esa-sceva/satcom-chunk-collection huggingface dataset:
# Noether's Theorem in Non-Local Field Theories
## 3 Non-Local Charged Scalar Field
### Angular Momentum Tensor  
The conservation of angular momentum arises from the invariance of the system with respect to rotation. Taking \(\Sigma_{\mu\
u}=0\) for the charged scalar field and substituting \(\delta^{\alpha}_{\mu}\mathcal{L}=0\) into Equation (16), one can write the expression for the angular momentum density in the following form:  
\[\mathfrak{M}^{\gamma}_{\alpha\beta} = \frac{i}{2}\sum_{l=1}^{\infty}f_{l}(m)\sum_{k=1,3,5,...}^{2l-1}( \partial_{\alpha_{k-1}}\dots\partial_{\alpha_{1}}\phi^{*})\delta^{\alpha_{1} \alpha_{2}}\dots\delta^{\gamma_{\alpha_{k}+1}}\dots\delta^{\alpha_{2l-1}\alpha _{2l}}\partial_{\alpha_{k+1}}\dots\partial_{\alpha_{2l}}\mathcal{R}_{\alpha \beta}\phi \tag{45}\] \[- \frac{i}{2}\sum_{l=1}^{\infty}f_{l}(m)\sum_{k=2,4,6,...}^{2l}( \partial_{\alpha_{k-1}}\dots\partial_{\alpha_{1}}\phi^{*})\delta^{\alpha_{1} \alpha_{2}}\dots\delta^{\alpha_{k-1}\gamma}\dots\delta^{\alpha_{2l-1}\alpha _{2l}}\partial_{\alpha_{k+1}}\dots\partial_{\alpha_{2l}}\mathcal{R}_{\alpha \beta}\phi\] \[+ \text{c.c.}\]  
The first terms of the series expansion are  
\[\mathfrak{M}^{\gamma}_{\alpha\beta}=\frac{1}{4m}\phi^{*}i\overleftrightarrow {\partial}^{\gamma}\mathcal{R}_{\alpha\beta}\phi+\frac{1}{4m}(\mathcal{R}_{ \alpha\beta}\phi)^{*}i\overleftrightarrow{\partial}^{\gamma}\phi+\dots. \tag{46}\]  
By performing contraction of the indices in Equation (45), we obtain  
\[\mathfrak{M}^{\gamma}_{\alpha\beta}= \frac{i}{2}\sum_{l=1}^{\infty}f_{l}(m)\sum_{k=1,3,5,...}^{2l-1}( \triangle^{(k-1)/2}\phi^{*})\triangle^{l-(k+1)/2}\,\partial^{\gamma} \mathcal{R}_{\alpha\beta}\phi \tag{47}\] \[- \frac{i}{2}\sum_{l=1}^{\infty}f_{l}(m)\sum_{k=2,4,6,...}^{2l}( \triangle^{(k-2)/2}\partial^{\gamma}\phi^{*})\triangle^{l-k/2}\,\mathcal{R}_{ \alpha\beta}\phi+\text{c.c.}\]  
The arguments presented in Appendix B enable the summation of the series in Equation (47), yielding  
\[\mathfrak{M}^{\sigma}_{\alpha\beta}=\frac{1}{2}\phi^{*}i\mathcal{D}^{\sigma} \mathcal{R}_{\alpha\beta}\phi+\frac{1}{2}(\mathcal{R}_{\alpha\beta}\phi)^{*} i\mathcal{D}^{\sigma}\phi. \tag{48}\]  
For \(\sigma=0\), we recover Equation (25). \(\mathcal{R}_{\alpha\beta}\) is not diagonal in the momentum representation, so the momentum-space representation of \(\mathfrak{M}^{\gamma}_{\alpha\beta}\) offers no significant advantages. Using the equations of motion, one can verify that  
\[\partial_{\sigma}\mathfrak{M}^{\sigma}_{\alpha\beta}=0. \tag{49}\]  
The conserved currents defined by Equation (48) correspond to the space-like components of the parameter \(\epsilon^{\alpha\beta}\), which describe a rotation; thus, the conserved charges are the components of the angular momentum tensor.
## Materials and Methods
### Spatiotemporal Analysis  
EOF analysis was used in this study to characterize the soil moisture pattern. EOF analysis decomposes a dataset into a series of orthogonal spatial patterns, which can be correlated with regional characteristics, such as soil texture, vegetation fraction, and topography factors [26; 54]. A dataset for EOF analysis typically consists of a set of observations (e.g., soil moisture) for \(m\) locations that have been observed \(n\) times. The dataset is described by a matrix \(X\) with dimensions (\(m\times n\)). An eigenanalysis is conducted using the covariance matrix of the dataset. The principal components (PCs) are given in the columns of vector \(F_{t}\), which is calculated using:  
\[F_{t}=X^{T}E_{t} \tag{5}\]  
where \(E_{t}\) is obtained from the equation \(R_{t}E_{t}=L_{t}E_{t}\) with \(R_{t}=\frac{1}{n}XX^{T}\), \(L_{t}\) is the matrix containing the eigenvalues and \(E_{t}\) is the matrix containing the eigenvectors. The eigenvectors, which are also called EOFs for simplicity, are treated as the spatial patterns in the analysis. The PCs describe how aligned the spatial patterns are with the variability at each time point. As in principal component analysis, the first EOF explains the largest portion of the total soil moisture variance, followed by the second, third, and the remaining EOFs. The portion of the variance explained by an EOF is obtained by dividing the respective eigenvalue by the sum of all the eigenvalues. To decide the reasonable number of significant EOFs that are used in this study, a North test [55] for the statistical significance of each EOF was conducted. A significance test with a 95% confidence interval was used to determine the error range of the eigenvalues \(\lambda_{i}\):  
\[e_{i}=\lambda_{i}(\frac{2}{n})^{1/2} \tag{6}\]  
where \(n\) is the number of the time series. If the difference between the two adjacent eigenvalues is not less than the error range above, the corresponding EOF is considered as effective.  
To investigate the soil moisture patterns and their relationships with geophysical factors, all of the time-varying observations, i.e., soil moisture, NDVI, and temperature datasets, were applied in the EOF analysis. With each EOF representing a spatial pattern, the percentage of variance that an EOF explains indicates the relative importance of this spatial pattern when compared with the others. When associated with PCs, we can determine the relative importance of the corresponding spatial pattern (EOF) over time: the higher the PC value, the more important the corresponding EOF at the specific time. Finally, the correlation analysis was carried out between the EOFs of soil moisture and the geophysical factors to study the controls on the retrieved soil moisture in this region.
## 2 Principles of Underwater Communication
### Communication Mediums in Water
#### 2.1.3 Underwater Optical Communication (UOC)  
UOC has demonstrated that it is capable of addressing the limitations of underwater acoustic and electromagnetic communication [15].  
Seawater behaves differently for RF and optical signal propagation. For RF signals seawater acts as a conductor and for optical it acts as a dielectric. Attenuation in a dielectric medium is lower than conductive medium. By using, blue-green optical window attenuation can be reduced further, making optical wireless communication ideal for underwater communication.  
The optical characteristics of water can be classified into two categories: Inherent Optical Properties (IOPs) and Apparent Optical Properties (AOPs) [19]. IOPs depend on the light source and the medium. For example, the absorption coefficient measures how much light is absorbed, the scattering coefficient measures how much light is scattered, and the attenuation coefficient measures the overall loss of light during propagation. AOPs depend on the medium and the geometric structure of the light field such as radiance, irradiance, and reflectance [19].  
Ref. [15] demonstrates the impact of water clarity on optical signal communication. According to the study, pure seawater shows the least attenuation and maintains a consistent strength up to 100 m distance. Clear ocean water experiences a slightly higher attenuation compared to pure seawater as the distance reaches 100 m. In contrast, coastal ocean water demonstrates a greater attenuation than clear ocean water which limits the effective range of optical communication. Signal strength attenuates exponentially in turbid harbor water and beyond 40 to 50 m, the signal become unpredictable.  
In UOC, the receiver should be pointed at the transmitter. To reduce pointing requirements, researchers utilize Photomultiplier Tubes (PMTs), which consist of large lenses with a wide range and wide field of view. Electronic switches adjust the receiver's FOV dynamically. PMTs incur issues such as being expensive, bulky, fragile, and unsuitable for multi-user environments [15].  
Blue and green LEDs and lasers are effective in underwater environments due to their lower attenuation coefficients compared to other colors [20].  
Lasers provide high performance compared to LEDs due to their monochromatic behavior, high intensity, and low dispersion quality [15].  
Even optical wireless communication allows high data rates underwater; it is limited to a shorter range of communication. There are several reasons for the short transmission range. Water absorbs and scatters light. The presence of chlorophyll, particulate matter, and other substances in water significantly affects the optical properties. Also, reflection and refraction limit the distance of optical waves underwater. The range of underwater optical links can be extended by increasing the power of light sources and incorporating optical amplifiers and repeaters. Multiple optical amplifiers or repeaters can be located between transmission terminals to mitigate losses due to absorption and scattering [21].
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 3 Methodology
### BLE-Based Positioning
#### 3.2.2 Weighted Centroid Algorithm  
The traditional centroid algorithm is coarse-grained and largely depends on the distribution of nearby iBeacon anchors. In order to decrease the influence of the anchor distribution and improve the positioning accuracy, a weighted centroid algorithm is proposed. The rationale behind this algorithm is to assign larger weights to the iBeacons that are closer to the smartphone. Thus Equation (1) can be extended to  
\[(\hat{x},\hat{y})=\bigg{(}\frac{x_{1}/d_{1}+\ldots+x_{N}/d_{N}}{1/d_{1}+\ldots +1/d_{N}},\frac{y_{1}/d_{1}+\ldots+y_{N}/d_{N}}{1/d_{1}+\ldots+1/d_{N}}\bigg{)}. \tag{2}\]  
Based on the free space path loss model, i.e., the Friss equation, the RSS at the receiver is  
\[P_{r}=\varepsilon P_{t}/d^{a}, \tag{3}\]  
where \(P_{t}\) is the transmit power, \(d\) is the distance between the transmitter and the receiver, and \(\varepsilon\) is a constant related to the antenna gains and the wavelength. Suppose all the iBeacons use the same transmit power, combing (1) and (3), we have  
\[(\hat{x},\hat{y})=\bigg{(}\frac{\sqrt[4]{P_{1}}x_{1}+\ldots+\sqrt[4]{P_{N}}x_ {N}}{\sqrt[4]{P_{1}}+\ldots+\sqrt[4]{P_{N}}},\frac{\sqrt[4]{P_{1}}y_{1}+ \ldots+\sqrt[4]{P_{N}}y_{N}}{\sqrt[4]{P_{1}}+\ldots+\sqrt[4]{P_{N}}}\bigg{)}. \tag{4}\]  
It is worth noting that, in the proposed weighted centroid algorithm, the distances between the smartphone and nearby iBeacons do not need to be precisely estimated. Only the size relationship of the distances is concerned, which is reflected by the RSS. That is why a simple Friss equation is adopted instead of more complicated path loss models.  
Figure 1: Architecture of the proposed positioning method.
## 3 Results and Discussion
Hence, in this study, we used the perovskite-like (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl material as a buffer layer between the ohmic contact metal and AlN layer to reduce the metal contact resistance. As shown in Figure 5, the Ag/(PEA)\({}_{2}\)PbBr\({}_{3}\)Cl on the AlN layer was almost non-conductive. After annealing treatment at 150 and 300 \({}^{\circ}\)C for 10 min, the conductivity of Ag/(PEA)\({}_{2}\)PbBr\({}_{3}\)Cl on the AlN layer improved. The resistance of the applied voltage at 9.9 V decreased to 1.7 and 1.1 \(\times\) 10\({}^{10}\)\(\Omega\) from 2.7 \(\times\) 10\({}^{10}\)\(\Omega\) of the as-deposited film, respectively. The mechanism  
Figure 4: (**a**) Photoluminescence (PL) and (**b**) normalized PL spectra of the perovskite-like (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl material on glass and AlN/sapphire substrate heterostructure, respectively. of conductivity increase is shown in Figure 6. The work function of the Ag metal and electron affinity of the (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl perovskite layer were 4.7 and 3.65 eV, respectively. The work functions of Ag and Ag\({}_{2}\)O were 4.7 and 5.7 eV, respectively, as shown in Figure 6a. The transportation of an electron from Ag to the (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl layer was difficult due to the 1.05 eV barrier, as shown in Figure 6b. Part of the Ag transformed into Ag\({}_{2}\)O after annealing due to processes occurring in the air environment and formed the Ag and Ag\({}_{2}\)O complex layer. This caused the Fermi level of the (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl layer to lower because of the thermal equilibrium [24]. Therefore, the electron in the Ag contact layer could easily transfer to the (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl layer via the thermionic-field emission (TFE) mechanism. The electrons in the Ag contact could acquire a small amount of energy, enabling them to penetrate the top of the potential barrier and reach the (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl layer via the tunneling effect, such that the conductivity between the Ag contact and (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl semiconductor layer improved, as shown in Figure 6c. Hence, the contact between the Ag and (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl was not an ohmic contact but a non-blocking contact due to the narrow potential barrier for electrons and the possibility of tunneling.
## 2 Data and Segmentation Methods
### Dataset  
Modern convolutional deep learning architectures require large and properly annotated datasets divided into three parts--training, validation and testing data. Training is the part of the dataset that is used for learning the feature representation of the input data. Validation data are used during training process to control the progress of training accuracy. Testing data are used for validating accuracy of the algorithm after the training process. They are not used during the training process and should represent the production data which the algorithm will be used for.
## 4 Experiments and Results
### Ablation Study  
To provide evidence of the efficacy of the modules we propose in improving network performance, we conducted a series of ablation experiments, including assessing the effectiveness of the RRD module, the GFA module, and the OHEM training strategy. We set the UNet as the baseline. These ablation experiments were conducted on the NEU-seg dataset, and the corresponding performances are shown in Table 3. Meanwhile, Figure 8 shows some sample images and their corresponding predictions with different modules in the ablation experiments.
# Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring
## 2 Previous Related Works
### DTSN Routing Algorithms  
At present, the typical data delivery strategy for DTSN is the direct transmitting algorithm [24], flooding transmitting algorithms [25; 26] and social relation-based algorithms [14; 27; 28; 29; 30; 31; 32].  
In the direct transfer algorithm, the initial network node only sends the message to the destination node and does not communicate with other nodes. Flooding strategy is one of the earliest opportunistic routing protocols. In epidemic routing [25], the network's initiation node delivers messages to all neighboring nodes until the message arrives at the final destination. Spray and waiting routing [26] is an improved flooding data delivery strategy, where the spray and waiting routing divides the message replication and delivery into two stages. In spray stage, a copy of the message is made, and the message is distributed to the neighbor node until reaching the destination in the next phase.  
The social relation routing is an intermittent connection network protocol based on the social characteristics of nodes. The typical routings are prophet routing [27] and bubbling routing [28]. Prophet routing uses the recording of encountered nodes to conduct delivery prediction, if the forwarding node's delivery prediction is higher than its own, the message is copied and forwarded. Bubble routing is a community-based message transmitting algorithm for DTSN, where node activity  
Figure 2: Research directions of DTSN.  
is used to determine the community ranking for forwarding decisions. The other DTSN social routing protocols, such as Sim Bet [29] and People Rank [30], estimate nodes' centrality and social distance to decide whether to forward messages through them. In the literature [14; 31; 32], the knowledge of graph theory and the energy model are introduced into the data delivery strategy.
# Efficiency Improvement Strategy for Multiple Operating Points in Doubly Fed Magnetic Geared Motor
## 3 Iron Loss Characteristics
### Iron Loss According to the Frequencies of the Inner and Outer Windings
Namely, it is reasonable to design the high frequency at the outer winding over the high-speed and high-torque region, and it would be better to lower the frequency at the outer winding over the high-speed and low-torque region. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Iron Loss** & **Inner** & **Outer** & **MPs** & **Total** \\ \hline Type I (\(f_{inner}\) & Hys.(W) & 233.3 & 262.0 & 2.1 &. \\
180 Hz and \(f_{outer}\) 360 Hz) & Eddy.(W) & 299.8 & 439.2 & 158.8 &. \\  & Excess(W) & 0.037 & 0.084 & 0.040 &. \\  & Total (W) & 533.2 & 701.3 & 161.0 & 1395.5 \\ \hline Type II (\(f_{inner}\) & Hys.(W) & 275.5 & 236.2 & 24.0 &. \\
270 Hz and \(f_{outer}\) 270 Hz) & Eddy.(W) & 452.4 & 382.7 & 171.8 &. \\  & Excess(W) & 0.097 & 0.084 & 0.041 &. \\  & Total (W) & 727.9 & 619.0 & 195.8 & 1542.7 \\ \hline Type III (\(f_{inner}\) & Hys.(W) & 354.6 & 227.7 & 50.1 &. \\
360 Hz and \(f_{outer}\) 180 Hz) & Eddy.(W) & 628.7 & 375.1 & 200.6 &. \\  & Excess(W) & 0.097 & 0.084 & 0.041 &. \\  & Total (W) & 983.4 & 602.9 & 250.8 & 1837.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Iron loss according to the frequency conditions (@2492 rpm and 210 Nm). Figure 7: Iron loss map in the constant torque region according to the winding frequency when the amplitude of the inner winding current is adjusted and the outer winding current is fixed at the rated current (168 Arms). (**a**) Type I (\(f_{inner}\) 180 Hz and \(f_{outer}\) 360 Hz); (**b**) Type II (\(f_{inner}\) 270 Hz and \(f_{outer}\) 270 Hz); (**c**) Type III (\(f_{inner}\) 360 Hz and \(f_{outer}\) 180 Hz); (**d**) Difference between Type III and Type I.
## 5 Discussion of Results  
From a methodological perspective, this study encountered a few challenges in data analysis, the most significant one being a moderate sample size. Even if datasets of more than 100 observations are not considered a small sample, they require careful treatment and validation to assure stability and robustness of results. We applied robust statistical solutions, such as a t-test based on t-Student distribution, which is dedicated to moderate samples. In the clustering procedure, we considered a few alternative methods. The multitude of available clustering criteria results from their various characteristics and usefulness addressing a diverse range of problems; thus, deciding what to use requires research expertise and robustness checks. The examined dataset was only clusterable to a moderate degree, and a trial to extract a large number of groups failed due to instability. The comprehensive analysis of possible groupings allowed robust partitioning, with significantly different values of variables. ANOVA and pairwise tests of differences in means among cluster groups revealed significantly diverse characteristics among obtained investors' profiles. For validity checking, we ran alternative partitioning (not reportedin detail); the results were similar, statistically weaker, and led to satisfying conclusions. In the validation procedure, we also considered bootstrapping to improve the statistical features of the sample; however, the results did not alter significantly, which confirms the robustness of outputs from pure data.  
From a conceptual perspective, this approach is innovative in a number of ways. Firstly, the cognitive biases were considered jointly, not separately; secondly, the problem was approached directly from individual survey data, not indirectly from market data; thirdly, unsupervised learning methods such as clustering and PCA were applied (rather than time-series, as has been used in the majority of studies). This innovative approach also presents methodological challenges in designing survey questionnaires and analytical paths. The lack of other similar studies using these methods limits the possibility of comparison with existing state-of-the-art. On the other hand, it opens the door for further scientific discussion on profiling of investors and understanding how their attitudes and perceptions can influence the market in the form of collective, non-rational behaviour.  
From an empirical perspective, we believe that the obtained profiles of investors present an interesting cross section of competencies, behaviour, rationality, and cognitive biases. Moreover, the size of groups suggests that TSE investors are widely diversified and that these groups have a significant impact on the financial market. Going forward, a challenge for the scientific community will be to validate these results by comparing them with studies on other markets or on the same market in the following years. This may help understand the unexplainable patterns in financial markets and more accurately predict potential financial crises.
# Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
## 2 Theory and Method
### GNSS-R Observables  
GNSS-R DDMs are usually processed to produce observables that could describe the scattering strength of reflected surface. Different surface roughness related observables have been proposed in [44; 45]. Those DDM observables have been employed in [40] with slight modification, which applied a pixel number based sea ice detection method with an incoherent summation.  
The standard DDM observable is usually denoted as \(DDM(\tau,f)\), where \(\tau\) and \(f\) represent delay and Doppler shift, respectively. Then the corresponding normalized DDM denoted as \(\overline{DDM}(\tau,f)\) is formulated as:  
\[\overline{DDM}(\tau,f)=DDM(\tau,f)/\left|DDM\right|_{\text{max}}, \tag{1}\]  
where \(\left|DDM\right|_{\text{max}}\) is the maximum absolute value of the standard DDM.
# Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms
## 3 Results and Discussion
### Maximum Crack Width Measurement
#### 3.2.2 Effect of Distance Away from Measuring Plane  
Figure 6a shows the relative error (RE) is sensitive to the distance away from the measuring surface. Crack F, captured at a distance of 1150 mm from the surface, had an RE of 25% which was higher than that of crack G, 13.5%, captured at 1350 mm. The larger error of crack F is most likely due to image processing error in the segmentation stage, more than  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Crack ID**} & **Distance to** & \multirow{2}{*}{\(\alpha_{\text{c}}\) (mm/Fixels)} & **Pixel Width** & **Converted** & **Actual Width** & **Absolute** & **Relative Error** \\  & **Surface (mm)** & & **(Pixels)** & **Width (mm)** & **(mm)** & **Error (mm)** & **(RE) (\%)** \\ \hline Indoors A & 250 & 0.391 & 10.8 & 4.23 & 4.19 & 0.04 & 0.95 \\ B & 250 & 0.205 & 21.0 & 4.39 & 4.03 & 0.26 & 6.56 \\ C & 460 & 1.071 & 3.162 & 3.39 & 3.54 & 0.15 & 4.30 \\ D & 460 & 1.071 & 1.00 & 1.07 & 1.14 & 0.07 & 6.02 \\ \hline Ouddoors E & 330 & 0.138 & 6.00 & 0.83 & 0.85 & 0.02 & 2.56 \\ F & 1150 & 0.357 & 7.00 & 2.50 & 2.00 & 0.50 & 25.0 \\ G & 1350 & 0.507 & 11.0 & 5.57 & 5.00 & 0.57 & 11.45 \\ H & 950 & 0.351 & 18.86 & 6.62 & 7.10 & 0.48 & 6.82 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measured maximum crack widths.  
it is due to the distance away from the surface. Figure 6b shows that the absolute error is affected by the distance to the measuring plane. The R\({}^{2}\) value of 0.827 shows that the absolute error is highly dependent on the distance to the measuring plane. The high level of variability can be explained by other factors that can affect the accuracy of the proposed method, such as the camera quality and parameter selection in segmentation processing. If a camera of low resolution is used, there will be an increase in errors with an increase in the distance away from the surface increases, especially for smaller cracks. This is in line with the study by [PERSON] et al. [23] that found when measuring smaller crack widths, larger errors can be expected.