esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
A decrease in vertebral canal diameter and/or area that results in compression of spinal cord and/or nerve roots is termed absolute stenosis, whereas a diameter that is less than normal but does not cause compression of neural elements is termed relative stenosis [35; 36]. Relative vertebral canal stenosis results in decreased available space for the spinal cord to compensate for extradural space-occupying conditions. Relative vertebral canal stenosis therefore predisposes animals to develop clinical signs when relatively mild space-occupying pathologies, such as age-related intervertebral disc protrusion or ligamentous hypertrophy, occur [35; 36]. The percentage of the vertebral canal of the spinal epidural space was minimal between L1 and L4 (27-41%), indicating that these segments are more subject to clinically significant epidural compressive lesions and that L5-L7 had a greater amount of vertebral canal available for the dural sac (47-87%). The large size of the epidural space between L5 and L7 also provides a secure space for epidural instruments or devices (i.e., catheters) in this region. The spinal epidural space surrounds the dural sac and is limited dorsally by the epidural fat, ligamentum flavour and periosteum, ventrally by the dorsal longitudinal ligament, IVVP and vertebral bodies, and laterally bordered by the vertebral pedicles and intervertebral foramina [1]. In MRI and CT imaging studies, severe stenosis is usually associated with subjective signs of the absence of epidural fat [37; 38; 39]. There are no quantitative studies using CT images that evaluated the dimensions or proportion of the lumbar epidural space in dogs. Limitations of the present study were associated with the small number of dogs included and the variety of dogs of different sizes evaluated. Further studies to increase the study population and standardize various breeds to allow analysis by breed (small, medium, large, and giant) size are necessary. These data should thus be interpreted with caution given that measurements were only acquired at the mid-vertebral level and did not include the intervertebral cross-sectional area parameters.
## 2 Materials and Methods
### Limits of Stability Assessment  
The assessment of limits of stability employed computerized dynamic posturography through the Iso-Free system (Techno body, Bergamo, Italy). Limits of Stability pertain to the maximum extent to which an individual can intentionally lean or sway their body while maintaining a stable base of support without losing balance or taking a step and the value is expressed as a percentage. This technique offers a comprehensive analysis of the area within which an individual can safely move while maintaining their base of support intact. The system consists of a circular platform integrated with several key components, including a stabilometric posture platform, a touch screen, a 3D camera, and specialized software. The stabilometric force platform plays a pivotal role in this assessment by analyzing the center of pressure when an individual is in a standing posture, effectively detecting the pressure exerted on the platform [25]. The entire limits-of-stability assessment was conducted in a serene and well-ventilated environment. Participants were instructed to position themselves on the stabilometric force platform with both feet together, adhering to standardized protocols (Figure 2).  
The participant's gaze was directed toward the screen located in front of them, focusing on the targets presented by the posturography device. Randomly appearing in all eight directions, these targets appeared momentarily on the screen. Participants were tasked with shifting their center of mass toward the target while maintaining their feet stationary. The posturography instrument meticulously recorded the degree of sway required for the individual to attain the target position from the center, following the shortest vertical or horizontal path. The scoring system assigned a maximum score of 100 for each direction, where lower scores indicated increased sway. The assessed limits-of-stability parameters encompassed reaction time, maximum excursion, and directional control.  
Figure 2: Limits-of-stability assessment using stabilometric force platform; (**A**) starting and (**B**) moving to limits-of-stability target.
## 3 LoMoB Localization Scheme
### Beacon Point Selection  
From the viewpoint of a sensor node, the sensor node can receive a beacon when the beacon is within the communication range of the sensor node; e.g., in Figure 4(b), the sensor node receives the beacons \(B_{1}\) to \(B_{6}\). The selection of beacon points from the received beacons is performed as in LDB. When a sensor node receives the first beacon from a mobile beacon, the first beacon is selected as a beacon point; _i.e._, \(B_{1}\) in Figure 4(b). If the sensor node receives no further beacons during a predefined time after receiving its last beacon, the last beacon is selected as a beacon point; _i.e._, \(B_{6}\) in Figure 4(b). The above process is repeated each time the mobile beacon passes through the communication sphere of the sensor node.  
As shown in Figure 4(b), the beacon points do not lie exactly on the communication sphere of the sensor node, because the sensor node receives a beacon at every beacon distance \(d\). When the communication range of the sensor node is \(r\), the beacon points are located between the distances \(r-d\) and \(r\) from the sensor node. Based on the distance range between a beacon point and a sensor node, the middle value of the range, \(r-d/2\), is estimated as the distance between the beacon point and sensor node in order to minimize the error in the estimated distance.  
Figure 4: System environment, beacon point selection, and projection for LoMoB. (**a**) System environment with a mobile beacon that has an omnidirectional transceiver; (**b**) Selection and projection of beacon points from among the received beacons.  
Sensor nodes are assumed to have a pressure sensor, and therefore they are assumed to know their depth [22]. With this depth information, the beacon points can be projected onto the horizontal plane in which the sensor node resides, as shown in Figure 4(b). When the \(z\) coordinates of a sensor node and the \(i^{th}\) beacon point are \(z_{s}\) and \(z_{i}\), the distance \(r^{\prime}_{i}\) between the sensor node and the \(i^{th}\) projected beacon point is \(\sqrt{(r-d/2)^{2}-(z_{i}-z_{s})^{2}}\).  
For a system using a directional transceiver, all the projected beacon points are located between the distances \(r_{b}-d\) and \(r_{b}\) from a sensor node; here, \(r_{b}\) is the radius of the circle formed by the beam for the sensor node. Subsequently, all the estimated distances between the sensor node and projected beacon points are identically \(r_{b}-d/2\).
## Materials and Methods
### Laying Hens and Feed Additives  
A total of 90 laying hens (Lohmann Brown), 20 weeks of age, were randomly distributed into 30 cages and divided into five groups (six replicates). Three hens per cage (0.125 m\({}^{2}\)\(\times\) hen\({}^{-1}\)) were housed for 13 weeks at the Agricultural Experimental Plant \"Swojec\" of Wroclaw University of Environmental and Life Sciences, within a standard 3-tier furnished cages system, with all three tiers (top, middle and bottom) consisting of two rows. Feed was supplied daily for each cage and the average daily feed intake (ADFI) was estimated once a week. Evaluation of the additives' effectiveness on ADFI was made on the basis of the relative increase value (RIV, %) calculated as the ratio of the difference between the control and treatment parameter means.  
Two additives were used in the experiment: beechwood biochar (BC) and the mixture consisting of 67% of the same BC, 24% of aluminosilicates as an anticaking agent and 9% of glycerin as an antidusting agent (BCM). Properties of the additives are presented in Table 1. Treatment feed mixtures were prepared weekly in 5 L plastic jars, separately for each cage to ensure even distribution of the investigated additives. All laying hens were fed with the same basal diet formulated according to nutrient recommendations for laying hens (Tables 2 and 3). The control group (C) was fed only with the basal diet. Treatment groups were fed with the addition of BC in the amount of 1% and 2% by mass (groups BC1 and BC2, respectively) and with the addition of BCM in the amount of 1.5% and 3% by mass (groups BCM1 and BCM2, respectively). The mass of BC in the BCM1 and BCM2 groups was numerically the same as in the corresponding BC1 and BC2 groups.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & \multicolumn{2}{c}{**Additive**} \\ \cline{2-3}  & **Beech Wood Biochar** & **Aluminosilicates** \\ \hline pH & 9.0 & 8.7 \\ Fiber Content & 75\% & – \\ Iodine Index & 140 mgI\({}_{2}\) x g\({}^{-1}\) & – \\ Methylene Index & 3 ml & – \\ Bulk Density & 350.0 g x dm\({}^{-3}\) & 741.2 g x dm\({}^{-3}\) \\ Ash Content & no more than 10.0\% (by mass) & – \\ Moisture Content & no more than 8.0\% (by mass) & no more than 6.2\% (by mass) \\ Volatile Matter & 14\% & – \\ Granulation & 0.0–1.0 mm & – \\ Specific Surface Area & 140 m\({}^{2}\) x g\({}^{-1}\) & – \\ \hline Mineral Composition & – & Clinoptilolite—up to 83\% (by mass) \\  & & Feldspars—up top 5\% (by mass) \\
0.600 mm Sieve Screenings & – & 0.2 \\ \(<\)0.125 mm Sieve Screenings & – & 7.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of additives used in the experiment.  
During the first four weeks of the experiment, no parameters were measured so that the hens could adapt to the new surroundings and the treatments could take effect.
# Failure Mechanism and Structural Analysis of Chain Slings with Non-Standard Connections
## 3 Failure Cause Analysis
### Review of Safety Factor  
The Safe Working Load (S.W.L) refers to the maximum allowable load that ensures safe lifting operations under the assumption of a static, straight-line loading condition. It is also commonly referred to as the working load or allowable load. If the load applied to the chain exceeds the S.W.L, permanent deformation, cracking, or damage may occur. Therefore, to ensure safe lifting operations, the applied load must always remain within the designated S.W.L. The safety factor is defined as the ratio of the breaking load to the maximum applied working load. As shown in Table 5, the Industrial Safety and Health Standards Regulations mandate a minimum safety factor of 5 for lifting accessories such as chains and wire ropes.  
However, as noted in Section 3.3, the product certificate provided by the supplier specifies the S.W.L of the chain as 1600 kgf and the breaking load as 6400 kgf. This yields a theoretical safety factor of 4, which is approximately 20% lower than the legal requirement.  
If the legally required safety factor of 5 were applied to this chain, the allowable S.W.L would be recalculated as 1280 kgf (6400 kgf \(\div\) 5). When using this chain in a four-leg sling configuration, the total lifting capacity would then be 5120 kgf (1280 kgf \(\times\) 4 legs). In the  
Figure 10: Breaking load test reports: (**a**) Test result of new chain sling-1��⃝; (**b**) Test result of new chain sling-2⃝.  
incident under investigation, the actual weight of the lifted object was found to be 5260 kgf, which exceeds the calculated S.W.L by approximately 1.02 times.  
Accordingly, it is concluded that the lifting operation in this case did not satisfy the minimum safety factor requirement and was carried out under improper and unsafe working conditions.
## 3 Results and Discussions
### Flexural Strength of PU-Cement Mortar  
The flexural strength of PU-cement mortar modified with or without NS particles is shown in Figure 8. The flexural strength of the control mortar (PUCM0-0) is 12.96 MPa. Adding 10% PU without NS led to an increase in strength of 6.17%. Under the same 10%PU content and reinforced with NS, the flexural strength revealed improved strength, as indicated in PUCM10-1, PUCM10-2, and PUCM10-3 specimens. This behavior is attributed to the combined effect of PU binder and NS in improving the flexural strength of cement-based composites. The positive effect of PU binder agrees with findings in the past literature [6; 26]; the viscoelastic properties of PU binder are responsible for this behavior. Due to the addition of 10% PU binder and 2% NS and 3% NS, the flexural strength was increased by 8.88% and 16.69%, respectively. Similarly, the reinforcing effect of NS was also noted in the specimens containing 15% PU binder. A moderate PU binder content obviously  
Figure 7: Compressive strength of PUCM modified with nano silica.  
improved cement mortar's flexural strength. The average flexural strength of PUCM15-0 was 14.10 MPa which is 8.79% higher than the reference specimen. For the specimens prepared with 15% PU, the flexural strength decreased and then increased with an increase in NS particles. PUCM15-3 demonstrated the highest strength of 14.77 MPa among this group. The higher PU binder content also negatively affects the flexural strength, as shown in Figure 8. All the specimens containing 25% PU revealed a reduced flexural strength compared to others. The flexural strength of PUCM25-0 was 31.34 MPa, and appeared to be the lowest strength among all the specimens. However, some of the lost strength was mitigated due to the reinforcing effect of NS, as revealed in the PUCM25-1, PUCM25-2, and PUCM25-3 specimens.
## 3 System Implementation
### Compress and Transmit  
To further reduce data capacity, the recording data is compressed before transmission. An effective way to compress audio data is using the Differential Pulse Code Modulation (DPCM) that encodes the analog audio input signal using the difference between the current and the previous sample [25]. The difference can be large or small because the sound signal is random. To accommodate it, ADPCM is more applicable [26]. In this work, we implemented the ADPCM on the embedded processing board. The influence of compression with ADPCM on audio quality and recognition will be reported in later chapters. After compression, a packet including audio, noise level, triggered time of recording, coordinates, and device number is transferred to the server through the 4 G wireless module.
## 2 Results
### An Increased Number of GAG-Binding Motifs Results in Higher Affinity for HS
TAMRA-labeling is indicated with [\({}^{*}\)] and the GAG-binding motifs are underlined in the amino acid sequences of the peptides. Subcloning of a Mixed Population of pgsB-618 Cells Results in HS\({}^{+}\) and HS\({}^{-}\) Subclones  
The IFT experiments already provided insight into the interaction between the peptides and the GAGs. Subsequently, we wanted to investigate this interaction in a physiologically more relevant setting, i.e., the binding of the peptides to cellular GAGs. Therefore, we used CHO-K1 cells and pgsB-618 cells in parallel for the experiments. According to Ex-pay [21], pgsB-618 cells are mutant CHO-K1 cells that are deficient in galactosyltransferase I, also known as \(\beta\)-1.4-galactosyltransferase 7 (\(\beta\)4 Gal-I7). This enzyme is required for the synthesis of the common tetrasaccharide linkage region in the biosynthesis of proteoglycans [22, 23]. Consequently, pgsB-618 cells are proteoglycan-deficient mutants. Upon receiving the pgsB-618 cells, their HS expression was evaluated using flow cytometry to confirm their inability to produce GAGs. Remarkably, 36.8% of the pgsB-618 cells stained positive for HS on their cell surface using flow cytometry, suggesting a mixed population of cells, consisting of both HS\({}^{+}\) and HS\({}^{-}\) subpopulations (Figure S1A). Two weeks later, the HS\({}^{+}\) subpopulation of the pgsB-618 cells even increased to 91.9% (Figure S1B). The expression of HS on CHO-K1 cells was also measured and, as expected, a uniform HS\({}^{+}\) cell population was observed (Figure S1A,B). PgsB-618 cells, deficient in GAG biosynthesis, were required for the experiments to serve as an appropriate negative control. To this end, subcloning of the pgsB-618 cells was performed and resulted in 21 subclones, each derived from a single cell. The HS expression on each of the 21 pgsB-618 subclones was evaluated using flow cytometry immediately after subcloning and appeared to be absent in 7 out of 21 subclones. One HS\({}^{-}\) subclone (clone 16) was selected for use in further experiments. In addition, one HS\({}^{+}\) subclone (clone 19) was chosen (Figure S1C). More than 3 months and 36 cell passages later, the HS expression was measured again, and the stability of both cell lines was confirmed (Figure 6). For reasons of simplicity, HS\({}^{-}\) pgsB-618 clone 16 cells and HS\({}^{+}\) pgsB-618 clone 19 cells will be named HS\({}^{-}\) and HS\({}^{+}\) CHO cells, respectively, in the following paragraphs. Figure 5: Overview of the K\({}_{\rm d}\) values obtained from the IFT experiments. The K\({}_{\rm d}\) values of the interactions of the different peptides with (ds)LMWH, HS and HA are displayed in nM. The amino acid sequences of the peptides are depicted on the right with TAMRA indicated in pink [\({}^{+}\)] and the GAG-binding motifs underlined.
## 5 Experimentation and Results
### Training and Results--Step 1  
The panel and cavity natural frequency were predicted using the GCN with the following parameters: learning rate 0.0004, weight decay 0.002, and number of epochs 800 for panel and 0.003, 0.0015, and 700 for cavity, respectively. Figure 11 shows the accuracy of the predicted natural frequency of the morphed panels.  
The frequency range of the first to fifth natural frequency of all sizes of morphed panels is from 71 Hz to 430 Hz. The r-square value of the proposed method is about 0.96.  
Figure 10: Cavity shape with cutting the volume from hexahedron and mode shapes up to 5 th mode: (a) cavity size 1.2 m \(\times\) 1.0 m \(\times\) 0.8 m and 1.3 m \(\times\) 0.8 m \(\times\) 0.8 m; (b) cavity size 1.5 m \(\times\) 1.0 m \(\times\) 0.9 m and 1.6 m \(\times\) 1.3 m \(\times\) 1.0 m.  
Figure 9: Cavity dataset with cutting the volume from 4 sizes of hexahedron: (a) cavity size 1.2 m \(\times\) 1.0 m \(\times\) 0.8 m, 1.3 m \(\times\) 0.8 m \(\times\) 0.8 m; (b) cavity size 1.5 m \(\times\) 1.0 m \(\times\) 0.9 m, 1.6 m \(\times\) 1.3 m \(\times\) 1.0 m.  
The cavity natural frequency and mode shape depends on the effective length of each facet, as explained in Equation (5), and the adjacency matrix for the GCN to predict the cavity natural frequency was setup based on the node connectivity of the 2D element, which represents the outer shape of the cavity to reduce the matrix size and setup time instead of the node connectivity of the 3D element.  
Figure 12 shows the accuracy of the predicted natural frequency of the cavity. The frequency range of first to fifth natural frequency of all sizes of cavities which are to be predicted with a proposed GCN is from 71 Hz to 430 Hz. The -r-square value of the proposed method is about 0.97.  
Generally, low-frequency modes are less sensitive to shape change than higher-frequency modes. In this study, the cavity first mode frequency is distributed below the 200 Hz range, so the high-frequency range in Figure 12b relatively shows more discrepancy than the low-frequency range.
## 3 3D BEM-DMST Model: Basic Formulation and High-Order Submodels
### Strut Correction  
Supporting struts play a paramount role for the stiffness and mechanical integrity of the structure in VAWTs. On the other hand, they have a detrimental effect on turbine performance, particularly at high TSRs when the flow is severely distorted by the interaction with non-aerodynamic spokes. With emphasis on the H-shaped VAWT investigated in this article, spokes are modelled as two radial flat plates with variable width as function of turbine radius. Geometric parameters of the model are available in [12] while the estimation of passive torque induced by strut drag force is evaluated according to Equations (13)-(16), as also suggested in [17]. The drag force contribution is calculated for discretised elements of the strut and the drag of the rectangular section is assumed according to [48] and equal to \(C_{Dst}=1.3\) for Reynolds number in the range \(10^{4}\)-\(10^{5}\). The velocity approaching the actuator disc is interpolated at the mid point position of each discretised strut element to calculate the relative velocity \(W_{st}\). The overall average passive torque estimation over a rotation is estimated in Equation (16) by integrating over the strut length and over the azimuthal angle.  
\[W_{st,j,k}^{2}=(V_{j}\,\cos\theta_{j}+\Omega\,r_{st,k})^{2}+(V_{j}\,\sin\theta _{j})^{2} \tag{13}\]  
\[\alpha_{st,j,k}=atan\left(\frac{V_{j}\,\sin\theta_{j}}{V_{j}\,\cos\theta_{j}+ \Omega\,r_{st,k}}\right) \tag{14}\]  
\[W_{st,j,k}^{\perp}=W_{st,j,k}\,\cos\alpha_{st,j,k} \tag{15}\]  
\[\overline{T}_{st}=\frac{N}{4\pi}\rho\int_{0}^{2\pi}\int_{D_{p}}^{R_{st}}C_{Dst }\,W_{st,j,k}^{\perp 2}\,t_{st}\,dr_{st}\ d\theta \tag{16}\]  
The strut geometry implementation benefits of the flexibility to deal with both straight horizontal arms and inclined ones.
# Cable Temperature Prediction Based on RF-GPR for Digital Twin Applications
## 1 Introduction
Among them, the numerical method calculates the entire temperature field under the given cable electromagnetic parameters and environmental parameters, including the finite element method, the finite difference method, etc. The thermal path method is mainly based on IEC standards, and the cable structure is equivalent to a one-dimensional model using the equivalent model principle and then calculated according to the law of heat conduction. The thermal path method can only calculate the average temperature of each layer of the cable structure, and the error of the cable temperature value calculated under a poor laying environment is large. In comparison, the finite element coupling calculation method has the advantages of simulating complex models and high calculation accuracy. However, under the same cable laying conditions, the time used to calculate the cable temperature by the finite element method also increases. When constructing cable, the multi-physical field coupling model is applied to the digital twinning cable frame, and the model-driven theory represented by the finite element method cannot meet the real-time requirement of digital twinning application. The digital twinning of cable can be realized not only by model drive but also by the artificial intelligence algorithm. The development of the artificial intelligence algorithm has provided a new research method for the early state recognition of cable. Machine learning and deep learning were applied in many power system fields [16; 17]. In [18], a prediction model was built by the machine learning algorithm to realize the accurate identification of the partial discharge mode of electrical equipment. Ref. [19] constructed the state prediction model of a power transformer by using the short-short memory network algorithm and studied the corresponding relationship between the characteristic variables and transformer operating state. Ref. [20] realized the predictive control of an NSI system in dual-output mode and six-phase mode based on the model prediction method of the finite control set. Ref. [21] established a prediction model based on the recursive neural network principle to realize the load prediction of a power system. Under the framework of digital twin technology, this paper innovatively proposes a digital twin cable temperature prediction method based on machine learning for digital twin application by combining finite element calculation and artificial intelligence. A certain number of basic data are provided through finite element calculation, and then the artificial intelligence method is used to learn, establish a reliable model training process, generate a cable temperature prediction model, and realize the rapid and accurate calculation of the cable temperature distribution in the cable trench. The calculation results meet the real-time requirement of cable temperature calculation in the application of the digital twin and provide technical support for the application of the digital twin.