esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Study on Longitudinal Joints in Quasi-Rectangular Shield Tunnels
## 2 Test Setup
### Testing Procedure  
Positive bending moments were respectively exerted on Type-A and Type-BPos specimens, as shown in Figure 7a. The bending moment \(M\) and axial force \(N\) at the joint can be determined by the vertical loads \(P\) and horizontal loads \(F\) through  
\[\left\{\begin{array}{c}M=P\text{-}l_{2}+G\text{-}l_{1}\\ N=F\end{array}\right. \tag{1}\]  
and the bending moment diagram is shown in Figure 8a. Herein, \(P_{c}\) is the reaction force at the vertical supports, equal to the sum of the vertical load \(P\) and the dead load \(G\). \(l_{1}\), \(l_{2}\), and \(l_{3}\) represent the distances from a support to the center of gravity of each segment, to the vertical load \(P\), and to the joint section, respectively.  
The width of the longitudinal joints in this research was 1.2 m, but this width can vary among different shield tunnels. In order to make the results independent of the segment width, the axial force and bending moment values are presented after normalization to 1 m width. As such, the units of axial force and bending moments become kN/m and kN\(\cdot\)m/m. For the positive cases (type-A and Type-BPos), in the first testing stage, loads \(P\) and \(F\) were  
Figure 8: Test loads: (**a**) bending moment diagram; (**b**) exerted axial forces and bending moments at the joint section.  
Figure 6: Cross-sectional view of the DJJPs (unit: mm): (**a**) Type-BPos; (**b**) Type-A; (**c**) Type-BNeg.  
Figure 7: Exerted loads: (**a**) positive case; (**b**) negative case.  
increased simultaneously in order to achieve a constant eccentricity of 0.21 m up to an axial load \(F=1250\) kN/m (\(M=262.5\) kNm/m). Then, in the second testing stage, only the vertical load \(P\) was increased to investigate the effect of the bending moment while the axial load \(F\) remained constant. A similar loading procedure was used for the negative cases (Type-A and Type-BNeg), as shown in Figure 8b. A constant eccentricity of 0.19 m was achieved there, up to an axial load \(F=1625\) kN/m (\(M=308.8\) kNm/m), after which only the vertical load \(P\) was increased. For this case, the exerted axial forces and bending moments at the joint section are shown in Figure 8b.  
During the tests, joint openings, joint deflections, bolt strains, and strains of the connecting reinforcements for the DJIPs and concrete were monitored. Strain gauges were pre-installed on the connecting reinforcements before the DJJP installation. In order to investigate the connection between the DJIPs and the concrete, the gauges were arranged to measure reinforcement strains in one transverse section, as well as along the most stressed reinforcement. The positions and numbers of the measurement points are shown in Figure 9 and Table 1.  
Figure 9: Measurement points: (**a**) joint opening, joint deflection, and bolt strains; (**b**) details of a bolt strain measurement point (unit: mm); (**c**) strain measurement points on the connecting reinforcements (unit: mm).
## 5 Security Analysis of Strike
### Cryptanalysis Attacks  
#### 5.3.1 Brute-Force Attacks  
Brute-force techniques aim to guess all possible choices in a given search space. The search space of our Strike cipher is enormous. Strike cipher uses a 128-bit _SKey_ and 32-bit _IVec_ to generate one _Kstream_ in each iteration. This requires the attacker to make \(2^{128}+2^{32}\) guesses to reveal both the _SKey_ and _IVec_, which is practically impossible with currently available resources. Hence, the Strike cipher is found to be secure against brute-force attacks.
## 3 S-N Curves and P-S-N Curves  
The S-N curves with the different average surface roughness are shown in Figure 5. The input data points of the curve fitting were obtained by taking the logarithm of the means of the fatigue life data and stress amplitudes. The material constants were estimated by the least square method and are listed in Table 3. The material constants satisfy the logarithmic Basquin equation \(\lg N_{f}=\lg c-n\lg S\), where \(S\) is the stress amplitude. As shown in Figure 5, (1) the mean of the fatigue life with \(R_{\text{a}}=0.4\)\(\upmu\)m is very close to that with \(R_{\text{a}}=0.8\)\(\upmu\)m or \(R_{\text{a}}=1.6\)\(\upmu\)m when the stress amplitude is very high; and (2) the mean of the fatigue life with \(R_{\text{a}}=0.8\)\(\upmu\)m is very close to that with \(R_{\text{a}}=1.6\)\(\upmu\)m, while the mean of the fatigue life with \(R_{\text{a}}=0.4\)\(\upmu\)m is significantly different from that with \(R_{\text{a}}=0.8\)\(\upmu\)m or \(R_{\text{a}}=1.6\)\(\upmu\)m when the stress amplitude is low. This phenomenon can be explained as surface damage due to machining not being the key factor of fatigue crack initiation and development of medium-carbon steel at higher-stress amplitudes than the endurance limit. It might be concluded that the mean of the fatigue life of the medium-carbon steel cannot be observably decreased when the average surface roughness is increased from \(R_{\text{a}}=0.8\)\(\upmu\)m to \(R_{\text{a}}=1.6\)\(\upmu\)m or even high at the low-stress amplitude.  
The P-S-N curves with \(R_{\text{a}}=0.4\)\(\upmu\)m, \(R_{\text{a}}=0.8\)\(\upmu\)m, and \(R_{\text{a}}=1.6\)\(\upmu\)m are shown in Figures 6-8, respectively. \(R\) is the survival probability. The data point (pink inverted triangle, blue triangle, red dot and black square) is the confidence lower limit which is calculated by [13]:  
\[x_{RL}=\overline{x}+h\sigma_{x}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Average Surface Roughness (\(\upmu\)m)** & \(n\) & \(c\) \\ \hline \(R_{\text{a}}=0.4\) & 9.84 & \(4.56\times 10^{30}\) \\ \(R_{\text{a}}=0.8\) & 9.44 & \(3.16\times 10^{29}\) \\ \(R_{\text{a}}=1.6\) & 9.80 & \(2.32\times 10^{30}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material constants with the different average surface roughness.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 8 Experimental Evaluation
### Implications for Real-World Implementation  
Discussion on how experimental results translate to practical applications: While our experimental results are promising, real-world implementation will likely see reduced performance due to network constraints. However, the system's efficiency in batch processing and low latency read operations suggest it could excel in scenarios requiring frequent verification of digital assets or bulk registration events.  
Considerations for deployment on the public Ethereum network:  
* Gas price volatility: Implementing dynamic gas price strategies to ensure transaction success during network congestion.  
* Block size limitations: Optimizing transaction size and frequency to work within Ethereum's block constraints.
* Network upgrades: Staying abreast of Ethereum's roadmap (e.g., Ethereum 2.0) and adapting the system to leverage new features and improvements. Economic analysis based on gas consumption and current Ethereum prices: Assuming an average gas price of 50 Gwei and an ETH price of $2000:
* Single registration cost: 126,276 \(\times\) 50 \(\times\) 1 \(\times\) 10\({}^{-9}\)\(\times\) 2000 = $12.63;
* Batch registration cost (per item): (550,000/5) \(\times\) 50 \(\times\) 1 \(\times\) 10\({}^{-9}\)\(\times\) 2000 = $11.00. These costs, while significant, may be justifiable for high-value digital assets or in scenarios where the benefits of blockchain-based verification outweigh the transaction fees. Potential challenges and mitigation strategies for large-scale adoption are as follows:
* High transaction costs: Implement layer-2 scaling solutions or explore alternative blockchain platforms with lower fees;
* User onboarding: Develop user-friendly interfaces and educational resources to facilitate adoption by non-blockchain users;
* Regulatory compliance: Engage with regulatory bodies to ensure the system meets legal requirements across different jurisdictions;
* Interoperability: Develop standards and APIs to facilitate integration with existing digital asset management systems.
## 5 Discussion
### Comparing Scenarios
occurrence distributions by making fewer candidates (in this case, 5 candidates compared to 10) cover more occurrences because cluster centroids are situated to be more distant from the main concentrations and therefore can cover outliers that are not covered by more candidate clusters. Choosing between great coverage and closeness to concentrations of crime is an interesting trade-off. If outlier regions are important to be covered, one possible solution to maintain the preferred number of clusters is prioritizing. A more realistic option is the inclusion of value judgments on the importance of neighborhoods based on socio-economic status or educational attainments (for instance, prioritizing more vulnerable regions for community-oriented policing) and/or the type of crime Weighting different crimes according to their degree of severity, or clear-up complexity, which according to [PERSON] et al. (2010), relates to the efficiency of allocated resources, is expected to impact the clustering process and integrate the coverage prospects without reducing the ability to allocate facilities closer to concentrated centers of criminality. Since this analysis is limited to  
Figure 10: Performance comparison (distance). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**N. of** & **Covered** & **Cov. \%** & **Non-Covered** & **Non-Cov. \%** & **Mean Distance** & **Std. Deviation** \\ \hline
5 & 1558 & 0.92 & 130 & 0.08 & 1570.58 & 989.13 \\ \hline
10 & 1521 & 0.90 & 167 & 0.10 & 1639.17 & 1045.29 \\ \hline
15 & 1523 & 0.90 & 165 & 0.10 & 1714.41 & 1038.34 \\ \hline
20 & 1568 & 0.93 & 120 & 0.07 & 1888.65 & 1026.29 \\ \hline
25 & 1584 & 0.94 & 104 & 0.06 & 1723.50 & 969.96 \\ \hline
30 & 1584 & 0.94 & 104 & 0.06 & 1723.50 & 969.96 \\ \hline
35 & 1602 & 0.95 & 86 & 0.05 & 1844.77 & 958.51 \\ \hline
40 & 1584 & 0.94 & 104 & 0.06 & 1718.15 & 943.99 \\ \hline
45 & 1597 & 0.95 & 91 & 0.05 & 1715.32 & 930.77 \\ \hline
50 & 1602 & 0.95 & 86 & 0.05 & 1819.48 & 964.39 \\ \hline
100 & 1608 & 0.95 & 80 & 0.05 & 1803.64 & 917.07 \\ \hline
1200 & 1625 & 0.96 & 63 & 0.04 & 1777.95 & 910.55 \\ \hline
1500 & 1625 & 0.96 & 63 & 0.04 & 1.775.22 & 911.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensitivity analysis of the number of candidates. the coordinates, we aim to expand these solutions to future assessments while considering the expertise, preference elicitations, and value judgments of public administrations. The proposed MIS manages to be ahead of the current MP configuration in all scenarios evaluated. The performance of the locations of the police facilities is visible and enables better and more efficient performance by covering more events and reducing the distance that police vehicles must travel to reach each crime location.
# A Lunar Backup Record of Humanity
## 2 Challenges
### Storage Capacity  
The system must have sufficient capacity to store the necessary existing and future data. Let \(C_{i}\) be a category of information stored (e.g., books, movies, journal articles, etc.) where \(1<i<n\), let \(q_{i}\) be the existing quantity of bytes of data associated with category \(i\), and let \(r_{i}\) be the growth rate of the total quantity of data (i.e., data rate/stored data) for category \(i\). Some categories of data demonstrate exponential growth, which is enabled by the accelerating growth in data creation and collection in the era of big data [23]. However, some categories may be better fit by a linear growth model because rates of knowledge creation are constrained. Hence, we let \(a_{i}\) be an indicator variable where \(a_{i}=1\) if the data for category \(i\) fit an exponential growth model and \(a_{i}=0\) if the data fit a linear growth model. We assume that the system must have sufficient capacity to store all initial and future data until its capacity is increased. Then, the necessary storage capacity of the system in bytes, \(S\), is,  
\[S=\sum_{i=1}^{n}q_{i}(a_{i}e^{r_{i}d}+(1-a_{i})(r_{i}d+1))\, \tag{1}\]  
where \(d\) is the duration of time, in years, before the system is replaced or repaired.  
Historically, space-based data systems (e.g., satellites) have served as data relays rather than large data storage centers [24]. Because of the adverse effects of the harsh conditions in outer space on equipment (e.g., radiation and extreme temperatures), shielding systems and cooling equipment would need to be developed before large-scale data storage would be feasible. Such systems are already being considered by private organizations, including Cloud Constellation and its satellite-based cloud infrastructure design known as Space-Belt [24]. The establishment of permanent operations on celestial bodies, such as the Artemis Base Camp, will also increase the demand for space-based storage solutions and accelerate the technological development.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Category** & **Quantity** & **Stored Data (Bytes) a** & **Data Rate (Bytes/yr) b** \\ \hline Books & \(3\times 10^{7}\) & \(6\times 10^{13}\) & \(6.7\times 10^{12}\) \\ Journal Articles & \(8.45\times 10^{7}\) & \(8.45\times 10^{13}\) & \(4\times 10^{12}\) \\ Films & \(5.4\times 10^{5}\) & \(1.08\times 10^{15}\) & \(3\times 10^{13}\) \\ Genetic Information c & N/ A & \(8.7\times 10^{15}\) & N/ A \\ Earth Imagery d  & N/ A & \(7.5\times 10^{14}\) & \(9\times 10^{15}\) \\ \hline \end{tabular}
\end{table}
Table 1: Initial stored data and data rate based on current information stored in existing databases.
# Functionally Graded AISI 316L and AISI H13 Manufactured by L-DED for Die and Mould Applications
## 4 Discussion  
In the present work, FGM samples constituted by AISI H13 hot-work tool steel and AISI 316L low carbon stainless steel have been produced by means of L-DED. Starting from 100% AISI 316L, a graded structure has been built until 100% AISI H13 composition has been reached, with 20% composition intervals. The objective of the study was to perform a microstructural analysis and to understand how the mechanical properties of the FGM evolved along the gradient. For that aim, a metallographic analysis and microhardness measurements in different areas of the FGM have been carried out. The following conclusions can be drawn from the obtained results:  
* Regarding metallurgical integrity of the added material, severe cracking and several lack-of-fusion defects have been observed. The first was concluded to be due to the brittle sigma phase formation. This issue has been also reported in the literature and it is associated with the successive heating and cooling cycles inherent to the L-DED process, which tend to promote sigma phase growth.
* The corrosion resistance of the graded structure did not follow a linear behaviour. The initial 100% AISI 316L and the intermediate layers with mixed composition showed similar corrosion resistance, while the upper layers composed of AISI H13 were strongly affected by the etching process. This was due to the nonlinearity of corrosion resistance according to chromium content range present in this work.
* As far as the microstructure of the FGM is concerned, it depended on the composition of the deposited materials and, consequently, a variation along the sample has been observed. Lower layers composed of AISI 316L and low AISI H13 contents, showed an austenite + ferrite microstructure; while intermediate layers with higher AISI H13 contents had an austenitic microstructure; and, finally, AISI H13 layers presented a martensite + austenite microstructure.
* The aforementioned microstructure variation strongly conditioned the hardness of the graded sample, and consequently, a nonlinear hardness profile has been measured. In fact, the hardness abruptly increased when reaching the upper three layers composed of AISI H13, while lower values of hardness were present in the rest of the FGM.
# New Step Size Control Algorithm for Semi-Implicit Composition ODE Solvers
## 2 Materials and Methods
### Truncation Error Estimators for Composition ODE Solvers  
In order to evaluate a new error estimation scheme, we compare it with several known adaptive step size algorithms. The first one estimates local error using the difference between two numerical solutions obtained by the same composition of different basic integration methods, which we denote as OCDM (_One Composition for Different Methods_). Figure 1 depicts the general scheme of the OCDM error estimator for an arbitrary composition solver.  
In this study, we will use two different _commutations_ of the basic CD method. The term \"commutation\" means the order in which state variables are calculated. This way provides two different solutions as if we used different basic methods of the same accuracy order.  
\[\Phi(\mathbf{x}_{n},h):\begin{cases}x_{n+0.5}=x_{n}+\frac{h}{2}f(x_{n},y_{n}, t_{n})\\ y_{n+0.5}=y_{n}+\frac{h}{2}g(x_{n+0.5},y_{n},t_{n})\\ y_{n+1}=y_{n+0.5}+\frac{h}{2}g(x_{n+0.5},y_{n+1},t_{n+1})\end{cases},\]  
Figure 1: Scheme of OCDM algorithm. \(\mathbf{x}_{n+1}\) is the main solution and \(\widetilde{\mathbf{x}}_{n+1}\) is the additional solution introduced for error estimation.  
The second considered method of error estimation uses two solutions found using the same basic method in different composition solvers, which we call DCOM (_Different Compositions for One Method_). The DCOM scheme is shown in Figure 2.
# Methods of Analyzing Microsized Plastics in the Environment
## 4 Thermal Analyses
### Thermogravimetric Analysis  
Another thermal analysis method is thermogravimetric analysis (TGA). This method confirms the qualitative and quantitative information of the sample by observing the dependence on time and temperature by measuring the weight loss of the sample while it is heated at a specific rate under preset atmospheric and temperature conditions [65]. Previous studies combined TGA and DSC for the analysis of microplastics in wastewater. However, only PE and polypropylene (PP) were clearly identifiable in these studies. Polyvinyl chloride (PVC), polyamide (PA), and polyester (PES) were not identified. When overlapping phase transition signals occur, such as those of polyethylene terephthalate (PET) and polyurethane (PU), it is difficult to distinguish them [66; 67].
###### Abstract  
Background: The effects of low-intensity resistance training on health and muscular performance have been widely reported, but its effects on motor skills such as balance and force matching have been overlooked. Hence, the purpose of this study was to determine the effects of low-intensity resistance training on a force-matching task and balance. Methods: The subjects from the intervention group (EXP; \(n=20\)) participated in a 12-week low-intensity resistance training program. The measurements of balance and force-matching ability were conducted before and after the intervention. To determine the accuracy and steadiness (variability) in the force matching task, we calculated the values of three errors: (1) absolute error (AE), (2) constant error (CE), and (3) variable error (VE). Results: In the force-matching task performed after the training, the values of two errors decreased: (1) AE (right leg, \(p=0.0008\); left leg, \(p=0.0008\)), and (2) CE (right leg, \(p=0.0064\); left leg, \(p=0.0440\)). Resistance training did not significantly affect VE and the parameters characterizing COP sway in the balance test. Conclusions: The 12-week low-intensity resistance training improved the accuracy of the force-matching task but did not change postural stability or postural strategies.  
force control; force errors; postural stability; postural strategy; proprioception; strength training +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Comparison of Ablation Experiments  
Ablation experiments were conducted to investigate the effectiveness of each module in the SL-CNN model. Five detailed evaluation metrics were selected, that is, overall accuracy (OA), average accuracy (AA), kappa, test loss, and mean F1 scores. The experimental results are listed in Table 4. As shown in the table, the SL-CNN model performed well in all aspects compared to a single module, particularly in the OA and Kappa terms, with improvements of 0.216% and 0.349, respectively, compared to the single RBC-F module. There were improvements of 0.359% and 0.581, respectively, compared to the single CBAM-F module. For the AA, test loss, and F1 score, the RBC-F and CBAM-F modules performed similarly when used alone in the model proposed in this study. However, both were inferior to the SL-CNN model with a combination of the two modules. Based on these results, the addition of both modules improved the classification performance of the model.
# Neural Models for Measuring Confidence on Interactive Machine Translation Systems
## 5 Experimental Setup
### Model Architecture  
We used the open-source toolkit NMT-Keras [43] to build the neural models for the MT system and the CM models. We tested the experiments with the recurrent neural network (RNN) architecture. All the systems used Adam [44] as the learning algorithm, with a learning rate of 0.0002. We clipped the \(L_{2}\) norm of the gradient to 5. The batch size was set to 50 and the beam size to 6.  
The RNN models used an encoder-decoder architecture with an attention model [45] and LSTM cells [46]. The dimension of the encoder, decoder, attention model, and word embeddings was set to 512. We used a single hidden layer of the encoder and the decoder.  
The MT and CM models have the same size and architecture. The main difference between both models lies in the output layer. While the MT model has the usual softmax layer that gives us the probability of each word of the vocabulary, the CM model has a relu layer that provides us with the confidence estimation of the last word sent by input.
## 3 Results
### Stripline Incorporating the Proposed Design Methodology
#### 3.2.4 Process Tolerance of the Distance Between the Air Cavities and the Metal Line  
Taking into account the feasibility of fabrication, it is known that the LTCC process allows for the fabrication of cavities within multilayer structures [31]. Considering factors such as alignment accuracy, material shrinkage rate, and thermal stress in the process, a minimum distance requirement between cavities and metal lines is necessary to prevent short circuits or open circuits. Increasing the distance between cavities and metal lines can enhance the reliability of the circuit. To evaluate the impact of this distance on the performance of the transmission line, \(w_{\text{a}}\) is increased from 0.48 mm to 1.28 mm, equivalent to the distance increasing from 23.5 \(\upmu\)m to 423.5 \(\upmu\)m. This variation far exceeds the typical processing tolerance of LTCC technology (\(\pm\)15 \(\upmu\)m). Keeping all the other parameters constant, the results are shown in Figure 11. In Figure 11a, when \(w_{\text{a}}\) increases from 0.48 mm to 1.28 mm, a 5.14% deterioration in the attenuation constant is observed. This is due to the electromagnetic field distribution, which is concentrated near the metal line. As the distance between the air cavities and the metal line increases, the loss reduction becomes  
Figure 10: Comparison of different air cavity densities: (**a**) attenuation constant; (**b**) return loss.  
less pronounced. In contrast, Figure 11b shows a 2 dB decrease in S11 as \(w_{\text{a}}\) changes from 0.48 mm to 1.28 mm, indicating only a slight variation. This is attributed to the fact that as the air cavities are moved farther from the metal line, the impedance mismatch caused by the cavities is reduced. Overall, the comparison suggests that the proposed design is not highly sensitive to changes in \(w_{\text{a}}\), as significant variations in \(w_{\text{a}}\) result in only minor performance changes. Consequently, the design offers wide process tolerance and can be implemented using various manufacturing processes.