esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Sample Noise Detection Based on Confidence Learning  
#### 3.1.1 Confidence Learning  
A confidence learning algorithm for noise sample recognition and cleaning of datasets [15] is proposed. The algorithm trains the dataset through the noise recognition model, and the trained model reversely test the samples in the original dataset. Its core idea is to take the newly learned knowledge as the criterion to re-examine the source of knowledge. The steps are as follows:  
1. Select noise detection model according to application requirements;
2. The threshold value of the sample being wrongly labeled as a certain type of label, which is defined as the self-confidence is determined by the model's prediction probability of the samples;
3. Compute the confusion matrix.  
In this paper, the structure of the noise detection model is consistent with that of the recognition model. The set of seven categories of label values in the expression dataset is denoted as \(\mathbf{Y}\) that is defined by the following Formula (1):  
\[\mathbf{Y}=\{y|0\leq y\leq 6,y\in\mathbb{Z}\} \tag{1}\]The set of the dataset samples is denoted as \(\mathbf{X}\) that is defined by the following Formula (2):  
\[\mathbf{X}=\left\{(\mathbf{x},\tilde{y})^{n}|\mathbf{x}\in\mathbb{R}^{d},\tilde{y }\in\mathbf{Y}\right\} \tag{2}\]  
where \(\mathbf{x}\) is \(1\times d\) sample pixel data, and \(d\) is the product of the length and width pixels of the sample image. \(\tilde{y}\) is the corresponding label of the sample, and \(n\) is the sample size of the dataset. The sample count matrix of labeled label \(\tilde{y}\) and potential real label \(y^{*}\) is denoted by \(\mathbf{C}\), \(\mathbf{C}\in\mathbb{N}^{\mathbb{Z}\times\mathbb{Z}}\), and the elements in \(\mathbf{C}\) are evaluated by the following Formula (3):  
\[\mathbf{C}_{i,j}=\left|\mathbf{X}_{i,j}\right| \tag{3}\]  
In the formula above \(\mathbf{X}_{i,j}\) is computed as the following Equation (4):  
\[\mathbf{X}_{i,j}=\left\{x|x\in\mathbf{X}_{g=i},p(j;x)\geq t_{j}\right\} \tag{4}\]  
where \(X_{\tilde{y}=i}\) is the subset of all samples labeled with \(i\) in the sample set. \(p(j;x)\) represents the predicted probability of sample \(\mathbf{x}\) for label \(j\). \(t_{j}\) is the self-confidence of label \(j\) and is computed as Equation (5):  
\[t_{j}=\frac{1}{\left|\mathbf{X}_{g=j}\right|}\sum_{x\in X_{g=j}}p(j;x) \tag{5}\]
# Design of a Compact Multiband Monopole Antenna with MIMO Mutual Coupling Reduction
###### Abstract  
In this article, the authors present the design of a compact multiband monopole antenna measuring 30 \(\times\) 10 \(\times\) 1.6 mm\({}^{3}\), which is aimed at optimizing performance across various communication bands, with a particular focus on Wi-Fi and sub-6G bands. These bands include the 2.4 GHz band, the 3.5 GHz band, and the 5-6 GHz band, ensuring versatility in practical applications. Another important point is that this paper demonstrates effective methods for reducing mutual coupling through two meander slits on the common ground, resembling a defected ground structure (DCS) between two antenna elements. This approach achieves mutual coupling suppression from \(-6.5\) dB and \(-9\) dB to \(-26\) dB and \(-13\) dB at 2.46 GHz and 3.47 GHz, respectively. Simulated and measured results are in good agreement, demonstrating significant improvements in isolation and overall multiple-input multiple-output (MIMO) antenna system performance. This research proposes a compact multiband monopole antenna and demonstrates a method to suppress coupling in multiband antennas, making them suitable for internet of things (IoT) sensor devices and Wi-Fi infrastructure systems.  
multiband; monopole antenna; Wi-Fi; MIMO; mutual coupling; mutual coupling suppression; IoT +
Footnote †: journal: Sensors
# Cooperative Friendly Jamming Techniques for Drone-Based Mobile Secure Zone
## 4 CFJ-DMZ Model
Finally, [PERSON] and D communicate inside the secure zone formed by the jamming-signal transmission of J. Here, because the distance between S and D is close, the transmission signal strength of S is reduced. The three units of J transmit cooperative jamming signals. As a result, the probability of success of the eavesdroppers decreases, improving the security of D2D communication in the secure zone. In this study, we conducted simulations and field experiments to prove the security of the CFJ-DMZ model. Octave was used for the simulations, and Raspberry Pi 3 was used in the field experiments. Section 5 discusses the security verification process and results. The experiments were focused on proving the security improvement inside the secure zone. Therefore, as a part of the process under the CFJ-DMZ scenario, we assumed that the source node has moved and that the friendly jamming drones have completed moving to form a secure zone.
## 3 Program Development and Implementation
### Real-Time Data Calculation and Display  
After data integration and conversion, calculations are performed within the C# program. During the program's operation, when the user clicks a valve or pump, it changes to the color of the pipeline, indicating that it is open, and the signal is sent to the calculation program. Next, the program analyzes the pipeline network to determine which segments allow fluid flow. Using an iterative method, it reads and evaluates the control states on the segments. When the number of open controls on a segment matches the number of controls shown in the CSV file, the program determines that the segment is in a flow-allowed state. When all the segments from the source to the terminal are in a flow-allowed state, the pipeline containing those segments is considered to allow fluid flow.  
Once the pipeline is confirmed to allow flow, the flow calculation and transmission process begins. The flow calculation proceeds as follows: based on the pump's power and the pipeline flow direction from the CSV, the program requests flow from the source segment to the pump inlet. If the maximum upstream segment flow exceeds the pump's rated flow, the system requests flow based on the pump's rated flow. If the upstream segment's maximum flow is below the pump's rated flow, the system adjusts the request according to the segment's flow limit.  
The flow is temporarily stored at the upstream node of each pump and continuously requested from upstream nodes until it reaches the source. When multiple branches exist at an upstream node, the system applies an equal flow distribution strategy, requesting flow evenly from each branch. If the maximum flow limit of any branch is lower than the average, the system adjusts the request to that branch's maximum flow and distributes the remaining flow to the other branches at the same level. When all branches reach their maximum flow limits, the system automatically adjusts the upstream node's maximum flow setting to the sum of the flow limits of all branches.  
Once flow is assigned to the source, it is distributed downstream to each branch node along the pipeline. Pump outlet handling follows a similar process, with the flow stored at the pump node being distributed downstream according to the maximum allowable value of the downstream segment. The flow is adjusted based on the principles of average distribution and maximum flow limits until it reaches the terminal.  
This entire process ensures accurate and balanced flow distribution from the source to the terminal segments. Ultimately, all calculated and distributed flow values are displayed in the pipeline node processing flow, ensuring the intuitive and logical clarity of the pipeline network operations. The network processing flow is illustrated in Figure 7.
# Study on Actuator Line Modeling of Two NREL 5-MW Wind Turbine Wakes
## 4 A Single NREL 5-MW Turbine Case
### Validation of Numerical Model
The comparison with the result of the BEM method is depicted in Figure 11. It shows that the local Reynolds number varies in different locations of the blade and the maximum value is near \(1.2\times 10^{7}\), which is far bigger than that given in the reference. Thus, the airfoil data become unreliable for the wind speeds greater than 11.4 m/s. In addition, the size of the wind turbine is so large that the dynamic stall should be considered at the high wind speed. The dynamic stall model is not used in this paper. Additionally, the blades need to adjust their pitch angles and maintain the tip speed ratio to maximize the power output when the wind speed exceeds the rated speed. In summary, large wind speeds are not considered in this work. To validate the effect of the tip loss model, the comparison of the force distributions with and without the tip loss model is given for \(U_{0}=11.4\) m/s (See Figure 12). There is no difference in the middle of the blade, but obvious disagreement is found at the tip if the model of tip loss is not included. Although the discrepancy in the force is small, the residual of power could rise up to 12% (See Table 8). Therefore, it seems to be significant to add the tip loss correction model when using ALM. Figure 11: Comparison of the local Reynolds number with the Blade-Element/Momentum (BEM) method. Figure 12: Comparison of radial force distributions with [PERSON] model and without the tip loss model.
# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 3 Explainable AFV
### Methodological Perspective
#### 3.2.1 Summarization Approach  
In AFV, extractive and abstractive explanations serve as two types of summarization methodologies, providing a summary along with the predicted label as a form of justification or explanation. Extractive explanations involve directly extracting relevant information or components from the input data that contribute to the prediction or fact-checking outcome. These explanations typically rely on the emphasis of specific words, phrases, or evidence within the input. On the other hand, abstractive explanations involve generating novel explanations that may not be explicitly present in the input data. These explanations focus on capturing the essence or key points of the prediction or fact-checking decision by generating new text that conveys the rationale or reasoning behind the outcome. It is important to note that terminology can vary across fields. For instance, in the explainable natural language processing (explainable NLP) literature, ref. [14] refers to extractive explanations as 'Highlights' and abstractive explanations as 'Free-text explanations'.  
The approaches to explainability employed by existing explainable AFV systems are primarily extractive. For example, the work of [23] presents the first investigation of the generation of explanations automatically based on the available claim context, utilizing the transformer model architecture for extraction summarization purposes. Two models are trained with the intention of addressing this issue. One model focuses on generating post hoc explanations, where the predictive and explanation models are trained independently, while the other model is trained jointly to handle both tasks simultaneously. The model that trains the explainer separately tends to slightly outperform the model trained jointly. In [34], the task of explanation generation as a form of summarization is also approached. However, their methodology differs from that of [23]. Specifically, the explanation models of [34] are fine-tuned for extractive and abstractive summarization, with the aim of generating novel explanations that go beyond mere extractive summaries. By training the models on a combination of extractive and abstractive summarization tasks, they enabled the models to generate more comprehensive and insightful explanations by both leveraging existing information in the input and generating new text to convey the reasoning behind the fact-checking outcomes.  
A potential concern is that these models (both extractive and abstractive) may generate explanations that, while plausible in relation to the decision, do not accurately reflect the actual veracity prediction process. This issue is particularly problematic in the case of abstractive models, as they can generate misleading justifications due to the possibility of hallucinations [3].
# An Overview of Position Sensorless Techniques for Switched Reluctance Machine Systems
## 4 Future Directions
### Reduce Coupling between Position Estimation and Control Methods  
For motor motion control, current chopper control (CCC), angle position control (APC), and voltage chopper control (VCC) are relatively mature control algorithms. Many position estimation methods are extremely dependent on these control strategies, which is weak compared with traditional position sensors. Many new controls, such as direct torque con  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Categories** & **Speed Range** & **Pre-Stored** & **Advantages** & **Shortcomings** \\ \hline Pulse injection methods & Startup and low speed & Small & Accurate, easy to implement & Negative torque ripple generation \\ \hline Additional components methods & Whole-speed & Without & Universal versatility & Introduced new components \\ \hline Electromagnetic-characteristics-based methods & Whole-speed & Small & Simple, effective and stable & Poor real-time performance \\ \hline Flux-current-based methods & Traditional & Medium and high speed & Large & Good real-time performance & Poor versatility, huge calculation \\ \hline Developed & Whole-speed & Medium & Without additional components & Poor versatility \\ \hline Inductance-based methods & Traditional & Medium and high speed & Medium & Small amount of computation & Large estimation error, difficult modeling \\ \hline Developed & Whole-speed & Medium & Small amount of computation & Poor real-time performance \\ \hline Intelligent-control-based methods & Whole-speed & Medium & Universal versatility, Good real-time performance & Difficult algorithm design \\ \hline Observer-based methods & Whole-speed & Without & Good robustness, universal versatility & Poor accuracy in low speed, difficult algorithm design \\ \hline Strategies based on a mix of multiple methods & Whole-speed & Medium & Accurate location estimation & Requires switching algorithm \\ \hline Hybrid method based on multiple features & Whole-speed & Small & Various methods, easy to implement & Poor real-time performance \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the various sensorless methods.  
trol (DTC) and torque sharing function (TSF) [154; 160; 161; 162], have outstanding performance in reducing torque ripple and vibration noise, and the current waveforms produced by these methods are completely different due to different control strategies [134; 135]. This forces the position estimation methods to be able to adapt to these new control strategies.
# An Interoperable UMLS Terminology Service Using FHIR
## 5 UMLS FHIR Terminology Service: Design and Implementation  
### Design: FHIR Extensions to ConceptMap  
To overcome the identified shortcoming in the analysis, the research designs three FHIR extensions to align the ConceptMap to the UMLS knowledge requirements for an interoperable representation of UMLS knowledge using ConceptMap.  
* An extension to capture the semantic type(s) of the SourceElement (UMLS concept) and TargetElement (Atom).
* An extension to capture the label attribute associated with the UMLS relationship (Relationship class, Figure 3).
* An extension to capture the type attribute of the UMLS relationship (Relationship class, Figure 3). As discussed, the type attribute only takes values from the UMLS REL set. Thus, the REL set is translated into an FHIR valueset, named UMLSRelationTypeCode. This extension is bound to  
\begin{table}
\begin{tabular}{c c} \hline \hline
**UMLS REL Values** & **ConceptMapEquivalence Values** \\
**(Description)** & **(Description)** \\ \hline \hline \multirow{4}{*}{—} & inexact (The target mapping overlaps with the source concept, but both source and target cover additional meaning, or the definitions are imprecise and it is uncertain whether they have the same boundaries to their meaning.) \\ \cline{1-1}  & disjoint (There is no match for this concept in the target code system.) \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont_.  
Figure 5: Overview of the research methodology for designing and developing UMLS FHIR terminology service.  
the UMLSRelationTypeCode valueset. This design is similar to the equivalence attribute of the TargetElement class in ConceptMap that is bound to the ConceptMapEquivalence valueset.  
Figure 6 shows the three FHIR extensions in the FHIR structure, and Figure A1 (Appendix A) shows the defined UMLSRelationTypeCode valueset in the JSON format. In Figure 6, Figure 6 shows the FHIR extension to capture the semantic type as an FHIR Coding datatype, Figure 6 shows FHIR extension to capture the label attribute of a UMLS relationship as a string datatype, and Figure 6 shows the extension bound to UMLSRelationTypeCode valueset, to capture the type attribute of a UMLS relationship (Relationship class, Figure 3). Figure A2 shows the profiled ConceptMap with the designed extensions in the FHIR structure.
## 2 Background
### SC and Fast-SSC Decoding  
The original SC decoding process [1] for Polar codes of length \(N\) can be obtained by message passing between nodes of a balanced binary tree [2] with \(2N-1\) nodes, named Polar Factor Tree (PFT). Figure 1 shows a PFT for \(N=16\). For non-systematic Polar codes, the decoded bits \(\hat{u}_{i}\), \(i\in[0,N-1]\) are estimated in the leaf nodes at layer \(s=0\) of the PFT. The \(N\) received channel values \(\mathbf{y}\) are input to the root node (\(s=n\)) as channel Log-Likelihood Ratios (LLRs) \(\alpha_{i}=\log(Pr(y_{i}|x_{i}=0)/Pr(y_{i}|x_{i}=1))\). In layer \(s\) of the PFT, a node \(v\) of size \(N_{v}=2^{s}\) receives a vector \(\mathbf{\alpha}^{v}\) of \(N_{v}\) LLRs from the parent node and returns a hard decision vector \(\mathbf{\beta}^{v}\) of the same length. The messages passed to the left and the right child nodes, \(\mathbf{\alpha}^{l}\) and \(\mathbf{\alpha}^{r}\), are computed by the f- and g-functions as defined in [16], respectively. The message to the parent node is the partial sum vector \(\mathbf{\beta}^{v}\), calculated by the combination of \(\mathbf{\beta}^{l}\) and \(\mathbf{\beta}^{r}\), denoted by h-function. In this way, the PFT is traversed depth first with priority to the left child, since its result is used to determine the message to the right child. In the leaf nodes, \(\mathbf{\beta}^{v}\) only has one element, and the estimated bit \(\hat{u}_{i}\) determined by  
\[\hat{u}_{i}=\begin{cases}0,&\text{if }i\in\mathcal{F}\text{ or }\alpha_{i}^{v}\geq 0\\ 1,&\text{otherwise.}\end{cases} \tag{2}\]  
Rate-0 nodes, whose leafs are all frozen bits, or Rate-1 nodes, whose leafs are all information bits, can be decoded without the need of traversing their subtrees, which is denoted by Simplified SC (SSC) decoding [2]. Alike, PFT tree pruning can be carried out for nodes that represent Repetition code (REP) nodes and Single Parity-Check code (SPC) nodes, resulting in Fast-SSC decoding [3]. The PFT in Figure 1 shows Rate-0, Rate-1, REP and SPC nodes with a node size restriction of \(N_{v,\text{max}}=4\) as wh  
Figure 1: Polar factor tree for \(\mathcal{P}(16,8)\) with \(\{u_{0},\ldots,u_{6},u_{8}\}\in\mathcal{F}\). Rate-0, Rate-1, Repetition code (REF) and Single Parity-Check code (SPC) nodes with a node size restriction of \(N_{v,\text{max}}=4\) are represented as white and black circles, red squares and green thombs, respectively.  
squares and green rhombs, respectively. In Fast-SSC decoding (with the aforementioned restriction), the subtrees of these nodes in layer \(s=2\) need not be traversed. This tree pruning largely reduces the size of the PFT and thus the number of visited nodes.
## 2 Materials and Methods
### Calibration of Sensors  
#### 2.3.1 Moisture Calibration  
Five sets of natural saline soil samples with mass moisture contents of 3%, 6%, 9%, 12%, and 15% were prepared by washing and drying. The samples were sealed and stored for 24 h to ensure an even distribution of moisture. The prepared samples were placed in a beaker. The CS655-1--CS655-5 sensor probes were inserted into the saline soil in sequence to obtain 3 readings, and the average value was determined. The mass moisture content measured by the drying method was used as the vertical axis, and the volumetric moisture content measured by the sensor was used as the horizontal axis to fit the test results, as shown in Figure 5. There was a satisfactory linear relationship between the mass and volumetric moisture contents, with a correlation coefficient greater than 0.98.
## 7 Smart Grid Communication and Metering  
Communication plays a critical role in the real-time operation of power systems. Different types of communication technologies are being developed for applications in smart grid development, such as wired (e.g., fiber optic and telephone) and wireless (in different frequency bands, namely VHF/UHF) technologies. Wireless technologies have additional advantages over wired technologies, such as low installation cost, rapid deployment, ease of use, etc., and are ideal for isolated applications [53]. To better understand the different forms of wireless technologies, comparisons are made in Table 10.  
Initially, wired systems such as telephones are used to communicate line utilization to the control center and dispatch operators performing switching operations at substations. Due to a number of power grid constraints, such as voltage limits, thermal overloads, and other complexities of hardwired grid connections, the communication networks in individual DERs in smart grids are becoming more complicated by the day as more DGs are integrated into power grids. Therefore, the success of a smart grid implementation depends on the adoption of a reliable and cost-effective communication system for metering, control, and monitoring purposes.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Wireless Technologies** & **Zigbee** & **Wi-Fi** & **WiMax** \\ \hline Design target & Low cost Low power & Indoor WLAN and small networks & Indoor or outdoor WAN, large networks \\ \hline Target application & No infrastructure, nearby devices & Indoor, high-capacity, small network & Outdoor, high capacity, large network \\ \hline Range channel & 10 \(\sim\)\(\times\)75 m & 10 \(\sim\)\(\times\)100 m & Up to \(\times\)50 km [27] \\ \hline Bandwidth & 5\(\times\)MHz & 5 or 10 MHz & \(>\)5 MHz [27] \\ \hline Maximum capacity & 250 kbps & 54 Mbps [54] & 10–50 Mbps [27] \\ \hline QoS mechanism & None & Limited to AP’s local QoS, 4 types: voice, video, best effort, and background & Network-wide QoS, 5 types: UGS, errPS, rtPS, nrtPS, and xBE \\ \hline Security measures & No security measures & Security at MAC with WEP encryption, etc. & MAC layer encryption and key exchange via licensed spectrum \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of wireless technologies.  
Figure 13: Architecture of synchro-phasor networks [52].