esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Experiments
### Throughput and Message Loss Ratio
For example, if the number of incoming control messages is 10,000, the number of user data messages becomes 252,144 (because the maximum PPS is 262,144 for a packet size of 512 bytes). The control messages in this test consist of Association Request  
Figure 4: Comparison of throughputs. Figure 5: Comparison of packet loss rates. messages and Configuration Request messages, as shown in Table 2. If the system receives those control messages, it should verify the validity of the establishing session, update session information to its session table, establish a session with a user device and a server and configure device parameters to its session table. The result of the evaluation is described in Figure 6. As shown in the Figure, the performance of the dual plane IoT system is almost the same regardless of the number of control messages, and only a 1.9% performance loss occurs. This number represents the overhead for packet relay between the data and control plane in the dual plane architecture. In contrast, the performance of the single plane architecture with DPDK drops sharply when the number of control messages exceeds 10,000, while that of the single plane architecture without DPDK approaches 0. The throughput of the single plane architecture with DPDK only shows nearly 89%, 77%, 60%, 36%, 24%, 18% and 13% of the throughput of the dual plane architecture with DPDK (while the number of control messages are increased from 10,000 to 40,000), even though both systems use the same amount of memory and the same number of cores. No matter how quickly the DPDK polls received packets, packet delay and queue congestion in the handling of time-consuming control message are unavoidable, leading to a performance degradation. These performance differences represent the contribution of the dual plane architecture. That is, the dual plane architecture can improve system performance and is more efficient than the single plane architecture, particularly in an environment where control messages are generated frequently. Second, the comparison of a single plane architecture with and without DPDK found that DPDK can relieve performance degradation. When there are few control messages, the performance of the single plane architecture with DPDK is much different than the performance of the single plane architecture without DPDK. We can verify that although the DPDK can increase the system throughput and can relieve performance degradation by improving packet IO performance, it still suffers from handling time-consuming control messages, which consequently leads to a performance degradation problem.
# Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2 vec and WordNet
## 3 Proposed Method
### Cosine Similarity  
The cosine similarity between two vectors was computed by their dot product divided by the product of their norm, as shown in Equation (7). The cosine similarity was in the range between 0 to 1. Two vectors were said to be similar when the cosine similarity was close to 1, and they were said to be dissimilar when it was close to 0 [27].  
\[cosim(v_{1},v_{2})=\frac{v_{1}\cdot v_{2}}{\|v_{1}\|\|v_{2}\|} \tag{7}\]  
Our idea is that the correct word sense is likely to have a high cosine similarity with its context. Before doing this, all the word sense's signatures and the context must be mapped to sentence vectors using the various configurations mentioned in the previous section.
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 2 Three-Phased Induction Motor
### Nameplate of a Three-Phased Induction Motor  
The nameplate of a three-phased induction motor first gives useful connection data, such that an electrical technician is able to correctly connect the motor to the network. Then,  
Figure 2: Schematic view of a terminal box and a star or delta connection.  
usually, secondary but useful information is provided, such as the power factor (\(\cos\varphi\) in Figure 3) at rated speed and the efficiency \(\eta\) at rated speed.  
As presented in the connection section, the stator windings must be connected according to the network phase-to-phase voltage. The minimum voltage value written on the nameplate corresponds to the rated supply voltage value that can be handled by a stator winding. When the phase-to-phase voltage value is equal to this value, the motor must be wired in delta (D), and when the network phase-to-phase voltage value is equal to the maximum voltage value written on the nameplate, the stator windings must be wired in star (Y), as presented in Figure 2.  
On the same line, the nameplate indicates, for each of the couplings, the value of the line current intensity that will be absorbed at rated speed. Using the electrical quantities provided, i.e., the voltages between phases, the line current intensities, and the power factor, it is possible to calculate the active power absorbed:  
\[P_{a}=\sqrt{3}\cdot U\cdot I\cdot\cos\varphi. \tag{3}\]  
Since the useful power is also provided on the nameplate, it is easy to deduce the efficiency of the machine operating at nominal speed, thanks to the following formula:  
\[\eta=\frac{P_{u}}{P_{a}}. \tag{4}\]
# Crushing Analysis and Optimization of Adjacent Variable Thickness Hexagonal Tubes
## 3 Results and Discussion
### Optimization Results and Discussion  
Table 6 shows the details of the validation of the surrogate model method. In general, the model has acceptable accuracy and can be used for subsequent optimization.  
Using the MOGA, Pareto fronts are obtained and shown in Figure 13. The values of \(-\)SEA\({}_{\text{w}}\) and F\({}_{\text{maxw}}\) are negatively correlated for AVTHTs, which indicates that an increase in SEA\({}_{\text{w}}\) (decrease of \(-\)SEA\({}_{\text{w}}\)) leads to an increase in the F\({}_{\text{maxw}}\). Because the two objectives are normalized values and cannot represent actual values, the pareto fronts are just meaningful for us to select appropriate thickness configurations. For example, if we prefer to design an AVTHT with the best performance for SEA\({}_{\text{w}}\), the leftmost point (Figure 13a) is selected, and then we use the corresponding \(t1\) to perform the simulation under all loading cases. Thus, we can investigate the specific crashworthiness performance of this design. Moreover, Figure 13b,c shows the curves of F\({}_{\text{maxw}}\) vs. \(t1\) and SEA\({}_{\text{w}}\) vs. \(t1\), and it can help us to determine more clearly which thickness configurations should be avoided in the design. For example, the rangs of \(1.55\leq t1\leq 1.6,1.85\leq t1\leq 1.95\) should not be the design domain. In the pareto results, increasing \(t1\) would not always increase the F\({}_{\text{maxw}}\) and SEA\({}_{\text{w}}\). For example, when \(1.75\leq t1\leq 1.8\), increasing \(t1\) would lead to decline of F\({}_{\text{maxw}}\) and SEA\({}_{\text{w}}\).  
In conclusion, the AVTHTs can basically achieve lower F\({}_{\text{maxw}}\) and higher SEA\({}_{\text{w}}\) levels with optimum designs. Certain considerable design thickness configurations can be obtained from the optimization results, which may be a way to achieve the excellent crushing performance of AVTHTs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indices** & \(\mathbf{F_{\text{maxw}}}\) & \(-\mathbf{SEA_{\text{w}}}\) \\ \hline R\({}^{2}\) & 0.9904 & 0.9926 \\ RAAE & 0.08564 & 0.0675 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy of the surrogate model.  
Figure 13: Pareto fronts of optimization results: (**a**) F\({}_{\text{maxw}}\) vs. \(-\)SEA\({}_{\text{w}}\); (**b**) F\({}_{\text{maxw}}\) vs. \(t1\); (**c**) SEA\({}_{\text{w}}\) vs. \(t1\).
# 40 W All-Fiber Er/Yb MOPA System Using Self-Fabricated High-Power Passive Fiber Components
## 3 Experimental Results  
A graph showing measured output power of the three-stage MOPA setup for operation on both the wavelengths (1555 and 1563 nm) versus pump power of the third high-power amplifier stage is shown in Figure 6. The maximum achieved output power was approximately 44 W at the pump power of 144 W at the wavelength of 915 nm. The achieved slope efficiency was at a level of approximately 33% in the case of the MOPA operating at the wavelengths of 1555 nm and 1563 nm. As can be seen, there was no decrease in the slope efficiency even at highest pumping power, which led us to assume that the output power was currently limited only by the available pump power. In both the cases, the input signal of 1.8 W from the second stage was amplified with a gain of approximately 13.9 dB. Taking into account that we used 10 mW seed laser, we characterized the presented MOPA system with a total gain at a level of approximately 36.4 dB.  
Figure 7 shows the recorded spectra of the amplified signal at the output of the three-stage MOPA setup. It was characterized by a very low amplified spontaneous emission (ASE) level of the output signal at the wavelengths of 1555 nm and 1563 nm. The registered OSNR was at a level over 40 dB in both the cases. In addition, during the measurement, we were controlling Yb-ASE emission in the spectral region of 1 \(\upmu\)m, and it was at a low level below 0.6 W, and we did not observe any spontaneous lasing.  
Figure 6: Characteristics of signal output power versus pump power in the case of the MOPA operating at 1555 nm and 1563 nm.  
Figure 7: Optical spectra of the output signal at the wavelengths of 1555 nm and 1563 nm.
# Free Surface Energy and Hansen Solubility Parameter Vector Field. Interface Thickness
## 3 Data and Discussion
### Contact Angle and the Solubility Parameters Vector Field  
The tensions that come into play when a liquid droplet is dropped over a solid surface are shown in Figure 11. The surface tension of the droplet versus air (or any given gas) is \(\sigma_{\text{LG}}\), the surface tension of the solid surface versus air is \(\sigma_{SG}\), and the interfacial tension between the solid surface and the liquid droplet is \(\sigma_{SL}\). The tension \(\sigma_{LG}\) is tangent to the surface of the droplet where the three phases come into contact. The angle formed by \(\sigma_{LG}\) is called the contact angle, \(\theta\).  
The cosine of the contact angle is as follows:  
\[cos\theta=\frac{\sigma_{SG}-\sigma_{SL}}{\sigma_{LG}} \tag{71}\]  
By substituting free surface energies with their expression as a function of the interaction vector it results in, the following is obtained:  
\[cos\theta=\frac{\left|\overset{\rightarrow}{S}_{SG}\right|^{2}-\left| \overset{\rightarrow}{S}_{SL}\right|^{2}}{\left|\overset{\rightarrow}{S}_{ LG}\right|^{2}} \tag{72}\]  
Or, in other words:  
\[\theta=arccos\left(\frac{\left|\overset{\rightarrow}{S}_{SG}\right|^{2}- \left|\overset{\rightarrow}{S}_{SL}\right|^{2}}{\left|\overset{\rightarrow} {S}_{LG}\right|^{2}}\right) \tag{73}\]  
If:  
\[\left|\overset{\rightarrow}{S}_{SG}\right|^{2}-\left|\overset{\rightarrow} {S}_{SL}\right|^{2}>\left|\overset{\rightarrow}{S}_{LG}\right|^{2} \tag{74}\]  
This means that the critical surface tension, \(\sigma_{c}\), has been surpassed and values \(cos\theta=1\) and \(\theta=0\) will be directly assigned.  
To confirm the validity of (72) and (73), the calculated values have been compared with the experimental values for the contact angles of various liquids on Poly(methyl methacrylate) (PMMA) and n-Octacosane surfaces (Table A5 in Appendix A). Figure 12 shows the correlation between the experimental and calculated values. Experimental contact angle estimation is usually not very accurate because a variety of factors like surface roughness or impurities can distort the measurement. Even so, for the same solid-liquid-gas system, there can be a wide range of contact angles. Within that range of possible contact angles, the largest is called the advancing contact angle and the smallest is known as the  
Figure 11: The tension balance of a liquid droplet dropped over a solid surface.  
receding contact angle. Despite potential experimental contact angle inconveniences, and the fact that small solubility parameter biases can be amplified when squaring parameters, the correlation can be classified as excellent: \(R^{2}\) = 0.981.
## 2 Materials and Methods
### Antibiotic Susceptibility Test  
The antibiotic susceptibility of _Salmonella_ strains was evaluated using the Kirby-Bauer disk diffusion test on Mueller-Hinton agar (MH, Becton Dickinson, Franklin Lakes, NJ, USA) [26]. In brief, bacterial cells cultivated in LB broth overnight were adjusted to an OD\({}_{600}\) of 0.6-0.7, which is equivalent to 5 \(\times\)\(10^{7}\) colony-forming units (CFU)/mL. The bacterial suspension was spread on MH agar using sterile swabs, and antibiotic disks (BBL\({}^{\text{TM}}\) Sensi-Disc\({}^{\text{TM}}\), Becton Dickinson) were placed on the agar plates. After incubation at 37\({}^{\circ}\)C, the diameters of the growth inhibition zones were measured to determine antibiotic susceptibility. The tested antibiotic disks included 10 different antibiotics, as follows: amoxicillin/clavulanic acid (AMC, 30 \(\upmu\)g), ampicillin (AM, 10 \(\upmu\)g), cephalothin (CF, 30 \(\upmu\)g), gentamicin (GM, 10 \(\upmu\)g), kanamycin (K, 30 \(\upmu\)g), nalidixic acid (NA, 30 \(\upmu\)g), neomycin (N, 30 \(\upmu\)g), ampicillin/sublactam (SAM, 20 \(\upmu\)g), sulfamethoxazole/trimethoprim (SXT, 25 \(\upmu\)g), and tetracycline (TE, 30 \(\upmu\)g).
# A System Design Perspective for Business Growth in a Crowdsourced Data Labeling Practice
## 5 Aggregating Results (Solution to RQ5)
They also define an F1 score, which is a summary statistic that provides an overall measure of performance but does not offer detailed insights into specific aspects of the labeling process. In contrast, our metrics provide a more granular analysis of performance in light of the labeling process. The metrics proposed provide a comprehensive overview of the labeling process, ensuring informed decision making. By aggregating the responses received per item, the administrator can evaluate the consensus existing among users and identify items needing further attention. Being able to measure the reliability of answers allows the administrator to evaluate the accuracy and consistency of user contributions, identify high-performing users and address potential problems with less reliable contributors. Assessing the volume of remaining work enables efficient resource allocation and prioritization of tasks. Additionally, validating the accuracy of golden items and analyzing reports on financial performance ensures that the labeling system maintains high standards and aligns with organizational goals. This comprehensive feedback loop helps administrators optimize the labeling process, enhance data quality, and ultimately improve overall system performance. While general accuracy metrics focus solely on the correctness of labels, the proposed metrics also consider the completeness and dominance of responses, the reliability of contributors, and the consistency of golden items. This holistic approach ensures that not only is the accuracy of individual labels assessed, but also the overall reliability and robustness \begin{table}
\begin{tabular}{|c|c|} \hline \multicolumn{2}{|c|}{**Graphical Representation**} \\ \hline  & Consistency level of \(i_{2}^{*}\) (\(\%\)) = \(\frac{|A,B|}{|A,B|}=\frac{2}{2}=100\%\) \\  & \(i_{5}^{*}(\%)=\frac{|A,B|}{|A,B,C|}=\frac{2}{3}=66.67\%\); \(i_{8}^{*}(\%_{0})=\frac{|A,C|}{|A,B,C|}=\frac{2}{3}=66.67\%\) \\  & High level of consistency (\(\%\)) = \(\frac{|i_{2}^{*}|}{|i_{2}^{*}+i_{3}^{*}+i_{4}^{*}|}=33.33\%\) \\  & Medium level of consistency (\(\%\)) = \(\frac{|i_{2}^{*}+i_{3}^{*}-i_{4}^{*}|}{|i_{2}^{*}+i_{3}^{*}+i_{4}^{*}|}=\frac{ 2}{3}=66.67\%\) \\  & Low level of consistency (\(\%\)) = \(\frac{|i_{2}^{*}|}{|i_{2}^{*}+i_{3}^{*}+i_{4}^{*}|}=\frac{0}{3}=0\%\) \\ \hline \end{tabular}
\end{table} 
Table 10: Metrics of the golden data associated with the hypothetical study case. of the labeling process. Finally, aggregating responses per item and distinguishing between ordinary and golden items allow for the identification of patterns and discrepancies that simple accuracy measures might miss.
## 4 Experimental Results and Analysis
### Energy Consumption Experiment  
To verify the inference energy consumption of the EDPNet model, energy consumption tests were conducted on the OrangePi5 Plus device. Different CPU frequencies were locked separately to obtain runtime power and inference time. The experimental results are shown in the Table 3.  
Figure 8: Comparison results of different algorithms.  
It can be seen from the above table that the power of EDPNet in operation is very low, only less than 4 w, so it is enough to meet the needs of edge computing. In addition, we conducted multiple high- and low-temperature tests on the OrangePi5 Plus to simulate real-world usage scenarios. The device was able to operate normally in extreme environments ranging from \(-40\)\({}^{\circ}\)C to \(55\)\({}^{\circ}\)C.
## 3 Methodology
### Cryptographic Techniques
#### 3.4.2 Advanced Encryption Standard Algorithm  
AES is a symmetric encryption algorithm commonly used to secure data, particularly in authentication processes, such as encrypting passwords or email addresses. AES operates on fixed 128-bit blocks and supports key sizes of 128, 192, and 256 bits. It uses multiple stages of substitution, permutation, and mixing to convert plaintext into ciphertext. AES is highly efficient, secure, and used in various database applications like banking, government, and telecommunications. Similar to RSA, this technique is used to address four cryptographic issues, as discussed in the MLS/DBMS model.  
Scintifically, AES operates on a block of plaintext \(P\) and key \(K\) to generate ciphertext \(C\). The encryption process includes several steps (rounds) of substitution, shifting, mixing, and key addition. For a given round \(r\), the transformation can be expressed in Equation (4), where \(C\) is the resulting ciphertext after applying the AES operations.  
\[C=AES(P,\ K) \tag{4}\]
# Additive Manufacturing for Surgical Planning and Education: A Review
## 4 Applications of 3D Printing in the Medical Field
### Surgical Plates and Pins  
AM technology is also suitable for unilateral displaced intra-articular calcaneal fractures involving the posterior facet that require precise anatomic restoration. The contralateral unaffected calcaneus, devoid of any fractures, deformities, or surgical history, serves as a template for pre-shaping the plate before the surgery. It also acts as a guide for achieving anatomic reduction during the operation. It is important to note that Sanders type 4 calcaneal fractures may not be suitable for this minimally invasive approach. Additionally, fractures older than 3 weeks might pose challenges for reduction using this method [103]. Patients with a unilateral calcaneal fracture undergo a CT scan of both calcanei, with a slice thickness of 1 mm (see Figure 13).  
Utilizing the mirror-imaging technique, the normal side calcaneus is transformed into a model resembling the injured side's calcaneus, likely similar to its pre-injury state (Figure 14). It takes approximately 3 h to print a 3D model using PBF polymer method.  
The shape and size of the locking plate are chosen by attaching the plate to the real-size calcaneus model. Careful examination of plain radiographs and CT scans of the fractured calcaneus helps determine the correct position for the plate and which holes to use. For an AO Locking Calcaneal Plate, three proximal holes are cut--one each on the anterior, middle, and posterior portions--to allow the plate to smoothly advance through the small incision. The plate is bent to fit the model, securing the fracture fragments effectively (Figure 14).  
Fluaroscopic images are taken with the plate attached to the model to aid in deciding the plate's proper position during its insertion through the incision (Figure 15-left). This can be done either before the operation or intraoperatively, just before inserting the plate through the incision. Both the pre-shaped locking plate and the 3D-printed model are sterilized before the surgery. The 3D-printed model serves as a reference for anatomical reduction of the fracture during the surgery.  
During the procedure, the patient is positioned in the lateral decubitus position with a thigh tourniquet. Figure 15 shows such a procedure.  
Figure 13: The Sanders system of fracture classification on the calcaneus [1; 2; 49]: **(a)** Normal calcaneus in transverse and coronal images; **(b)** Type II; **(c)** Type III; and **(d)** Type IV [103].  
Figure 14: The design, fabrication, and application of 3D-printed cast. **(A,B)** The reconstruction and design of 3D model based on CT data. **(C–E)** The 3D-printed cast with five cannulas for insertion of 1.5 mm K-wires. **(F,G)** CT images of a patient (male, 42 years of age) with displaced intra-articular calcaneal fracture, showing sustentaculum tali nondisplaced. **(H,I)** A fractured calcaneus treated with percutaneous reduction and 3D-printed cast assisted screw fixation [104].  
Figure 15: _Cont._
## 2 Methodology
### Research Methods
#### 2.1.2 Calculation of Indicator Weights  
(1) Constructing a Judgment Matrix  
The relative importance between the indicators \(i\) and \(j\) is quantitatively analyzed by experts using the 1-9 scale method, denoted by \(x_{ij}\), and the judgment matrix \(X_{ij}\) is constructed accordingly, where \(x_{ji}=\frac{1}{x_{ij}}\). The weight vector of the indicators \(w_{i}=(w_{1},w_{2},...,w_{n})\) is calculated by Equations (1) and (2), where \(w_{1},w_{2},...,w_{n}\) represents the weight of the evaluation indicators.  
\[X_{ij}=\begin{bmatrix}1&x_{12}&\cdots&x_{1n}\\ \frac{1}{x_{12}}&1&\cdots&\vdots\\ \vdots&\vdots&\ddots&x_{nn-1}\\ \frac{1}{x_{1n}}&\cdots&\frac{1}{x_{nn-1}}&1\\ \end{bmatrix}_{n\times n}\]  
\[\overline{w_{i}}=\sqrt[n]{\prod\limits_{j=1}^{n}x_{ij},i=1,2,...,n} \tag{1}\]  
\[w_{i}=\frac{\overline{w_{i}}}{\sum\limits_{i=1}^{n}\overline{w_{i}}},i=1,2,...,n \tag{2}\]  
(2) Consistency Test  
In order to ensure the consistency of the logic of expert judgment, it is necessary to test the consistency of the indicator weights using Equations (3)-(5). When \(CR<0.1\), the judgment matrix passes the consistency test, indicating that the indicator weights are more reasonable; otherwise, there is need to re-judge the relative importance of the indicators. The randomized consistency index (RI) is an important metric used for consistency testing in AHP, and its values are shown in Table 1.  
\[\lambda_{\text{max}}=\frac{1}{n}\sum\limits_{i=1}^{n}\frac{(AW)_{i}}{w_{i}} \tag{3}\]  
\[CI=\frac{\lambda_{\text{max}}-n}{n-1} \tag{4}\]  
\[CR=\frac{CI}{RI} \tag{5}\]  
where \(W\) is the standardized weight vector. \(CI\) is the consistency index. \(n\) is the matrix order. \(\lambda_{\text{max}}\) is the maximum eigenvalue of the judgment matrix. \(CR\) denotes the consistency ratio.  
Figure 1: Suitability evaluation index system.