esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 3 Energy Management of an HRES Based on Deep Q-Network
### Markov Decision Process Model for the EMS
#### 3.2.2 Actions and Action Variables  
Given the state at the current time step \(s_{t}\), the EMS controller chooses an action and moves to next state by opening or dispatching the operation of following elements: \(DG\), fuel cell, electrolyzer, and battery system. The action set \(A\) is formed by 4-component control signals:  
\[A=\left\{\sigma_{Battery}\times\sigma_{DG}\times\sigma_{FC}\times\sigma_{EL}\right\} \tag{12}\]  
The control actions of the battery system are discharging \((-1)\), stopping \((0)\), and charging \((1)\), that is:  
\[\sigma_{Battery}=\left\{-1,0,1\right\} \tag{13}\]  
The control action variable of the diesel generator is in Equation (14), including stop, operating 25%, 50%, 75%, and full capacity, that is:  
\[\sigma_{DG}=\left\{0,0.25,0.5,0.75,1\right\} \tag{14}\]  
The control action variables of \(FC\) and electrolyzer are defined as \(\sigma_{FC}\) and \(\sigma_{EL}\), respectively, including ON \((0)\) and OFF \((1)\), that is:  
\[\sigma_{FC}=\left\{0,1\right\} \tag{15}\]  
\[\sigma_{EL}=\left\{0,1\right\} \tag{16}\]
## 3 Experimental Results and Analysis
### Grasping Algorithm Experiments
#### 3.6.2 Workpiece Grasping Experiment  
As shown in Figure 17, this is the main process for target object grasping.  
To evaluate the grasping performance of the proposed algorithm and verify its robustness, four distinct object poses were defined, as illustrated in Figure 18. For each pose, 25 grasping trials were conducted under consistent experimental conditions.  
The experimental results are shown in the Table 10. Postures 1 and 3 achieved higher success rates, while postures 2 and 4 showed a noticeable drop in grasp success rates. An analysis of the failed cases reveals two main causes:  
1. In Postures 2 and 4, occlusions in the point cloud led to insufficient feature points, resulting in reduced pose estimation accuracy.
2. Under these postures, the gripper was more likely to collide with the object, which caused grasp failures.  
Figure 17: Grasping process diagram: (**a**) original point cloud; (**b**) YOLOv8-URE detection result; (**c**) illustration of target point cloud segmentation; (**d**) illustration of pose estimation for the registered target point cloud; (**e**) Illustration of grasping pose. The green part represents the segmented target point cloud.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Control Algorithm** & **Proposed Algorithm** \\ \hline Running Time (s) & 10.5949 & 4.18396 \\ _RMSE_ & 0.317391 & **0.052965** \\ _MAE_ & 0.267475 & **0.051005** \\ \hline \hline \end{tabular} The best results for each metric are highlighted in bold.  
\end{table}
Table 9: Algorithm comparison results.  
Figure 18: Workpiece placement pose for grasping: (**a**) Pose(1); (**b**) Pose(2); (**c**) Pose(3); (**d**) Pose(4).
# CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements
## 2 Materials and Methods
### Laboratory Sensor Calibration  
The laboratory distance calibration experiment was conducted at the SmartSense iHuB, Agriculture Victoria, Horsham, VIC, Australia. The laboratory trial was designed to calibrate the capabilities of the LiDAR under ideal conditions, mounted on a platform with the CBM sensor pointed towards a hard, flat ground surface (bigger than the minimum FoV of the sensor). The height of the platform carrying the CBM sensor was adjusted at different reference distances from the ground surface (Figure 3). The sensor was triggered for approximately 5 s at nine specified positions ranging from 40 cm to 265 cm in steps of approximately 30 cm, collecting 9 points \(\times\) 8 detectors = 72 readings. The LiDAR unit was calibrated against these reference measurements. Hereafter, these calibration settings were internally applied on the Raspberry Pi to tune the measurements that were captured from the LiDAR unit to provide precise distance profiles automatically.  
Figure 2: The CropBioMass (CBM) sensor design and development. (**a**) 3D model parts for the sensor case, (**b**) printing of the modeled parts using a 3D printer, and (**c**) encasing of the hardware units inside the printed 3D model case.
# Collaborative Multi-Robot Formation Control and Global Path Optimization
## 2 Improved Artificial Potential Field Method
### Repulsive Potential Field Function Adjustment  
To solve the defect that the target is not reachable during the robot movement, the repulsive potential field function is now adjusted as Equation (4).  
\[U_{rep}(x)=\left\{\begin{array}{l}\frac{1}{2}\eta(\frac{1}{\rho(X,X_{0})}-\frac {1}{\rho})\cdot\rho^{n}(X,X_{0}),\;\rho(X,X_{0})\leq\rho_{0}\\ 0,\;\rho(X,X_{0})>\rho_{0}\end{array}\right. \tag{4}\]  
where \(\rho(X,X_{0})\) is the Euclidean distance between the robot and the obstacle, \(\eta\) is the repulsion gain coefficient, and \(n\) is the regulation constant. The negative gradient of the repulsive field function can be obtained according to Equation (4), and the improved APF repulsive function is as follows:  
\[F_{rep}(X)=-\Delta[U_{rep}(X)] \tag{5}\]  
\[\Delta[U_{rep}(X)]=\left\{\begin{array}{l}-(F_{rep1}+F_{rep2}),\;\rho(X,X_{ 0})\leq\rho_{0}\\ 0,\;\rho(X,X_{0})>\rho_{0}\end{array}\right. \tag{6}\]  
where  
\[F_{rep1}(X)=\eta(\frac{1}{\rho(X,X_{i})}-\frac{1}{\rho_{0}})\cdot\frac{\rho^{ n}(X,X_{g})}{\rho^{2}(X,X_{i})} \tag{7}\]  
\[F_{rep2}(X)=\frac{n}{2}\eta(\frac{1}{\rho(X,X_{i})}-\frac{1}{\rho_{0}})\cdot \rho^{n-1}(X,X_{g}) \tag{8}\]  
From the above equation, the repulsive force \(F_{rep1}(X)\) decreases to 0 when the robot moves toward the target point; the gravitational force \(F_{rep2}(X)\) gradually increases when \(0<n<1\). This ensures that the robot can reach the specified position even if there is an obstacle near the target point. In this case, the combined force on the robot is given by Equation (9).  
\[F=F_{a}+\sum_{i=1}^{m}F_{rep,i} \tag{9}\]  
where \(m\) is the number of effective obstacles. Assuming that the combined force \(F\) is at an angle \(\theta\) with the \(x\)-axis, and \(k\) is the coordinate point of path, the next coordinate \((x_{k+1},y_{k+1})\), at which the robot moves, is calculated as follows, where \(l\) is the position shift length of the robot:  
\[\left\{\begin{array}{l}x_{k+1}=x_{k}+l\cdot\cos\theta\\ y_{k+1}=y_{k}+l\cdot\sin\theta\end{array}\right. \tag{10}\]
## 3 Methodology
### SEA Model
#### 3.2.2 Boundary Conditions  
The force applied to the fuselage during the operation should be considered in the SEA model. This force is employed to predict the interior noise levels within the cabin. To this end, the operational forces applied to structural linking points from the wings to the fuselage of the tiltrotor are computed based on classical transfer path analysis and within the matrix inversion procedure. The operational forces applied to structural linking points from the wings to the fuselage are computed at six points. Figure 5 shows these linking points.  
The forces are determined via experimental data. This procedure necessitates the following information:  
Figure 4: Cavity subsystems and the mesh elements. (**a**) Cavity 1, (**b**) Cavity 6, and (**c**) mesh elements.  
Figure 5: Structural linking points of wings to the fuselage in the VSEA model.  
* The frequency response functions (FRFs) at the linking points. These data are provided during experimental measurements by impacting with an instrumented hammer the linking points between the wings and fuselage. \[H=\frac{a}{F}\] (14) \(H\) is the accelerometer FRFs, \(a\) is the acceleration, and \(F\) is the impact force.
* The data on accelerations in operating conditions are measured at the same structural points as during the operational conditions.  
A system of linear equations with an equal number of equations and unknowns typically provides a unique solution; however, it can introduce substantial inaccuracies [18]. In other words, minor adjustments in the input data can result in significant variations in the obtained results. There are several options available to enhance the estimation of the forces, such as generating an over-determined system of equations. This can be achieved by adding extra equations using the information recorded on some of the internal panels of the tiltrotor when exciting the structural points. Equation (15) represents a system of equations for an over-determined system:  
\[\begin{bmatrix}\frac{a}{F_{1}}&&\frac{a}{F_{n}}\\ \vdots&&\vdots\\ \frac{a_{n}}{F_{1}}&&\frac{a_{n}}{F_{n}}\\ \frac{a_{n+1}}{F_{1}}&&\cdots&\frac{a_{n+1}}{F_{1}}\\ \vdots&&\vdots\\ \frac{a_{n+p}}{F_{1}}&&\frac{a_{n+p}}{F_{n}}\end{bmatrix}\begin{bmatrix}F_{1} \\ \vdots\\ F_{n}\end{bmatrix}=\begin{bmatrix}a_{1}\\ \vdots\\ a_{n}\\ a_{n+1}\\ \vdots\\ a_{n+p}\end{bmatrix} \tag{15}\]  
where \(n\) is the number of structural points, and \(p\) is the number of panels used to over-determine the system of equations. Equation (15) is used to calculate the operational forces, and they are used as the boundary conditions of the model.
## 3 Experiments on the 3D Quartz Tuning-Fork Probe
### 3D Trigger Positioning System  
Based on the mentioned quartz tuning fork probe combined with a 3D nanopositioning stage, a feedback control module, and a signal processing circuit, a 3D trigger positioning system is constructed, as shown in Figure 8. The quartz tuning fork probe keeps vibrating at its resonant frequency. The amplitude of the probe is detected by the amplitude feedback circuit. When the probe tip contacts the sample surface in a certain direction, the vibrating amplitude of the probe attenuates because of the force interaction between the probe tip and the sample surface. The phase feedback circuit using Phase Locked Loop (PLL) is also designed as an alternative for the system. To verify the amplitude-distance model that we established, only the amplitude feedback circuit is used in the experiment part. The 3D nanopositioning stage used in this study is produced by Physik Instrumente Co., Ltd (Karlsruhe, Germany). The nanostages in the \(Y(X)\) and \(Z\) directions are P-611.2S and P-753.1 CD, respectively. The \(X(Y)\) stage has a closed-loop resolution of 2 nm and a positioning repeatability of less than 10 nm. The \(Z\) stage has a closed-loop resolution of 0.05 nm and positioning repeatability of \(\pm\)1 nm.
# Assessing the Probability of Winning a Point in Men's Padel: A Comprehensive Analysis
###### Abstract  
Background--The number of studies on padel has grown significantly in recent years, reflecting the growing importance of the sport. However, more research is still needed on a comprehensive analysis of performance indicators. This study had a double objective: on the one hand, to analyze the probability of winning a point as a function of several variables and, on the other hand, to develop a match analysis. Methods--A total of 980 points from the Menorca 2020 World Padel Tour Master Final Men's category were examined using observational methodology. The participants were the eight pairs who competed in the final rounds of the tournament. To obtain the results, various analytical techniques were used, such as descriptive analysis and the chi-square test, with a significance level of \(p<0.05\). Results--The results indicated that most points were initiated on the first serve (88.5%) and were most often won by the serving pair (59.3%). Short rallies predominated (42.2%), with the serving pair most likely to win the point (71%). As the rally became longer, the probability of winning the point decreased, reaching 57% for medium rallies and 47% for long rallies. Almost half of the points were completed in less than 10 s, with an average point duration of 15 s. Most points were finished from the middle area and near the net (41.4% and 36%, respectively), mainly by smashes (25.9%) or volleys (28.9%), with the cross-court trajectory being the most effective. Conclusions--Short rallies (0-6 shots) were the most common, with an average point duration of approximately 15 s. Most points ended with a shot from the middle of the court, using a volley or a smash with a cross-court trajectory. There is a clear relationship between proximity to the net on the final shot and an increased probability of winning the point.  
notational analysis; performance; observational analysis; match statistics; padel coaching
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 1 Introduction  
Today's method for the generation and distribution of electric power was designed and constructed in the last century and has remained unchanged since. The traditional power grids are primarily radial and built for centralized power generation. Reliability is ensured by having excessive capacity and one-way power flow from the power plant to the consumer through high voltage transmission lines, often over long distances. With the demand for electric energy continuously increasing, and the existing conventional grid being at the end of its life cycle, increasing amounts of distributed renewable energy sources (RES) and energy storage systems (ESS) require new ways of managing and controlling the power grid and distributing the power in a more efficient, effective environmentally sustainable and economical manner. The next-generation power grids are often referred to as Smart Grids (SG). SGs are achieved by overlaying a hierarchical communication infrastructure on the power grid infrastructure [1; 2; 3; 4].  
Since 1 January 2019, most end-users in Norway should have installed smart electricity meters as part of the implementation of Advanced Metering Infrastructure (AMI) in the Norwegian power grid [5]. By the end of 2020, 3.2 million, or 99% of the electricity meters in Norway were smart meters [6]. In EU, it was committed by the member states to achieve a rollout of close to 200 million smart meters for electricity by 2020. About 71% of European consumers then will have a smart electricity meter installed [7]. Globally it is expected that 800 million smart meters will be installed by 2020 [8]. The installation of these metering devices can be seen as one of the first steps toward a smarter grid system, as implementing a SG is not a one-time event, but rather an evolutionary process. The smart meters havethe ability to collect and report consumption data to the utilities provider several times per hour, rather than the consumer having to report every month manually. The smart meters also open up for the consumer to feed the grid with electricity from, i.e., solar panels or electric vehicles. Other possibilities with smart metering are a higher degree of monitoring and control of the grid, automatic fault detection, and reports [1,9].  
The rest of the paper is organized as follows: Section 2 gives an overview of Smart Grid infrastructure, domains, architecture, and applications. Section 3 presents Smart Grid communication technologies and network structures. Section 4 addresses challenges of Smart Grid communications, and the privacy and security of Smart Grid communication. The organization of this paper is summarized in Figure 1.
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 2 Preliminaries
### Tokenization and Embeddings  
Tokenization is a crucial preprocessing step in NLP that converts text into tokens, which are smaller units of meaning. In the context of LLMs, tokenization is essential for transforming clinical reports into a format that the model can understand and process. Effective tokenization ensures that the context and semantic meaning of the clinical data are preserved. This is particularly important in healthcare, where the accuracy of the converted data can directly impact patient outcomes. Proper tokenization allows the LLMs to process the text more efficiently, reducing computational resources and time. Consistent tokenization ensures that similar clinical terms are represented uniformly, which is crucial for maintaining the integrity of the converted data. The process involves breaking down the text into individual words or subwords, which are then converted into numerical representations called embeddings.  
Embeddings capture the semantic meaning of the text, allowing the LLMs to understand the context and relationships between different parts of the clinical report. In LLMs, embeddings are generated through a process that maps tokens to high-dimensional vectors. These vectors are used by the model to understand and generate text. There are several types of embeddings, including word embeddings, subword embeddings, and contextual embeddings. Word embeddings represent individual words in a continuous vector space, capturing semantic and syntactic similarities. Techniques like Word2 Vec [15] and GloVe [16] are commonly used to create word embeddings. Subword embeddingsbreak words into smaller units, such as morphemes, to handle out-of-vocabulary words and rare terms more effectively. Byte pair encoding (BPE) and WordPiece are popular methods for subword tokenization. Contextual embeddings consider the context in which a word appears, allowing the same word to have different embeddings based on its usage. Models like BERT [1] and RoBERTa [2] use contextual embeddings to capture nuanced meanings.
# A Denoising Method for Seismic Data Based on SVD and Deep Learning
## Appendix A  
The formula for SVD is as follows:  
\[\text{D}\ =\ \text{USV}^{\text{T}}\]  
D is a matrix of m \(\times\) n, and U is a matrix of m \(\times\) m. S is a matrix of m \(\times\) n, S is 0 except for the elements on the main diagonal, and each element on the main diagonal is called a singular value \(\sigma_{\text{k}}\). V is a matrix of n \(\times\) n and U\({}^{\text{T}}\)U = I, V\({}^{\text{T}}\)V = I.  
\[\left(\text{D}^{\text{T}}\text{D}\right)\text{v}_{\text{k}}\ =\ \lambda_{\text{k}} \text{v}_{\text{k}}\]  
From the above formula, we can get n eigenvalues and n eigenvectors of D\({}^{\text{T}}\)D. All eigenvectors of D\({}^{\text{T}}\)D form matrix V and each eigenvector of V is called the right singular vector.  
\[\left(\text{D}\text{D}^{\text{T}}\right)\text{\mu}_{\text{k}}\ =\ \lambda_{\text{k}} \text{u}_{\text{k}}\]  
From the above formula, m eigenvalues and m eigenvectors of DD\({}^{\text{T}}\) can be obtained. All eigenvectors of DD\({}^{\text{T}}\) form a matrix U, and each eigenvector of U is called the left singular vector.  
\[\text{D}^{\text{T}}\ =\ \text{VSU}^{\text{T}}\ \text{D}^{\text{T}}\text{D} =\ \text{VSU}^{\text{T}}\text{USV}^{\text{T}}\ =\ \text{VS}^{\text{2}}\text{V}^{\text{T}}\ \text{\to}\ \sigma_{\text{k}}\text{=}\ \sqrt{\lambda_{\text{k}}}\]  
Here's an example of the definition matrix D is:  
\[\text{D}\ =\ \begin{bmatrix}1&2\\ 2&1\\ 1&1\end{bmatrix}\]  
\[\text{D}^{\text{T}}\text{D} =\begin{pmatrix}1&2&1\\ 2&1&1\end{pmatrix}\begin{pmatrix}1&2\\ 2&1\\ 1&1\end{pmatrix}=\begin{pmatrix}6&5\\ 5&6\end{pmatrix}\]  
\[\text{DD}^{\text{T}} =\begin{pmatrix}1&2\\ 2&1\\ 1&1\end{pmatrix}\begin{pmatrix}1&2&1\\ 2&1&1\end{pmatrix}=\begin{pmatrix}5&4&3\\ 4&5&3\\ 3&3&2\end{pmatrix}\]  
The eigenvalues and eigenvectors of D\({}^{\text{T}}\)D can be found:  
\[\lambda_{1} = 11;\ \text{v}_{1} =\begin{pmatrix}\frac{1}{\sqrt{2}}\\ \frac{1}{\sqrt{2}}\end{pmatrix};\ \lambda_{2} = 1;\ \text{v}_{1} =\begin{pmatrix}-\frac{1}{\sqrt{2}}\\ \frac{1}{\sqrt{2}}\end{pmatrix};\]The eigenvalues and eigenvectors of DD\({}^{\rm T}\) can be found:  
\[\lambda_{1} = 11{\rm u}_{1} = \begin{pmatrix}\frac{3}{2}\\ \frac{3}{2}\\ 1\end{pmatrix};\;\lambda_{2} = 1,\;{\rm u}_{2} = \begin{pmatrix}-1\\ 1\\ 0\end{pmatrix};\;\lambda_{3} = 0,\;{\rm u}_{3} = \begin{pmatrix}-\frac{1}{3}\\ -\frac{1}{3}\\ 1\end{pmatrix};\]  
From \(\sigma_{\rm k}\)=\(\sqrt{\lambda_{\rm k}}\), we know that the singular values are \(\sqrt{11}\) and \(1\), so we get a singular value decomposition of D:  
\[{\rm D} = {\rm USV}^{\rm T}=\begin{pmatrix}\frac{3}{2}&-1&-\frac{1}{3}\\ \frac{3}{2}&1&-\frac{1}{3}\\ 1&0&1\end{pmatrix}\begin{pmatrix}\sqrt{11}&0\\ 0&1\\ 0&0\end{pmatrix}\begin{pmatrix}\frac{1}{\sqrt{2}}&\frac{1}{\sqrt{2}}\\ -\frac{1}{\sqrt{2}}&\frac{1}{\sqrt{2}}\end{pmatrix}\]
# Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts
## 5 Experiment
### Results
#### 5.3.4 Runtime and Convergence
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Query** & \multicolumn{2}{c}{**(San Bernardino,/base/bibliness/bibs_location/state,?)**} \\ \hline
**Method** & \multicolumn{2}{c}{**SoLE**} & \multicolumn{2}{c}{**SoLE\_OTI**} \\ \hline  & **California** & **0.793200** & San Bernardino County & 0.688559 \\ Top 5 entities and & San Bernardino County & 0.560985 & **California** & **0.106467** \\ their scores & Oregon & 0.131643 & United States of America & 0.105158 \\  & United States of America & 0.117618 & Oregon & 0.061081 \\  & Washington & 0.093031 & Nevada & 0.044814 \\ \hline
**Query** & \multicolumn{2}{c}{**(National League sports league,/baseball/baseball\_league/teams,?)**} \\ \hline
**Method** & \multicolumn{2}{c}{**SoLE**} & \multicolumn{2}{c}{**SoLE\_OTI**} \\ \hline  & **Colorado Rockies** & **0.967590** & Milwaukee Brewers & 0.945655 \\ Top 5 entities and & Houston Astros & 0.967565 & Houston Astros & 0.904532 \\  & Montreal Expos & 0.632854 & Cleveland Indians & 0.826663 \\  & Milwaukee Brewers & 0.506580 & Baltimore Orioles & 0.728855 \\  & Oakland Athletics & 0.476932 & **Colorado Rockies** & **0.708710** \\ \hline
**Query** & \multicolumn{2}{c}{**(?/finance/currency/countries\_used,Austria)**} \\ \hline
**Method** & \multicolumn{2}{c}{**SoLE**} & \multicolumn{2}{c}{**SoLE\_OTI**} \\ \hline  & **Euro** & **0.446783** & [PERSON] & 0.321364 \\ Top 5 entities and & Screen Actors Guild Award for & 0.303476 & [PERSON] & 0.243579 \\ their scores & Outstanding Performance by a Cast & & & \\  & in a Motion Picture & & & \\  & /m/0 bs0 bh & 0.250963 & Salzburg & 0.113729 \\  & sailing & 0.214201 & Miami & 0.112626 \\  & record producer & 0.075033 & **Euro** & **0.084402** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of three specific queries (predictions) on FB15K. Boldface entities are the right answers according to the test sets. Figure 5: Convergence times of embedding learning in ComplEx and SoLE on datasets FB15K and DB100K, respectively. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{2}{c}{**FB15K**} & \multicolumn{2}{c}{**DB100K**} \\ \cline{2-5}
**Method** & \multicolumn{2}{c}{**Grnd. Gen. (s)**} & \multicolumn{2}{c}{**Em. Learn.**} \\  & \multicolumn{2}{c}{**Gen. (s)**} & \multicolumn{2}{c}{**(s/epoch)**} & \multicolumn{2}{c}{**Gen. (s)**} & \multicolumn{2}{c}{**(s/epoch)**} \\ \hline ComplEx & - & 8.64 & - & 9.74 \\ SoLE & 39.37 & 17.79 & 19.52 & 21.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Runtime on FB15K and DB100K. “Grnd. Gen.” is the time required for the Grounding Generation stage, and “Em. Learn.” for training per epoch.
## 3 Results and Discussion
No significant effect of the distance of the wells from the antenna was observed. The changes in parameter values for wells #2, #3, and #5 were almost identical, although each well was \(\sim\)1 cm further from the antenna. This is due to the small difference in field strength at these distances, which means that the expected effect on the wells was almost the same and the resulting differences were imperceptible.