esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Work  
Various systems have applied data-driven methods to rental recommendations, often using collaborative filtering or fuzzy logic to handle user preferences [3]. These approaches, however, typically lack real-time learning or geospatial features. Some platforms have added augmented reality or basic filtering, but their models remain static and shallow [2].  
Real-estate recommender systems have evolved from simple collaborative filtering [6] to sophisticated hybrid approaches. One solution [7] proposed a fuzzy logic-based system achieving 72% user satisfaction, while another [8] demonstrated that incorporating user behavioral patterns improved recommendation accuracy by 34%. However, these systems lack real-time adaptability. Recent work [9] has introduced deep learning for property recommendations, achieving state-of-the-art results on a dataset. Our approach extends this by incorporating reinforcement learning for continuous improvement.  
Reinforcement learning (RL) offers dynamic, long-term optimization and is increasingly used in domains like e-commerce. Its use in housing is limited, and our system is among the first to apply RL to student-focused rental recommendations based on campus proximity and budget [1].  
Geospatial tools like interactive maps and rent heatmaps enhance decision-making by visualizing neighborhood trends. We extend this with real-time visualizations using live GeoJSON and listing data [4]. MongoDB and other NoSQL databases support flexible housing data storage, geospatial queries, and keyword searches. When combined with automated data scraping, they enable up-to-date insights critical for smart-city applications [5].  
The uniquely developed application combines RL, live mapping, real-time data collection, and a modern full-stack design, features not jointly addressed in prior housing systems.
# Flexural Performance of Novel Nail-Cross-Laminated Timber Composite Panels
## 3 Experimental Results and Analysis
### Ultimate Bending Capacity
#### 3.4.1 Calculation for 3-Layer CLT Panels  
As shown in Figure 14: \(b=705\) mm, \(h_{1}=h_{2}=38\) mm, \(\overline{h_{1}}=38\) mm, \(h_{tot}=114\) mm, \(L=2150\) mm, \(E=11,200\) MPa,\(f_{b}=11.8\) MPa, \(G_{R}=50\) MPa. Hence: \(E_{1}=E_{2}=E\), \(A_{i}=b_{i}\times l_{i}\), \(l_{i}=\frac{b_{i}l_{i}^{3}}{12}\), \(\gamma_{1}=\frac{1}{1+\left(\pi^{2}\frac{E_{1}A_{1}}{l^{2}}\frac{\overline{b_ {1}}}{C_{R}b^{6}}\right)}\), \(\gamma_{2}=\frac{1}{1+\left(\pi^{2}\frac{E_{2}A_{2}}{l^{2}}\frac{Accordingly, \(A=b\times h=705\times 38=26,790\) mm\({}^{2}\), \(a=\frac{38}{2}+\frac{38}{2}=38\) mm.  
\(\gamma_{1}=\gamma_{2}=\frac{1}{1+\frac{n^{2}-112200(705.38)}{2150^{2}}}=0.59\)  
\(\text{I}=\frac{bl^{3}}{12}=\frac{205\times 38^{38}}{12}=3.22\times 106\) mm\({}^{4}\)  
Hence, \(E_{eff}=\sum\limits_{i=1}^{2}\left(E_{i}l_{i}+\gamma_{i}E_{i}A_{i}a_{i}^{2} \right)=2E\left(1+\frac{\gamma Aa^{2}}{I}\right)=2\times 11,200\times 3.22\times 1 06\left(1+\frac{0.59\cdot 26790.38^{2}}{3.22\times 10^{6}}\right)=584.24\times 1 09\) N\(\cdot\)mm\({}^{2}\)  
\(I_{eff}=52.16\times 106\) mm\({}^{4}\)  
Thus, \(M_{r}=\varphi f_{b}\frac{I_{eff}}{(\gamma_{1}a_{1}+0.5 bl_{1})}=0.9\times 11.8 \times\frac{52.16\times 10^{6}}{0.59\times 38+0.55\times 38}\times 10^{-6}=13.37\) kN\(\cdot\)m
# Review of Liquid Argon Detector Technologies in the Neutrino Sector
## 3 Dual-Phase Experiments
### DUNE (Dual-Phase Far Detector)  
In a similar way to single-phase LArTPCs under the ICARUS program, the development of dual-phase LArTPCs for neutrino detection has followed a path of progressively larger detectors-initially using a 10 \(\times\) 10 \(\times\) 21 cm TPC volume containing \(\approx\)3 kg of LAr [75; 76], followed by a 200 kg 40 \(\times\) 76 \(\times\) 60 cm iteration [77]. Much of the most recent development has been performed in the context of the dual-phase DUNE program, which includes the 3 \(\times\) 1 \(\times\) 1 m WA105 and 6 \(\times\) 6 \(\times\) 6 m dual-phase ProtoDUNE (\"ProtoDUNE-DP\") detectors (both described below in Section 3.2), as well as the dual-phase DUNE far detector module (\"DUNE-DP\") itself. The latter is conceptually depicted in Figure 39.  
DUNE-DP will have an active LAr mass of 12,096 tonnes (10,643 tonnes fiducial) [74] contained within a single 60 \(\times\) 12 \(\times\) 12 m TPC. All of the designs and technologies to be implemented on DUNE-DP will be scaled-up versions of those used by the WA105 and ProtoDUNE-DP experiments, and so the successful construction and operation of these detectors represent a critical step towards the realisation of DUNE-DP.
## 2 Cluster Head Selection
### Assisted Schemes  
There are numerous advantages to using distributed cluster-based algorithms, but since a single node does not have a general understanding of the topology and characteristics of the entire network, distributed schemes provide no guarantee either about the fair placement of CHs or about the number of CHs selected within the network. Moreover, heuristic algorithms impose transmission of large  
Figure 3: Nodes with Max-min hops to any other sensor nodes.  
number of control messages on restricted resources of sensor nodes, which decreases the overall network lifetime. In this respect, _Bs assisted_ and _CHs assisted schemes_ are proposed to provide fair placement of the optimum number of CHs and to mitigate energy expenditure of nodes in re-clustering stages of a balanced cluster.
## 2 Materials and Methods
### Peak Decay Analysis  
The peak decay curve can be used to describe the decay pattern of the strong dynamic loads' waveforms. It analyzes the waveform between the \(\rm T_{Start}\) and \(\rm T_{End}\) times. Figure 1 shows an example of a typical peak decay curve of a strong dynamic load signal, calculated as follows:  
* Determine the peak values on the velocity curve of the original signal after the peak.
* Fit these peak values using spline interpolation to form the upper envelope after the peak.
* Based on the exponential decay law, fit the upper envelope after the peak using a fitting formula. \[v_{a}=mt^{-n}\] (8)  
In Equation (8), \(v_{a}\) represents the post-peak fitting velocity; \(m\) is the fitting coefficient related to the peak velocity; and \(n\) is the decay coefficient, where a larger \(n\) leads to faster decay of \(v_{a}\), and vice versa [34].
## 6 Conclusions and Future Work  
In this paper we analysed the relation between readability indices and the World Wide Web. We looked at the topic from two different angles: How to determine readability of documents on the web and on the potential of exploiting web resources to indicate parameters for new approaches to determine readability.  
Concerning the application of readability measures on web documents we showed that the introduction of content extraction filters into the process leads to significantly improved estimates. Further, we developed bias adjustments for CE based SMOG and FRE indices that lead to still better estimates for the readability of web documents. Given that we focused in our analysis on news documents, it remains to investigate how the CE methods operate on other type of documents or documents with different levels of readability.  
On the other hand we found indications that corpus statistics of the web can be exploited to obtain language independent measures for readability. We showed that the distribution of terms into frequency classes reproduces very nicely the intuitively perceived difficulty of text categories. However, when looking at the document level, the latter results require some further investigations. Predicting the readability for a single document simply based on the expected frequency class does not provide results of the desired quality yet. Overcoming data sparsity in single documents and using more characteristics and features of the frequency class distribution seems a promising approach here.  
In future work we will address exactly this task of feature engineering on the frequency class distribution in individual documents. We are confident, that by applying smoothing techniques and identifying a set of suitable features it will be possible to estimate the readability also of individual documents based on the frequency classes of the contained terms. Once such a metric is established, an interesting question will be if the observations can be generalized to other languages, thereby providing a language independent readability metric.
# Piecewise Function Hysteretic Model for Cold-Formed Steel Shear Walls with Reinforced End Studs
## 3 Hysteretic Model of CFS Shear Walls with Reinforced End Studs
### Model Verification  
In order to verify the proposed hysteretic model, comparative analysis on the experimental results of the CFS shear walls described by [PERSON] [22] was carried out, where the end stud sections of specimens include square-89 \(\times\) 100 \(\times\) 0.9 and square-140 \(\times\) 140 \(\times\) 1.5 (hereinafter referred to as 89-type and 140-type walls, respectively); all specimens were sheathed with gypsum wallboards combined with bolyvian magnesium boards on both sides, whose shear modulus are 484 and 124 N/mm\({}^{2}\), respectively; the shear performance of screw connections was determined according to the test results of [PERSON] et al. [27], as detailed listed in Table 2.  
Firstly, the elastic lateral stiffness \(K_{\rm{e}}\) and shear capacity \(F_{\rm{P}}\) of each specimen was determined according to the simplified method that was described in Section 3.1.3; on this basis, the discrete coordinate method was used to establish the load-displacement skeleton curves of those specimens. Limited to space, only the calculated curves of typical 89-type and 140-type walls were depicted in Figure 10. It can be observed that the calculated results, which agree well with the test results, can reflect the shear behavior of the wall with reinforced end studs like nonlinearity, stiffness, and strength deterioration. It also can be concluded from Table 3 that the calculated and test results are of high consistency in both elastic stiffness \(K_{\rm{e}}\) and shear capacity \(F_{\rm{P}}\), with errors being within 11% and 15%, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Failure Modes**} & \multirow{2}{*}{\begin{tabular}{c} **Steel** \\ **Thickness** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **12 mm Gypsum Wallboard** \\ **(mm)** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **12 mm Bolivian** \\ **Magnesium Board** \\ \end{tabular} } & \multirow{2}{*}{\(f_{\rm{e}}\)/**kN**} \\ \cline{3-3} \cline{5-6}  & & & & & & \\ \hline \multirow{2}{*}{Screw being pulled through} & 0.9 & 0.70 & 0.66 & 0.63 & 0.74 & 1.40 \\  & 1.2 & 0.62 & 0.73 & 0.50 & 0.70 & 1.43 \\ \multirow{2}{*}{Screw being sheared off} & - & - & - & - & - & 1.78 \\ \hline \hline \end{tabular}  
* \(f\) and \(d_{\rm{e}}\) are the shear strength and elastic deformation of a screw connection sheathed with a single-layer wallboard, respectively;\(f_{\rm{e}}\) is the shear strength of a screw connection in the perimeter of the wall with reinforced end studs.  
\end{table} 
Table 2: The shear performance of screw connections [27]1. Figure 10: Comparisons of the load-displacement skeleton curves of typical specimens. (**a**) 89-type wall (specimen W89-2); (**b**) 140-type wall (specimen W140-1). Secondly, a piecewise function was adopted to establish the hysteretic loop relative to each governing deformation, and the hysteretic model of a wall was further established.
# Enhanced YOLO Network for Improving the Efficiency of Traffic Sign Detection
###### Abstract  
One important task for autonomous driving is the precise detection and recognition of road traffic signs. This research focuses on a comprehensive set of 72 distinct traffic signs that are prevalent on urban roads in China, with the goal of developing an enhanced You Only Look Once (YOLO) network model tailored for this specific task. The modifications include the omission of the terminal convolution module and Conv3 (C3) module within the backbone network. Additionally, the 32-fold downsampling is replaced with a 16-fold downsampling, and a feature fusion module with dimensions of 152 \(\times\) 152 is introduced in the feature layer. To capture a more encompassing context, a novel hybrid space pyramid pooling module, referred to as Hybrid Spatial Pyramid Pooling Fast (H-SPPF), is introduced. Furthermore, a channel attention mechanism is integrated into the framework, combined with three other improved methodologies. Upon evaluation, the enhanced algorithm demonstrates impressive results, achieving a precision rate of 91.72%, a recall rate of 91.77%, and a mean average precision (mAP) of 93.88% at an intersection over union (IoU) threshold of 0.5. Additionally, the method also achieves an mAP of 75.81% for a variety of IoU criteria between 0.5 and 0.95. These achievements are validated on an augmented dataset established for this study.  
traffic sign detection; feature fusion; object detection Article
## 3 Simulations
### Effect of the Secondary Source Location
The noise reduction is 5.5 dB with the secondary source placed at \(\mathbf{r}_{\mathrm{s1}}\), while it is enhanced to 21.2 dB with the secondary source at \(\mathbf{r}_{\mathrm{s2}}\), as shown in Figure 7b. Figure 5: (**a**) The angular spectrum estimation error when the source is at different locations. (**b**) The NR at the receiver when the secondary source is located at different positions in the \(z=-0.6\) m plane, and the target direction is \(\theta=0\). Figure 6: The distribution of SPL and NR when the target direction is \(\theta=0\) (**a**) SPL without control, (**b**) SPL with control and (**c**) NR when the secondary source located at \(\mathbf{r}_{\mathrm{s1}}\), (**d**) SPL without control, (**e**) SPL with control and (**f**) NR when the secondary source located at \(\mathbf{r}_{\mathrm{s2}}\). Figure 8 shows the distribution of SPL and NR when \(\theta=\pi/6\) and \(\varphi=\pi/2\), where better noise reduction in the target direction can be achieved by placing the secondary source at \(\mathbf{r}_{\text{s2}}\) rather than \(\mathbf{r}_{\text{s1}}\). For instance, the average SPL at the 7 orange points spaced 0.3 m apart in Figure 8c,f is reduced by 5.4 dB and 10.6 dB, respectively, when the secondary source is located at positions \(\mathbf{r}_{\text{s1}}\) and \(\mathbf{r}_{\text{s2}}\).
## 3 Methodology
### Segmentation of Non-Negative Logarithmic Likelihood Difference Based on Gamma Distribution  
To enhance local consistency, a 3D adaptive block is obtained by an improved entropy rate superpixel method. Under speckle noise, pairwise similarities cannot be calculated directly. In order to reduce speckle noise impact, the Gamma distribution statistical characteristics of the SAR image are taken into account. For the intensity image, the probability density function of the Gamma distribution is defined as  
\[P(x)=\frac{\beta^{\gamma}x^{\gamma-1}}{\Gamma(\gamma)}e^{-\beta x} \tag{9}\]  
where \(\gamma\) is the shape parameter, \(\beta\) is the scale parameter, and \(\Gamma(\cdot)\) is the Gamma function. The non-negative logarithmic likelihood difference value [29] is adopted to represent the intensity similarity between pixels based on Gamma distribution, which is defined as  
\[d(i,j)=|\log P(i)-\log P(j)| \tag{10}\]  
where \(i\) and \(j\) are intensity values of two pixels, and \(P(i)\) is the value obtained by substituting \(i\) into the probability density function (9) of the Gamma distribution, which reflects the statistical characteristic on the local neighborhood of the pixel with \(i\) value and suppresses the effect of speckle noise. The logarithmic transformation of \(P(i)\) is performed to improve the sensitivity to the difference in likelihood probability values without changing the monotonicity of the original data. The absolute value of the difference between \(\log P(i)\) and \(\log P(j)\) represents the similarity between two pixels with \(i\) and \(j\) values, replacing (8) in the original method to reduce the impact of speckle noise, so that pixel similarity is not represented by pixel gray value difference directly.  
Using Formula (10), the superpixel results by the improved superpixel algorithm are shown in Figure 1(a). The region-based statistical characteristic can reflect the change rule of most data in the image, so it can suppress the noise. From Figure 2, the superpixels obtained by the improved method have adaptive sizes and shapes. In particular, the region in the red rectangle box is significantly more attached to the edge than the previous shown in Figure 1(b). The red rectangle area is zoomed in Figure 1(c),d, from top to bottom; it contains dark gray crop features, black vegetation features, white bare land features, and dark black river features. As can be seen from the two figures, the superpixel edge is better attached to the boundary of the black vegetation area in Figure 1(c), while in Figure 1(d), the superpixel edge is far away from the boundary of the vegetation area. Then, the shape and size of each block according to local structure can be adaptively adjusted, instead of the boundary being far away from the shape contour of the local object.
## 4 Classification Process
### Threshold Segmentation  
The HY-2A/B's AGC and PP values were used for comparison purposes in order to draw the scatter plots, as shown in Figure 15.  
In Figure 15, the distributions of the PP values from the seawater appear to be dense. However, that was not the case for the sea ice. The PP values of the sea ice were observed to be greater than those of the seawater. In addition, there was major overlapping of the AGC from the two different surfaces. In the Antarctic regions, the data range of the PP values from the seawater were determined to be larger than that of the Arctic regions.  
It was found in this study that the small values of the AGC from the HY-2A had many discrete values. These small values existed at the joints where the two tracking packets switched. However, this had not occurred in the HY-2B. Therefore, the improvements of the HY-2B instrument had been demonstrated in the results of this study.  
Therefore, in accordance with the findings detailed Section 3.4.2, along with other previous related sections, this study selected the PP values the parameters of the classification threshold. The threshold values were then set, as detailed in Table 2.  
Results of the daily classification were shown in Figure 16. Figure 16A,C showed the results of the Ku band from HY-2A/B, meanwhile Figure 16B,D showed the results of C band form HY-2A/B. Comparing the accuracy of the two different places, the accuracy rates of the Arctic regions changed in a smaller rang than the Antarctic regions. It was observed in the daily classification of the November  
Figure 15: Scatter plots of the Automatic Gain Control (AGC) and Pulse Peaking (PP) values: Figure 15A–D used the data of the HY-2A ALT; Figure 15E–H used the data of the HY-2B ALT; Figure 15A,B,E,F are the scatter plots of the Arctic regions; Figure 15C,D,G,H are the scatter plots of the Antarctic regions.  
data that a high level of accuracy had been maintained. Generally speaking, the accuracy level for the sea ice was higher than that for the seawater.  
It was found that a simple threshold segmentation with a single parameter could also achieve a high accuracy rate (Table 3), and there were small differences observed between the HY-2A ALT and HY-2B ALT data. Comparing the result of sea ice and seawater in Table 3, the average accuracy rates of the seawater for threshold segmentation were higher than 96% both in Arctic and Antarctic. However, the average accuracy rates of the sea ice were lower than 90% except band C from HY-2B ALT in arctic.
## 4 Case Studies
### A Numerical Case Study on a Synthetic Dataset of Cylinders
#### 4.2.2 Reference MD Space Based on Defective Samples  
This subsection offers an additional perspective on the classification performance of the synthetic dataset based on a reference MD space constructed by defective samples only.  
Classification results of the training and testing datasets, shown in Figure 12, are inferior to the results obtained in Section 4.2.1, with an accuracy of 93.10% and 92.33%, respectively.  
As it can be observed from Figure 12, the SMD values of the defective samples are spatially dispersed in the entire SMD space [0,1]. Alternatively, the SMD values of healthy samples are highly concentrated in the range of 0.6-0.7. It can be interpreted by the fact that the resonance frequencies in defective samples vary greatly, thus yielding a large variation in MD as well. This also offers a good understanding of the obtained concentrated SMD values for healthy samples due to the small  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Accuracy** & **Testing Accuracy** & **Number of Features** \\  & **(\%)** & **(\%)** & **(\(\boldsymbol{-}\))** \\ \hline \hline
**IMCS\({}_{\text{PSO}}^{\text{BPSO}}\)** & 99.43 \(\pm\) 0.14 & 98.81 \(\pm\) 0.24 & 8.00 \(\pm\) 1.63 \\ (SMD-R1) & 99.43 \(\pm\) 0.14 & 98.81 \(\pm\) 0.24 & 8.00 \(\pm\) 1.63 \\
**IMCS\({}_{\text{PSO}}^{\text{BPSO}}\)** & 92.58 \(\pm\) 0.46 & 92.35 \(\pm\) 0.91 & 2.10 \(\pm\) 0.32 \\
**IMCS\({}_{\text{PSO}}^{\text{BPSO}}\)** & 98.23 \(\pm\) 0.36 & 98.15 \(\pm\) 0.51 & SMD-R1: 8.00 \(\pm\) 1.63 \\ (SMD-R1+SMD-R2) & & & SMD-R2: 2.10 \(\pm\) 0.32 \\
**IMCS\({}_{\text{RF}}^{\text{BPSO}}\)** & 99.39 \(\pm\) 0.07 & 99.02 \(\pm\) 0.20 & SMD-R1: 8.00 \(\pm\) 1.63 \\ (SMD-R1+SMD-R2) & & & SMD-R2: 2.10 \(\pm\) 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification performance of the algorithms based on difference MD spaces over 20 independent runs.  
Figure 12: (**a**) Classification on the training dataset; (**b**) classification on the testing dataset.  
variations in resonance frequencies among healthy samples. Although the presented classification results are not completely satisfactory compared to the results in Section 4.2.1, it further provides an additional opportunity to explore the in-depth study by constructing a two-dimensional SMD space in which both reference MD spaces can be examined.