esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Measurement Environment  
This study involved performing propagation measurements in different scenarios at the Henderson WestCity mall parking levels 1, 2, 3, and 4. The measurements were conducted to obtain significant results under various test conditions. Two types of files, a small text file (71 bytes) and a larger image file (1.05 MB), were used for the measurement tests. Colligo workshop software was utilized for file transfer and sharing, while WirelessMon was used to collect data such as the signal strength, bandwidth, data rate, and transfer time. Figure 4 illustrates the measurement area at Henderson WestCity shopping center.  
The measurements were performed on different levels of the WestCity mall parking lot. Level 1 (Figure 4(a)) had dimensions of 35 m wide by 100 m long, while the remaining levels (including Level 2) had dimensions of 35 m wide by 50 m long, as shown in Figure 4(b). The mall's exterior flooring and walls are made of concrete, and each level has concrete ramps, lifts, and stairs. There is approximately a 4 m separation between each floor level. The measurements were conducted across all parking levels of the mall.  
To conduct the field measurements, we took the following preparation steps, as shown in Figure 6. Figure 5(a) shows that the intended route that each car is allocated is in three segments: the start position, start-to-end parking measurements, and the end position. The most important part of this segment is the parking measurement. Figure 5(b) shows that the distance of the parking measurement is 1m from approach to departure. Figure 5(c) shows that the measurement process continues to cover up to 100 m.  
Figure 4: Field trial measurement environment (**a**) city mall parking lot and (**b**) experimental vehicle equipped with wireless laptop and cards.  
Figure 5: WestCity Auckland shopping mall parking layouts.  
The flowchart of the field measurement process is shown in Figure 7. It involved setting up the necessary hardware and installing the required software. The same procedure was repeated for another computer. The Wi-Fi connections of both computers were checked, and their IP addresses were provided to establish communication between them. The required distance for measurement was determined and marked as the destination. Data collection was then initiated, starting with the first measurement and proceeding to subsequent measurements.
# Argininemia: Pathophysiology and Novel Methods for Evaluation of the Disease
## 4 Novel Methods of Evaluation
### Novel Evaluation Methods
#### 4.2.6 Discovery of Novel Mutations  
ARG1, the gene encoding the enzyme ARG1, is located on chromosome 6q23 and consists of 8 exons [89]. It was first cloned in 1986 and to date more than 43 pathogenic mutations have been identified [90; 69]. The majority of these mutations is missense/nonsense or small deletions, but also whole-gene deletion and complex rearrangement have been described [91; 92; 92]. The identified mutations are fairly uniformly distributed across the eight exons as well as at several exon-intron boundary splice sites [69]. The mutations disrupt the binuclear Mn\({}^{2+}\) cluster and interfere with the metal-activated hydroxide mechanism, affecting the active site, bridging residues, creating steric clashes, and influencing regions essential for oligomerization -with the latter hindering the assembly of the protein trimer [93; 94].  
The disease is autosomal recessive, with almost half of the patients being compound heterozygous and the other half homozygous [23]. Most affected individuals have private mutations, although prevalent mutations have been identified in specific populations (e.g., Portuguese, French-Canadian) [95; 96].  
While the genotype-phenotype correlation remains vague, a surprising correlation exists between responsiveness to dietary protein restriction and different genetic mutations of the ARG1 gene [97; 98]. Patients with at least one moderately mutated allele respond well to dietary treatment, as reflected by a great reduction in plasma arginine levels (<300 umol/L), while patients with two severely mutated alleles respond poorly with their plasma arginine levels remaining high (>400 umol/L). The characterization of a mutation as moderate or severe is based on the degree of metabolic defect that it causes [98].  
Recently, case reports of arginineic patients with novel mutations of the ARG1 gene have been published. The researchers mainly used technics such as whole exome sequencing to identify these mutations [99,100].
## 3 Experiments and Results
### Evaluation Index  
In this paper, experimental evaluation metrics including Accuracy, Precision, Recall, and F1 score are employed, which are calculated based on the confusion matrix presented in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classes** & **Positive Comment Number** & **Negative Comment Number** \\ \hline Book & 2100 & 1751 \\ Pad & 5000 & 5000 \\ Phone & 1163 & 1158 \\ Fruits & 5000 & 5000 \\ Shampoo & 5000 & 5000 \\ Water Heater & 100 & 475 \\ Mengniu Dairy & 992 & 1041 \\ Clothes & 5000 & 5000 \\ Computer & 1996 & 1996 \\ Hotel & 5000 & 5000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset data distribution.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Environment** & **Environment Configuration** \\ \hline Operating System & Win10 \\ CPU & i5-7300 HQ CPU @ 2.50 GHz \\ Memory & 8 GB \\ Deep Learning Framework & TensorFlow 2.1.0\textless{}cpu \\ Programming Language & Python 3.7 \\ Word Segmentation Tool & jieba \\ Feature Vector Training Tool & Word2 Vec (gensim 3.8.3) \\ Programming Environment & Anaconda 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental environment and configuration.  
In Table 3, TP and TN, respectively, represent the number of positive and negative samples predicted correctly.  
Accuracy _Acc_ represents the proportion between the predicted samples conforming to the label and the total samples, as shown in Equation (8).  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN}. \tag{8}\]  
Recall rate _Recall_ represents the proportion of positive samples with correct prediction in all positive samples, as shown in Equation (9).  
\[PRcall=\frac{TP}{TP+FN}. \tag{9}\]  
Precision _Precision_ represents the probability that all predicted positive samples are actually positive samples, as shown in Equation (10).  
\[Precision=\frac{TP}{TP+FP}. \tag{10}\]  
Value \(F1\) score \(F1\) represents the final classification effect obtained by weighted harmonic calculation of accuracy rate \(Precision(P)\) and recall rate \(Recall(R)\), as shown in Equation (11).  
\[F1=\frac{2}{\frac{1}{P}+\frac{1}{R}}=2\cdot\frac{P\cdot R}{P+R}. \tag{11}\]  
Among them, \(P=\frac{TP}{TP+FP}\).
## 3 Research Framework and Methodology  
This research contains four phases, and the research framework is shown in Figure 1. The first phase involves processing industry patent information, which includes searching for the patent and downloading technological and geographical information, such as titles, abstracts, applicants, and addresses. The next phase involves mining technological information, which includes topic acquirement based on the LDA algorithm and technology-applicant association based on co-occurrence analysis. The following phase is about the identification and evaluation of the IIC cluster from the perspectives of technology and geography. And the final phase is visualizing and analyzing the result of the clusters with a complex network.
# Automatic Roadway Features Detection with Oriented Object Detection
## 5 Analysis and Results
### Oriented Objects Detection
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Appetences**} & \multirow{2}{*}{
\begin{tabular}{c} **Oriented** \\ **Class** \\ \end{tabular} } & \multicolumn{5}{c}{**Average Precision [\%]**} \\ \cline{3-8}  & & & **Model 1** & **Model 2** & **Model 3** & **Model 4** & **Model 4** & **Model 5** \\  & & **Class** & **(No Orientation)** & **(\(\emptyset\))** & **(\(\sin(\emptyset\)) and cos(0))** & **cos(20) without** & **cos(20))** \\ \hline \multicolumn{8}{c}{Oriented Classes} \\ \hline Marking & 6494 & Yes & 00.01 & 40.94 & 62.45 & 55.30 & 62.92 \\ Right Barriers & 1827 & Yes & 22.53 & 43.38 & 55.84 & 54.25 & 56.31 \\ Left Barriers & 1946 & Yes & 39.14 & 93.9 & 91.30 & 88.68 & 91.96 \\ Right Curb & 1091 & Yes & 1.45 & 36.73 & 43.18 & 39.23 & 51.72 \\ Left curb & 1215 & Yes & 0.54 & 64.74 & 71.13 & 59.05 & 73.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average precision per class for different models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Number** & **Model** & **mAP [\%]** \\ \hline
1 & No orientation [5] & 33.4 \\
2 & \(\theta\) & 50.0 \\
3 & \(\sin(\emptyset)\) and cos(\(\emptyset\)) & 50.7 \\
4 & \(\sin(20)\) and cos(\(20\)) without data augmentation & 43.1 \\
5 & \(\sin(20)\) and cos(\(20\)) & 50.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Map score for each experiment. For qualitative comparison, Table 5 shows output samples from the different object detector variants, summarized as follows:  
* The model that does not use data augmentation (model 4) is less accurate than the other models and produce more redundant predictions. * The model that does not use orientation (model 1), the predicted boxes are bigger than object areas, especially for the \"curb\" and \"marking\" classes which are oriented by nature. * The model that predicts \(\theta\) directly (model 2), the predicated orientations are not accurate or wrong, as in sample numbers 2, 3, 5, 6, 8, and 9. This model produces lots of redundant object predictions for the same object, but with different orientations. Due to non-accurate orientation predictions, IoU between duplicate object predictions becomes small, especially for objects with narrow width like lane markings, and hence non-maximum suppression cannot detect them as duplicates. * The model that predicts \(\sin(\theta)\) and \(\cos(\theta)\) (model 3) results into better predictions, the number of duplicate predictions decreases. * The model that predicts \(\sin(20)\) and \(\cos(20)\) (model 5) results in less redundant predictions due to its accurate orientation angle predictions capacity as in sample numbers 3, 6, 8, and 9. The above findings combined verifies the research hypothesis, discussed in Section 3.2 that solving the orientation encoding discontinuity problem leads to more accurate orientation detection of undirected objects as clearly shown in sample numbers 3, 5, 6, 8, and 9 in Table 5 of model 5 versus model numbers 2 and 3.
## 4 Analysis and Interpretation of Results
### Correlation Links Between Individual Production Sources and Residual Load
#### 4.3.1 Analysis of the German Market  
Germany, as the country with the most advanced RES integration in Europe, represents a key case study country for studying the structural links between RES generation, pricing and residual load. The overall correlation analysis provides a consistent picture of the negative relationships between renewable generation and electricity market prices.  
The negative correlations between wind power and residual load (r = \(-\)0.49), as well as between wind and price (r = \(-\)0.41), suggest that increased wind generation significantly reduces the need for conventional generation (residual load) while also leading to a decrease in the market price of electricity. This relationship has become even stronger over the years, with the correlation between wind and residual load reaching \(-\)0.62 in 2025.  
Solar shows weaker but still negative links to both prices and residual load (r = \(-\)0.16 and \(-\)0.09, respectively). A more pronounced drop in the correlation is noticeable especially from 2020 onwards, when the Solar/Residual Load correlation reaches values as high as \(-\)0.42 (2024), which is related to seasonal surpluses in the summer months when demand is low. The strong positive correlation between residual demand and price (r = 0.82) underlines the importance of conventional resources for price stabilisation--the larger the difference between demand and RES generation (i.e., higher residual load), the higher the price. In 2015-2018, the structure of the relationships is relatively stable. However, from 2019 onwards, there is a deepening of negative correlations between RES and prices, which is a consequence of the increasing saturation of the grid with RES generation. For example, the Wind/Price correlation reaches as high as \(-\)0.57 in 2022. Similarly, the Solar/Price correlation gradually declines to \(-\)0.39 in 2025.
# Global Structure-Aware Drum Transcription Based on Self-Attention Mechanisms
## 3 Proposed Method
### Transcription Models  
The transcription model is used for estimating the tatum-level onset probabilities \(\boldsymbol{\phi}\in[0,1]^{M\times N}\), where \(\phi_{m,n}\) represents the probability that drum \(m\) has an onset at tatum \(n\). The drum score \(\mathbf{Y}\) is obtained by binarizing \(\boldsymbol{\phi}\) with a threshold \(\delta\in[0,1]\). The encoder of the transcription model is implemented with a CNN. The mel spectrogram \(\mathbf{X}\) is converted to latent features \(\mathbf{F}\in\mathbb{R}^{D_{F}\times T}\), where \(D_{F}\) is the feature dimension. The frame-level latent features \(\mathbf{F}\) are then summarized into tatum-level latent features \(\mathbf{G}\in\mathbb{R}^{D_{F}\times N}\) through a max-pooling layer referring to the tatum times \(\mathbf{B}\) as follows:  
\[G_{d,n}=\max_{\frac{b_{n-1}+b_{n}}{2}\leq t<\frac{b_{n}+b_{n+1}}{2}}F_{d,t}, \tag{1}\]  
where \(b_{0}=b_{1}\) and \(b_{N+1}=b_{N}\) are introduced for the brief expression. The decoder of the transcription model is implemented with a bidirectional GRU (BiGRU) or a self-attention mechanism (SelfAtt) followed by a fully connected layer. The intermediate features \(\mathbf{G}\) are directly converted to the onset probabilities at the tatum level. In the self-attention-based decoder, the onset probabilities are estimated without recursive computation. To learn the sequential dependency and global structure of drum scores, thepositional encoding \(\mathbf{E}\in\mathbb{R}^{D_{F}\times N}\) are fed into the latent features \(\mathbf{G}\) to obtain extended latent features \(\mathbf{Z}\in\mathbb{R}^{D_{F}\times N}\). The standard positional encodings proposed in [26] are given by  
\[E_{d,n}= \begin{cases}\sin\left(\frac{1}{10,000^{2d/D_{F}}}n\right)&(d\equiv 0 \text{ mod }2),\\ \cos\left(\frac{1}{10,000^{2d/D_{F}}}n\right)&(d\equiv 1\text{ mod }2),\end{cases}  \tag{2}\]  
In this paper, we propose tatum-synchronous positional encodings (denoted SyncPE):  
\[E_{d,n}= \begin{cases}\sin\left(\frac{\pi}{2+\left\lfloor d/2\right\rfloor}n \right)&(d\equiv 0\text{ mod }2),\\ \cos\left(\frac{\pi}{2+\left\lfloor d/2\right\rfloor}n\right)&(d\equiv 1 \text{ mod }2),\end{cases}  \tag{3}\]  
where \([\cdot]\) represents the floor function. As shown in Figure 2, the nonlinear stripes patterns appear in the encodings proposed in [26] because the period of the trigonometric functions increases exponentially with respect to the latent feature indices, whereas the proposed tatum-synchronous encodings exhibit the linear stripes patterns. As shown in Figure 3, the extended features \(\mathbf{Z}\) are converted to the onset probabilities \(\mathbf{\phi}\) through a stack of \(L\) self-attention mechanisms with \(I\) heads [26] and the layer normalization (Pre-Norm) [57] proposed for the simple and stable training of the transformer models [58, 59].
## 5 Case Study
For instance, concerning the RES-based generators, the considered capability curves are: (i) semi-circular, for static generators greater than or equal to 400 kW (i.e., max reactive power Q\({}_{\text{max}}\) equal to the rated apparent power S\({}_{\text{n}}\)), (ii) limited semi-circular, for static generators smaller than 400 kW (i.e., Q\({}_{\text{max}}\) = \(\pm\)0.436 S\({}_{\text{n}}\), corresponding to a 0.9 power factor); (iii) rectangular, associated to the Doubly Fed Induction Generator (DFIG) and Full Converter (FC) wind turbines (i.e., Q\({}_{\text{max}}\) = \(\pm\)0.312 S\({}_{\text{n}}\), corresponding to a 0.95 power factor). The simulations perform power flow (PF) calculations with a discretisation time of 1 h (\(\Delta t=1\)). Since three representative days (working day, semi-holiday and holiday) for each season (winter-spring-summer-autumn) are considered, a total number of \(N\) = 288 time intervals has been calculated [34]. The simulations have been performed for each feeder included in the Case Study with the approach described in the previous Sections. In particular, the market potential has been calculated for two different cases:  
* Case A (\"only DG\"), where the flexibility is provided only from the DG units, according to the participation level reported in Table 2;
* Case B (\"DG + end-users\"), with the flexibility offered from all DER available in the feeders, following the indications reported in Table 2.  
For the inherent features that characterise the test network (i.e., it is constituted by representative feeders, integrates the real DG installed power, delivers a quantity of energy to the loads according to the open data available to the served territory, uses typical profiles for load demand and production according to the irradiation level of the territory and on historical data of wind production, etc. ), it can be considered representative of a real case, and, thus, the results achievable from the application of the proposed approach can be considered representative as well.
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 3 Methods
### Methods of Parameter Estimation
#### 3.2.13 Generalized Exponential Distribution (EG2-Gupta)  
Parameter \(\alpha\) can be accurately obtained only by numerical methods, because the coefficient of variation and the L-coefficient of variation are nonlinear equations presented in Appendix B.  
For estimation with MOM, parameter \(\alpha\) has the following approximate forms depending on \(\mathrm{C}_{\mathrm{v}}\):  
\[\alpha=\exp\left(\begin{array}{l}-0.00031096-2.262833217\cdot\ln(\mathrm{C} _{\mathrm{v}})+0.260607141\cdot\ln(\mathrm{C}_{\mathrm{v}})^{2}-\\ 0.211003998\cdot\ln(\mathrm{C}_{\mathrm{v}})^{3}+0.094017376\cdot\ln(\mathrm{C }_{\mathrm{v}})^{4}+\\ 0.045622834\cdot\ln(\mathrm{C}_{\mathrm{v}})^{5}+0.049036309\cdot\ln(\mathrm{C }_{\mathrm{v}})^{6}+\\ 0.016045195\cdot\ln(\mathrm{C}_{\mathrm{v}})^{7}+0.002009549\cdot\ln(\mathrm{C }_{\mathrm{v}})^{8}\end{array}\right) \tag{130}\]  
\[\beta=\frac{\psi(\alpha+1)+\gamma_{\mathrm{e}}}{\mu} \tag{131}\]  
where \(\gamma_{\mathrm{e}}\) represent the Euler-Mascheroni's constant and \(\psi(\alpha)\) represents the digamma function.  
For estimation with the L-moments, parameter \(\alpha\) has the following approximate forms depending on \(\tau_{2}\):If \(0.05\leq\tau_{2}\leq 0.5\)  
\[\alpha=\exp\left(\begin{array}{l}-2.705734288-6.147348286\cdot\ln(\tau_{2})-5.63 695924\cdot\ln(\tau_{2})^{2}-\\ 4.843168776\cdot\ln(\tau_{2})^{3}-2.533007093\cdot\ln(\tau_{2})^{4}-\\ 0.9053571\cdot\ln(\tau_{2})^{5}-0.19830547\cdot\ln(\tau_{2})^{6}-\\ 0.026613323\cdot\ln(\tau_{2})^{7}-0.001663193\cdot\ln(\tau_{2})^{8}\end{array}\right) \tag{132}\]  
if \(0.5<\tau_{2}<1\)  
\[\alpha=\frac{1344.7635811\cdot\tau_{2}^{5}+536.9323004\cdot\tau_{2}^{6}-92.281 2201\cdot\tau_{2}^{2}}{1+0.0001378\cdot\tau_{2}+0.0005466\cdot\tau_{2}^{2}+0.00 05326\cdot\tau_{2}^{3}} \tag{133}\]  
\[\beta=\frac{\psi(\alpha+1)+\gamma_{\rm e}}{\rm L_{1}} \tag{134}\]
# Three-Dimensional Stopping Sight Distance Calculation Method under High Slope Restraint
## 5 Instance Verification  
### S.S.D. Calculation for a Two-Lane Rural Highway in Guizhou Province  
In order to verify the accuracy of the above calculation method, this section will calculate the S.S.D. of the turning section of the actual high road slope. The actual project verified in this paper is a two-lane rural highway in Guizhou province, with a design speed and speed limit of 40 km/h. The roadbed width of general sections is 8.5 m, the width of single lane is 3.5 m, the width of hard shoulder is 0.75 m, the side ditch is rectangular side ditch, the width is 0.6 m, and the lining size of side ditch is 0.25 m (Figure 8). Six curves of PI115 (Point of Intersection, No.115), PI144, PI152, PI317, PI324, and PI369 are selected for 3D S.S.D. calculation. The calculation parameters of each curve are shown in Table 1.  
Figure 8: Calculate highway semi-cross section layout.  
Figure 7: Flow chart of sight distance calculation.  
Considering the convenience of calculation, the above calculation methods were programmed in MATLAB (MathWorks Inc., Natic, MA, USA), and the curve parameters were input and calculated, and the results were shown in Table 2:  
From the above calculation results, it is not difficult to find that the difference between horizontal S.S.D. and three-dimensional S.S.D. is not large. However, the difference of the curve on the straight slope section is larger. This is because the radius of the vertical curve is often much larger than the horizontal S.S.D., resulting in a small arc length (three-dimensional S.S.D.) with horizontal S.S.D. as a chord (Figure 6). In contrast, the vertical slope of the straight slope section has a significant effect on the growth of the 3D S.S.D. (Figure 5).  
Therefore, when calculating S.S.D. in the vertical curve section, the horizontal S.S.D. can be used instead of the three-dimensional S.S.D., which will greatly reduce the workload of the engineering personnel.