esa-sceva/satcom-chunk-collection huggingface dataset:
# LaRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh
## 3 System Overview  
This section introduces an overview and practical challenges of LoRaWAPS. LoRaWAPS plans to deploy a large number of low-cost, low-power positioning anchors which are networked using the LoRa Mesh protocol. Through the coordination of a large number of anchor points, full signal coverage is provided for the region to avoid larger positioning errors caused by poor signal quality. The system architecture is shown in Figure 1. The system consists of a server, a gateway, a number of positioning anchors, and a number of targets.  
Tags are used to track the locations of goods or persons. The tag is portable and has long battery life. It is equipped with a large-capacity battery that can last for several years with a single charge. The tag weighs less than 100 g. LoRa technology enables wireless communication and ranges between tags and positioning anchors.  
Location anchors are placed within the region. The location coordinates of the anchors are known to the server. The wireless networking among anchor points and gateways is completed through LoRa Mesh protocol. Distance information can be obtained by TOF ranging between the anchor point and the tag. Data packets such as distance information and task instruction between the anchor point and the tag are transmitted bidirectionally between the gateway and the anchor point through wireless mesh networking. The anchor can be powered by solar energy or its own integrated large-capacity battery instead of  
Figure 1: System architecture.  
external power sources. Therefore, external power cables and network cables are not required for anchor deployment.  
The gateway is the source node in the entire LoRa Mesh network. It communicates with anchor points and tags through the wireless network, and is also connected to the server through wired access, playing the role of uploading and transmitting.  
The server is the \"brain\" of the system, which grasps the position coordinates and links state information of all anchor points. The server sends the ranging instruction to the anchor point and label through the gateway, receives the ranging result from the anchor point and label through the gateway, and estimates the location coordinates of the label based on collected data.
## 2 Previous Works
### Decision Level Fusion  
The third technique depends on the decision of fusion or refining an initial classification map with some post-processing methods. In this integration, deep learning models such as CNNs are first applied to obtain a classification map of the area. Then, the image data is segmented using a segmentation algorithm. Finally, the classification map obtained by the CNN is refined by a majority filtering method or some other post-processing methods [14; 15] (Lv 2018, Liu 2019,). [PERSON] et al. (2018) [16] proposed a method to refine a classification map produced by a CNN using image segmentation. They showed a significant improvement in classification accuracy over other traditional classifiers. [PERSON] et al. (2020) [17] applied a combination of OBIA and CNN to identify rock glaciers in mountainous landscapes. [PERSON] et al. (2020) [18] studied urban tree cover changes and their relationship with socioeconomic variables. They used data from satellite, Google Earth imageries, and light detection and ranging (LiDAR). In their approach, OBIA was used to refine and improve the tree heatmap obtained by a CNN. In addition, [PERSON] et al. (2020) [19] incorporated the multiresolution segmentation into the classification layer of U-net and DenseNet architectures for land cover classification. They also used a voting method is applied to optimize the classification results. While studies have highlighted the significance of the decision level fusion techniques, this method does not fully utilize the OBIA method as no features are used for classification.
## 4 Results and Discussions
### Comparison of 3D Position Estimation Results
The U-Net++ and Xception models were used in our framework as a backbone to detect the QR code area for the QR code decoding in the video frame. We continue to develop the deep learning field, including the segmentation model, and expect that the improved segmentation model and backbone will improve the 3D position estimation performance. Moreover, the association with the estimated position error of the inventory and the position of the drone may be further studied. In addition, DBSCAN was used in the proposed framework to detect the outliers of estimated drone positions. DBSCAN can be used to detect the incorrectly estimated drone 3D position in the video frame because of continuity. Our framework can estimate 3D position for all types of objects regardless shapes or surfaces if the QR code is attachable. We used the ground QR code for 3D position estimation of the inventory QR code. Even if the QR code used to estimate the 3D position of the inventory QR code is attached to the wall or somewhere else, the proposed framework can perform the 3D position estimation of the inventory QR code. Instead, the QR code used for position estimation should have a 3D coordinate for the attached position. However, the proposed framework has a limitation in that it cannot estimate the 3D position of the invisible object, since only the 3D position of the object within the video frame captured by the drone's camera can be estimated. Furthermore, because of the limitations of the segmentation model, it does not guarantee 100% accuracy of outlier detection, due to the detection and decoding error of the QR code in certain frames, or errors in the estimated drone 3D position. We proposed the error correction method of 3D position estimation utilizing the continuity of the video frame. However, the proposed framework requires segmentation results of all video frames. In addition, 3D position estimation is performed for all pairs of time points at which the QR code is recognized. As a result, as the video frame increases by \(n\) times, the time consumption on segmentation increases by \(n\) times and the time spent on position estimation increases by \(n^{2}\) times. In the proposed framework, the performance of deep-learning-based QR code segmentation is very important. We constructed the synthesized QR code dataset for training the QR code segmentation model. High-quality data are required for training the deep learning model. Research related to efficient data augmentation has been conducted [61]. In addition, a hyperparameter optimization method has been proposed to improve the performance of deep learning models [62]. If the data augmentation and hyperparameter optimization methods are applied in the future, the performance of the QR code segmentation model may be improved. Although this study focused more on inventory management automation using the QR code and the drone-mounted monocular camera, it can be supplemented with additional efforts, such as existing studies and solutions for object detection.
# Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda
## 2 Materials and Methods  
### Study Area  
The current study area focuses on the southeast region of the state of Texas, the largest state in the contiguous United States, with three climatic regions. The regions differ from each other based on hydrometeorological conditions, topography and land cover. The western region is typically arid and dry, the central and eastern regions are under humid climatic conditions, and the southeastern region is predominantly wet, with subtropical weather conditions [15; 62; 63]. The southeast region is one of the most vulnerable regionsin the United States to extreme precipitation events such as hurricanes, tropical storms, and flash-floods. Tropical Storm Imelda struck the southeast coastal regions of Texas and caused massive flooding in the Houston and Galveston areas extending along the I-10 corridor from Winnie eastward to Fannett, Beaumont, Vidor, and Orange Texas. It was the second most expensive natural disaster in the contiguous United States in 2019 costing a total of 3.5 billion. Our current study area ranges from 93.4\({}^{\circ}\) to 106.7\({}^{\circ}\) West in longitude and 25.7\({}^{\circ}\) to 36.6\({}^{\circ}\) North in latitude. Within this study area, data from radar and GPM IMERG products were used for precipitation estimate analysis. Figure 1 shows the path tracking of the storm as it made landfall in Freeport, TX on 17 September 2019, and continued inward in the latter days.
## 3 Bearing Fault Diagnosis Based on Deep Learning
### Network Model and Detailed Parameters  
The bearing vibration signal sequence has the time correlation property, and the recycle neural network has good time correlation sequence processing ability. Therefore, this paper introduces a GRU recurrent neural network to process the sequence features extracted from the convolutional layer. By combining the convolutional neural network and GRU network, the time-dependent sequences can be processed more efficiently. The utilization of the features can be improved by automatically extracting the intrinsic features of the signal using the convolutional neural network. The GRU network can then enable further processing of the features to improve the network's ability to process time-correlated sequences. The approach combining the advantages of both neural networks can increase the ability of the network to cope with bearing fault signals in complex situations, especially strong noise situations [43].  
The network model proposed in this paper is shown in Figure 7. After the time-domain signal is extracted by FFT features, the first layer of the multi-scale large convolutional kernel, multi-layer 3 \(\times\) 1 convolutional layer and 2 \(\times\) 1 pooling layer, and GRU recurrent neural network are then used for feature extraction to further classify the rolling bearing state.  
Figure 6: Multi-scale first-layer wide convolution kernel.  
Figure 7: Structure of the network model.  
The detailed network parameters are shown in Table 4, with a Relu activation function layer following each convolutional layer. The purpose of introducing the activation function is to make the otherwise linear model nonlinear, allowing the model to handle linearly indistinguishable problems.  
To suppress neural network overfitting, Dropout is introduced in this paper after the first large convolutional kernel and GRU layer of the multi-scale. Usually, Dropout is artificially set to 0.5 or 0.3, so the probability of lost neurons \(p\) is set to 0.3 for all Dropout layers, and the I2 regularization factor is introduced to \(10^{-4}\) in each convolutional and GRU layer.
## 2 Materials and Methods
### ANN Modeling
#### 2.7.6 Experimental Data  
The experimental test was conducted from 16 October to 19 November, 2019, in Xinzhou, Shanxi Province, China. During the test period, the local ambient temperature changed a lot (e.g., the \(T_{0}\) on 16 October varies from 8.6 to 12.8 \({}^{\circ}\)C while the \(T_{0}\) on 17 November varies from \(-\)6.2 to 0.32 \({}^{\circ}\)C). In order to investigate the influences of ambient conditions on the drying behavior of the drying system, 327 groups of drying data for three days (17 October, 30 October, and 19 November) with significant \(T_{0}\) difference were selected to be analyzed in the present work. The experimental data were measured every 5 min interval, and the data with obvious errors (e.g., the imprecise measurement of the grain moisture sensor caused by the ice phenomenon inside the grain at the beginning of the test, or the random error caused by the system failures) were eliminated during the process. The selected data were normalized followed by Equation (13) before the ANN modeling. The partial experiment data and the related calculation results are tabulated in Table 3.  
\[X=\frac{X_{i}-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}} \tag{13}\]  
Figure 4: The train process of the established models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Day**} & \multicolumn{8}{c}{**Input Neurons**} & \multicolumn{8}{c}{**Output Neurons**} \\ \cline{2-13}  & \(t\)**/min** & \(T_{0}\) & \(RH_{0}\) & \(T_{a,in}\) & \(T_{a,out}\) & \(T_{c}\) & \(T_{f,in}\) & \(RH_{a,out}\) & \(T_{r}\) & \(MC\) & \(\eta_{ex,b}\) & \(Er\) \\ \hline \multirow{6}{*}{17 October} & 40 & 8.65 & 44.76 & 102.6 & 34.1 & 30.8 & 927.5 & 99.9 & 97.6 & 29.5 & 0.43 & 19.39 \\  & 75 & 9.21 & 54.8 & 108.2 & 32.3 & 28.2 & 861.1 & 99.9 & 102.5 & 28.1 & 0.52 & 20.44 \\ \cline{1-1}  & 125 & 9.5 & 54.8 & 117.7 & 38.8 & 30.3 & 930.7 & 99.2 & 97.8 & 24.3 & 0.51 & 19.43 \\ \cline{1-1}  & 270 & 12.56 & 55.54 & 114.4 & 35.3 & 31.2 & 867.1 & 77.6 & 96.6 & 17.2 & 0.54 & 19.18 \\ \cline{1-1}  & 435 & 8.53 & 53.32 & 107.7 & 46.1 & 35.4 & 880.9 & 64.2 & 104.1 & 14.8 & 0.51 & 20.79 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Partial experiment data and the related calculation results.
## 2 Passive Systems
### Chipless Techniques
The power level between a wet and a dry condition varies more than and it can be read at a distance of 3 m. In addition to humidity, it is also possible to measure other parameters like temperature. A possible approach is to use a transducer based on a capacitor with a micro-fluid channel that changes its permittivity according to the temperature. In this way, temperature changes would change the reflected power. The work presented in [103], uses this technique for a frequency of 29.75 GHz resulting in a variation of between 24 \({}^{\circ}\)C and 33 \({}^{\circ}\)C. Instead of encoding information in the reflected power, phase modulation encodes the reflecting signals at different phases, see Figure 12. To do that, the transponder needs to have different load terminations and high impedance stubs. [PERSON] et al. [104], tested this theory developing three different patch antennas and measure their response with and without a stub length of 10.9 mm. The results have shown that it is possible to change the reflection by a 30\({}^{\circ}\) phase shifter, changing the unique code. In [93], [PERSON] et al. presents the development of a high order PSK chipless tag. The tag is composed of line sections which can be removed and added (glued) to create a re-configurable tag that can realize different ID. The final results show a chipless device capable of 16-PSK.
# Improvements to Supercomputing Service Availability Based on Data Analysis
## 2 System Configuration  
### Hardware Configuration  
The Nurion system is a cluster-type supercomputer [9], the hardware architecture of which is illustrated in Figure 1. It consists of various infrastructure nodes connected by a high-performance interconnect, including computing nodes, storage systems, login nodes, management nodes, datemover (DM) nodes, and web servers.  
The computing nodes that perform parallel processes at high speeds consist of an 8305 Intel many-core processor Knights Landing Nodes (KNLs) [10; 11] and a 132 Xeon server processor Skylake Nodes (SKLs) [12]. Each KNL node has 68 cores per socket and 96 GB (16 GB \(\times\) 6) memory, while each SKL node has two sockets, each of which has 20 cores and 192 GB (16 GB \(\times\) 12) memory. The interconnection network uses a 100 Gbps Intel Omni-Path Architecture (OPA) [13], and the storage uses a Lustre parallel file system [14], consisting of a user home directory (/home01), an application directory (/apps), and a user working directory for global shared scratch (/scratch) with 21 PB or more available space. To improve the speed of programs using large-scale bulk data, a burst buffer (/scratch_ime) with 800 TB or more storage capacity was deployed to provide file input/output (I/O) at a maximum speed of 800 GB/s. Tape storage with a capacity of 10 PB or more was developed to backup important system data and for long-term storage of user data. This was connected to the DM node, which handles data transfer through the  
Figure 1: Hardware architecture of the Nurion supercomputer.  
fiber channel (FC) network. The infrastructure nodes, such as the login nodes, for users of the supercomputing service and the management node for system administrator access, are connected to the Korea Research Environment Open NETwork (KREONET) [15] through a 10 Gbps Ethernet.
### Fuzzy Inference System  
The fuzzy inference system deals with imprecise and fuzzy data. It calculates the output by using fuzzy logic. Fuzzy logic will return output between the range of 0 to 1 [21]. Principally, it is used in the development of expert systems for classification and makes the decision on it as the human brain does. The decision depends on the rules stored in the knowledge base of the inference system. The rules are in the IF-THEN manner [22].  
There are four main elements used in the fuzzy inference system. These elements are given below:  
* Fuzzifier: It transforms given inputs into fuzzy values.
* Fuzzy Knowledge Base: Facts and rules are stored into the knowledge base
* Inference Engine: It maps the rules corresponding to fed inputs and gives results.
* Defuzzifier: It transforms the result of the inference engine into crisp values.  
These elements are combined with each other, as elaborated in Figure 2.
## 1 Introduction
Thirdly, default topologies are always mapped to the nodes, whether the knowledge of the workloads is known or not, creating an overhead and resulting in a dip in performance, not only at the task at hand but also in the system as a whole. To rectify these challenges, we propose and extend the idea of our previous work [12] that has the aptitude of utilizing a variety of different metrics such as late element frequency in the incoming workload, as well as both currently recorded throughput of the system and the runtime measured latency value to control the inherent latency problem as quickly and as efficiently as possible. Unlike the default system, it makes the task tracker's buffering timeout and max out-of-order-ness dynamic, which can be changed and maintained according to the Service Level Agreements (SLAs) with the users. We propose three varying modes: automated, semi-automated, and manual, to successfully maintain the trade-off between latency and throughput using max-out-of-order-ness and window correctness. In case of manual mode, the system require the administrator to provide target latency, priority proportion of throughput and correctness, lower limit of throughput, and lower limit of window correctness to decide and forward the decision to the streaming environment. Semi-automate mode requiresthe administrator to provide target latency and preferred throughput, while in automated mode the system only needs target latency as an input. Following suit from our proposal, Section 2 highlights the aforementioned problem we must handle, and Section 3 introduces our proposed system. Section 4 explains a detailed use-case scenario, Section 5 offers a thorough evaluation of our work, Section 6 describes some similar projects, and Section 7 concludes the paper.
## 3 Results and Discussion
### Anti-Inflammatory Potential  
#### 3.4.1 Inhibition of Albumin Denaturation  
Inflammation is a complex defense mechanism of the body that fights off dangerous microbes or damaged cells [54; 55]. Nonetheless, this response needs to be controlled to avoid the development of clinical disorders linked to the immune system [3]. Disorders of inflammation are the body's response to tissue damage. The body is more vulnerable to the development of cancer, neurological illnesses, and autoimmune diseases when unchecked acute inflammation leads to chronic inflammation [56; 57]. Conversely, chronic inflammation is a long-term response that is characterized by a gradual alteration in the cells that are present at the site of inflammation [57; 58]. The immune response involves the nuclear factor-kappa B signaling pathway. It is essential to inflammatory processes due to its involvement in the transcription of cytokines such as nitric oxide (NO), interleukin 18 (IL-18), tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\)), and interleukin-6 (IL-6). Substances that  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Compound** & **MW, g/mol** & **XLOGP3** & **ESOL Log S** & **TPSA, Å2** & **RB** & **BA** & **SA** & **LD\({}_{\text{30}}\), mg/kg** \\ \hline
**5** & 483.58 & 6.60 & \(-\)7.35 & 107.41 & 3 & 0.55 & 3.22 & 5000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ADME parameters of the SQ (**5**) representing drug-likeness.  
can disrupt this route are of interest to the pharmaceutical sector [59; 60; 61]. Patients with inflammatory illnesses are most frequently prescribed glucocorticoids or nonsteroidal anti-inflammatory drugs. The severe adverse effects of these drugs, like bleeding and stomach ulcers, have decreased their therapeutic effectiveness and usually lead to patients stopping their therapy [62]. In this context, the pharmaceutical industry has focused its efforts on finding novel bioactive compounds that have fewer adverse effects and superior therapeutic outcomes.  
Based on its structure, the in silico calculations predicted the anti-inflammatory potential of SQ (**5**). Therefore, the main goal of this research was to establish the anti-inflammatory potential of SQ. In vitro antidenaturation of albumin was used to evaluate its anti-inflammatory properties. The obtained data showed that the IC\({}_{50}\) of the compound was 350 \(\upmu\)g/mL, while the IC50 of diclofenac was 471.3 \(\upmu\)g/mL, which confirms a better anti-inflammatory effect of SQ (**5**). We can assume that the better anti-inflammatory effect of SQ is also due to the presence of a sulfonate group in the tosylate counterion [63; 64].