esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Wind-Redox Flow Battery System for Decarbonizing Off-Grid Mining Operations
## 4 Design, Sizing, and Modeling of the Wind Energy and Storage System
### Sub-Model 3: Model of the Response to Electrical Demand  
The electrical demand response model simulates the behavior of the 25 kV network under different load conditions. This sub-model is essential for monitoring and controlling:  
* Battery management (state of charged and discharged energy);
* Unused surplus energy;
* Generator consumption.  
To introduce flexibility, a randomization factor is applied to simulate variations in electrical demand. The model uses Equation (1) (previously introduced) with a random fluctuation range of \(-5\%\) to \(+10\%\), ensuring a realistic simulation of demand changes.  
A key objective of this sub-model is to ensure that the 25 kV network maintains a surplus of energy, enabling efficient battery storage and load management. Additionally, it anticipates future increases in electrical demand, given that the Raglan mine plans to expand nickel production.  
Complete decarbonization is not immediately feasible since the ore-drying process relies entirely on diesel generators. Thus, the model accounts for the continued operation of some generators to meet heat demand, ensuring a pragmatic transition to renewable energy.  
Two scenarios were tested to analyze the behavior of the 25 kV network under different supply and demand conditions:Electricity demand is satisfied:  
* The energy supply exceeds demand, generating excess power.
* The surplus is directed toward charging storage batteries.
* If batteries are fully charged, the excess energy is curtailed (rejected).
* This is the ideal case for the Raglan mine, as it maximizes the utilization of renewable energy.
* Electricity demand is not satisfied:
* The 25 kV network draws from battery storage to cover the deficit.
* If battery reserves are insufficient, additional power must be generated.
* In this case, an emergency recourse to diesel generators is triggered.
* Each generator has an estimated power output of 2.88 MW.  
Figure 12 illustrates Sub-Model 3's operating structure and details how energy demand, supply, and storage interact within the MATLAB simulation framework.
## 2 Methodology
### Model Fitness Criteria  
Three criteria are used in this study to evaluate the effectiveness of the model. The root mean square error (RMSE) and mean square error, which gauge the value discrepancy, and the mean absolute error (MAE). These are both the actual values as well as the values the model anticipated. A technique called MAE can be used to assess the precision of continuous variables. We can determine the average size of the errors once we have a set of forecasts. It does not consider the direction of the magnitude.
# Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images
## 2 Methods
### Evaluation Matrices  
The extraction accuracy of each organ was evaluated using the three indices (Dice index, sensitivity, and false positive rate) shown in the Formulas (1)-(3) below: the number of voxels in the output region that matched the correct region was defined as a true positive (\(TP\)), and the number of voxels in the output region that were not included in the correct region (i.e., the number of voxels in the over-extracted region) was defined as a false positive (\(FP\)). Finally, the number of voxels in the correct regions that were not included in the output regions (i.e., the number of voxels in the undetected regions) was defined as a false negative (\(FN\)).  
\[Dice\ index=\frac{2 TP}{2 TP+FP+FN}\times 100[\%] \tag{1}\]  
\[Sensitivity=\frac{TP}{TP+FN}\times 100[\%] \tag{2}\]  
\[False\ positive\ rate=\frac{FP}{TP+FP}\times 100[\%] \tag{3}\]  
The Dice index indicates the relative volume overlap between the output and correct regions. Furthermore, the sensitivity indicates the percentage of correct outputs among the correct regions, and the FP rate indicates the percentage of outputs that are not in the organ region out of all the outputs.
### Artificial Intelligence Applied to Concrete Production  
In recent years, the use of artificial intelligence techniques to estimate and model a wide range of problems has increased, especially in civil engineering [14]. In our review of the literature, we found the application of artificial intelligence techniques in the concrete production process. The central interest has focused on predicting compressive strength using supervised machine learning techniques. The use of artificial intelligence techniques and statistical approaches such as artificial neural networks [15], adaptive neuro-fuzzy inference systems (ANFIS) [16], genetic programming [17], support vector machines [18], regression or classification trees [19], and some ensemble methods [20], has been very relevant in predicting concrete compressive strength based on mix proportions [21]. This application is quite useful due to the complexity and uncertainty of its calculation, caused by the variable nature of the materials, the quality of the machinery, and the use of chemical and mineral admixtures. In Tables 1 and 2, the literature findings are summarized, identifying the variables to predict, describing the analyzed data, and outlining the methods applied. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Output Variable** & **Input Variables** & **Methods** & **Source** \\ \hline \multirow{4}{*}{Compressive Strength} & Cement content, water, fine and coarse aggregates, silica fume, nano silica, fly ash, super plasticizer, and temperature. & \multirow{4}{*}{[22]} \\ \cline{2-2} \cline{4-4}  & Cement content, water, fine and coarse aggregates, silica fume, nano silica, fly ash, super plasticizer, and temperature. & & \\ \hline \multirow{4}{*}{Compressive Strength} & Cement content, water-to-binder ratio, cement-sand ratio, metakaoIn-to-binder ratio, and superplasticizer. & Random forests and firefly algorithm. & \multirow{4}{*}{[23]} \\ \cline{2-2} \cline{4-4}  & Water content, cement, fine aggregate, coarse aggregate, binary SCMs (SCM1 and SCM2), the chemical composition of each SCM including SiO2, CaO, Fe2O3, Al2O3, MgO, physical properties of binary SCMs, and age of specimens. & \multirow{4}{*}{[24]} \\ \cline{2-2} \cline{4-4}  & Tensile strength of cement, curing age, crushed stone, stone powder content, fineness modulus of sand, water-to-binder ratio, water-to-cement ratio, water, sand ratio, and slump. & Gene expression programming. & \multirow{4}{*}{[25]} \\ \cline{2-2} \cline{4-4}  & Cement, blast furnace slag, pozzolana, water, coarse aggregate, fine aggregate, and age. & & \\ \hline \multirow{4}{*}{Compressive Strength} & Cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, age. & \multirow{4}{*}{Artificial neural networks.} & \multirow{4}{*}{[26]} \\ \cline{2-2} \cline{4-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of AI techniques found in the literature for prediction of concrete strength.
# Conductive Ink Printed Fabric Antenna with Aperture Feeding Technique
## 3 Antenna Design
### Measurement
It is thus important to note the comparable performance of the screen-printed fabric patch antenna with the reference copper model with respect to the realized antenna gain, radiation efficiency, and front-to-back ratio. With just a 24% drop in radiation efficiency compared with the copper model, the conductive PET can replace a copper sheet and achieve comparable results. Following the successful verification of the screen-printed fabric antenna as being superior to the ink-jet-printed antenna and electrically comparable with the copper-based model, the next section will focus on developing a new feeding mechanism that eliminates the use of an SMA directly on the fabric antenna. The antenna in Figure 2a and the results in Table 1 will thus serve as benchmarks in determining the performance of this feeding approach.
## 4 Hardware Evaluation
### Power Consumption
Switching the power rails off has the adverse effect of the eSRAM losing its content and, therefore, the application losing its state. As reinitializing the application, saving and restoring its state variables results in significant wake up time penalties, we defined the SmartFusion sleep mode with the integrated AFE and ACE powered off, the FPGA put in reset mode, the required MSS peripherals running on 32 kHz and the Cortex-M3 halted, waiting for interrupt. We found the latter SmartFusion configuration to yield the lowest-power mode from which the system can wake up in only a few clock cycles. While the 50 mW power draw is significantly higher than that of our previous microcontroller plus IGLOO flash FPGA approach [17], we consider it a trade-off for the high-bandwidth, on-chip AMBA APB interface between the MSS and the FPGA fabric. As microcontrollers with ultra-low-power sleep mode already exist and the IGLOO flash FPGAs consume less than 60 \(\mu\)W in _Flash*Freeze_ mode [23], we expect manufacturing technology to lower the  
Figure 3: Power consumption comparison of the CC1000 commodity radio-frequency (RF) chip, the Marmote SDR platform and the USRP N210 in various power modes. static power consumption for next generation flash FPGA-based SoPCs significantly. The _Joshua_ radio front-end had two main components, the MAX2830 transceiver chip and the TCXO during sleep mode measurements. As the RF transceiver chip consumes \(<\)1 mA in shutdown mode, the main contributor to the 20 mW power is the TCXO. Even though the current _Joshua_ module always keeps the TCXO on, this was a design decision, and it could be turned off in future versions. In receive mode, Marmote SDR consumes 287.4 mW, approximately 12-times more power than the CC1000 (24.4 mW) and 50-times less than the USRP N210 (14,400 mW). Out of the 287.4 mW, the _Teton_ board SmartFusion and AFE dissipate approximately 80 mW and 15 mW, while the MAX2830 and the TCXO on the _Joshua_ board, around 172 mW and 20 mW, respectively. Comparing the transmit mode power consumption at 0 dBm nominal transmit power, the Marmote SDR dissipates 851.7 mW, the CC1000, 24-times less (34.3 mW), while the USRP N210, 51-times more (14700 mW). The SmartFusion, AFE and TCXO consume the same as in receive mode, while the MAX2830 transmit section and the integrated power amplifier draw roughly 280 mW and 450 mW, respectively. Thus, considering a 6,000 mAh 3.7 V Li-Ion battery, the Marmote SDR is able to continuously operate for over 24 h in transmit mode (0 dBm) and to run for over 10 days in sleep mode.
## 3 Results and Discussion
### Additional Experimental Validation of WD-YOLO  
#### 3.4.1 Visual Comparison of Heatmaps  
In this experiment, we assessed the effectiveness of the trainable dual-scale convolution module DSConv integrated into WD-YOLO by comparing heatmaps generated from different models, including YOLOv10n, YOLOv10l, and WD-YOLO. We utilized Grad-CAM to produce heatmaps for a set of images from the PlantDoc dataset. As depicted in Figure 11, these heatmaps visually demonstrate the areas of the images that are most significant in the models' prediction processes. By comparing the heatmaps from these three models, our goal was to ascertain the extent of each model's focus on relevant features of plant diseases.  
The YOLOv10n model demonstrated broad activation across the heatmap, suggesting a generalized approach to disease detection. While this approach is effective for covering larger areas, it sometimes misses finer details critical for early disease identification. In contrast, the YOLOv10l model's heatmaps showed more focused activations, indicating enhanced pinpointing of visible disease symptoms, albeit with occasional inconsistencies in capturing all relevant anomalies.  
Our WD-YOLO model, enhanced with the DSConv module, represents a significant advancement. The heatmaps from this model displayed highly concentrated activations that precisely overlaid the diseased regions, capturing smaller and less apparent spots that previous models frequently overlooked. This high level of precision in the WD-YOLO  
Figure 11: Heatmap comparison results. (**a**) Original Image. (**b**) YOLOv10n. (**c**) YOLOv10l. (**d**) WD-YOLO. (**e**) Original Image. (**f**) YOLOv10n. (**g**) YOLOv10l. (**h**) WD-YOLO.  
Figure 10: Comparison of detection results.  
heatmaps underscores the DSConv's ability to enhance feature extraction, leading to superior accuracy in disease localization. The focused detection capability of WD-YOLO is particularly beneficial for precision agriculture applications, where exact identification of plant health issues is crucial. Moreover, the integration of DSConv into WD-YOLO not only sharpens the model's focus but also optimizes its computational efficiency. Despite leveraging the foundational structure of YOLOv10n, WD-YOLO achieves enhanced performance with a reduced computational demand, indicating that the DSConv module facilitates a more efficient analysis without sacrificing accuracy.  
Overall, the comparative analysis using heatmaps clearly illustrates that our WD-YOLO model with DSConv significantly outperforms its predecessors. It achieves this by combining meticulous attention to detail with the robustness needed to handle diverse and complex agricultural environments, making it an exemplary tool for advancing plant disease detection technology.
## 3 Undifferentiated Cells (Stem Cells)
### Embryonic Stem Cells (ESCs)  
#### 3.1.1 General Information  
The first reports regarding the successful isolation of ESCs from the inner cell mass (ICM) of blastocysts were related to the experiments performed by two groups coordinated by [PERSON] and [PERSON] in 1981 [60; 61]. In addition to the mouse, scientists have also succeeded in the isolation of ESCs from other species including non-human primates (like the rhesus monkey) and humans [62; 63]. Although there are ethical issues about the isolation of ESCs in the case of humans [64], new approaches such as in vitro fertilization (IVF) and nuclear cloning have been developed to remove these problems [65]. Under the defined conditions, these cells can differentiate into all the three germ lines (Figure 4).  
Generally, culturing ESCs requires a notable commitment for time and resources, which makes it an overwhelming process [66]. However, it is necessary to culture these cells in the most optimal conditions because the following applications would depend on the starting sources [67]. Previously, the use of mouse embryonic fibroblasts (MEFs) [68] or human feeders [69] as a feeder layer was the most common method to culture ESCs since they provide a large number of growth factors required for the robust growth of all types of ESCs. However, there are some difficulties related to MEFs  
Figure 3: The pathways and cytokines involved in the differentiation of osteoclast progenitors into functionally active osteoclast. With permission from Reference [58]. CTR: calcitonin receptors, TRAP: tartrate-resistant acid phosphatase.  
including their complexity, non-defined components, and xenogeneic origin with respect to the culture of human ESCs (hESCs) [70]. Therefore, the use of the human-originated feeder layers was considered to overcome the issues associated with the MEFs. In this regard, human fetal and adult fibroblast feeders have been applied to long-term undifferentiated growth of hESCs [69]. Other adult human cells have also been used to culture and expand hESCs including the fallopian tube, the foreskin, marrow-derived stromal cells, and the uterine endometrium [71]. In total, feeder cultures are not suitable for clinical application because of the release of undefined factors into cultures. Therefore, feeder-free systems with a defined medium were developed for culturing ESCs.
# FLGQM: Robust Federated Learning Based on Geometric and Qualitative Metrics
## 3 Results and Discussion
### Experimental Results
#### 3.2.2 The Variant of FLGQM  
FLGQM has two key features: it uses cosine similarity and Euclidean similarity to constrain the size and direction of the local model, and it uses union clients to perform quality metrics on the local model. Considering that the union client cannot run alone, we consider a variant of FLGQM called FLGQM-NoQMS. In this variant, we remove the union client and only consider the effect of geometric metric scores.  
Table 4 shows the accuracy of this variant with median, Trim_mean, Krum, and FLGQM under different attack methods. It is evident that the accuracy of FLGQM-NoQMS decreases relative to FLGQM, indicating that the union client has a role in improving the robustness of federated learning. By incorporating quality metrics alongside geometric metrics, we can effectively counteract poisoning attacks that may go undetected if relying solely on geometric metrics. Through distributed quality assessment, we gain additional insights into the local models, enabling us to implement a comprehensive defense strategy against poisoning attacks and achieve higher accuracy rates. However, FLGQM-NoQMS is able to achieve similar results relative to methods such as the median. This shows that robustness with certain effects can be achieved by using only geometric metric scores.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Attacks** & **FedAvg** & **Median** & **Trim\_mean** & **Krum** & **FLTrust** & **MAB-** & **FLGQM-** & **FLGQM** \\ \hline \multirow{8}{*}{MNIST} & No attack & 98.51 & 98.25 & 98.22 & 95.01 & 97.84 & 97.95 & 97.98 & 98.39 \\  & LF attack & - & 97.79 & 97.41 & 93.51 & 97.40 & 96.60 & 95.94 & **98.03** \\  & LIE attack & - & 96.25 & 88.62 & 94.49 & 97.14 & 97.44 & 96.83 & **98.05** \\  & Fang attack & - & 96.17 & 95.64 & 94.75 & 97.10 & 97.45 & 95.53 & **97.97** \\  & AGRT attack & - & 95.18 & 95.84 & 74.19 & 97.03 & 97.14 & 87.64 & **97.63** \\ \hline \multirow{8}{*}{CIFAR-10} & No attack & 73.68 & 72.83 & 72.58 & 66.36 & 73.29 & 73.70 & 73.15 & 73.55 \\  & LF attack & - & 69.59 & 69.35 & 57.63 & 73.21 & **73.66** & 65.59 & 73.53 \\ \cline{1-1}  & LIE attack & - & 48.38 & 35.77 & 41.68 & 72.95 & 72.62 & 69.28 & **73.30** \\ \cline{1-1}  & Fang attack & - & 58.73 & 66.57 & 42.43 & 73.19 & 72.18 & 67.30 & **73.39** \\ \cline{1-1}  & AGRT attack & - & 23.06 & 53.77 & 10.00 & 72.52 & 70.55 & 56.34 & **72.63** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of compared federated learning algorithms on MNIST and CIFAR-10 with different attacks.
## 5 Conclusions
**Data Availability Statement:** The data presented in this study are available in the Google Earth Engine Data Catalog at [[[https://developers.google.com/earth-engine/datasets](https://developers.google.com/earth-engine/datasets)]([https://developers.google.com/earth-engine/datasets](https://developers.google.com/earth-engine/datasets))] (MODIS MCD12Q1 v6.1 [63] (accessed on 19 January 2025), NTSG Landsat Gross/Net Primary Production [64] (accessed on 11 January 2025), Hansen Global Forest Change [66] (accessed on 11 January 2025), Monitoring Trends in Burn Severity [67] (accessed on 8 January 2025), MODIS MCD64A1 v6.1 Burned Area [68] (accessed on 8 January 2025), CHIRPS [69] (accessed on 8 January 2025), LandTrendr Landsat 5/7/8 [62] (accessed on 21 January 2025), MODIS MOD09 GA v6.1 [70] (accessed on 20 January 2025), MODIS MOD10A1 v6.1 [71] (accessed on 10 January 2025)); USGS Earth Explorer at [[[https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)]([https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)) (accessed on 22 January 2025)] (NAIP); and ORNL DAAC at [[[https://daac.ornl.gov/VEGFATION/guides/PhenoCam_V2.html](https://daac.ornl.gov/VEGFATION/guides/PhenoCam_V2.html)]([https://daac.ornl.gov/VEGFATION/guides/PhenoCam_V2.html](https://daac.ornl.gov/VEGFATION/guides/PhenoCam_V2.html)) (accessed on 24 January 2025)] (PhenoCam [65]). **Acknowledgments:** We appreciate our colleagues at the University of North Georgia, Institute for Environmental and Spatial Analysis, for their support and feedback on the project: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. As thru-hikers of the AT (A.R.I., D.F.R) and PCT (D.F.R. ), we also thank the Appalachian Trail Conservancy and Pacific Crest Trail Association organizations for their work preserving and protecting these vital landscapes. **Conflicts of Interest:** Author [PERSON] was employed by the company Jacobs Engineering but this company does not have any direct financial interest in the monitoring or management of the National Scenic Trail conservation lands considered in this research project. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Results and Discussions
### Threats to Validity Discussion  
Various factors can threaten the reliability of test results and monitoring data. It is essential to identify these threats to take corrective actions. In the case of the communication system being tested on GNU radio, the main limitation is the minimum sampling frequency, which is determined by the bandwidth of the communication link. If the sampling frequency is too high, signal processing could be impossible on resource-limited machines. It could also result in data loss if too many data are acquired or the monitoring frequency is too high. To prevent this, the testbed monitors the CPU usage of the system under test and that of the broker to alert in case of processing resource starvation. The monitoring data can be corrupted if the transmission nodes are not synchronized or the synchronization needs to be more precise. A synchronization mechanism that is precise enough for the monitoring data frequency needs to be used to avoid this.  
Figure 20: Evolution in time of some parameters of the tested adaptive OFDM transmission system developed in GNU radio.  
Ensuring that the IP packets and Ethernet frames sent and received by the application are adequately fragmented and defragmented is essential. If these operations fail, it can cause issues with the data flow at the output of the system being tested, resulting in incorrect statistics. To avoid this problem, the IP and Ethernet headers should be set according to the defragmentation mechanism implemented in the testbed.
## 4 Simulation Results
### Applications of Hyperparameters  
Considering the results in Section 4.2, we observed that those obtained in [1] do not improve. The hyperparameters of the different, previously created ML models are analyzed in this subsection to determine if improving performance metrics is possible after applying PCA.  
ML models such as DT, RF, SVM, KNN, and MLPC involve several of the hyperparameters we set before running. In contrast to direct, first-level model parameters determined during training, these second-level tuning parameters must often be carefully optimized to achieve maximal performance [18].  
To select an appropriate hyperparameter configuration for the specific dataset at hand, users of ML algorithms can resort to default values of hyperparameters specified in implementing software packages, or manually configure them, for example, based on recommendations from the literature, experience, or trial and error [18].  
There are many techniques with which to search for hyperparameters, from a simple grid or random search to iterative procedures or iterated F-racing [18]. This work uses simple grid and random search techniques to search for hyperparameters, training the models with the PCA dataset.  
In Appendix A, Table 11, shows the different ML models' input variables, data types, and hyperparameters, with their possible values. After an exhaustive search, the best hyperparameters found for the PCA dataset are show in Table 6: