esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Intelligent Fault Diagnosis of Sensor Faults
### Edge Computing-Based Fault Diagnosis Method  
Edge computing is a new computing model that deploys computing and storage resources (such as Cloudlets, and fog nodes) to networks closer to mobile devices or sensors. IoT-based edge computing falls into three main categories: local devices adapted to specific definitions and good targets, local data centers providing significant data processing and storage capacity, and regional data centers closer to the data source. In response to the limitations of agricultural land, edge computing can be applied to work over long distances, and the processing of data generated by sensors through edge computing can improve response times, reduce the amount of communication transmitted to the cloud, and avoid latency problems in data processing [143]. Control monitoring of data using edge computing allows data consistency and integrity analysis, identification, and removal of erroneous data. When an edge device fails, the edge computing system can notify the user which component is at fault, which facilitates sensor fault detection. [PERSON] et al. proposed a real-time detection algorithm based on edge computing and cloud computing to improve the average repair time by pre-processing the original video data when performing fault diagnosis on video surveillance systems [144]. [PERSON] et al. point out that sensor-based data collection systems suffer from significant latency and data redundancy, and edge computing is often applied closer to the source of data generation, i.e., data processing is performed locally, sharing the reduced data redundancy and solving latency problems [145]. Since edge computing will control in real-time, monitor the process of data processing, and send the analysis results to the cloud, it is convenient for managers to view the working status of sensors in time and make judgments on whether the sensors produce faults. To solve some of the problems of wireless sensor data collection, wireless sensors are combined with edge computing to establish data collection algorithms. In wireless sensor networks, through edge servers, invalid data is filtered, sensing parameters are obtained, and the collected data is transmitted to the cloud or edge servers to improve sensor networks' intelligence and computing capability [146], laying the foundation for its fault diagnosis. [PERSON] et al. proposed that the data distribution of edge computing was heterogeneous and required distributed data processing and storage. On the basis of edge computing, they proposed High-Performance Computing (HPC), which can identify data anomalies by performing quality checks at the edge layer, which can help troubleshoot sensors to ensure accuracy and effectiveness. Users can view, analyze, and operate the data stored in the cloud and use the machine learning tools stored in the cloud database to analyze the data and make judgments when the sensor is faulty [147].
# PALANTIR: An NFV-Based Security-as-a-Service Approach for Automating Threat Mitigation
## 6 Validation
### Testbed Environment
#### 6.1.1 Use Case 1: On-Premises Testbed for E-Health Environment  
The testbed infrastructure for the protection of on-premises medical environments (e.g., medical offices) is presented in Figure 6. The lightweight deployment model aligns with this case. Under the provisions of such model, a standalone CPE HW equipment is purposely prepared to support the baseline PALANTIR security and detection capabilities. The equipment is scaled according to the requirements imposed by the hosted PALANTIR components and limited by the requirements of the Lightweight deployment mode; which features a lightweight, reduced footprint device. However, depending on the actual size and scale of the medical office's network, more advanced CPEs may be available. The CPE installed on-premises at the medical office is able to function in a standalone manner, without exposing local data. The only implemented interconnections are solely relevant (i) to manage and monitor the status of the CPE hosting the PALANTIR components; and (ii) to share detected attacks that may be related to other end-users.  
The testbed topology at the medical office is split into two zones: (i) the office equipment and users and (ii) the guest network where the customers are connected. The CPE lies past beyond these two networks, connecting them with the modem/gateway for internet access. At the medical office, the critical assets are (i) the Medical Records database, withholding all patient-related data (e.g., personal data related to operations, medicines or exams); and (ii) the equipment used for specific exams, which is connected to the local network in order to provide access to the results (e.g., ultrasound or X-ray equipment). The above setup may be categorised as a cyber-physical system. Under the provisions of this use case, this environment is prone to attacks that aim at compromising the network and networked assets; in the case of ransomware attacks on the medical data stored in the medical database, the data leakage of sensitive patient information or the generic availability attacks can deteriorate the ability to use networked assets or access remote services.  
The CPE (referred to as the \"Palantir Box\" in the middle of Figure 6) hosts the PALANTIR components that provide the baseline protection for the services in the medical office. The CPE inspects all incoming traffic and, in addition, logs from various monitored network nodes such as servers, PCs, and medical devices. When a particular type of attack is detected, specific measures are applied to mitigate it. In addition to this, the security service provider will be also notified of the incident and the end-user will also receive an additional notification.  
Figure 6: The testbed architecture of use case 1.
## 6 Methodology and Performance Metrics Measurement
### Normal Condition  
In this condition, the initial configuration requires the intelligent distribution of data and Internet traffic between two ISPs called ISP-A and ISP-B, to maximize network resilience and performance. The data traffic is going from the overlay network (Tunnel) and Internet traffic is going from the underlay network. Because of its capacity and speed, ISP-A serves as the primary connection in this system for the majority of data traffic. In addition, ISP-B acts as a backup connection for data flow, guaranteeing load sharing to effectively disperse network traffic. Conversely, internet traffic is routed via ISP-B, which serves as a primary link, making use of its unique benefits, including reduced latency or cost. Automatic failover techniques ensure continuous connectivity and minimize service disruption in the case of a link breakdown or degradation on the major pathways by smoothly redirecting the impacted traffic to the secondary links. The quick and automatic reaction helps to create a more resilient and efficient network architecture by reducing downtime and guaranteeing constant connectivity. This dual-ISP strategy offers a strong failover mechanism, enhancing overall network efficiency and providing a dependable and resilient network architecture.  
In this experiment for data traffic, ISP-A is working as a primary link and ISP-B is working as a secondary link. Similarly, for Internet traffic ISP-B is working as a primary link and ISP-A is working as a secondary link. In case of link failure, the traffic shifts to the secondary link accordingly.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Prediction Intervals Formulation and Evaluation Metrics
#### 2.5.5 Prediction Interval Normalised Average Deviation (PINAD)  
PINAD is an index for describing the deviation of the PIs from the actual values quantitatively [21; 31]. It is expressed as given in Equation (24)  
\[PINAD=\frac{1}{mR}\sum_{i=1}^{m}dv_{i} \tag{24}\]\(dv_{i}\) is defined in (25)  
\[dv_{i}=\begin{cases}L_{i}^{\alpha}-y_{i},&\text{if}\quad y_{i}<L_{i}^{\alpha},\\ 0,&\text{if}\quad L_{i}^{\alpha}\leq y_{i}\leq U_{i}^{\alpha},\\ y_{i}-U_{i}^{\alpha}&\text{if}\quad y_{i}>U_{i}^{\alpha}\end{cases} \tag{25}\]
# Molecular Subtyping and Outlier Detection in Human Disease Using the Paralique Algorithm
## 3 Experimental Results
### A Search for Unrecognized Subtypes
#### 3.2.2 Breast Cancer  
Genetic factors have long been known to play a significant role in breast cancer. Studies have shown that in families with at least four breast cancer cases, most can be linked to mutations in either BRCA1 or BRCA2 genes [43; 44]. Moreover, breast cancer has a variety of known subtypes that significantly impact prognosis and treatment. For example, tumors negative for estrogen receptors, progesterone receptors, and the expression of HER2 are indicative of triple-negative breast cancer, a subtype identified with higher risk of recurrence and a five-year mortality rate [45].  
GEO series GSE10810 data were also derived from the Affymetrix Human Genome U133 Plus 2.0 Array, although values for only 18,382 probes were provided. This study was designed to investigate links between gene co-expression and phenotypic breast cancer differences [46], and contains data for 31 tumor samples and 27 healthy tissues.  
Filtering reduced the number of probes to 11,531. Our tools produced two paracliques of size 22 and 5 that persisted to a threshold of 0.8 and left four tumor samples unclassified. The 100 most differentially expressed genes between these putative subtypes include SLC39A6, S1004, AGR3, Cd24, and epcam, all of which have been reported in the literature as biomarkers for distinct breast cancer phenotypes [47; 48; 49; 50; 51].
## 1 Introduction
Both the controlled and uncontrolled interferences from IEEE 802.11 networks are taken into account. Figure 1: The 2.4-GHz industrial, scientific and medical (ISM) spectrum. Channel allocation for heterogeneous technologies with RF emissions within the band: IEEE 802.11, Bluetooth, microwave oven (MWO), IEEE 802.15.4. * The often overlooked influence of device calibration on spectrum sensing-based interference classification is analyzed, showing that the classifier accuracy is subject to the intrinsic hardware variations of the employed devices. However, we show that this factor can be easily corrected by means of a straightforward calibration process. The remainder of this article is structured as follows. In Section 2, relevant work available in the literature about interference classification and mitigation in WSNs is presented. Section 3 provides a general background of the topic, discussing the various sources of cross-technology interference, with specific interest in the IEEE 802.11 standard. In Section 4, the basic concepts and mathematical formulation for SVMs are explained. In Section 5, a detailed description of the proposed solution is given, highlighting feature selection and the structure of the proposed classifier. In Section 6 and Section 7, the experimental setup and the results from experiments are described. In Section 8, the achieved results are discussed, and lastly, conclusions and final considerations are drawn in Section 9.
## 3 Experimental Results  
The different fault types considered for this study are MF and four independent failure modes such as HDF, OSF, PWF, and TWF.  
In this study, firstly, the experiments which were conducted with SML such as DTs, SVMs, and k-NNs were performed using MATLAB2021b. In continuation to our previous study, training and evaluation processes for DL classification algorithms like 1D-LeNet, 1D-AlexNet, and 1D-VGG16 were performed on a Python-based Google Colaboratory (Colab) environment utilizing NVIDIA graphics processing units (GPUs), specifically the NVIDIA-SMI version 535.104.05 with CUDA Version 12.2, and with access to 15 GB of RAM.  
In the all following experiments, we used 5-fold cross-validation and then 4-fold was used for training and the remaining 1-fold for testing (80% for training and 20% for testing) and we obtained the average results for the performance metrics for all fault classification studies with an imbalanced and balanced dataset with and without feature normalization.  
In the Table 3 below, it is shown how the imbalanced and balanced datasets were generated as the number of rows labeled failure and non-failure for each fault types. Balanced datasets were generated 100 different times from the imbalanced data, each time ensuring the inclusion of all faulty data for each fault type, while non-failure data were randomly sampled to achieve balance each time.
## 1 Introduction
An automatic steering wheel alignment system was developed by [PERSON] et al. at China Agricultural University, which used a combination of LIDAR and alignment sensor contacts to detect and change the steering wheel deflection angle [13]. For corn harvesters, [PERSON] et al. at Shandong Agricultural Machinery Research Institute developed an autonomous alignment direction self-correction system that triggered the system via a sensing switch and also controlled the harvester travel direction using hydraulics [14]. [PERSON] et al. developed an electro-hydraulic circuit and integrated it into a small farm tractor's traditional hydraulic steering circuit [15]. To achieve speed and position control of the steering, a PID controller was employed to operate the hydraulic valve. [PERSON] et al. adopted a wheeled mobile robot as a test platform to effectively minimize the process of manually tweaking parameters in the path tracking task [16], allowing for improved driving of the controller's DC motor. [PERSON] et al. in South China Agricultural University designed an electric steering wheel rice transplanter steering control system [17], adopted electric steering wheel as steering actuator, studied the automatic steering control of rice transplanter, constructed a model of rice transplanter steering system, and designed a nested steering control algorithm based on PID. The system offers good dynamic responsiveness and control stability, according to the research. Through analysis, it has been determined that both electro-hydraulic control methods and motor control methods are extensively used in agricultural machinery and have been intensively investigated [18; 19; 20; 21; 22]. Consequently, there is a clear lack of research on the control method for automatic row alignment of corn harvesters, and it is necessary to design a set of control methods with high accuracy and low cost that are suitable for automatic row alignment of corn harvesters. In general, the steering fine-tuning control is implemented by altering the oil circuit of the hydraulic valve train. The advantages of electronically controlled hydraulic steering are great control precision and rapid reaction. However, its system adjustment is complicated and tough to implement in practice. The electric steering wheel can match the functional requirements of the end-effector of this study [17] because to its ease of installation, broad applicability, and manual automated switching function. Therefore, to address the aforementioned issues, this study takes the 4 LZ-8 self-propelled corn harvester of Shandong Wuzheng Group as the carrier, designs an automatic alignment system with Hirschmann PLC controller as the main control unit, touching alignment mechanism as the system input, and electric steering wheel as the actuator, establishes the system kinematic analysis model, adopts adaptive fuzzy PID control algorithm, and realizes the automatic alignment system of corn The system kinematic analysis model is established, and the adaptive fuzzy PID control algorithm is used to realize the closed-loop control of the automatic alignment system of corn harvester.
## 4 M2M-Based Division of MAC Layer Protocols
### CSMA-TDMA Hybrid MAC  
This Scalable hybrid MAC protocol for M2M communication is proposed in [30]. This protocol divides time into frames and each frame is further divided into four parts, which are notification period, contention period, announcement period, and transmission period. These frame fragments reduce collisions between packets during their exchange but pose problems of extra delays and extra battery drainage.
## 3 Results and Discussion
### Non-Retrofitted Joints  
The obtained results show that there are three main buckling modes. They are the local and overall buckling of the brace and the chord ovalization. These buckling modes are illustrated in Figure 3. The common elephant-foot buckling is observed in the brace local buckling. The brace overall buckling happens out of the plane of the joint because the high flexural stiffness of the chord prevents in-plane buckling.  
Figure 4 shows the distribution of the buckling modes of the non-retrofitted CHS T-joints along with their critical buckling load in terms of the most important normalized geometric properties. The most buckling occurs in brace local buckling. Generally, the chord ovalization buckling mode has the lowest critical load. As it is observed in Figure 4a, for the joints with \(r/t<10\) the critical modes are the brace overall buckling or the chord ovalization, with the brace overall buckling showing more buckling load. By increasing  
Figure 3: The main buckling modes of the CHS T-joints: (**a**) the brace local buckling; (**b**) the brace overall buckling; and (**c**) the chord ovalization.  
Figure 2: Load-displacement curve of the verified CHS T-joint under axial brace compressive load.  
the \(r/t\) ratio, the brace local buckling is observed such that for the joints with \(r/t>30\) the brace local buckling is the most critical mode.  
From Figure 4b, for the ratio of the chord's \(R/T\), if this ratio is less than 10, no chord localization occurs. For a ratio higher than 15, no brace overall buckling is observed. The brace's \(l/t\) ratio in Figure 4c can separate the brace local buckling from the other buckling modes for values more than 600 or less than 100. The brace to the chord length ratio \(l/L\) shows no clear sign of separation between the buckling modes or their critical load (Figure 4d). For the chord to the brace thickness ratio \(T/t\) in Figure 4e, values of more than 3 will lead to the brace local buckling. However, values less than 1.5 will lead to the two other buckling modes. The brace's \(l/r\) ratio (Figure 4f) cannot clearly divide the buckling modes.  
Figure 4: Distribution of the buckling modes and their critical buckling load (kg) in terms of dimensionless values: (**a**) the brace’s \(r/t\); (**b**) the chord’s \(R/T\); (**c**) the brace’s \(l/t\); (**d**) the ratio of the brace to the chord length \(l/L\); (**e**) the ratio of the chord to the brace thickness \(T/t\); and (**f**) the brace’s \(r/T\).
## 4 Conclusions  
The purpose of this study was to obtain a new type of composite hygroscopic building material and put forward methods for its application research. Through the preparation of hygroscopic materials, sample tests, large-scale space experiment and different climate region analysis, conclusions can be drawn as follows:  
(1) A series of experiments assessing the properties of the new composite hygroscopic material are conducted. The microstructure and adsorption properties of multiple sets of CHM-WSE are studied. The mechanical properties and thermodynamic properties experiment is conducted of specimen has excellent hygroscopic property and a specific surface area of 33.331 m\({}^{2}\)/g, aiming to meet the engineering requirements. Importantly, the structural characteristics of the hygroscopic material are firstly considered for quantifying its performance of engineering requirements.  
(2) A large-scale space comparison experiment is established. The test results show that the temperature and humidity uniformity in the room with CHM-WSE are better than that in the room with CM at 1.5 m from the floor. The relative humidity of the room with CHM-WSE is 14.3% lower than that of the room with CM in winter. The relative humidity of the room with CHM-WSE is lower than that of the room with CM by 6-10% in spring. Furthermore, the room with CHM-WSE has better moisture buffering performance than the room with CM, and the practical moisture buffering performance is better in spring and summer alternating season than in winter.  
(3) The influence of CHM-WSE on the indoor humidity environment and energy consumption in the multi-climate zone is studied. The simulation results show that the relative humidity fluctuation of the room with CHM-WSE is about 10% lower than that of the room without CHM-WSE, which has better hygroscopic ability and energy saving potential, and is more suitable for hot summer and cold winter region.  
The raw materials of the CHM-WSE presented in this paper are common environmental materials, which are cheap and easy to handle. The CHM-WSE not only meets the requirements of mechanics and thermodynamics, but also regulates indoor humidity environment, improves indoor comfort and reduces building energy consumption. It is a new type of composite hygroscopic building material which could replace the traditional mortar.  
**Acknowledgments:** The authors are grateful for the financial support of the Major Science and Technology Projects of Hunan Province, China (No. 2010 FJ1013), the International Science and Technology Cooperative in Project of China (No. 2010 DFB63830), National Natural Science Foundation of China (Grant No. 51378186) and National Key Technology Support Program (No. 2015 BAJ03B00).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], [PERSON] and [PERSON] contributed materials/test/analysis tools; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Effects of Simulated Nitrogen Deposition on the Bacterial Community of Urban Green Spaces
## 1 Introduction
Other analyses have confirmed that there are a large number of bacteria that find their critical habitats in soil, and that their activities are strictly related to underground processes [35]. Bacteria that adjust to living in the soil can also affect plant growth and health through their direct and indirect effects in ecological processes in relation to N cycling [36]. Studying the feedback of urban soil bacteria to N deposition is therefore of primary importance to understand the effects of regional environmental changes on ecosystem processes regulated by soil bacteria. Although the structure of bacterial communities entails particular importance for the stability and productivity of urban green spaces, there are still few studies on the effect of anthropogenic N deposition on these habitats. In this respect, since (a) nitrogen is the primary nutrient for soil fertilization and (b) life is N-dependent and N-limited in almost all environments, one could not predict a priori whether N deposition would be deleterious for microbial ecology, or it could instead boost its development and diversification, as is the case for crops and weeds. The hypothesis we aimed at testing here was that, in spite of its nature of essential macro-element for nutrition, sustained N additions to a soil could lead to a fast depression of the main ecological indicators for bacterial diversity in soil. To unveil the potential effects of different degrees of N deposition on the bacterial communities of urban green areas, we set up a simulated atmospheric N deposition-controlled experiment. Lawns, planted with _Cynodon dactylon_, which is one of the most popularly used grasses for turf, yards, and recreational greens, were selected as the research object. We analyzed the bacterial diversity and community structure of urban green space undergoing deliberate supplementation of N deposition by Illumina MiSeq 16S NGS metabarcoding. The present study aimed at (1) detecting the dynamics of the urban green space bacterial diversity along an N loading gradient;and (2) investigating the direct and indirect effects of N deposition on soil microbial communities in urban green spaces.