esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study: Structural Factor Analysis for Public Transportation Bus
### Result Analysis
#### 3.5.2 Effect Group Analysis  
In general, the failure-causing factors within the effect group are primarily receivers of influence, meaning their states are significantly affected by changes or failures in other components. By identifying these effect group factors, this study can comprehensively understand the fault propagation network. Out of the 30 failure-causing factors, 12 factors belong to the effect group. The components involved are the transmission computer (CF1), transmission (CF2), transmission speed sensor (CF4), water tank fan pump motor (CF5), high-pressure fuel pump (CF12), ABS/ASR sensor circuit (CF13), air brake cylinder (CF17), handbrake/rear transmission safety cable (CF19), parking handbrake pump (CF20), suspension airbags (CF23), power steering fluid inlet hard hose (CF24), and power steering pump (CF25).  
The transmission computer (CF1) is central within the effect group because of the relatively highest centrality degree. The transmission computer (CF1) also plays a dual role that is both influenced by and influences other components. The negative cause degree of \(-0.2315\) that underscores the transmission computer (CF1) is predominantly an effect rather than a cause, suggesting that transmission computer failures are often the result of upstream issues within the vehicle system. Meanwhile, the high influenced degree of \(1.5033\) further supports that the transmission computer (CF1) is notably susceptible to upstream failures. These characteristics of vulnerability, centrality, and dual influence necessitate the prioritization of CF1 in maintenance management. For the same reason, the transmission speed sensor (CF4), high-pressure fuel pump (CF12), transmission (CF2), and water tank fan pump motor (CF5) are ranked higher in centrality degree within the effect group, which require prioritization maintenance management decision-making.  
The ABS/ASR sensor circuit (CF13), parking handbrake pump (CF20), and power steering pump (CF25) typically exhibit relatively a higher centrality degree and a relatively lower cause degree within the effect group. This suggests that these three factors have a low impact on the overall vehicle system but are susceptible to influences from other factors. Therefore, the ABS/ASR sensor circuit (CF13), parking handbrake pump (CF20), and power steering pump (CF25) are unlikely to be significant failure-causing factors for the public transportation, indicating that maintenance resources should not be focused on them. Similarly, the air brake cylinder (CF17), handbrake/rear transmission safety cable (CF19), power steering fluid inlet hard hose (CF24), and suspension airbags (CF23) exhibit relatively lower (\(D-R\)) values and the lowest centrality degree values. This indicates that the air brake cylinder (CF17), handbrake/rear transmission safety cable (CF19), power steering fluid inlet hard hose (CF24), and suspension airbags (CF23) do not have a significant impact on the entire system. Consequently, maintenance resources should not be concentrated on these components either.
## 6 Experimental Validation
### Discussion  
In broad terms, the design and features of ReproRun are served more efficiently by SDR1. The following variations are present in the two SDR platforms due to their inherent hardware and firmware features:  
In SDR1, one of the cores of the ARM A9 (processor 0) hosts a bare-metal application linked with a partial bitstream and other necessary ReproRun functions that enable the run-time firmware reconfiguration, whereas the other core, Processor 1, hosts the Linux distribution of Analog Devices. The native version of the AMP framework was only applied in SDR1 since the architecture of the FPGA device in SDR2 does not favor its full use. For this reason, ReproRun was designed in a different way in the case of SDR2, since processor 0 was implemented using a MicroBlaze soft microprocessor IP core, while processor 1 was represented by a host processor. As a result, the REWIRE controller in SDR2 features a subset of the functionalities of its SDR1 counterpart.  
In SDR2, it was required to integrate the PR glue logic with the RFNoC firmware without compromising its native operation. The RFNoC FPGA images running at the SDR2 can be built to include up to ten DSP functions, which then can be interconnected based on a GRC project. The latter provides a hardware-in-the-loop environment. In SDR1 Analog Devices provides the necessary HDL reference design, a Linux distribution running at the PS, and several other tools for a user to be able to prototype its own hardware-software (HW-SW) system. It is important to highlight that the user code integration in SDR1 is simpler and less dependable compared to SDR2 which uses the RFNoC framework. The native functionality of the RFNoC HDL firmware of SDR2 was modified and extended to contemplate ReproRun, resulting in an elevated code integration effort. As a general conclusion, it has to be noted that the conceptual idea of the RFNoC framework is to be used in its native version and not modified to add PR features. On top of that the SDR2 features an important limitation for being able to apply run-time PR, as it is further detailed in Appendix A.
## 2 Nanodiamonds Synthesis Techniques
### Milling of High-Pressure, High-Temperature (HPHT) Microdiamonds  
The HPHT synthesis technique resembles the natural process of diamond formation, where a carbon precursor, usually graphite, is brought to a state of high pressure and high temperature. Inside a chamber, the temperature is brought to \(\sim\)2000 \({}^{\circ}\)C and a set of anvils increases the pressure up to several GPa [36]. This technique allows for the formation of bulk or microdiamonds, which must be milled to obtain NDs [21]. The milling process does not allow for good control of nanoparticle size and shape [37], so additional work-up is required. For instance, an acid treatment to remove the contaminants coming from the milling process or centrifugation and filtration to isolate NDs with a narrower size distribution.
## 3 Methods  
### Calculations of Drought Indices  
#### 3.1.1 The Precipitation Condition Index  
The PCI was calculated by comparing current precipitation to long-term maximum and minimum precipitation. The precipitation used to calculate the PCI was obtained from the CHIRPS dataset. The equation used to calculate the PCI is shown below.  
\[PCI_{i}=\frac{CHRIPS_{i,j}-CHRIPS_{i,j,min}}{CHRIPS_{i,j,max}-CHRIPS_{i,j,min}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Spatial Resolution** & \
\begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} & **Time Range** & **Sources** & **Product Name** \\ \hline Rainfall & 0.25 degree & Monthly &
\begin{tabular}{c} 1 January 2000-30 \\ December 2021 \\ 1 February 2000-30 \\ December 2021 \\ 2000-30 \\ December 2021 \\ 1 February 2000-30 \\ December 2021 \\  
In this context, i represents the pixel, and j represents the month. CHRIPSi,j,max denotes the maximum precipitation for the i-th pixel in the j-th month, while CHRIPSi,j,min denotes the minimum precipitation for the i-th pixel in the j-th month. This index ranges from 0 to 1, with values close to 0 (1) indicating dry (wet) periods.
## 3 Methodology
### Cross-Lingual Online Substitution  
Inspired by constructive learning, recent work generates code-switched samples with multilingual dictionaries or through word alignment models [26; 27], trying to close the representation gap between different languages. However, these data augmentation methods need other explicit cross-lingual dictionaries (which is hard to obtain for zero-shot language pairs) or additional models such as MUSE [41] (which is computationally expensive). Differently, we propose to bridge the representation gap among different languages through cross-lingual online substitution (CLOS) without external resources. Given a training sample \((\mathbf{x},\mathbf{y},L_{i},L_{j})\) and a sampled intermediate language \(L_{k}\), we attempt to generate code-switched synthetic sentences rather than sentences of which all words are from language \(L_{k}\). Concretely, we investigate two variants in our work:  
**Hard substitution.** We build vocabulary \(V_{k}\) for each language \(L_{k}\) and \(V\) as the joint vocabulary of all languages for training. At the decoding step \(t\) of online back-translation, the decoded word \(\mathbf{x}_{k}^{t}\) is randomly substituted for its synonym of another language with a probability of \(p\). We use cosine similarity of a word's encoder output to determine the most semantically similar word. As illustrated in Figure 1, the German word 1\"Katze\" and \"Tisch\" in the back-translated sentence are replaced by the French word \"chat\" and Spanish word \"mesa\", respectively. The new code-switched synthetic sentence is paired with the original Chinese sentence for training. Since the word representation depends on the context, the word with similar meanings across different languages can share a similar representation.  
**Soft substitution.** Different from the discrete nature of hard substitution, we randomly replace \(x_{k}^{t}\) with a soft word, which is a probabilistic distribution over the vocabulary, as shown in Figure 2. Soft substitution can capture a mixture of multiple candidate words with adequate variations. Suppose \(E\) is the embedding matrix of all \(V\) words, the embedding of soft word \(w\) is:  
\[e_{w}=\sum_{i=0}^{|V|-|V_{k}|}p_{i}(w)E_{i} \tag{7}\]  
where \(p_{i}(w)\) is the normalized cosine similarity of \(x_{k}^{t}\) with the \(i\)th word in the word set of \(|V|-|V_{k}|\), and \(\sum_{i=1}^{|V|-|V_{k}|}p_{i}(w)=1\).  
Figure 1: Cross-lingual online substitution by randomly replacing words with their synonyms from other languages. Hard substitution determines the synonyms by ranking the embedded similarities.  
Figure 2: Cross-lingual online substitution by randomly replacing words with their synonyms from other languages. Soft substitution replaces the embedding of one word by a weighted combination of multiple semantically similar words.
## 2 Materials and Methods
### System Model
#### 2.1.4 Problem Modeling  
The optimization objective of this paper is to minimize the system's delay and energy consumption. Define \(U_{n}(t)\) as the system utility of UAV \(n\) in time slot \(t\), as shown in Formula (22).  
\[U_{n}(t)=w_{1}E_{n}(t)+w_{2}T_{n}(t) \tag{22}\]  
Here, \(w_{1}\) and \(w_{2}\) are the weight factors indicating energy consumption and delay, both initialized as 1. If \(w_{1}\geq w_{2}\) it means more emphasis is placed on the energy-saving situation of the system; otherwise, it means the system is more sensitive to the task delay situation. The objective function of this paper is defined as shown in Formula (23).  
\[\underset{w_{n}(t),\gamma_{\text{m0}}^{n}(t),\sum_{\text{s}}^{n}U_{n}(t)}{ \sum_{\text{s}}^{n}U_{n}(t)}, \tag{23}\]  
\[\gamma_{\text{mk}}^{n}(t),P_{n}^{t}(t)\]  
\[0\leq\gamma_{mk}^{n}(t)\leq 1, \tag{24}\]  
\[0\leq\gamma_{m0}^{n}(t)\leq 1, \tag{25}\]\[\gamma^{n}_{m0}(t)+\sum_{k}\gamma^{n}_{mk}(t)=1,\forall n \tag{26}\] \[0\leq P^{t}_{n}(t)\leq P_{max} \tag{27}\]  
Here, the constraints (24-26) ensure that, in a multi-drone and multi-edge-cloud-assisted MEC system, tasks generated by mobile devices will be partially stored on drones after being collected, with the remaining tasks being sent to other edge cloud base stations. Constraint (27) ensures the maximum transmission power of drones. Limiting the maximum transmission power can effectively address the energy consumption of drones and extend flight time. Reasonable setting of transmission power can protect wireless spectrum resources, avoid spectrum waste and pollution, and promote effective management and utilization of spectrum resources. Equations (3)-(5) are also constraints for the objective function in this paper. Constraints (3)-(5) can restrict the flight height, horizontal, and vertical flight distances of drones. By limiting the flight range of drones, it helps to manage communication resources and energy consumption reasonably. By controlling the flight range, communication resources can be more effectively allocated and the flight time of drones can be extended, enhancing the efficiency and performance of the system. Furthermore, ensuring that drones fly within the appropriate range helps to avoid collisions with other aircraft or obstacles, thus enhancing flight safety.
# Wireless May Benefit Blockchain +
## 2 System Model
### Byzantine Faults and Wireless Connection  
Byzantine faults in safety-critical systems are real and occur with failure rates far more frequently than \(10^{-9}\) faults per operational hour [41]. A typical example of a Byzantine fault is a digital signal that is stuck at \"1/2\", e.g., a voltage that is anywhere between the voltages for a valid logical \"0\" and a valid logical \"1\". Such Byzantine faults usually propagate through traditional fault containment zones, thereby invalidating system architectural assumptions.  
The problem is that the wireless connection can increase the odds of Byzantine faults. Specifically, we identify \"clustered occurrence\" of faults attributed to interference among each other [35] as the key characteristic that the wireless connection adds to Byzantine faults, which yields that the nodes located in certain areas face a higher probability of disconnection. We attribute such clustered disconnections to interference among nodes. In fact, an ad hoc wireless network is more susceptible to interference [42] due to its decentralized network structure where no central mediator exists to coordinate scheduling among nodes.
# Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem
## 5 Computational Results
### Comparison between the Integrated PM Schedule and Non-PM Schedule
The reason for this result is that the delay in the initial scheduling makespan caused by the failure is less than the impact of PM activities. Figure 9c shows that the quartile graph of QRIR and its variation trend are the same as SRIR. We also can find that the QRIR of the schedule that inserts PM activities under the condition of high machine failure probability and large CM time is better. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{_tc,\(\theta\)_} & \multicolumn{2}{c}{\(SRIR\)(\%)} & \multicolumn{2}{c}{\(PIR\)(\%)} & \multicolumn{2}{c}{\(QRIR\)(\%)} \\ \cline{2-7}  & \(mean\) & \(std\) & \(mean\) & \(std\) & \(mean\) & \(std\) \\ \hline
20,0.5 & 32.28 & 8.07 & 0.68 & 0.71 & 40.48 & 8.91 \\
20,1.0 & \(-\)0.66 & 5.12 & \(-\)0.75 & 0.57 & 2.90 & 14.21 \\
20,1.5 & \(-\)2.54 & 3.18 & \(-\)0.35 & 0.29 & \(-\)25.23 & 18.92 \\
40,0.5 & 42.70 & 6.86 & 3.55 & 1.64 & 48.04 & 7.22 \\
40,1.0 & 17.68 & 6.55 & \(-\)0.03 & 0.51 & 22.98 & 10.62 \\
40,1.5 & \(-\)0.70 & 3.03 & \(-\)0.93 & 0.66 & \(-\)7.07 & 13.72 \\
60,0.5 & 47.24 & 6.11 & 6.28 & 2.53 & 51.87 & 7.72 \\
60,1.0 & 26.79 & 7.19 & 0.86 & 0.79 & 30.13 & 10.10 \\
60,1.5 & 9.67 & 4.72 & \(-\)0.53 & 0.82 & 12.78 & 10.80 \\
80,0.5 & 53.17 & 8.41 & 9.85 & 2.69 & 56.33 & 9.65 \\
80,1.0 & 34.95 & 8.88 & 2.07 & 1.10 & 39.13 & 11.11 \\
80,1.5 & 20.36 & 10.58 & 0.15 & 0.72 & 23.89 & 13.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Relative difference between \(\delta_{p}\) and \(\delta_{pm}\).
# A Survey on Deep Learning in 3D CAD Reconstruction
## 7 Datasets
### Fusion 360 Gallery Reconstruction Dataset  
[PERSON] et al. [9] proposed the Fusion 360 Gallery reconstruction dataset, which is the first artificial 3D CAD geometry dataset designed for machine learning. The dataset contains 8625 artificial design sequences (i.e., CAD programs), which are simple to operate and only involve sketching and extrusion modeling. Through basic CAD modeling operations and Boolean operations, various complex 3D designs can be created.  
In contrast, many existing 3D CAD datasets focus on providing mesh geometry representations [7; 116; 117]. However, the standard format for parametric CAD is boundary representation (B-Rep), which provides richer surface and curve analysis. The Fusion 360 Gallery reconstruction dataset uses the B-Rep format, which not only facilitates fine control of 3D shapes but also records a clear design construction history, providing valuable information for analysis and modeling.
## 4 Results and Discussions
### Comparison of NSGA and PSO Results  
The results obtained from NSGA-II have been cross-validated using another evolutionary algorithm, PSO, for multi-layered structures consisting of PP/GNP/epoxy within the X-band frequency range. This section details the findings, comparing results from different evolutionary algorithms.  
Specifically, the comparison involves a two-layered structure comprising 1.5% MWCNT/PP/epoxy (1.0520 mm thick) and 2.7% GNP/PP/epoxy (4.4569 mm thick), totaling 5.506 mm in thickness, evaluated within the X-band frequency range. Figure 11 illustrates the outcomes, highlighting how the NSGA-II and PSO algorithms optimize the structure's performance metrics under these conditions. This comparison aids in validating the effectiveness and reliability of the NSGA-II results through consistency with alternative optimization approaches, thereby enhancing confidence in the conclusions drawn regarding the composite's electromagnetic properties within the specified frequency range. Moreover, RL comparison with the literature is presented in Table 3.  
Figure 11: RL data comparison for 2 layers composed of GNP/PP and MWCNT/PP using NSGA—II and PSO in X-band.
## 3 Results and Discussion
### Effect of Magnetite Loading on Biogas Production and COD Removal
Therefore, the addition of 4 g to the 10 L UASB reactor results in the highest biogas production. This is in agreement with studies by [25] that show there is an optimum quantity for nanoparticles in stimulating biogas production. In addition to biogas production, the degradation of organic content in the form of chemical oxygen demand (COD) increases exponentially with the addition of magnetite. The trends in Figure 4 confirm that the highest COD removal of >90% is achieved in the digester dosed with 4 g of magnetite. The reduction in organic content corresponds to the generation of biogas with microorganisms degrading and converting it into biogas.
# Effectiveness of High-Intensity Therapy in Rehabilitation of Parkinson's Disease Patients
## 4 Results
### Baseline Characteristics  
A total of 100 individuals diagnosed with idiopathic Parkinson's disease (PD) were selected for this study based on predefined inclusion criteria. Participants were randomly allocated in a 1:1:1:1 ratio into four intervention groups: Exergame (female, n = 12; male, n = 13), Cycling (female, n = 11; male, n = 14), Agility (female, n = 12; male, n = 13), and Robot (Cmill) (female, n = 11; male, n = 14). The mean disease duration was comparable across all groups, averaging 7.2 years (\(\pm\)1.5). The physical characteristics of participants, including age, height, weight, Hoehn and Yahr stage, and MMSE scores, are summarized in Table 1, where detailed group distributions are also provided. Statistical analysis revealed no significant differences between the four groups regarding these variables (all \(p\) > 0.05), ensuring a balanced allocation. During the intervention period, Borg Rating of Perceived Exertion (RPE) values were recorded across all therapy sessions. The mean RPE scores for each group were as follows: Exergame (14.9 \(\pm\) 0.5), Cycling (14.6 \(\pm\) 0.4), Agility (14.7 \(\pm\) 0.6), and Robot (Cmill) (14.2 \(\pm\) 0.5). There were no statistically significant differences in perceived exertion levels between the four groups (\(p\) > 0.05), indicating a consistent training intensity across intervention types. When evaluating functional assessments, significant baseline differences were detected between groups in the 10-Meter Walk Test (F = 3.42, \(p\) = 0.047) and COP WEC Sway (F = 9.87, \(p\) < 0.001). No other initial outcome measures, in cluding the Berg Balance Scale, Dynamic Gait Index, or Functional Independence Measure, demonstrated statistically significant between-group differences before the intervention.
## 3 Results
### Spatial Variability
#### 3.2.2 MERRA  
The daily precipitation estimates by MERRA had the highest correlation and Willmott coefficients in CNR (r = 0.24 and d = 0.43). The highest and smallest errors in the daily precipitation estimates were obtained in GBC (RMSE = 16.11 mm day\({}^{-1}\)) and in CCR (RMSE = 11.07 mm day\({}^{-1}\)) (Figure 7A). The monthly precipitation had the highest correlation in CNR (r = 0.82) and the highest Willmott coefficients in MTP and GBC with both had d = 0.87. The smallest error in monthly precipitation estimates was obtained in PRR (RMSE = 71.60 mm month\({}^{-1}\)) (Figure 7B). The annual precipitation estimates did not show any significant correlation in DMT and SJRC, had the highest correlation coefficients in CBA (r = 0.78), and the highest error in MTP (RMSE = 355.65 mm year\({}^{-1}\)) (Figure 7C).  
Figure 6: Taylor diagrams showing the comparisons of (**A**) daily (mm day\({}^{-1}\)), (**B**) monthly (mm month\({}^{-1}\)), and (**C**) annual (mm year\({}^{-1}\)) measured and estimated precipitation, as well as (**D**) the performance diagram of the **GLDAS** product at each of the weather stations in the state of Mato Grosso, Brazil. In panels (**A**–**C**), the Pearson correlation coefficient (r) is represented by the azimuth angle (black outlines), the RMSE was represented by the red dashed outlines, and the standard deviation (SD) was indicated by the x and y axes, respectively. In panel (**D**), the probability of detection (POD) is represented on the x and y axes; the BIAS was indicated by the dashed black lines; and the critical success index (CSI) is represented by continuous black lines. For (**A**), the daily values for the entire study period were averaged, while for (**B**), the total monthly values for all months during the study period were averaged, and the annual values in (**C**) were the annual values averaged for all years during the study period.  
The daily precipitation estimates by the MERRA in MTP showed the highest performance in detecting precipitation events as indicated by the categorical indices with CSI = 0.42, FAR = 0, and BIAS = 0.42. The lowest performance was shown in CCR with CSI = 0.23, FAR = 0, and BIAS = 0.23. Apparently, MERRA had consistently estimated the occurrence of precipitation events as indicated with the high values of probability of occurrence (Figure 7D).