esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 SSB Signal Generation and SSBN Mitigation Techniques
### SSBN Estimation and Cancellation Approach  
The Rx-DSP that comprises the SSBN estimation and cancellation technique in addition to the Rx-EDC is depicted in Figure 6(d). This technique presents compensation performance that is comparable to that of the iterative SSBN cancellation scheme. Unlike the iterative SSBN cancellation scheme, the need for multiple symbol decision-based SSBN reconstruction procedures that demand multiple IFFT/FFT pairs is prevented. Furthermore, the scheme entails having two replicas of the detected signal. A copy is stored in the memory, while the other is sent to an SSLF that offers partial elimination of the SSBN terms. Then, a noniterative SSBN estimation and cancellation is executed to construct an approximation of the SSBN. Subsequently, the resulting signal is subtracted from the signal waveform that is stored in the memory [29; 59]. It is noteworthy that no additional unwanted beating product is presented by the scheme, as is usual in the case of the linearization filtering schemes. Consequently, the SSBN estimation and cancellation scheme presents an improved compensation gain in particular, at the higher OSNR values. Nonetheless, the dependency on the symbol decision accuracy is the main limitation of the scheme. This limitation results in performance degradation at lower OSNR levels [29].
## 5 Discussion
### Hope and Regret on Transformers  
Several insights drawn from the comparisons between the performances of different transformers, of the tasks of regret detection and hope speech detection, are presented as follows:  
* Generally, the transformers were expected to perform better on longer texts, such as Reddit posts, than shorter texts, such as tweets. This is because transformers are designed to capture long-range dependencies in the text and can handle large amounts of context. On the other hand, including the post titles in the regret detection task may provide additional context and information that helps the transformer models better understand the content of the Reddit posts. This could explain the higher scores achieved by the transformer models in the regret detection task, compared to the hope speech detection task, where only the text of the tweets is used.
* In terms of the complexity of the tasks, the task of regret detection from Reddit posts comprises a three-class classification task, while the task of hope speech detection from tweets is a four-class classification task. Including an additional class in the hope speech detection task may make it more challenging for the models to classify the tweets accurately. This could explain the lower scores achieved by the transformer models in this task when compared to the regret detection task.
* The dataset size can impact the performance of different transformers, in terms of hope speech detection and regret detection tasks. A larger dataset may provide more diverse and representative samples of the underlying distribution, which can help the transformer learn more accurate and generalizable patterns in the data. This can lead to an improved performance of the task. However, in this comparison, longer texts and more context (ReDDIT dataset) resulted in a better improvement than the number of samples (PolyHope dataset).
* Results in Tables 4 and 6, revealed that transformers were effective in both datasets, with slight differences. Based on the results in Table 4, the roberta-based model performs exceptionally well in terms of the regret detection task, with an averaged macro F1-score of 0.83 when the text and title of the Reddit posts are used. The xnet-based, cased model also performs well, with an averaged macro F1-score of 0.82. These results suggest that the roberta-based and xnet-based, cased models may be well-suited for tasks involving longer texts. Regarding the hope speech detection task, as shown in Table 6, the pert-based, uncased model performs particularly well, with an averaged macro F1-score of 0.72. The roberta-based and distilbert-based, uncased models also perform well, with an averaged macro F1-score of 0.70. These results suggest that the pert-based, uncased, and roberta-based models may be well-suited for tasks involving shorter texts. Overall, these results suggest that the transformer models perform well on tasks involving long and short texts. Overall, the roberta-based model was the best-performing transformer, with high performance in both datasets.
## 4 Results
### Analysis of Thermal Damage Distribution  
A comprehensive analysis and comparison of thermal damage, both theoretically and experimentally, across different laser powers, are presented in this study. Significant disparities can be observed between the thermal damage profiles predicted by the two distinct conditions, as depicted in Figure 9. Moreover, the total volume of tissue damage, as illustrated in Figure 10, reveals substantial discrepancies between the models with \(\alpha=0\) kJ/kg and \(\alpha=2260\) kJ/kg. Specifically, the volume of damaged tissue is notably larger in the \(\alpha=0\) kJ/kg model compared to the \(\alpha=2260\) kJ/kg model, with the maximum magnification reaching 360 times at a laser power of 10 W. Furthermore, as shown in Figure 9, the extent of the thermal damage in the \(\alpha=0\) kJ/kg model extends beyond the boundaries of the model. This renders it unsuitable for accurately estimating the thermal damage length, given the significantly higher simulation error compared to that observed experimentally.  
The thermal damage length between the condition of \(\alpha=0\) kJ/kg and experimental values under different laser powers along the axial direction is illustrated in Figure 11. A high level of agreement was observed between the \(\alpha=2260\) kJ/kg condition and the experimental data. In comparison to the experimental damage length, the minimum deviation is 5.11% with an average experimental length \(l=4.28\) mm at \(P=10\) W. Meanwhile,  
Figure 8: Temperature distribution at various time points when the duration reaches 10 s. (**A**) The laser power \(P=10\) W at point (0,1); (**B**) the laser power \(P=14\) W at point (0,3).  
Figure 9: The two-dimensional thermal damage distribution under varying laser powers for two distinct conditions.  
the maximum deviation is 18.38% with an average experimental length of \(l=7.55\) mm at \(P=18\) W. Thus, the \(\alpha=2260\) kJ/kg model was proven highly suitable for analyzing the thermal damage distribution during the interaction between the laser and the tissue.  
At least three valid experimental data points were selected. During the experiment, sources of error include instrument response time, sensitivity, the presence of large blood vessels or fascia tissue in the isolated pig liver, and other factors. These data points were subsequently averaged to compute the final mean square error. The resulting average uncertainty was determined to be \(\pm 49.3\)\({}^{\circ}\)C for the temperature experiment and \(\pm 1.0\) mm for the length of laser ablation.
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 5 Optimization on the ABE Scheme
### Hybrid ABE Scheme  
The hybrid scheme consists of four algorithms. The Setup algorithm and the KeyGen algorithm are the same as their counterparts in the ABE scheme. The encryption and decryption algorithms work, as follows.  
Encrypt (\(M_{pk}\), \(A\), \(M\)): Given the master public key \(M_{pk}\), an access structure \(A\) and a message \(M\), this algorithm proceeds, as presented in Algorithm 11.  
```
1 Randomly generated a SM4 \(\sec\) ret key and an \(IV\)
2\(C_{1}\gets SM4.\textbf{Encrypt}(M,key,IV)\)/* Encrypting in CBC mode.*/
3\(C_{2}\gets ABE.\textbf{Encrypt}(M_{pk},A,key\parallel IV)\)
4 Output \((C_{1},C_{2})\)
```  
**Algorithm 11**\(\text{Encrypt}(M_{pk},A,M)\)  
Decrypt (\(M_{pk}\), \(U\), \(SK_{U}\), \(C\)): Given the master public key \(M_{pk}\), a set of attributes \(U\), the private key \(SK_{U}\), and the ciphertext \(C\), the decrypt algorithm proceeds as presented in Algorithm 12.  
```
1\((C_{1},C_{2})\gets C\)
2 key\(\parallel IV\gets ABE.\textbf{Decrypt}(M_{pk},U,SK_{U},C_{2})\)
3\(M\gets SM4.\textbf{Decrypt}(C_{1},key,IV)\)/* Decrypting in CBC mode.*/
4 Output \(M\)
```  
**Algorithm 12**\(\text{Decrypt}(M_{pk},U,SK_{U},C)\)  
The hybrid scheme is more space-economic than the scheme in the previous section. Let m be the length of plaintext and n be the access structure's size. The size of the ciphertext of this hybrid ABE scheme is \(m+n*(32+96)\) bytes, which is much smaller than before. For example, when the plaintext's length is 1 GB and the access structure's size is 8, the ciphertext's length is only 1 GB + 1 KB.
## 2 Materials and Methods  
The fabrication of the _n_-type Yb0,3 Co4 Sb12 powder followed the same process described in reference [33]. To fabricate samples for the measurement of the coefficient of thermal expansion (CTE), Yb0.3 Co4 Sb12 powder was loaded into a graphite die (\(\Phi\)30 mm) and consolidated in a spark plasma sintering (SPS) system at 620 \({}^{\circ}\)C under a pressure of 60 MPa and held for 10 min. Ti powder (4 N, 45 \(\upmu\)m) was loaded into a graphite die (\(\Phi\)30 mm) and consolidated at 1100 \({}^{\circ}\)C under a pressure of 60 MPa and held for 20 min in the same SPS system. The final dimension of the samples for CTE measurement was 4 \(\times\) 4 \(\times\) 20 mm\({}^{3}\). To fabricate the Ti/Mo/Yb0.3 Co4 Sb12 joints, Yb0.3 Co4 Sb12 powder was consolidated following the same sintering process as mentioned above. The surfaces of the resulting Yb0.3 Co4 Sb12 bulks were polished and cleaned ultrasonically before they were dried and loaded into the chamber of a magnetron sputtering system. The chamber was evacuated to 3 \(\times\) 10\({}^{-3}\) Pa and heated to 150 \({}^{\circ}\)C before Ar was introduced. Mo was sputtered at 1.5 Pa under 1.5 kW for 50 min. Then, Ti was sputtered at 1.5 Pa under 3 kW for 120 min. The resulting samples were cut into joints with a final dimension of about 3 \(\times\) 3 \(\times\) 6 mm\({}^{3}\). The joints were then sealed in quartz ampoules under vacuum. In the thermal shocking test, the ampoules were moved from room temperature into a furnace at 600 \({}^{\circ}\)C and kept inside for 5 min. Then, they were taken out into a water tank at 0 \({}^{\circ}\)C for another 5 min. In the isothermal aging test, the ampoules were kept at 550 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, and 650 \({}^{\circ}\)C for different time periods of time. The CTE were measured with a Linseis-L75 VS dilatometer (Princeton Junction, Princeton, NJ, USA). The interfacial microstructure of the joints was investigated by a scanning electronic microscope (ZEISS, SUPRA-55-SAPPHIRE, Oberkochen, Germany), and the chemical composition of the interfacial diffusion layers was analyzed using an Energy Dispersive Spectrometer (OXFORD, X-MaxN, Oxfordshire, UK). The contact resistivity of the joints was measured on the homemade four-probe platform (Haida Tech, Beijing, China) mentioned in our previous paper [34]. All the data for the diffusion layer thickness and the contact resistivity presented in this paper are the root mean square of three parallel samples, and the error bars are the root mean square errors.
# Evaluation of Diversification Techniques for Legal Information Retrieval +
## 2 Legal Document Ranking Using Diversification
### Diversification Overview  
Result diversification is a trade-off between finding relevant to the user query documents and diverse documents in the result set. Given a set of legal documents and a query, our aim is to find a set of relevant and representative documents and to select these documents in such a way that the diversity of the set is maximized. More specifically, the problem is formalized as follows:  
**Definition 1** (Legal document diversification).: _Let \(q\) be a user query and \(N\) a set of documents relevant to the user query. Find a subset \(S\subseteq N\) of documents that maximize an objective function \(f\) that quantifies the diversity of documents in \(S\)._  
\[S=\underset{\begin{subarray}{c}|S|=k\\ S\subseteq N\end{subarray}}{\operatorname{argmax}}\;f(N) \tag{1}\]  
Figure 1 illustrates the overall workflow of the diversification process. At the highest level, the user submits his/her query as a way to express an information need and receives relevant documents, as shown in the left column of Figure 1, where different names and colors represent the main aspects/topics of documents. From the relevance-oriented ranking of documents, we derive a diversity-oriented ranking, produced by seeking to achieve both coverage and novelty at the same time. This list is visualized in the right column of Figure 1. Note that Document E was initially hidden in the left column, the relevance-oriented ranked result set list.  
Significant components of the process include:  
* Ranking features, features of legal documents that will be used in the ranking process.
* Distance measures, functions to measure the similarity between two legal documents and the relevance of a query to a given document. We note that in this work, as in information retrieval in general [9], the term \"distance\" is used informally to refer to a dissimilarity measure derived from the characteristics describing the objects.
* Diversification heuristics, heuristics to produce a subset of diverse results.  
Figure 1: Diversification overview. Each document is identified by name/color representing the main aspects/topics of documents. A diversity-oriented ranked list of the documents is obtained in the right column, through diversification of the relevance-oriented ranked result set list in the left column.
## 4 Inversion and Verification of Mechanical Parameters
### Verification of Inversion Model  
The 1000 parameter samples are divided into two groups. Some 800 samples were chosen for training the metamodel, and the remaining 200 samples were reserved for testing and inversion. A comparative evaluation between the XGBoost and PSO-XGBoost inversion results was undertaken. Figure 10 presents the outcomes of both models, highlighting a significant contrast in the performance across four input parameters in the testing set. Equation (11) and mean squared error (MSE) were employed to evaluate the outcomes, which could be expressed as follows:  
\[\text{MSE}=\frac{\sum\limits_{i=1}^{n}\left(y_{i}-y_{i}^{*}\right)^{2}}{n} \tag{12}\]  
Figure 10 reveals that the MSE attained in elasticity modulus (E) amounts to 0.162, with a corresponding R\({}^{2}\) value of 0.835. However, an entirely different outcome emerged when employing the XGBoost model for the same inversion task. In this case, the MSE was substantially reduced to a value of merely 0.033, while the R\({}^{2}\) value increased significantly to 0.955. The same trends were observed in the inversion results for Poisson ratio (\(\upmu\)), cohesion (c), and internal friction angle (\(\upvarphi\)), as shown in Figure 10b-d. The MSE values for the XGBoost inversion model were found to be 4.19 \(\times\) 10\({}^{-5}\), 0.0027, and 0.796, while the corresponding R\({}^{2}\) values were 0.806, 0.735, and 0.821 respectively. In contrast, the PSO-XGBoost inversion model exhibited improved performance, with MSE values reduced to 9.67 \(\times\) 10\({}^{-6}\), 6.61 \(\times\) 10\({}^{-4}\), and 0.184. Meanwhile, the R\({}^{2}\) values significantly enhanced to 0.943, 0.934, and 0.948. Therefore, the results indicate that the PSO-XGBoost model demonstrates superior precision in the parameter inversion of the surrounding rock mass in RBSR tunnel operation.  
Figure 9: The crown displacement, B–C convergence, and D–E convergence data generated by the FEM calculation.
## 2 Materials and Methods
### Batch Adsorption Experiments  
Adsorption experiments were conducted with 25 mL Cr(VI) solution in 250 mL Erlenmeyer flasks. The effects of influencing factors on the adsorption performance were investigated separately, including modifier mass ratio (0.05-0.7), adsorbent dosage (1.0-10.0 g\(\cdot\)L\({}^{-1}\)), solution pH (2.0-10.0), initial concentration (2-50 mg\(\cdot\)L\({}^{-1}\)) and coexisting ionic strength. The initial pH was adjusted with HCl and NaOH solution. The effects of initial pH values on the Cr(VI) removal were assessed at a Cr(VI) concentration of 10 mg\(\cdot\)L\({}^{-1}\) and an adsorbent dosage of 8 g\(\cdot\)L\({}^{-1}\). The effects of initial Cr(VI) concentrations on the Cr(VI) removal were also performed at a pH of 6.0 and an adsorbent concentration of 8 g\(\cdot\)L\({}^{-1}\). Isotherm experiments were conducted at different initial Cr(VI) concentrations (5-70 mg\(\cdot\)L\({}^{-1}\)) for 24 h at 298 and 380 K, respectively. Kinetic experiments were performed at different time intervals (i.e., 15, 30, 60, 90, 120, 150, 180, 240, 300, 360, 480 and 720 min) at 25 \({}^{\circ}\)C by adding 20.0 mg FeBC to 25 mL of Cr(VI) solution with initial Cr(VI) concentrations of 10 and 20 mg\(\cdot\)L\({}^{-1}\), respectively.  
The flasks containing the adsorbent and the adsorbent mixture were placed in a digital thermostatic shaker and mixed at 150 r\(\cdot\)min\({}^{-1}\) for 2 h. Then, the mixtures were filtered with a 0.45 \(\upmu\)m sieve. In addition, Cr(VI) concentration in the filtrate was determined by ultraviolet-visible (UV-vis) spectrophotometer (2800A, Unico Instrument Co., Ltd., Shanghai, China).  
The Cr(VI) removal efficiency (\(\eta\)) and adsorption capacity (\(q\)) of BC and FeBC were calculated by  
\[\eta=\frac{C_{0}-C}{C_{0}}\times 100\% \tag{1}\]  
\[q=\frac{C_{0}-C}{W}\cdot\frac{V}{1000} \tag{2}\]  
where \(\eta\) is the Cr(VI) removal efficiency (%), \(C_{0}\) and \(C\) are the initial and residual Cr(VI) concentrations (mg\(\cdot\)L\({}^{-1}\)), respectively, \(q\) is the adsorption capacity (mg\(\cdot\)g\({}^{-1}\)), \(V\) is the solution volume (L) and \(W\) is the adsorbent mass (g).
## 7 Limitations of the Study
### Mesh Accuracy  
The dynamic test used a coarse mesh for the concrete box, which reduced the accuracy of the results as energy was transferred to from the load sphere to the bolt through the concrete. By incorporating a finer mesh, more accurate results can be produced. Figure 27 shows a concrete box that incorporates finer mesh and compares it to the mesh used in the study.  
Figure 27: Comparison of proposed concrete box mesh with current mesh.  
Dynamic testing with the parameters of a 28 mm bolt diameter, 922 MPa steel yield strength, 200 mm/s load cell velocity and 110 kg load cell mass was carried out to compare the results of two meshes. The load vs. displacement of both meshes are shown below in Figure 28. The finer concrete mesh produces a load vs. displacement graph with less fluctuations and a more definable ultimate stress of 379.6 kN. On the other hand, the coarser concrete mesh produces multiple fluctuations, which creates difficulties determining the ultimate stress of the bolt. However, although the finer mesh is more accurate, the computational time of the finer mesh increased by 1229%, which is not feasible to run many tests.
# Manufacturing Processes of Integral Blade Rotors for Turbomachinery, Processes and New Approaches
## 3 Summary and Conclusions  
The ongoing evolution of the aeronautical sector and, more specifically, the monolithic components or integral blade rotors (IBRs) of the aircraft engine implies the need for new or alternative manufacturing processes offering a more productive solution in terms of costs and time.  
The manufacturing and repairing processes of these complex components imply a high number of scientific-technological challenges described hereafter:  
* Complex design requirements and geometries: IBRs are classified as sculpted or free-form surfaces. For these geometries' generation, different methods as B-Splines or NURBS are used. Therefore, additionally to the complexity of these geometries manufacturing, in some cases the accessibility for the tool along with the tough dimensional requirements are a handicap for manufacturers.
* Difficult-to-cut materials: Present machining difficulties due to elevated cutting forces, high temperatures, limited cutting conditions and excessive tool wear.
* High-cost production processes: Inside the IBRs manufacturing chain, many stages are presented such as, roughing, intermediate semifinishing, finishing and abrasive final operations in order to achieve desired tolerances. These stages imply higher machining times and tool wear.  
This work presents a literature review describing different manufacturing processes, conventional and non-conventional processes such as Casting, EDM, SEDM, ECM/PECM, additive manufacturing, conventional 5-axis milling process and super abrasive machining, with special emphasis on the last two processes, milling and SAM, due to the fact that 5-axis conventional milling process is the most commonly used for these type of components. Finally, the evolution of mathematical algorithms for machining tool-paths optimization is discussed.  
Conceptualization, H.G.-B.; writing--original draft preparation, A.C.-O.; writing--review and editing, A.L.; L.N.L.d.L. paper structural coherence and integration of super alloys machining concepts. All authors have read and agree to the published version of the manuscript.  
This research received no external funding.  
We are grateful to Basque Excellence university Groups IT IT1337-19, and Ministry of economy project IBRELIABLE (DPI2016-74845-R), and Elkartek PROCODA KK 2019-004.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Rib Design for Improving the Local Stiffness of Gearbox Housing for Agricultural Electric Vehicles
## 4 Parameter Study of the Rib Design
### Parameter Study 2: Length versus Height  
Based on the results of parameter study 1, the width of the ribs was fixed at 4 mm and the results of the local stiffness evaluation were compared by varying the length and height (Table 7). In consideration of the shape of the housing, the lengths of the ribs were selected to be 10 mm, 15 mm, and 20 mm, and the heights of the ribs were selected to be 15 mm, 20 mm, 25 mm, 30 mm, and 35 mm. As the length and height of the ribs changed, they showed different mass distributions. In parameter study 2, the stiffness increase rate was divided by the mass of the rib, and this value was referred to as the efficiency (%/g); these data were then compared. Because the design of the ribs led to an increase in the material cost, the increase rate of stiffness against the rib mass was considered as an important indicator in manufacturing. In the results, the efficiency was the highest with 2.31 %/g when the rib length was 20 mm and the height was 25 mm. If the target stiffness is met when the actual housing is manufactured, the rib design can be considered by comparing the efficiency.