esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of High Availability for Linux Container Infrastructures
## 3 Materials and Methods
### CRI-O and Kubernetes as a Container Orchestrator  
CRI-O was developed as a standardized plugin for the Kubernetes environment, to run and monitor the functionality of containers. It serves as a link and layer between containers and the Kubernetes CRI (Container Runtime Interface) and is based on an older version of the Docker architecture. Compared to Docker, it is less demanding on system resources; its primary benefit lies in the reduction of resource consumption. CRI-O can directly run containers created by Kubernetes without the need for additional code or tools. When Kubernetes needs to launch a container, it simply calls a command on CRI-O, and a specially crafted daemon for CRI-O works with any Open Container Initiative (OCI) compliant program to launch and create that container. If Kubernetes needs to interact with CRI-O, a request is made to its Container Request Interface (CRI), which in turn makes a request to the CRI-O service. This service executes instructions based on the specification and provides Kubernetes with information about the new container, such as its ID and status [38; 39; 40].  
Kubernetes is an open-source container orchestration platform that enables automation of the deployment, extension, and management of container applets. Using Kubernetes, developers can improve the responsiveness and stability of an application by distributing container clusters to service zones. However, one of its limitations is the lack of decoupling for single individual users, requiring the creation of dedicated clusters for each of them. Kubernetes uses service replication across multiple nodes in a cluster and maintains information about the states of these containers, see Figure 3. In a high-availability environment, Kubernetes uses methods such as automatic replacement of failed nodes, self-healing, and load balancing. Automatic failed node replacement moves applications to other nodes if a failure occurs. Self-healing attempts to restart failed containers automatically. The load balance ensures a fair load distribution among the nodes in the cluster [39; 41].  
Figure 3: Kubernetes Architecture.
## 1 Introduction  
The area of sensor networks research has recently gained a great deal of attention [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. Various sensors are used in both civilian and military tasks. Sensing devices can be deployed in either static or dynamic settings, where the positions of each sensor can be permanent or dynamically changing (such as in the case of sensors installed on air vehicles). Multiple sensor systems are commonly represented as networks, since besides collecting important information, sensing devices can transmit and exchange information via wireless communication between sensor nodes. Therefore, network (graph) structures are convenient and informative in terms of efficient representation of the structure and properties thereof. To analyze and optimize the performance of sensor networks, mathematical programming techniques are extensively used.  
The purpose of this paper is to give a brief review of some of the recent developments in mathematical programming as applied to sensor network research. Various types of optimization problems can be formulated and solved in this context [9; 11; 12]. Moreover, the pursued tasks can vary from optimizing the network performance to network interdiction, where the goal is to disrupt enemy networks by interfering with communication network integrity. We will outline the formulations and briefly describe the solution methods used to tackle these problems.  
In many cases, the identified optimization problems are challenging from the computational viewpoint. In particular, the theoretical computational complexity of many of these problems was proven to be _NP-hard_. Therefore, efficient algorithms need to be developed to ensure that the near-optimal solutions are found quickly. This is essential to ensure that the decisions regarding efficient operations of sensor networks could be made in a real-time mode, which can be crucial in many applications. Moreover, the uncertain factors that commonly arise in real-world situations also need to be incorporated in the mathematical programming problems, which makes them even more challenging to formulate and solve. In this paper, we will address some of the challenges arising in this area.  
In particular, we will describe several important classes of problems that have recently been addressed in the literature. We will start the discussion with the description of recent promising developments in the area of sensor network localization, which allow identifying global positions of all the nodes in a network using limited and sometimes noisy information. It turns out that semidefinite programming techniques can be efficiently used to tackle these problems. Next, we will discuss the problems of single and multiple sensor scheduling for area surveillance, including the setups under uncertainty. We will also address the issues of wireless communication network connectivity and integrity, as well as network interdiction problems.
## 2 Materials and Methods
### System Modeling Process
#### 2.1.3 Classification Techniques  
As the patient's sensed vital information was gathered (on a large scale) using WBAN sensors, the DHS periodically stored these data in a transactional database (the PDS) for analysis. The massive scale of data involved made it possible to increase the efficiency of the classification techniques during this process. When a large amount of data is collected, a classification process can be used based on the SVM classifier, which is one of the most widely used algorithms in the medical sciences regarding its satisfactory results in terms of accuracy, precision and recall. The SVM was therefore used to classify blood pressure values as safe (normal) and risky (abnormal). This process requires the application of medical rules that are predefined by the medical staff and/or a binary-based classification that can detect and report the presence of potential anomalies. Our learning model was based during the training process on the following blood pressure references: ideal blood pressure, which is considered as safe and pre-high blood pressure, high blood pressure and low blood pressure that are risky. Once our model was well trained, it would be able to classify any BP value based on the learned knowledge. The generated model could therefore be applied at the SSS level, which uses two different classification techniques in order to assess the degree of danger of the anomalies previously detected. The work in this paper made it possible to initially classify the medical information of a patient in binary mode based on relational criteria. Following this, a second classifier based on a probabilistic approach was used to notify the broker agent. The aim of this was to reconsider the problem in order to quantify it to extract the maximum amount of useful knowledge. This involved switching from a binary mode (safe (0)/risky (1)) to a probabilistic based model. For example, if a B.P reading represents a high value of a blood pressure, which is classified as risky, the probability of its impact relative to a reference value (mean and standard deviation of the same sample) can quantify this anomaly compared to the remaining medical information in order to assess its risk to the patient's health. This was fully explained in the section entitled \"Probabilistic Approach\". (a) Data classification using support vector machines:  
SVMs are a set of supervised learning techniques that are used for classification, regression and outlier detection [26]. Unlike other classifiers, a SVM finds the optimum line separating the closest points of different classes, called support vectors, and draws this by carrying out vector subtraction. Figure 8: Interaction diagram of the alerts broking process. Table A1 in Appendix A contains a table that defines the variables used during the classification procedure.
# Scour Detection with Monitoring Methods and Machine Learning Algorithms--A Critical Review
## 2 Conventional Monitoring-Based and Machine Learning-Based Methods to Identify Scour
### Direct Monitoring-Based Studies
#### 2.1.2 Cluster 2--Machine Learning-Based Research
One should highlight that this parameter was not fixed: studies resulted in different conclusions about \"the most effective parameter\" because their training data, i.e., input was different from each other. Since all the studies claimed to make better predictions than empirical formulas, a comparison in terms of outperforming frequently used, (i.e., reference) algorithms can be meaningful. Specifically, the researchers adopting extreme learning machines, gradient tree boosting, radial basis function neural networks, and reduced error pruning tree algorithms, were successful in performing better than reference algorithms such as artificial neural networks and support vector machines.
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 2 Materials and Methods
### Machine Learning and Fuzzy Logic Models for Soil Temperature Estimation
#### 2.2.6 Extreme Gradient Boosting (XGB)  
XGB is a powerful machine learning algorithm based on the gradient-boosted decision trees (GBDT) framework, originally introduced by [PERSON] and [PERSON] [53]. XGB constructs tree-based models in a sequential manner, where each new tree is trained to correct the residual errors of the previous ensemble, reducing both bias and variance to improve the overall accuracy of the model. In this study, the key hyperparameters--including learning rate, maximum tree depth, minimum child weight, and number of boosting rounds (trees)--were systematically optimized. XGB is known for its superior prediction accuracy and computational efficiency, which is facilitated by advanced features, such as built-in handling of missing values, regularization to control overfitting, and support for parallel processing. Despite these advantages, the performance of the model is very sensitive to the configuration of the hyperparameters, which makes the tuning process relatively complex.
# Optical Boundaries for LED-Based Indoor Positioning System
## 5 Results and Discussions
### Defining Optical Boundaries to Compensate for Receiver Tilt  
The results in Section 5.5 consider a horizontal receiver in parallel to the plane of the transmitter. However, in reality, the receiver could be tilted. When tilt occurs, the BER, especially at the boundary region, worsens. At the full angle, this poor BER causes a greater delay in receiving packets which carry positioning information and thereby causes a delay in the positioning time. Repeating the process of Section 5.5 and including \(4^{\circ}\), \(8^{\circ}\), and \(12^{\circ}\) angle of tilt in the angle of incidence \(\varphi\) according to (22), the positioning times are presented in Figures 15 and 16 for the 4-bit and 12-bit packets.  
Figure 14: Reduction of APT in the overlap region through the use of receiver defined threshold angle (TA) instead of the conventional full angle (FA) for 12-bit packets in (21).  
Figure 13: Reduction of APT in the overlap region through the use of a receiver defined threshold angle (TA) instead of the conventional full angle (FA) for 4-bit packets in (21).  
The characteristics plots in Figures 13-15 show optimal cycle times for low APT between regions of low cycle times and high cycle times. This is due to two occurrences. Firstly, at very low cycle times, packets are not adequately separated to allow for pseudo-orthogonality using PDM [66]. The probability of collision in this region is high, and the average positioning time is high in this region due to packets lost in collisions. However, if the cycle times are significantly increased (at very high cycle times), there is a long wait before the packets are received. The trade-off between the delay caused by high probability of collisions at low cycle times and the delay caused by long waits at high cycle times lead to the optimal cycle times.  
Figure 16: Reduction of the effect of receiver tilt on APT in 12-bit packets through the use of receiver defined threshold angle (TA) instead of the conventional full angle (FA).  
Figure 15: Reduction of the effect of receiver tilt on APT in 4-bit packets through the use of receiver defined threshold angle (TA) instead of the conventional full angle (FA).  
The effect of tilt in terms of positioning time shows that by defining the optical boundary, for a 4\({}^{\circ}\) tilt which is expected in a person walking, the APT is 0.52 s for the 4-bit and 1.6 s for the the 12-bit packets. Whereas, if the conventional full angle is used, the APT increases to 11.25 s for the 4-bit packets and 2343 s for the 12-bit packets. This shows a large amount of positioning time delay when boundary conditions are not specified at the optical receiver. For a 12\({}^{\circ}\) angle of tilt, using the 4-bit packet, the positioning time is 0.7 s, which still meets the criteria for human positioning. Therefore, defining the threshold angle based optical boundary makes the receiver robust and resistant to little tilts which could be experienced in practical scenarios.
# Evaluating the Impact of Pre-Configured Uplink Resources in Narrowband IoT
## 4 Experiments
### Scalability  
The introduction of PURs significantly improves the network scalability in NB-IoT by allowing more devices to connect compared to the default RAP and EDT. This mechanism is particularly effective for managing uplink communication from multiple IoT devices. By pre-allocating uplink resources, PURs enable UE to transmit data without dynamic resource scheduling, which can be complex and resource-intensive, especially in large-scale deployments. In addition, the simulations showcase eNB's capability to serve up to 62,000 UEs under idealized radio conditions in an idle environment within one day. Additionally, a dedicated PUR setup with a 10% PUSCH BLER can manage up to 52,000 UEs.  
The DL spectrum usage in the default RAP is significantly impacted by system broadcasts, even in the absence of user data. Specifically, broadcasts such as the Master Information Block (MIB), Narrowband Primary Synchronization Channel (NPSS), Narrowband Secondary Synchronization Channel (NSSS), SIB1-NB, and additional System Information (SI) messages consume 30% of the DL spectrum. This leaves only 70% available for user-specific transmissions. As the number of connected devices increases, the spectrum usage ratio inevitably rises. However, the introduction of EDT and PURs offers a substantial reduction in the overhead associated with small data transmissions. This reduction in spectrum usage, by a factor of 3.7 [11] and 5.6, respectively, effectively increases cell capacity by the same factor, making PURs a highly recommended approach.  
In addition to DL constraints, the UL spectrum also plays a crucial role in determining cell capacity. Although the UL does not carry broadcast information, its available spectrum is reduced by the configuration of RA windows, which reserve portions of the spectrum regardless of actual RA activity. Therefore, a careful balance must be maintained between allocating sufficient intervals for RA windows and preserving spectrum for application data transmission. In well-configured scenarios, RA collisions are minimal, ensuring that the successful data transfer remains at optimal levels. However, as the number of connected devices increases further, the likelihood of RA collisions rises, potentially preventing devices from accessing the cell. In such cases, it will be necessary to optimize the RRC configuration to maintain efficient cell access and performance.  
Figure 9: The energy consumption of EDT and the default RAP are shown, including the measured outcomes from this study, as well as those referenced from the LENA-NB paper [11].
## 4 Concerns about and Encounters with Underwater Wireless Sensor Network
### Underwater Acoustic Communications Characteristics
#### 4.3.2 Transmission Loss  
The term \"transmission loss\" refers to the decrease in the sound volume from the source to the receiver (TL). This made a variety of empirical formulations to quantify the transmission loss. [PERSON] calculated the signal transmission loss as follows [41]:  
\[\alpha=\frac{0.12\text{f}^{2}}{1+\text{f}^{2}}+\frac{44\text{f}^{2}}{4100+ \text{f}^{2}}\left[\frac{\text{dB}}{\text{Km}}\right] \tag{1}\]  
\[\text{ss}=20\text{log}\tau \tag{2}\]  
\[\text{TL}=\text{ss}+\text{ }\alpha\text{ }\alpha^{*}0.001 \tag{3}\]  
f: freq. in kilo Hz, r: distance in mts, ss: spherical signal spreading, \(\alpha\): attenuation.  
The phrase suggested in [PERSON]'s formula was then offered as a more precise expression for the attenuation factor.  
\[\alpha=\frac{0.12\text{f}^{2}}{1+\text{f}^{2}}+\frac{44\text{f}^{2}}{4100+ \text{f}^{2}}+\left(2.75*10^{-3}\text{f}^{2}+0.003\right) \tag{4}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Depth (m)** & **Salinity (PPM)** \\ \hline
0 & 38.46 \\ \hline
50 & 37.01 \\ \hline
100 & 36.01 \\ \hline
100 & 36.22 \\ \hline
500 & 35.80 \\ \hline
1000 & 36.90 \\ \hline
1500 & 35.05 \\ \hline \end{tabular}
\end{table}
Table 3: Salinity contingent on the water depth.
# Hybrid Uncertainty Metrics-Based Privacy-Preserving Alternating Multimodal Representation Learning
## 4 Results and Discussion
### Experimental  
#### 4.1.1 Datasets  
1. CREMA-D: CREMA-D [2] is an audio-visual dataset for emotion recognition research, containing 7442 video clips of 2-3 s duration, featuring facial and vocal emotional expressions from 91 actors. The emotional states are categorized into six classes: happy, sad, angry, fearful, disgusted, and neutral. The dataset is randomly split into a training set of 6698 samples and a test set of 744 samples at a ratio of 9:1.
2. MVSA: MVSA-Single [9] is a standardized image-text dataset for multimodal sentiment analysis, comprising 5129 Twitter-derived social media pairs (4511 validated samples after curation). Each entry includes an image and its corresponding textual tweet, annotated via a single-annotator protocol with ternary sentiment labels (positive/negative/neutral). Through a systematic curation process involving eliminating inter-modal polarity conflicts and prioritizing the adoption of non-neutral labels when either modality exhibits neutrality, the dataset ensures label consistency while preserving authentic cross-modal interaction patterns.
## 3 Results
### Experiment II: Identification of DNA-Binding Proteins
The main reason is that, taking the form \(K\left(V_{i},V_{j}\right)=e^{-\gamma\left\|V_{i}-V_{j}\right\|^{2}}\), the RBF kernel can non-linearly map samples into a higher dimensional space so it can handle the non-linearly separable data. Accordingly, the RBF kernel is also adopted in this paper. The model selection of this kernel involves two parameters to be decided: the penalty parameter \(C\) and the kernel parameter \(\gamma\). We first convert each of the 1585 protein sequences into a 28-D vector, and then the vectors belonging to Set I are scaled and fed to the SVM. With an optimization procedure using a grid search strategy in LIBSVM, the parameter pair (C, \(\gamma\)) is determined as (8, 0.5) (It should be pointed out that the optimal values for one round of cross-validation may not be the same for another.). In literature, a set of metrics are often used to measure the prediction quality. To make it intuitive and easy to understand for readers, here we adopt the definition and notations used in [40; 41; 56; 57; 58; 59; 60] to describe the corresponding evaluation metrics:  
\[S_{n}=1-\frac{N_{+}^{+}}{N^{+}},\]  
\[S_{p}=1-\frac{N_{+}^{-}}{N^{-}},\]  
\[Acc=1-\frac{N_{+}^{+}+N_{+}^{-}}{N^{+}+N^{-}},\]  
\[MCC=\frac{1-\left(\frac{N_{+}^{+}}{N^{+}}+\frac{N_{+}^{-}}{N^{-}}\right)}{ \sqrt{\left(1+\frac{N_{+}^{-}-N_{+}^{+}}{N^{+}}\right)\left(1+\frac{N_{+}^{+}- N_{+}^{-}}{N^{-}}\right)}},\]  
\[F1\_M=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text {Recall}},\]  
where \(N^{+}\) is the total number of DNA-binding proteins investigated, while \(N_{-}^{+}\) the number of DNA-binding proteins incorrectly predicted to be of non DNA-binding proteins; \(N^{-}\) the total number of non DNA-binding proteins investigated, while \(N_{+}^{-}\) the number of non DNA-binding proteins incorrectly predicted as DNA-binding proteins. \(\text{Precision}=\frac{N^{+}-N_{+}^{+}}{N^{+}-N_{+}^{-}+N_{+}^{-}}\), \(\text{Recall}=1-\frac{N_{+}^{+}}{N^{+}}\). It should be pointed out that the set of metrics above is valid only for the single-label system (such as the case at hand). For the multi-label systems whose existence has become more frequent in system biology [61; 62; 63; 64] and system medicine [65], a completely different set of metrics as defined in [66] is needed. With the best pair (C, \(\gamma\)) obtained in the training stage, Set II is fed to the SVM. We find that \(N_{-}^{+}=17\) and \(N_{+}^{-}=22\). We thus have  
\[S_{n}=95.28\%,S_{p}=94.57\%,Acc=94.90\%,MCC=0.8978,F1\_M=94.62\%.\]  
Repeating the above random division procedure three times, we perform three cross-validation tests and list the results in Table 6. As can be seen, the accuracy (_Acc_), [PERSON]'s correlation coefficient (_MCC_), and F1-measure (_F1_M_) in each cross-validation test are greater than 94.90%, 0.8977, and 94.59%, respectively. This result indicates that our method is promising in identifying DNA-binding proteins.
## 2 GTLR-BiSAR Imaging Geometry and Signal Properties  
In this section, the geometry of the GTLR-BiSAR system is described first. Then, the signal model is established and the signal expressions in different domains are deduced and analyzed in detail.