esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In the study, the durability and engineering performance of binary blended concrete with ground granulated blast-furnace slag (GGBS) were evaluated based on the CaO content and ratio, and the optimal CaO content range to secure durability was derived. The results of the study are summarized as follows.  
(1) Regression analysis on the CaO/SO\({}_{3}\) and SO\({}_{3}\)/Al\({}_{2}\)O\({}_{3}\) molar ratios in the triangular graph revealed that the molar ratios decreased when the GGBS replacement ratio increased. This indicated that it is necessary to consider long-age strength when the GGBS replacement ratio increased.  
(2) While the compressive strength exhibited a tendency to increase when the CaO content increased within 28 d, similar compressive strengths were observed at 56 d, irrespective of the CaO content. The rate of increase in compressive strength after 28 d decreased when the CaO content increased. Additionally, within 28 d, the compressive strength exhibited a tendency to increase when the basicity increased.  
(3) When the GGBS replacement ratio ranged from 0 to 50%, the specimens at eight weeks of age exhibited similar carbonation depths. However, when the replacement ratio exceeded 50%, the carbonation depth significantly increased. Conversely, the chloride penetration depth significantly decreased when the GGBS replacement ratio was less than 25%, although it remained at similar levels when the replacement ratio ranged from 25% to 70%.  
(4) The relative dynamic elastic modulus remained at similar levels when the GGBS replacement ratio ranged from 0 to 45% although it increased to 86.8%-94.5% when the replacement ratio exceeded 45%. Additionally, the weight reducing ratio reached or exceeded 99.9% when the GGBS replacement ratio exceeded 20%.  
(5) The optimal CaO content to satisfy all of the four parameters set in the study to secure durability performance (carbonation depth, chloride penetration depth, relative dynamic elastic modulus, and weight reducing ratio) was observed as between 53% and 56% (GGBS replacement ratio: 27.5%-47.1%).  
The durability and engineering performance evaluation data based on the CaO content and ratio and optimal CaO content ranged to secure durability (which were derived in the study) can provide guidelines on mixing proportions when high volume GGBS concrete is applied to local construction sites in the future. They are also expected to be used as basic data to set chemical composition criteria for the development of binders to improve durability.  
In the future, we will proceed with research on ternary blended concrete that has been replaced with high volume industrial by-products. For this, more detailed micro structural analysis is needed.  
Conceptualization, J.L. and T.L.; Investigation, J.L. and T.L.; Resources, J.L.; Writing--original-draft, J.L.; Writing--review and editing, J.L. and T.L. All authors have read and agreed to the published version of the manuscript.  
The study received no external funding.  
The authors declare no conflicts of interest.
## 2 Background
### Safety Assurance in HRCS  
As a critical aspect of manufacturing applications, the enforcement of safety controls has been built upon ISO 12100:2010 guidelines [76] that establish a structure of standards for identifying and reducing machine-associated risks. In [4], this structure was adapted to HRCS composition with the purpose of having a more balanced safety outlook for collaborative environments.  
Type-C standards possess the highest implementation priority and include norms related to robot's intuitive manipulation, behavior monitoring, and biomechanical limitation for safe interaction with human operators. Regarding robot physical integrity, Type-B standards comprise directions on the installation of safety guards, laser curtains, danger signals, and pressure-sensitive devices. Ultimately, human well-being assurance in HRCS is assisted by Type-A standards for proper oversight of mental workload, physical ergonomics, material handling, and risk assessment analysis. In the case of collaborative environments, ISO/TS 15066:2016 guidelines [77] are harnessed for the implementation of safety strategies such as  
* Safety-rated monitored stop (SMS): A robot is stopped from any movement if a human operator enters a pre-designated safety area of the workstation.
* Hand-guiding (HG): A robot is enabled to be manually controlled by a human operator without the need for complementary interfaces or devices.
* Speed and separation monitoring (SSM): A robot work area is divided into safety areas where speed and distance are adjusted based on a human operator's location.
* Power and force limiting (PFL): A robot works within certain levels of torque where injuries are not expected to be caused by a human operator in case of collision.
# Analyzing the Performance of the S3 Object Storage API for HPC Workloads
## 2 Materials and Methods
### Modifications of Benchmarks  
For IO500, an optimistic S3 interface backend using the libS3 client library is implemented for IOR in the sense that it stores each fragment as one independent object, and as such, it is expected to generate the best performance for many workloads.  
For identifying bottlenecks, it supports two modes:  
* Single bucket mode: created files and directories result in one empty dummy object (indicating that a file exists), every read/write access happens with exactly one object (file name contains the object name + size/offset tuple); deletion traverses the prefix and removes all the objects with the same prefix recursively.
* One bucket per file mode: for each file, a bucket is created. Every read/write access happens with exactly one object (object name contains the filename + size/offset tuple); deletion removes the bucket with all contained objects.  
As such, the libs3 implementation gives us the flexibility to test some optimistic performance numbers. The S3 interface does not support the \"find\" phase of IO500, which we, therefore, exclude from the results.  
MD-Workbench recognizes datasets and objects and also offers two modes:* One bucket, the D datasets are prefixed by the process rank.
* One bucket per dataset.  
In both modes, objects are atomically accessed fitting directly the S3 API.  
The libS3 used in IO500 is the latest one which only supports AWS signatures v4 [26] while the current release of MD-Workbench supports an older version of libs3, which uses the AWS signature v2. As such, it is ideal for the benchmarking of some S3 compatibles systems that only support the v2 signature, like the one found at DKRZ (The German Climate Computing Center).
# A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
## 3 Experimental Setting
### Parameters Setting of Different Algorithms  
In terms of hardware, a server with 128G RAM, 2048 GB hard disk space is selected. The type of GPU is NVIDIA Tesla K40c, and the type of CPU is Intel Xeon E5. For the experimental software, the Keras 2.4.3 tool based on Tensorflow version 2.4.0 under python is selected as the development conditions for the deep learning methods. The parameters of the seven algorithms are as follows:  
The parameters setting of EMD-BiGUR, EMD-GRU, BiGRU, and GRU are as follows:  
EMD-BiGUR: The raw acceleration data and decomposed subsequence decomposed by EMD are inputted into BiGRU algorithms. BiGRU used Tanh activation function, and  
Figure 9: Results of decomposition for A_1 acceleration data sets.  
the neurons number is 60. Then, the BiGRU layer linked two fully connected layers, whose neuron sizes are 512 and 256.  
EMD-GRU: The raw acceleration data and decomposed subsequence decomposed by EMD are inputted into GRU algorithms. The activation function of Tanh is used for the GRU layer with 60 neurons. The GRU layer linked two fully connected layers whose neuron sizes are 512 and 256.  
BiGRU: The BiGRU used Tanh's activation function, and the number of neurons in the hidden layer is 60. Then, the BiGRU layer linked two fully connected layers whose neuron sizes are 512 and 256.  
GRU: The GRU layer used Tanh's activation function, and the number of neurons in the hidden layer is 60. The GRU layer linked two fully connected layers whose neuron sizes are 512 and 256.  
The common parameter settings for the mentioned algorithms, such as EMD-BiGRU, EMD-GRU, BiGRU, GRU, are as follows: The initial learning rate is 0.01, and the optimizer is selected Adma. The algorithms adopt 300 epochs iteration. The mean square error (MSE) is used for the loss function, and the Mini-Batch is set to 128.  
The parameters setting for machine learning methods such as GBR, RFR, and SVR are set as follows:  
Gradient Boosting Regressor (GBR): the learning rate is 0.1, and the maximum depth is 6. The range of maximum number of \(n\_estimators\) is set to [10,100], which is optimized by using a grid search  
Random Forest Regressor (RFR): The range of maximum number of \(n\_estimators\) is set to [10,100], which is optimized by using a grid search  
Support vector regressor (SVR): a Gaussian RBF function is used as the SVR kernel function, and a grid search is used to determine the penalty parameters \(c\) and kernel parameters \(g\). The search range \(c\) and \(g\) are [\(10^{-4},10^{4}\)] and [\(2^{-4}\),\(2^{4}\)], respectively.  
Figure 10: Results of decomposition for A_2 acceleration data sets.  
Figure 11: Results of decomposition for A_3 acceleration data sets.  
Figure 12: Results of decomposition for A_4 acceleration data sets.
# Automatic Modulation Recognition Using Compressive Cyclic Features
## 3 CS-based AMR
### Feature Selection  
As a case study of CS-based AMR, we investigate the modulation pool consisting of 4 QAM, 32 QAM, 16 QAM, 16 PSK, 8 ASK. The theoretical values of constellation cumulants of candidate modulations, are given by Table 1. Accordingly, the feature vector is formatted as  
\[F=\big{[}|C_{x}^{\hat{\beta}_{0}}(\mathbf{0})_{(8,0)}|,\ |C_{x}^{\hat{\beta}_{0}}( \mathbf{0})_{(4,0)}|\big{]}, \tag{11}\]  
where \(\beta_{0}=1/\rho+(n-2q)T\Delta f/\rho\), corresponding to \(\alpha=1/\rho\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(C_{s,\mathbf{4,0}}\) & \(C_{s,\mathbf{6,0}}\) & \(C_{s,\mathbf{8,0}}\) \\ \hline
4 QAM & \(1\) & \(0\) & \(-34\) \\
32 QAM & \(-0.19\) & \(0\) & \(-1.9926\) \\
16 QAM & \(-0.68\) & \(0\) & \(-13.9808\) \\
8 ASK & \(-1.2381\) & \(7.1889\) & \(-92.018\) \\
16 PSK & \(0\) & \(0\) & \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cumulant values of candidate constellations.
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 4 Model Performance and Results Analysis
### Second Computational Approach Analysis  
The second computational approach improved upon the first by introducing refined parameter initialization based on empirical distributions, leading to more stable convergence. Additionally, it applied L2 regularization, effectively mitigating overfitting while ensuring numerical stability. However, it still did not incorporate cross-validation, limiting its ability to assess generalizability. The results showed that the 1 PL-IRT model once again performed exceptionally well, achieving AIC (1936.50) and BIC (2424.11) while maintaining a high AUC (0.8217). Notably, the DTA-IRT model closely followed the standard model in terms of AIC/BIC, suggesting that subtracting a scaled response time factor may provide slight improvements in estimation. The TP-IRT model also showed high performance with an AUC of 0.8220, which was nearly identical to the 1 PL-IRT model. The TWD-IRT model continued to underperform, showing high AIC (3598.07) and BIC (4085.68), with a substantial drop in AUC (0.400), confirming that multiplicative response time transformations introduce noise rather than valuable predictive information. Interestingly, the NRT-IRT and ART-IRT models yielded poor AUC values (0.339 and 0.429, respectively), indicating a failure to effectively differentiate between high- and low-ability students. These findings highlight that while some response-time-based modifications may enhance the estimation process, certain transformations, particularly those involving multiplication, can significantly degrade model performance.
## 4 Experiments
### Pan-Tilt Mixed Motion  
Holding the end effector, Pan and Tilt motion are mixed to create a cone-shaped trajectory based on the remote center. The motion of the trajectory as shown in Figure 10b is controlled within a maximum torque of 1.20 N\(\cdot\)m as shown in Figure 10a. As shown in Figure 10c, the remote center position error of up to 4.08 mm and an average of 1.80 \(\pm\) 1.03 mm is maintained for approximately 3 min of continuous operation. Orientation errors for mixed motion are within 0.70\({}^{\circ}\) maximum. The torque in the \(x\)-axis direction and the torque in the \(y\)-axis direction are evenly repeated with similar magnitudes for the cone-shaped rotational motion, providing a guarantee of isotropic operating sensitivity.  
Figure 8: Experimental results in pan motion: (**a**) measured torque; (**b**) trajectory of desired and actual orientation; (**c**) position error of remote center; (**d**) orientation error of motion.  
Figure 7: Experiments with a prototype of a surgical assistant robot.  
Several surgeons have surveyed the performance of this surgical assistant robot through dry testing and animal experiments and suggested that the accuracy of RCM is sufficient to be less than 5 mm for 12 mm-diameter trocars, but the operating sensitivity of direct training should be slightly improved.
## 5 Isovalent Dopants  
Tetravalent or isovalent atoms can be introduced in the Ge lattice mainly as substitutional atoms. Apart from C, which is mainly introduced during the processing (although at smaller concentration as compared to Si lattices) [1], isovalent atoms are considered in Ge as co-dopants that will be useful to constrain A-centers (or other oxygen-related defects) and reduce the diffusivities of \(n\)-type dopants (also refer to Section 3 above).  
Considering oxygen-related defects in Ge [108; 109; 110; 111; 112; 113] (refer to Section 7 for what follows), the main benefit for introducing isovalent dopants is to anchor or attract the lattice vacancies, which would otherwise bind with the oxygen atoms [39; 112]. In DFT calculations, it was calculated that the Sn\(V\) defect is more bound compared to the \(V\)O defect by 0.2 eV [112], and thus, if there is enough Sn in the lattice (i.e., more than the existing oxygen) most vacancies will be trapped by the Sn atoms limiting the concentration of the \(V\)O pairs. This is analogous to the defect engineering strategies employed using isovalent dopants to minimize the formation of the deleterious oxygen-vacancy defects in Si [86]. A difference between Si and Ge as host lattices is that in the latter, the concentration of oxygen is far lower [1]. Therefore, smaller oxygen-vacancy clusters are likely to form.  
For \(n\)-type dopants, the co-doping with isovalent atoms again aims to control the behavior of the \(n\)-type dopant by depriving it from vacancies. In that respect, previous studies have considered the co-doping with large isovalent dopants such as Sn and Hf which relax (and thus are bound) near lattice vacancies [54]. In particular, the DFT studies by [PERSON] et al. [54] showed that the Sn\(V\) and Hf\(V\) defects are strongly bound. The interaction with these large isovalent atoms does effectively increase the activation energy of diffusion of \(n\)-type dopants and thus succeeds in decelerating (and constraining) \(n\)-type dopants in the Ge lattice (refer to Figures 3 and 4) [54]. This effect is more significant for the larger isovalent dopant (i.e., Hf). As discussed in Section 3 above, the introduction of isovalent dopants fails to deal with the deactivation issues, and thus, other defects engineering strategies such as co-doping with F should be more efficient.  
Figure 3: Diffusion of the \(PV\) pair via the ring mechanism of diffusion in the presence of an oversized dopant atom. Reproduced by permission from the PCCP Owner Societies [54].
## 3 Factors Governing Survival and Occurrence of Opportunistic Pathogens in Premise Plumbing Systems
### Disinfection  
Secondary disinfection (chlorination) performed by utilities can assist in the control of many opportunistic premise plumbing pathogens, as well as traditional pathogens. Chloramine in particular is valued for its potential to reduce the risk of Legionnaires' disease and the occurrence of _P. aeruginosa_ at a community level [37; 157]. Despite this, the absence of disinfect residuals and OPPP regrowth observed towards the end of main distribution systems is reason to question the suitability of this strategy [36; 44].  
In-building and on-site chemical disinfection is recommended for facilities frequented by at-risk populations, such as hospitals and aged care facilities [153]. Chlorine, chloramine, and chlorine dioxide are commonly applied chemical disinfectants in such systems. Indeed, many facility owners and operators choose to install supplemental disinfection treatment systems on the basis of disease prevention for economic or insurance reasons. More often, the decision to employ additional treatment is motivated by an outbreak of disease or by the detection of OPPPs in building water samples. Particularly for small facilities, installation, operation, and maintenance can be too costly and complicated for facility owners and operators.  
As mentioned in Section 2, the application of chlorine, and possibly chloramine can be detrimental to premise plumbing infrastructure, with potential to cause serious pinhole leaks in copper and stress corrosion failure of stainless steel [57]. An investigation of continuous chlorination revealed an increase in the incidence of pinhole leaks by up to 30 times [158].  
The physical and chemical characteristics of the water entering the premise system will also influence the effectiveness of disinfection technologies to varying degrees [159; 15]. For example, increased water temperatures have little impact on the effectiveness of copper or silver ions relative to chlorine and chlorine dioxide disinfectant residuals, which are lessened as reactions with organic materials and/or pipe surfaces proceed quicker and further. The acidity or alkalinity of finished water can also determine the effectiveness of chlorine, monochloramine, and copper ions to a greater extent than that of chlorine dioxide and silver ions. Many other physical and chemical parameters can also impact the performance of specific disinfectants for the control of opportunistic pathogens.  
Another major weakness in this method results from the disturbance of biofilm populations and subsequent release of opportunistic pathogens. Furthermore, not all disinfectants are equally effective against bacteria within biofilms [44; 160; 161]. In addition to the protection provided by biofilms, the prolonged exposure to chlorine may select for chlorine or even drug resistance of certain OPPPs, including _Mycobacterium Avium_[162].
## 4 Experimental Results
### Compound Fault Diagnosis in Noisy Conditions  
The proposed method is also evaluated under noisy conditions. The AE signals (Dataset 1 and Dataset 2) are added with three white-noise levels (signal-to-noise ratio (SNR) = 0, 5, and 10 dB), where SNR is defined as the ratio of the power of a signal (meaningful input) to that of the background noise.  
\[\text{SNR}_{dB}=10\log_{10}\left(\frac{P_{signal}}{P_{noise}}\right) \tag{5}\]  
The experimental results in noisy conditions are shown in Table 4, which indicate that in lower SNR values, the accuracy of the proposed method becomes relatively worse, but still provides high accuracy. When the SNR values are positive, the average accuracy is more than 89.85% and 92.11% for Dataset 1 and Dataset 2, respectively.
# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
## 1 Introduction
This design also enhances the overall robustness against missing or noisy data in the first stage. Even if the C-SDFormer's real-time SOC estimation is adversely affected by such uncertainties, the voltage drop in the MSD battery still provides a clear hardware-based signal to indicate a low battery state. As a result, the system can reliably detect when the battery is nearing depletion, regardless of model inaccuracies or data gaps. This battery was specifically designed to guarantee the reliable operation of IoT devices, particularly where batteries are not actively monitored in real time during the first stage. By issuing a low battery alert, the MSD battery mitigates the risk of unexpected device shutdowns. Hence, even under noise or missing data conditions, the second stage of the MSD battery architecture ensures continued device reliability and uninterrupted operation in remote metering applications. To evaluate the effectiveness of the proposed C-SDFormer model, we collected data from smart remote meters deployed in households. The model was subsequently trained on this dataset and its performance was validated through a series of experimental analyses. These experiments were designed to demonstrate the model's capability in accurately predicting SOC and improving battery life management for IoT devices in remote metering. The C-SDFormer model utilizes temperature, voltage, and number of communications as input features for SOC prediction. Furthermore, the efficiency of the C-SDFormer architecture was verified through ablation studies, where the contributions of individual layers to its performance were systematically analyzed. The model also exhibited superior accuracy and robustness compared to existing methods in SOC prediction. Furthermore, the low-battery alert enabled by the MSD primary battery enhanced the robustness of IoT devices in remote metering and ensured their continuous operation. The rest of this paper is organized as follows. Section 2 introduces the related works on SOC estimation based on deep learning. Section 3 explains the details of the remote metering life prediction method. Section 4 describes the experimental setup. Section 5 demonstrates the SOC estimation and prediction performance in comparison with existing methods and provides a detailed discussion of the experimental results. Finally, Section 6 concludes this paper.