esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
SDC errors caused by SEU can affect the execution of routing programs, which may further lead to the emergence of SH and GH nodes in satellite networks, reducing the reliability and availability of the satellite networks. To solve this problem, we propose a digital-twin-based detection and protection framework for SDC-induced SH and GH nodes in satellite networks. By analyzing and detecting the digital-twin process files of the satellite network routing mechanism, the proposed framework can detect SH and GH nodes caused by SDC errors in the satellite network as early as possible before the satellite carries out actual data forwarding, allowing the failed nodes to be recovered in time. At the same time, this framework does not interfere with the normal operation of the satellite network routing mechanism when there is no SDC error or when the SDC errors do not cause SH or GH nodes to form. We evaluate the proposed detection and protection framework through experiments. Experiment results show that the detection algorithm proposed in this paper has high accuracy (99.3-100%), precision (98-100%), and recall (100%) compared to existingapproaches, and the additional time cost of routing convergence caused by this framework is relatively low (3.1-28.2%).  
At present, the proposed framework mainly focuses on satellite network routing mechanisms based on link delay. We also note some shortcomings of this method, such as increasing the communication cost of satellite systems. In addition, compared to hardware-level fault injection methods or irradiation experiments, the fault coverage rate of software-based fault injection simulation methods is lower. In the future, we will improve the proposed framework by integrating other routing mechanisms, such as routing mechanisms based on link congestion and QoS. At the same time, we will consider extracting the features of the routing update process file to reduce the communication cost of the satellite system. We will also try using fault injection methods that are closer to the real radiation environment.  
Conceptualization, G.Q. and Y.Z.; methodology, G.Q. and T.Y.; software, T.Y. and G.Q.; investigation, G.Q. and Y.Q.; validation, G.Q., Y.Z. and T.Y.; result analysis, T.Y. and Y.Q.; writing--original draft preparation, G.Q. and T.Y.; writing--review and editing, Y.Z., G.Q. and Y.Q.; supervision, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (General Program) under Grant No. 61572253.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 5 FGI Challenges and NGN Solutions for Future Networks
### Content Delivery  
Content is a major revenue source to content providers and indirectly in future also to network and service providers, as it is often content which binds end users to a network or service.  
Content delivery is a challenge over a best-effort network, especially if there may be capacity bottlenecks in both core and access networks. Such bottlenecks lead to a degraded content consumption experience due to the interrupted and/or slower-than real-time delivery. The need to add capacity creates friction in the business models as the costs and revenues of added capacity is not always equally shared [16].  
Content delivery network (CDN) is a solution to cache and replicate both file-type content (content-on-demand, software distribution, _etc._) and streaming content (broadcast, game rendering).  
Over-the-top CDN is commonly deployed over the internet. This distribution of content delivery offloads a central media content server. However, there are not any real-time guarantees. This impedes the delivery of real-time premium services. Integration of CDN technologies in NGN networks and services is currently being studied [17] and standardized [18], in order to have guaranteed high-quality content delivery over NGN. Interconnection of (telco) CDNs is being studied [19] and standardized [20]. Such integrations of CDN technology within networks may also be applicable to FGI.
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 3 AODV-EOCW Protocol
### Protocol  
The AODV-EOCW routing protocol is carried out by two stages, namely, the discovery stage and the route establishment stage, as depicted in Figure 1. First, the node initializes itself. Afterwards, if the node is required to send a message, it checks whether there is an entry in the routing table that directly reaches the destination. If there is no routing table entry that directly reaches the destination, the node will perform the route discovery process; otherwise, it will directly send messages according to this routing table entry.  
In the route discovery stage, RREQ carries information on parameters such as the congestion, hop count, and energy, so the next hop node can easily obtain the status of the previous hop node.  
In the route establishment stage, since the node can identify the status of the previous hop node, the most reasonable path can be calculated using the combined weighting method and the scores of the routing table entries. In the meantime, the node also records the path with a score that is not the highest as the backup route. If the link in the routing table fails, the backup route can be enabled, which can largely reduce the network's energy consumption and congestion caused by the repeated flooding of RREQ.
## 2 Concept of Operations
### Integrated Broadcasting Signal Structure  
Due to the limited communication capacity of any given pLEO platform \(i\) (where \(i=1,\ldots,N\)), a non-dedicated, low-power signal for coordinating clock comparisons and ensemble clock estimates is introduced alongside its primary communication functions. It is assumed that both the non-dedicated clock signal and the primary communication data utilize different pseudo-random-number (PRN) sequences that are orthogonal to one another. These two types of signals are then transmitted at the same frequency and phase over crosslinks  
\[s^{i}(k)=s^{i}_{c}(k)+s^{i}_{clk}(k)\,, \tag{1}\]  
where an on-demand pLEO server horizon \(T\) is discretized to some constant quanta of the size \(\Delta T\), e.g., \(\Delta T=1\) minute and each unit of \(\Delta T\) represents one time step \(k\).  
As shown in (1), the signal integration method presented here is characterized by three key properties: (i) a low-latency yet discreet physical-layer approach that embeds the clock signal \(s^{i}_{clk}(k)\) into the existing communication signal \(s^{i}_{c}(k)\) without needing additional bandwidth or power resources; (ii) radio backward compatibility, which allows existing communication receivers and remote timing systems to continue functioning as intended without any modifications; (iii) the use of the same radio frequency coupled with different PRN sequences, which enables the use of traditional communication receivers' radio frequency circuits.
# Mdpj _sciences_
## 5 Discussion and Countermeasures
### Discussion
#### 5.1.6 Quantum-Safe  
Quantum-safe security requirements are essential to protect cryptographic systems against the potential threats of quantum computers, which can break traditional algorithms like RSA and ECIES. KEM addresses these requirements by enabling secure key exchanges based on PQC techniques, which rely on hard mathematical problems believed to be resistant to quantum attacks.
## 3 System Design and Platform Subsystems
### Telecommunications Subsystem (TS)  
The Telecommunications Subsystem consists of a UHF transmitter and VHF receiver. Four deployable dipole antennas are used for the UHF and VHF. The Transceiver provides the uplink and downlink communications between the satellite and ground stations.  
A UHF transmitter is used for transmitting telemetry, science and image data from the satellite to the ground station. It transmits at 9.6 kbps data rate using the BFSK modulation technique. The frequency range for the transmission carrier is from 430 MHz to 450 MHz.  
Table 1 shows the data volume generated by the payloads. The major portion of the payload data volume is generated from the RGB camera with 1,643,530 Bytes and 389,130 Bytes produced for SXGA format images and VGA format images respectively. Considering the SXGA format download a total of 22 min is required to download the payload data using the 9600 bps UHF transmitter. STK simulations verify a total of 28 min per day Ground Station access time available for data download. It should be noted that these calculations do not consider using any on-board image compression.  
A VHF receiver receives telecommands from ground station to the satellite. The receiver uses baseband modulator with 1.2 kbps data rate and AFSK modulation. The frequency range for the transmission carrier is from 130 MHz to 150 MHz.
# Adaptive HVAC System Based on Fuzzy Controller Approach
## 2 Literature Review
### Existing Non-Predictive Control  
A controller that implements non-predictive control relies heavily on fixed timetables. It uses such data to create a model that predicts when a building will be filled and uses that information to regulate HVAC operation. In a setting where activities follow a predetermined daily schedule, this sort of control strategy may be useful. Commercial structures like offices, labs, and corporate environments are fantastic illustrations that reflect the literature [22; 47; 48; 49; 50]. However, this strategy would not be effective in places where occupancy does not adhere to a set timetable. Most of the non-predictive controllers, such as those in [43; 51], employ binary algorithms and demonstrate that frequent on/off cycling tends to shorten the lifespan of electrical appliances. As such, more advanced controllers in [52] using decision control algorithms such as fuzzy logic were designed as improvements to binary algorithms with sets of values other than zeros and ones, allowing for additional control.  
The research in [12] proposes controlling HVAC energy usage according to demand, as the controller can postpone operation whenever electricity prices are at their highest. A similar approach in [7] uses a scheduling strategy to determine which control signal will activate energy consumption. This strategy uses a rule-based technique to quickly access the power grid voltage or rate stabilization and is an excellent example of model-based control.  
The schedule-based controller previously used in [7; 53; 54; 55; 56] reduces energy costs and prevents the usage of appliances during times of high demand. For instance, it is possible to schedule standby equipment to operate when energy costs are lower so that other appliances in the house may operate. Both runtime and static techniques may be used to provide this control.
## 2 Data Acquisition and Preprocessing
### Data Preprocessing
#### 2.2.1 Image Mosaicking and Generating Subgraphs  
After obtaining 83 local images of the maize-breeding research field, all of the images were processed to obtain complete leaf-coverage information. Because of restrictions due to flight altitude and CCD size, most of the images acquired from the UAV remote-sensing platforms are image sequences with a small amplitude, low overlap and a large tilt angle. Furthermore, the RGB cameras carried on the UAV platforms contained mostly non-metric ordinary digital sensors, so the internal camera parameters are not known with precision. To solve these problems, we used the Photoscan Software (Agisoft, St. Petersburg, Russia) to preprocess the images as follows: (1) The selection of aerial photos, (2) image mosaicking, (3) generate digital elevation model (DEM) and orthophoto. We then used the Arcgis 10.2 to manually separate a single species from the orthophoto map according to the scope of the planting plot. Next, we used Photoshop (Adobe Inc., CA, USA) to generate several \(1000\times 1000\) pixel subgraphs in which the large-area ridges were removed.
## 1 Introduction  
In a phased-array transceiver for 28 GHz 5G new radio (NR), the down-conversion mixer is a crucial component in each digital channel for conversion of the receiving RF signals to intermediate-frequency (IF) or baseband (BB) signals [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. The basic requirements of a down-conversion mixer include a small chip area, low supply voltage (V\({}_{\rm DD}\)) and power (P\({}_{\rm DC}\)), and decent input impedance (Z\({}_{\rm in}\)) matching, conversion gain (CG), 3 dB CG bandwidth (f\({}_{\rm 3 dB}\)), noise figure (NF), LO-RF isolation, and power linearity (such as an input 1 dB compression point (P\({}_{\rm 1 dB}\)) and third-order intercept point (IIP3)). Recently, several millimeter-wave (mm-wave) down-conversion mixers have been reported [1; 2; 3; 4; 5; 6; 7; 8]. For instance, in [2], a wideband (24-40 GHz) down-conversion mixer employing body-effect control for CG enhancement in 65 nm CMOS was demonstrated. However, a P\({}_{\rm DC}\) of 10.3 mW and LO input power (LO\({}_{\rm in}\)) of 5 dBm leaves room for improvement. In [3], a 23-25 GHz folded down-conversion mixer using cross-coupled PMOS transistors (CCPTs) for CG boosting in 0.13 \(\upmu\)m CMOS is reported. However, a P\({}_{\rm DC}\) of 16.8 mW and f\({}_{\rm 3 dB}\) of 2 GHz are not satisfactory. In [8], a 88-100 GHz down-conversion mixer using the parallel of CCPT and series-RL as the core IF load (i.e., CCPT-RL-based core IF load) for CG and IF-bandwidth enhancement in 90 nm CMOS is reported. Yet, a V\({}_{\rm DD}\) of 1.2 V (due to the cascade of three transistors) still has room for improvement. To demonstrate that low V\({}_{\rm DD}\) and P\({}_{\rm DC}\), wideband, and decent CG and NF can be achieved for a down-conversion mixer for 28 GHz 5G NR, we report a 6.5 mW 12.4-32 GHz down-conversion mixer with a CG of 14.4 \(\pm\) 1.5 dB and NF of 7-9.7 dB in 90 nm CMOS using the body-self-forward-bias (BSFB) technique, the CCPT-RL-based core IF load, and \(\lambda\)/4-TL-C-based coupler (constituting a \(\lambda\)/4 transmission line (TL) and a coupling capacitor (C\({}_{\rm c}\))). In this paper, mixer circuit design is introduced in Section 2, the measurement results of the mixer and comparisons with previous work is discussed in Section 3, and a conclusion is presented in Section 4.
# Parametric Analysis of Control Techniques for 15 MW Semi-Submersible Floating Wind Turbine
## 4 Controller Evaluation
### DLC Simulation  
Additional simulations based on the design load cases (DLCs) were performed to verify the control performance of the overall control algorithm with the feedforward, IPC, peak shaving, and the nacelle feedback control, along with the selected control parameters from the parametric study. These DLC simulation conditions include the environmental  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{4}{*}{**Controllers**} & \multirow{4}{*}{**Control**} & \multicolumn{4}{c}{**Control Performance in Dynamic Simulation [\%]**} \\ \cline{3-6}  & & \multicolumn{2}{c}{**(Controllers – Baseline)/Baseline * 100**} \\ \cline{3-6}  & & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Standard Deviation**} & \multicolumn{2}{c}{**Damage Equivalent Load**} \\ \cline{3-6}  & & \multicolumn{2}{c}{**Generator**} & \multicolumn{2}{c}{**Rotor**} & \multicolumn{2}{c}{**Platform**} & \multicolumn{2}{c}{**Tower-Base**} & \multicolumn{2}{c}{**Blade-Root**} \\  & & \multicolumn{2}{c}{**Power**} & \multicolumn{2}{c}{**Speed**} & \multicolumn{2}{c}{**Pitch**} & \multicolumn{2}{c}{**FA Bending**} & \multicolumn{2}{c}{**FW Bending**} \\ \hline \multirow{4}{*}{Feed Forward} & 1 & 0.0 & \(-\)4.4 & \(-\)5.8 & \(-\)0.8 & \(-\)1.5 \\  & 2 & 0.0 & \(-\)7.3 & \(-\)11.1 & \(-\)0.4 & \(-\)1.8 \\  & 3 & 0.0 & \(-\)11.2 & \(-\)20.1 & 3.6 & 1.8 \\  & 4 & \(-\)1.0 & 3.8 & \(-\)31.4 & 84.2 & 200.1 conditions that maximize the platform motion and mooring line tension of the target FOWT under operation with the proposed control algorithm. The conditions cover the rated wind speed range, where maximum thrust occurs, the cut-out wind speed range, where the highest wind speed is reached, and severe sea states (SSS). By comparing the control performance under these severe simulation conditions, the control performance and dynamic characteristics of the proposed control algorithm can be easily observed, thus ensuring the reliability of the control system. The presented DLCs are divided into three specific scenarios and summarized in Table 3.  
To more thoroughly analyze the signals related to the stability of the floater, additional comparisons were made for the x-directional (fore-aft) acceleration of the rotor-nacelle assembly (RNA) and the mooring line tensions, in addition to the fatigue loads and platform motions. The mooring line numbers are indicated in Figure 1. As a result, the rotor speed variations were reduced by up to 49% through the feedforward control. The platform pitch motion and tower loads were mitigated by up to 20% and 15%, respectively, due to the peak shaving and nacelle feedback controls. The blade loads were alleviated by up to 23% via the IPC, and the platform yaw motion was also reduced by up to 13% as the tower-top yaw moment in the fixed frame is reduced. Regarding the mean power, there was up to a 5% reduction in cases near the rated wind speed due to the blade pitching by the peak
# Self-Service Data Science in Healthcare with Automated Machine Learning
## 1 Introduction  
After reviewing the literature on the state of data analysis in healthcare, it becomes evident that there is still significant progress to be made in the application of analytics in healthcare [1; 2]. One of the ways to progress, is the automation of the knowledge discovery process in healthcare [1]. Automation of the knowledge discovery process can increase the adoption of analytics by enabling domain experts to contribute to the knowledge discovery in the field using state-of the art techniques in adaptive analytic systems, especially with the amount of data that is available in healthcare. The introduction of the Electronic Health Record (EHR) accelerated the digitalisation of this formerly unavailable dataset. Combining this available EHR data with analytics provides great potential to improve the healthcare industry by making the data available for automatic processing [3].  
The concept of self-service data science was first described in [4] and has been defined as \"_the engineering discipline in which analytic systems are designed and evaluated to empower domain professionals to perform their own data analyses on their own data sources without coding in a reliable, usable and transparent manner within their own daily practices_\" [5]. Figure 1 visualises self-service data science research in the context of adjacent data science research disciplines. Bridging purely foundational and purely applied research processes, both applied data science [6] and self-service data science studies pursue a translational (e.g., application-oriented) research process, often including CRISP-DM as the knowledge discovery process of choice [7].  
The Machine Learning (ML) community has noticed the need to enable access for non-expert users to ML techniques. The need to enable non-experts to use machine learning is one of the drivers that gave birth to the fast-paced research area of AutoML [8, 9]. The AutoML community aims to automate all steps in the process of creating a machine learning pipeline. However, to the best of our knowledge, no AutoML applications were tested in real-world situations with non-expert users in the healthcare domain. Hence our research question is: _How can we support the knowledge discovery process of domain experts in healthcare using automated machine learning?_ To answer this question, we answer three sub-research questions in Section 4:  
1. Which AutoML method performs best on a benchmark test, given medical datasets?
2. What are the requirements of healthcare professionals for starting to use AutoML in their daily practice?
3. How does the selected AutoML method suit healthcare professionals in their knowledge discovery process?  
Each question is answered in a subsection of Section 4.
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 4 Design of the Subtree-Size and Traffic Aware Scheduler  
Orchestra is an elegant autonomous scheduler that organizes the communication resources for TSCH-based networks without any overhead or central unit. The information needed for allocating the cells is obtained from RPL. However, as already discussed in Section 2.3, Orchestra has its shortcomings, mainly in high traffic load situations. Packets might become lost due to queue overflow, or delays might increase due to congestion. In the case of high upward traffic load, the nodes that suffer the most are the root's children. All the traffic destined for the root passes through these nodes. The proposed scheduler improves the behavior of Orchestra in high traffic load situations by allocating more transmission opportunities for the children of the root, and consequently more Rx cells for the root.  
If traffic bursts are not detected by the children of the root, the cells on the unicast traffic slotframe are allocated as in RBS Orchestra: one Rx cell for each node and as many Tx cells as the number of RPL neighbors. If the children of the root detect high traffic loads, additional cells are allocated for these nodes and the root. A combination between two parameters is used to determine how many additional cells should be allocated and when will these cells be allotted. The number of remaining packets in the queue is used to predict the increase in traffic, thus congestion. The subtree size of the root's children determines how many additional cells are needed for each child. Information about the subtree size can be collected when RPL is in storing mode. In this case, each node keeps a routing table with entries for all its descendants. The number of entries indicates the subtree size.  
The slotframes dedicated to EBs and RPL control traffic have the same structure and follow the same rules as in Orchestra. The proposed scheduler contains a new rule implemented for the unicast data slotframe. This rule has the same design as all the other rules that govern the functioning of Orchestra with some additions. In each rule, including the new one, specific functions are called when a node selects or changes its parent, when a child is added/removed, or when a packet is ready to be sent. In our implementation, specific functions are also called when a packet is received and when a packet is sent. Whenever a packet is received, a function that determines the subtree size of each root's child is triggered together with another function that decides if adding/removing more cells is necessary. This is done by checking the state of a flag previously inserted in the data packets. Whenever a packet is sent, congestion is checked by counting the remaining packets in the queue. Additional design and implementation details are discussed in the following paragraphs. Note that the description of how cells are allocated refers only to the unicast slotframe.