esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
For a more detailed qualitative assessment, a scanning electron microscope (FE-SEM) (SU8000 in-line, Hitachi High-Tech, Tokyo, Japan) was employed. This instrument uses a focused beam of high-energy electrons to elucidate the external morphology (texture) based on electron-sample interactions. The advantage of SEM lies in its ability to produce images that reveal spatial variations at the surfaces of solid specimens, offering a more precise depiction of the surface roughness topography of the tested samples at an impressive magnification of 20,000\(\times\). At baseline, all samples exhibited surface roughness levels that were excessively high for clinical applications. However, after undergoing post-milling procedures, either before or after the final crystallization process, there was a significant improvement in surfaces smoothness across all samples (\(p\leq 0.05\)). For lithium disilicate (LDS), the timing of the post-milling procedures did not significantly affect surface roughness values (\(p=0.129\)). In contrast, zirconia-reinforced lithium silicate (ZLS) samples showed a noticeable difference--surfaces polished before the crystallization were significantly rougher compared to those polished after the crystallization process.
# Joint Invariants of Linear Symplectic Actions
## 5 From Joint to Differential Invariants
### Jets of Curves in Symplectic Vector Spaces  
Locally a curve in \(\mathbb{R}^{2n}\) is given as \(\boldsymbol{u}=\boldsymbol{u}(t)\) for \(t=x_{1}\) and \(\boldsymbol{u}=(x_{2},\ldots,x_{n},y_{1},\ldots,y_{n})\) in the canonical coordinates \((x_{1},x_{2},\ldots,x_{n},y_{1},\ldots,y_{n})\), \(\omega=dx_{1}\wedge dy_{1}+\cdots+dx_{n}\wedge dy_{n}\). The corresponding jet-space \(J^{\infty}(V,1)\) has coordinates \(t,\boldsymbol{u},\boldsymbol{u}_{t},\boldsymbol{u}_{tt},\ldots\), and \(J^{k}\) is the truncation of it. For instance, \(J^{1}(V,1)=\mathbb{R}^{4n-1}(t,\boldsymbol{u},\boldsymbol{u}_{t})\). Please note that \(\dim J^{k}(V,1)=2n+k(2n-1)\).  
In the case of dimension \(2n=2\), the jet-space is \(J^{k}(V,1)=\mathbb{R}^{k+2}(x,y,y_{x},\ldots,y_{x\_x})\). Here \(G=\operatorname{Sp}(2,\mathbb{R})\) has an open orbit in \(J^{1}(V,1)\), and the first differential invariant is of order 2:  
\[I_{2}=\frac{y_{xx}}{(xy_{x}-y)^{3}}.\]  
There is also an invariant derivation (\(\mathcal{D}_{x}\) is the total derivative with respect to \(x\))  
\[\
abla=\frac{1}{xy_{x}-y}\mathcal{D}_{x}.\]  
By differentiation we get new differential invariants \(I_{3}=\
abla I_{2}\), \(I_{4}=\
abla^{2}I_{2}\), etc. The entire algebra of differential invariants is free:  
\[\mathcal{A}_{G}=\langle I_{2}\,;\,\
abla\rangle.\]  
In the general case we denote the canonical coordinates on \(V=\mathbb{R}^{2n}\) by \((t,\boldsymbol{x},y,\boldsymbol{z})\), where \(\boldsymbol{x}\) and \(\boldsymbol{z}\) and \((n-1)\)-dimensional vectors. \(G=\operatorname{Sp}(2n,\mathbb{R})\) acts on \(J^{\infty}(V,1)\). The invariant derivation is equal to  
\[\
abla=\frac{1}{(ty_{t}-y+\boldsymbol{x}\boldsymbol{z}_{t}-\boldsymbol{x} \boldsymbol{z})}\mathcal{D}_{t}.\]  
and the first differential invariant of order 2 is  
\[I_{2}=\frac{\boldsymbol{x}\boldsymbol{z}_{tt}-\boldsymbol{x}_{tt}\boldsymbol{ z}_{t}+y_{tt}}{(ty_{t}-y+\boldsymbol{x}\boldsymbol{z}_{t}-\boldsymbol{x} \boldsymbol{z})^{3}}.\]  
There is one invariant \(I_{3}\) of order 3 independent of \(I_{2}\), \(\
abla(I_{2})\), one invariant \(I_{4}\) of order 4 independent of \(I_{2}\), \(\
abla(I_{2})\), \(I_{3}\), \(\
abla^{2}(I_{2})\), \(\
abla(I_{3})\), and so on up to order \(2n\). Then the algebra of differential invariants of \(G\) is freely generated ([7], SS4) so:  
\[\mathcal{A}_{G}=\langle I_{2},I_{3},\ldots,I_{2n}\,;\,\
abla\rangle.\]
## 4 Results
### Evaluation Methodology  
We evaluate our contributions through four main experiments. First, we assess the effectiveness of our reconstruction pipeline using a single-LiDAR, single-camera setup on the Argoverse dataset [16], comparing it directly against Semantic [15] and BAAM [13]. Second, we measure the classification accuracy of our proposed pose quality metric. Third, we analyze the robustness-accuracy tradeoff between our method (MM-VSM) and BAAM, which was selected due to its state-of-the-art neural-based 6 DOF pose estimation. Finally, we conduct ablation studies to isolate and quantify the impact of LiDAR loss versus multi-view reconstruction.  
For consistency with prior evaluations [15], we primarily utilize the Argoverse dataset, as it provides annotated images, LiDAR data, and tracking labels. We also conduct further ablations using data collected from the ZalaZONE proving ground to specifically evaluate the impact of LiDAR loss in the presence of multi-view data.  
As our method is not a simple object detector--it can only estimate poses of vehicles it has reconstructed first--the evaluation metrics and methodology are non-trivial. We only wish to evaluate whether the accuracy of the pose estimation as object detection (meaning object presence detection) depends largely on our input 2D semantic keypoints and whetherr or not the model was reconstructed previously. In our experiments, therefore, we evaluate on the subset of the datasets that was detected by OpenPifPaf and BAAM, and we compare which estimates the detected object's pose most accurately. BAAM was chosen as the primary baseline due to its superior performance compared to other classical geometry-based approaches (see [15]) or deep learning-based approaches [14; 37; 44]. These either rely on geometrical assumptions which are not always true or are outperformed by BAAM, or their source is not available.  
In summary, direct comparisons with our previous method, Semantic [15], and BAAM [13] clearly illustrate our improvements and the advantages and disadvantages of our method.
# Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management
## 1 Introduction
Empirical studies have suggested that LSSVMs are at least as accurate as conventional SVMs but with higher computing efficiency [27]. To improve the predictive accuracy of the LSSVM model, the parameters of the LSSVM must be optimized because the performance of the LSSVM depends on the selected regularization parameter (C) and the kernel function parameter (\(\sigma\)), which are known as LSSVM hyperparameters. Modern evolutionary optimization (EA) techniques appear to be more efficient in solving constrained optimization problems because of their ability to seek the global optimal solution [28]. Researchers always seek to improve the effectiveness of the methods that they use. Metaheuristics have become a popular approach in tackling the complexity of practical optimization problems [29; 30; 31; 32; 33]. Owing to the continuous development of artificial intelligence (AI) technology, many intelligent algorithms are now used in parameter optimization, including the genetic algorithm (GA) [34] and the particle swarm optimization algorithm (PSO) [35]. Many studies have also shown that the firefly algorithm (FA) can solve optimization problems more efficiently than can conventional algorithms, including GA and PSO [36; 37]. In this study, metaheuristic components are incorporated into the standard FA to improve its ability to find the optimal solution. The efficiency of the optimized method (i.e., enhanced FA) was verified using many classic benchmark functions. Therefore, a new hybrid classification model (Optimized-OAO-LSSVM) that combines the OAO algorithm for decomposition and the enhanced FA to optimize the hyperparameters for solving multi-class engineering problems is established. To validate the accuracy of prediction of the proposed Optimized-OAO-LSSVM model, its prediction performance was compared with that of previously proposed methods and other multi-class classification models. After the optimized classification model is verified, an intelligent and user-friendly system that can classify multi-class data in the fields of civil and construction engineering is developed. The rest of this study is organized as follows. Section 2 introduces the context of this investigation by reviewing the relevant literature. Section 3 then describes all methods that are used to develop the proposed system and to establish its effectiveness. Section 4 elucidates the metaheuristic optimized multi-level classification system. Section 5 validates the system using case studies in the areas of civil engineering and construction management. Section 6 draws conclusions and presents the contributions of this study.
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 3 Selected Use Cases
### Tele-Operated Driving
#### 3.1.1 Automotive Requirements  
The requirements for tele-operated driving from an automotive perspective are demanding. As the vehicle is controlled remotely, it is of great that the sensor suite provides sufficient information to enable safe tele-operation and overcome above challenges of reduced situational awareness. A minimal requirement is 360\({}^{\circ}\) coverage of the vehicle's surroundings with camera images. An additional hardware requirement is drive-by-wire functionality to execute the control commands that are generated remotely. ToD is meant to be a fallback procedure for automated driving. Thus, it is only applicable for vehicles with AD functionality. Although the automation is not running during the tele-operation, a safety concept such as an emergency braking system needs to be active, e.g., to safeguard the case of loss of the mobile data connection. As indicated above, another great challenge for ToD technology is latency. It creates a requirement for each component. For instance, times for processing sensor data and video compression algorithms, or actuation delay needs to be kept as small as possible in order to provide information with minimum possible age to the tele-operator.
# Growing Kratky Basil in Trombe Wall Cavity: Energy Performance
## 4 Discussion
### Indirect Benefits of the Green Trombe Walls
#### 4.4.3 Attainable CO\({}_{2}\) Offset  
The carbon dioxide offset achieved by the Trombe wall can be estimated through two approaches: by calculating the carbon sequestered in the biomass of plants cultivated within the TW and by comparing the CO\({}_{2}\) concentrations within the TW cavity before and after the experiment. The first metric represents the minimal offset, while the second provides a more comprehensive estimate of the TW's overall carbon mitigation potential during its operation.  
Hydrooponically grown basil plants, composed of approximately 90% water, derive about 35% of their dry weight from carbon [65; 66]. Using the experimental yields of 20.55 kg net and 86.72 kg of gross yields per year, the corresponding carbon sequestered is calculated as 0.71925 kg (equivalent to 2.637 kg of CO\({}_{2}\)) and 3.036 kg (equivalent to 11.13 kg of CO\({}_{2}\)), respectively.  
CO\({}_{2}\) concentration within the TW cavity was also monitored to estimate the effect of plant presence on the air composition (Figure 15). Over a typical 24 h period, daily CO\({}_{2}\) concentrations were recorded with means of 439.35 ppm during the experiment (with plants) and 451.037 ppm (without plants). This difference indicates that the TW space retains approximately 0.00916 m\({}^{3}\) less CO\({}_{2}\) every day in the presence of plants, translating to an annual offset of 3.345 m\({}^{3}\) of CO\({}_{2}\), equivalent to 6.62 kg of CO\({}_{2}\).These results highlight the potential for green Trombe walls to serve as functional carbon sinks while maintaining their role in passive building systems.  
Figure 15: CO\({}_{2}\) concentrations in the Trombe wall during a typical baseline and experimental day.
## 3 System Design
### System Design Overview
The core algorithm governing a relay node's transmission behavior is detailed below (Algorithm 1):  
```
1:procedureRelayTransmission(\(\mathit{upstream\_addrdr}\),\(\mathit{downstream\_addrdr}\))
2:\(\mathit{listener}\leftarrow\) CreateSRTListener(\(\mathit{upstream\_addrdr}\))
3:\(\mathit{caller}\leftarrow\) CreateSRTCaller(\(\mathit{downstream\_addrdr}\))
4:while\(\mathit{connection\_active}\)do
5:\(\mathit{packet}\leftarrow\mathit{listener.receive}()\)
6:if\(\mathit{packet}\
eq\mathit{null}\)then
7:\(\mathit{caller.send}(\mathit{packet})\)
8:endif
9: CheckConnectionHealth()
10:endwhile
11:endprocedure
```  
**Algorithm 1** Node Relay Transmission Algorithm  
Based on the P2P chain and IPv6 foundation, the system employs a layered protocol stack design to organize its functionalities. At the application layer, responsibilities include video encoding/decoding and playback control. The SRT layer then provides crucial services such as reliable transport, congestion control, and secure data transfer. Beneath SRT, the UDP layer handles the underlying datagram transmission. Finally, the IPv6 layer offers fundamental network addressing and routing capabilities, forming the core communication backbone. While the data transmission architecture is fundamentally decentralized, the system still requires a lightweight coordination mechanism to maintain the node list and topology information. The coordination server is designed with a RESTful API. It primarily performs node registration and deregistration via POST/DELETE /api/nodes endpoints, topology querying and updates using GET/PUT /api/topology, and health status monitoring accessible via GET /api/health. This setup ensures efficient management of the dynamic network without centralizing the actual data stream. To further enhance system robustness and efficiency, a Load Balancing Mechanism is incorporated. This mechanism prevents any single node from bearing an excessive forwarding load by supporting parallel transmission across multiple chains. When a specific link is detected to be overloaded, the coordination server can dynamically create new transmission links to distribute the load. Load assessment metrics, which inform these decisions, include CPU utilization, memory consumption, network bandwidth usage, and packet loss rate, enabling proactive and adaptive load management.
# Scan Matching by Cross-Correlation and Differential Evolution
## 5 Experimental Evaluation  
A variety of computer experiments were carried out to evaluate the ability of DE with cross-correlation as fitness function to perform point-based laser scan matching for self-localization of an autonomous mobile robot. The experiments used a series of simulated laser scans of an indoor environment describing the sensor readings of a virtual mobile robot moving through a room. The location data, corresponding to the simulated robot trajectory, was used as the ground truth.
# Assessment of Energy-Saving Practices of the Hospitality Industry in Macau
## 3 Results
### Green Leadership and Innovation (GLI)
Although it is not compulsory for a hotel to do carbon audits, it is found that 35.7% of hotels have done so. Specifically, 63.6% of 5-star hotels do so to help them improve the energy policy and greenhouse gas emissions, which indicates that the managers of these hotels in Macau, especially the 5-star hotels, have higher environmental protection and energy-saving awareness. For instance, since 2008, through a series of measures of energy saving and environmental protection, the MGM Grand Macau successfully reduced 24% of total energy consumption, which is about 13.5 million kWh electricity utilization, leading to an indirect reduction of 9300 tons of carbon dioxide emissions [41]. However, it should be pointed out that only 16.6% of 3-star hotels do that, and no 2-star hotel does it. Hence, more efforts should be made for the hotels to do carbon audits so as to improve energy and environmental performance. Staffs' energy-saving and environmental protection awareness reflect many aspects of energy utilization status, such as people's knowledge, personal consideration and behaviors, and the attitudes towards sustainable energy utilization and environmental protection. Since the contribution of staffs' awareness is enormous for energy efficiency improvement [31], it is very meaningful for a hotel to provide training for their workers to help the crews to accomplish their daily work in an energy-efficient manner as required by GLIC3 (staff training and proposal adoption). For instance, as significant amounts of water are used to clean guestrooms and public areas, Altira Macau trains its employees regularly and encourages them to reduce water use as far as possible by making use of alternative cleaning methods, like the use of alcohol-based hand cleaner. However, only 32.1% of hotels in Macau have such training programs, especially there is no 2-star hotel that has such a program [42]. It is very meaningful for a hotel in Macau to apply for financial support from a government fund to improve energy-efficiency as indicated by GLIC4 (EPEC fund). The government of Macau SAR set up the \"environmental protection and energy conservation fund\" in 2011 to promote energy saving, environmental protection, and to encourage commercial enterprises and social organizations to use facilities and equipment that are environmentally friendly and efficient in energy and water saving [33]. In this study, it is found that 82.1% of hotels have submitted proposals or intend to apply for such funding for the purchase of energy conservation devices. According to statistical reports [43], up to May 2014, the government received about 5000 proposals, and 240 million MOP have been granted. The money was mainly used to replace T8 bulbs and air conditioners by T5 bulbs and energy-saving air conditioners. It is noticed that some of the 5-star hotels have already installed such devices when they were built, which moderately explains why the 5-star hotels have a relatively lower adoption rate in this index than the average one.
## 4 Results and Analysis of the Experimental Results
### Mengzi-BiLSTM-Crf
#### 4.1.5 Ablation Experiment  
Through Table 4, it has been observed that in the context of BiLSTM-CRF, the BiLSTM sequentially models the vectors encoded by the pre-trained model for the input sequence, effectively capturing continuous sequential information. This type of continuous sequential information proves to be beneficial for entity recognition, resulting in favorable outcomes in the dataset.  
Figure 6: Named entity F1 value of different tags under Weibo dataset.
## 2 Materials and Methods  
### MPI Systems  
Different imaging device-reconstruction algorithm-correction combinations were considered. For data acquisition the following imaging devices were used: two SPECT/CT Symbia T2 devices (Siemens Healthineers, Erlangen, Germany) with low-energy high-resolution (LEHR) collimators, and one cardio-centric SPECT/CT Symbia Intevo Excel(Siemens Healthineers, Erlangen, Germany) and one cardio-centric Symbia Intevo device, both with IQ SPECT and SmartZoom collimators. Clinical acquisition protocols were used (Table 1). Reconstruction parameters were selected according to the recommended algorithm settings. The acquisition protocol for the Symbia T2 included SPECT imaging only. Raw SPECT data were reconstructed with the IRR algorithm (Flash3D, Siemens Medical Solutions). A double-energy window was used for imaging, allowing the implementation of the SC. Reconstruction settings were selected based on the matrix size, pixel size, and number of projections used for acquisitions: 15 iterations, 8 subsets and a 13.2 mm Gaussian post-filter (pixel size 6.6 mm).  
IQ SPECT data acquisition included both the SPECT and CT. The latter was performed using the following acquisition parameters: 130 kV, reference tube current exposure time product 55 mAs (Care Dose 4D, Siemens Healthineers), collimation 2 \(\times\) 4 mm, pitch 1.0, slice 5 mm. The image data obtained by this device were reconstructed with IRR algorithm IQ Flash 3D with 10 iterations, 3 subsets and 10 mm FWHM Gaussian post-filter (pixel size 4.8 mm), either with both SC and AC or correction-free.  
Overall, four different imaging device-reconstruction algorithm-correction combinations, hereinafter referred as _MPI systems_, were evaluated: SPECT with IRR (SPECT-IRR), SPECT with IRR and SC (SPECT-IRR-SC), cardio-centric IQ SPECT with IRR, SC, and AC (IQ-IRR-SC-AC), and correction-free cardio-centric IQ SPECT with IRR (IQ-IRR).
## 3 Results and Discussion
### Mode-I Interlaminar Fracture Toughness Measurements  
The interlaminar fracture toughness was determined in terms of the Mode-I strain energy release rate, in propagation (\(\mathrm{G_{I_{c,PROP}}}\)). The \(\mathrm{G_{I_{c}}}\) load versus displacement representative curves for both reference and \(\mathrm{PCF_{4.25\,W}}\) samples are shown in Figure 11. For the calculation of the strain energy release rate, Equation (5) was used. All the values summarized in Table 3 were based on Mode-I fracture with a pre-crack of 50 mm. The comparative values obtained from \(\mathrm{G_{I_{c}}}\) tests are illustrated in Figure 12a. From the results, it can be seen that the fabricated laminates preserved their interlaminar fracture toughness, as the \(\mathrm{G_{I_{c,PROP}}}\) strain energy release rate values were almost similar--1268 \(\pm\) 56 J m\({}^{-2}\) and 1254 \(\pm\) 80 J m\({}^{-2}\) for reference and \(\mathrm{PCF_{4.25\,W}}\) sample, respectively. Furthermore, the propagation resistance curves (R-curves) in Figure 12b, display representative curves of Mode I interlaminar fracture toughness as a function of the delamination length observed for both the reference and \(\mathrm{PCF_{4.25\,W}}\) sample. The experimental data showed that the values of \(\mathrm{G_{I_{c}}}\) were gradually increased within the crack growth phase between 50-60 mm and reached steady-state fracture toughness values at delamination length values above 60 mm.  
Figure 10: Electrical conductivity (\(\sigma\)) of all irradiated yams.