esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application to Cultural Heritage Science
### Meteorites  
Muonic X-ray spectroscopy can be a very useful tool for the study of extraterrestrial samples, especially for the analysis of light elements with a non-destructive approach. In [PERSON] et al. [39], a study was carried out on meteorite fragments from carbonaceous chondrites, called Murchinson and Allende. From the results, the former contains more extraterrestrial organic material than the latter, as shown in Table 3.  
The Murchinson meteorite is characterized by a significant signal from carbon, showing that it contains a large amount of organic material, as well as magnesium, carbon, silicon, iron, calcium, and sulphur. On the other hand, only magnesium, silicon, iron, potassium, and calcium were found in the Allende meteorite. In addition to these elements, aluminium has also been observed, but this is reportedly from the sample holder. Besides the multi-component analysis, the study shows another interesting application;  
Figure 11: Depth profile of copper, tin and lead concentration (wt.%) of the neck of the boat in boat n.6779. The tin concentration is similar in all the measured areas, while lead and copper vary more significantly.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element and** & **Energy (keV)** & \
\begin{tabular}{c} **Murchison** \\ **(Counts)** \\ \end{tabular} &
\begin{tabular}{c} **Allende** \\ **(Counts)** \\ \end{tabular} \\ \hline Calcium Ca–(Ma) & 55 & n.d. & 53 \(\pm\) 23 \\ \hline Magnesium Mg–(L\(\alpha\)) & 56 & 896 \(\pm\) 66 & 183 \(\pm\) 23 \\ \hline Aluminum Al–(L\(\alpha\)) & 66 & 10,796 \(\pm\) 130 & 136 \(\pm\) 30 \\ \hline Carbon C–(K\(\alpha\)) & 75 & 626 \(\pm\) 52 & 6 \(\pm\) 27 \\ \hline Silicon Si–(L\(\alpha\)) & 76 & 824 \(\pm\) 58 & 175 \(\pm\) 32 \\ \hline Iron Fe–(M\(\alpha\)) & 94 & 1319 \(\pm\) 63 & 265 \(\pm\) 39 \\ \hline Oxygen O–(K\(\alpha\)) & 133 & 4785 \(\pm\) 111 & 800 \(\pm\) 38 \\ \hline Potassium K–(L\(\alpha\)) & 140 & n.d. & 94 \(\pm\) 27 \\ \hline Calcium Ca–(L\(\alpha\)) & 156 & 213 \(\pm\) 41 & 83 \(\pm\) 28 \\ \hline Aluminum Al–(K\(\alpha\)) & 346 & 9542 \(\pm\) 100 & 359 \(\pm\) 27 \\ \hline Silicon S–(K\(\alpha\)) & 516 & 121 \(\pm\) 33 & 11 \(\pm\) 9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The muonic X-rays detected (along with the transitions) in the Murchison and Allende meteorites and the concentrations of each element. The data was taken and adapted from Ref. [39].  
given the possibility of depth profiling, one can measure extraterrestrial samples within their containing tubes, thus avoiding contamination from terrestrial materials. Another study on meteorite fragments is reported in [40]. Muon analysis can be a valid option in this particular field of research, to add further information but also to avoid some of the disadvantages related to other techniques, such as severe radioactivation of the sample, difficulty of bulk analysis and limited sensitivity.
# Wearables for Industrial Work Safety: A Survey
## 3 State-of-the-Art Techniques in the Field of Industrial Wearables
### Data Transmission  
Historically, wearable solutions that appeared in the medical domain were based on a wired communication architecture, where wearable devices transmit their collected data to external processing units via wired links [44; 118]. However, relying on wired connectivity restricts user mobility. This limitation was the main reason behind considering the wireless alternative for wearable communications. The migration from wired to wireless connectivity for data transmission is a trend in healthcare monitoring systems and industrial wearables in general. On top of the industrial wearable applications that are provided in Table 2, several examples were studied in the literature and utilized different wireless communication technologies [119; 120; 121]. These technologies can be classified based on various metrics, among which we choose the range. As a result, we provide in Table 5 a summary of the main short-range, mid-range, and long-range connectivity solutions currently employed in industrial wearable systems. Due to the battery lifetime consideration, most market-available wearable devices generally rely on smartphone-aided operations using short-range and mid-range communication technologies. In this architecture, the smartphone pre-processes the data sent by the wearable device and acts as a gateway to transmit the pre-processed data to the cloud (if
## 3 Results  
### PSDs and PRIDs  
The PSDs retrieved by the FC method for the UKCW dataset were found to broadly follow power law distributions, with the main difference between stations being the overall concentration of the particle population (Figure 3a). Two obvious outliers are present, corresponding to samples from the turbid waters of the Bristol Channel; close inspection reveals structures that may be closer in nature to models such as the double gamma distribution proposed by [PERSON] [31], and that indeed may be identified to a lesser degree in the other samples as well. Nonetheless, the power law approach remains a reasonable approximation for a large majority of the dataset, and was used for PSD extrapolations accordingly for all samples including the Bristol Channel ones.  
Figure 3: Collective view of **(a)** all 50 UKCW PSDs and **(b)** UKCW PRIDs produced by the FC method. Note that real refractive index values above 1.15 are not precise, but still indicate high refractive indices [1]. **(c)** Total, organic, inorganic, and fluorescent PSDs for a typical sample of the UKCW dataset and **(d)** power law extension of the total, organic and inorganic PSDs. Note that the extended organic and inorganic PSDs intersect the extended total PSD; therefore the sum of the extended organic and inorganic PSDs is not exactly equal to the extended total SPM. To evaluate the error thus introduced, SPM values are modelled both from the total PSD and by summing model ISM and OSM values. Panels **(a,b)** of the figure were adapted and modified with permission from Figure 2 of [PERSON] et al. [2].  
Particle refractive index distributions were found to be fairly homogeneous across all samples (Figure 3b), with distribution peaks found between 1.05-1.15 and within expectations for the real refractive index of the most common components of marine particle populations [9; 32]. By exploiting the particle composition information given by the PRIDs, the total PSD of each UKCW sample was further separated into an inorganic PSD, an organic PSD, and a fluorescent fraction, which is itself a sub-fraction of the organic PSD (Figure 3c). These were used to calculate ISM, OSM & POC, and ChLA respectively, while SPM was calculated both from the total PSD and as the sum of the model ISM and OSM. To account for the fraction of the particle population undetected by the FC method, the total organic and inorganic PSDs were extended between 0.05 and 2000 \(\upmu\)m as suggested by [PERSON] et al. [23], following Equation (8) (Figure 3d); note that since the PSD extrapolations only approximate the particle populations outside the FC method detection range, the sum of the extended organic and inorganic PSDs generally is not exactly equal to the extended total PSD, making the dual calculation of SPM both from total PSD and as the sum of model ISM and OSM values a useful check of the error so introduced.
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 5 Literature Review
### Power Distribution Network
#### 5.2.1 Performance Calculation  
Performance evaluation under disruptions is the milestone of resilience assessment, where system modeling-based approaches prevail. Still, in [130] and [134], field data are used to calculate the resilience of recent natural disasters like: 2010 earthquake and tsunami in Chile, 2011 earthquake and tsunami in Japan, 2011 earthquake in New Zealand, and hurricanes: Isaac (2012), Sandy (2012), and Ike (2008). This fits post-recovery evaluation given the availability of the information a posteriori [130]. This is also a useful experience for upcoming events when included in a proactive analysis for response and restoration [134]. An alternative to system physical and operational modeling is exposed in [129], where a machine-learning-based agent is leveraged to compute the number of outages, the outage duration, and the number of unserved customers; from clusters of focal variables used to estimate a multivariate resilience manifold.  
Other than these options, the reviewed literature stipulates using system modeling due to a lack of data in the case of HILP extreme events. One can recall all aspects of the model: contingency, fragility, restoration, and functionality; which are achieved in different ways. Works in [127; 128; 136; 137; 139] suggest using simulation-based frameworks to implement the quantification procedure, while [123; 124; 131; 135; 138] opt for complete analytical formulation. A good compromise is found in [122; 125; 126; 132; 133] with a hybrid analytical-simulation modeling, for example [133] where the functional model is experimental, and remaining contingency, fragility, and restoration models are posed as optimization problems.  
In the case of generic events, the model omits handling contingency and fragility, because direct impact scenarios are applied in the study; except for [137], which needs Matlab graph analysis libraries to compute quantities that contribute to the failure scenarios selection.
_applied sciences_  
_Article_  
Development of Virtual Sensor Based on LSTM-Autoencoder to Detect Faults in Supply Chilled Water Temperature Sensor  
[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 2, [PERSON] 3 and [PERSON] 1,*(c)  
1 Department of Building and Plant Engineering, Hanbut National University, Daejeon 34158, Republic of Korea; [EMAIL_ADDRESS] (SJ); [EMAIL_ADDRESS] (A.J.)  
2 Department of Architectural Engineering, Hanbut National University, Daejeon 34158, Republic of Korea; [EMAIL_ADDRESS] (D.L.); [EMAIL_ADDRESS] (S.K.)  
3 Division of Architecture and Architectural Engineering, College of Engineering Sciences, Hanyang University, Ansan 15588, Republic of Korea; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
Supply chilled water temperature (SCWT) is an important variable for the efficient and stable operation of heating, ventilation, and air conditioning (HVAC) systems. A precisely measured value ensured by the continuous reliability of the temperature sensor is essential for optimal control of an HVAC system because temperature sensor faults can affect the chiller operation and waste energy. Therefore, temperature sensor fault-detection strategies are imperative for maintaining a comfortable indoor thermal environment and ensuring the efficient and stable operation of HVAC systems. This study proposes a fault-detection method for an SCWT sensor using a virtual sensor based on a long short-term memory-autoencoder. The fault-detection performance is evaluated considering a case study under various sensor fault scenarios to evaluate changes in indoor thermal comfort and energy consumption after correcting sensor faults detected by the virtual sensor. The results verify excellent fault-detection performance in various fault scenarios (F-1 scores ranging from 0.9350 to 1.000). After correcting the SCWT fault, indoor thermal comfort is steadily maintained without additional energy consumption (indoor set-point temperature unmet hour reduced by a maximum of 105.7 hours, and energy consumption decreased by up to 1.8%).  
supply chilled water temperature sensor; sensor fault; indoor thermal comfort; energy consumption; fault detection; virtual sensor
## 3 Comparison of Application Cases and Methods  
This study employs a publicly accessible dataset for gearbox fault diagnosis, originally compiled by [PERSON], [PERSON], and colleagues in 2014, serving as a foundational resource for experimental validation [23]. The test rig underwent evaluations under varied pinion conditions, with vibration signals meticulously captured by accelerometers operating at a sampling rate of 10 kHz over a 10-second duration. The compiled data are organized into three distinct packages, each representing a specific fault type. These encompass datasets characterizing the healthy state, gear breakage, and gear wear conditions, providing a comprehensive spectrum for in-depth analysis and evaluation.  
The core components of the test stand unit's drive system primarily include a motor that functions as the drive input. This motor propels the gearbox via a belt and, in turn, the gearbox's output drives the brake system, also connected by a belt. Data acquisition is facilitated by an accelerometer strategically positioned on the drive end of the induction motor [24]. With a sampling frequency set at 10 kHz and a sampling duration of 10 seconds, the parameters for data capture are meticulously defined. The motor operates at a speed of 1420 rpm/min. Given that the pinion gear is equipped with 15 teeth and the large gear with 110 teeth, the calculated meshing frequency equates to 355 Hz, derived from the formula (1420/60) \(\times\) 15. However, spectrum analysis reveals an actual meshing frequency proximate to 365 Hz, offering nuanced insights into the intricate dynamics of gear engagement and operation.
## 3 Results and Discussion
### Combustion Chamber Temperature  
Figure 5 shows the average temperature of the combustion chamber for the tested fuels. This parameter indicates the ability of a blend for combustion phasing. Notice that the study only presents the temperature distribution for a full load rate, representing the critical condition from the analyzed cases since it holds the highest heat release rate and maximum pressure peaks.  
Figure 4: Influence of fuel on heat release rate for (**a**) 50%, (**b**) 75%, (**c**) 100% of the engine load.  
According to the results, the maximum temperature is achieved by ABE 5 + HHO, followed by the gasoline, ABE 5, ABE 10 + HHO, and ABE 10. Interestingly, the temperature after combustion presents a reverse trend concerning the blends, which can be associated with the higher heat release rate during combustion, minimizing the temperature in this stage. The maximum temperature reaches a value of 2229 \({}^{\circ}\)C for ABE 5 + HHO, supporting the energetic contribution derived from the dual-fuel operation. Moreover, as the ABE content escalates, the combustion temperature drops between 20-45%. This pattern can be explained considering the lower heating value and intensified latent heat of the ABE blends. Notably, increasing the latent heat promotes a temperature drop in the intake stage, resulting in a lower temperature at the end of the compression stage. Moreover, incorporating hydroxy gas in the blends intensifies the temperature due to the higher hydrogen and oxygen content that stimulates the chemical energy conversion of the air/fuel mixture [42]. On average, the combustion chamber temperature of ABE 5 and ABE 10 decreased by 6.21% and 12.23% compared to the gasoline fuel. In contrast, HHO enrichment increases the temperature of ABE 5 and ABE 10 by up to 394 \({}^{\circ}\)C and 341 \({}^{\circ}\)C, respectively.
## 3 Synthesis of Transition-Metal-Sulfide-Based Nanomaterials
### WS\({}_{2}\) Computational Studies  
[PERSON] et al. studied the effects of engineering the edge layers of WS\({}_{2}\) on the electrocatalytic reduction of CO\({}_{2}\) using DFT calculations [166]. The aim of the modeling work was to investigate the doping of the WS\({}_{2}\) structure with Zn, Fe, Co, and Ni, which showed a change in the bond strength between the W and S atoms and increased electrical conductivity [166]. In addition, the catalytic activities of the composite materials could be increased, and an optimum catalyst was observed in the calculations using Zn as the dopant with an over-potential of \(-\)0.51 V. [PERSON] et al. on the other hand, used DFT calculations to study the effect of Fe doping on WS\({}_{2}\)[167]. The results showed that the Fe has a preferential binding to two sulfur atoms on the edge site and showed breaking of the W-S bonds at the edges of the nanomaterial. The results also showed that CO\({}_{2}\) was activated on the WS\({}_{2}\) with bond angles of 129\({}^{\circ}\), and the binding occurred though physisorption. These computational studies performed on WS\({}_{2}\) do provide some mechanistic insight into the CO\({}_{2}\)RR using WS\({}_{2}\)-based nanomaterials. These insights and mechanisms observed with WS\({}_{2}\) are very similar to those in MoS\({}_{2}\)-based materials. These insights include modeling the rim-edge and distortions of the O=C=O bond angles after adsorption, and the importance of the metal sites in the reaction mechanism.  
Table 1 shows a summary of the different products generated using layered MoS\({}_{2}\), TiS\({}_{2}\), WSe\({}_{2}\) and their respective nanocomposites. As can be illustrated in the table, these nanomaterials are very versatile for their applications in the CO\({}_{2}\)RR. The majority of the common small molecule products (CO, CH\({}_{3}\), CH\({}_{3}\)OH, CH\({}_{3}\)CH\({}_{2}\)OH, and C\({}_{2}\)H\({}_{4}\)) observed in the CO\({}_{2}\)RR were formed using MoS\({}_{2}\), TiS\({}_{2}\), WSe\({}_{2}\) and their respective nanocomposites. However, formate was not shown as a reaction product but was a postulated product using computation.  
Non-Layered First Row TMS Nanomaterials and Composite Nanomaterials Applied to CO2 Reduction Reactions
# Check-QZP: A Lightweight Checkpoint Mechanism for Deep Learning Frameworks
###### Abstract  
In deep learning (DL) frameworks, a checkpoint operation is widely used to store intermediate variable values (e.g., weights, biases, and gradients) on storage media. This operation helps to reduce the recovery time of running a machine learning (ML) model after sudden power failures or random crashes. However, the checkpoint operation can stall the overall training step of the running model and waste expensive hardware resources by leaving the GPU in idle sleep during the checkpoint operation. In addition, the completion time of the checkpoint operation is unpredictable in cloud server environments (e.g., AWS and Azure) because excessive I/O operations issued by other running applications interfere with the checkpoint operations in the storage stacks. To efficiently address the above two problems, we carefully designed Check-QZP, which reduces the amount of data required for checkpoint operations and parallelizes executions on the CPU and GPU by understanding the internal behaviors of the training step. For the evaluation, we implemented Check-QZP and compared it with the traditional approach in real-world multi-tenant scenarios. In the evaluation, Check-QZP outperformed the baseline in all cases in terms of the overall checkpoint time and the amount of data generated by the checkpoint operations, reducing them by up to 87.5% and 99.8%, respectively. In addition, Check-QZP achieved superior training speeds compared to the baseline.  
deep learning; checkpoint; quantization; parallelism; compression; performance and storage capacity +
Footnote †: journal: Applied sciences
## 1 Introduction  
Construction and composition of sediments record significant information about river processes related to flow dynamics, geomorphology, weathering condition, provenance, and tectonic setting [1; 2; 3; 4]. The geochemical compositions of various sediments are widely used to identify their precipitation process involved, and a lot of studies have been carried out on sediments from different ages and places [5; 6; 7; 8; 9; 10]. The main factor controlling the initial geochemical characteristics of sediments is provenance, and the provenance is again related to regional tectonic setting [1; 11]. However, provenance rocks undergo weathering before transport, which has a close relationship with the paleo-climate [4; 12; 13]. In this process, the sediments keep the original geochemical feature of immobile elements, such as Al, Ti, Sc, rare earth elements (REEs) and so on, and the mobile elements, such Na, K et al., would be removed [14]. During transporting, heavy and light minerals are sorted, resulting in the enrichment of stable heavy minerals, such as zircon. Hence, the geochemical compositions of sediments could be used to deduce the provenance as well as paleo-climate, and tectonic setting of their formation effectively [4; 12].  
Rivers transport detrital material, and mold the surface through erosion, transport and accumulation [15]. At the same time, the river system can respond sensitively to the changes in external environmental factors, such as climate, structure, and human activities [16; 17; 18]. Major rivers in the world have been investigated by several geochemists [4; 13; 19; 20]. As one of the seven major river systems in China, the Songhua River system is a unique laboratory related to tectonic setting, topography and climate development [21; 22]. Due to the complex provenance distribution of the Songhua River system, the study of provenance is still very limited. [PERSON] et al. [15] analyzed the provenance and river process by the composition of heavy minerals from the sediments of Songhua River. The heavy minerals cannot efficiently reflect the weathering degree of the provenance. However, the chemical composition can identify the weathering and sorting process of the river sediments.  
In this paper, we present the results of a comprehensive study of the petrography, major, trace, and rare earth elements (REEs) of sand collected from Holocene Wenquanhe and Late Pleistocene Guxiangtun Formations. The aims are to define the geochemical characteristics, weathering conditions, and provenance of these formations, and constrain their tectonic setting in the Quaternary Period.