esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Influence Mechanism of CBR on the Performance of Courtyard Buildings  
Through actual measurement, it is found that compared with open courtyards, CBR can effectively improve the energy efficiency of buildings and significantly improve indoor thermal comfort in winter. Through different CBRTs, a buffer space is added to the envelope of HTSTO, which was originally directly exposed to the external environment, thereby improving the building's ability to resist external climate fluctuations (Figure 7). During sunny winter days, the courtyard covered by CBRT acts as a conservatory to absorb solar energy and store heat. On winter nights, the enclosed courtyard can transport the heat stored during the day to various functional rooms, and can effectively reduce indoor heat loss at night.  
CBRT improves the performance of courtyard buildings mainly by collecting solar energy, storing solar energy, and slowing down indoor heat dissipation. In the cold winter, the temperature of the courtyard covered by CBRT is 3-6 degrees Celsius higher than that of the open courtyard, which reduces the temperature difference between the courtyard and the heated room. The reduction in temperature difference effectively alleviates the heat loss of the heated room leaking to the courtyard through walls, windows, and ventilation. Furthermore, heat leaking from the warm rooms will be collected by the enclosed  
Figure 6: Monitoring data for different vernacular buildings.  
courtyard, slowing the loss of heat to the exterior of the building, thereby providing a warmer courtyard.  
Although T-CBRT and NT-CBRT can also effectively improve the thermal comfort of courtyard space in winter, they have slightly different operating mechanisms. Specifically, T-CBRT has better solar energy collection effect due to better light transmittance, while NT-CBRT provides a more stable indoor thermal environment due to the thermal insulation performance of roofing materials. For T-CBRT, the light transmittance of the roof material is the most critical parameter affecting its performance. The use of high light transmitting-materials not only helps to improve the efficiency of CBRT in collecting solar energy, but also helps to improve the indoor light environment [53]. For NT-CBRT, the height of the roof and the volume of the buffer space provided are important factors affecting its performance.
# Panchromatic and Hyperspectral Image Fusion Using Ratio Residual Attention Networks
## 4 Results
### Comparative Experiment
#### 4.5.3 Impact of Different Parameters on Fusion Results  
To investigate the impact of the parameter \(\alpha\) on the fusion performance of the proposed method, a detailed statistical analysis was conducted using multiple values of \(\alpha\). The experimental results are summarized in Table 5. Initially, four values (0.1, 0.5, 1.0, and 5.0) were selected. Further, additional fine-grained values around 1.0 (i.e., 0.8, 0.9, 1.1, and 1.2) were included to explore the model's sensitivity near the optimal region.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Q** & **ERGAS** & **SAM** & **CC** & **RMSE** & **Time (s)** \\ \hline GFPCA & 0.6996 & 7.7778 & 5.2997 & 0.8094 & 10.4624 & 0.6142 \\ HySure & 0.8001 & 4.3335 & 5.2672 & 0.8256 & 9.3447 & 28.1752 \\ SFIM & 0.8202 & 9.6464 & 4.1682 & 0.8439 & 15.1758 & **0.4894** \\ CNMF & 0.8946 & 6.2779 & 3.7672 & 0.9146 & 8.5365 & 3.8101 \\ DRCNN & 0.7630 & 8.4678 & 4.2339 & 0.8089 & 19.3449 & 0.5679 \\ DRSAN & 0.8683 & 4.5246 & 3.9532 & 0.8796 & 8.4892 & 0.7247 \\ HT & 0.8985 & 4.3310 & 3.6842 & 0.9271 & 7.5032 & 1.9435 \\
**Proposed** & **0.9028** & **4.1904** & **3.6381** & **0.9298** & **7.3851** & 0.6534 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation metrics of different fusion methods on the Chikusei dataset.  
Figure 7: Comparison of spectral reflectance differences at four randomly selected locations on the Chikusei dataset: (a) (42,189), (b) (58,93), (c) (90,147), and (d) (20,149). Each curve shows the reflectance difference between a specific fusion method and the reference hyperspectral image across spectral bands. The closer the curve is to 0 (the black dashed line), the higher the spectral fidelity. Note that all negative values in the plot are indicated using proper minus signs (e.g., \(-1\) instead of hyphens).  
As shown in the table, when \(\alpha=1.0\), the proposed fusion model consistently achieves the best performance on both the EO-1 and Chikusei datasets across multiple evaluation metrics, including Q, ERGAS, SAM, CC, and RMSE. This demonstrates that spatial and spectral loss components contribute equally and synergistically to the final fusion quality. Moreover, the results for nearby values, such as \(\alpha=0.9\) and \(\alpha=1.1\), are also competitive, suggesting that the model is relatively stable and robust to small changes in this parameter.
# Temporal-Guided Label Assignment for Video Object Detection
## 4 Experiment
### Ablation Study  
#### 4.3.1 Effect of Support Frame Number N  
We adopted the uniform sample strategy from the Temporal ROI Align [11] method. That is, the sampling stride is dynamically adjusted according to the length of the video. Table 1 shows the effect of different values of N. When N increased from 2 to 8, the performance increased from 81.0 mAP(%) to 82.0 mAP(%). Compared with the baseline, when N was 2, the accuracy dropped because of the introduction of interference. When N exceeded 8, the accuracy continued to increase within a certain range, but the accuracy no longer increased or decreased after exceeding a certain value. The performance might continue to rise, but we did not continue to verify this due to the limitation of computing resources. The bottleneck was the relation module and fusion module, which were calculated N times in the training phase. Therefore, this did not affect the inference speed. This showed that as the number of support frames increased, the fused weight map was more stable and less prone to large deviations; therefore, N was chosen to be 8 as default.  
4.3.2 The Effectiveness of Temporal-Guided Label Assignment and the Feature Instructing Module (FIM)  
TROI-Var denotes the variant of Temporal ROI Align [11], which introduces the label assignment strategy in AutoAssign [18], and it is the baseline of our model. As shown in Table 2, temporal-guided label assignment only uses the label assignment information to model the relationship between themselves without additional feature information. This improved performance over the baseline, which means the framework is effective. FIM further enhanced the performance of the framework. This demonstrated that the relation model established by features is significant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\#N** & **2** & **4** & **6** & **8** \\ \hline mAP(\%) & 81.0 & 81.4 & 81.7 & 82.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The effect of support frame number N.
## 4 Hydrogeological Context  
From the hydrogeological point of view, the study area belongs to the Almazan Sur Groundwater Body (ES020 MSBT000400050), belonging to the Duero River Basin, with a defined surface area of 1032 km\({}^{2}\) and in which two groundwater formations of interest are defined:  
* The main aquifer--Jurassic--is made up of dolomites, carniolas and limestones of the formations corresponding to the \"Infralias\" and \"Lias\" of the Iberian Mountain Range (Cordillera Iberica). They have sections of variable permeability due to fissuring and/or karstification, and they behave as free aquifers.
* From a hydrogeological point of view, the Upper Cretaceous carbonate series of the domain of the Northern Edge of the Central System (Sistema Central), with permeability associated with fissuring and/or karstification, are of interest. They constitute a--Cretaceous--aquifer that overlaps and disconnects from the Jurassic aquifer due to the existence of a low-permeability detrital formation between the two, made up of the sands of the \"Utrillas\" Fm.  
The Jurassic aquifer formation is confined and disappears to the north, under the Cretaceous aquifer formation, which, at the same time, goes under Tertiary materials towards the Almazan basin. There, it constitutes the main deep aquifer, which feeds the Tertiary detrital materials and significant springs and watercourses, constituting the important Groundwater Body of the Almazan Basin, as well as that of the Paramo de Escalote. The following figure (Figure 2) shows the definition of the Almazan Sur Groundwater Body and the bordering bodies, as well as the location of the research area within the body.  
The present research focuses on the Jurassic aquifer formation shortly before it is confined by Cretaceous materials, as it is expected to provide a higher quality source of resources and solve the existing problems at Liceras associated with the poor hydrogeological characteristics of the Triassic materials with a high content of salts due to their lutile and gypsum materials. In the area, the public body Confederacion Hidrografica del Duero has a borehole used as a control piezometer for the Jurassic materials, called PZ0250005, located 1.6 km to the north of Liceras. This borehole is 205 m deep and with very plastic red and grey clays at the bottom (possibly Keuper). The piezometric level is monitored periodically (Figure 3), with the base level at 103 m depth in the driest periods and around 70 m depth in the periods of greatest recharge.  
Figure 1: (**A**) Geological Cartography (Geological Map of Spain 1:50,000, sheet n\({}^{\circ}\) 404 (Aylilón) [16]) with the position of the TDEM surveys carried out (**B**) and interpretative geological cross-section showing the main structural features over the TDEM survey line.
## 4 Numerical Analysis and Results
### Applied Final Model of the Tissue System  
The developed models of components such as rib bones (distinguishing between the compact and spongy parts), muscles, adipose tissue, and the liver itself were put together [33]. In the final stage, a simplified geometric model was developed (Figure 9), which was eventually adapted for further numerical analysis and validated using the Computerized Imaging Reference Systems (CIRS) Model 039 calibrator.  
In the numerical simulations, the soft tissues and liver were modeled with 0.5 mm Hex elements. The rib bones with the separation of the external compact part were modeled using the FEM method. The internal spongy part was modeled using SPH-type elements. Discretization was assumed at 0.5 mm with the use of Tetra-type elements. The indenter was modeled using the Johnson-Cook model with material parameters for steel in the elastic range.  
For a simplified model imitating liver, muscle, ribs, and skin, numerical simulations were performed in the frequency range of 1.0-5.0 MHz and with a liver stiffness of 1.5-12.5 kPa, as well as steatosis below 300 dB/m. Example results for forcing a vibration pulse at 3.0 MHz are shown in the Figure 10. The duration was determined to avoid imaging the reflection of the wave from the end of the liver, since the other organs were not included in the model. The time steps were chosen to avoid imaging wave reflection from the end of the system.  
Figure 8: Individual time steps for 3 MHz excitation.  
Figure 9: Adopted model for the numerical analysis.  
The propagation of the longitudinal wave associated with interference can be seen already at the level of the ribs. The duration was determined to avoid imaging the reflection of the wave from the end of the liver, since the other organs were not included in the model.
# Some New Bounds for the Inverse Sum Indeg Energy of Graphs
###### Abstract
Denote by \(\mu_{1}\geq\mu_{2}\geq\ldots\geq\mu_{n}\) the ordered eigenvalues of \(\mathcal{C}\). The multiset \(Sp_{isi}=Sp_{isi}(G)=\{\mu_{1},\mu_{2},\ldots,\mu_{n}\}\) will be called the _ISI spectrum_ of the graph \(G\). We say \(\mu_{1}\) is the _ISI spectral radius_ of \(G\). Extending the energy concept to the ISI matrix, the _ISI energy_ of a graph \(G\) can be defined as follows  
\[\varepsilon_{isi}=\varepsilon_{isi}(G)=\sum_{i=1}^{n}\left|\mu_{i}\right|\,. \tag{4}\]In recent years, researchers have found that graph energy and its variants have diverse, amazing and, to some extent, unanticipated utilizations in crystallography [34; 35], the analysis and comparison of protein sequences [36; 37], the theory of macromolecules [38; 39], network analysis [40; 41; 42; 43; 44; 45], and so on. It is noted that there is a very close relationship between the \(\varepsilon(G)\) and \(\varepsilon_{isi}(G)\) of graphs. Therefore, we can use \(\varepsilon_{isi}(G)\) to obtain the \(\varepsilon(G)\) of numerous kinds of graphs. Consequently, it has not only theoretical importance, but also practical significance in \(\varepsilon_{isi}(G)\) research. In 2018, [PERSON] et al. [13] normalized almost all kinds of degree-based graph energies into a unified form, and they derived some bounds on these energies of graphs. In this paper, novel bounds for \(\mu_{1}\) and \(\varepsilon_{isi}(G)\) were acquired, and these bounds can not be deduced from the results in [13]. In this paper, we also need the _general Randic index_  
\[R_{\frac{1}{2}}(G)=\sum_{v_{i}v_{j}\in E(G)}\sqrt{d_{i}d_{j}}\,\]  
which was introduced by [PERSON] and [PERSON] [46], and the _Zagreb indices_ introduced by [PERSON] and [PERSON] [6] in 1972. The first and second Zagreb indices of a graph \(G\) are denoted by \(M_{1}(G)\) and \(M_{2}(G)\), respectively, and defined as  
\[M_{1}(G)=\sum_{v_{i}\in V(G)}d_{i}^{2}\,\ M_{2}(G)=\sum_{v_{i}v_{j}\in E(G)}d_{i }d_{j}\.\]  
We structure this paper in four parts. Some subsequently used definitions, notations and results are offered in Section 2. Section 3 gives some bounds for \(\mu_{1}\) and characterizes the corresponding graphs. Several novel bounds on \(\varepsilon_{isi}(G)\) are established in Section 4.
## 2 Data and Methodologies  
### PPP-B2b Message and GNSS Observations  
Using our custom-developed GNSS receiver and self-developed decoding software, the PPP-B2b corrections were received and decoded in real time at station BJ01 (40.07\({}^{\circ}\) N, 116.27\({}^{\circ}\) E) in Beijing, China. These corrections were then stored for further study. The receiver is equipped with a K803 GNSS OEM module provided by GomNav Technology Ltd. (ShangHai, China), designed to support the PPP-B2b signal with a centre frequency of 1207.14 MHz and a bandwidth of 20.46 MHz. The PPP-B2b data are acquired and transmitted to an Advanced RISC Machine (ARM) processor via the RS-232 communication standard in binary format. Our in-house developed software on the ARM processor then converts these binary data into ASCII format, enabling the real-time estimation of ZTD and coordinates using the PPP software running on the ARM processor. Furthermore, the software includes functionality for storing PPP-B2b data and transmitting the real-time stream to a Linux server, which broadcasts these corrections to users via the Internet. This capability facilitates broader access to real-time corrections for researchers and practitioners. Additional technical details regarding the structure and processing of PPP-B2b information are provided in the Appendix A. Researchers interested in utilizing these corrections can request access by contacting the authors directly via email. The performances of the clock, orbit, and DCB broadcast on the PPP-B2b signal between DOY 069-311 in 2022 were compared with the final clock and orbit products from GBM and DCB from the MultiGNSS Experiment (MGEX) project. It should be noted that our GNSS receiver could not continuously record the PPP-B2b information in May and June 2022 due to a power supply issue. The GNSS observations were available at 32 stations in RINEX format and were downloaded from the Crustal Dynamics Data Information System (CDDIS) (Noll 2010) or from our own observation network.
## 3 Collaboration Performance Evaluation Based on Combination of AHP and EW
### Determining the Comprehensive Weight Based on Combination of AHP and EW
#### 3.2.2 Determining the Objective Weight Based on EW  
Supposing there are \(m\) evaluation objects and \(n\) evaluation indicators, the collected data forms the following matrix:  
\[A=\begin{vmatrix}a_{11}&a_{12}&\cdots&a_{1n}\\ a_{21}&a_{22}&\cdots&a_{2n}\\ \cdots&\cdots&\cdots&\cdots\\ a_{m1}&a_{m2}&\cdots&a_{mn}\\ \end{vmatrix} \tag{6}\]  
After standardization of indexes, the standardized index matrix \(A^{\prime}=(a^{\prime}_{ij})_{m\times n}\) is obtained. The proportion \(f_{ji}\) of the \(j\)-th evaluation object in the \(i\)-th index is calculated:  
\[f_{ji}=\frac{a^{\prime}_{ji}}{\sum_{j=1}^{m}a^{\prime}_{ji}} \tag{7}\]  
Then, the entropy value \(e_{i}\) of the \(i\)-th index can be calculated:  
\[e_{i}=-\frac{1}{\ln m}\sum_{j=1}^{m}f_{ji}\ln f_{ji} \tag{8}\]  
where \(e_{i}\) is the entropy value of the \(i\)-th index, and \(m\) is the number of evaluation objects. The entropy weight \(s_{i}\) of the \(i\)-th index can be calculated as:  
\[s_{i}=\frac{1-e_{i}}{\sum_{i=1}^{n}1-e_{i}} \tag{9}\]
# Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
###### Abstract  
Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.  
driving assistance; intelligent vehicles; traffic sign detection; convolutional neural network; traffic sign recognition +
Footnote †: journal:  
_Article_
## 3 Dual-Band Nested Antenna Array Design
### Antenna Array for 3.5 GHz
elements are sandwiched between the two substrates. The distances between the horizontal and vertical individual elements were both adjusted to 0.5 \(\lambda_{0}\) for minimum mutual coupling and side-lobe level, where \(\lambda_{0}\) denotes the free-space wavelength. The simulated results reveal a \(-\)10 dB impedance bandwidth of B\({}_{\text{match}}\) = 90 MHz and an axial ratio AR \(<\) 3 dB over a bandwidth of B\({}_{\text{AR}}\)\(\approx\) 65 MHz around f = 3.5 GHz as shown in Figure 4b,c. The dimensions of the CP array including all design elements are listed in Table 3.  
Figure 4: (**a**) Exploded view of the 2 \(\times\) 2 bare patch antenna array (metallized layers in yellow, dielectric substrates in green); (**b**) simulated frequency dependence of the input reflection coefficient 1\(S_{11}\)(f) 1\({}^{2}\); (**c**) simulated axial ratio AR(f) versus frequency. \begin{table}
\begin{tabular}{c c c c} \hline
**Design Element** & **Symbol** & **Detail** & **Dimension (mm)** & **Impedance (f)** \\ \hline Patch & L\({}_{\text{P}}\) & Length & 19.3 & \\ Truncated corner & L\({}_{\text{r}}\) & Length & 4 & \\  & L\({}_{\text{s}}\) & Length & 8 & \\ Slot & W\({}_{\text{s}}\) & Width & 1.5 & \\  & L\({}_{\text{gs}}\) & Length & 2 & \\ Ground slot & W\({}_{\text{gs}}\) & Width & 8 & \\ Feed to ground slot & L\({}_{\text{d}}\) & Length & 5 & \\  & W\({}_{\text{f}}\) & Width & 0.5 & \\
50 \(\Omega\) microstrip line & L\({}_{\text{o}}\) & Length & 6 & \\  & & Width & 1.5 & \\ \hline \end{tabular}
\end{table} 
Table 3: Geometrical dimensions of the 2 \(\times\) 2 patch antenna array.
# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
###### Abstract  
The spectrum of Internet of Things (IoT) applications is exponentially growing, driving the demand for better energy performance metrics. In conjunction, Low Power Wide Area Networks (LPWAN) have evolved as long-range connectivity enabler with low management cost. The integration of LPWAN communication assists in reliable IoT operation with extended lifetime. Notable LPWAN technologies that contend for many of the IoT applications are LoRaWAN, DASH7, Sigfox, and NB-IoT. Most of the end-devices such as sensors and actuators are battery powered, therefore investigating energy consumption becomes crucial. To estimate the consumed power, it is important to analyze the energy consumption in wireless communication. This paper describes an empirical evaluation of energy consumption for LPWAN wireless technologies. We measure the current consumption of LoRaWAN, DASH7, Sigfox, and NB-IoT and derive the respective battery lifetime. These measurements help to quantify the energy performance of different protocols. We observe that LoRaWAN and DASH7 are more energy efficient when compared to Sigfox and NB-IoT. Finally, a case study on energy consumption is done on precision agriculture in the greenhouse, showing that battery lifetime in real applications can drop significantly from the ideal case. These results can be used for increasing the effectiveness of the IoT application by selecting the right technology and battery capacity.  
Keywords:LPWAN; LoRaWAN; DASH7; Sigfox; NB-IoT; energy efficiency; power consumption Article