esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Validation of the PT-JPL-Thermal ET
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Monthly \(ET\)** & \(\rho\) & \(\mathbf{e_{2}}\) & \(\mathbf{MAE}\) & \(\mathbf{Bias}\) & \(\mathbf{RMSE}\) \\  & & & & **(mm/day)** & \\ \hline PT-JPL-t vs. WATEN & 0.78 \({}^{*}\) & 0.59 & 0.74 & 0.13 & 0.9 \\ PT-JPL-t vs. WATEN\({}_{\text{1 month-lag}}\) & 0.94 \({}^{*}\) & 0.87 & 0.39 & 0.13 & 0.51 \\ MOD16 vs. WATEN & 0.48 \({}^{*}\) & \(-0.27\) & 1.17 & \(-0.92\) & 1.58 \\ MOD16 vs. WATEN\({}_{\text{1 month-lag}}\) & 0.18 \({}^{*}\) & \(-0.42\) & 1.22 & \(-0.94\) & 1.67 \\ \hline \hline \multicolumn{4}{l}{\(ET\)**average seasonality**} \\ \hline PT-JPL-t vs. WATEN & 0.83 \({}^{*}\) & 0.67 & 0.68 & 0.13 & 0.78 \\ PT-JPL-t vs. WATEN\({}_{\text{1 month-lag}}\) & 0.99 \({}^{*}\) & 0.96 & 0.25 & 0.13 & 0.29 \\ MOD16 vs. WATEN & 0.65 \({}^{*}\) & \(-0.28\) & 1.15 & \(-0.92\) & 1.54 \\ MOD16 vs. WATEN\({}_{\text{1 month-lag}}\) & 0.17 \({}^{*}\) & \(-0.43\) & 1.18 & \(-0.92\) & 1.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Pearson correlation coefficient \(\rho\) significant \({}^{*}\) at \(p\) < 0.001, the Nash-Sutcliffe \(e_{2}\), the Mean Absolute Error (\(MAE\)), the bias, and the Root Mean Square error (\(RMSE\)) for the monthly \(ET\) and average climatology \(ET\) derived from the PT-JPL-thermal and MOD16 vs. WATEN.
## 2 Materials and Methods
### Proposed Method
Therefore, to deal with this problem, the proposed approach must consider a condition that if a neighbor exists in the testing set, the proposed approach should increase the number of \(k\) for neighbors' selection as long as all the selected neighbors are from the training set. Figure 1: An example of the proposed method for phase prediction in HEAs. In Step (**a**), we extracted the related features/descriptors from the HEA database, and then (**b**) created an interaction network based on the similarities between HEAs. (**c**) In this example ZrHfTiCuNi alloy is considered as a phase prediction sample, hence related community of this HEAs is extracted from the interaction network. (**d**) The ZrHfTiCuNi has four neighbors; therefore, three of them were selected which are highlighted in dark blue if k is three. (**e**) Finally, phase can be predicted by voting on neighbors’ labels as Amorphous for ZrHfTiCuNi. The dataset used in this study referred to Table 1 in [1] that the HEA interaction network is made on [19]. This dataset contained 90 HEA compounds with seven descriptors which were grouped into five phases including FCC, BCC, HCP, Multiphase, and Amorphous. The descriptors included \(\delta\), \(\Delta H_{mix}\)(kJ/mol), \(S_{c}\)(\(k_{B}\)_per atom_), \(\varphi\), \(e_{RMS}\), and VEC, which expressed the atomic size difference, mixing enthalpy, configurational entropy, thermodynamic, energy storage density, and valence electron concentration, respectively. The compounds connected to the target compound are called neighbors. The number of \(k\) was determined by the trial-and-error method. The number of \(k\) means the number of neighbors that were selected for voting. Since the model was trained according to the method described above, it predicted the phase.
# MBPPE: A Modular Batch Processing Platform for Electroencephalography
## 2 Materials and Methods
### Preprocessing
#### 2.3.1 Filter  
In the preprocessing of EEG signals, filters serve as an efficient tool for artifact removal, particularly when artifacts and EEG signals reside in distinct frequency bands [13]. MBPPE provides two types of filters: finite impulse response (FIR) filters and infinite impulse response (IIR) filters. The design process for the FIR filter involves the window function method, using the Hanning window by default, while the IIR filter uses a Butterworth filter. Both filters offer users the flexibility to customize parameters like filter frequency and order. They also provide three basic filtering methods: band-pass, low-pass, and high-pass.  
In terms of implementation specifics, the FIR filter coefficients are calculated via the firwin method in the SciPy [14] library. Conversely, the Butterworth filter coefficients are generated through the SciPy library's butter method. Subsequently, the filttilt method is utilized for bidirectional data filtering based on these filter coefficients. This involves applying a linear digital filter twice, once forward and once backward.  
It is noteworthy that the advantages of FIR filters lie in their intrinsic stability, and they do not introduce phase distortion, thereby effectively preserving the temporal information of events. Conversely, the IIR filter, while requiring less computation, does not offer the same level of stability.
## 5 Conclusions  
In this paper, a large-scale wireless temperature monitoring system for LPG storage tanks was developed and applied to a real-world monitoring project. The main conclusions are outlined below:  
Figure 21: Environment temperature monitoring data.  
Figure 20: LPG storage tank temperature monitoring data.  
(1) Hardware for the monitoring system was developed which includes wireless temperature sensors, fiber optic high temperature sensors, onsite monitoring system, and the remote access system. The hardware of the monitoring system allows the multi-parameter monitoring, wireless sensing and harsh environments adaptive, local and remote access.  
(2) Software for the monitoring system was developed which can configure the sensors' data acquisition interval, diagnose sensor's health status, display the monitoring parameters, such as inner temperature, surface temperature, inner-surface temperature difference, and the environment temperature, both in 3D vision and in history curves. The temperature changing rate and the maximum history temperature are calculated by the software automatically. All the data can be exported to Excel file format. The software for the mass concrete fulfills the requirements of automatic monitoring and data management.  
(3) The engineering practice validated the feasibility of the developed system and it has advantages, such as being easy to deploy, easy to use, and highly reliability.  
Upon validation, the system can be widely applied to the monitoring of LPG storage tanks and other liquid or chemical storage tanks, which will significantly improve the reliability and safety management of these critical facilities.
## 2 Algorithm Principle
### State Equation  
We combine the attitude error, velocity error, and position error of the INS with the first-order Markov drift of the gyroscope and the first-order Markov offset of the accelerometer. Here, due to the limited space, we only list the related equations and omit the tedious deduction process; the detailed process can be found in [23; 24]. We take the  
Figure 2: Schematic diagram of INS+LEO2-satellite alternate switching ranging integrated navigation algorithm.  
navigation coordinate system as the geographic coordinate system, and we can obtain the state variables as follows:  
\[\mathbf{X_{S}}=[\mathbf{\varphi_{E}},\mathbf{\varphi_{N}},\mathbf{\varphi_{U}}, \mathbf{\delta V_{E}},\mathbf{\delta V_{N}},\mathbf{\delta V_{U}},\mathbf{ \delta L},\mathbf{\delta\lambda},\mathbf{\delta h},\mathbf{\xi_{E}},\mathbf{ \xi_{N}},\mathbf{\xi_{U}},\mathbf{\
abla_{E}},\mathbf{\
abla_{N}},\mathbf{ \
abla_{U}}]^{T} \tag{5}\]  
where \(\mathbf{E}\), \(\mathbf{N}\) and \(\mathbf{U}\) represent the east, north and up directions of the local Cartesian coordinate system (ENU coordinate system), respectively; \(\mathbf{\varphi_{E}}\), \(\mathbf{\varphi_{N}}\) and \(\mathbf{\varphi_{U}}\) represent the roll angle, pitch angle and yaw angle of the carrier, respectively; and \(\mathbf{\delta L}\), \(\mathbf{\delta\lambda}\) and \(\mathbf{\delta h}\) represent the latitude, longitude and altitude errors of the carrier, respectively. \(\mathbf{\delta V_{i}}\) represents the velocity error in all directions; \(\mathbf{\xi_{i}}\) is the first-order Markov drift of the gyroscope in the carrier coordinate system; \(\mathbf{\
abla_{i}}\) is the first-order Markov offset of the accelerometer, and \(i=\mathbf{E},\mathbf{N},\mathbf{U}\).
## 4 Discussion
### Future Research  
Future work could involve creating an EU-BEM, European Union building energy model, encompassing all EU countries that adhere to common frameworks for obtaining the necessary information to develop these models. This would enable the analysis of decarbonization progress at a supranational scale, facilitate data pooling among different countries, and evaluate the effectiveness of various energy renovation strategies and policies within the community framework. This would allow for the following:  
* Evaluating different policies and technologies for reducing energy consumption and emissions from buildings.
* Obtaining progress indicators in different MSs in a consistent method and using the same procedures.
* Gathering large-scale information to support data-driven decision-making.
* Developing strategies to achieve climate goals.
* Evaluating and comparing the performance of different renovation policies and strategies.
* Assessing in what way some renovation policies have performed better than others, depending on the chosen case scenario.
* Increasing the amount of data to enhance the algorithm's understanding of the patterns defining energy behavior.  
To make the EU-BEM a reality, several challenges must be addressed, mainly concerning data availability, processing, privacy, and standardization. While it is possible to process INSPIRE cadastral buildings data across Europe, a thorough examination and standardization of each country's data is still required.  
Moreover, the integration of data-rich sources such as building information modeling (BIM), digital twins, and digital building logbook (DBL) data represents a significant advancement in both precision and scope [68]. These technologies provide access to detailed information about key energy-related characteristics, thereby substantially enhancing the model's predictive capabilities. Additionally, this integration addresses current gaps associated with the lack of data on construction systems, offering a more robust foundation for evaluating the energy performance of the building stock.
## 5 Experiments and Data Analysis
### 5G Experimental Base Station Testing  
In the first measurement scenario, an experiment is conducted using a 5G base station in a 5G laboratory. The CSI-RS signal parameters of 5G base stations in the laboratory are shown in Table 4. The measurement scenario is shown in Figure 13a, and the model is shown in Figure 13b.
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 2 Materials and Methods
### Stability Proof of Satellite Control  
In this section, the stability of the proposed self-organizing control is proved based on the Gaussian equations and [PERSON]'s theory. The selection criteria of potential function variables for satellite control are discussed.
## 3 Methodology
### Index for Interferometric Performance Evaluation  
The coherence of InSAR quantifies the stability of the radar signal's backscattering characteristics at the wavelength scale. It serves as a metric for assessing the quality of an interferogram and offers valuable insights into the nature of the ground surface. Additionally, coherence is instrumental in detecting subtle variations in scatter distribution, which can indicate small-scale changes in the terrain or surface properties.  
The coherence coefficient of images \(S_{1}\) and \(S_{2}\) can be quantitatively represented as:  
\[\gamma=\frac{\sum\left|S_{1}(j,k)\times S_{2}^{*}(j,k)\right|}{\sqrt{\sum\left| S_{1}(j,k)\times S_{1}^{*}(j,k)\right|\times\sum\left|S_{2}(j,k)\times S_{2}^{*}(j, k)\right|}} \tag{3}\]  
where * denotes the complex conjugate, \(S_{1}\) and \(S_{2}\) represent single-look complex SAR images, and \(0<\gamma<1\). A value of 0 signifies incoherence, while 1 indicates complete coherence. This coefficient is crucial for assessing measurement accuracy.  
The backscatter intensity, a power ratio of transmitted versus returned pulses in the SAR system, is represented in intensity values and converted into normalized backscatter coefficients (\(\omega^{0}\)) in dB during interferometric processing. The coherence between the images is influenced by backscatter intensity, with higher intensity ensuring greater coherence.  
Thermal decorrelation, denoted by \(\gamma_{thermal}\), is due to the thermal noise induced by the characteristics of the SNR of the radar system.  
\[\gamma_{thermal}=\frac{1}{1+SNR^{-1}} \tag{4}\]\[SNR=10 lg(P_{s}/P_{n}) \tag{5}\]  
\(P_{s}\) is the average received signal power, \(P_{N}\) is the power of thermal noise in the receiver system, and \(SNR\) is the signal-to-noise of the radar system. Baseline decorrelation is a geometric decorrelation related to the baseline, whereby the larger the perpendicular baseline, the greater differences between the primer and secondary SAR images. The baseline decorrelation could be simply defined as:  
\[\gamma_{baseline}=\left\{\begin{matrix}1-B_{\perp}/B_{c}&B_{\perp}<B_{c}\\ 0&B_{\perp}>B_{c}\end{matrix}\right. \tag{6}\]  
\[B_{c}=RB_{w}tan(\theta_{inc}-\alpha)/f \tag{7}\]  
where \(B_{c}\) represents the critical baseline; \(f\) is the radar frequency; \(B_{w}\) denotes the bandwidth; and \(\alpha\) denotes the terrain slope.
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
###### Abstract  
A wide-scale outdoor remote deployment involves a large number of low-cost nodes that are powered by green energy, such as solar. We deal with such a system for landslide monitoring where the tiny nodes with ultra-low memory as little as 2 KB are directly connected to the Internet using cellular networks, thereby constituting Cellular IoT's (C-IoT). This makes them vulnerable to a wide range of Denial of Service (DoS) attacks during their cellular commutative communications. Further, due to memory constraints, the nodes are not able to run resource-hungry security algorithms. Existing IoT protocols also cannot offer resiliency to DoS attacks for these memory-constrained devices. This paper proposes the Voice Response Internet of Things (VRITHI), which addresses the above issues by using the voice channel between the nodes. To the best of our knowledge, this is the first solution in the IoT domain where both the voice and data channels are being used for collaborative communications. Evaluation results demonstrate that VRITHI is able to reduce external DoS attacks from 82-65% to less than 28% and improves real-time communications in such a memory-constrained environment. In addition, it also contributes to green IoT energy saving by more than 50% in comparison with other IoT protocols.  
Internet of Things; green IoT; real-time; sensor applications; landslides Article
## 4 Experiments and Results
### Robustness Analysis
#### 4.3.2 Watermark Extraction Results  
After embedding the watermark information into the experimental BIM data, watermark extraction and detection were performed to check whether the watermark was successfully embedded. When the watermark data are not subjected to any attack, the water-mark should be detected completely, i.e., the NC should be 1. The watermark detection was performed without attacking the BIM data, and the results are presented in Table 5.  
As shown in Table 5, the NC values for all three BIM data watermark extractions were 1. This indicates that after the pre-element screening and embedding condition judgment, the watermark information was correctly embedded into the carrier and extracted completely via the blind detection process.  
\begin{table}
\begin{tabular}{c c c} \hline
**BIM Data** & **Redundancy (Mb)** & **Ratio (\%)** \\ \hline Office building & 0.02 & 0.14 \\ Gymnasium & 0.39 \(\times\) 10\({}^{-2}\) & 0.02 \\ Structural model & 0.05 & 0.09 \\ \hline \end{tabular}
\end{table}
Table 4: Results of redundancy increment.  
Figure 7: File increment comparison.
# MinHash-Based Fuzzy Keyword Search of Encrypted Data across Multiple Cloud Servers
## The Proposed Fuzzy Keyword Search Scheme Based on MinHash
### The Proposed Scheme
\[\text{E}^{\prime}({\text{w}_{i}})=\text{g}^{\text{r}(s^{\prime}+\delta_{i})}. \text{h''}=(\text{h}\cdot\text{g}^{\delta_{i})},1\leq\text{i}\leq\text{d}\] (6)Trapdoor (s', t, w\({}_{1^{\prime}}\), w\({}_{2^{\prime}}\),..., w\({}_{\rm w}\)'): The authorized user enters s', t and keywords w\({}_{1^{\prime}}\), w\({}_{2^{\prime}}\),..., w\({}_{\rm w^{\prime}}\) to be retrieved, randomly selects t\({}^{\prime\prime}\in\ Z_{\rm p}^{*}\) and calculates Y = (g\({}^{\rm r}\))\({}^{\prime\prime}\). For each keyword w\({}_{\rm i}\) to be searched, the user calculates the fingerprint value of the keyword w\({}_{\rm i}\) by using the keyword fingerprint generation algorithm MinHash (s', w\({}_{\rm i}\)), which is the trapdoor T\({}_{\rm i}\), 1 \(\leq\) i \(\leq\) w. The trapdoor T = (T\({}_{\rm i}\), T\({}_{\rm 2}\),..., T\({}_{\rm w}\), Y) is sent to the server S.
8. Search (T, I, C): After the cloud server S receives the search request from the authorized user, it enters h\", calculates the Jaccard distance of the fingerprint Si in the trapdoor T and index I\({}_{\rm w_{i}}\) and sends I\" to servers S\({}_{\rm 1}\), S\({}_{\rm 2}\),..., S\({}_{\rm N}\). Then, server S\({}_{\rm j}\) (j = 1, 2,..., N) calculates m = A(u\({}_{\rm i}\))\(\cap\)A(f\({}_{\rm x}\)) according to Jaccard similarity to query file access table of f\({}_{\rm x}\) and u\({}_{\rm i}\) in user table. If m \(\geq\) X\({}_{\rm i}\), f\({}_{\rm x}\) will be added to the result, otherwise u\({}_{\rm i}\) has no access. S\({}_{\rm j}\) would continue searching for files with high similarity and, when the process completes, send the accessed files to the cloud server S. All the other n-1 servers repeat the same work in parallel. Server S will send the top-t (1 \(\leq\) t \(\leq\) k) files to the authorized user or \"could not find relevant documents\" to the user. 9. Decrypt (K, C): The authorized user inputs the packet key K and the received ciphertext C' and decrypts the top-t ciphertext documents to recover the plaintext files.
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 6 Security Assurances and IoT Threat Model
### IoT Threat Model
#### 6.2.2 Threat and Attacks on Availability  
Several factors adversely affect the availability of an IoT network and relevant services. Loss or theft of nodes such as sensors and repeaters; jamming or interference or blocking or loss of the radio signals; disruption of the supply chain of network equipment; DDoS on the gateway devices or application servers; among others, could limit the effectiveness of an IoT network [43]. In 2015, a false data injection (FDI) cyber attack on a power grid in Ukraine resulted in the loss of service for over 22,500 customers [48]. The Mirai botnet [49] (2016) is an instance of a DoS attack where a malware named \"Mirai\" enslaved IoT devices such as cameras, routers, and digital video recorders to cause large-scale attacks to shut down websites and networks. Other variants of Mirai malware, such as Okiru, the Satori, the Masuta, and the PureMasuta, still live on the internet to exploit IoT devices to cause further cyber-attacks as anonymous agents ([[https://bit.ly/3](https://bit.ly/3) GViHH]([https://bit.ly/3](https://bit.ly/3) GViHH) (accessed on 21 June 2022)). An infected IoT device that can launch further attacks is called a bot. An IoT network of bots, also called a botnet of things, could deliver many HTTPS requests per unit of time, which can throttle a web server [23]. Attacks such as node capture attacks, malicious code injection, false data injection, replay (freshness) attacks, cryptanalysis, and side-channel attacks, sleep deprivation attacks, spoofing attacks, sinkhole attacks, wormhole attacks, man-in-the-middle, eavesdropping, and interference could adversely affect the availability of an IoT network [27].
# Concrete Silos: Failures, Design Issues and Repair/Strengthening Methods
## 1 Introduction
Both American and European design codes namely American Society of Agricultural Engineers ANSI/ASAE EP433 DEC1988 (R2011), American Concrete Institute ACI 313-97, British Standard BS EN 1991-4:2006, Australian Standard AS 3774-1996 [1] are reviewed to determine whether conformity to standards could help reduce cases of failure associated with silos. The study is important because there are no globally accepted standards and timelines for reinforced concrete repair and strengthening of the reinforced concrete circular silos; this has elevated the susceptibility to failure [3]. The research also considers the material and structural factors from the perspective of emerging technologies such as fiber-reinforced polymers, carbon fibers, carbon nanotubes, and new concrete repair methods (such as laser interferometry) [4] to augment the durability of the silo structures [5; 6]. The review article focuses on four aspects, namely the design, construction (failure), usage/maintenance (repair), because these variables predict the sustainability of reinforced concrete silo hoppers [7]. In the 20 th century, construction engineer observed that silo construction poses the greatest risk of failure compared to other construction projects. Such an assessment was accurate, considering that nearly 30 percent of concrete silos failed [7]. The main question is if advances in materials science and nanotechnology and development of new and novel forms of concrete reinforcements would help to mitigate the risk of silo failure--new materials that have been developed in the recent past include CNTs and graphene, which were discovered in 1991 and 2004, respectively [8]. Both materials have proven effective in the reinforcement of concrete structures. From the researcher's point of view, the material advances only address part of the problem because there are other factors that are associated with the failure of reinforced concrete structures; these include the disregard of the design codes, the pressure associated with the incorporation of the inserts, the incorporation of sub-standard materials, inaccurate estimation of the flows [7]. The engineering and material factors that lead to failure are discussed in the next section.
## 3 Methodology
### Bias Correction Methods
#### 3.1.2 Local Intensity Scaling (LOCI) of Precipitation  
The LOCI method [20] adjusts the biases in the frequency and intensity of precipitation which prevents the model-simulated raw data having an excessively large number of drizzle days. It is a two-step process; first, a threshold for a wet day for a month, m \(\mathrm{P_{threshold}}\), m is determined from the time series of model-simulated raw precipitation data (\(\mathrm{P_{raw,m,d}}\)), so that the threshold should match the observed (\(\mathrm{P_{obs,m,d}}\)) wet day frequency, and in the second step, scaling factor (\(\mathrm{S_{m}}\)), is calculated using (4):  
\[\mathrm{S_{m}}=\frac{\mu(\mathrm{P_{obs,m,d}}\mid\mathrm{P_{obs,m,d}}>0)}{\mu( \mathrm{P_{raw,m,d}}\mid\mathrm{P_{raw,m,d}}>\mathrm{P_{threshold,m}})} \tag{4}\]  
Equation (4) is used to verify that the corrected model precipitation mean is equal to that of the observed precipitation data and calculated using (5):  
\[\mathrm{P_{cor,m,d}}=\begin{cases}0\,,&\text{if }\mathrm{P_{raw,m,d}}<\mathrm{P_{ threshold,m}}\\ \mathrm{P_{raw,m,d}}\times\mathrm{S_{m}}&\text{otherwise}\end{cases} \tag{5}\]