esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems
## 3 Applications of Knowledge Graphs in the Power System Operation
### The Overall Construction Process of KG in the Power System Field
#### 3.1.3 Knowledge Reasoning  
Through ontology construction and knowledge extraction and fusion, we can obtain the overall framework of the power system KG. By processing complex KG for knowledge reasoning, we can discover more potential knowledge based on existing knowledge sets. Section 2.5 introduces several knowledge reasoning methods applicable to specific situations in the field of power systems. Through the application of knowledge reasoning technology, KG can gradually improve and provide service functions such as recommendation, Q&A, and decision-making.  
Deductive and inductive reasoning, mentioned in Sections 2.5.1 and 2.5.2, respectively, apply to different research contexts in the field of power. (1) As one of the deductive reasoning methods, the method based on generative rules strictly follows the prescribed mechanism to execute the rules to achieve the corresponding objectives. Although the process of matching rules is highly interpretable, the method is less flexible and difficult to adapt to the complex and real-time emergent situations in the electric power domain. Therefore, the method is only applied in the definition of rules such as grid dispatching regulations and customer service business processes to provide users with a logically rigorous knowledge search. (2) Common inductive reasoning methods in DKG are case-based reasoning and representation learning-based reasoning. 1 Case-based reasoning first requires the establishment of a historical case events base. The similarity between the current event and the base case is then calculated to recommend a high-matching historical treatment solution for the operator as a recommendation function. 2 The reasoning based on representation learning [91] automatically captures and infers the required features in the process of vector representation to complete the link prediction of multiple relationships. As a more used inference method in the power field, representation learning inference can achieve efficient inference in KG, and the score function of the triplet is highly interpretable. However, the drawback is that the inference process is limited to the form of a triad, which cannot combine other information at the same time, and the inference results may not be accurate enough on some occasions. 3 The latest research on knowledge inference reads KG features by neural network models [92] and adopts the \"massive data + self-learning\" model to cope with the increasingly complex grid structure and increasingly unpredictable customer behavior. However, the black-box nature of neural network machines to read and generate prediction results automatically makes it difficult to interpret the inference results.
## 5 Basic Experiments
### Evaluation Procedure  
In our testing stage, we used the data set \(\mathbb{D}_{2}\), which is independent of the training set \(\mathbb{D}_{1}\). When performing retrieval using a query \(\mathsf{Q}\), we computed \(\delta_{\mathsf{D}}(\mathsf{S}^{\mathsf{Q}})\) for all \(\mathsf{D}\in\mathbb{D}_{2}\) and obtained a ranked list of documents, as described in Section 3. We excluded the document containing the query from the database \(\mathbb{D}_{2}\) so that there are no trivial retrieval results.  
For evaluating the results, we considered three evaluation measures. First, we used precision at one (P@1), which is 1 if the top-ranked document is relevant, and 0 otherwise. However, not only the top rank is of relevance in our retrieval scenario. This was taken into account by our second evaluation measure, called \(R\)-precision (\(\mathsf{P}_{R}\)). Here, \(R\in\mathbb{N}\) denotes the number of relevant documents for a given query. Note that this number may be different for different queries. \(\mathsf{P}_{R}\) is defined as the proportion of relevant documents among the first \(R\) ranks. Third, we used average precision, which is a standard evaluation measure for information retrieval that takes the entire list of ranks into account. It is defined as the mean of the precision scores for the ranks with retrieved relevant documents. This measure is not as well interpretable as P@1 or \(\mathsf{P}_{R}\), but it is the most comprehensive evaluation measure that we used. For a more detailed explanation, we refer to the book by [PERSON] et al. (2013) (Chapter 8).  
For our experiments, we used a set of queries, which we created by equidistantly sampling 10 queries from each recording of our test set \(\mathbb{D}_{2}\), resulting in 3300 queries. The evaluation results were then averaged over all queries. In the case of average precision, the averaged measure is referred to as mean average precision (MAP).
###### Abstract  
Inversion analysis is usually an efficient solution to process the monitoring data of earth-rockfill dams. However, it is still difficult to obtain calculation results that are consistent with monitoring data due to different construction statuses. To deal with this situation and to introduce a new solution to improve calculation accuracy, the general method of inversion analysis based on back-propagation neural networks and the original step-by-step inversion method assuming that the parameters of the constitutive model vary with construction periods are introduced and verified in this work. Then, both methods are applied in the inversion analysis of a high gravelly soil core rock-fill dam during construction periods. Moreover, the relationship between the inversed material parameters and the stress values of the core wall is discussed. The material parameters are further optimized to obtain more accurate displacement values. The results show that the step-by-step inversion method has a higher accuracy in vertical compression values compared with the conventional inversion method, the trend of material parameter \(K\) is more significant than other parameters, and the proposed variable parameter constitutive model has an accuracy between the step-by-step and conventional inversion methods. Conclusions can be drawn that the original step-by-step inversion method has more advantages than the conventional method and the variable parameter constitutive model proposed in this paper might be more suitable for the analysis of a high earth-rockfill dam during construction periods.  
rock-fill dam; inversion analysis; Duncan-Chang; material parameter optimization; BP neural network 28 March 2022  
13 May 2022  
15 May 2022  
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## 3 Results and Discussion
### Investigating the Synthesis Products  
The X-ray diffraction analysis of the combustion products of the Ti-NiB powder mixture has demonstrated the presence of NiTi, NiTi2 and TiB2 phases (Figure 4). A quantitative assessment has shown that the phases are distributed in the following proportions: 40 wt.% NiTi:6 wt.% NiTi2:54 wt.% TiB2.  
Figure 3: A thermogram of combustion of the samples of the Ti-NiB powder mixture.  
Figure 2: Images of spin combustion in the samples of the NiB-Ti mixture: (**a**) combustion initiation, (b) 20 s, (c) 23 s, (d) 37 s and (e) 51 s.  
Figure 5a,b shows SEM images of fine-ground products of combustion of the Ti-NiB mixture and the structure of the particles of the materials obtained. The results of the elementary particle analysis are shown in Figure 5c. It was determined that after fine-grinding of the products of combustion of the 63.5 wt.% NiB + 36.5 wt.% Ti system, a powder was obtained and the powder particles had an irregular sharp-corner shape. The particle size did not exceed 150 \(\upmu\)m. It has been shown that SHS powder particles have a composite structure and consist of a matrix (area 1) and reinforcement inclusions that are uniformly distributed inside it. From the results of EDS analysis, it was determined that composite SHS powder particles contain the following elements: Ti, Ni, B, Al and Fe.  
Figure 6 shows the SEM image of a local area of a composite SHS powder particle (Figure 5, area 3). Figure 6 show maps of the distribution of chemical elements in a local area of a composite particle. It has been proven that titanium is the main element of the composite particle and it is present in both the reinforcement inclusions and the matrix material. It has also been established that the matrix contains nickel and there are areas where aluminium elements are present. It has been found that boron is concentrated in the area of reinforcement inclusions.  
Figure 4: A typical X-ray diffraction pattern of the products of combustion of the Ti-NiB mixture.  
Figure 5: (**a**) SEM image of fine-ground products of combustion of the Ti-NiB system, (**b**) SEM image of the structure of the self-propagating high-temperature synthesis (SHS) powder and (**c**) elementary analysis of SHS powder particles.  
By comparing the results with the XRD data, it has been found that the products of combustion of the Ti-NiB powder mixture consist of an intermetallic NiTi-NiTi2 matrix within which irregular-shaped titanium diboride (TiB2) particles are distributed (Figure 7a). The particle size lies in the range of 0.1 to 4 \(\upmu\)m, and the average particle size is 0.57 \(\upmu\)m (Figure 7b). The iron in the structure of SHS powder particles was presumably scraped from the steel balls of the grinder.
# Parallel Complex Quadrature Spatial Modulation
## 2 System Model and Related Works
### Complex Quadrature Spatial Modulation  
To increase the spectral efficiency, CQSM transmits two complex signal symbols, \(s_{a}\) and \(s_{b}\), compared to a single symbol in QSM. To make it possible to recover the two signal symbols at the receiver side, \(s_{a}\) and \(s_{b}\) are drawn from distinct constellations \(\Omega_{a}\) and \(\Omega_{b}\), respectively. The received vector is given as follows,  
\[\mathbf{y}=\begin{cases}\mathbf{h}_{l}s_{a}+\mathbf{h}_{l}s_{b}+\mathbf{n},& \text{if}\quad l\
eq k\\ \mathbf{h}_{l}(s_{a}+s_{b})+\mathbf{n}=\mathbf{h}_{l}s_{c}+\mathbf{n},&\text{ if}\quad l=k,\end{cases} \tag{4}\]  
where \(s_{c}\in\Omega_{c}\) and \(\Omega_{c}=\Omega_{d}\oplus\Omega_{b}\) with \(\oplus\) denoting the Minkowski sum. As such, the full signal constellation of CQSM is of size \((|\Omega_{a}|+|\Omega_{b}|+|\Omega_{a}|\times|\Omega_{b}|)\). The modulation set \(\Omega_{b}\) is a rotated version of \(\Omega_{a}\) and is obtained as follows,  
\[\Omega_{b}=\{s_{a}e^{i\theta_{opt}}|s_{a_{i}}\in\Omega_{a}\},1\leq i\leq M, \tag{5}\]  
where \(\theta_{opt}\) is the optimal rotation angle that maximizes the error performance of the communication system. As such, CQSM achieves \(2\log_{2}(N_{t}M)\) bits\(/\mathrm{s}/\mathrm{Hz}\) at a cost of only one additional RF chain at the transmitter.
# New Eldercare Robot with Path-Planning and Fall-Detection Capabilities
## 2 Mechanical Design and Kinematics
### Kinematic Calculations  
To control robot movements and velocity, differential drive is chosen for the robot. The difference in RPM between the wheels makes the robot move forward or backward or rotate. The kinematic modeling equations of the robot with a differential mechanism are shown in Equations (1)-(3) and its layout is shown in Figure 3. The robot velocity in the x-direction and y-direction and robot rotation velocity are denoted \(\dot{x}\), \(\dot{y}\), and \(\dot{\Theta}\), respectively. The terms \(v\) and \(\omega\) denote the robot linear displacement and linear velocity, respectively. \(R_{\mathrm{z}}\) is the rotation matrix of the robot, \(r_{R}\) is the radius of the robot's right wheel, and \(r_{L}\) is the radius of the left wheel. \(\dot{\varphi}_{R}\) and \(\dot{\varphi}_{L}\) are the right and left wheels' rotation velocities respectively. Finally, \(2b\) is the width of the robot base.  
\[\begin{bmatrix}\dot{x}\\ \dot{y}\\ \dot{\Theta}\end{bmatrix}=R_{\mathrm{z}}(\theta)\begin{bmatrix}v\\ 0\\ \omega\end{bmatrix}=\begin{bmatrix}\cos(\theta)&-\sin(\theta)&0\\ \sin(\theta)&\cos(\theta)&0\\ 0&0&1\end{bmatrix}\begin{bmatrix}v\\ 0\\ \omega\end{bmatrix} \tag{1}\]  
\[\begin{bmatrix}v\\ \omega\end{bmatrix}=\begin{bmatrix}\frac{r_{R}}{2}&\frac{r_{L}}{2}\\ \frac{r_{R}}{2b}&-\frac{r_{R}}{2b}\end{bmatrix}\begin{bmatrix}\dot{\varphi}_ {R}\\ \dot{\varphi}_{L}\end{bmatrix} \tag{2}\]  
By combining the Equations (1) and (2),  
\[\begin{bmatrix}\dot{x}\\ \dot{y}\\ \dot{\Theta}\end{bmatrix}=\begin{bmatrix}\frac{r_{R}}{2}\cos(\theta)&\frac{r_ {L}}{2}\cos(\theta)\\ \frac{r_{R}}{2}\sin(\theta)&\frac{r_{L}}{2}\sin(\theta)\\ \frac{r_{R}}{2b}&-\frac{r_{R}}{2b}\end{bmatrix}\begin{bmatrix}\dot{\varphi}_ {R}\\ \dot{\varphi}_{L}\end{bmatrix} \tag{3}\]  
Figure 1: Manufacturing stages of the new eldercare robot from CAD design to a full assembled robot.  
Figure 2: Mechanical design of the new eldercare robot (all dimension in mm).
# Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training
## 5 Experiments
### Dataset  
Text-to-speech systems depend on the availability of a corpus containing pairs of speech and corresponding text. This study explores voice data of the Central Kurdish dialect for TTS systems. We started by creating an audio- and text-pairing dataset featuring a male individual who spoke in Central Kurdish. The recording process was carried out by a male dubber in a recording studio. The 6078 sentences that we collected from the text corpus were categorized into 12 categories, including sports, science, literature, health, and everything else. Training sentences were then created using the collected information, resulting in 4255 (70%) sentences. The validation set contained 608 (10%) random sentences. The testing set contained 1215 (20%) sentences that were randomly selected from the overall dataset. The sentences were then improved through various web sources. Table 2 illustrates the subjects and the number of sentences. The process of recording speech files ended after 30 days. The dataset can be accessed through the following link [37].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Topics** & **No. of Sentences** \\ \hline News & 888 \\ \hline Sport & 631 \\ \hline Health & 463 \\ \hline Interview & 1240 \\ \hline Science & 65 \\ \hline Religion & 24 \\ \hline Economic & 275 \\ \hline General information & 224 \\ \hline Politics & 66 \\ \hline Education and literature & 1399 \\ \hline Article & 420 \\ \hline Social & 383 \\ \hline Total & 6078 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the sentences of the speech corpus.  
Figure 7: The architecture of the duration prediction model \(f_{duration}\).  
Some features of the recorded files can be summarized as follows: (a) 6078 WAV files and over 13.63 h of recorded speech were captured; (b) the output of the files was recorded at a rate of 22,050 kHz; (c) the quantization process was carried out using 16 bits of signed data; (d) the stored speech audio files were in the format known as PCM, and a mono channel was utilized to record the audio streams; (e) the shortest audio file length was 0.502 s; (f) the longest audio file length was 16.781 s; (g) the mean audio file length was 8.076 s.  
The audio files are stored in wave format, while the text sentences are saved in an Excel file. The audio files are organized in a single folder. The audio file's name includes the extension names, while the transcript is the text of the speech referenced to the audio file with an ID which is the name of the audio file. The dataset was prepared to comply with the Gaussian distribution to be more effective in training models avoiding bias in record length. A statistical figure of the dataset has been created to show more clarity on the number of audio records of similar length recordings as depicted in Figure 8.
## 3 Method
### Verify Downscaled Result  
To evaluate the correlation between the downscaled SIF result and GOSIF, the downscaled results were resampled back to a 0.05-degree spatial resolution. To assess the accuracy of downscaled SIF data, this study calculated the correlation between GOSIF, downscaled SIF and MODIS GPP. Furthermore, to reveal the advantages of the RF method, the direct resampling (nearest neighbour and bilinear) results were used as comparisons with the downscaled SIF result based on the RF method.  
Figure 2: The flowchart of this study.
## 2 Mathematical Modeling
### Sliding Mode Controller Design
#### 2.1.1 Derivation of System Error Dynamics  
In general, the output tracking error of the system could be described as,  
\[e=x_{d}-x, \tag{4}\]  
furthermore, the error of each state could be computed as,  
\[\begin{bmatrix}e_{1}\\ e_{2}\\ e_{3}\end{bmatrix}=\begin{bmatrix}x_{d}-x_{1}\\ \dot{x}_{d}-x_{2}\\ \dot{x}_{d}-x_{3}\end{bmatrix}, \tag{5}\]  
Figure 1: Schematic diagram of the proposed system.  
where \(\hat{x}_{d}\) and \(\hat{x}_{d}\) are the desired velocity, and desired acceleration, respectively. Thus, the error dynamics of the system can be described as  
\[\begin{split}\dot{e}_{1}&=e_{2},\\ \dot{e}_{2}&=e_{3},\\ \dot{e}_{3}&=-\frac{1}{\tau}e_{3}-\frac{1}{\tau}u. \end{split} \tag{6}\]  
As far as our purpose is to track the desired speed of the vehicle our main goal is to design a control input that makes the velocity error \(e_{2}\) zero in a steady state.
# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 3 Experiments
### Evaluation Indices  
We mainly evaluate the results from two aspects: pose estimation and shape reconstruction. For the evaluation of the nine degrees-of-freedom object pose, we calculate the percentage of the area under the curve for the ADD and ADD-S metrics:  
\[\text{ADD}=\frac{1}{|M|}\sum_{x\in M}\big{\|}(Rx+t)-(\bar{R}x+\bar{t})\big{\|}^ {2} \tag{13}\]  
\[\text{ADD-S}=\frac{1}{|M|}\sum_{x_{1}\in M}\min_{x_{2}\in M}\big{\|}(R_{x_{1} }+t)-(\bar{R}_{x_{2}}+\bar{t})\big{\|}^{2} \tag{14}\]  
where M is the object model. We use the ground truth pose of the first frame to define the canonical coordinate system for each video for pose evaluation.  
For the evaluation of 3D shape reconstruction, we measure the similarity between the generated mesh and the ground truth model using the Chamfer distance. The Chamfer distance between two point clouds \(x_{1}\) and \(x_{2}\) is calculated as:  
\[d_{\text{CD}}=\frac{1}{|M_{1}|}\sum_{x_{1}\in M_{1}}\min_{x_{2}\in M_{2}}\|x_{ 1}-x_{2}\|_{2}+\frac{1}{|M_{2}|}\sum_{x_{2}\in M_{2}}\min_{x_{1}\in M_{1}}\|x_ {1}-x_{2}\|_{2}. \tag{15}\]  
Specifically, we first uniformly sample the model to obtain a 3D point cloud and then calculate the Chamfer distance between the two point clouds. For all methods, we use the same resolution (5 mm) to sample points for evaluation. For our self-collected dataset, due to the lack of ground truth poses, we focus on using the reconstructed results for evaluation. In the framework used in this paper, the accuracy of reconstruction and tracking are interdependent and complementary. Correct understanding of geometric shapes often stems from accurate pose estimation, and accurate modeling simplifies the tracking task.
# Magneto-Optical Traps for Cold Atomic Gravimetry: Research Status and Development Trends
## 3 Classification and Application of MOTs
### Rise of Single Beamed MOTs
During installation, the operator only needs to adjust the perpendicularity of the sensor head and it is ready for measurement after a 1 h warm-up time. In 2019, [PERSON] and [PERSON] et al. [75; 76] developed a new mobile CAG, which used an inverted MOT with a through-hole pyramidal shape. It was realized to measure the absolute gravity at Berkeley Hills in a field vehicle-mounted environment. A plane mirror was used as a reflector in this MOT, which eliminates the effect of the pyramid top angle defect and the wavefront aberration caused by the pyramid edge on the system. The MOT achieved a loading time of 150 ms to trap about \(5\times 10^{7}\) Cesium atoms. After the magnetic field was turned off, the temperature of the atomic cluster was further cooled to 2 \(\upmu\)k. In laboratory operation, they achieved a sensitivity of 37 \(\upmu\)Gal/\(\surd\)Hz and a stability of 2 \(\upmu\)Gal in half an hour. In the field, the mobility allowed them to measure gravity in the field with a resolution of around 0.5 mGal/\(\surd\)Hz. This structure is simpler and more efficient than a conventional pyramidal CAG [77]. As key means of miniaturizing CAG, pyramidal MOTs have been widely used.
# User-BS Selection Strategy Optimization with RSSI-Based Reliability in 5G Wireless Networks
## 3 System Model and Problem Formulation
### Rssi  
RSSI is a method of receiving signal strength indicating ranging. In an actual application environment, since a wireless signal is affected by various obstacles, reflections, multipath propagation, temperature, and propagation mode, electromagnetic wave transmission loss conforms to the lognormal shadow model, which can be described by the modified path-loss model [29]:  
\[PL(d)=PL(d_{0})+10n\log_{10}(\frac{d}{d_{0}})+X_{\sigma}, \tag{4}\]  
where \(PL(d)\) is the loss after signal propagation distance \(d\), \(PL(d_{0})\) is the loss after signal propagation distance \(d_{0}\), \(n\) is the propagation factor (usually 2\(\sim\)5), and \(X_{\sigma}\) is the shielding factor that is a Gaussian random noise variable with mean 0 and variance \(\alpha\).  
\(PL(d_{0})\) in (4) can be calculated by the outdoor radio free space propagation model. The free space propagation model [32] is:  
\[PL(d_{0})=32.44+10n\log_{10}(d)+10n\log_{10}(f_{c}), \tag{5}\]  
where \(f_{c}\) is the frequency of the propagated signal, and \(d\) is the distance between the sending and receiving nodes; usually, \(d_{0}=1\) m. Our approach is not restricted by this specific formula and it can be straightforwardly extended to any path-loss forms.  
The signal strength of the anchor node received by the unknown node is:  
\[RSSI(d)=P_{s}+P_{a}-PL(d), \tag{6}\]  
where \(P_{s}\) is the transmitting power of the node signal, \(P_{a}\) is the antenna gain, and \(PL(d)\) is the loss after signal propagation distance \(d\). According to Formulas (4)-(6), the distance can be calculated.  
In the WINNERII C2 model [33] that simulates the wireless channel of the cellular connection in tahe 5G network environment, the path-loss value at distance \(d\) from the urban macrocell base station can be expressed as:  
\[PL(d)=27+22.7\log_{10}(d)+20\log_{10}(f_{c})+X_{\sigma}. \tag{7}\]