esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Data Splitting and Oversampling  
In this phase, we divide the dataset into training and testing subsets to develop a severity prediction model utilizing a subset of the selected features. We also address the issue of data imbalance by applying oversampling techniques to enhance the effectiveness of model training. Initially, we divided the patient dataset into training (3956/5651, 70%) and testing (1696/5, 651, 30%) datasets. To prevent bias during the model training, we randomly shuffled the training data and employed a stratified sampling technique. We also conducted five-fold stratified cross-validation, dividing the dataset into five folds. One fold was used as the validation dataset, and the remaining four as the training dataset. To ensure comprehensive validation, we repeated this cross-validation process 10 times. Each fold served multiple times as both the training and validation dataset, significantly improving the model's robustness and reliability by extensively evaluating it against various data subsets.  
As described in Table 2, our target variable, CSS, has a severe data imbalance. Such an imbalance can be problematic for machine-learning algorithms, especially those based on optimization techniques, as they can be biased toward classes with a greater frequency of occurrence. This can negatively affect the predictive performance of minority classes.  
Figure 4: Results of selected feature importance according to the data-driven strategy.  
To mitigate this imbalance, we utilized two oversampling techniques commonly used in medical research: the Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) [46; 47; 48]. SMOTE synthesizes the data points for a minority class by utilizing the Euclidean distance to the nearest neighbors. Based on the original attributes, these new samples are very similar to the actual data. However, ADASYN generates synthetic samples by considering the distance between the majority and minority classes, thereby enhancing the minority class data. This method is often considered more sophisticated than SMOTE.  
It is important to only perform oversampling on the training data and not on the validation or test sets. Oversampling before splitting the dataset can lead to duplication or the synthesis of instances in the validation or test sets, which can inflate the performance metrics. Therefore, we applied oversampling only to the training dataset, which contained the data for 3956 patients. Table 3 lists the oversampling ratios for Types 1, 2, and 3 of the target variable _CSS_ using SMOTE and ADASYN.
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 4 Proposed Methods
### Intrusion Detection System
#### 4.4.1 Detect of DoS and Fuzzy Attack  
First, we discuss the detection of DoS and fuzzy attacks. DoS attacks involve the injection of a large number of high-priority data frames, which disrupt the normal transmission of data frames. On the other hand, fuzzy attacks entail injecting a substantial volume of randomly generated data frames. This results in the disruption and disturbance of various vehicle functions, leading to a state of chaos within the system.  
In our approach, we gather \(K\) CAN frames to form a test window. Subsequently, we calculate the similarity between the test window and the normal state. This similarity  
Figure 4: Thresholds and normal data.  
measure is then compared against the threshold to detect DoS and fuzzy attacks. If it is not within the threshold, an alarm is triggered.  
We illustrate the detection of two attacks in Figure 5. Specifically, Figure 5 illustrates the detection of a DoS attack, while Figure 5 demonstrates the detection of a fuzzy attack. The figure depicts the threshold and the distribution of data in two datasets. In this figure, the color blue denotes normal data, while red represents abnormal data. We observe that the blue points are concentrated within the threshold range, while the red points are predominantly located outside the threshold. Notably, the Hamming distance for DoS attacks is found to be below the threshold. Conversely, the Hamming distance for fuzzy attacks exceeds the threshold.  
By comparing the calculated Hamming distance with the threshold, we can determine the type of attack based on whether the distance is above or below the threshold. This comparison serves as an attack fingerprint, allowing for us to classify and identify the specific type of attack. Additionally, in the case of zero-day attacks, if they induce significant changes in the data field beyond the threshold range, our model is capable of detecting them.
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 5 Proposed WLAN Aware Cognitive MAC (WAC- MAC) Protocol
### Sensing Time Slot in Superframe  
WAC-MAC protocol adds a new time slot known as sensing time slot in the superframe structure, as shown in Figure 10. Each beacon interval starts with a beacon frame used by the coordinator for coordinator discovery and synchronization. Each sensor node then performs the spectrum sensing in the sensing time slot. The goal of spectrum sensing is to determine if the spectrum is idle or active using energy detection. The sensing time slot is greater than the maximum backoff value for WLAN transmissions. This is done to identify if the channel is empty due to WLAN contention or extended inactive period.  
Note that IEEE 802.15.4 devices do adopt CSMA/CA strategy at the MAC level for media access (i.e., manage contention between simultaneous IEEE 802.15.4 transmission). However, the additional sensing phase introduced in this paper is to determine the extended inactive period between the clusters of WLAN frames, which are notably longer. This additional sensing phase serves two purposes: (1) to determine whether media is busy or idle; (2) to determine whether media is idle due to WLAN contention window or due to WLAN extended inactive period. IEEE 802.15.4 devices can transmit data in these extended inactive pockets, which will reduce the overlapping probability of ongoing IEEE 802.15.4 devices transmission with new WLAN communication. In the proposed system model, two types of sensing are performed; the first one is for WLAN active transmission, and second one is for media access control.  
After the sensing time, if the medium is idle, the sensor node proceeds with the transmission using CSMA/CA. In case the channel is busy, the IoT nodes use the adaptive wake-up scheduling mechanism explained in the next subsection. Note that CFP in the superframe is not considered in this work since we assume that each IoT sensor node has continuous data to be transmitted to the network coordinator (sink node) and no GTS requests are required to be sent. Thus, the active period consists of CAP only.  
Figure 10: Proposed superframe structure and media access control scheme for Internet of Things (IoT) network.
## 5 Simulation Results  
### Experiment Setup  
We do simulations to prove the performance optimization that our presented computation offloading strategy can bring. An edge computing system is considered in this paper. The system is composed of the edge cloud and mobile devices, in which mobile devices have relatively intensive computation tasks. The simulation settings are listing as follows. We set the transmission bandwidth \(B=5\) MHz and transmitting power \(P_{n}=0.5\) W, and the relevant noise power \(\sigma=10^{-10}\). The channel gain \(H_{n}\) is denoted by \(H_{n}=127+30\times\log d\), where \(d\) is the distance between edge cloud and mobile device \(n\).  
By default, the mobile devices have 50 computation tasks in total. For task \(u_{n,k}\), the number of CPU cycles \(w_{k}\) subjects to normal distribution, which its average is 1000 M Cycles. The data size \(s_{k}\) obeys normal distribution, and its average value is 3 MB, and the completion time \(D_{n,k}\) is generated by a normal distribution with an average of 1 s. Furthermore, the computational capability of mobile devices is assumed 1 GHz. Relative to the research field, the above parameters are typical values, and will not affect the results obtained.
Sensors_**2012**, _12_, 7350-7409; doi:10.3390/s120607350  
_Sensors_  
ISSN **1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**A Survey on the Taxonomy of Cluster-Based Routing Protocols for Homogeneous Wireless Sensor Networks**  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 1 and [PERSON] 3**  
1 Department of Electrical Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia; E-Mail: [EMAIL_ADDRESS]  
2 School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada; E-Mail: [EMAIL_ADDRESS]  
3 School of Information and Telecommunication Engineering, Tokai University, Tokyo 259-1292, Japan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +60-14-237-1382; Fax: +60-37-967-5316.  
_Received: 30 March 2012; in revised form: 14 May 2012 / Accepted: 20 May 2012 / Published: 30 May 2012_  
**Abstract:** The past few years have witnessed increased interest among researchers in cluster-based protocols for homogeneous networks because of their better scalability and higher energy efficiency than other routing protocols. Given the limited capabilities of sensor nodes in terms of energy resources, processing and communication range, the cluster-based protocols should be compatible with these constraints in either the setup state or steady data transmission state. With focus on these constraints, we classify routing protocols according to their objectives and methods towards addressing the shortcomings of clustering process on each stage of cluster head selection, cluster formation, data aggregation and data communication. We summarize the techniques and methods used in these categories, while the weakness and strength of each protocol is pointed out in details. Furthermore, taxonomy of the protocols in each phase is given to provide a deeper understanding of current clustering approaches. Ultimately based on the existing research, a summary of the issues and solutions of the attributes and characteristics of clustering approaches and some open research areas in cluster-based routing protocols that can be further pursued are provided.  
**Keywords:** wireless sensor networks; homogeneous networks; clustering protocol; cluster head; cluster formation; data aggregation; failure management; resource-aware design; survey
## 3 Results and Discussion  
### Bulk Density  
Figure 6 shows the bulk density of fiber-cement flat sheets in terms of comparison between young coconut fiber (YCF) and mature coconut fiber (MCF) with 5% to 9.5% by weight of binder. The density of fiber-cement board mixed with YCF ranged from 1399 to 1882 kg/m\({}^{3}\), while the density of fiber-cement board using MCF had a density of 1498 to 1993 kg/m\({}^{3}\). The results showed that the density of fiber-cement board mixed with MCF was higher than that of fiber-cement board made with YCF. This can be explained by the formation of natural coconut fiber. Generally, the density of raw fiber from mature coconuts is higher than that of raw young coconuts due to the increased lignin content, which also makes them tougher and stiffer. [PERSON] and [PERSON] [17] indicated that the density of concrete mixed with coconut was lower than 1900 kg/m\({}^{3}\). Furthermore, it can be observed that incorporating coconut fiber into the fiber-cement flat sheets at levels ranging from 5% to 9.5% weight of binder resulted in a decrease in bulk density, which was dependent on the volume of coconut fiber added. [PERSON] and [PERSON] [3] revealed that the density of fiber-cement mortars decreased with increased coconut fiber content (from 2110 kg/m\({}^{3}\) to 1800 kg/m\({}^{3}\)). They also demonstrated that the reduction in density resulted in an increase in porosity and water absorption in fiber-cement flat sheets mixed with coconut fiber compared to non-fiber-cement mortars.
# Review of Weed Detection Methods Based on Computer Vision
## 3 Traditional Machine Learning Weed Detection Methods
### Traditional Features and Their Advantages and Disadvantages for Common Weed Detection
#### 3.1.2 Shape Features  
Shape features play an important role in image analysis for weed detection. They mainly include shape parameters, region-based descriptors, and contour-based descriptors. Generally, shape parameters include 11 kinds: perimeter, area, diameter, minor axis length, major axis length, eccentricity, compactness, rectangularity, circularity, convexity, and solidity. These parameters are the most intuitive, easy to implement, and unaffected by lighting. Region-based descriptors include Hu moment invariants and two-dimensional Fourier descriptors (FDs). Hu moment invariants are a shape descriptor proposed by [PERSON] (1962) [65]. They are a normalized function based on shape boundary and its internal region information and contain seven invariant moment parameters in total. They are independent of geometric translation, scaling, or rotation and are robust to noise. Two-dimensional FDs describe the shape region by establishing feature points in the region plane and carrying out Fourier transforms on rows and columns at the same time. Contexture-based descriptors mainly include spatial position descriptor, curvature scale descriptor, and one-dimensional FD.  
These shape features have been successfully applied in the species recognition task of plant leaf images [66; 67; 68]. For example, [PERSON] et al. [69] used five shape descriptors, namely, beam angle statistics, FD, Hu moment invariants, multiscale fractal dimension, and Tensor Scale Descriptor (TSD), in shape analysis to describe the contour shape of aquatic weeds. [PERSON] and [PERSON] [51] extracted four major shape factors, Hu moment invariants, and FDs to distinguish weeds and crops on different classifiers. [PERSON] et al. [39] used eight shape features and Hu moment invariants combined with color features to detect weeds in a soybean field.  
Different species of plants have distinct shape features, but the shape of the leaves can be distorted by disease, insects, and even human and mechanical damage. Most research is conducted under the ideal condition of specific leaves without background. In a field environment, problems of overlap or occlusion of plant leaves occur. Therefore, the task of weed identification is difficult to complete by only basing on shape features. They should be combined with other features to improve accuracy.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 3 Kubernetes Architecture
### Networking and Service Identification  
Kubernetes networking simplifies the intricacies of inter-service communication. Each pod is assigned a distinct IP address, and services facilitate stable, discoverable endpoints.  
* Cluster Networking establishes a flat network architecture, enabling direct communication among all pods without requiring NAT. It is generally administered through CNI plugins such as Antrea, Calico, Flannel, or Cilium.
* Services function as reliable front-ends for pod access, frequently load-balanced and reachable through DNS. Kubernetes features an internal DNS service that autonomously allocates DNS records to services and pods.
* Ingress Controllers regulate external access to services, typically through HTTP/S routing protocols and TLS termination, and play a crucial role in securely managing north-south traffic.  
This system enables dependable communication between services internally within the cluster (east-west) and externally (north-south).
## 3 Materials and Methods
### Proposed Method
Step 4: calculate the ambiguity of the cross-phase of the \(\pm\)DOR\({}_{2}\) signal by using the coarse time delay estimation from Step 3. Step 5: modify the cross-phase of the \(\pm\)DOR\({}_{2}\) signal by integer multiples of \(2\pi\). Step 6: synchronize the modified \(\pm\)DOR\({}_{2}\) signal, \(-\)PNc or +PNc, and carrier to estimate the high-precision interferometry delay. The primary advantage of the proposed method is that when there is a pseudo-code ranging clock component, the DOR beacon used for ambiguity resolution in the detector downlink signal can be detuned, and furthermore, without any additional changes to deep space transponders, the downlink signal power configuration is adjusted to enhance the measurement signal power and to improve the measurement accuracy or increase the detection distance. The accuracy of interferometry delay measurement is proportional to the effective bandwidth. Under the same conditions, the wider the effective bandwidth, the higher the accuracy of the time delay measurement, yet the problem is that the ambiguity of the time delay becomes smaller. According to the CCSDS standard, two groups of DOR beacons are set for the X-band, DOR\({}_{1}\), and DOR\({}_{2}\). DOR\({}_{1}\) primarily resolves the time delay ambiguity, while \(\pm\)DOR\({}_{2}\) ensures measurement accuracy. For the method proposed in this paper, when the pseudo-code clock component is used to replace the DOR\({}_{1}\) signal, the effective bandwidth of the coarse time delay estimation becomes narrower. If the same time delay measurement accuracy is achieved, the cross-spectrum phase measurement accuracy must be improved, otherwise, the time delay measurement accuracy will deteriorate. However, as long as this does not result in integer ambiguity resolution errors, the final delay measurement accuracy aligns with traditional methods, because the \(\pm\)DOR\({}_{2}\) signal beacons remain unchanged. There is only one set of DOR beacons for the S-band, and it can be directly equivalent to the coarse delay estimation process in the X-band. When the DOR signal is used, the effective bandwidth is wider, and the measurement accuracy is higher than that of the pseudo-code clock component. Therefore, the method proposed in this paper is more suitable with a weak downlink signal power in the X-band.
## 1 Introduction
### Remote-Sensing Big Data
The remote-sensing data set, including onsite pictures, attribute information, etc., are vital for model training and algorithm testing in regard to the process of machine learning for many research fields of remote-sensing applications, such as target recognition, land cover and land utilization classification [12].
## 2 Description of Proposed Algorithms
### Algorithm Based on Teacher-Learner and Local Descent Methods
#### 2.2.3 Step-by-Step Algorithm  
Two variants of the TL have been implemented: the original (v.1, \(N_{LOC}=0\)) and the hybridized with the modification of trust region (v.2, \(N_{LOC}>0\)) method for local descents. See Algorithm 2.
```
1. Set the algorithmic parameters. Choose starting point \(x^{0}\in X\). Suppose \(x^{REC}=x^{0}\), where \(x^{REC}\) is the record point. Compute \(f^{REC}=f(x^{REC})\). Suppose \(K=0\), where \(K\) is the algorithm iteration number.
2. Create a random initial set of agents (a group of students) \(P^{0}=\big{\{}p_{1}=x^{0},p_{2},\ldots,p_{m}\big{\}}\), \(p_{j}\in X,\,\,j=\overline{1,m}\).
3. Carry out a selection of the initial population: perform \(m\) local descents, each of \(N_{LOC}\) iterations of the local search method, each starts with its agent (student) of the initial population, get \(\{s_{1},s_{2},\ldots,s_{m}\}\).
4. Improve the record point, take \(x^{REC}=\text{argmin}\big{\{}f(s_{i}),l=\overline{1,m}\big{\}}\).
5. Substitute in the group \(P^{0}\) of the students \(\{p_{1},p_{2},\ldots,p_{m}\big{\}}\) by the agents \(\{s_{1},s_{2},\ldots,s_{m}\}\). On the \(K\)-th iteration:
6. Calculate a measure of biodiversity \(M_{bio}=1/(m-1)\sum\limits_{l=1}^{m-1}\|p_{l+1```
10 Evaluate all students: in a cycle for \(l=\overline{1,m}\) execute \(N_{LOC}\) iterations of the local search method, each starts with its own agent \(p_{l}\), get \(\{s_{1},s_{2},\ldots,s_{m}\}\).
11 Improve the record point, take \(x^{REC}=\operatorname*{argmin}\{f(s_{l}),l=\overline{1,m}\}\).
12 Select \(m\) best students from the two multitudes \(P^{K}\) and \(\{s_{1},s_{2},\ldots,s_{m}\}\) to the new group, get \(P^{K+1}\).
13 Set \(K=K+1\), move to stage 6.
```  
The iteration is complete.
## 4 Model Proposal
VCs can also be used to grant temporary authorization to another entity to access the credential in its entirety or only some of its claims, and can also be issued by IoT medical devices to transmit data as VCs to the patient, for example, in the case of a blood pressure meter that transmits data to the mobile application of the patient. The proposal can be applied for enabling secure communications between personal medical devices and patient/doctor devices. It guarantees secure communications between personal medical devices such as blood pressure meters and user/doctor devices. In the SSI paradigm, so-called peer DIDs allow us to establish a secure connection between the blood pressure meters and the applications on the patient's mobile phone or on the doctor's terminal. This is because, in addition to the secure communication channel between meters and applications provided by the DIDcomm protocol, thanks to peer DIDs, the two parties can use keys shared only between them. In this way, no one can impersonate the doctor and read the blood pressure data. In addition, the proposal can ensure data privacy, exploiting Zero Knowledge Proofs (ZKPs) and in particular the BBS+ signature scheme. Let us consider a use case where a patient gives temporary permission to a doctor to access their credentials. The credentials can be related to personal information and clinical data. The patient can allow the doctor to read all the claims in the credentials or only some subsets of the claims. In emergency situations, in case the patient is unable to give consent for access to their data by themselves, the doctor could access the data thanks to the delegation by the patient issued as a VC previously. In turn, the patient is sure that the doctor can only access precise data, exploiting ZKPs as defined in the VC. The patient has full control over granting and revoking access to clinical data and other personal data.
## 3 5G and Beyond
### Cloud-Based 5G/6G Systems
#### 3.2.4 Virtualized Cloud-RAN (V-CRAN)  
In order to better answer requirements of emerging services (e.g., with lower latency), NFV and SDN in the C-RAN can be deployed to virtualize necessary functions and resources (i.e., decoupling data and control planes) [38]. This method of virtualization toward access networks forms a new type of C-RAN, called virtualized-CRAN or V-CRAN.