainties [20].  
An upsurge in BIM-related research over the last five years indicates a growing interest among construction management researchers in understanding the implications of BIM [6]. This shift marks a positive trajectory in the construction sector, moving away from the perception that BIM was primarily used for promotional purposes and client negotiations [21]. Studies have demonstrated significant cost benefits associated with BIM implementation in construction projects. For instance, [PERSON] et al. [22] and [PERSON] et al. [23] have highlighted various advantages of BIM, such as technical prowess, improved scheduling, enhanced decision-making support, and economic benefits. BIM'sintegration has led to expedited decision-making, early conflict detection in the design phase, reduced design time, and enhanced data integrity, all crucial in mitigating cost overruns and enhancing project efficiency [24].  
Moreover, [PERSON] et al. [25] underscore how BIM facilitates effective decision-making and execution across all project phases, offering tools for tracking project progress during construction and enabling swift remote assessments of construction performance. Despite its innovative potential in the architecture, construction, and engineering (ACE) industries, BIM still needs to be utilized further [26]. Recent findings by [PERSON] et al. [27] from a BIM study in a developing country's construction industry suggest that increased BIM usage correlates with reduced project delays and reduced cost overruns. These insights underscore the transformative potential of BIM in enhancing construction project management and efficiency.
# Protograph LDPC Code Design for Asynchronous Random Access +
## 3 Code Design for the Asynchronous Random Access Channel
### Code Optimization  
Good base matrices are found by some optimization algorithm, such as differential evolution [39]. The goal is to find protographs that maximize a gain function. To this end, differential evolution creates a generation of candidate base matrices for which the gain function is evaluated. By introducing perturbations on the base matrix entries, a new generation of base matrices is obtained. They may replace the original ones if their gain function is higher. After a certain number of generations, we choose those base matrices that maximize the gain function.  
The gain function is usually a function of the protograph ensemble's iterative decoding threshold, which can be seen as the worst channel parameter for which symbol error probability vanishes (assuming that the block length and the number of decoding iterations go to infinity). We will give a more precise description in the following.  
Iterative decoding thresholds can be found using (quantized) density evolution [15] or a suitable approximation, such as extrinsic information transfer (EXIT) analysis [40] if certain Gaussian assumptions can be made. EXIT analysis simplifies the computation of the protograph code ensemble's compared to (quantized) differential evolution. To compute iterative decoding thresholds, we make use of the channel LLRs distributions for each codeword bit. In the following sections, we discuss the effect of the interference model on the channel LLR distributions and thus on the code design.
## 2 Materials and Methods
### Mortar Preparation  
Table 3 summarizes the main composition characteristics of the different systems studied. It should be noted that the mixtures to manufacture the different mortars are based on the following classification:  
* Ordinary Portland cement systems (CEM I): a type I 42.5R Portland cement was used and hydrated with water.
* Alkaline-activated materials: these were composed of a subindustrial waste (slag) and activated using alkaline solutions with basic pH (pH > 13). In this sense, two solutions were used: 4 M NaOH solution (AAS-N) and a commercial sodium silicate solution (waterglass) with a SiO\({}_{2}\)/Na\({}_{2}\)O ratio of 1.2 (AAS-WG).
* Hybrid materials: these mortars were composed of ordinary Portland cement (20 wt %) and industrial and ceramic wastes (fly ash, blast furnace slag and two ceramic wastes), of which the amount was 80 wt % Furthermore, in these hybrid systems, a percentage of Na\({}_{2}\)SO\({}_{4}\) (at 3% and 5 wt %) was added to favor the hydration process of the materials. Water was used as the activating solution.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**wt \%** & **CaO** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **MgO** & **Fe\({}_{2}\)O\({}_{3}\)** & **SO\({}_{3}\)** & **K\({}_{2}\)O** & **Na\({}_{2}\)O** & **LOI \({}^{*}\)** \\ \hline CEM I & 61.8 & 21.5 & 6.6 & \(<\)0.01 & 2.5 & 5.9 & 1.1 & \(<\)0.014 & 2.35 \\ Slag & 38.0 & 35.0 & 11.7 & 12.0 & 0.2 & 1.9 & 0.3 & \(<\)0.014 & 2.10 \\ Fly ash & 4.1 & 46.6 & 28.1 & 0.7 & 17.0 & 1.1 & 1.1 & \(<\)0.014 & 1.00 \\ Porcelain waste & 2.5 & 64.7 & 21.2 & 0.5 & 2.3 & 0.05 & 1.2 & 8.5 & 1.00 \\ Porous waste & 8.7 & 62.5 & 22.7 & 0.3 & 2.6 & 0.3 & 1.8 & \(<\)0.014 & 2.00 \\ \hline \end{tabular} \({}^{*}\) LOI = loss of ignition.  
\end{table}
Table 1: Chemical composition by X-ray fluorescence.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**CEM I** & **Slag** & **Fly Ash** & **Porous Waste** & **Poreclaim Waste** \\ \hline D\({}_{10}\) (\(\mu\)m) & 2.9 & 3.8 & 0.1 & 0.6 & 2.5 \\ D\({}_{50}\) (\(\mu\)m) & 19.6 & 13.2 & 6.5 & 10.7 & 11.5 \\ D\({}_{90}\) (\(\mu\)m) & 65.3 & 34.9 & 35.4 & 78.9 & 95.8 \\ \hline \end{tabular}
\end{table}
Table 2: Particle size distributions of the different materials studied.  
For the preparation of all mortar systems, standardized sand was used, with a 3:1 sand/cementitious material ratio. For each proposed system, 4 \(\times\) 4 \(\times\) 16 cm specimens were made at different curing ages (1, 7 and 28 days). It should be noted that, in each evaluated system, a different liquid/solid ratio (L/S) was used that was appropriate for each, but in all cases, the ratio was selected in equal consistency accordance with the UNE-EN 1015-6 standard to appropriately compare the results (values of the diameter of spread of 150 \(\pm\) 10 mm).
## 5 Experiments
### Effect of Magnetic Material  
In this experiment, magnetic materials mainly refer to soft magnetic materials. The soft magnetic material can achieve maximum magnetization with the minimum external magnetic field. In the experiment, permalloy was used as the magnetic material. Permalloy is a Fe-Ni-based soft magnetic alloy with extremely high magnetic permeability under a weak magnetic field. Thus, the magnetic flux will accumulate when the permalloy is added to the coils, as shown in Figure 33. The sensitivity of the sensor can be improved.  
In the experiments mentioned above, the running speed of the detected wire rope is relatively high (\(\approx\)5 m/s). According to the Faraday law of electromagnetic induction, the wire rope's slow speed leads to a weak output voltage. Based on extensive experimental verification, in most applications, such as slow speed or considerable lift-off distance, permalloy can significantly improve the detection ability of magnetic sensitive sensors to magnetic leakage flux of the defects. As shown in Figure 34, the experimental condition is that the lift-off distance is 30 mm at a relative speed of 2 m/s. The peak to peak value of the single broken wire signal was increased from 275 mV to 466 mV. We obtain good results with this simple method.  
In summary, the testing device in the more than 30 mm detecting distance adopts the coil whose size is A = 5 mm, B = 1 mm, C = 1 mm, and the inner space is full of permalloy.
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 4 Virtual World Architecture Inspection
### Hybrid-II  
The second hybrid class, denoted by Hybrid-II (Figure 8d), introduces cloud resources to the P2P architecture to improve the overall performance. In [141], for instance, special server nodes play the roles of zone masters. They do not retain the game state but only help clients in intra/interzonecommunication. The Hybrid-II architecture was initially proposed by [135] after identifying the heterogeneous peer resource issue. In their design, cloud resources are introduced to complement peer resources, and both are virtualized to nodes (called virtual nodes, VN). The VNs construct two P2P overlays for providing the common services: the state action manager (SAM) and the positional action manager (PAM). The SAM nodes, organized in a structured overlay, manage the state of virtual objects [90]. The PAM nodes, organized in an unstructured overlay, manage user position for neighbor and object discovery [142]. In an application of Hybrid-II architecture, a client firstly receives the nearby users and objects by querying the PAM service. Then the client can interact with them through the SAM service for state update and state synchronization.