esa-sceva/satcom-chunk-collection huggingface dataset:
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 4 Application and Usage of Brainwave-Based Fine-Tuned Model in Architectural Spaces
### Application of Fine-Tuned Model to Architectural Space: 360\({}^{\circ}\) VR Experience
#### 4.2.3 Exploratory Analysis of Results  
In the DDP (A) group, participant #1 exhibited the highest proportion of 'happy' (57.42%) when EEG activity from the frontal and parietal lobes was analysed. For #2, observations from the occipital and temporal lobes indicated that 'neutral' (50.02%) was the most dominant state, followed by 'happy' (35.13%) and'sad' (14.85%). In the case of #3, EEG activity in the parietal lobe showed 'happy' (48.96%) as the leading emotion, but 'neutral' (33.14%) and'sad' (17.90%) were also well represented, suggesting potential classification bias. Participant #4, whose EEG signals were analysed from the temporal and occipital lobes, exhibited nearly equal proportions of 'neutral' (41.55%) and 'happy' (42.33%), indicating no dominant emotional state.  
In the Long Island (B) group, participant #1 showed an even distribution of 'happy' (40.23%), 'neutral' (39.34%), and'sad' (20.43%) when EEG activity from the frontal and temporal lobes was considered. For #2, analysis of the occipital lobe alone revealed a dominant 'neutral' state (55.23%), with lower proportions of 'happy' and'sad'. Participant #3, whose EEG signals were derived from the frontal and parietal lobes, had 'happy' (44.60%) as the highest proportion, but 'neutral' (35.10%) and'sad' (20.30%) were also present. Finally, for #4, EEG activity from the frontal, temporal, and occipital lobes combined resulted in'sad' (28.77%) being more pronounced, while 'happy' (34.78%) and 'neutral' (36.45%) showed similar proportions, suggesting a balanced emotional distribution across states (Table 5).  
Figure 7: Application of fine-tuning model using 360 VR.
## 3 Results and Discussion
### Composite Material Performance Testing
#### 3.1.2 Thermogravimetric Analysis  
Since thermoplastic composites are usually molded at high temperatures, it is necessary to investigate the thermal resistance of several carbon fibers in the air. The results for the thermogravimetric tests of virgin carbon fibers, de-pulped carbon fibers, and CF2 in the air are shown in Figure 3b. When the weight loss mass reaches 1%, the resistance temperatures of CF, CF-D, and CF2 are 189.579 \({}^{\circ}\)C, 307.079 \({}^{\circ}\)C, and 324.579 \({}^{\circ}\)C, respectively; when the weight loss mass reaches 5%, the resistance temperatures of CF, CF-D, and CF2 are 572.079 \({}^{\circ}\)C, 532.079 \({}^{\circ}\)C, and 559.079 \({}^{\circ}\)C, respectively; and when the bearing temperature reaches the highest temperature of hot compression molding, at 370 \({}^{\circ}\)C, the weight losses of CF, CF-D, and CF2 are 1.85%, 1.62%, and 1.4%, respectively. It can be seen that CF2 exhibits the best heat-resistant properties. The optimal ratio identified in this research enhances the heat-resistant properties of the composite material, fully satisfying the requirements of hot compression molding.
## 4 Results and Discussion
This means although no water in liquid form remained in the reactor, only 73% of expected gas was adsorbed during hydrate formation. This difference can be explained by two main reasons: (1) a small minority of formed large cages stayed un-occupied and (2) some possible water in liquid form is stocked in between two formed hydrate chunks. On the other hand, the ratio between the amounts of adsorbed hydrogen over adsorbed propane is around 2.42, as compared to an expected value 2 based on type II hydrate (assuming one H\({}_{2}\) molecule per small cage). This higher adsorption of the hydrogen is in agreement with the first assumption mentioned above regarding formation of propane hydrate with a small minority of empty large cages. This means that a part of the adsorbed hydrogen fills, in part, the available large cages, e.g., with double occupation a likelihood at present pressures in the general range of 20-30 bar. Figure 6: (**a**) High-pressure apparatus used for hydrogen growth and hydrogen storage study, (**b,c**) the formed hydrate immediate after pressure release and opening the reactor, (**d**) Number of propane moles adsorbed during the formation of the hydrate in the first 7 days under 4 bar pressure of propane, (**e**) Number of hydrogen moles adsorbed during the storage step in the second 7 days under 25 bar pressure of hydrogen at 1.5 °C. On the other hand, the maximum hydrogen storage capacity of a fully occupied type II gas hydrate, 8 propane and 16 hydrogen molecules in a unit cell with 136 water molecules will be 1.13 wt%. This value can increase in the case of partial occupancy of propane in the large cages (Table 2). Based on the number of adsorbed hydrogen and propane molecules in the hydrate formed using 10 mL of water, the overall hydrogen stored in the formed propane hydrate is about 1.04 wt%. This observation also supports the second assumption regarding the existence of liquid-form water trapped on the surface of between formed hydrate chunks. It is worth mentioning that the higher observed value vis-a-vis the previously reported values of hydrogen storage in C\({}_{3}\)H\({}_{8}\)+H\({}_{2}\) hydrate, 0.33% at 270 K [41], and 0.04% at 263 K [42] is due to lower selected working temperature. According to our MD results, the diffusion coefficient at 273 K (0.0694 \(\times\) 10\({}^{-5}\) cm\({}^{2}\)/s) is approximately 1.5 times larger than that of 260 K (0.0441 \(\times\) 10\({}^{-5}\) cm\({}^{2}\)/s), suggesting a strong dependence of the uptake rate on temperature.
# A Practical and Efficient Node Blind SignCryption Scheme for the IoT Device Network
## 4 Functionality Comparisons and Security Analysis
### Efficiency Comparisons  
In this section, we evaluate the efficiency of our approach in the following. First, there is an assumption that the prime numbers \(p_{1}\), \(p_{2}\), \(p_{3}\) and \(p_{4}\) are 1024 bits in length; \(Ha\) is computation time for one hash computation; \(SE\) is the time for a symmetric encryption operation, and \(SD\) is time for a symmetric decryption operation. Meanwhile, we also define that \(Ex\) is the computation time for one modular exponentiation operation in a 1024-bit module, \(Mu\) is the time for one modular multiplication in a 1024-bit module, \(M_{ecc}\) is the time for a number performing another point addition over an elliptic curve [13], and \(Pa\) is the time for the computation time of a bilinear pairing operation of two elements over an elliptic curve. Then, we assume that \(Ex\approx 8.24M_{ecc}\) for the ARM CPU to process at 200 Mhz in [14]. From the above assumption, we can discover that there exists some relation in the following, where \(Ex\approx 240 Mu=600 Ha\approx 3 Pa\) and \(Ad\approx 5 Mu\) in [15; 16; 17; 18; 19; 20; 21]. From the above computation time evaluation, we can see that our approach total computation time is \(33 Mu+6 Ha+2\oplus+1 SE+1 SD\). Then, the result is approximate to \(36\)\(Mu\) modular multiplication operations. Comparing with [2], we can see that our approach is much faster under the 1024-bit prime numbers. In the following two simulation results shown in Figures 1 and 2, our approach provides the QR-signature simulation and RSA signature simulation, respectively. On the other hand, we implemented our approach on a Ubuntu 20.04 operating system with Intel Core i5-1135G7 CPU @ Base 2.4 GHz up to 4.2 GHz CPU and 8 GB memory. This simulation is carried out by using GO language and python language with \"crypto/encoding/Matplotlib\" library on the 10 nodes to 50 nodes, where are shown in Figures 1 and 2, respectively.  
This means that the probability of \(\mathcal{F}\) to output a forged signature and for this signature to pass the verification function with non-negligible probability is less than \(\varepsilon\).  
\[Adv[S^{\prime}_{i}\longleftarrow\mathcal{F}^{Si_{S}(sk_{s},m)}|Ver(S^{\prime}_ {i},m,pk_{S})=1]<\varepsilon.\]
# Autonomous Multiple Tramp Materials Detection in Raw Coal Using Single-Shot Feature Fusion Detector
## 5 Results and Analysis  
### Comparison with Other Methods  
In this section, we have selected some widely used detectors such as SSD, Yolov3, Yolov3-tiny, DSSD, and Faster R-CNN for the comparison experiments, and to compare the results of each kind of tramp materials detection AP and all types of mAP are counted, as shown in Table 4. When the input image size is 300 pixels \(\times\) 300 pixels, compared with SSD and DSSD, TMRC-SSD has achieved the best mAP which reaches 0.9612, which is 8.3 and 6.9% higher than that of SSD and 6.9% higher than that of DSSD, which shows that the three modules--MDCS, CLFF and AFM--designed to improve detection accuracy are effective. Yolov3 uses the FPN structure in feature extraction, and uses the multi-scale prediction method to perform on the feature maps of the three scales. YOLOv3-tiny is a streamlined version of YOLOv3. It only retains two independent prediction branches. The speed is improved but the detection accuracy is not as high as YOLOv3. Faster R-CNN is a two-stage detection network, which, by processing large-resolution input images and adding RPN structure to detect the tramp materials.  
For the detection effect of three different sizes of objects, we use different detection networks to conduct comparative experiments. The results are shown in Table 5. YOLOv3-tiny's backbone network is relatively shallow, and it is difficult to extract high-level semantic features of the image, and only uses two feature layers for detection, and loses part of the scale information; therefore, the detection accuracy of small targets is only 88.56%. TMRC-SSD uses the feature fusion method to fuse the low-level features and high-level semantics of the image extracted from the shallow feature map and the deep feature map, and uses a multi-scale prediction strategy to detect targets of different sizes. In small object and large objects, the detection accuracy on the two types reached 95.57% and 97.06% respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Backbone** & **Input Size** & **Gangue** & **Bolt** & **Stick** & **Iron Sheet** & **Iron Chain** & **mAP** \\ \hline SSD & VGG-16 & 300 \(\times\) 300 & 0.9267 & 0.9216 & 0.9287 & 0.9336 & 0.6827 & 0.8787 \\ Yolov3 & Darknet-53 & 320 \(\times\) 320 & 0.9719 & 0.9217 & 0.9776 & 0.9691 & 0.9016 & 0.9484 \\ Yolov3-tiny & Darknet-19 & 416 \(\times\) 416 & 0.9461 & 0.8554 & 0.9418 & 0.9174 & 0.8669 & 0.9055 \\ DSSD & ResNet-101 & 300 \(\times\) 300 & 0.9117 & 0.9135 & 0.9056 & 0.9776 & 0.7518 & 0.8920 \\ Faster RCNN & ResNet-101 & 600 \(\times\) 1000 & 0.9728 & 0.9141 & 0.8625 & 0.8663 & 0.7507 & 0.8733 \\ TMRC-SSD & M-DenseNet & 300 \(\times\) 300 & 0.9663 & 0.9714 & 0.9719 & 0.9529 & 0.9434 & 0.9612 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance with different methods for five type of objects.
# Demonstration of Wastewater Recycling in a Slaughterhouse
## 2 Materials and Methods
### Legal Regulations for the Reuse of Slaughterhouse Wastewater  
Regulations play a crucial role in controlling the discharge and reuse of SWW [22]. Various regulations worldwide set limits for emissions, such as the \"Urban Wastewater Treatment Directive 91/271/EEC\" in the EU and the \"Effluent Limitations Guidelines and New Source Performance Standards for the Meat and Poultry Products Point Source\" in the USA [23; 24]. Limit values for wastewater discharge are referenced from the German Waste Water Ordinance (AbwV) [25] and the \"Norme Tunisienne NT 106.002\"--Normes Tunisienne relatives aux rejets d'eaux usees dans les reseaux publes d'assainsissement. Targets for potential use of treated wastewater include direct water recycling and water reuse without direct product contact. For assessment purposes, references are made to the \"European Drinking Water Regulation\" and \"EU Regulation on minimum requirements for water reuse\" [26; 27]. Process water recycling regulations stipulate the drinking water quality according to Directive 98/83/EC [26]. In addition, reuse without product contact places special demands on germ-free conditions due to aerosol formation. Guidelines for evaluating water quality for agricultural irrigation are provided by the European Commission [28], and the Tunisian standard \"Environment Protection--Use of reclaimer water for agricultural purposes--Physical, chemical and biological specifications\" [29]. Table 1 summarizes the limit values for each category according to existing regulations.
# An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks
###### Abstract  
Clustering is an effective way to prolong the lifetime of a wireless sensor network (WSN). The common approach is to elect cluster heads to take routing and controlling duty, and to periodically rotate each cluster head's role to distribute energy consumption among nodes. However, a significant amount of energy dissipates due to control messages overhead, which results in a shorter network lifetime. This paper proposes an energy-centric cluster-based routing mechanism in WSNs. To begin with, cluster heads are elected based on the higher ranks of the nodes. The rank is defined by residual energy and average distance from the member nodes. With the role of data aggregation and data forwarding, a cluster head acts as a caretaker for cluster-head election in the next round, where the ranks' information are piggybacked along with the local data sending during intra-cluster communication. This reduces the number of control messages for the cluster-head election as well as the cluster formation in detail. Simulation results show that our proposed protocol saves the energy consumption among nodes and achieves a significant improvement in the network lifetime.  
wireless sensor network; clustering; routing; network lifetime Article
# Operational Optimisation of a Non-Recuperative 1-kWe Organic Rankine Cycle (ORC) Engine Prototype
## 3 Data Analysis
### Overall ORC Performance  
The performance of an ORC engine can be quantified using various indicators. The net power output, \(\dot{W}_{\rm net}\), is obtained by subtracting the power consumed by the pump, \(\dot{W}_{\rm p}\), from the gross power output, that is, the electrical power generated through vapour expansion, \(\dot{W}_{\rm exp}\):  
\[\dot{W}_{\rm net}\,=\,\dot{W}_{\rm exp}\,-\,\dot{W}_{\rm p}\quad. \tag{3}\]  
As a heat engine, the ORC system converts the heat extracted from the heat-source stream, \(\dot{Q}_{\rm evap}\), into useful work. The first-law performance indicator used to quantify the effectiveness of this conversion is the thermal efficiency, \(\eta_{\rm th}\), defined as the ratio of the net power output to the heat input:  
\[\eta_{\rm th}\,=\,\frac{\dot{W}_{\rm net}}{\dot{Q}_{\rm evap}}\quad, \tag{4}\]  
where \(\dot{Q}_{\rm evap}\) is calculated from a heat balance across the evaporator on the working-fluid side:  
\[\dot{Q}_{\rm evap}\,=\,\dot{m}_{\rm wf}\left(h_{\rm evap,out}\,-\,h_{\rm evap,in }\right)\quad. \tag{5}\]  
Finally, the rational performance of the system is reported using the exergy efficiency, \(\eta_{\rm exp}\), that is the ratio between the actual net useful power output to the ideal reversible power:  
\[\eta_{\rm exp}\,=\,\frac{\dot{X}_{\rm out}}{\dot{X}_{\rm in}}\,=\,\frac{\dot {W}_{\rm net}}{\dot{m}_{\rm hs}\left(x_{\rm hs,out}\,-\,x_{\rm hs,in}\right)}\quad. \tag{6}\]
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 10 Conclusions and Future Study  
We showed the existence of the domain gap when training a real-world CNN-based multispectral cloud detector on data from one EO mission and evaluating it on data from another mission. To address the domain gap, we proposed domain adaptation tasks framed in two different settings of an EO mission: (i) offline adaptation and (ii) online adaptation. For offline adaptation, our results show that only a small fraction of weights need to be updated (in a supervised manner) without noticeable impacts on performance. This enables more sophisticated and robust models to be deployed and remotely updated. Whereas for online adaptation, our results show the viability of test-time adaptation algorithms on space hardware. This enables us to directly update models onboard in an unsupervised manner. The collection of results and insights reported in this paper should provide a timely reference for EO mission planners who are increasingly interested in satellite-borne machine learning. Our source code is publicly available at: [[https://github.com/andrewpatrick/domain-adaptation-cloud-detection](https://github.com/andrewpatrick/domain-adaptation-cloud-detection)]([https://github.com/andrewpatrick/domain-adaptation-cloud-detection](https://github.com/andrewpatrick/domain-adaptation-cloud-detection)) (accessed on 11 September 2024).  
For future work, we plan on: (i) investigating other means of performing online adaptation that can satisfy the requirements in accordance with EO mission standards and (ii) showing how our work can be extended to hyperspectral EO applications by simply increasing the number of spectral bands for the cloud detection and domain adaptation algorithms. However, representative or real-world performance of the enhanced/extended algorithms on hyperspectral data cubes can only be evaluated using a significantly more capable compute payload than what is available for our current investigation.  
Conceptualisation, A.D., A.-D.D. and T.-J.C.; data curation, A.D.; formal analysis, A.D.; funding acquisition, T.-J.C.; investigation, A.D. and Y.W.L.; methodology, A.D., A.-D.D., Y.W.L. and T.-J.C.; project administration, T.-J.C.; software, A.D.; supervision, A.-D.D., Y.W.L. and T.-J.C.; validation, A.D.; visualisation, A.D.; writing--original draft, A.D.; writing--review and editing, A.D., A.-D.D., Y.W.L. and T.-J.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by SmartSat CRC, Research Program 2: Advanced Satellite Systems, Sensors and Intelligence. [PERSON] is SmartSat CRC Professorial Chair of Sentient Satellites.  
Datasets Sentinel-2-Cloud-Mask-Catalogue and Landsat-9-Level-1 are available at [[https://universityofadelaide.box.com/s/f60](https://universityofadelaide.box.com/s/f60) mhnbv8 tbysgxrv9 dps3v6 zoazg0 ic]([https://universityofadelaide.box.com/s/f60](https://universityofadelaide.box.com/s/f60) mhnbv8 tbysgxrv9 dps3v6 zoazg0 ic) (accessed on 11 September 2024).  
The authors declare no conflict of interest.
## 4 Simulation Study on Disturbance Transient Control  
To validate the proposed method's ability to enhance the disturbance-rejection capability of bus voltage and achieve bus voltage tracking based on existing methods, two sets of meticulously designed disturbance-rejection-simulation experiments were conducted. These include the bus voltage disturbance rejection under load disturbance and bus voltage disturbance rejection under uncertainty disturbance. The benchmark comparison strategies include traditional PID control (parameters set according to reference [21], with a proportional parameter of 0.71 and an integral parameter of 182), Linear Parameter Varying (LPV) controller [16] and traditional linear Active Disturbance Rejection Control (ADRC). The simulation parameters for the hybrid energy storage system (HESS) are shown in Table 1.  
To ensure the experiment's accuracy, this paper presents three evaluation metrics for measuring the effectiveness of bus voltage tracking [16]:  
\[IAE=\int\limits_{0}^{t}{|e(t)|dt} \tag{46}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Title 3** \\ \hline Battery Rated Voltage/V & 12 \\ Battery Rated Current/A & 20 \\ Battery Internal Resistance/\(\Omega\) & 0.0012 \\ Capacitor Rated Voltage/V & 16 \\ Capacitor Open Circuit Current/A & 10 \\ Capacitor Internal Resistance/\(\Omega\) & 0.0021 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of values for HESS parameters [16].  
\[rip=(V_{dcmax}-V_{dcmin})/V_{dc} \tag{47}\]  
\[\eta_{a}=V_{dc}/V_{DC} \tag{48}\]  
Robustness and comprehensive performance evaluation are the reasons for choosing \(IAE\) to evaluate the curve-tracking performance in this paper.  
The \(rip\) parameter ensures the stable representation of sparse signals by the observation matrix, meaning that even in the presence of noise or incomplete data, the signal can be accurately reconstructed. This is crucial for precise curve tracking.  
Dynamic response and robustness evaluation are the reasons for using the \(\eta_{a}\) to evaluate tracking performance.  
The Integral of Absolute Error \(IAE\) represents the absolute error between the tracking curve and the target curve, which \(e(t)\) denotes the tracking error. A smaller absolute tracking error indicates higher precision. The ripple \(rip\) of the tracking curve represents the rapidity of bus voltage tracking. It is calculated by subtracting the minimum value from the maximum value of the tracking curve and dividing it by the target voltage value. Similar to \(IAE\), a smaller value indicates better tracking performance. The tracking accuracy \(\eta_{a}\) represents the stability of the tracking effect, calculated by dividing the bus-tracking voltage by the bus voltage. The closer this value is to 1, the better the overall tracking performance. Here, \(V_{dc}\) is the bus-tracking voltage, \(V_{dcmin}\) and \(V_{dcmax}\) are the minimum and maximum tracking voltages, respectively.