esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Fuzzy Vector Signature with Reusability
## 5 Performance Analysis
### Computation Cost  
For the fuzzy extractor, we considered the computational cost required for obtaining a signing key \(K_{ID}\) by running the **FE.Rep** algorithm that takes as input helper data \(P_{ID}\) and a biometric data \(bio^{\prime}\). This is the step for [A2] in Figure 1. We assume that a reproduced value from **FE.Rep** is straightforwardly used as the singing key corresponding with the verification key \(pk_{ID}\). If a hash function \(H\), such as SHA-256, is used as a digital locker [17], \(K_{ID}\) is locked with \((\mathsf{nonce}_{i},H(\mathsf{nonce}_{i},\{bio\}_{i}))\bigoplus(K_{ID}||0^{ \mathsf{S}})\) for a positive integer \(s\), where \(\{bio\}_{i}\) is the set of biometric data corresponding to a subset \(i\) among \(n\) components. Therefore, with a new \(bio^{\prime}\), the unlocking algorithm needs to perform a hash computation \(|H|\) plus an XOR operation \(|X|\) per each subset \(i\) until \(K_{ID}\) is obtained. Consequently, the **FE.Rep** algorithm in Reference [17] must compute \(d\cdot(|H|+|X|)\) operations as a worst case scenario. Since [18] also requires solving a \((\tau,m)\)-secret sharing scheme, the **FE.Rep** algorithm chooses a set of \(\tau\) shares among \(m\) unlocked values and then solves a secret-sharing scheme, leading to performing additional \(\frac{d}{m}\big{(}\binom{m}{\tau}\cdot\tau m(m-1)\big{)}\,|X|\) operations. In contrast, the FVS scheme needs to run the **FVS.Sign** algorithm to generate a signature \(\sigma_{ID}\) by taking  
Figure 3: Comparison between previous and our construction.  
\((bio^{\prime},\text{SP},R)\). This is the step for [A2] in Figure 2. In our FVS scheme, the **FVS.Sign** algorithm needs \((n+4)\) exponentiations in \(\mathbb{G}_{1}\) for the dimension \(n\) of biometric data. Compared to Reference [20], the signing operation is reduced by about half, which is shown in Table 3.  
In order to measure the actual amount of calculation, we considered how much computation the user should perform by substituting the numbers in Table 2 directly. For instance, let \(n=512\) and \(\ell=80\). The fuzzy extractor by [PERSON] et al. [17] must complete \((61.6\times 10^{5})(|H|+|X|)\) operations, and when setting \((\tau,m)=(5,32)\), the fuzzy extractor by [PERSON] et al. [18] needs to compute approximately \((53.6\times 10^{3})|H|+(16.7\times 10^{11})|X|\) operations--which still seems to be a burdensome amount of calculation to perform on a personal device. To compare, when considering 0.3 ms for an exponentiation in \(\mathbb{G}_{1}\)[34], the **FVS.Sign** algorithm in ours takes about \((512+4)\cdot 0.3\approx 155\) ms. Since \(\delta=1/2\), the user is required to run the step [A2] of the authentication protocols twice, such that the **FVS.Sign** algorithm, which is computed by the user, takes about 310 ms.
###### Abstract  
Recently, there has been active research on utilizing GPUs for the efficient processing of large-scale dynamic graphs. However, challenges arise due to the repeated transmission and processing of identical data during dynamic graph operations. This paper proposes an efficient processing scheme for large-scale dynamic graphs in GPU environments with limited memory, leveraging dynamic scheduling and operation reduction. The proposed scheme partitions the dynamic graph and schedules each partition based on active and tentative active vertices, optimizing GPU utilization. Additionally, snapshots are employed to capture graph changes, enabling the detection of redundant edge and vertex modifications. This reduces unnecessary computations, thereby minimizing GPU workloads and data transmission costs. The scheme significantly enhances performance by eliminating redundant operations on the same edges or vertices. Performance evaluations demonstrate an average improvement of 280% over existing static graph processing techniques and 108% over existing dynamic graph processing schemes.  
dynamic graph processing; graph scheduling; GPU; data transfer cost
###### Abstract  
Extending set-point temperatures in residential buildings has a significant impact on energy demand and thermal comfort. European governments have adopted this strategy to mitigate the energy crisis. Previous studies attempting to quantify energy savings by extending set-point temperatures were limited due to a lack of building stock characterisation, poor climate representation, and the absence of uniformity in the reference set-point temperature. In this study, a large-scale simulation was conducted, which included six building models covering 90% of southern Europe Koppen-Geiger climates, where 20 \({}^{\circ}\)C and 25 \({}^{\circ}\)C were the reference heating and cooling set-point temperatures, respectively. This also accounted for the thermal characteristics of the older building stock, built more than 15 years ago, and the new buildings built under the latest version of Directive 2010/31/EU. The results show that reducing the heating set-point temperature by 1 \({}^{\circ}\)C can lead to an average demand reduction of 20%, while raising the cooling set-point temperature by 1 \({}^{\circ}\)C can lead to a 25% cooling demand reduction. The oldest building stock shows a higher absolute savings potential. Adjusting thermostats by 1 \({}^{\circ}\)C in Spanish homes during the winter season could represent a saved natural gas volume of 1.8 million normal cubic meters, nearly 40% of the gas demand of households in 2022. These findings suggest that extending the set-point temperatures in residential buildings can be a promising strategy towards a more energy-efficient society without compromising the occupant's thermal comfort.  
energy crisis; energy policy; thermostat; potential energy savings; residential buildings +
Footnote †: journal:  
Article Extending the Thermal Comfort Band in Residential Buildings: A Strategy towards a Less Energy-Intensive Society
###### Abstract  
This paper proposes an adaptive coordinated multi point (CoMP) scheme using pre-coding in order to improve the reliability of wireless communication systems based on orthogonal frequency division multiplexing (OFDM). In the conventional scheme, the received signal to noise ratio (SNR) of the mobile is degraded due to the interference signal. Therefore, the bit error rate (BER) performance is degraded, as is, the reliability of the system, due to inter-cell interference (ICI). Therefore, the BER performance of the system is reduced and the transmitter must perform unnecessary re-transmission. The proposed scheme uses the CoMP scheme to improve reliability in a heterogeneous network (HetNet) system. Additionally, the proposed scheme uses the constructive spatial phase coding (SPC) scheme to mitigate the received SNR reduction due to the interference signal. Therefore, the proposed scheme can prevent performance degradation caused by the interference signal. When a mobile is located in the cell edge, the transmission signal is distorted by the transmitted signal from the adjacent cell. The proposed scheme can reduce the SNR of the interference signal by using the destructive SPC scheme. Therefore, this scheme can improve the reliability of the system. The simulation results show that the BER performance of the proposed scheme is better than that of the conventional scheme. As a result, the proposed scheme improves the reliability of systems by adaptively applying the CoMP and SPC schemes.  
Keywords:cell edge; CoMP; HetNet; interference mitigation; OFDM; pre-coding; SPC +
Footnote †: journal: Applied Signal Processing
# Analysis of Subharmonic Oscillation and Slope Compensation for a Differential Boost Inverter
## 3 Behavior of the Differential Boost Inverter
Namely, within the phase interval defined by two critical phase angles, two different branches of duty cycle values appear instead of one a kind of bubble emerges [18]. It can be observed that the onset of bubbling phenomenon depicted in Figure 5 is gradual. First, for a relatively small value of the parameter \(k_{p}\), the cycle is smooth, then, for increasing \(k_{p}\), it becomes disrupted in a small phase interval. Thereafter, as \(k_{p}\) is further increased, the interval \((\varphi_{1},\varphi_{2})\) of \(\varphi\) during which SO takes place grows up as can be seen in Figure 5. If the proportional gain is further increased, this interval gets wider and the phenomenon usually spreads through the whole line cycle. Figure 5 also shows that successive period doubling inside the SO interval may also take place in the first half cycle where the voltage reference is positive, i.e., when \(D>0.5\). When \(k_{p}\) becomes even larger, the bubbles start appearing even in the second half cycle of voltage reference where \(D<0.5\). Therefore, even for \(D<0.5\), the voltage loop may have a destabilizing effect since when the proportional gain \(k_{p}\) is increased beyond a critical value \(k_{p}\approx 0.8\), SO and the associated bubbling starts appearing for \(D<0.5\) and even in the presence of slope compensation. Therefore, the ramp slope needed for eliminating SO is larger than the one obtained when ignoring the effect of the voltage loop. This destabilizing effect of the voltage loop is similar to the one reported in [27] for the buck converter and in [24] for the boost  
converter. Similar behaviors have been obtained when other parameters such as the input voltage \(V_{g}\) or the inductance \(L\) are varied.
## 5 Optimization
### Energy Storage  
Another aspect that can substantially improve the efficiency of a smart grid is the energy storage. Basically, it is the problem of keeping energy available directly on the grid, in storage components efficient enough to minimize energy losses. In cases of energy production peaks, when there is an overproduction of energy, having a distributed storage system increases the overall efficiency and compensates the variability of Renewable Energy Sources (RES), also enabling local optimization strategies for energy consumption.  
An agent-based technique is exposed by [PERSON] _et al_. [47]. Basically, they propose a game-theoretic framework that analyses the Nash equilibrium of an electricity network, and develops learning strategies for agents that dynamically adapt to the energy market. As regards storage devices, they embrace the so-called Vehicle-to-Grid (V2G) view, where the unused energy is stored in the batteries of electric vehicles (EVs) or Plug-in Hybrid Electric Vehicles (PHEVs). Since this practice can raise problems of peaks in energy demand, a Multi-Agent System is adopted in order to optimise usage  
Figure 12: Example PowerMatcher agent cluster [42].  
and storage of electricity. In particular, the proposed system models a situation where each device is represented as an intelligent software agent, and every agent can try to \"buy\" the needed amount of energy at every time, meanwhile learning what is the most profitable amount of energy to buy, according to the specific usage. The authors claim that, implementing their technique, a single consumer may save up to 13% on his electricity bill [47].  
Other approaches related to the V2G view have been proposed. For example, [PERSON] _et al_. [48] used a multi-objective evolutionary algorithm to schedule a smart charging of the batteries in the EVs. In their work, they presented a simulation environment that takes into account different scenarios and parameters. Different charging stations are simulated, in different locations. For each of them, there are an expected number of car arrivals. The simulation defines arrival and departure time, as well as initial and requested charge levels. For each car, a specific battery type is simulated, with different charging curves. All of these parameters are modelled as parameterized Gaussian curves, in order to obtain a realistic simulation. Subsequently, authors established a set of hard constraints (_i.e._, grid capacity, battery characteristics) and soft constraints (objectives, such as minimize battery degradation) to be respected. Afterwards, the algorithm tries to find the optimal scheduling for the battery charging, in order to minimize the total energy cost. Results of this optimization can be appreciated in Figure 13, which shows how the charging schedules are optimized so that more cars are charged when the price is cheaper. The used power (in blue) decreases when the energy price (in green) rises. This is another example of how smart charging of EVs may result in energy savings.
## 2 Research Methods
### Inclusion and Exclusion Criteria
#### 2.1.1 Selection of Keywords  
Multiple well-known research databases and repositories, including IEEE, Science Direct, PubMed, Wiley, Taylor & Francis, JSTOR, ACM Digital Library, EBSCOhost, Springer, Emerald and IET, were searched exhaustively for research publications. The papers in the aforementioned databases were searched using keywords like sensors, wearable sensors, biosensors, AI, ML, healthcare, telemedicine, and e-health.
# A Review of Small-Scale Vapor Compression Refrigeration Technologies
## 3 Small-Scale Vapor Compression Refrigeration (VCR) Systems
### Modeling
As a conclusion, the authors emphasize how challenging the size reduction is for this type of system, maintaining a competitive performance as this depends on different variables: a correct compressor configuration (relationship between displacement and revolutions), the heat exchangers design, and the choice of the refrigerant fluid, being these the three main focused aspects of their work. The analysis also considered refrigerant selection attending current socio-environmental needs. Many refrigerants of current interest were compared, including R450A, R513A, R152a, R448A, R449A, R452A, and R600a, with the latter being the best choice based on the favorable characteristics found as high COP and low compression ratio. Another application where small-scale VCR systems have been modeled is in food preservation, specifically fish refrigeration on fishing boats. Through a model developed in CoolPack [80], [PERSON] et al. [81] evaluated the performance of a refrigeration system operating with R134a and R600a for \(-10\)\({}^{\circ}\)C evaporation temperature and condensation temperatures between 32 and 44 \({}^{\circ}\)C. The results showed that the system has better performance when operating with R600a, reaching COP values between 3 and 5, and a cooling capacity from 450 to 610 W.  
As a summary of this section, Figure 17 shows the typical modeling approaches for small-scale VCR systems. Although not all studies share the same input and output variables, those listed are the most common. As can be seen, some propose the cooling capacity as input, while others obtain it as output, depending on the particular needs and objectives of each case. On the other hand, the compressor efficiency can refer to isentropic or volumetric, and finally the values of temperature, pressure, and enthalpy as outputs refer to each thermodynamic state that comprises the refrigeration cycle. Figure 17: Common modeling strategies used for small-scale VCR systems.
# Study on the Performance of Optical Lenses under High Fluxes of Solar Radiation
## 3 Results and Discussion
### Results from Tests with Xenon Arc Lamp
However, a xenon arc lamp emits radiation with wavelengths starting at circa 250 nm, as shown in Figure 9b (from the work of [PERSON] and [PERSON] [22]). The range between 250 nm and 290 nm, where the borosilicate glass (but not the silica glass) is strongly absorbing the xenon light, seems to be the key reason to explain the fracture of all the tested lenses made of BK7 glass. [PERSON] and [PERSON]. [22] have also mentioned that the region of the xenon arc lamp spectrum with wavelengths \(\leq\)290 nm \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Lens Type** & **Type of Support** & **Irradiation** & **Irradiation** & **Maximum** & **Comment** \\  & **and Mask** & **Power (W)** & **Time (min)** & **Temperature 1 (\({}^{\circ}\)C)** & \\ \hline DB150 & M, 1 inch & 180 & \(\approx\)10 & \(\approx\)100 & Fractured after \(\approx\)10 min \\ DB200 & M, no mask & 311 & \(\approx\)3 & \(\approx\)84 & Fractured after \(\approx\)3 min \\ DS151 & A, 1 inch & 180 & 60 & \(\approx\)205 & No changes observed in the lens \\ DS151 & M, 1 inch & 180 & 60 & \(\approx\)127 & No changes observed in the lens \\ DS151 & A, 1 inch & 350 & 60 & \(\approx\)189 & No changes observed in the lens \\ DS151 & A, 1 inch & 852 & 60 & \(\approx\)342 & No changes observed in the lens \\ DS201 & M, 1 inch & 180 & 60 & \(\approx\)126 & No changes observed in the lens \\ DS201 & M, 1 inch & 350 & 60 & \(\approx\)191 & No changes observed in the lens \\ DS201 & M, 1 inch & 852 & 60 & \(\approx\)340 & No changes observed in the lens \\ CB100 & A, no mask & 311 & 60 & \(\approx\)182 & No changes observed in the lens \\ CB100 & A, no mask & 607 & \(\approx\)10 & \(\approx\)262 & Fractured after \(\approx\)10 min \\ CS100 & A, no mask & 311 & 60 & \(\approx\)148 & No changes observed in the lens \\ CS100 & A, no mask & 607 & 60 & \(\approx\)213 & No changes observed in the lens \\ \hline \hline \end{tabular}  
* \({}^{1}\) Measured by thermocouples located in the back of the lens, at shadow, but very close to the lens border.  
\end{table} 
Table 4: Summary of irradiation tests performed with xenon arc lamp. can be filtered using a borosilicate glass of Pyrex type in order to become more similar to the solar radiation spectrum. The unfiltered spectrum of the xenon arc lamp presents also a series of peaks in the region between 800 and 1000 nm, as shown in Figure 9b, which do not appear in the solar radiation spectrum. However, both silica and borosilicate glasses are transparent in this spectral range, in the close infrared region (as discussed later), and the associated energy should be too low to actually break the glass.
## 2 Related Work
### TP Monitoring  
Considering the crucial impact of the TP, scientists have recognized the great value of observation data on the TP and the implications for scientific research. Over the last several decades, a number of comprehensive observation stations have been built on the TP by Chinese research institutions. The Institute of Tibetan Plateau Research Chinese Academy of Sciences allied 21 stations to form the Tibetan Observation Network and Research Platform (TORP) [20] with the aim of providing long-term and in situ monitoring data. In 2013, the High-cold region Observation and Research Network for land surface processes and environment of China (HORN) was officially established to provide long-term and stable support to 17 comprehensive observation and research stations across the TP, and most TORP stations were included. Along with the ongoing advances in wireless technologies, several large-scale scientific experiments, such as the Global Energy and Water Cycle Experiment (GEWEX), Asian Monsoon Experiment on the Tibetan Plateau (GAME/Tibet, 1996-2000) [21], Coordinated Enhanced Observing Period (CEOP) Asia-Australia Monsoon Project on the Tibetan Plateau (CAMP/Tibet, 2001-06), Heihe Watershed Allied Telemetry Experimental Research (HiWATER) [22], and Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-III) [23; 24] have been conducted in recent decades and have significantly enhanced the Earth system observations and research on the Earth science system of the TP.  
While the ground observation network plays an important role in scientific research, these systems cannot retrieve data in the farmost areas of the TP due to the high construction cost and notable dependence on public networks. Thus, remote sensing has drawn much research attention regarding effective monitoring in inaccessible areas where in situ observations are absent [25; 26]. However, the high uncertainty in satellite-retrieved environmental variables has resulted in monitoring data discontinuities on a temporal-spatial scale. Additionally, considering the sparsity of observation stations and uncertainty in remote sensing monitoring, researchers [27; 28] have obtained environmental data in the farmost areas of the TP via interpolation and integrated learning methods. Although scientists have made great efforts to mitigate the problem of insufficient observation data on the TP, none of the presented works addressed observation data retrieval requirements in the farmost areas on the TP.
# Joint Location-Allocation Model for Multi-Level Maintenance Service Network in Agriculture
## 5 Case Study
### Case Description  
In this part, the maintenance service network is built, including three provinces in East China, namely Shandong, Anhui, and Jiangsu, and the maintenance data are obtained from a leading agricultural machinery manufacturer in China. As shown in Figure 3, there exist 251 service units in agriculture, and maintenance demands in these service units are shown in Figure 4. The manufacturer wants to construct 25 service stations in these 251 service units. In addition, the manufacturer can provide 75 service vehicles to be allocated to these service stations. The maximal service capacity per service vehicle is 300, and the usage costs per service vehicle are CNY 800.  
In order to satisfy the maintenance demands efficiently, the maximal service distance for service stations is set as 200 km. As for spare part centres, the construction costs for per spare part centre are set as CNY 10,000, and the maximal distribution distance for spare part centres is set as 360 km. In addition, the travelling distance between two service units is obtained from Baidu Map, and the service mileage costs (from service stations to service units) are set as CNY 0.5.  
Figure 2: Benders decomposition for MLFLAPs.
# Preliminary Results on Tropospheric ZTD Estimation by Smartphone
## 3 Results
### Case Study 1
Concerning the smartphone receiver, the two results are quite different; as already mentioned, if the online software is considered (CSRS-PPP), the solution is smoothed and stable over time because the software is not able to produce an estimation and it provides model-derived ZTD values, whereas considering the open-source one (RTKLIB), the solution is less stable, even if the values are quite reasonable. As shown in Figure 10, the differences between these two solutions vary from \(-0.04\) m to \(0.3\) m, with a mean value of \(0.2\) m and a standard deviation of \(0.1\) m, as summarized in Table 3.  
Figure 8: Comparison of ZTD values for TOSM considering CSRS-PPP (modelled, blue) and RTKLIB (estimated, orange). Figure 9: Differences in ZTD values for TOSM with CSRS-PPP and RTKLIB. Considering the TORI GNSS station, the results are definitely better than in the previous case: again, CSRS-PPP software provides more stable and smoothed ZTD solutions if compared to the RTKLIB ones (Figures 11 and 12), but the ZTD residuals are smaller than for TOSM because they are derived from an estimation process rather than from a model. The range of difference is from \(-\)0.01 m to 0.03 m, as seen from Figure 13, with a mean value of 0.007 m and a standard deviation of 0.01 m (Table 3). For the comparison shown in Figure 13, it should be noted that solutions falling in the convergence time have been excluded. The considered time interval, therefore, is from 11:00 a.m. to 1:00 p.m. UTC. This improvement is undoubtedly due to the quality of raw measurements, which are better in the case of TORI than TOSM. Nevertheless, even using the smartphone, it is possible to reach feasible results and interesting values for ZTD using RTKLIB.