esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Potential of Energy Savings in Terms of Primary Energy Per Year (MW/h)
The primary energy consumption per year (MW/h\(\cdot\)m\({}^{2}\)) of the selected solution within a POS was applied to the useful area (m\({}^{2}\)) of the corresponding group of buildings. As a result, the primary energy consumption per year of the current building stock is 288,054.83 MW/h (Table 11). The total primary energy savings per year that could be reached if the buildings were renovated are 180,495.13 MW/h (Table 12). However, this would correspond to the best-case scenario, which is 100% of the building stock is renovated. Currently, on average, less than 1% of the national building stock is renovated each year in Europe [1], meaning that over 1804.95 MW/h of primary energy savings are reached (Table 13). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SFH1**} & \multicolumn{2}{c}{**SFH2**} & \multicolumn{2}{c}{**SFH3**} & \multicolumn{2}{c}{**MFH1**} & \multicolumn{2}{c}{**MFH2**} & \multicolumn{2}{c}{**MFH3**} \\ \hline  & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** \\ \hline W1S2 & 5266.13 & 621.47 & 1273.87 & 1732.45 & 11.21 & 0.00 & 45,014.69 & 16,772.92 & 71,971.74 & 34,598.80 & 242.71 & 131.70 \\ W2S2 & 1437.97 & 17.61 & 0.00 & 76.55 & 0.00 & 0.00 & 50.41 & 5.62 & 841.25 & 428.01 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Total primary energy savings per year (MW/h) of 100% of EVha building stock. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SFH1**} & \multicolumn{2}{c}{**SFH2**} & \multicolumn{2}{c}{**SFH3**} & \multicolumn{2}{c}{**MFH1**} & \multicolumn{2}{c}{**MFH2**} & \multicolumn{2}{c}{**MFH3**} \\ \hline  & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** \\ \hline W1S2 & 10,042.95 & 1565.08 & 1779.90 & 2423.07 & 17.75 & - & 80,515.80 & 36,100.95 & 100,562.12 & 48,391.22 & 591.14 & 277.70 \\ W2S2 & 2742.33 & 44.35 & - & 192.78 & - & - & 111.55 & 13.91 & 1604.34 & 1077.90 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Primary energy consumption per year (MW/h) in the current status of the building stock. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SFH1**} & \multicolumn{2}{c}{**SFH2**} & \multicolumn{2}{c}{**SFH3**} & \multicolumn{2}{c}{**MFH1**} & \multicolumn{2}{c}{**MFH2**} & \multicolumn{2}{c}{**MFH3**} \\ \hline  & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** \\ \hline W1S2 & 1024.95 & 1565.08 & 1779.90 & 2423.07 & 17.75 & - & 80,515.80 & 36,100.95 & 100,562.12 & 48,391.22 & 591.14 & 277.70 \\ W2S2 & 2742.33 & 44.35 & - & 192.78 & - & - & 111.55 & 13.91 & 1604.34 & 1077.90 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Selected solutions from the results of all the POS applicable to the building stock.
## 5 LMS Channel Models
### Four-State Markov Channel Model
Another work in the literature [61] proposes the use of a four-state Markov model for LMS satellite channel for a dual-satellite MIMO communications able to improve the BER performance under the same two considered satellites' signal-to-noise ratio condition.
## 3 Results
### Visual Quality Evaluation  
Subjective evaluation is evaluating the quality of the images with the naked eyes and making judgments according to the subjective feelings of image quality [22]. In this analysis, a team of observers compared the fused image with the input images and valued the quality of the fused image by employing various optical parameters such as spatial details, geometric pattern, size of objects, and color [14]. The subjective evaluation method is simple and intuitive, and it can make a quick and convenient evaluation of the images [22]. For visual quality evaluation, we need a good RVI with the same spatial resolution of FVI, which would only give proper judgment, as we already calculated FVI and RVI in different resolutions during objective quality evaluation in previous section. Then, we compared them with their visual quality for different features in different parts of the images. A total of 384 VI maps were prepared, including 360 FVI (6 VIs @ 5 fusion algorithms @ 4 SRR @ 3 types of image fusion) maps and 24 RVI maps (6 GF2 (1 m) + 6 GF1 (8 m) + 6 GF2 (4 m) + 6 GF4 (50 m)). Due to such large numbers, individually, it is difficult to show their visual quality, so subjective quality was analyzed in different parts of the images selectively. To analyze the FVI quality more clearly, water area, forest areas, built-up areas, lakes, roads, etc., were compared with their respective RVI. Different image fusion algorithms showed different influences on different VIs. Some image fusion algorithms had a good performance at the edges and some had good results in vegetation areas, rivers, etc. The following table (Table 8) shows the visual quality of FVI results from same-sensor, multi-sensor and multi-sensor resampled abbreviated as Same-Sen., Multi-Sen. and Multi-Sen(R), respectively.
## 4 Case Study  
The effectiveness of the proposed UC model is verified by the modified IEEE RTS-24 bus system, which consists of 10 thermal generation units, two wind farms, two nuclear power stations and two energy storage power stations. The cost coefficients of the generators are listed in Tables 1 and 2. The daily load demand and wind power generation are shown in Figure 2. The quadratic function of the fuel cost and the constraints of minimum up/down time are linearized by reference to the literature [22]. The proposed UC mathematical model is solved with GAMS MINLP solver.  
The total installed capacity of the wind farms connected to lines 8 and 21 is 200 and 50 MW, respectively. The installed capacity of the nuclear power plant connected to buses 1 and 18 is 100 and 300 MW, respectively.  
In the test system, the wind generation is linked to both bus 8 and bus 21. The Gaussian mixture parameters of wind power forecast error are \(c_{1}^{(1)}=0.7\), \(u_{1}^{(1)}=0\), \(\sigma_{1}^{(1)}=8\), \(c_{2}^{(1)}=0.3\), \(u_{2}^{(1)}=4\), \(\sigma_{2}^{(1)}=2\), \(c_{1}^{(2)}=0.8\), \(u_{1}^{(2)}=1\), \(\sigma_{1}^{(2)}=4\), \(c_{2}^{(2)}=0.2\), \(u
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 3 Formation Control: Virtual Linkage Approach
### Moving in Formation Using Virtual Linkage
#### 3.3.2. Moving the Virtual Linkage  
This involves simply moving the virtual linkage's coordinate frame \(\{R\}\) along the predefined reference trajectory \(r_{R}^{d}=\zeta_{R}(t)\) with attitude \(q_{R}^{d}=q(t)\). Then, the coordinate frame of the \(j\)th virtual link \(\{vj\}\) will follow its desired trajectory, \(X_{vj^{\prime}}^{d}\) given by:  
\[{}^{w}T_{vj}\Big{(}X_{vj}^{d}\Big{)}={}^{w}T_{R}\Big{(}X_{R}^{d}\Big{)}\cdot{}^ {R}T_{vj}(\theta_{d}) \tag{5}\]  
where \({}^{R}T_{vj}(\theta_{d})\) is determined by the predefined formation shape and can be expressed using the Denavit-Hartenberg convention [30]. The desired trajectory and attitude of the \(j\)th link is illustrated using \({}^{w}T_{vj}\Big{(}X_{vj}^{d}\Big{)}\) when the virtual linkage \(\{R\}\) follows the trajectory and attitude \(X_{R}^{d}=\Big{(}r_{R}^{d},q_{R}^{d}\Big{)}\).  
Hence, the desired position of the agent \(i\) which belongs to the \(j\)th virtual link can be represented as follows:  
\[{}^{w}r_{i}^{d}={}^{w}T_{vj}(X_{vj}^{d})\cdot{}^{vj}p_{i} \tag{6}\]
## 3 Results and Discussions
### Material Damping  
One of the key features of rubberized concrete is its ability to dampen vibrations. For this purpose, the material damping was determined by means of the signal decaying function.  
From the analysis of the experimental data, it was observed that the recorded signal consisted of a transient part, right after the impact, and a steady-state part. The steady-state part of the signal was fitted by means of a SineDamp equation, as shown in Figure 11. The material damping, \(\xi\), was computed by means of Equation (6):  
\[\xi=\frac{\lambda}{\sqrt{\lambda^{2}+\omega^{2}}} \tag{6}\]  
Figure 10: Prediction of the dynamic modulus of elasticity.  
where \(\lambda\) is the signal rate of decay and \(\varpi\) is the pulsation of the signal. Both the rate of decay and the pulsation were extracted from the SineDamp function used to fit the steady-state response.  
The obtained values for the material damping ratio are summarized in Table 3. It can be observed that the replacement of natural sand by rubber particles leads to a three-fold increase in the material damping.  
Contrary to what was expected, higher percentages of rubber replacement did not necessarily mean better damping. The explanation may lie in the interfacial transition zone (ITZ) between the rubber particles and the cement matrix. A recent study [26] indicated that even for lower replacement percentages of sand by rubber aggregates, up to 15% replacement, the ITZ exhibited a higher concentration of voids and weaker bonding. The presence of voids was also reflected in lower values for the fundamental frequency of vibration, and further research should be dedicated to this topic.
# Analytical Framework of CP-Free Multiuser OFDM System for Coordinated Multi-Point at mmWave
## 2 System Model
### Block Diagram  
The conceptual block diagram of the CoMP-JT-based downlink CP-free multiuser OFDM wireless communication system is shown in Figure 3. Figure 3 shows four users that are assumed to receive their data in binary form. The binary data are first passed through the channel encoder for forward error correction (FEC) in a redundant way and the FEC-encoded binary data are digitally modulated to generate the complex symbols [25]. From the perspective of discriminating orthogonal spreading sequence from user, to user, the complex, digitally modulated signal is encoded with the individually assigned spreading code under the application of complex Hadamard transformation (CHT) technique [26]. On the basis of 5G frame structural information provided at [27], the orthogonally spread  
Figure 2: A scenario of CoMP-JT-based mmWave signal transmission from two BSs to four users.  
signal is converted from serial-to-parallel and processed with localized subcarrier mapping before OFDM modulation. In localized subcarrier mapping for the purpose of generating CP-free OFDM signals, null subcarriers are introduced at both ends of the data subcarriers with the consideration that the time for null subcarriers is longer than the delay spread of the multipath channel. The CP-free OFDM signals are converted again from parallel-to-serial and are windowed with a pulse-shaping filter for interchannel interference reduction [28]. The filtered signal is then passed through an IBO-based high-power amplifier (HPA) [29]. To eliminate multiuser interference (MUI) completely, a channel-dependent transmit precoding scheme is implemented for each power-amplified signal of the individual user. At the final stage of the transmitting section, all the precoded signals are summed up and transmitted from the antennas [30].  
At the receiving end of each user, zero-forcing (ZF) is used as a linear signal detection scheme to detect all the transmitted signals [31]. The detected signal is power-scaled to restore its original signal power, matched-filtered based on user's own precoding/beamforming weight, and serial-to-parallel-converted prior to OFDM demodulation. The OFDM-demodulated signal is converted back from parallel-to-serial, processed with subcarrier demapping, and despreaded by multiplying with the user's own spreading code used by the inverse CHT (ICHT) technique to nullify interfering signals completely. The despreaded signal will pass through the digital demodulator and channel decoder, and eventually retrieve the transmitted binary data.  
Figure 3: Block diagram of CoMP-JT-based downlink CP-free multiuser OFDM communication system.
# Experimental Study of the Air Demand of a Spillway Tunnel with Multiple Air Vents
## 4 Calculation Equation for Air Demand
### Convergence and Sensitivity Analysis  
The relationship between inverted roughness and the interfacial drag coefficient established in Section 4.2 was incorporated into the model described in Section 4.1 to predict the air velocities through multiple vents. Due to the inherent nonlinear interactions among the system variables, analytical derivation of the convergence properties and sensitivity coefficients through conventional theoretical methods proved impractical. To address this limitation, we implemented a parametric sensitivity analysis by perturbing five key operational parameters--water velocity, water depth, total loss coefficients of vents, inverted roughness, and tunnel length--from baseline conditions (\(a_{1}=a_{2}=a_{3}=56\) cm\({}^{2},U_{w}=12\) m/s, \(h_{w}=0.08\) m, \(L_{1}=L_{2}=2\) m, \(L_{3}=4\) m). The resultant sensitivity characteristics are quantitatively summarized in Table 3. We assigned five distinct initial air velocities (\(U_{a,1}^{0}\)) and analyzed their numerical residuals for the semi-iterative loop (see Figure 14).  
Parameter variations exert the strongest influence on vent 1#, with vents 2# and 3# exhibiting decreasing sensitivity. Water velocity emerges as the dominant control parameter, where a 10% increase causes a 21.94% rise in air velocity through vent 1#. A comparative analysis reveals that 10% perturbations in water depth, total loss coefficients of vents, inverted roughness, and tunnel length induce minimal effects, producing less than 3.5% variation in air velocity. These findings emphasize the critical importance of high-accuracy water velocity measurements for operational modeling. A convergence efficiency analysis showed that initial values closer to the final solution required fewer iterations, with the maximum computational time in MATLAB 2023b remaining below 1.26 s--demonstrating sufficient efficiency for preliminary design applications.  
Figure 14: Residual evolution vs. iteration count (\(k\)) for five air velocity (\(U_{u,1}^{0}\)) initializations.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\%} & \multicolumn{4}{c}{**AU\
## 5 Experiments and Analysis  
This section illustrates two cases of IEEE 14 bus system and 24 bus system [31] to show the effectiveness and advantages of the proposed scheme. The scheme takes DoS attacks into account and considers the worst case of attacks under the constraints in the Stackelberg game model mentioned above. In the Stackelberg game model, the scheme uses the optimal structure design to enhance the system performance by reinforcement learning. We assume sub-system parameters are as follows:  
\[\begin{array}{l}A_{i}=\left(\begin{array}{cccc}0&-\sum_{j\in D_{i}}T_{ji}& 0&0&0\\ 1/M_{i}&-D_{i}/M_{i}&1/M_{i}&0&0\\ 0&0&-1/T_{di}&1/T_{di}&0\\ 0&-1/(T_{gi}R_{gi})&0&1/T_{gi}&K_{i}/T_{gi}\\ 1&-b_{i}&0&0&0\end{array}\right),\\ B_{i}=\left(\begin{array}{cccc}0&1/M_{i}&0&0&0\end{array}\right)^{T},\\ \end{array}\]where \(M_{i}\) and \(D_{i}\) are inertia and damping constant respectively, \(T_{gi}\) and \(T_{di}\) are the governor and gas turbine constant respectively, and \(R_{gi}\) and \(T_{ij}\) are the regulation and synchronizing constant respectively [20]. The related parameters of sub-system are listed in Table 1[24].  
The constraints of DoS attacks are set as follows:  
\[\sum_{i=1}^{14}\sum_{j\in N_{c}(i)}\delta_{ji}\leq 1.4.\]  
The subsystems in grid communicate with each other by communication channels. The above constraint means that the sum of all communication channel jamming possibilities is 1.4, or the average jamming possibility is 0.1 for each communication channel. The larger \(C\) in Equation (26) is, the more cyber resources for attackers would be taken. In the following simulations, we set the sampling period to 0.1 s.
## 5 Conclusions  
The methodology proposed in this study addresses the limitation of manual input prompts by employing a text detection network, ensures accurate text segmentation using the SAM, and ultimately achieves precise text removal and background reconstruction via a latent diffusion model. Extensive testing across various datasets validated the superiority of the text detection network designed with specific modules over comparable alternatives, and our text erasure technique clearly outstrips existing solutions.  
In the subsequent stages of our research endeavors, we shall concentrate on several pivotal domains to further augment the performance and practicality of our method. Initially, our objective is to refine the model architecture, boosting its robustness and adaptability to accommodate a broader spectrum of text types and varied backgrounds. This may entail enhancements to the text detection and segmentation procedures in order to address more intricate and variable circumstances. Furthermore, we intend to enhance the model's inferential efficiency, striving to expedite processing velocities while maintaining accuracy levels. Ultimately, we will investigate the integration of our proposed technique with other systems and applications to guarantee its compliance with the requirements for large-scale practical deployment across diverse sectors.  
Conceptualization, X.W. and W.Z.; methodology, W.Z., X.W. and B.Z.; software, W.Z. and B.Z.; validation, W.Z. and L.L.; formal analysis, X.W. and W.Z.; investigation, X.W.; resources, X.W. and W.Z.; data curation, W.Z.; writing--draft, W.Z.; writing--formal, X.W. and W.Z.; visualization, W.Z. and L.L.; supervision, X.W.; project administration, X.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This study was supported by funding from the National Key Research and Development Program of China under grant number 2021 YFC3340402.  
Not applicable.  
The original contributions presented in the study are included in the article.  
We thank Zhiduoduo Technology Co., Ltd. for providing the experimental data used in this paper.  
The authors declare no conflicts of interest.  
Figure 8: Actual-scene application effect.
# A Collection of Best Practices for the Collection and Analysis of Bioacoustic Data
## 2 Species Identification
The training dataset for this classifier comprised vocalizations recorded during a 4.5-month shipboard visual and acoustic survey and included 153 different schools of dolphins recorded across a variety of behavioral states and times of day. The samples used for analysis included only schools that had visual confirmation of species identity, were composed of a single species, and had no other species within range of the hydrophone. A total of 1000 manually validated calls per species were extracted from these recordings, resulting in a large dataset to be used for training and testing the classifier. The recordings were all made using the same recording platform (a towed hydrophone array) to ensure sampling consistency, and a sampling rate (500 kHz) sufficiently high to capture each species' entire range of vocal frequencies. To reduce the likelihood that vocalizations from given individuals would be included in both training and testing datasets, all vocalizations from each individual school were included in only one or the other dataset. This classifier performed well on the test dataset, classifying 84% of schools to the correct species. Because the training and test datasets were collected using consistent methods and drew from a sample of recordings large enough to capture temporal and behavioral variability of all five species within the study area, the resulting species classifier can now be applied with confidence to document the presence and movements of these five species in the California Current. One caveat, though, is that the [PERSON] et al. [58] classifier was trained using data collected using a specific method, a towed hydrophone array, which could in theory impact the structure of the training data samples. Thus, it is yet not clear whether the classifier could be applied reliably to different locations or to data collected using different sampling methods. Before doing so, it would be helpful to test the classifier on a visually validated dataset, to determine whether the performance is consistent under different conditions.
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 5 Experiment and Analysis
### Performance Evaluation
#### 5.2.3 Time Consumption  
In Table 3, we count the time taken to complete measurements for \(N_{\text{a}}\) agents when using different methods. The results show that the elapsed time of SAL-Normal increases linearly with increasing agents and the elapsed time of the proposed SAL method does not change as the agent number increases. In SAL-Normal, the anchor serves each agent one by one, so all agents are located in turn. However, in the proposed SAL method, all agents send Resp messages to the anchor (quasi-)simultaneously, and the anchor can estimate the TOA and AOA of the agents in a single  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Errors** & **Methods** & \(N_{\text{a}}=1\) & \(N_{\text{a}}=2\) & \(N_{\text{a}}=3\) \\ \hline \multirow{2}{*}{90\% error} & SAL-Normal & 14.0 cm & 13.6 cm & 13.8 cm \\  & Proposed SAL method & 13.6 cm & 16.0 cm & 16.8 cm \\ \hline \multirow{2}{*}{Median error} & SAL-Normal & 6.8 cm & 6.5 cm & 6.7 cm \\  & Proposed SAL method & 6.5 cm & 7.3 cm & 7.5 cm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Localization error comparison in E2.  
Figure 8: The tracking results of three agents (red cross, magenta cross, and green cross) moving on the preset route (blue line).  
estimated CIR. We find that the efficiency of measurement increases as the number of agents increases when using the proposed SAL method. Therefore, our method can improve measurement efficiency in multi-agent localization scenarios while ensuring decimeter-level localization accuracy. Note that the actual workflow can be further optimized, so the time consumption of the two method can be further reduced. The table is intended to illustrate the overall difference in time consumption between the two methods.