esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 4 Discussion
### Temporal Resolution  
Temporal resolution was directly affected by the autonomy of data collection in this study. From highest to lowest frequency observations were terrestrial camera, UAV, satellite, and PhenoCam data. The high temporal resolution of PhenoCams makes these data uniquely powerful for examining long-term patterns and trends. For example, the Face Estate tower PhenoCam was installed on 9 March 2017, and has collected 43,254 images as of 19 June 2020. Further, there are over 400 PhenoCams collecting similarly large datasets [22]. Neither ground-based nor UAV-based approaches can reach these temporal resolutions, largely because each sensor platform requires an operator, whereas PhenoCams do not. Even when UAVs can be deployed at high frequencies, data volume, orthorectification, and post-processing become major bottlenecks in the processing pipeline. Additionally, ground-based methods, such as tripod-mounted cameras, must be physically moved from location to location during  
Figure 5: GCC\({}_{\text{UAV}}\) regressed against LAI (R\({}^{2}\) = 0.81; RSE = 0.538) using a 15 m buffer size (**A**), and gap fraction (RSE = 3.20) using a 20 m buffer size (**B**); error bars indicate standard deviation. At right, LAI (**C**) and gap fraction (**D**), modeled for the clipped raster extent using GCC\({}_{\text{UAV}}\) using equations 2 and 3, respectively, for the entire clipped raster extent.  
a sampling period, adding additional effort and time. Satellite imagery partially mitigates these issues, but can be limited by cloud-cover--particularly in the tropics or in mountainous areas--as well as temporal resolution, which may be solved with the development of CubeSat constellations, capturing daily, high-resolution imagery.
## 3 IoT Architectures
### IoT Architecture for Smart Homes  
A sensor creates data, but it contributes little value to the home environment on its own. A thermostat is often not called \"smart\" because the homeowner must regulate the temperature based on the outside temperature, humidity, and other factors. It is possible to maintain a stable temperature, but this is automation, not \"smartness.\" Only when all environmental data is collectively analysed based on collected patterns and decisions made without the intervention of the consumer can it be called a \"smart\" ecosystem. The way sensors interact, how and where sensor and equipment usage pattern information is processed, how this data is interpreted and patterns are detected, and how the systems can be interacted with by the consumer and vice versa is decided by the smart home infrastructure.  
The symbiosis of numerous components, such as sensors, connections, and applications, that generate a complex, heterogeneous infrastructure to effectively control home devices and provide advanced services to consumers can be described as a smart home. Figure 7 depicts cloud-based smart home architecture in general. The internal network consists of terminal equipment, cameras, processors, and actuators. These machines link with a firewall at the network's edge, which makes linking internal networks to the internet even simpler. The communication difference between end-users, sensors, software, and the cloud is bridged by a gateway computer [76].  
Figure 5: Healthcare models of IoHT.  
Figure 6: The system architecture of IoT with fog computing.  
Figure 7: IoT-based smart home architecture.  
A smart home gateway architecture that is configured in terms of accepted communication protocols and can convert heterogeneous data from sensors into a standard format. In general, the gateway is a device that accepts several communication protocols with the end devices for interoperability. It is efficient enough to do any computation at the edge of the network before uploading the data to the cloud. The gateway further adds a layer of security to the smart home network as it connects contact between end devices and the outside world, meaning all communication is filtered before instructions are transmitted to the end devices. This effectively includes ways to improve usability, stability and security while at the same time taking advantage of higher computational power and scalable architecture. The cloud can connect multiple services from third parties as well, such as data visualization, control of smart home products, or access and role management for consumers.
## 3 Results
### Hepatic Fatty Acid Contents in Mice Fed APAP and EtnGpl
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Ctrl**} & \multicolumn{2}{c}{**APAP**} & \multicolumn{2}{c}{**AM + APAP**} & \multicolumn{2}{c}{**PL + APAP**} \\ \hline Initial body weight (g) & 24.4 & \(\pm\) 0.4 & 24.3 & \(\pm\) 0.4 a & 24.4 & \(\pm\) 0.4 a & 24.3 & \(\pm\) 0.3 a \\ Final body weight (g) & 44.7 & \(\pm\) 1.5 & 40.5 & \(\pm\) 0.8 b & 46.5 & \(\pm\) 1.1 a & 41.0 & \(\pm\) 1.1 b \\ Body weight gain & 9.4 & \(\pm\) 0.8 * & 5.9 & \(\pm\) 0.4 b & 9.7 & \(\pm\) 0.6 a & 6.6 & \(\pm\) 0.7 b \\ during APAP treatment (g) & 261.6 & \(\pm\) 0.8 & 243.9 & \(\pm\) 7.6 a & 295.6 & \(\pm\) 19.8 a & 269.5 & \(\pm\) 6.8 a \\ during APAP treatment (g) & & & & & & & & \\ Plasma AST (IU/L) & 54.5 & \(\pm\) 6.1 & 59.3 & \(\pm\) 6.1 a & 48.4 & \(\pm\) 5.5 a & 57.8 & \(\pm\) 6.5 a \\ Plasma ALT (IU/L) & 14.4 & \(\pm\) 3.1 & 10.5 & \(\pm\) 1.0 a & 8.6 & \(\pm\) 1.0 a & 11.6 & \(\pm\) 1.7 a \\ Plasma TNF-\(\alpha\) (mg/mL) & 19.3 & \(\pm\) 0.6 & 20.8 & \(\pm\) 0.6 a & 22.6 & \(\pm\) 1.2 a & 21.0 & \(\pm\) 1.4 a \\ Liver weight (g) & 2.2 & \(\pm\) 0.2 * & 1.8 & \(\pm\) 0.1 b & 2.2 & \(\pm\) 0.1 a & 1.9 & \(\pm\) 0.1 b \\ Liver GSH (\(\mu\)mol/mg protein) & 18.4 & \(\pm\) 3.8 & 16.4 & \(\pm\) 3.2 a & 19.9 & \(\pm\) 2.0 a & 16.0 & \(\pm\) 2.8 a \\ Liver MDA (nmol/mg protein) & 11.6 & \(\pm\) 0.9 * & 16.2 & \(\pm\) 1.5 a & 10.2 & \(\pm\) 0.7 b & 10.6 & \(\pm\) 1.1 b \\ Spleen weight (mg) & 140.0 & \(\pm\) 10.7 & 113.3 & \(\pm\) 9.4 a & 120.8 & \(\pm\) 6.2 a & 125.8 & \(\pm\) 8.4 a \\ \hline \end{tabular}
\begin{tabular}{c} All values are presented as the mean \(\pm\) standard error of the mean (\(\alpha\) = 12 except for feed intake). Feed intake was expressed based on three data points and was calculated as the amount per mouse because each cage with one feed they housed four mice. Different letters indicate significant differences among groups treated with APAP at \(p\) = 0.05, as determined using a one-way analysis of variance by [PERSON]’s test. Asterisks indicate significant differences between the Ctrl and APAP groups at \(p\) < 0.05, as determined using the \(t\)-test. ALT, alanine aminotransferase; AM, ascidian muscle; APAP, acetaminophen; AST, aspartate aminotransferase; Crtl, control; EtnGpl, ethanolamine glycerol phospholipid; GSH, glutathione; MDA, malondialdehydedehyde; PL, porcine liver.  
\end{table} 
Table 3: Changes in the parameters of hepatic injury and oxidative stress in mice fed APAP and EtnGpl. rich diet increased the hepatic levels of DHA and docosapentaenoic acid (22:5_n_-3) and the DHA/ARA ratio. In contrast, plasmalogen levels in the livers were extremely low (<0.5 nmol/mg protein) among all the groups.
## 3 Aging of Lithium-Ion Batteries
### Calendar Aging  
Calendar aging refers to the degradation behaviors of capacity and power capability with saving under the same SOC level for a long period. Moreover, calendar aging is closely related to environmental conditions such as storage temperature; that is, the speed of aging varies according to the conditions mentioned above.  
According to the study in [13], under 25 degrees Celsius, the capacity fade can be described in Equation (1).  
\[C_{f\_cal}=0.1723\cdot e^{0.007388\cdot SOC}.t^{0.8} \tag{1}\]  
where \(C_{f\_cal}\) represents the percentage of capacity fade, \(SOC\) refers to the storage SOC level, and \(t\) represents the storage period (month).  
Figure 5 shows the calendar aging curve drawn by Equation (1). From this figure, it can be seen that the curve is nonlinear and tends to be flat as the storage time increases. In other words, the higher the storage SOC level is, the faster the aging rate will be.
# Privacy and Security in Federated Learning: A Survey
## 4 Privacy in Federated Learning
### Privacy-Preserving Defense Strategies
#### 4.2.6 Concealing Iterations  
[PERSON] et al. [54] have suggested iterations concealing as a method of avoiding privacy threats. In typical FL, the client model iterations are visible to multiple actors of the system, such as the central server and the clients chosen to participate in each round. Thus, a malicious server may be able to infer data through iterations thanks to GAN-based attacks. By concealing iterations, each client should make the learning phase inside a Trusted Execution Environment (TEE). A TEE provides the ability to run code on a remote trusty machine, regardless of the trust placed in the administrator. Indeed, a TEE limits the abilities of any party, including the administrator, to interact with the machine. This specificity results in three properties that make TEE trusty:  
* Confidentiality: The running code and its execution state are not shared outside of the machine and cannot be accessed.
* Integrity: Because of access limitations, the running code cannot be poisoned.  
* Attestation: The running virtual machine can prove to an external entity what code is executing and the inputs that were provided.  
Thus, thanks to TEE, the model iterations can be concealed, and the model parameters may be encrypted inside the TEE before being shared. Nonetheless, this approach might not be used across clients, especially with devices such as smartphones. In fact, TEE may not be powerful enough for training and may involve too many computational and memory costs. These weaknesses may have a significant impact on devices' autonomy, performance, and user experience. For these reasons, in its current state, TEE cannot yet be used on limited-capacity edge devices.
## 4 Results and Discussion
### Meta-Learning for Rapid Training of Multi-Machine Models for Anomaly Detection and Prediction  
For the sake of versatility, meta-learning is applied to multi-machine anomaly detection and prediction models with different datasets, such as factory chiller vibration data, publicly available datasets for analyzing the vibration and current of Paderborn bearings, and SECOM semiconductor analysis.  
(1) Rapid Training of Multi-Machine Models for Anomaly Detection  
The proposed PQUM-DNS method is compared with the general DAE method. PQUM-DNS achieves similar AUC values with minimal data compared to those of the DAE method when used to train models for new machines. This is because PQUM-DNS used meta-learning to train a versatile model applicable to various conditions (Metatrain model). Therefore, training a new machine model only requires a small amount of data for fine-tuning, resulting in a rapidly adaptable anomaly detection model. Test results for different data types, such as chiller vibration, SECOM, and Paderborn current and vibration datasets are presented in Table 5 and Figure 16. Compared with the DAE, PQUM-DNS reduces the required data for training new machine models on average by approximately 75%, with a decrease in AUC performance of only 0.35%, which is a very close AUC value.  
2. Anomaly Prediction:  
PQUM-DNS detects machine degradation levels through anomaly detection and evaluated these levels using various prediction algorithms. Seven prediction algorithms are compared and one based on RMSE calculations is selected based on the predicted and actual values. The performances of these algorithms on different datasets are presented in Table 6 and Figure 17. The Holt-Winters algorithm demonstrates the best performance, with the lowest RMSE value of approximately 0.037, making it the chosen algorithm for anomaly prediction in the PQUM-DNS.
# Magnetic Properties of NiZn Ferrite Nanofibers Prepared by Electrospinning
## 2 Materials and Methods  
### Fabrication of NiZn Ferrite Nanofibers via Electrospinning  
The nanofibers were prepared by a typical electrospinningning process as follows. Ni(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O (13478-00-7, 98%, EP, Samchun Chemicals, Seoul, Korea), Zn(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O (10196-18-6, 98%, EP, Daejung Chemicals, Sireung, Korea), and Fe(NO\({}_{3}\))\({}_{2}\cdot\)9H\({}_{2}\)O (7782-61-8, 99%, GR, Kanto Chemical, Tokyo, Japan) in a molar ratio of 1:1:4 were mixed in N,N-dimethylmethanamide (DMF, 68-12-2, 99.5%, EP, Daejung chemicals, Sireung, Korea) as a solvent at a 150 mmol/L of molar concentration. The nitrate crystals were mixed using a magnetic stirrer until they were completely dissolved in DMF solvent. Polyvinyl pyrrolidone (PVP, 9003-39-8, 98.3%, M.W. 1,300,000, Alfa Aesar Co., Tewsbury, MA, US) was added to the completely dissolved solution at a weight ratio of 18 wt%, 20 wt%, and 22 wt% and stirred for 48 h to prepare the electrospinning precursor solution. The precursor solutions were labeled A, B, and C, and the compositions of the precursors are shown in Table 1. The prepared precursor was placed in a polypropylene syringe, connected with polyethylene tubing, and mounted on a syringe pump. A stainless-steel nozzle adapter and a 27-gauge plastic nozzle were attached to the end of the tubing, and pressure was applied through the pump to infuse at a constant flow rate of 0.5 mL/h. The nozzle adapter was connected to the power supply, and the aluminum foil collector was grounded. A voltage of 15 kV was applied at the electrode while maintaining a 15-cm electrode-to-electrode distance. The relative humidity was 40% for room temperature. The collected composite nanofiber sheets were dried in a dry oven at 80 \({}^{\circ}\)C for 2 h. The dried composite nanofiber sheet was heated to a temperature of 750 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min atmospheric environment, fixed for 4 h, and then cooled to obtain a ceramic nanofiber with a Ni\({}_{0.5}\)Zn\({}_{0.5}\)Fe\({}_{2}\)O\({}_{4}\) ferrite single phase. The morphologies and diameters of the nanofiber samples were confirmed by field emission-scanning electron microscopes (FE-SEM) image analysis. The heat treatment temperature of the composite fiber was determined by confirming the pyrolysis behavior using thermogravimetric analysis. The crystal phase was determined by comparing the XRD pattern with the Crystallography Open Database. VSM measured the hysteresis loop of each average diameter sample.
# Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding
## 4 Experimental Result
### Method Comparison to References  
As described in Section 1, there are several references related to this proposed method. We proposed a new method with more benefits than the mentioned references. Our proposed method could be used for both audio watermarking or audio steganography with compression due to the controllable parameter between the payload, the audio quality, and the compression ratio. Besides, our proposed method produced the encoded audio, which could not be attacked by a general signal processing attack, i.e., Stirmark benchmark, except the additive noise as described in Section 3.5. Table 8 displays the comprehensiveness comparison between our proposed method and the previous references, which also used CS as the embedding or compression method and the audio as the object to embed or to compress. From the previous references in Table 8, the reference [1] only described the robustness as only one performance parameter, although his method had the same purpose as our method. The reference [2] proposed the hiding method only. The reference [3] proposed the audio compression scheme only. In detail, we could only compare the performance to [4] because of its comprehensiveness performance, and the performance parameters were the closest to our method.  
Table 9 displays the performance comparison between our proposed method and [4]. In [4], [PERSON] described the performance of four techniques. The audio quality was quite imperceptible since SNR = 28 dB. Our method was also quite imperceptible since the ODG range was [\(-\)0.94 \(-\)0.74]. Although [4] had better robustness to additive noise attack with SNR 20 dB where maximum BER = 3% and our method obtained maximum BER = 13%, our method had an outstanding payload compared to the payload in [4]. Note that the experiments in [4] only used one clip to obtain the performance in BER, SNR, and payload. In contrast, our method obtained the average performance from the simulation results of 50 clips. Our method also reported the compression ratio with the range of [1.47 4.84], while [4] did not report the compression ratio.
###### Abstract  
The accurate forecasting of oil recovery factors is crucial for the effective management and optimization of oil production processes. This study explores the application of machine learning methods, specifically focusing on parallel algorithms, to enhance traditional reservoir simulation frameworks using black oil models. This research involves four main steps: collecting a synthetic dataset, preprocessing it, modeling and predicting the oil recovery factors with various machine learning techniques, and evaluating the model's performance. The analysis was carried out on a synthetic dataset containing parameters such as porosity, pressure, and the viscosity of oil and gas. By utilizing parallel computing, particularly GPUs, this study demonstrates significant improvements in processing efficiency and prediction accuracy. While maintaining the value of the \(R^{2}\) metric in the range of 0.97, using data parallelism speed up the learning process by, at best, 10.54 times. Neural network training was accelerated almost 8 times when running on a GPU. These findings underscore the potential of parallel machine learning algorithms to revolutionize the decision-making processes in reservoir management, offering faster and more precise predictive tools. This work not only contributes to computational sciences and reservoir engineering but also opens new avenues for the integration of advanced machine learning and parallel computing methods in optimizing oil recovery.  
distributed machine learning; HPC; artificial intelligence; cuML; enhanced oil recovery Article
# Biosynthesis Progress of High-Energy-Density Liquid Fuels Derived from Terpenes
## 4 Strategies for Constructing Efficient Microbial Cell Factories for Terepenes
### Terpene Synthase Mining and Engineering  
#### 4.1.1 Mining of Terpene Syntheses  
In microbial cells, the synthesis of monoterenes and sesquiterpenes relies on exogenously introduced terpene synthases to catalyze the formation of the corresponding targets from precursor GPP/FPP. Therefore, the activity and selectivity of terpene synthases play a crucial role in the efficiency and purity of terpene synthesis. For terpenes that have not been successfully synthesized in microbes or have low production yields, mining the corresponding synthases is the first step in achieving their efficient synthesis (Figure 2A). Effective ways to obtain new or better terpene synthases include database mining and the exploration of microbial or plant genomes/transcriptomes [89]. [PERSON] et al. expressed 22 terpene synthases derived from bacteria in _E. coli_, and 15 of them were found to be active in generating corresponding products, including monoterenes, sesquiterpenes and diterpenes, with some showing high activity, which was conducive to the subsequent large-scale synthesis of terpenes in microbes [90]. Recently, our research group discovered a (+)-cubenene synthase in _A. chrysogenum_, which was the first report of a (+)-cubenene synthase, ultimately achieving the synthesis of (+)-cubenene with a titer of 597.3 mg/L in _S. cerevisiae_[91]. [PERSON] et al. screened three linalool synthases from plants, including _Valeriana officinalisis_, _Lippia dulcis_ and _Ocimum basilicum_, finding that the linalool synthase from _V. officinalis_ was the most active enzyme [92]. [PERSON] et al. successfully synthesized longifolene in yeast for the first time, with a titer of 36.8 mg/L, by screening three potential longifolene synthases in pine trees [93].  
Figure 2: (**A**) Mining and engineering of terpene synthases for overexpression of terpen. (**B**) Optimization of terpene synthetic pathways and upstream or downstream pathways to improve terpene production. (**C**) Subcellular localization of mitochondria and peroxisome for terpene synthesis. (**D**) Increasing cellular tolerance by overexpressing efflux pumps. (**E**) Adaptive laboratory evolution for improving the terpene production.
## 3 Results
### Antioxidant Profile  
#### 3.3.1 Total Phenolic Compounds (TPC) Content  
Table 2 shows that the analyzed Leccio del Corno monovarietal extra virgin olive oil samples are rich in total phenolics (TPC), with values of 3.29 mg GAE/g. This amount disagrees with that of [PERSON] et al. [26] who found an average total phenolic content of 0.33 mg GAE/g. On the other hand, it is well known that the phenolic content of extra virgin olive oils can differ according to cultivar, weather conditions, degree of ripeness of the drupes, and processing techniques [27; 28; 5; 29].  
Our results suggest that they are highly nutritious and organoletically valuable. This aspect is particularly important and is in line with the results of the sensory analysis. In fact, the present sample (Figure 2) also shows a high median for the bitter and pungent attributes (5 and 5, respectively), characters that correlate with total polyphenols. The latest research [30] confirmed that phenolic compounds account for the bitter and pungent organoleptic characteristics of extra virgin olive oil.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**N\({}^{\circ}\)** & **COMPONENT 1** & **LRI 2** & **LRI 3** & **(\%) 4** \\ \hline
1 & capric acid, C10:0 & 1368 & 1373 & 0.5 \(\pm\) 0.02 \\
2 & palmitic acid, C16:0 & 1965 & 1962 & 11.0 \(\pm\) 0.65 \\
3 & linoleic acid, C18:2\(n\)6 & 2120 & 2123 & 3.4 \(\pm\) 0.05 \\
4 & oleic acid, C18:1\(n\)9 & 2136 & 2141 & 80.8 \(\pm\) 1.32 \\
5 & stearic acid, C18:0 & 2175 & 2180 & 4.3 \(\pm\) 0.08 \\ \hline \hline \end{tabular} \({}^{\dagger}\) The components are reported according to their elution order on the capillary column (VF-5 ms). 2 Linear Retention Indices measured on VF-5 ms column. 3 Linear Retention indices from literature. 4 Percentage mean values of Fatty acids content. Data are means \(\pm\) standard deviation of three (\(n=3\)) replicates.  
\end{table}
Table 1: Fatty acid content (percentage mean value \(\pm\) standard deviation) of monovarietal extra virgin olive oil from the Leccio Del Corno cultivar (LC) as determined by GC–MS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **TPC** & **ABTS\({}^{\bullet+}\)** & **FRAP** \\ \cline{2-4}  & **mg GAE/g** & **mmol TE/g** & **mmol Fe2+/g** \\ \hline Leccio Del Corno & 3.29 \(\pm\) 0.05 & 1.27 \(\pm\) 0.06 & 1.31 \(\pm\) 0.005 \\ \hline \hline \end{tabular} \({}^{\dagger}\)TPC: total phenolic compounds; ABTS\({}^{\bullet+}\): radical scavenging activity assays; FRAP: ferric reducing antioxidant power; TE: Trolox equivalent. Data are means \(\pm\) standard deviation of three (\(n=3\)) replicates.  
\end{table}
Table 2: Total phenolic compounds (TPC) and total antioxidant capacity (ABTS\({}^{\bullet+}\) and FRAP) of monovarietal extra virgin olive oil from the Leccio Del Corno cultivar.