esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation and Analysis
### Experimental Results and Analysis with IOMeter Tool
#### 5.3.2 I/O Performance
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**\% of Read**} & \multirow{2}{*}{**Total**} & \multicolumn{2}{c}{**Average Response Time (ns)**} & \multicolumn{2}{c}{**Overhead (\%)**} & \multicolumn{2}{c}{**Improvement (\%)**} \\ \cline{3-8}  & & **BL** & **MC** & **AP** & **MC vs. BL** & **AP vs. BL** & **AP vs. MC** \\ \hline
0 & 155,704 & 2,438,562 & 2,530,800 & 2,442,056 & 3.78 & 0.14 & 3.64 \\
25 & 151,518 & 2,089,008 & 2,187,306 & 2,095,965 & 4.71 & 0.33 & 4.38 \\
50 & 146,353 & 1,755,354 & 1,871,839 & 1,758,980 & 6.64 & 0.21 & 6.43 \\
75 & 151,477 & 1,406,386 & 1,545,373 & 1,410,152 & 9.88 & 0.27 & 9.61 \\
100 & 151,442 & 1,112,380 & 1,271,339 & 1,110,935 & 14.29 & 0.06 & 14.23 \\ Average & & & & & 7.81 & 0.20 & 7.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The I/O performance for Baseline, Meta-Cure, and Asymmetric Programming with different percentages of read operations (BL: Base Line; MC: Meta-Cure; AP: Asymmetric Programming). The response time of the read operation is another important issue. We used a workload with random accesses of 50% and a read operation of 50% to verify the performance of the Asymmetric Programming read operation. The workload lasted for five minutes. The experimental results shown in Figure 12 indicate that Asymmetric Programming can achieve nearly the same response time as the baseline scheme for read operations, while Meta-Cure has a response time over two times longer for read operations. This is because Meta-Cure must read one or more redundant backup pages for critical pages to restore the damaged metadata. That means an overhead cost of two or more times in the response time of read operations. We conducted this experiment using NAND flash with 1000 P/E cycles. As the P/E cycles increase, the uncorrectable page errors will also increase. The response time of Meta-Cure will increase correspondingly. However, Asymmetric Programming improves the reliability of metadata in the programming process without the need to conduct any extra read operations. Therefore, Asymmetric Programming will not cause any overhead in terms of response time for read operations.
## Appendix A  
Referring to the error equation as Equation (24), this study defines the Lyapunov function as Equation (A1):  
\[V=e^{T}Pe,\] (A1)  
where the matrix \(P=P^{T}>0\). By differentiating Equation (A1), the derivative of the Lyapunov function can be obtained as Equation (A2):  
\[\dot{V}=\dot{e}^{T}Pe+e^{T}P\dot{e}\] (A2)  
By substituting Equation (24), Equation (A2) can be further rearranged as Equation (A3):  
\[\dot{V}=\left(e^{T}A^{T}Pe+e^{T}PAe\right)+2\left(e^{T}Pb\right)\left(\delta a_ {m}+\eta\right)\] (A3)  
Then, referring to Equation (28), Equation (A3) can be rewritten as Equation (A4):  
\[\dot{V}=-e^{T}Qe+2\left(b^{T}Pe\right)^{T}\left(\delta a_{m}+\eta\right)\] (A4)  
Because the uncertainty \(\eta\) is bounded by \(\rho\) as Equation (26), Equation (A5) can be deduced from Equation (A4):  
\[\dot{V}<-e^{T}Qe+2\left(b^{T}Pe\right)^{T}\left(\delta a_{m}+\rho\frac{b^{T}Pe }{\left\|b^{T}Pe\right\|}\right)\] (A5)  
Case (A): in the case that the additional term \(\delta a_{m}\) is designed as Equation (27) with \(\left\|b^{T}Pe\right\|>\sigma\), then \(\delta a_{m}=-\rho\frac{b^{T}Pe}{\left\|b^{T}Pe\right\|}\).  
\[\text{Since }2\left(b^{T}Pe\right)^{T}\left(\delta a_{m}+\rho\frac{b^{T}Pe }{\left\|b^{T}Pe\right\|}\right)=0\text{ and }Q=Q^{T}>0,\,\dot{V}<0.\]Case (B): If the additional term \(\delta a_{m}\) is designed as Equation (27) with \(\left\|b^{T}Pe\right\|\leq\sigma\), then \(\delta a_{m}=-\frac{\rho}{\sigma}b^{T}Pe\), and thus \(2\big{(}b^{T}Pe\big{)}^{T}\Big{(}\delta a_{m}+\rho\frac{b^{T}Pe}{\left\|b^{T}Pe \right\|}\Big{)}=-2\frac{\rho}{\sigma}\big{\|}b^{T}Pe\big{\|}^{2}+2\rho\big{\|} b^{T}Pe\big{\|}\). Therefore, Equation (A5) can be rewritten as Equation (A6):  
\[\dot{V}<-e^{T}Qe+\left(-2\frac{\rho}{\sigma}\big{\|}b^{T}Pe\big{\|}^{2}+2\rho \big{\|}b^{T}Pe\big{\|}\right)\] (A6)  
Since the minimum eigenvalue of the matrix \(Q\), \(\lambda_{\min}(Q)\), satisfies the inequality \(\lambda_{\min}(Q)\|e\|^{2}\leq e^{T}Qe\), to achieve \(\dot{V}<0\), Equation (A7) must be satisfied:  
\[2\frac{\rho}{\sigma}\big{\|}b^{T}Pe\big{\|}^{2}-2\rho\big{\|}b^{T}Pe\big{\|}+ \lambda_{\min}(Q)\|e\|^{2}>0\] (A7)  
Since Equation (A7) is a quadratic inequality with a minimal value of \(-\frac{\rho\sigma}{2}+\lambda_{\min}(Q)\|e\|^{2}\) when \(\big{\|}b^{T}Pe\big{\|}=\frac{\sigma}{2}\), there exists a matrix \(Q\) such that Equation (A8) is satisfied:  
\[-\frac{\rho\sigma}{2}+\lambda_{\min}(Q)\|e\|^{2}>0\] (A8)  
Then, \(\dot{V}<0\) and \(\|e\|>\sqrt{\frac{\rho\sigma}{2\lambda_{\min}(Q)}}\).  
The description of Case (A) denotes that there exists a set \(S_{A}\) so that the solution trajectory of Equation (24) will reach and enter the set \(S_{A}\) since \(\dot{V}<0\) is outside \(S_{A}\). The description of Case (B) denotes that there exists a set \(S_{B}\) with \(\|e\|\leq\sqrt{\frac{\rho\sigma}{2\lambda_{\min}(Q)}}\) and \(S_{B}\subseteq S_{A}\). Therefore, the descriptions of Case (A) and Case (B) denote that the solution trajectory of Equation (24) will be bounded within the region between set \(S_{A}\) and set \(S_{B}\).
# A Fast Approach to Optimize Trad Pattern Shape for Tire Noise Reduction
## 2 Tire-Pavement Interaction Noise
### Prediction Model of Tire Pattern Noise  
Tire pattern noise is mainly generated by the interaction between the tread pattern and the road surface. The leading and trailing edges play a significant role in tread noise generation. At the leading edge, noise is mainly generated due to the impact between the tread blocks and road surface when the blocks enter the contact patch, and the vibrations are excited by time-varying impact forces. The vibration wave does not propagate very far because of tread rubber's high damping property. For the trailing edge, the noise generation mechanism is similar to that at the leading edge. These patterns located in the contact area cause minimal vibration due to their strong adhesion to the road surface. In this study, the vibration caused by the adhesion between these blocks and the road surface is negligible.  
As described in Section 2.1, the relationship between sound pressure and impact force can be expressed as [36]:  
\[p\propto a\propto F \tag{3}\]  
where \(a\) is the acceleration of the tread element, and it relates to the tread impact force at the leading edge, which is necessary for predicting tire noise. Here, the impact force (forcing function) is derived from the discrete impact of each tread element on the pavement. These repeated impacts induce vibrations, which propagate through the air and surrounding structure, resulting in noise. However, accurately calculating the impact force for an actual commercial patterned tire is a significant challenge. Therefore, it is assumed that a pulse is generated when the tread blocks make contact with the road surface. On the contrary, no force is generated by the tread voids or grooves. Accordingly, the force function can be described as follows:  
\[F(x,y)=\gamma(x,y)\delta(x,y) \tag{4}\]  
where \(\gamma(x,y)\) is the amplitude of the exciting force on the tire tread surface, represented by \(x,y\), and it is related to the tire structure, material, etc. \(\delta\) is the Dirac delta function, which is written as:  
\[\delta(x,y)=\left\{\begin{array}{ll}0&\text{for groove}\\ 1&\text{for block}\end{array}\right. \tag{5}\]  
Integrating Equation (4) along the meridian direction of the tire tread, the impact force between the tire and road surface during rolling can be expressed as:  
\[F(\varphi)=\int_{leading}\gamma(x,\varphi)\cdot\delta(x,\varphi)d_{x}\ \ \ \text{for all}\ \varphi =0\dots 2\pi \tag{6}\]  
The spectrum of sound pressure in the frequency domain can be expressed as:  
\[F(t)=\frac{a_{0}}{2}+\sum_{n=1}^{\infty}\left(a_{n}\cos n\omega t+b_{n}\sin n \omega t\right) \tag{7}\]where \(\omega=2\pi/T\) is the frequency, \(T\) is the cycle of rotation, and \(a_{n}\) and \(b_{n}\) can be expressed by:  
\[\begin{array}{c}a_{n}=\frac{2}{T}\sum\limits_{i=1}^{N}F(t)\cos n\omega t_{i}\\ b_{n}=\frac{2}{T}\sum\limits_{i=1}^{N}F(t)\sin n\omega t_{i}\\ c_{n}=\sqrt{a_{n}^{2}+b_{n}^{2}}\end{array} \tag{8}\]  
where \(c_{n}\) is the amplitude for the \(n\)-th harmonics calculated using the Fourier transform of the exciting force in Equation (7).
## 3 Methodology
### Proposed Algorithm
If there is no active server to accommodate the VM, the algorithm selects an appropriate host from the set of inactive servers that has the most energy-efficient metric (line 6). The chosen host will be associated to the VM (line 7). Finally, when all the VMs are allocated to their hosts, the algorithm returns the final set of VM-PM pairings. Let there be \(n\) number of VMs in set \(\mathbb{V}\) and \(m\) number of PMs in set \(\mathbb{H}\). The algorithm takes \(\mathcal{O}(n\log n)\) to sort VMs in descending order based on resource demands. For each VM, the algorithm checks all active PMs to find a suitable host and then checks all inactive ones in case there is no active host available. All calculations for \(\mathbb{E}^{e}(h)\) take a constant amount of time (\(\mathcal{O}(1)\)) and can be ignored. Thus, the overall time to sort VMs and to check all hosts takes \(\mathcal{O}(n\log n+nm)\). As the number of PMs is smaller than the number of VMs in the cloud system, we can express the complexity for the algorithm as \(\mathcal{O}(n\log n+n^{2})\). As \(n\) increases, the overall complexity can be simplified to \(\mathcal{O}(n^{2})\). The fundamental concept of including the \(\mathbb{R}^{w}(h)\) factor in the choice of PM is to enhance the utilization of a physical machine's resources across various dimensions while ensuring a balanced distribution of remaining resources. It also helps with mitigating unnecessary potential migrations due to resource contention, thus increasing the energy optimization for the whole data center. Since \(\mathbb{R}^{w}(h)\) represents the normalized resource wastage of the host, \((1-\mathbb{R}^{w}(h))\) reflects the normalized resource usage, which we include in our calculation of the energy-efficiency factor (\(\mathbb{E}^{e}(h)\)). This means that the host that has an acceptable amount of resource usage will be prioritized for hosting the VM and thus lead to more utilized hosts while minimizing the number of powered-on servers overall. This indicates a reduction in the total power consumption of the data center caused by decreasing the number of active hosts. To reduce the data center's power consumption, overloaded and underloaded machines are not considered for hosting. This exclusion occurs when a physical machine's remaining capacity surpasses a specific threshold, either exceeding an upper threshold or falling below a lower one. Consequently, VMs on underloaded PMs will be migrated to other hosts, enabling the shutdown of those machines to save energy.
# Blockchain-Based Multistage Continuous Authentication for Smart Devices
## 4 System Model
### System Architecture
#### 4.3.2 Authentication  
When a legitimate user wishes to access the services of a service provider, then the authentication process needs to be completed. The authentication process is shown in Figure 16. The specific steps for authentication are as follows:  
Step 1: The user enters the \(ID_{i}\), biometric information, and \(Info\) into the client.  
Step 2: The client extracts current login time \(T_{1}\) and sends the \(A=PK(hashInfo^{\prime},T_{1})\) to the service provider. Then, the service provider decrypts \(A\) with a service-side private key \(SK\) and obtains \(hashInfo^{\prime}\), \(T_{1}\).  
Step 3: The service provider sends \(B=EPK(hashInfo^{\prime},T_{1},T_{0})\) to the client. The client decrypts \(B\) with its own private key \(ESK\) and obtains \(hashInfo^{\prime}\), \(T_{1}\), and \(T_{0}\). The client compares the results with \(T_{1}\) saved in step 2; if they are equal, the service side verification is completed. At the same time, users can check if the \(T_{0}\) moment matches the registered login time to avoid someone from stealing it.  
Step 4: The client extracts the user's biometric template \({\omega_{i}}^{\prime}\) and recovers the user's random key \(R_{i}^{\prime}\) based on \({\omega_{i}}^{\prime}\) and the locally stored public information \(P\). Then, the random key is hashed to generate \(HR_{i}^{\prime}\). Then, the client inputs \(ID_{i}\), \(HR_{i}^{\prime}\), and \(C=PK(hashInfo^{\prime})\) to the service provider.  
Step 5: The service provider retrieves the user's index \(ID_{i}\) in the blockchain and decrypts \(C\) using the private key \(SK\) to obtain \(hashInfo^{\prime}\). Then, the decrypted information is compared with the \(hashInfo\) entered for registration. If the comparison is successful, the system will return the file hash address in the IPFS. The user's credential information is verified.  
Step 6: The service provider downloads the user's \(HP_{i}\) in the IPFS system. If the \(HR_{i}\) is the same as the \(HR_{i}^{\prime}\), the user's biometric authentication succeeds.  
Figure 15: Process of user registration.
## 1 Introduction
This study developed a simulation model using the \"Trace 3D Plus\" software to specifically focus on the thermal environment and energy efficiency in residential buildings located in hot climates. While other studies, as discussed earlier, have analyzed passive design strategies or adaptive solutions, this research takes a comprehensive approach by testing a wide range of passive solutions--such as building orientation, roof and wall modifications, glazing options, shading techniques, and natural ventilation--across different urban settings and house types. Additionally, the study assesses the cumulative effects of combining these solutions, offering a holistic understanding of their impacts on both the thermal environment and energy demand. This comprehensive approach has not been previously explored in such depth. As a result, the findings presented in this work offer a novel contribution to the field. The relevance of this research lies in its focus on practical, location-specific solutions for hot climates, where thermal environment challenges are particularly pronounced. Moreover, the inclusion of an energy demand analysis--evaluating how much thermal energy still needs to be managed after applying passive measures--makes this study highly actionable for real-world applications in sustainable building design. The methodology of selecting cities with the highest air temperatures ensures that the findings are applicable to other thermally stressed regions.
## 1 Introduction  
Energy consumption in buildings is a field of extensive research worldwide. Buildings are one of the fastest growing sectors in energy consumption in the last decades [1, 2, 3], which is due to population growth, industry development, expansion of cities, and improvement of living standards [4]. The aforementioned implies a growth in the demand for thermal comfort and indoor air quality [5]. This sector has a contribution between 20-40% of the global final energy consumption [6, 7], where these percentages vary according to region. For example, buildings in European Union Member States represent about 40% of total final energy consumption and 36% of carbon emissions [8]. Electricity is one of the most widely used energy sources in buildings. It is estimated that buildings consume about 60% of the global electricity [9], which has increment its use in more than 19% between 2010 and 2018 [10]. The energy consumption of buildings is mainly affected by factors such as weather conditions, building characteristics, and the operating conditions of building systems [6]. Heating, ventilation, and air-conditioning systems (HVAC) are used to provide indoor thermal comfort and their demand depends mainly on local climate, building design and internal loads. These systems are the largest consumers of energy in buildings with about 40% of it [7; 11]. Also, the energy demand for HVAC is affected by the thermal properties of the building envelope (walls, doors, roofs, windows, etc.) mainly in warm climate locations [12; 13; 14]. Artificial lighting is the second most energy-consuming system in buildings, ranging from 20% to 45% [15; 16]. Educational buildings are among the main consumers of energy in the commercial building sector, where in countries, such as USA, Australia and UK, the energy consumption of these buildings is between 10% and 13% [7; 17]. The frequency of use and occupancy of classrooms could considerably increase the energy bills [15]. In the case of Ecuador, commercial buildings account for 20.5% of the total Ecuadorian electricity demand [18; 19]. Therefore, it is necessary to look for initiatives that help to improve the efficiency in the use of energy in the buildings maintaining the levels of comfort of the users, with minimum cost and reduction of their carbon footprints.
## 2 Results and Discussion
### Compared with Other Methods  
The performance of an extraction method based on C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) was compared with previously reported extraction methods for the determination of food additives. As can be seen in Table 4, the proposed method was demonstrated to have lower LODs values compared to other methods, and also had good RSD values and recovery.  
In addition, this study also compared it with PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) without C18 modification. The results showed that the materials without C18 modification were inferior to those without C18 modification in terms of detection limit and recovery. In the process of adsorption, PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) belongs to physical adsorption and had no selectivity, while C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) belongs to chemical adsorption and had a certain selectivity, so the adsorbed material was purer, thus reducing the matrix effect and increasing the detection accuracy of the instrument.
## 4 Stationary Sensor Network
### Wireless Sensor Networks
The network manager than assigns this new node a transmission schedule. The network manager may allocate more slots to a node to increase throughput. Due to clock drift, nodes are also required to re-synchronize regularly. The joining and synchronization process may take some time, depending on the size of the network, external interference, and the length of the slot frame. As such, a traditional TSMP network does not lend itself readily to applications which require mobile agents and fast response times. The TSMP routing table, and transmission schedule are updated at most once every slot-frame. A MAV agent may change its location, and thus its neighbors, much more quickly than a centralized controller can keep track off. As such, a modification must be made to the TDMA-based network schedule to accommodate the dynamics of the MAV. Aside from supporting scheduled communications between neighboring nodes, TSMP also supports multihop communications via the Routing Protocol for Low power and Lossy Networks (RPL) [25]. RPL is a gradient-based routing algorithm which provides multiple data paths between nodes. Data flows, one hop at a time from one node to another. This permits nodes that would otherwise be out of range to communicate with each other. Detailed information on such routing is given in [13,25]. While TSMP architecture is designed to facilitate low throughput communications, it can be augmented to allow sporadic bursts of large data as shown in the next section.
## 2 Materials and Methods
### Methods
#### 2.3.4 Evaluation of the Relationship between GPP and Downscaled SIF  
The coefficients of determination (R\({}^{2}\)) obtained from linear regression were employed to assess the degrees of linear association between GPP and three different SIF datasets: raw 0.05\({}^{\circ}\) SIF, downscaled SIF EC footprint-averaged (\(\text{SIF}_{\text{ECA}}\)), and downscaled SIF EC footprint-weighted (\(\text{SIF}_{\text{ECW}}\)). This analysis aimed to validate and assess the correlation between the downscaled SIF and GPP. \(\text{SIF}_{\text{ECA}}\) represents the average SIF value across 90% of the cumulative EC flux footprint range. On the other hand, \(\text{SIF}_{\text{ECW}}\) was calculated  
Figure 3: EC flux footprints for SHB1 and SHB2. The colors of the monthly flux footprints denote the cumulative contribution gradient, with the outermost color representing 90% to 80% and values toward the center decreasing by 10% increments.  
by assigning a weight to each flux footprint contribution within the 0-10%, 10-20%, \(\cdots\), 70-80%, and 80-90% ranges. To ensure the sum of the weighting coefficients of SIF is 1, each part has a weight of 10/90 (as shown in Equation (1)).  
\[\text{SIF}_{\text{ECW}}=\sum_{\text{k=1}}^{\text{NI}}\frac{\text{SIF}_{\text{k} }}{\text{N1}}\times\frac{10}{90}+\sum_{\text{k=1}}^{\text{N2}}\frac{\text{SIF} _{\text{k}}}{\text{N2}}\times\frac{10}{90}+\cdots\sum_{\text{k=1}}^{\text{NS}} \frac{\text{SIF}_{\text{k}}}{\text{N8}}\times\frac{10}{90}+\sum_{\text{k=1}}^{ \text{NS}}\frac{\text{SIF}_{\text{k}}}{\text{N9}}\times\frac{10}{90} \tag{1}\]  
The variables N1 to N9 refer to the total number of pixels within the image that fall within each of the nine cumulative footprint ranges, from the range of 0-10% to 80-90%. \(\text{SIF}_{\text{k}}\) represents the value of SIF for the specific pixel k located within the corresponding footprint range.  
Additionally, the EC flux footprint-averaged VI (\(\text{VI}_{\text{ECA}}\)) and EC flux footprint-weighted VI (\(\text{VI}_{\text{ECW}}\)) were computed using an identical approach to assess variations in the correlation between \(\text{VI}_{\text{ECA}}\) and GPP, as well as between \(\text{VI}_{\text{ECW}}\) and GPP. It is worth noting that EC observations in the Saihanba area began on 1 August 2020, while the SIF and VIs data were from 2020 to 2021. Therefore, this study of the correlation between SIF, VIs, and GPP considered only data after 1 August 2020.
# Study of Partial Discharge Inception Voltage in Inverter Fed Electric Motor Insulation Systems
## 4 Analysis of PDIV and Modelling Tool Performance for Variable Temperature
### Performance of PDIV Models for Variable Temperature
#### 4.2.3 Lusuardi Model  
In contrast to the Dakin and parallel-plate models, the Lusuardi model considers the effect of the temperature on the surrounding air properties. The Lusuardi model was  
Figure 11: Comparison of experimental measurements and Dakin PDIV estimation for different temperatures (sample group B).  
Figure 12: Comparison of experimental measurements and parallel-plate model (sample group C).  
proposed in [26] and defined as Equation (11). In Figure 13, the experimental results compared to the Lusuardi model for sample groups B (greater change of permittivity with temperature) and C (lower change of permittivity with temperature) are illustrated.  
It can be seen in Figure 13 that the model best fits to sample group C, whose permittivity is less dependent on the temperature. In this case, the model results remain inside the uncertainty range of the measurement, with a maximum error of around 10%. Adversely, due to the higher influence of the permittivity on sample group B, the calculated values for the PDIV at high temperatures are outside of the measurement uncertainty. However, the accuracy could be considered acceptable as the maximum error is around 12%. As the author of [26] mentions, the difference between the obtained results for both sample groups may be attributed to the influence of the relative permittivity. Therefore, the estimation error is higher when the permittivity variation with the temperature is increased.  
The main drawbacks of the Lusuardi model are the requirement of an experimental PDIV value and the non-consideration of the relative permittivity. All in all, this model could accurately estimate the PDIV at operating temperatures.