esa-sceva/satcom-chunk-collection huggingface dataset:
# Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)
## 2 Method
### BEM Importing Software
#### 2.2.3 Cypethern He  
Initially, three options for BEM software were considered to import IFC: CYPETHERM HE, Space Boundary Tool (SBT), and Open Studio. Nevertheless, only CYPETHERM HE was actually available. The SBT software, from the Lawrence Berkeley National Laboratory, is no longer updated (since 2014). On the other hand, Open Studio can import gbXML, as was previously explained, but when trying to import IFC files, an intermediate program called BIMserver was necessary. The installation process of the BIMserver required a high level of expertise and, therefore, this workflow was discarded.  
CYPETHERM HE is continuously updated and its interoperability between BIM and BEM is an easy and intuitive process. Despite the fact that an intermediate software is needed to transfer the IFC data to CYPETHERM HE (Open BIM Analytical Model), the workflow is simple and does not require expert knowledge.  
In the CYPETHERM HE import interface, it is possible to select a typology directory, which refers to the project materials. In this case, as it was not possible to transfer the envelope materials and its thermal properties from Revit and there was no typology directory created in CYPETHERM HE, it was necessary to generate all the construction systems in this tool. Once this typology is generated, it can be saved and be used in any other project.  
Before importing the IFC file into the BEM tool, the Open BIM Analytical Model software was used to create an analytical model and verify the extracted data, as it refers to its dimensions, geometry, materials, and any inconsistency between the building geometry elements. As a result, the IFC data of the residential case was extracted correctly from the BIM authoring tool. All the 13 thermal zones and the envelope materials were transferred. Furthermore, the model was constructed properly and its dimensions were the same as in the BIM (see Figure 4).  
On the other hand, the industrial warehouse model in the Open BIM Analytical Model software seemed to be constructed properly, but only 19 thermal zones were imported. It was found out that two thermal zones of the first floor were missing in the IFC data. No reason was found, as to why these zones were not extracted, as the same procedure was followed within all the thermal zones.  
In order to cast light on the problem, the IFC of the warehouse was imported into the Solibri Anywhere software and the same issues appeared as in the Open BIM Analytical Model. The reason for this seemed to be connected with the generation of the model from the IFC as a consequence of a wrong transfer of the BIM data to the IFC format and, therefore, was not dependent on the type of software used. This is clearly an interoperability problem related to the IFC format.
## 4 Problem Definition and Modeling  
This section presents a detailed introduction to the programming model of CSLP, encompassing the objective function and constraints.  
The problem of charging station location can be framed as an optimization problem, where the objective function \(gain(p)\) is defined to identify the optimal plan \(p^{*}\) that best fulfills our expectations. The objective function \(gain(p)\) in our study comprises three components: the profit term \(profit(p)\), the cost term \(cost(p)\), and the fairness term \(fairness(p)\). Each term within the objective function will be discussed in detail below.
# Local Phase Segregation Induced by Ion Milling in 2:17-Type Sm-Co Based Magnets
## 2 Materials and Methods  
A commercial magnet with a nominal composition of Sm\({}_{13}\)Gd\({}_{12}\)Co\({}_{50}\)Cu\({}_{8.5}\)Fe\({}_{13}\)Zr\({}_{3.5}\) (wt.%) was provided by the Southwest Applied Magnetism Research Institute of China (Mianyang, China). The ingot was prepared using the conventional powder metallurgy method by induction melting high purity (99.9%) constituent elements under the protection of high purity argon with 5 wt.% more Sm added to compensate for the Sm volatilization. Then, the as-cast ingot was crushed into powders with an average size of \(\sim\)4-6 \(\upmu\)m by jaw crusher and jet-milling. The obtained powders were pressed under \(\sim\)150 MPa in a magnetic field higher than 10 kOe, followed by isostatic compaction under \(\sim\)200 MPa. Subsequently, the green compact was sintered for \(\sim\)1 h at \(\sim\)1215 \({}^{\circ}\)C, and solution-treated for \(\sim\)4 h at \(\sim\)1175 \({}^{\circ}\)C, followed by cooling to room temperature using high-speed argon flow. Then, the solution-treated magnet was aged for 24 h at \(\sim\)810 \({}^{\circ}\)C and slow cooled to room temperature.  
The average structure of the powder magnet was characterized using a Rigaku X-ray diffractometer with Cu K\({}_{\alpha}\) radiation. Two slices were cut from the final magnet along the [001] axis for TEM specimen preparation. The TEM specimens were prepared by standard mechanical polishing, dimpling to a thickness of 20-30 \(\upmu\)m, and followed by ion milling by Ar\({}^{+}\) in a Gatan 691 precision ion polishing system (PIPS). One specimen (named S1) was milled at 5 keV for each gun with an incident angle of 10\({}^{\circ}\) until hole perforation was achieved. The other (named S2) was also milled under the same conditions as specimen S1 before hole perforation, and then milled at 3 keV for each gun with an incident angle of 6\({}^{\circ}\) for 15 min. The phase structure and elemental distribution were identified using a JEOL 2100F TEM (JEOL, Tokyo, Japan) and a Thermo Fisher Talos F200S high-resolution scanning TEM (HR-STEM) (Thermo Fisher, Waltham, MA, USA) equipped with an EDS detector. The HR-TEM images were analyzed using digital micrography software (Version 3.22.1461.0, Gatan).
## 5 Discussion  
### The Performance Improvement of the Baseline Model after Applying the PDEC Framework  
In Figure 4, it is evident that PDEC exhibits a notable enhancement in performance compared with traditional baseline methods on the FB15K-237 dataset. Notably, the TransE and DistMult methods achieve the most significant improvements, both achieving approximately 20% improvement.  
Figure 8: The correlation between granularity of predicate decomposition and KGC performance (NELL-995).  
Figure 7: The correlation between granularity of predicate decomposition and KGC performance (FB15K-237).  
Figure 9: The correlation between granularity of predicate decomposition and KGC performance (YAGO3-10).  
As Figure 5 illustrates, PDEC demonstrates the most significant performance enhancement on the YAGO3-10 dataset when compared with traditional methods. Among the various methods, the CompIEX approach demonstrates the most remarkable improvement, achieving approximately 85% improvement, closely followed by DistMult with approximately 53% improvement.  
On the NELL-995 dataset, Figure 6 reveals that PDEC demonstrates the least improvement over traditional methods. Nevertheless, the TransE and DistMult methods still exhibit the most notable improvements, achieving 13.5% and 14%, respectively.  
As shown in Table 2, drug rediscovery experiments have shown that the PDEC framework can improve KG reasoning performance on larger datasets. Among them, TransE achieved the most significant performance improvement after adopting the PDEC framework, reaching 13.8%. In addition, PDEC achieved performance improvements of 7.9%, 2.0%, and 5.2% for DistMult, RotatE, and CompGCN, respectively.
# A Channel Compensation Technique Based on Frequency-Hopping Binary Offset Carrier Modulated Signal
## 4 Simulation Results
In the meantime, the latter should not involve the code tracking process, which otherwise behaves as extra noise during the demodulation of the FH-BOC signal. The performance on the BER over different power ratios \(m^{2}\):\(m^{2}\) is depicted in Figure 16.  
Figure 14: Acquisition performance on false positive and false negative error. (**a**) Type I Error Rate. (**b**) Type II Error Rate. Figure 15: Tracking performance on key parameters measurement. (**a**) Tracking on Phase Parameter. (**b**) Tracking on Code Delay Parameter. (**c**) Tracking on Frequency Parameter. Generally, it can be identified that the BER performance behaves better, with the \(Eb/No\) and power ratio increasing. However, an exceptional one is the power ratio of \(m^{2}\):\(n^{2}\) = 9:1 for it holds near BER of \(10^{-1}\), regardless of the value of \(Eb/No\), mostly due to an inappropriate power ratio, since only 10% power has been allocated to FH-BOC signal, which brings acquisition and tracking loop difficulty for achieving detection and signal alignment. As a consequence, such an unfavorable condition causes OFDM symbol demodulation failure. Additionally, the power ratio equal to 7:1 is the one outperforms the rest, which almost tells us that an appropriate power allocation has been found. In the meantime, we can identify that the most prominent one does not always beat the BER performance, since below \(Eb/No\) = 15 dB, it behaves worse than others, e.g., the power ratio of 5:1 also behaves worse than either 3:1 or 1:1 below the condition \(Eb/No\) = 12 dB. Such performance capture emerges at smaller \(Eb/No\) for lower power ratio as an abnormal phenomenon, mainly because the detection scheme of the FH-BOC signal demands that it should surpass a certain energy threshold. In other words, when less energy allocation belongs to the FH-BOC signal as a power ratio raising, in order to reach the signal acquisition threshold, a larger \(Eb/No\) is required. As the comparison to our proposed signal on BER performance analysis, a pure OFDM signal denoted as \"other\" in Figure 16 has been tested in the same simulation environment, with an original pilot symbols arrangement and correlated signal compensation algorithm on frequency and phase. It can be evaluated that our proposed signal, the one with the optimal power ratio allocation, behaves better than the pilot-inserted OFDM signal when \(Eb/No\) > 18 dB. At the BER level of \(10^{-5}\), our optimal signal requires about \(8 dB\) less on \(Eb/No\) in the performance comparison.
# Efficient Health Data Transmission Method in a Wireless Body Area Network for Rural Elderly
## 4 Opportunistic Routing Protocol for a WBAN Based on a Time-Varying Link
### Expected Anycast Transmission Cost  
The transmission probability between \(v_{i}\) and \(v_{j}\) is calculated according to Equation (4). In the candidate set of \(v_{i}\), the probabilities of each candidate node receiving data packets are independent of each other, so the probability of \(v_{i}\) successfully transmitting data is:  
\[p_{i}^{t}=1-\prod\limits_{j\in Ca_{i}}(1-p_{ij}^{t}) \tag{8}\]  
The expected transmission time of one anycast transmission between \(v_{i}\) and the candidate set \(Ca_{i}\) can be calculated by the following:  
\[de_{iCa}=\frac{1}{1-\prod\limits_{j\in Ca_{i}}(1-p_{ij}^{t})} \tag{9}\]  
It can be seen from the above that the priority of monitoring data is different. For emergency data, it should be ensured that they reach the sink as soon as possible. The energy in the WBAN is limited. If all nodes choose the routing with the minimum expected transmission time, energy consumption may be too fast and uneven, resulting in a sharp decline in the network performance. Therefore, for periodic data with low real-time requirements, the residual energy should be considered when selecting the next hop:  
\[de_{iCa}=\begin{cases}\sum\limits_{j=1}^{|Ca_{i}|}p_{ij}^{t}\frac{LCS_{ij}}{1- l}\\ \frac{1}{1-l}\prod\limits_{j\in Ca_{i}}(1-p_{ij}^{t}),\\ \frac{|Ca_{i}|}{1-l}\frac{p_{ij}^{t}(\frac{E_{0}}{E_{j}}+\frac{LCS_{ij}}{1-l})} {1-l},\\ \frac{|C_{i}|}{1-l}\prod\limits_{j\in Ca_{i}}(1-p_{ij}^{t}),\\ \end{cases}\quad\phi=0 \tag{10}\]
## 3 Experimental Results
### Comparison of Multi-Focus Image (i.e., Clock)  
The evaluation of the first multi-focus image is the clock, illustrated in Figure 9. Figure 9a represents the original image. Figure 9b,c illustrate left-focused and right-focused images, respectively. The term \"left-focused image\" refers to the fact that the image's left side is focused while the right side is not. A \"right-focused image\" is one in which the image's right side is focused, but its left side is not. Figure 9d shows that the all-in-focus image is created when the approach is implemented. _E(F)_, _AG_, _CC_, _QABF_, _SSIM_, _QE_, _SF_, and _QW_ are calculated to assess the proposed methodology performance. Finally, the performance of the proposed approach is compared to that of other methods previously published in the literature. The results of the comparison are shown in Tables 3-8 of the report. The letter X indicates that metrics are not calculated for the article depicted in the image. According to the literature [21; 22; 24; 27; 28; 30], the proposed method is more successful than those approaches, and the best outcomes of methods are indicated in bold.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Input Images** & _RMSE_ & _PSNR_ & _SD_ & _SF_ & _SSIM_ & _E(F)_ & _QABF_ & _AG_ & _CC_ & _QE_ \\ \hline Book & 9.8416 & 38.2341 & 60.9389 & 25.0770 & 0.9085 & 7.4362 & 0.9376 & 13.0210 & 0.9892 & 0.9165 \\ Clock & 4.4502 & 41.6810 & 51.1783 & 9.2224 & 0.9576 & 7.4180 & 0.9835 & 6.1301 & 0.9816 & 0.8997 \\ Flower & 5.1433 & 41.0524 & 39.6769 & 22.3338 & 0.9753 & 7.2315 & 0.9915 & 14.7327 & 0.9669 & 0.9058 \\ Lab & 4.0437 & 42.0970 & 47.7172 & 13.4651 & 0.9639 & 7.1517 & 0.9908 & 7.5892 & 0.9776 & 0.9196 \\ Leaf & 11.9434 & 37.3935 & 46.7165 & 32.0547 & 0.7898 & 7.4633 & 0.9582 & 26.9816 & 0.9219 & 0.8305 \\ Flowerpot & 5.3262 & 40.9006 & 53.5593 & 24.1377 & 0.9638 & 7.5211 & 0.9908 & 14.4599 & 0.9750 & 0.9131 \\ Pepsi & 2.7651 & 43.7477 & 45.6264 & 14.2884 & 0.9730 & 7.1431 & 0.9955 & 8.5574 & 0.9819 & 0.9439 \\ Balloon & 1.7621 & 45.7045 & 45.5287 & 21.0675 & 0.9904 & 7.4875 & 0.9990 & 10.2507 & 0.9840 & 0.9534 \\ Leopard & 2.1108 & 44.9204 & 66.0408 & 20.0953 & 0.9099 & 7.4777 & 0.9988 & 13.6941 & 0.9893 & 0.9491 \\ Wine & 10.0433 & 38.1460 & 72.2601 & 51.7072 & 0.8938 & 7.6116 & 0.9784 & 35.6507 & 0.9492 & 0.8705 \\ Craft & 4.6617 & 41.4794 & 31.8556 & 13.4799 & 0.9374 & 6.5221 & 0.9823 & 7.5230 & 0.9660 & 0.8807 \\ Desk & 4.8765 & 41.2838 & 47.6417 & 16.1341 & 0.9448 & 7.3882 & 0.9871 & 9.4234 & 0.9624 & 0.9216 \\ \hline \hline \end{tabular}
\end{table}
Table 1: For certain trailed images, the outcomes of the proposed method.  
Figure 9: (Clock): (**a**) original image; (**b,c**) multi-focus input images; and (**d**) proposed fusion.
## 4 Simulation Results
### Comparative Analysis  
To justify the effectiveness of the proposed MGOA-WAVLCN, it was compared with one existing method, namely DYRP-VLC [16]. For analyzing the performances, the DYRP-VLC [16] was also implemented and simulated for the same specifications mentioned in the Table 2.  
Table 7 shows the performance analysis for the comparison between the DYRP-VLC [16] with MGOA-WAVLCN. Here, the comparison is taken for varying nodes (i.e., 50, 100, 250, and 500). From the comparison, it is concluded that the MGOA-WAVLCN outperforms the DYRP-VLC [16], because the DYRP-VLC [16] was considered only the hop count during the route generation. Moreover, the routing overhead of the DYRP-VLC [16] was high, because it continuously transmitted the request messages for a certain number of times to identify the transmission path. However, the MGOA-WAVLCN considers important parameters in the fitness function such as transmitting optical power of nodes, channel gain, number of hops, and distance. Therefore, the MGOA with an appropriate fitness parameter is used to select an optimal node during the route generation. In the routing path generation, the failure node is avoided by considering the optical power of the nodes in the MGOA which was used to improve the PDR.
## 8 Discussion
### Need for Standardization  
* _Uniform Protocols:_ The smooth cooperation of many stakeholders in agriculture is hampered by the lack of established protocols. Creating consistent standards for the use of blockchain technology guarantees that all parties function under the same framework, promoting consistency, interoperability, and confidence.
* _Regulatory Compliance:_ There are a number of regulations that apply to the agricultural industry. To guarantee that blockchain solutions follow these rules and enable compliance and legal recognition, standardization is required. The navigation of regulatory environments with more clarity and assurance is made easier by using a standardized strategy.
* _Data Security and Privacy:_ Ensuring a consistent level of data security and privacy across the agricultural supply chain requires standardization. Defined standards may include data ownership, access restrictions, and encryption techniques, guaranteeing that private agricultural data are always safeguarded.  
* _Smart Contract Standards:_ Standardized coding techniques are required for the use of smart contracts in agriculture. Having a standardized set of smart contract requirements improves their auditability, security, and dependability, which raises the overall efficacy of blockchain-based solutions.
## 3 Treatment Approaches for Gaseous VOC Emissions
[PERSON] _et al._[11] reported biofilter removal efficiencies up to 97% for VOCs generated in the composting process of different organic wastes at pilot plant scale with loading rates ranging from 0.55 to 40 g C m\({}^{-3}\) biofilter h\({}^{-1}\). Removal efficiencies were highly dependent on the composted waste. The same authors also pointed a biofilter basal emission of VOCs of approximately 50 mg of C m\({}^{-3}\). However, in the literature, VOC biofiltation is frequently studied in laboratory scale biofilters using synthetic gases with two or three mixed compounds or even a single compound [47, 48], which can be very different from those conditions found in full-scale facilities, as previously commented. Another aspect to comment in biofilter operation and maintenance at an industrial level is the need for attention to packing material needs for a correct performance of the gas treatment equipment. In fact, watering of the material is needed, as well as a periodic replacement of the biofilter material. [PERSON] _et al._[18] reported real data on two full scale biofilters treating gases from the OFMSW composting process during filtering material replacement. Old biofilter material showed lower removal efficiencies than the new material. The average VOC removal efficiencies with the old material for the two biofilters studied were 42 and 65%, whereas the average values of all data with new material were 74 and 71%. These efficiencies correspond to average elimination capacities of 11 and 8.6 g C m\({}^{-3}\) biofilter h\({}^{-1}\) for the old material and 17.1 and 27 g C m\({}^{-3}\) biofilter h\({}^{-1}\) for the new material. These results indicate that the biofilter performance was improved as a result of material replacement. In one of the two biofilters the authors observed the pattern reported by [PERSON] _et al._[45] in relation to VOC removal in biofilters: a first stage of dominance of the adsorption process followed by a decrease of the removal efficiency attributable to the saturation of the adsorption/absorption capacity and to the microorganisms acclimation period and, finally, an increase in the removal efficiency because of the biodegradation dominance [49]. For the second biofilter, the removal efficiency followed a similar pattern to the applied VOC loading rate during the entire period of the study (before and after biofilter material replacement). Even though biofilters are widely implemented in full scale treatment plants for organic waste treatment facilities, the complete removal of VOC is difficult to achieve [20]. Proper maintenance operations are thus needed to maximize biofilter performance. Figure 1: Scheme of a biofilter.
## 3 Results
### Four-Class Problems  
#### 3.3.1 Multi-Class MMFT Four-Class Cross-Session Classification  
The impact of multi-class cross-subject classification on transfer learning performance is further investigated based on a four-class cross-session classification problem [43]. To validate the classification performance, multi-class MMFT is compared with MEKT and ML algorithms. To facilitate the investigation, nine four-class EEG sessions are used. Subsequently, when multi-class MMFT is employed, eight sessions are assigned as source domains, while a single session is assigned as a TD, and all nine sessions take a turn as a target domain. Moreover, when a traditional BCI is implemented, both LDA and RegTree are trained using eight sessions, while a single session is used to predict the classifiers, respectively.  
Consequently, a highest classification accuracy of 99% was recorded across Se7 as a TD and (Se1-Se6 and Se8-Se9) as SDs when MEKT is employed, while a CA of 70.5% was noted across Se4 as a target domain and (Se1-Se3 and Se5-Se9) as source domains when multi-class MMFT is employed. Compared with ML algorithms, an inferior CA of 43%, which is the best performance achieved by LDA, is noted when Se9 is used to test and (Se1-Se8) is used to train. In a similar manner, RegTree did not show traits of any improvement, with a highest accuracy of 40% achieved when Se5 was used to test and (Se1-Se4 and Se6-Se9) was used to train the classifiers.  
Notably, a combination of (Se1-Se8) as source domains proved to contain non-related sources. Hence, an inferior classification performance of 24.5% is observed across Se9 when multi-class MMFT is employed. The complexity of a four-class problem and a combination of non-related sources proved to pose a significant challenge for MEKT, with poor performances recorded across Se2-Se6 as TDs. Furthermore, LDA achieved a lowest accuracy of 31% when Se4 was used to test and (Se1-Se3 and Se5-Se9) was used to train the classifier, while RegTree achieved a lowest accuracy of 27% when Se8 was used to test and (Se1-Se7 and Se9) is used to train the classifier.  
From these results, one finds that sessional variations can significantly affect the performance of TL for a four-class cross-subject classification problem due to the complexity of a four-class problem. However, as sessions took turns as target domain, some sessions demonstrated to yield superior CA with different combinations of eight source domains. Hence, related domains with high transferability have proven to be more effective in enhancing the prediction rate of individual sessions, while non-related domains deteriorate the prediction rate, as illustrated in Figure 9.