esa-sceva/satcom-chunk-collection huggingface dataset:
# Hypergraph-Clustering Method Based on an Improved Apriori Algorithm
## 4 Experiments and Analysis  
### Dataset Description  
The HDHPA method was validated for clustering problems using 6 different protein-protein interaction (PPI) network datasets and the MNIST dataset.  
* Protein-protein interaction network: The vertices, attribute values, and edges correspond to proteins, GO (gene ontology [33]) terms, and protein-protein interactions, respectively. The GO terms mainly include molecular functions, cellular components, and biological processes. The experiments selected 6 PPI network datasets from real protein interaction network datasets [34], including FAA4 (from yeast), Natoc_0297 (from Cryptococcal Natrophomonas), 16 items (from Nattococcus), HOXA10 (from chromosome 7), NNMT (from cancer cells), and MBP (from Escherichia coli). Partial PPI networks constructed from the datasets are shown in Figure 3.
* MNIST: A classic handwritten digit recognition dataset, consisting of 60,000 grayscale images divided into 10 classes from \"0\" to \"9\". In this study, 1000 samples were selected from the dataset for experimentation.  
In Figure 3, the larger the blue vertices, the more proteins it interacts with, and conversely, the smaller the vertices, the fewer proteins it interacts with in the network [35]. Edges represent protein-protein associations, indicating that they are clear and meaningful, that is, proteins work together to share functions, but this does not necessarily mean that they physically bind to each other [36]. Colored edges represent the predicted degree of protein-protein interaction, increasing from orange-yellow-green-blue-purple (more about PPI network supplements as shown in Appendix A), indicating that the closer the edge color is to purple, the more homogeneous features it contains and the more similar biological information it contains. Unlike the MNIST, we can see from Figure 3 that the PPI network basically follows a power law distribution, with more sparse edge structures that are closer to real networks to verify the robustness of the HDHPA from multiple perspectives.
## 3 Data and Methods
### Methods
#### 3.2.1 The Optimal Band of RapidEye Images for Surface Offset Monitoring  
The band radiation characteristics determine the effect of surface deformation monitoring. In this study, images acquired in the period without deformation (Table 2) were used to identify the optimal band for monitoring surface deformation. Typically, the offset in the initial offset field is the sum of decorrelation noise, attitude jitter distortions, and terrain shadow, among others. The error levels of offset fields acquired from different bands reflect the robustness of surface deformation monitoring. Here, the root-mean-square error (RMSE) of the deformation values serves as an accuracy evaluation index, guiding the selection of the optimal band for deformation monitoring.  
The RMSE of each offset field (Table 3) was obtained from the images in Table 2 by SPC with COSI-Corr 1.5 software. Band 5 has the highest percentage of minimum RMSE in the east-west (E-W) and north-south (N-S) offset fields and the minimum mean RMSE in all the offset fields. Therefore, Band 5 was selected as the optimal band for error analysis and deformation monitoring.
## 2 The Evolution and Trends of 6G Network Architecture
### Opportunities for Fundamental Change
#### 2.2.1 Removal of/Reduction in the Transport Network  
Many are unaware that the transport network, including its core functionalities, is essentially a legacy element carried over from previous generations (4G, 3G, and 2G). Originally introduced due to limitations in Internet capabilities, today's well-developed transport fiber infrastructure renders maintaining private national-scale networks unnecessary for operators. The cellular community might focus entirely on the wireless edge (air interface + radio access network + control plane) and use a segmented Internet fiber infrastructure for cellular traffic if a thorough reevaluation is conducted. Although this technique requires adaptations to operations, the tools to support it are available.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 6 Experimental Results
### Evaluation under Variable Light Conditions  
The benefits in terms of achievable throughput of the proposed approach have been evaluated under variable light conditions. The residual energy of a single sensor node was tracked when exposed to fluorescent lighting, typical of an indoor environment, then without any available environmental energy (the lights were off) for 2 h and finally exposed to indoor light again. The whole experiment lasted for 5 h, starting with the storage fully charged, and the results are shown in Figure 14. In this evaluation, only PW-MAC has been compared to SNW-MAC, as it allows higher throughput than UPMA-X-MAC, as we have previously seen.  
Figure 14 shows that the EM successfully adapts the throughput of the node to keep it sustainable. In the first part of the experiment, i.e., before the light is turned off, and in the last part, i.e., after 4.3 h, the energy buffer is full, and environmental energy is available. In that case, the EM aims at keeping the amount of residual energy in the ENI interval. When the residual energy is in the range \([E^{up}_{ENI},E^{max}_{R}]\), the energy budget is increased by an amount of \(\Delta e_{B}\), in order to avoid energy waste, which corresponds to the rules (R7) and (R8) of Figure 2b. When the residual energy is in the ENI interval, the energy budget is either left unchanged or adjusted by \(\pm\Delta e_{B}\) according to the sign of \(\Delta e_{R}\). These cases correspond to the risk of saturation and energy neutral interval scenarios introduced in Section 3.1.  
Figure 12: Setup of the star network.  
When no environmental energy is available, the energy budget is progressively decreased, following the rule R1. One and a half hours after the beginning of the experiment, the minimal energy budget is reached, and the energy budget is no longer decreased to ensure minimum quality of service. However, the energy buffer keeps discharging, as no energy is harvested. This corresponds to the discharging state scenario introduced in Section 3.1. When environmental energy is available, 2.4 h after the beginning of the experiment, the energy buffer starts recharging, and the energy budget is progressively increased until the ENI interval is reached, which corresponds to the rule R3 and to the charging state scenario. Once the ENI interval is reached, the energy budget is still increased to avoid saturation, which corresponds to the rules R7 and R8.  
From this evaluation, we observe that the throughput using SNW-MAC is in all conditions higher than with PW-MAC, showing the better energy efficiency of SNW-MAC. Especially, the throughput of the proposed approach is up to 2.5-times higher than PW-MAC in periods where harvested energy is available.  
These results demonstrate the ability of the EM to achieve energy neutrality with different MAC protocols and the benefits of its combination with the highly efficient SNW-MAC protocol, which exploits a ULP WuRx to enable asynchronous communication.  
Figure 13: Results of the experimentations on a star network.
# Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification
## 3 Proposed Method
### MSAAN Model
#### 3.3.3 Channel Attention  
In DIFSCL, the introduction of the channel attention mechanism is intended to compensate for the potential information loss caused by spectral dimension compression, allowing our feature extraction network to focus on features across three dimensions rather than solely on spatial domain features. Specifically, the channel attention mechanism first performs a global average pooling operation, aggregating each height and width feature channel into a single numerical value to form a fixed-length channel descriptor. The global average pooling is calculated as follows:  
\[z_{i}=\frac{1}{H\times W}\sum_{j=1}^{H}\sum_{k=1}^{W}x_{i}(j,k) \tag{14}\]  
In this context, \(x\) represents the input feature map, and \(i\) denotes a channel of \(x_{i}\), where \(x_{i}(j,k)\) is the pixel value of the \(i\)-th channel at position \((j,k)\). After obtaining the pooling results, the importance of each channel is learned through a two-layer fully connected network. First, a dimensionality-reducing fully connected layer reduces the descriptor's dimension from \(C\) to \(C/r\). This is followed by a ReLU activation function, and then the dimension is expanded back to \(C\) through a second fully connected layer, as shown below:  
\[s=\sigma(W_{2}\cdot\text{ReLU}(W_{1}\cdot z)) \tag{15}\]  
In this context, \(\sigma\) represents the Sigmoid function, while \(W_{1}\) and \(W_{2}\) are the weight matrices associated with the fully connected layers, and \(r\) signifies the reduction ratio. The output activation weight vector \(s\) will be used to reweight the channels of the input feature map \(X\), achieving dynamic adjustment of the features.
## 2 Materials and Methods
### Procedures, Data Collection, and Equipment
#### 2.3.1 Anthropometric Measurements and Body Composition Analysis  
Body height was measured to the nearest 1 mm with a calibrated InLab S50 stadiometer (InBody Co., Seoul, Republic of Korea) in accordance with the relevant guidelines. Body mass (measured to the nearest 0.1 kg), the body mass index (BMI), and body composition parameters, including percent body fat (PBF) and skeletal muscle mass (SMM), were determined by bioelectrical impedance with an InBody 270 Bioelectrical Impedance Analyzer (BIA) (Biospace Co., Inc., Seoul, Republic of Korea). This foot-to-foot, hand-to-hand, and hand-to-foot device features two stainless steel foot-pad electrodes mounted on a platform scale and a tetrapolar 8-point tactile electrode system with two stainless steel handles. Bioelectrical impedance analysis has been found to be a viable tool for body composition measurements and competitive with other methods, such as DXA [43, 44]. The platform scale uses a single load cell to measure body mass (and stature) and calculate the BMI. The PBF is calculated by summing the results of segmental lean analysis to determine the total lean body mass, fat mass, and the proportion of fat mass to total body mass. Muscle mass percentage (M%) is calculated by evaluating water content in the segmental regions with the use of the provided equations. Visceral fat level (VFL) is estimated with the regression equations (provided), which--according to the manufacturer--were derived by comparing visceral fat in computerized tomography scans and impedance in the torso region in the segmental lean analysis.
## 2 Methodology
### Problem Formulation
Constraints are intended to bring engineering considerations into the optimization process such that the final solution is physically reasonable, as well as being mathematically optimal. Minimum and maximum altitude boundaries define design space where Earth-observing satellite can safely operate without suffering excessive atmospheric drag or radiation. Maximum aperture and propellant fraction limits are prescribed to ensure that ReCon satellites can be readily manufactured with current technologies and be safely launched in reality.
## 5 Conclusions and Future Work  
The five ISM-HAB missions flew successfully, providing valuable data regarding ISM interference and the dynamics of the balloon with a moderate personnel effort and with relatively low-cost hardware. The payload was easily refurbished and updated from  
Figure 23: Single-sided amplitude spectrum pitch oscillations at different altitudes (note the different vertical scales for different altitudes).  
one mission to the other. We conclude that the sounding balloon platform concept is particularly well-suited for testing and experimentation in telecommunications.  
The landing site location is impossible to predict with accuracy sufficient to ensure that the payload will be accessible for recovery; therefore, there is a clear need for active control of the payload gondola trajectory at re-entry, to ensure that undesired landing zones are avoided and, possibly, to land the payload in a pre-determined area. This could be achieved by placing the payload, or at least the data storage unit, on a recovery vehicle such as a fixed-wing glider or a multi-rotor drone, so that the final part of landing is performed in a controlled way. Such solution, however, would introduce a large penalty in the tiny mass budget of the sounding balloon system. A more attractive solution is the use of a steerable parafoil in place of the passive parachute, controlled by autonomous navigation during the last phase of descent. This is the object of future work to be carried out towards the achievement of a fully functional system. In case payload hardware recovery is not mandatory, data transfer can be done in near-real time with suitable low-mass radio equipment, such as S-band Cubesat-like transceivers, or--for small data rates--by means of low power, long-distance IoT telecommunication techniques, such as LoRa.  
Within the frame of this project, a first attempt at the characterization of the dynamic oscillatory environment of the platform was made. For the future, we plan to enhance the collection of dynamic data using inertial sensors of better sensitivity and accuracy, with the ultimate goal of making available a payload attitude stabilization system for the high-altitude portion of the flight. With such a feature, the possibilities of this platform would extend to other research areas such as astronomy, Earth imaging and to space-related technology development and demonstration.  
Conceptualization, S.M. and M.G.; Data curation, M.G.; Investigation, M.G.; Methodology, S.M.; Project administration, S.M.; Writing--original draft, S.M. and M.G.; Writing--review and editing, S.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was carried out in the frame of a collaboration between Eutelsat SA, MBI SrI and two Departments of the University of Pisa: DII-Information Engineering and DICI-Aerospace Division. The authors gratefully acknowledge the help of [PERSON], [PERSON], [PERSON] and [PERSON] of MBI SrI. The authors declare no conflict of interest.
## 3 Laboratory Model Tests
### Testing Procedure  
The sand was placed into the test box using pluviation technique. The height of fall was 100 cm to obtained the assumed unit weight of the backfill (\(\gamma=18.5\times 10^{3}\) N/m\({}^{3}\)). First, a 15 cm layer of soil foundation was formed at the bottom of test box. Then, a temporary support for the model, in the form of a wooden element, was positioned in front of the wall face in order to keep the facing surface in its initial place during construction. The model was built layer by layer from the bottom to the top, with 5 cm spacing between the soil and reinforcement layers. Each layer of soil was flattened with a grader. After this, a layer of reinforcement (consisting of three strips connected to the facing element) was placed on the sand, and then another sand layer was set down. This procedure was repeated until the structure was complete. Next, after the temporary support was removed, five displacement transducers were fixed on the wall facing at 5 points with regular intervals (Figure 6a). The actuator with the load cell and footing was then placed over the top of the model. The displacement transducers and the load cell were connected to a digital data logger. Finally, loading was applied on top of the structure through a footing that was pushed downwards at a constant displacement rate of 0.22 cm/min. The model was loaded continuously until failure was achieved with continuous measurements of the loading force, footing displacement, and facing deformation. The strip footings were loaded at a rate equal to those employed by other authors [94].
## 5 Conclusions  
The conducted research enables the formulation of the following conclusions:  
1. The tribological tests conducted on the high-carbon steel under various loading conditions and test durations demonstrated that, as expected, higher mass wear values were generally observed for samples subjected to higher loads and longer test durations. However, a straightforward relationship between the tribological test parameters and the different heat treatment parameters of the material was not always  
Figure 7: Dependence of the average hardness of the tested samples on the annealing time.  
apparent. Therefore, statistical tools were employed to facilitate the interpretation of the obtained results.
* The use of ANOVA (Analysis of Variance) allowed for the identification of statistically significant differences between the annealing time and the material's abrasive wear resistance. No clear statistical differences were observed in the mass loss of samples with increased load or extended test duration. The only parameter significantly affected by the tribological test parameters was the depth of wear. Higher loads resulted in increased volumetric wear of the material. However, the absence of a similar relationship for mass loss suggests that significant plastic deformation occurred in the material without corresponding mass loss.
* The lack of significant statistical differences under different test conditions indicates that there is no substantial variation in the wear mechanism of the tested materials. Scanning studies confirmed that the predominant wear mechanisms were microcutting and grooving of the material. The worn surfaces exhibited numerous grooves and also showed signs of material flaking. No significant differences were observed among the analyzed samples concerning their wear mechanisms.
* The stable wear behavior of the material within the same wear mechanism also underscores the significant role of the material matrix in determining its abrasive wear resistance. The studied alloys featured a pearlitic matrix with secondary cementie precipitates. The annealing process led to the dissolution of the smallest particles present in the material and a gradual homogenization of particle size distribution. The process of homogenizing cementie particle sizes resulted in improved tribological wear resistance of the material.  
Conceptualisation, J.K.; investigation, L.F., M.K., and S.L.; methodology, L.F.; software, P.M. and M.K.; supervision, J.K.; validation, L.F. and S.L.; writing--original draft, L.F.; writing--review and editing, L.F. and J.K. All authors have read and agreed to the published version of the manuscript.  
This research was financed by the Ministry of Education and Science (AGH--research subsidy No. 16.16.110.663).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to thank [PERSON] and [PERSON] for their support of this research.  
The authors declare no conflicts of interest.
## 3 Coordinate Control Strategy of Distributed Parallel-Series
### Control Strategy of MMC-HVDR  
According to the coupling mechanism in Section 2, the MMC should maintain the DC voltage on the MV side. The MMC should also maintain the DC voltage in the HV side by indirectly controlling the AC voltage of the MF transformer according to Section 3.1.  
For the MMC, the arm voltage \(v_{\textit{ap}}\) and van of phase A include three components that operate the DC component \(v_{\textit{dc}}\), the fundamental frequency fluctuation component \(u_{\textit{a}}\), and the second harmonic fluctuation component \(u_{\textit{acm}}\):  
\[\left\{\begin{array}{l}v_{\textit{ap}}=\frac{v_{\textit{dc}}}{2}-u_{\textit {a}}-\frac{u_{\textit{an}}}{2}\\ v_{\textit{an}}=\frac{v_{\textit{dc}}}{2}+u_{\textit{a}}-\frac{u_{\textit{an }}}{2}\end{array}\right. \tag{9}\]  
According to Equation (9), the MMC has three degrees of freedom, which can be used to achieve DC voltage control and AC voltage control simultaneously.  
The charging and discharging processes accompany the change in the submodules' voltage during power fluctuation. The capacitor voltage of submodules can be chosen as the indicator for the energy balance of the MMC. According to Equation (7), the AC voltage of transformer \(V_{T}\) will increase if the injected active power increases, and \(V_{T}\) will decrease if the injected active power decreases. An average capacitor voltage-AC voltage controller can be used to keep the energy balance. With the average capacitor voltage  
Figure 3: The control block of the whole system.  
control, the MMC and HVDR converter will automatically transmit the active power from wind turbines to the HVDC link.  
\[\left\{\begin{array}{l}V_{Tref}=K_{p}(v_{c-avgref}-v_{c-avg})+\frac{K_{i}}{s}(v_ {c-avgref}-v_{c-avg})\\ V_{dref}=K_{pv}(V_{Tref}-V_{Td})+\frac{K_{in}}{s}(V_{Tref}-V_{Td})\\ V_{qref}=K_{pv}(V_{Tqref}-V_{Tq})+\frac{K_{in}}{s}(V_{Tqref}-V_{Tq})\\ \end{array}\right. \tag{10}\]  
The dynamic equation for the DC voltage can be expressed as [23]:  
\[\frac{dv_{dc}}{dt}\approx N\frac{dv_{c-avg}}{dt}=\frac{1}{C}i_{cap} \tag{11}\]  
with the changes in DC port voltage, the capacitor voltage can also be controlled.  
\[v_{dc}=V_{dc-ref} \tag{12}\]  
where \(K_{pc}\), \(K_{ic}\) are the PI controller coefficient. The control block is shown in Figure 3b. Once the capacitor voltage exceeds its limit, the DC voltage will increase and block the output of the MVDR.
# Making Optimal Location-Sizing Decisions for Deploying Hybrid Renewable Energy at B&Bs
## 4 Methodology
### Case Study of the B&B
#### 4.3.1 Solar and Wind Energy Data  
This study uses a meteorological report of Pingtung station from the Taiwan Central Weather Bureau, ranging from 2011 to 2020 [69]. The months of April, July, October, and January are selected to cover different seasons in Taiwan, namely spring, summer, autumn, and winter. Figure A1 (in Appendix A) summarizes the 24-h solar radiance and wind speed data in Pingtung using 10-year average data for each season-representing month. The figures also present that these hourly data can be summarized in solar/wind supply for a 6-h time period: 00:00-06:00, 06:00-12:00, 12:00-18:00, and 18:00-24:00 for the first, second, third, and fourth time periods, respectively. In order to conduct more realistic simulations of energy supply, this study considers formulas from published research [70; 71] and technical specs of REGDs (Tables A1 and A2 in Appendix A).  
In financial terms, RE technologies available in the current market may vary in maturity. Therefore, different approaches should be considered to provide an accurate estimate of the costs of producing and saving energy. For conventional REGDs, this study adopts the work of [PERSON] and [PERSON] [72], which calculates the LCOE based on the existing feed-in tariff. In this study, the LCOE is assumed to be 40% lower than the Taiwanese feed-in tariff [73], that is, USD 0.120/kWh for conventional solar panels and USD 0.200/kWh for wind turbines. For unconventional REGDs, this study adopts an established formula [74] that calculates LCOE based on costs, performance, and reliability. The capital costs are taken from [62; 75; 76], the operation and maintenance costs are assumed to be 1.5% of the capital costs, and the project's economic life is assumed to be 25 years. An interest rate of 7.0% is used since the interest rate of various RE projects ranges from 5% to 10% [77]. Accordingly, the LCOEs for unconventional REGDs are as follows: USD 0.171/kWh, USD 0.139/kWh, and USD 0.488/kWh for the Tesla Solar Roof, the Ciel & Terre Floating Solar, and the Wind Tree, respectively. For comparison, B&Bs can pay electricity from the power grid for an average of USD 0.136/kWh, depending on the timing and amount of consumption (Figure A2 in Appendix D). Following a procedure similar to unconventional REGDs and the corresponding data [63; 78], the LCOE for saving energy or unit battery cost can also be calculated, that is, USD 52.72/kWh.