esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The results of measurements after the implementation of the first and second stages of noise-reducing measures showed a decrease in noise propagation to the outdoor environment. The noise values measured did not exceed 60 dB (see Table 4) but still did not comply with the permissible noise level in accordance with [19].  
The results of measurements after the implementation of the third stage of noise-reducing measures showed a further decrease in the noise propagation into the outdoor environment. Nevertheless, the permissible noise levels for residential houses B, D and F were exceeded (see Table 4).  
After the implementation of the fourth stage of noise-reducing measures, noise measurements in residential areas I and II were conducted only at three critical locations:  
* residential house B,
* residential houses D and F.  
The proposed noise-reducing modifications reduced the noise level at the locations under consideration to below 50 dB as shown by Table 4 and Figure 6. Noise values measured after each stage of the implemented measures take a linear trend.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Testing of Measurement Results of Individual Stages**} \\  & **Stage 0–Stage 1** & **Stage 0–Stage 2** & **Stage 0–Stage 3** & **Stage 0–Stage 4** \\ \hline \multicolumn{5}{c}{**Morgan–Pitman’s test**} \\ \(\mathrm{H_{0}}\) & & \(\sigma_{1}{}^{2}-\sigma_{2}{}^{2}=0\) & & \\ Test statistic & 0.4 & 0.8 & \(-0.03\) & 2.86 \\ Critical value & 2.8 & 2.8 & 2.8 & 12.7 \\  & ITSI \(<\) CV & ITSI \(<\) CV & ITSI \(<\) CV & ITSI \(<\) CV \\ Result & \(\mathrm{H_{0}}\) not rejected & \(\mathrm{H_{0}}\) not rejected & \(\mathrm{H_{0}}\) not rejected & \(\mathrm{H_{0}}\) not rejected \\  & ✓ & ✓ & ✓ & ✓ \\ \hline \multicolumn{5}{c}{**Student’s Paired _t_-test**} \\ \(\mathrm{H_{0}}\) & & \(\mu_{1}-\mu_{2}=0\) & & \\ Test statistic & 0.9 & 2.0 & 3.3 & 4.5 \\ Critical value & 2.6 & 2.6 & 2.6 & 4.3 \\  & TS \(<\) CV & TS \(<\) CV & TS \(>\) CV & TS \(>\) CV \\ Result & \(\mathrm{H_{0}}\) not rejected & \(\mathrm{H_{0}}\) not rejected & \(\mathrm{H_{0}}\) rejected & \(\mathrm{H_{0}}\) rejected \\  & ✓ & ✓ & ✗ & ✗ \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical testing of measurement results.  
Figure 8: Values of noise emissions measured in residential areas I and II during operation (Work) and out of operation (Stop)—residential house D.  
Figure 9: Values of noise emissions measured in residential areas I and II during operation (Work) and out of operation (Stop)—residential house—F.  
At certain frequencies (3150 Hz and higher), noise levels are equivalent to those reached during operation.  
The proposed noise-reducing modifications of machinery cladding at dumping hoppers, implemented modifications of fencing, and installation of acoustic barriers have reduced noise levels in the monitored areas below 50 dB, as shown in Table 4.
# Entity Alignment Method Based on Joint Learning of Entity and Attribute Representations
## 2 Materials and Methods
### Embedding Learning
#### 2.3.1 Structural Embedding Learning  
Structural embedding learning is performed using the TransE model, i.e., by minimizing the loss function \(L_{SE}\) (Equation (5)):  
\[L_{SE-direct}=\sum_{(h,r,t)\in T_{R}}\sum_{(h^{\prime},r^{\prime},t^{\prime}) \in T^{\prime}_{R}}\left[\gamma+\left\|\mathbf{h}+\mathbf{r}-\mathbf{t}\right\|_{L_{2}}- \left\|\mathbf{h}^{\prime}+\mathbf{r}^{\prime}-\mathbf{t}^{\prime}\right\|_{L_{2}}\right] _{+} \tag{5}\]  
where \(T_{R}\) is the set of valid relation triples; \(T^{\prime}_{R}\) is the set of corrupted relation triples, where \(T^{\prime}_{R}=\{(h^{\prime},r,t)|h^{\prime}\in E\}\cup\{(h,r,t^{\prime})|t^{ \prime}\in E\}\cup\{(h,r^{\prime},t)|r^{\prime}\in R\}\); and \(\gamma\) is the margin hyperparameter.  
The TransE model exhibits fewer parameters, low complexity and simplicity, and good efficiency in the construction of large KGs. However, its simplicity limits its applications to complex relation modeling, multisource information fusion, and relation path modeling. To address the inadequacies of TransE in handling complex KG relations, we embedded entities and relations into two separate spaces: entity space \(\mathbb{R}^{m}\) and relation space \(\mathbb{R}^{n}\) (\(m\) and \(n\) are the dimensionalities of these spaces, and \(m\) and \(n\) can be equal to each other). Each triple has an entity vector \(\mathbf{h},\mathbf{t}\in\mathbb{R}^{m}\) and relation vector \(\mathbf{r}\in\mathbb{R}^{n}\). First, the head and tail entities of the entity space \(\mathbb{R}^{m  
relation between a pair of entities (\(P(h,t)=\{p_{1},p_{2},\cdots,p_{N}\}\), with \(p=r_{1}\circ r_{2}\circ r_{3}\cdots\circ r_{l}\) being the path between the pair of entities), the loss function shown in Equation (8) can be used to learn the triple of the entity pair as:  
\[L_{SE-multistep}=\sum_{(h,r,t)\in T_{R}}\left\{\frac{1}{Z}\sum_{p\in P(h,t)}R(p |h,t)\sum_{(h,r^{\prime},t)\in T^{\prime}_{R}}\Bigl{[}\gamma+\left\|\mathbf{p}-\mathbf{ r}\right\|_{L_{2}}-\left\|\mathbf{p}-\mathbf{r}^{\prime}\right\|_{L_{2}}\Bigr{]}_{+}\right\} \tag{8}\]  
where \(R(p|h,t)\) is the plausibility of relation path \(p\), \(Z=\sum\limits_{p\in P(h,t)}R(p|h,t)\) is the normalization factor, and \(\mathbf{p}\) is the embedding vector representation of the relation path, defined as \(p=r_{1}+r_{2}+\cdots+r_{l}\) in this study.  
Combining Equations (7) and (8), the loss function for structural embedding learning can be expressed as:  
\[L_{SE}=L_{SE-direct}+L_{SE-multistep} \tag{9}\]
# Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article
## 5 Clinical Applications of Ultrasound Elastography  
### Liver  
Chronic liver diseases involve a long-term and progressive condition where the liver sustains ongoing inflammation and damage, particularly months or years of damage, resulting in significant liver deterioration that is considered one of the most prominent causes of death in the United States, accounting for 1.4% of deaths and being the 12 th most common cause of death. It primarily affects patients with high alcohol consumption as well as those with a metabolic syndrome of some sort, such as obesity or diabetes mellitus. Chronic liver disease (CLD) is primarily caused by various illnesses, including alcoholic liver disease, which affects 35% of Americans in the US, hepatitis viral disease (both hepatitis B and hepatitis C), which affects nearly 5 million Americans, and non-alcoholic fatty liver disease, which affects 20-30% of Americans, among others [70; 71].  
CLD can aggravate and eventually lead to liver fibrosis, cirrhosis, or portal hypertension, which are all considered life-threatening for the patient. The medical term for any type of scarring is fibrosis, whereas cirrhosis denotes late and extensively severe fibrosis.  
Increased blood pressure within the portal system, known as portal hypertension, causes varices, as we will discuss later in this section. Ultrasound elastography widely assesses CLD patients, ensuring an accurate diagnosis without the need for invasive intervention. Imaging the patient in a slight (30\({}^{\circ}\)) left lateral decubitus fashion showcases the right liver lobe and avoids cardiac motion artifacts, as well as vascular, biliary and rib shadowing, and superimposition. This position, combined with shear wave elastography techniques, has proven to show the most accurate results in CLD diagnosis [14; 72; 73].
## 5 AEDG: The Proposed Scheme  
Besides the setup of the CDS-based elliptical route, the selection and rotation of GNs and the MST-based association of the MNs with the GNs, we also propose an efficient data gathering scheme to enhance the network throughput and to preserve the network's energy. To prolong the network lifetime, the AEDG employs the AUV to collect the data from the GNs. To minimize the energy consumption, we use an SPT algorithm by associating the MNs with the GNs and devise a criterion to limit the association count of the nodes. Moreover, the role of the GNs is rotated to balance the energy consumption. We also develop the sub-optimal elliptical trajectory of the AUV by using the CDS to enhance the throughput of the network. The detailed flow chart of our proposed scheme is shown in Figure 8.  
Figure 8: AEDG: protocol operation.  
In our proposed protocol, the surface sink is deployed on the water surface. This surface sink has both radio and acoustic modems. Underwater sensor nodes are deployed at the bottom of the region. The AUV moves in an elliptical path in order to collect the data from the GNs. The following assumptions are taken for the proposed protocol.  
* The AUV has unlimited power, memory and computational capability.
* The trajectory of the AUV is predefined (elliptical).
* The nodes are randomly deployed in the underwater environment.
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 3 FANET Routing Protocols, Mobility Models, and Reconnaissance Scenarios
### Reconnaissance Scenarios  
A reconnaissance scenario is to discover an enemy in a targeted area. The enemy should have difficulty predicting the path of our UAVs. Therefore, UAVs should reconnoiter in an irregular route rather than a plan-based, regular route. Mobility models described in Section 3.2 helps to generate an irregular route for UAVs.  
We suggest a reconnaissance scenario with multiple UAVs. Multiple UAVs can cover a larger reconnaissance area, as well as can help FANETs provide wider network coverage. In addition, the failure of a UAV does not cause a failure of the entire FANET network. In other words, multiple UAVs increase the redundancy and reliability of FANETs.  
In our scenario, multiple UAVs reconnoiter a targeted area according to a reconnaissance mobility model. Once a UAV detects an enemy, the UAV should efficiently send the information of the detected enemy to GCS. In this scenario of sending information, other UAVs serve as communication nodes that transmit information to GCS. FANET protocols described in Section 3.1 help to transmit information from a UAV to GCS via several UAVs.  
To be more specific in our scenario, we set up the following conditions. First, the GCS has a fixed position in the center of the reconnaissance area. Second, each UAV periodically sends packets to the GCS during its mission. Third, we assumed that the altitudes of UAVs are almost the same (We will discuss the altitude of UAVs in Section 6.4).  
Based on the specific scenario, we conduct a routing protocol performance analysis. In our routing protocol performance analysis for the reconnaissance scenario, we consider the following characteristics of FANETs. In FANETs, the node density is very low and a node is frequently disconnected from the network. In FANETs, nodes frequently join and leave the network, so it is difficult to maintain the connectivity of nodes. Meanwhile, to deliver packets, a node should be connected to the network at the moment. In FANETs, node mobility affects network connectivity. For example, a mobility model can allocate nodes to one side. In this case, if the nodes are close to GCS, the packet delivery performance can be high, while the mobility model fails to achieve the original reconnaissance purpose to reconnoiter the target area. A mobility model that evenly reconnoiters the target area has a high reconnaissance rate. Therefore, in this paper, we evaluate routing protocols from the perspective of mobility models to be used for our reconnaissance scenario.
## 3 The Fully Assembled Sample Environment  
### General Remarks  
The in situ SANS/DLS instrument setup was designed with portability and modularity in mind, to guarantee the compatibility with various SANS instruments at the ESS and to enable a fast change of sample environments with only minimal work necessary. For the portability of the setup, all equipment and electronics needed for the light scattering measurements were mounted in 19 inch racks inside a box. This box is constructed from aluminum profiles (Rose und Krieger, Minden, Germany) and fits into the footprint of a EUR-pallet. A photograph of the complete setup can be seen in Figure 7. As versatile interface a 120 cm \(\cdot\) 90 cm breadboard (M-SG-34-2, Newport, CA, USA) is mounted on top of the aluminum box. The breadboard is screwed down on the aluminum box to prevent the shifting of the optical setup and to guarantee a stable transport of the complete setup e.g., by forklift or crane. For transport using a crane, a crane eye is installed on each edge of the box. This base construction is identical for all FlexiProb sample environments and can be easily moved to the instrument cave using a pallet jack.  
The setup is constructed, so that it can be put into operation with only a few connections made. Power is supplied by a single 32 A IEC 60309 plug. The thermostat cycle needs to be connected and the RS232-cable needs to be connected to the thermostat. Further pressurized dried air, the laser interlock and an Ethernet connection are required for operation. The patch panel layout of the setup is suitable for connectors available at ESS instruments such as SKADI and LOKI.  
The general architecture of the interacting systems is described in the scheme in Figure 8. The user interacts with the setup either via the networked instrument control system (NICOS) [42; 43] client or the DLS-GUI program, for direct control of the data fits. The measurement is controlled by the NICOS server, where the single NICOS devices can communicate with the input output controllers (IOCs) in the experimental physics and industrial control system (EPICS) [44] server. From the EPICS server, the hardware devices are controlled via their respective protocols, namely USB, Ethernet, an Ethernet-to-RS232-converter (EX-6034, EXSYS, Steinbach, Germany) or by the direct control of the power supply with switchable power outlets (Multibox, ANTRAX Datentechnik, Herford, Germany).  
Figure 7: Photograph of the complete SANS/DLS setup with carrier structure.
## 4 Results  
In this section, we present the LoRa coverage obtained via the conventional Free Space Path Loss (FSPL) method (Section 4.1) and compare it against the O-FDTD simulations (Section 3.2) that provide spatially-resolved LoRa RSSI and coverage maps in an urban environment. To demonstrate the accuracy of the O-FDTD simulations, we performed experiments on the university campus area and move the LoRa transceiver position while recording the time between packets per GPS position (Section 4.3). Ultimately, we quantified the differences between O-FDTD simulated and experimental signal coverage maps (Section 4.4).
## 1 Introduction
[26] proposed a fuzzy control algorithm to improve the seismic performance of a cable-stayed bridge which improves seismic performance by determining the input voltage from the response of MR dampers, and the simulation results showed that the semi-active fuzzy control algorithm can effectively improve the seismic performance of cable-stayed bridges. [PERSON] et al. [27] performed numerical simulations of a large-span structure based on a semi-active control algorithm in the trust domain and pointed out the potential effectiveness of semi-active control for seismic control of large-span structures. [PERSON] et al. [28] performed seismic tests on a three-span bridge and proposed a semi-active control algorithm based on optimal polynomial control that can achieve a balance between multiple objectives with low energy consumption. [PERSON] et al. [29] proposed a hybrid control algorithm that could improve the performance of two separate control algorithms, which effectively improved the seismic performance of a multi-span bridge. It is clear from the above literature that the application of semi-active control in bridge seismic control is feasible and effective. Compared with conventional prestressed concrete box girder bridges, PC composite girder bridges with CSWs are widely used in seismic-prone areas due to their lower self-weight and better seismic performance. However, how good is the performance of semi-active control in the seismic performance of such composite structure bridges, and can it significantly reduce their seismic response? There are no reports on the application of semi-active control methods in PC composite girder bridges with CSWs. This paper will carry out research of the semi-active control method in this composite girder bridge, and the research results will provide some reference for the design of seismic reduction of this composite structure.
## 3 Experiment and Results
Figure 4: Measured (**a**) electrical power spectrum and (**b**) waveforms of the generated triangular pulse trains with a repetition rate of 5 GHz. Figure 5: Electrical power spectrum and waveforms of the generated square waveform with repetition rates of 8 GHz (**a**) and (**b**), 10 GHz (**c**) and (**d**). Figure 6: Electrical signal spectrum and waveforms of the generated triangular pulse trains with repetition rates of 8 GHz (**a**) and (**b**), 10 GHz (**c**) and (**d**). Finally, in view of the fact that this scheme is not sensitive to the phase difference of the RF signals of the two arms, we have also realized the generation of the square and triangular waveforms experimentally when we adjusted the phase shifter, making the phase of the 5 GHz signal for 60\({}^{\circ}\), 90\({}^{\circ}\), and 180\({}^{\circ}\), respectively. The applied RF signal power also needed to be adjusted accordingly to achieve a certain modulation depth. When making the phase for 180\({}^{\circ}\) by tuning the phase shifter, the required RF signal power was the smallest and the system efficiency was the highest. As shown in Figure 7, when the phase changes from 180\({}^{\circ}\) to 90\({}^{\circ}\), the RF power arises from 14.46 dBm to 17.43 dBm. When the phase was changed to 60\({}^{\circ}\), the input power increased to 20.51 dBm. This is very consistent with the theoretical calculation, as shown in Figure 2. The RMSEs between the generated waveforms and the theoretical waveform are 0.053, 0.050, and 0.058, respectively.
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 2 Background and Related Work
### Localization Free Routing Protocols
In EEDBR, a sender node maintains depth, residual energy and ID information of its neighbours. Neighbours with smaller depth are selected and their list of IDs is included in data packet sent by sender. Receiving nodes hold the packet for holding time which is based on its residual energy \(E_{r}\). This may create problems as two nodes with same residual energy would have same holding time and could transmit at the same time. Moreover, EEDBR involves more number of hops in forwarding process and eventually brings increased delay and enhanced energy consumption. Hop-by-Hop Dynamic Addressing Based (H\({}^{2}\)-DAB) [37] routing protocol is another localization free protocol. In H\({}^{2}\)-DAB, depending on the depth of nodes, different size of addresses are given to different nodes. Deeper nodes have large size address and shallower nodes have less size address. Connectivity based Routing Protocol (CRP) protocol is proposed in [38] in which next forwarder node is directly related to Connectivity Index (\(CI\)) of neighbour node. where \(CI\) is defined as \"number of neighbouring nodes closer to sink from a forwarding node\". Hop count based cross layer approach Channel Aware Routing Protocol (CARP) is proposed in [39]. On the basis of recent history of successful transmissions a particular node is selected a next forwarder. CARP is hop count based protocol with varying power levels. Another localization free protocol, Adaptive Mobility of Courier nodes in Threshold-optimized DBR (AMCTD), is proposed in [40]. In this protocol, forwarding of packet is improved and based on weight, which is function on depth, residual energy and some priority values assigned to them. An improvement in AMCTD named as improved AMCTD (iAMCTD) is proposed in [41] in which different routing metrics are defined for different depth regions. These metrics include Localization free-SNR (\(LSNR\)), Signal Quality Index (\(SQL\)) and Energy Cost Function (\(ECF\)) and Depth Dependent Function (\(DDF\)). Moreover hard and soft threshold based simulation scenario is adopted in which a sensed event's value greater than hard threshold is reported to sink. Moreover, authors employed courier nodes in [40; 41] for data gathering tasks. Here mobility of nodes start when certain fraction of total nodes have died, which is not efficient and full use of deployed resources. Holding time calculation process does not guarantee optimum value of holding time ensuring packet suppression and minimum end to end delay.
# Design, Implementation and Power Analysis of
## 3 Results
### Power Analysis of PARSLAD and the Light Bulbs
2 & \(>\)500 & 0.5 W LED bulb off 1 & 5.13 & 0.01 & 0.0513 & 0.224694 & 0.91 & 1.86 \\
3 & \(<\)500 \& \(\&\) 300 & 0.5 W LED bulb dim 1 & 5.05 & 0.03 & 0.1515 & 0.663701 & 2.69 & 5.51 \\
4 & \(<\)300 & 0.5 W LED bulb on 1 & 5.01 & 0.06 & 0.3006 & 1.316628 & 5.33 & 10.93 \\
5 & \(>\)500 & 60 W Incandescent bulb off 1 & 5.14 & 0.02 & 0.1028 & 0.480264 & 1.82 & 3.74 \\
6 & \(<\)500 \& \(\&\) 300 & 60 W Incandescent bulb dim 1 & 5.05 & 0.04 & 0.202 & 0.88476 & 3.58 & 7.34 \\
7 & \(<\)300 & 60 W Incandescent bulb on 1 & 5.01 & 0.08 & 0.4008 & 1.755504 & 7.11 & 14.57 \\
8 & \(<\)500 & 8 W LED light bulb off 1 & 5.22 & 0.01 & 0.0521 & 0.228198 & 0.92 & 1.89 \\
9 & \(<\)500 & \(\&\) 300 & 8 W LED bulb light bulb off 1 & 5.21 & 0.03 & 0.1563 & 0.648594 & 2.77 & 5.68 \\
10 & \(<\)300 & 8 W LED bulb light bulb on 1 & 5.20 & 0.06 & 0.312 & 1.368312 & 5.54 & 11.36 \\ \hline \end{tabular}  
* Arduino connected to DDR, PIR sensor and relay. \end{table}
Table 7: Power and cost analysis of 60 W incandescent, 8 W LED and 0.5 W LED light bulbs.
## 4 Numerical Results
### Results of Parametric Analysis
#### 4.3.1 Influence of \(D/t\) Ratio  
The plots in Figures 8 and 9 illustrate the relationship between the CLS and the \(D/t\) ratio of YPT and RHT pipes, respectively. The plots in Figure 8 are obtained for YPT pipes with YPL = 1.50%, while the plots in Figure 9 are obtained for RHT pipes with PLUS ratio = 0.634. The plots for combined axial compression + bending in Figures 8c and 9c are both obtained for pipes subjected to compression factor, \(f_{c}\) = 0.6. The CLS trends are presented for all five levels of internal pressure considered.  
Figure 7: Moment-curvature response and stress evolution in RHT DT4 pipes under uniform bending (PL/US = 0.691,\(f_{p}\) = 0.4).  
The most obvious aspect of the plots above is the nonlinear negative correlation between the CLS and the \(D/t\) ratio. The plots indicate the tendency for the CLS to reduce as the \(D/t\) ratio increases, and even more evident is this phenomenon in YPT pipes, especially at high levels of internal pressure. It is also reasonable to deduce that the influence of internal pressure on the CLS vs. \(D/t\) ratio trends is minimal in RHT pipes compared to YPT pipes, as the change in the slope of the CLS vs. \(D/t\) ratio trends for RHT pipes is observed to be generally much lower than for YPT pipes. The relationship between the CLS of YPT pipes and the \(D/t\) ratio becomes nearly linear at internal pressure = 80%. For intermediate \(D/t\) ratios (DT2 and DT3) and high \(D/t\) ratios (DT4), RHT pipes are observed to have a higher deformational capacity than YPT pipes at internal pressure \(\leq\) 60% YS. However, at high internal pressure (\(f_{p}\) = 0.8) and/or low \(D/t\) ratio (DT1), the deformational performance of YPT pipes tends to supersede that of RHT pipes. The CLS of pipes under uniform bending is generally observed to surpass the CLS under uniform axial compression, while the CLS under combined axial compression and bending is somewhat bounded by the uniform axial and uniform bending CLS values.