esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dynamic Analysis  
#### 3.2.1 Thermodynamic Analysis  
The thermodynamic results, which were selected to be presented, were the annual energy efficiency, the annual exergy efficiency, and the annual hydrogen production, for different values of the net electricity load, and the TES tank volume. Particularly, according to Figures 7 and 8, both the annual energy and exergy efficiencies increased when the net electricity load increased; therefore, when the net electricity production was getting higher and the hydrogen generation decreased, the configuration performed better energetically and exergetically. Moreover, the aforementioned efficiency rates had a slightly decreasing rate if the TES tank volume increased. Figure 7 indicates that the energy efficiency reached values from 9.77% to 17.7%, while, as it is shown in Figure 8, the exergy efficiency ranged from 10.24% to 18.99%. The maximum energy efficiency value of 17.7%, and the maximum exergy efficiency value of 18.99%, were achieved when the net electricity load was considered to be equal to 175 kW, and the tank volume was assumed at 10 m\({}^{3}\). Furthermore, the annual hydrogen production in kg is illustrated in Figure 9. The hydrogen production amount decreased slightly with the increase in the tank volume, but noticeably when the net electricity load increased. When the net electricity generation increased, the electricity which was provided to the PEM water electrolyzer was getting lower. As a result, the hydrogen production rate decreased. If the entire ORC electricity production fed the electrolyzer, and the net electricity load was equal to zero, the annual hydrogen production amount was varied from 5836 to 5913 kg. If the net electricity load was equal to 175 kW, the produced hydrogen amount was negligible. That is why this value was selected as the maximum examined one for the net (base) electricity load.  
Figure 6: Hydrogen production rate depending on the electricity input to the electrolyzer.  
Figure 8: Annual exergy efficiency for different values of net electricity load and TES tank volume.  
Figure 7: Annual energy efficiency for different values of net electricity load and TES tank volume.
# Influence of Silane-Modified Coal Gangue Ceramsite on Properties of Ultra-High-Performance Concrete
## 1 Introduction
modified the surface of rubber aggregate using SCA [23] and compared it with concrete containing unmodified aggregate. The results revealed that, under identical conditions, the compressive strength and splitting tensile properties of concrete treated with SCA improved by 10-20%. At present, numerous researchers employ SCA to modify inorganic materials and additives in concrete, aiming to improve the bonding effect between inorganic materials and cementitious components, thereby optimizing various macro-properties [22]. Examples of such inorganic materials include recycled concrete aggregate [24], fly ash floating beads [25], and basalt fiber [26]. From the above research, it is evident that modifying inorganic materials with SCA is an effective approach. Theoretically, modifying CGC aggregate with SCA can improve the properties of CGC concrete. In addition, although there is some research on the modification of inorganic materials, no studies have explored the content of SCA-modified CGC in UHPC. Therefore, based on the preceding analysis, this study first uses CGC fine aggregate (0.075 mm-2.36 mm) and combines cement, fly ash, and silica fume as cementing materials. The mix proportion of UHPC was determined through the MAA model, supplemented by insights from previous experiments. To mitigate the adverse impact of CGC's high water absorption on UHPC performance, CGC was soaked in a KH-560 solution of SCA for surface hydrophobic modification. Firstly, the effectiveness of aggregate modification was assessed via contact angle and water absorption tests. Infrared spectrometry and scanning electron microscopy were used to observe changes in functional groups and the morphology of the modified aggregate. Then, the modified aggregate was mixed into UHPC at various mass substitution rates to evaluate alterations in working performance, apparent density, and mechanical properties. For each group, three specimens were prepared to test the flexural and compressive strength at different ages. The interface, pores, and hydration products of UHPC, both before and after modification, were analyzed using a scanning electron microscope, X-ray diffraction (XRD), thermogravimetry, and mercury intrusion porosimetry. This research aims to facilitate the diversified utilization of CGC in the future.
## 6 The Network between the Cluster Heads and the 2 nd Base Station  
The second base station is situated very far from the cluster heads due to the requirement of avoiding spectrum collisions. A separate network with built-in redundancy is required to cater for the failures affecting communication between cluster heads and the base station. Figure 5 shows the network between the cluster heads and the second base station.  
Figure 5: Networking Topology connecting cluster heads and redundant base station.  
Nodes 1, 2, 3, 4 are cluster heads. Nodes 5, 6, 7 are first layer relays, and nodes 8 and 9 are the intelligent relays. Node 10 is the base station. Redundancy is maintained between the cluster heads and the first layer's relays and between the first and second layers, as is the case with second layer relays and the base stations. Redundancy among the nodes is achieved by making available two paths from one node to the next superseding nodes. Each node is intelligent such that the node can run a path-finding algorithm.  
The FTA equivalent of such a network can be developed using the linear cluster concept. No probability model can be developed as the network follows no specific structure. An algebraic path-finding algorithm is implemented in each node to select a path based on the distance and traffic on a specific path.
## 5 Discussion
### Room Volume and Acoustic Cues Weighting Strategies  
This study investigated the influence of volume on auditory distance perception. Results in Section 4.5 demonstrated a slight influence on the reported distances of stimuli generated by the intensity-based model. This specific result illustrates the influence of the reported volume on the compression effect of participants on the intensity-based model.  
In the CW group, the maximal value of the nonlinear compression coefficient \(a\) tends to a smaller value when volume increases. This can be regarded as a reduction in the number of strategies observed for producing auditory distance judgments when the volume increases. In large volumes (\(>\)100 m\({}^{3}\)), participants seem to only rely on a reverberation-related cue to produce an auditory distance judgment. Under the hypothesis that a largervolume generally leads to the perception of a more reverberant environment [37; 38; 39; 40], this effect can be explained by the expectation induced by these environments. Participants who expect a large variation in a reverberation-related cue may be more likely to rely on it.  
This effect is not observable on the compression coefficients associated with participants of the FW group who benefited from a longer visual spatial boundary. It is possible that the statistical effect observed on the \(a\) coefficient values is due solely to the seven participants with a room volume greater than to 100 m\({}^{3}\). These participants potentially adopted the same strategies for reporting distances, that imply mainly relying on a reverberant-related cue, without being influenced by their perceived acoustic environment.
# Lithological Mapping Research Based on Feature Selection Model of Relief-RF
## 4 Results  
### Comparison between Lithological Maps of Different Multispectral Data  
Figure 5 shows the classification maps of Sentinel-2, Landsat-8 OLI and ASTER and their comparison with the reference maps. The details on the lithological map of Sentinel-2 data were more pronounced than those of the Landsat-8 OLI and ASTER maps, especially for discrimination J\({}_{1-22}\). The continuity and integrity of this rock unit is the best of all the three types of multispectral data.  
The classification accuracies of different multispectral data are shown in Table 5. Sentinel-2 data are better than the two multispectral data in terms of lithological mapping. The overall accuracy of Sentinel-2 (60.71%) is higher than that of ASTER (55.47%) and Landsat-8 OLI (55.94%). Amongst the lithological classes, the classification effect and average accuracy of Sentinel-2 presented better performance in nine lithological units, namely, Q\({}^{\mathrm{p1}}\)\({}_{4}\), N\({}_{\mathrm{2y}}\), E\({}_{\mathrm{3g}}\), K\({}_{\mathrm{qm}}\), Z\({}^{\mathrm{2}}\)\({}_{\mathrm{d}\mathrm{k}}\), Z\({}^{\mathrm{1b}}\)\({}_{\mathrm{dk}}\), Z\({}^{\mathrm{1a}}\)\({}_{\mathrm{dk}}\), J\({}_{1-2}\) and C\({}^{\mathrm{c}}\)\({}_{\mathrm{zh}}\). The classification effect and average accuracy of the Q\({}_{\mathrm{1q}}\) and E\({}_{1-21}\) of Landsat-8 OLI were better than those of Sentinel-2. The classification average accuracies of Q\({}_{\mathrm{3}}\)\({}^{\mathrm{p1}}\), J\({}_{\mathrm{3h}}\) and C\({}^{\mathrm{b}}\)\({}_{\mathrm{3h}}\) of ASTER were better than those of Sentinel-2.
# EMG-Driven Shared Control Architecture for Human-Robot Co-Manipulation Tasks +
## 2 Materials and Methods
### Low-Level Layer
#### 2.2.2 Admittance Control
In detail, with reference to Equations (6)-(8), when in _no-contact_ mode, the \(\mathbf{K}_{k}^{p}\) gain is set to \(\mathbf{O}_{3\times 3}\) to allow the operator to freely move the end-effector, and \(\mathbf{K}_{d}^{p}\) is set as \(\mathbf{K}_{d}^{p}=\text{diag}\{k_{d,\text{low}},k_{d,\text{low}},k_{d,\text{low}}\}\), with _low_ element \(k_{d,\text{low}}\) to assure smooth teleoperation to the user; when in _contact_ mode, the \(\mathbf{K}_{k}^{p}\) gain is still set to \(\mathbf{O}_{3\times 3}\), while \(\mathbf{K}_{d}^{p}\) is set as \(\mathbf{K}_{d}^{p}=\text{diag}\{k_{d,\text{low}},k_{d,\text{low}},k_{d,\text{high}}\}\) with _high_ element \(k_{d,\text{high}}\) to guarantee both a stable contact with a stiff surface parallel to the \(xy\) plane of the arm base frame (e.g., a table where the workpieces are placed) and a smooth hand-guiding experience on the other two directions; finally, when in _hold_ mode, the \(\mathbf{K}_{k}^{p}\) gain is set to \(\mathbf{K}_{k}^{p}=\text{diag}\{k_{k},k_{k},k_{k}\}\) with \(k_{k}>0\) and \(\mathbf{x}_{r}\) set to the end-effector position at the time of the state switch, while \(\mathbf{K}_{d}^{p}\) is set as \(\mathbf{K}_{d}^{p}=\text{diag}\{k_{d,\text{low}},k_{d,\text{low}},k_{d,\text{high}}\}\) as in _contact_ mode, to make the end-effector hold the position while being in contact with a stiff environment on the \(z\) coordinate and compliant with external forces coming from the other two directions. In all the possible states, the orientation of the end-effector is handled by letting the operator always adjust it with low damping, i.e., \(\mathbf{K}_{k}^{o}=\mathbf{O}_{3\times 3}\) and \(\mathbf{K}_{d}^{o}=\text{diag}\{k_{d,\text{low}},k_{d,\text{low}},k_{d,\text{low}}\}\). Finally, the virtual mass in Equation (6) is kept constant in all the states.
## 5 Parameter Estimation for Trajectory Prediction  
In Section 3, we explained that the distance parameter \(d_{pre}\) affects the accuracy of the predicted trajectory. However, the determination of the parameter was not clarified. This section presents a practical method for estimating the consistent value of the distance parameter \(d_{pre}\) from the intersection driving data.
# Western Mediterranean Precipitation Extremes, the Result of Quasi-Resonant Sea-Atmosphere Feedbacks
## 4 Discussion
### Incidence of Global Warming  
While northern and western Europe, due to its oceanic influence, is moderately impacted by anthropogenic warming [31], it experiences severe droughts or torrential rains, the frequency of which is increasing. The Mediterranean basin is no exception to this rule. Indeed, as mentioned in the introduction, numerous studies show an increase in torrential rains over the Mediterranean basin in recent decades, which is one of the reasons why this heavily populated basin has been declared a hotspot.  
Unlike the EREs observed at mid-latitudes, which essentially result from baroclinic instabilities of the atmosphere induced by SST anomalies of the oceans, they are mainly influenced by the Mediterranean Sea as well as the enclosed or semi-enclosed seas of central and northern Europe. This property gives the Mediterranean basin a particular behavior due to the rapid warming of the seas involved (Figure 8).  
EREs are potentially impacted by global warming whether sea-atmosphere feedbacks occur resonantly or non-resonantly (Table 2). Indeed, the latent heat released by water vapor is an important energy source of developing cyclonic eddies. Consequently, non-resonant  
Figure 8: Maximum yearly sea surface temperature over time in the (**a**) western Mediterranean, (**b**) Adriatic, (**c**) Black Sea, and (**d**) Baltic. Red lines represent the trend.  
events (tropical-like cyclones whose genesis cannot be linked to a precise SST anomaly) are favored by an increase in the surface temperature of the western Mediterranean. Quasi-resonant events (extratropical cyclones whose genesis is clearly linked to a coherent SST anomaly), for their part, are favored by an increase in the surface temperature of peripheral closed or semi-closed seas, especially the Adriatic.
## 4 Experimental Simulations
### Experimental Results and Analysis
#### 4.3.4 Comparison Experiments of Different Sampling Methods  
In order to solve the problem of unbalanced dataset, this paper adopts a hybrid ADASYN and RENN sampling method (ADRDB algorithm) to process the dataset, in order to verify the effectiveness of the proposed method, this section sets up a comparison experiment of different sampling methods: under the same experimental conditions, the model uses seven different methods, SMOTE, ADASYN, random oversampler, ENN, RENN, random undersampler, and ADRDB, to handle the imbalanced data set. The RFP  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Feature Selection Method**} & \multicolumn{4}{c}{**Evaluation Indicators (\%)**} \\ \cline{3-6}  & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{3}{*}{NSL-KDD} & CNN & 98.22 & 98.23 & 98.22 & 98.18 \\  & GRU & 98.67 & 98.95 & 98.67 & 98.78 \\  & CNN-GRU & **99.69** & **99.65** & **99.69** & **99.70** \\  & CNN & 84.51 & 84.08 & 84.51 & 84.29 \\  & GRU & 83.69 & 83.81 & 83.68 & 83.74 \\  & CNN-GRU & **86.25** & **86.92** & **86.25** & **86.59** \\  & CNN & 92.94 & 93.24 & 92.94 & 92.04 \\ CIC-IDS2017 & GRU & 98.15 & 98.36 & 98.15 & 98.16 \\  & CNN-GRU & **99.65** & **99.63** & **99.65** & **99.64** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of single and hybrid models.  
algorithm is then used to filter the features. Finally, the classification experiments are conducted by the proposed model in the paper, and the model parameters are set as shown in Table 1. The resulting detection accuracy is shown in Table 10. The bold text indicates the evaluation indicators of the proposed model.  
As can be seen from the table, comparing a variety of different sampling methods, the ADRDB algorithm proposed in this paper has better processing effect for unbalanced samples. The reasons for the analysis are that the single oversampling methods such as Random Oversampler, SMOTE and ADASYN cannot effectively discriminate the noisy data and are prone to generate a large amount of noisy data in the process of synthesizing new samples, which in turn leads to the degradation of the classification effect of the model; the single undersampling methods such as Random Undersampler, ENN and RENN are prone to losing key information of most classes of samples, which in turn leads to the degradation of the classification effect of the model. In this paper, the proposed hybrid sampling method samples the majority and minority samples separately, and rejects the noisy data by the DBSCAN algorithm, which not only avoids the loss of key information, but also reduces the influence of noisy data on the classifier model, thus achieving better results.
# A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices
## 3 Methodology
### Experimental Setup  
To conduct our experiments, a Raspberry Pi 4 Model B is considered as the IoT device. Table 2 lists the specification of the specific version of the device used. A custom OS image is burnt in a 32 GB SD card and inserted in the Raspberry Pi's storage slot. For interacting with the device, we have used PuTTY (version 0.79), an open-source software that utilizes the SSH protocol to remotely access IoT devices. Experiments are conducted under controlled conditions, with each Machine Learning model trained on its respective dataset using the selected libraries. The performance metrics, execution time, memory utilization, power consumption, and CPU temperature are recorded for individual experiments.  
The choice of the Raspberry Pi 4 Model B for our experiments is driven by several key factors that align with the objectives of our study. Firstly, the Raspberry Pi 4 Model B is widely recognized as a cost-effective and versatile IoT device, making it an ideal representative for real-world IoT applications. Its compact form factor and low power consumption align with the principles of sustainable computing, a core focus of our study, which aims to evaluate the environmental impact of machine learning models in IoT contexts. Moreover, its affordability ensures that the findings of our research are accessible and applicable to a broad audience, including educational institutions and industry professionals.
## 4 Experiment Results
### Comparative Model Accuracy Performance Analysis
Figure 12: Accuracy comparison of DenseNet, VGG, MobileNet, ResNet, SqueezeNet, and EfficientNet models. The horizontal axis represents different CNN models, while the vertical axis quantifies classification accuracy. Sky blue was used to indicate the DenseNet model, orange for the VGG model, green for MobileNet, red for ResNet, brown for SqueezeNet, and yellow for EfficientNet. ResNet shows excellent performance, while MobileNet, VGG, and SqueezeNet show lower performance.
## 7 Implication and Recommendations  
The research aimed at enhancing industry threat-detection capabilities by introducing a continuous and intelligent monitoring of easement activity for the purpose of detecting potential threats typically involved in external interference incidents. The outcomes of the research include increasing awareness of threats and lowering the reaction time to prevent incidents for the infrastructure operator. While the current solution focuses on remote location relying on satellite communication to transmit alerts, the system can be adapted to take advantage of other wireless networks such as the conventional 4G. This will allow the deployment of the solution in risk hotspots such as areas under development in (sub)urban areas and road crossings. As one of the most significant risks to the pipeline industry, successful management of external interference will ensure the reliability and continuity of supply for operators and stakeholders, while also guaranteeing the safety of the community and the environment. The benefits of this research can be realized for pipelines both locally and globally and could potentially be broadened to other industries owning linear infrastructure, e.g., electricity transmission network service providers.
# Functional and Sensorial Properties of Chicken Sausage
## Results and Discussion
### Sensory Profiling  
The colour, taste, aroma, texture, juiciness, hardness and overall acceptability of chicken sausage formulations were evaluated. The sensory properties of BP-containing sausage are described in Table 6.  
It is worth noting that supplementation of Berangan banana peel had an additional advantage on sensory attributes than sausages formulated with Saba banana peel and control. The control sample had a significantly (\(p<0.05\)) higher appearance score than Saba banana peel treatments at all concentration levels. In contrast, sausage with Berangan banana peel, BBF2 and BBF4 were comparable with the control sample.  
Colour acceptance was significantly (\(p<0.05\)) reduced in the treatments SBF4, SBF6, and BBF6, which may be due to the lower yellowish found in these treatments (Table 6). The results are in agreement with those of [36], where the inclusion of 4-6% of banana peel led to a decrease in the colour acceptance of the fish patties.  
On the other hand, all treatments showed a significant reduction in aroma acceptability compared to the control. This outcome agrees with those reported by [37], who noticed that adding banana peel flour in cereal reduced the aroma acceptability.  
The addition of SBF2, BBF2, and BBF4 resulted in almost similar scores on taste (\(p>0.05\)), indicating that low concentrations of banana peels had little or no impact on the typical taste of sausage (2-4% of banana peel). However, an increase in banana peel level 6%, decreased (\(p<0.05\)) the taste score of cooked sausage. Ref. [36] also reported results in line with the abovementioned, where the inclusion of banana peel did not affect the taste of fish patties.  
Moreover, the hardness and juiciness decreased with the increasing concentration of both Saba and Berangan banana peels. A similar trend was observed by [38], who noticed that adding dietary fibre to meat products decreases hardness and juiciness because dietary fibre may disturb the protein-protein or protein-water gel network.  
Generally, overall acceptance showed that berangan banana peel had a higher score than Saba banana peel (\(p<0.05\)). Berangan banana peel may be accepted by the panelists because it has better water holding capacity (WHC) and oil holding capacity (OHC) than the saba banana peel (Table 3). Water-and-oil binding properties are important for determining the quality of texture and sensory properties of meat product [39].  
Moreover, the treatments BBF2 and BBF4 scored significantly (\(p<0.05\)) higher overall acceptability; meanwhile, SBF2 had a slightly (\(p>0.05\)) lower score than the control. This outcome is in line with those observed by [40], who found that the incorporation of green banana flour did not deteriorate the sensory properties of the low-fat sausages.