esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Algorithm
### Partitioning Phase
#### 3.1.1 Local Density Is Calculated Using Kernel Density Estimation  
Kernel density estimation [35] (KDE) is a nonparametric density estimation function that can estimate the probability distribution of data without assuming a density distribution. The motivation of this paper is to partition the subclusters using KDE to evaluate the density differences of the data in each dimension. The kernel density estimation function can be expressed as Equation (3):  
\[f(x)=\frac{1}{\sqrt{2}nh}\sum_{i=1}^{n}\exp\left(-\frac{x-x_{i}}{h}\right) \tag{3}\]  
where \(h\) represents the kernel density bandwidth and the optimal bandwidth \(h_{d_{o}}\)[35] is defined as follows:  
\[h_{d_{o}}=0.9n^{-\frac{1}{d+2}}*\min\quad\left(\text{Std}\quad\left(X_{v}, \frac{\text{IQR}(X_{v})}{8}\right)\right) \tag{4}\]  
where \(n\) represents the data size, \(h_{d_{o}}=1,2,3,...,v\) denotes the bandwidth of the \(vth\) dimension, and \(IQR\) is the same as defined above.
# Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building
## 4 Proposal and Experiment
### Unified API Design  
In order to solve the above-mentioned problems, the development of a unified API for interoperability among IoT devices becomes a fundamental aspect. Unifying the device API would ease and accelerate the new service development in the smart building, which brings innovation and productivity. However, unifying of device API is considered challenging, because the heterogeneous devices with different functionalities have different specifications and configurations. A unified API has to cover all IoT devices and simplify the properties of such devices.  
Based on the exploration of device properties, we designed a unified API for receiving the monitored state information in real-time. The details of API is interpreted by the following Figure 3. The following set of properties has been contained: (1) 'ucode' [22] that used as the identifier of the sensor node; (2) 'name' that assigned with the description of the node; (3) 'data', that composed by the sub-properties of 'instance' and 'time', with 'instance' showing the sensed value and 'time' indicating the timestamp.  
The 'ucode' [22] is a 128-bit fixed-length identifier. It could be used as a unique identification for associating the objects in different databases. While accessing the API for retrieving data, the 'ucode' is required to be given whereas the time duration is not necessary. The system would capture the monitored sensing data during the time window for a response if the parameters of 'from' and 'to' were assigned. Meanwhile, if the timestamp was omitted, only the latest data value would be responded.  
Table 1 lists several examples of sensor data that could be retrieved from the unified API. The timestamp value is automatically detected by the system, such as the example  
Figure 3: The unified API design.  
Figure 2: Overview of our proposal for knowledge extraction.  
of '2020-10-07 09:46:54'. In addition, as sensor features consists of both numerical values and logic state (on/off) causing computation complexity, the state data are converted to numerical data with logical meanings, where 1 represents on/active/open, etc; 0 represents off/inactive/close, etc.
## 3 Results and Discussion
### Optimization of Cycle Times
#### 3.2.2 Switching Time  
During switching time optimization, the adsorption/desorption and heat recovery times were kept constant at 300 and 50 s, respectively. Figure 5 shows the average cooling capacity and COP. It is very clear that the optimum switching time considering cooling capacity and COP is between 40 and 50 s. The reason is that the beds are fully pre-cooled/pre-heated and further heating/cooling of the beds increases the switching time; thus, the average cooling capacity is reduced since during the switching time, the chiller is not producing a refrigeration effect.
# Sparse-View Artifact Correction of High-Pixel-Number Synchrotron Radiation CT
## 3 Results
### Selection of the Optimal Sparsity Ratio  
To enable the application of the method to actual CT imaging, our attention is directed towards establishing a projection sparse ratio, aiming to achieve high-quality artifact correction while expediting experiments with reduced projection data. Accordingly, we conducted artifact correction at 100, 200, 300, and 400 views, as depicted in Figure 7. Figure 7(a1-e1) illustrates the local zoomed-in image of the red box region in Figure 7a-e. The experimental results demonstrate that a higher number of projections leads to richer image information after algorithmic correction, becoming closer to the ground truth. Additionally, Figure 7f,g presents quantitative image quality evaluations using PSNR and MS-SSIM for varying sparsity levels in the test set. It is noteworthy that although the image quality improves with an increasing number of projections after applying the algorithm, the rate of improvement diminishes. Meanwhile, the experimental time and radiation damage to the samples also increase, indicating a diminishing return. The trade-off between rapid experimentation and artifact correction is a critical consideration. For this case, we believe that the choice of the sparsity ratio should depend on the experimental task. For example, if it is necessary to observe a salient target (e.g., the red arrow in Figure 7), it is appropriate to choose 100 views, which can save 11/12 of the experiment time. After being processed by the deep learning method, the signal-to-noise ratio of the image is improved, which is conducive to subsequent data processing operations such as feature segmentation. Moreover, if the focus is on restoring the detailed information of the slice (such as the blue  
Figure 6: Correction of aliasing artifacts in the TiW dataset under 200 views. (**a**) FBP reconstruction slice, (**b**) slice processed using the proposed method, (**c**) reconstructed slice image at 1200 views. (**a1–c1**) Magnified images of the areas within the red box in (**a–c**). (**a2,b2**) Absolute error images between (**a,b**) and the label images. The display window is [0, 0.06] for the absolute error images.  
arrows in Figure 7), it is a suitable choice to choose 400 views, which can save 2/3 of the projection data.
# A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
## 4 Proposed System Model
### Discriminative Performance Evaluation Metrics  
Because every ML model is unique to its architecture, it becomes necessary to exhaustively explore each model's drowness for diagnostics while also considering other factors such as model complexity, computational costs, parametirization, etc. This presents the need to employ standardized diagnostic/discriminative performance evaluation metrics. These metrics include accuracy, sensitivity, precision, F1-score, and false alarm rate (FAR) [40]. These criteria are defined, respectively, as (4)-(8).  
\[\text{Accuracy }=\frac{TP}{TP+FP+TN+FN} \tag{4}\]  
\[\text{Sensitivity }=\frac{TP}{TP+FN} \tag{5}\]  
\[\text{Precision }=\frac{TP}{TP+FP} \tag{6}\]  
\[\text{F1-Score }=\frac{2*\text{Sensitivity }*\text{Precision }}{\text{Precision }+\text{sensitivity}} \tag{7}\]  
\[\text{FAR }=\frac{FP}{FP+TN} \tag{8}\]  
where \(TP,FP,TN\), and \(FN\), respectively, are the number of correctly classified classes, number of incorrectly classified classes, number of incorrectly labeled samples belonging to a class that were correctly classified, and the number of incorrectly labeled samples belonging to a class that were incorrectly classified.  
Although these metrics provide a global perspective for evaluating model classification performance, it may become necessary to evaluate each model's class-specific performance to ensure a more comprehensive performance assessment. For instance, a classifier may return an overall classification accuracy of 90% over a five-class problem set. This high accuracy may emanate from the model's strengths for correctly classifying three out of the five classes, whereas it may flaw on the remaining two classes. On the contrary, another model may return the same level of accuracy but its class-specific classification performance may be fairly uniform for each class; hence it would be more reliable than the previous model. This is usually the case, and presents the need for the confusion matrix, which provides an avenue for evaluating each model's class-specific diagnostic performance.
## 3 Experimental Phenomena and Results
### Experimental Results  
Table 6 presents the data for the specimens examined in the experiment. Comparative analysis of the experimental results reveals that when the bonded anchorage lengths of the grout anchor steel bars are L\({}_{\rm{ab}}\) and 1.2 L\({}_{\rm{ab}}\), the failure mode is predominantly steel bar pull-out failure. This occurs even with low- to medium-strength grouting materials equivalent to C40, C50, and C60, corroborating the conclusion that the basic anchorage length prescribed in the code is reasonable. Conversely, when the anchorage length is less than L\({}_{\rm{ab}}\), all specimens, regardless of grouting material strength, experienced slip during loading and exhibited anchorage failure and corrugated pipe pull-out failure before reaching the yield stage. The ultimate load in these cases was significantly lower than that observed with steel bar tensile failure, suggesting that using low-strength grouting materials can compromise the bond anchorage performance in grout anchor connections, thereby failing to meet their performance requirements.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Specimen** & **Yield Load** & **Ultimate** & **Yield Strength** & **Ultimate** & **Elongation of Grout** & **Free-End Slip** & **Destruction** \\
**Number** & **(dN)** & **Load (kN)** & **(MPa)** & **Strength (MPa)** & **(mm)** & **(mm)** & **Mode** \\ \hline
40-0.4 & — & 20.22 & — & 131.35 & 14.0 & — & anchor failure \\ \hline
40-0.6 & — & 35.21 & — & 228.73 & 15.3 & — & anchor failure \\ \hline
40-0.8 & 62.61 & 70.32 & 406.92 & 456.81 & 38.7 & — & anchor failure \\ \hline
40-1.0 & 63.23 & 90.32 & 410.75 & 586.73 & 79.9 & 3.8 & tensile failure \\ \hline
40-1.2 & 65.95 & 90.72 & 428.42 & 589.33 & 65.9 & 1.7 & tensile failure \\ \hline
50-0.4 & — & 20.53 & — & 133.37 & 13.1 & — & anchor failure \\ \hline
50-0.6 & — & 35.26 & — & 229.05 & 14.0 & — & anchor failure \\ \hline
50-0.8 & 62.45 & 80.05 & 405.68 & 520.01 & 48.0 & — & anchor failure \\ \hline
50-1.0 & 64.41 & 90.31 & 418.42 & 586.67 & 82.1 & 2.7 & tensile failure \\ \hline
50-1.2 & 68.04 & 90.35 & 441.99 & 586.92 & 59.2 & 1.8 & tensile failure \\ \hline
60-0.4 & — & 50.22 & — & 326.24 & 14.5 & — & anchor failure \\ \hline
60-0.6 & — & 53.21 & — & 345.66 & 20.0 & — & anchor failure \\ \hline
60-0.8 & 63.09 & 80.25 & 409.84 & 521.31 & 47.5 & — & anchor failure \\ \hline
60-1.0 & 67.58 & 90.33 & 439.01 & 586.79 & 65.1 & 1.9 & tensile failure \\ \hline
60-1.2 & 69.68 & 96.05 & 452.65 & 623.95 & 61.6 & 0.9 & tensile failure \\ \hline
50-0.8-200 & 63.12 & 83.62 & 410.03 & 543.21 & 48.1 & — & anchor failure \\ \hline
50-0.8-400 & 63.16 & 84.25 & 410.29 & 547.30 & 47.8 & — & anchor failure \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results.
# Effects of Gradient Warming on Carbon and Water Fluxes in Zoige Plateau Peatland
## 3 Results
### Correlations of Carbon and Water Fluxes with TS and SWC  
The TS was found to be significantly negatively correlated with GPP (\(p<0.05\)) and E (\(p<0.01\)), while it exhibited a positive correlation with RE (\(p<0.01\)), NEE (\(p<0.001\)), and ET (\(p<0.01\)). The SWC exhibited a significant negative correlation with GPP (\(p<0.05\)) and a pronounced positive correlation with T (\(p<0.01\)). WUEc and WUEe exhibited significant negative correlations with both RE and ET (\(p<0.01\)). Moreover, the ratio of T to ET (T/ET) showed a significant negative correlation with GPP (\(p<0.05\)) and a positive correlation with RE (\(p<0.01\)) (Figure 5).  
Figure 4: Dynamics of canopy water use efficiency (WUEc, **a**), ecosystem water use efficiency (WUEe, **b**), and T/ET (**c**) under gradient warming treatments.  
Figure 3: Dynamics of soil evaporation (E, **a**), plant transpiration (T, **b**), and evapotranspiration (ET, **c**) under gradient warming treatments.
# First Request First Service Entanglement Routing Scheme for Quantum Networks
## 5 Evaluation Results
### Evaluation on Large-Scale and Dynamic Networks  
We also evaluate the FRFS routing scheme on a network topology with 20 nodes and 40 edges, which is shown in Figure 7. S is the quantum server which contains an entangled photon source and a WSS. A, B are users and other nodes are WSSs. The loss of WSS is assumed as 5 dB.  
As the topology may change dynamically in a large-scale network, the scheme is first evaluated on the full graph G in Figure 7 and then evaluated on the graph G with some edges cut. The assigned wavelength channels for users A and B are CH33 and CH35 respectively. The lowest paths under different topologies are calculated by the FRFS routing scheme, which is shown in Table 5. The edge loss of the path found between user A and user B is 15.3 dB when no edges of graph G are cut (without the double loss of the WSS in S and the insertion loss of intermediate nodes H, L, O). Assuming that the unit loss of the fiber is 0.2 dB/km, the fiber length between A and B can reach 76.5 km, which is longer than the point-to-point links in most metropolitan area networks [24; 32; 55; 56]. Thus the evaluation results show that the scheme can be applied in the large-scale metropolitan area network.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **Path** & \(I_{A}\) **(dB)** & \(I_{B}\) **(dB)** & **CC\({}^{m}\) (kcps)** & \(R^{s}\) **(cps)** \\ \hline \(G\) & AHLSOB & 21.9 & 18.4 & 10.68 & 171.26 \\ \(G\)-\(e_{AH}\) & AELSOB & 23.4 & 18.4 & 7.56 & 121.12 \\ \(G\)-\(e_{OB}\) & AHLSIFB & 21.9 & 21.4 & 5.35 & 85.74 \\ \(G\)-\(e_{SL}\) & AHTSOB & 22.2 & 18.4 & 9.97 & 159.80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Paths found by the FRFS routing scheme. \(G\) represents the full graph in Figure 7, \(G\)-\(e_{AH}\) represents the graph \(G\) without edge between node \(A\) and \(H\).  
Figure 7: Network topology with 20 nodes and 40 edges. S is the quantum server which contains an entangled photon source and a WSS. A, B are users and other nodes are WSSs. The loss of WSS is assumed as 5 dB. The numbers on the edges indicate the link losses. The WSSs in the network can support 16 channels which contain CH26, CH27, \(\cdots\), CH41, CH42.
# Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark
## 2 Literature Review
### Dataset Quality and Reliability Issues  
Data quality is important and greatly affects the reliability and robustness of an IDS. Data quality problems impact model predictions and impact security experts' decisions that rely on IDS results. From an IDS perspective, it is important to not only train ML-based models on clean and reliable data, but it is also important to diversify the data to cover a vast range of traffic patterns and minimize the model's biased output towards limited traffic patterns. In ML, some of the common data quality issues consist of noisy or insufficient labeled data, imbalanced data, duplicate data, incomplete data, and inconsistent data. A dataset consisting of a few attack patterns cannot fulfill the IDS purpose. In the preprocessing steps of ML-based modeling, data quality issues are fixed using statistical or manual techniques to ensure models are trained with clean and reliable data only.  
In [48], the authors emphasized the impact of poor-quality data on ML-based models' performance. The authors argued that very few research studies pay attention to data requirements and quality issues. The authors presented eight data quality issues relevant to ML-based IDS. These include reputation, relevance, comprehensiveness, timeliness, variety, accuracy, consistency, and de-duplication. In [49], the authors empirically analyzed data quality issues and stated that the data quality issues in ML-based models cause compounding negative events, create downstream effects, and become a technical burden to manage over time.  
Today, many ML-based IDS solutions are proposed by training the model on a single dataset. These models are proposed to assume that the training labels are accurately identified. However, this assumption is not fully accurate because the labels are manually created by a security expert who may lack the appropriate judgment; similarly, imbalanced class labels can impact the performance and accuracy of an ML model [50]. There are different approaches adopted by the researchers to mitigate dataset quality and reliability issues, such as removing noise in the labels, increasing unique input data instances to statistically represent the population, utilizing techniques such as bootstrapping or SMOT (Synthetic Minority Over-sampling Technique) [5] to overcome class imbalance issues. In this paper, we improved the data quality issues that may cause biased results by performing various preprocessing steps to clean the noise from the data and trained various models on multiple datasets containing unique attack classes and traffic patterns.
## 4 Algorithm Description  
### Overview  
ADG adopts an adaptive and schedule-based strategy for the data gathering at mobile wireless sensor networks. A round of data gathering task in ADG could be roughly divided into 4 phases:  
1. **Meta-data Extraction**: nodes collect network meta-data to facilitate cooperative data collection. Each node extracts and calculates meta-data such as locations, number of contacts, contact duration, _etc_. 2. **Proxy Node Selection**: \(MC\) collects the meta-data, extracts parameters and calculates a set of proxy nodes. Proxy nodes act as intermediate storage, where other nodes would send their sensed data to them through opportunistic communications. 3. **Visiting Order Scheduling**: \(MC\) adopts a schedule-based strategy for the data gathering. The visiting schedule of proxy nodes includes three aspects: (a) when should \(MC\) start its journey for the data gathering; (b) which nodes are going to be visited by \(MC\); and (c) where and according to which order \(MC\) is going to visit the nodes and pick up the data. 4. **Data Gathering from Proxy Nodes**: \(MC\) travels to each of the predicted data gathering points according to the schedule and collects the sensed data from the proxy nodes. When a round of data gathering ends, \(MC\) goes back to the \(sink\) area to upload the collected data and replenish its energy. Steps 2-4 are repeated as the data gathering task is epoch-based. The meta-data gathering and proxy node selection are preparation process for the coming of mobile data collector: data is routed and stored at the proxy nodes until \(MC\) comes and picks them up. Compared with other message forwarding protocols in MON, data messages in ADG have clearer targets--the proxy nodes, which have high probability to encounter \(MC\) and uploads their data when following the optimized data gathering schedule. Figure 2 illustrates an example of data gathering schedule within an epoch, where the length of a round of data gathering is 60 min (\(\Upsilon\)) and the length of epoch is 12 h (8:00-20:00). According to the optimized schedule, \(MC\) would begin the data gathering trip at 9:17, and there are 5 nodes at the set of to-be-visited proxy nodes, where each time slot of visiting is denoted by the colored rectangle. The overall goal of ADG is to gather the maximal amount of data from nodes during the constrained time period at the right place, and to avoid redundant or long hops of message transmissions.
## 1 Introduction  
The long-term effects of the space environment on the central nervous system (CNS) still remain largely unknown. Human space travelers experience a unique environment that affects homeostasis and physiological adaptation. In particular, neuro-psycho-physiological health deserves special attention to ensure successful long-term space missions. In recent years, studies have started to address neural function and behavior in space. It has been reported that cerebellar, sensorimotor, and vestibular brain areas seem to be the most affected [1]. Microgravity reduces central venous and intracranial pressure; nonetheless, intracranial pressure is not reduced to the levels observed in the 90\({}^{\circ}\) degrees seated upright posture on Earth. Instead, the basal levels over 24 h in microgravity pressure in the brain are slightly higher than those on Earth, which may cause the remodeling of the eye in astronauts [2]. Moreover, a space flight-associated neuro-ocular syndrome (SANS) has been recently described [3]. At the cellular level, it has been shown that microgravity induces apoptosis, alters the cytoskeleton, and affects the differentiation, proliferation, and metabolic status in different cells [4]. Nonetheless, only a few aspects regarding the sensitivity of human neural stem cells (hNSCs) in microgravity have been reported. An interesting article from [PERSON] and collaborators [5] showed that murine cerebellar neural stem cells (NSCs) respond to simulated microgravity by the arrest of their cell cycle in S-phase and enhanced apoptosis. They also found that these changes were transient and upon return to normal gravity (1 G) these cells recovered their stemness and a normal cell cycle.  
The objective of the present study was to determine how the space environment influences human-induced pluripotent stem cell-derived NSCs, their biology, and their fate choice capability toward the neuronal phenotype.