esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Algorithm Tuning and Validation
#### 3.2.1 Algorithm Structures and Optimization
An empirical relationship was developed between \(a_{d}(410)\), \(b_{br}(550)\) and \(a_{dg}(410)\), and then \(a_{g}(410)\) was found by subtracting \(a_{d}(410)\) from SAA retrievals of \(a_{dg}(410)\):  
\[a_{d}(410)=0.06822\times a_{dg}(410)+1.623\times b_{br}(550)+0.0002123 \tag{9}\]  
Due to a paucity of \(a_{d}(\lambda)\) and \(b_{br}(\lambda)\) observations in GOCAD, this relationship was tuned for multiple linear regression using the IOCCG synthetic dataset (\(\text{r}^{2}=0.76\), \(RMSD=0.07\), bias \(=-0.004\)\(\text{m}^{-1}\), \(MAPD=75\%\), N \(=464\)). \(a_{g}(410)\) was expanded using Equation (1) to other wavebands with the empirical retrieval for \(S_{g}(412\)-\(555)\) (derived as per Equation (8) and Table 4). Regression statics for the optimization data are shown for the QAA and GIOP in Figure 6, with slightly better results in the GIOP. Although the current version of GOCAD is less well populated with some optical properties than others (i.e., data collection targeted carbon-related properties and only included others if they happened to be in the same SeaBASS file), the digital structures for each property mentioned in this section are included in the database, and future algorithm investigation (particularly using SAAs) would greatly benefit from incorporation of these data into GOCAD or a similar, climate-scale, global database.
# Effects of Distracted Pedestrian Behavior on Transportation Safety: Causes and Contributing Factors
## 1 Introduction
### Strategies Implemented to Address Pedestrian Distraction
#### 1.4.1 Technology-Based Interventions  
Technology-based solutions can address pedestrian distractions by utilizing innovations to improve awareness, communication, and safety. Notable technology-based strategies include the following:  
* Smartphone Applications: Applications have been specifically designed to enhance pedestrian safety and address the issue of distracted walking. One example is the StreetBit app developed by researchers at the University of Alabama, which uses Bluetooth technology to deliver auditory and visual warnings to distracted pedestrians as they approach equipped street corners. A field study with 385 participants demonstrated the app's effectiveness, with 71% of users stopping their distracting activities and 70% considering the app beneficial for their safety [25].
* Wearable Devices: Devices such as smartwatches, fitness trackers, or augmented reality glasses can provide real-time safety information and alerts to pedestrians. These devices may feature proximity sensors, haptic feedback, or visual displays to warn users of nearby vehicles, obstacles, or unsafe crossing conditions [26].
* Vehicle-to-Pedestrian Communication Systems: Vehicle-to-pedestrian (V2P) communication systems allow vehicles to detect nearby pedestrians and share safety-related information via wireless communication protocols. These systems can alert drivers to the presence of pedestrians and warn pedestrians about approaching vehicles, reducing the risk of collisions [27; 28; 29; 30].
## 6 Experimental Simulation and Analysis of the Results
### Comparison of Network Lifetime  
Figure 10a shows the comparison of network lifetime. PEAFC-CL ensures the optimal effect regardless of whether network lifetime is measured in terms of the death time of the first node or after the first node dies. In terms of the time slope of node death in the network, the PEAFC-CL protocol ensures a better effect than the other three protocols. By regarding the distance between nodes as the condition, the CNF-Degree protocol optimizes the coverage of the network based on node degree and  
Figure 8: Impact of \(\beta\) on network performance.  
Figure 9: Comparison of the statistics of the number of CHs formed.  
then optimizes the energy consumption of the network to ensure that its network energy efficiency is superior to that of the LEACH protocol. However, EBACR regards the fading loss of nodes and residual energy of nodes as the conditions for selecting the CHs and relay node; thus, it improves network lifetime. The PEAFC-CL protocol follows the change in active nodes in the network from the optimal CH number; however, it adopts the optimal channel and energy as the reference factors in CH selection to make a node with high energy and a good channel become the CH. The energy factor plays the role of balancing energy consumption in the network and avoiding \"energy hot\", whereas the channel efficiency factor enables the CH node that plays an important role in transmitting data stably, thus avoiding retransmission and an increase in transmitting power because of unstable signals and reducing the energy consumption caused by abnormal data transmission.  
In Figure 10b, the efficiency of the four protocols is assessed by investigating the death time of the first node in the network under different channel variance intensities. PEAFC-CL ensures the best effect, followed by EBACR and then LEACH. This ranking occurs because PEAFC-CL integrates the fading loss between the CH and BS and the link conditions reflected by MAC, which ensures a superior channel efficiency factor. Only the loss of nodes is regarded as the parameter in the EBACR protocol; the distance between nodes in the CNF-Degree is not always entirely consistent with the communication loss of fading channel characteristics.
# Investigation into the Effect of Atmospheric
## 4 Results, Analysis and Discussion
### Analysis of Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Set 3 Great AQI Diff** & \multicolumn{2}{c}{**L1 (dBHz)**} & \multicolumn{2}{c}{**L2 (dBHz)**} \\ \hline PRN & **Mean** & **S.D.** & **Mean** & **S.D.** \\ \hline
08 & 0.077 & 0.125 & \(-\)0.012 & 0.257 \\
16 & \(-\)0.029 & 0.1556 & \(-\)0.075 & 0.251 \\
26 & \(-\)0.047 & 0.166 & \(-\)0.087 & 0.230 \\
27 & 0.003 & 0.127 & 0.015 & 0.156 \\ Overall & 0.001 & & \(-\)0.039 & \\ \hline
**Data set 4 Great AQI diff** & \multicolumn{2}{c}{**L1 (dBHz)**} & \multicolumn{2}{c}{**L2 (dBHz)**} \\ \hline PRN & Mean & S.D. & Mean & S.D. \\ \hline
14 & 0.185 & 0.132 & 0.094 & 0.314 \\
16 & 0.049 & 0.173 & 0.052 & 0.263 \\
26 & 0.084 & 0.162 & 0.079 & 0.215 \\
27 & \(-\)0.019 & 0.152 & \(-\)0.050 & 0.176 \\ Overall & 0.075 & & 0.044 \\ \hline
**Data set 5 Great AQI diff** & \multicolumn{2}{c}{**L1 (dBHz)**} & \multicolumn{2}{c}{**L2 (dBHz)**} \\ \hline
10 & 0.092 & 0.097 & 0.072 & 0.171 \\
12 & 0.168 & 0.147 & 0.101 & 0.346 \\
14 & 0.193 & 0.147 & 0.108 & 0.414 \\
18 & 0.088 & 0.109 & 0.058 & 0.205 \\ Overall & 0.135 & & 0.085 & \\ \hline
**Data set 6 Similar AQI** & \multicolumn{2}{c}{**L1 (dBHz)**} & \multicolumn{2}{c}{**L2 (dBHz)**} \\ \hline  & Mean & S.D. & Mean & S.D. \\ \hline
08 & 0.077 & 0.125 & 0.013 & 0.220 \\
16 & 0.041 & 0.150 & \(-\)0.041 & 0.286 \\
26 & \(-\)0.019 & 0.121 & \(-\)0.041 & 0.209 \\
27 & 0.050 & 0.124 & 0.025 & 0.164 \\ Overall & 0.037 & & \(-\)0.011 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: 95% confidence level statistical results of the L1 and L2 CNR differences of selected satellites in two consecutive sidereal days in the Datasets 3 to 6 collected in December 2015. Data with the satellite elevation angle greater than 30\({}^{\circ}\) is used. When we put a very high mass fraction of PM\({}_{2.5}\)/PM\({}_{10}\) particles as 0.3 [27] and the relative permittivity of PM\({}_{2.5}\) as 30 and PM\({}_{10}\) as 3.8 [28] in (12), the path delays of PM\({}_{2.5}\) and PM\({}_{10}\) particles
# Testbed for LoRaWAN Security: Design and Validation through Man-in-the-Middle Attacks Study
## 4 Experimental Results
### Bit-Flipping Attack  
After confirming the feasibility of executing a replay attack, we investigated the possibility of injecting a fake message. Specifically, the procedures for decryption, modification and re-encryption are detailed in Figure 15.  
This time, instead of just repeating the received packet of the victim as is, we aimed at modifying its payload. Specifically, our goal has been to replace the original application payload with the \"False\" message encoded in ASCII. For that, we created a new \(A_{i}\) block and encrypted it using the AES-128 ECB with the AppSKey (obtained as discussed in Section 4.2) and thus created a block S. We added zeros to the new payload and executed the XOR operation. This created an encrypted value of the FRMPayload. Then, we parsed the value and added it to the original header. In order to make the NS accept the message, we also had to calculate and pass the new MIC value. To calculate the MIC value, it is  
Figure 15: Bit-flipping attack: the procedure for creating a fake message.  
necessary to create a block \(B_{0}\) and attach the original message to this block. We encrypted this new message using the AES-128 in CMAC mode, using the network relational key NwkSKeyas the key. Only the first 4 byte values are parsed from the resulting sequence and attached to the message. We sent this fake message to the server, and the server received it and added it to the database, as depicted in Figure 16. Note, that the value \"_RnnFsc2U_ =\" in the Figure 16 is in Base64 format. Converting to hexadecimal it becomes 46616c7365, which corresponds to \"_False_\" message encoded in ASCII.
# Fast Type-II Hartley Transform Algorithms for Short-Length Input Sequences
## 2 Preliminary Remarks  
The discrete Hartley transform (DHT) is one of the orthogonal transforms used, among others, for the analysis and processing of sounds and signals. The DHT advantages are primarily because it can successfully replace the discrete Fourier transform in many applications. If we apply the DHT to real-valued signals, then real-valued results are obtained in contrast to the Fourier transform. The use of the latter to real-valued signals in general gives complex-valued results. The real-data processing significantly simplifies the implementation of calculations [1; 2; 3].  
For example, the direct generalized DHT can be represented by the following expression [23]:  
\[X_{k}\ =\ \sum_{n\ =\ 0}^{N-1}x_{n}\ cas(n+n_{0})(k+k_{0})\frac{2\pi}{N},\ k\ =\ 0,\ 1,\ \ldots,\ N-1, \tag{1}\]  
where  
\[cas\theta\ =\ cos\theta+sin\theta\ =\ \sqrt{2}cos(\theta-\pi/4); \tag{2}\]  
\(X_{k}\) is the output sequence after the direct generalized DHT is performed, \(x_{n}\) is the sequence of input data, and \(N\) is the number of signal samples. By expression (1), we have the DHT-I if \(n_{0}\)= 0, \(k_{0}\)= 0; the DHT-II if \(n_{0}\)= 1/2, \(k_{0}\)= 0; the DHT-III if \(n_{0}\)= 0, \(k_{0}\)= 1/2; and the DHT-IV if \(n_{0}\)= 1/2, \(k_{0}\)= 1/2.  
Therefore the DHT-II can be obtained as follows [23]:  
\[y_{k}\ =\ \sum_{m\ =\ 0}^{N-1}x_{n}\ cas\frac{\pi(2n+1)k}{N},\ k\ =\ 0,\ 1,\ \ldots,\ N-1, \tag{3}\]  
where \(y_{k}\) is the output sequence after the DHT-II is performed.  
In matrix notation, DHT-II can be represented as follows:  
\[\mathbf{Y}_{N\times 1}=\mathbf{C}_{N}\mathbf{X}_{N\times 1}, \tag{4}\]  
where \(\mathbf{Y}_{N\times 1}\ =\ [y_{0},\ y_{1},\ \ldots,\ y_{N-1}]^{\mathrm{T}}\), \(\mathbf{X}_{N\times 1}\ =\ [x_{0},\ x_{1},\ldots,\ x_{N-1}]^{\mathrm{T}}\), \(c_{kl}\)= \(\alpha s\frac{\pi(2n+1)k}{N}\), \(k\), \(l\)= 0, \(\ldots,N-1\).  
In this paper, we use the following markings and signs [5]:  
* \(\mathbf{I}_{N}\) is an order \(N\) identity matrix;
* \(\mathbf{H}_{2}\) is a \(2\times 2\) Hadamard matrix;
* \(\mathbf{1}_{N\times M}\) is a \(N\times M\) matrix of ones (a matrix where every element is equal to one);
* \(\otimes\) is the Kronecker product of two matrices;
* \(\oplus\) is the direct sum of two matrices.  
DHT-II in matrix notation is as follows:  
\[\left[\begin{array}{c}y_{0}\\ y_{1}\\ \vdots\\ y_{N-1}\end{array}\right]\ =\ \left[\begin{array}{cccc}c_{0,0}&c_{0,1}&\ldots&c_{0,N-1} \\ c_{1,0}&c_{1,1}&\ldots&c_{1,N-1}\\ \vdots&\ddots&\vdots\\ c_{N-1,\ 0}&c_{N-1,\ 1}&\cdots&c_{N-1,N-1}\end{array}\right]\left[\begin{array}{c}x_{0} \\ x_{1}\\ \vdots\\ x_{N-1}\end{array}\right] \tag{5}\]  
An empty cell in a matrix means that it contains zero. The multipliers are marked as \(s_{m}^{(N)}\).
## 4 Results  
### Fraction Maps Derived from MESMA  
After the procedure of MESMA, four fraction maps including water, vegetation, impervious surface, and soil can be derived and are shown in Figure 5. Red areas represent high fractions while blue areas mean low fractions.  
Figure 4: Spatial distribution of training and testing points for “water” class.  
Figure 5: Fraction maps derived from multiple endmember spectral analysis (MESMA) including (**a**) water; (**b**) vegetation; (**c**) impervious surface; (**d**) soil.  
Figure 5 indicates that MESMA can generate precise fraction maps for the entire post-flood regions. The peripheral regions of the reservoir (on the left side of the image) and the Yuyao River, which also belong to the flooded areas, have a high value of water fractions (Figure 5a). The extent of Yuyao City, which manifests itself with high values in the impervious surface fraction map, is clearly depicted in Figure 5c. The high vegetation fractions are mainly located outside the city core (Figure 5b) while the soil fractions are relatively lower and scattered among the vegetated areas (Figure 5d). Above all, the fraction maps derived from MESMA shows distinct differences between each other, which can improve the between-class separability of different land cover types.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 5 Industrial Application Perspective
### Work Safety
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Use Cases** & **Features** & **Protocol** \\ \hline Smart City & Large coverage area, latency, cost & Sigfox and LoRaWAN [25; 31; 71] \\ \hline Intelligent Logistics and Transportation & Low latency, high QoS, large coverage & NB-IoT [31; 95] \\ \hline Smart Farming and Agriculture & Large coverage area, latency, cost, low & Sigfox and LoRaWAN [29; 99; 100] \\ \hline Smart Home & Short range, lower latency, cost, low & Sigfox and LoRaWAN [29; 104] \\ \hline Terminals for Retail Sales & Low latency, high QoS, large coverage & NB-IoT [29; 106] \\ \hline Smart Environment & Low latency, high QoS, large coverage & NB-IoT, LTE-M [111; 113] \\ \hline Smart Metering, Energy, and Grid & Long range, low power, robust & NB-IoT [111; 112] \\ \hline Manufacturing and Automated & Long range, low power, robust QoS, & Sigfox and LoRaWAN \\ Industries & readability and cost & and NB-IoT and RedCap [10; 34; 118] \\ \hline Wearables and Health & Long range, low power, Robust QoS, & Sigfox and LoRaWAN \\  & readability and cost & and NB-IoT and RedCap [22; 34; 50; 133; 132] \\ \hline Work Safety & Low power, cost and readability & Sigfox and LoRaWAN \\  & & and NB-IoT and RedCap [34; 133] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Suitable IoT communications protocol based on application and physical features.
# MSEN: A Multi-Scale Evolutionary Network for Modeling the Evolution of Temporal Knowledge Graphs
## 3 The Proposed Method
### Model Overview  
In this section, a Multi-Scale Evolutionary Network (MSEN) is proposed to solve the problem of feature extraction at different scales. The overall framework of a MSEN is illustrated in Figure 1. There are three parts in our model: (1) A local memory encoder that applies a hierarchical transfer-aware graph neural network (HT-GNN) to obtain structural features within each timestamp's knowledge graph snapshot. The local encoder also performs temporal encoding of entities and relations using a gated recurrent unit (GRU); (2) a global memory encoder that employs a time-related graph neural network (TR-GNN) to mine temporal-semantic dependencies across the historical knowledge graph, yielding entity and relation embeddings; (3) a decoder that integrates the local and global encoder outputs and utilizes a scoring function to predict future entities.  
Specifically, the input is a series of temporal knowledge graph snapshots. The local encoder extracts per-timestamp features using HT-GNN and temporal dynamics via the GRU. The global encoder identifies long-range patterns using TR-GNN. Finally, the decoder combines local and global embeddings to predict future entities using the scoring function. This multi-scale approach allows MSEN to capture both contextual knowledge and global evolutions for enhanced temporal reasoning.
## 2 Materials and Methods
### Input Data
#### 2.3.4 Load Demand  
Detailed load characteristics are critical for designing an efficient and reliable HES tailored to the specific energy demands of the site. HOMER relies on random or synthetic load generation, using monthly average data to produce hourly values over an entire year, which can deviate from real consumption patterns.  
However, in this study, hourly load data were directly measured on-site for the entire year, ensuring a more precise representation of demand and eliminating uncertainties introduced by randomly generated profiles.  
The studied load profile represents the total electricity demand for the semi-remote community of Aras de los Olmos, Spain, including residential homes, small businesses, local public facilities, street lighting, and communal infrastructure. The load is entirely AC, with an average daily demand of 3516.1 kWh/day and an average power of 146.51 kW. The loadfactor is 0.32, calculated as the ratio of the average load to the peak load, indicating moderate variability. All data were obtained through direct measurements at main distribution points, ensuring accuracy and reliability.  
During a typical day in January, as shown in Figure 4, the load begins at 213.516 kW at midnight, gradually decreases to its lowest values around midday, and then steadily rises again, reaching 232 kW late at night. This daily pattern highlights higher energy demand during the evening and nighttime compared to daytime.  
The load exhibits seasonal variations, as shown in Figure 5, with the peak demand occurring in August, reflecting increased energy consumption during summer. Lower energy requirements are observed during the winter months. Variability in load distribution for each month is evident from the seasonal profile, indicating changing demand patterns throughout the year.  
As is shown in Figure 6, the yearly heatmap illustrates the hourly and seasonal variability of the load. Peak loads, reaching up to 453.41 kW, are more prominent during summer evenings, especially in August, while lower loads dominate the winter months  
Figure 4: Daily load profile on a typical day in January.  
Figure 5: Monthly load profile.  
and daytime hours. The figure clearly illustrates daily and seasonal variability, providing evidential support for renewable energy and storage system sizing, optimization scenarios, and reliability analysis presented in this study.
## 5 Simulation Results
### Comparative Assessment of Different Power Control Schemes
Additional work that would broaden the scope, enhance the applicability and resolve limitations of the proposed method include the following:  
* Present applications in systems with multiple BESS units, operated independently or in portfolios. For the case of BESS portfolios, modification of the proposed method is necessary to aggregate all units in a single virtual entity, using its total active power and equivalent SOC for control purposes. * Address operating conditions with no on-line thermal units, where the system relies solely on RESs and storage, including conditions of 100% inverter-based resources. * Accommodate more sophisticated market functionalities in the economic dispatch module and address the case of larger storages, performing energy arbitrage alongside balancing.
###### Abstract  
This study investigated the effect of different synthesis parameters including pre- and post-hydrothermal treatment on the formation of a high-quality SSZ-13 membrane layer. The membranes were identified initially by the gas tightness test, then were characterized by single gas permeation measurements applying H\({}_{2}\), He, CO\({}_{2}\), N\({}_{2}\), CH\({}_{4}\), and SF\({}_{6}\) at room temperature. The results showed how each parameter affects the performance of the membrane, including structural defects in the formed selective layer, CO\({}_{2}\) permeance, and the ideal CO\({}_{2}\)/CH\({}_{4}\) permselectivity. This work focused on optimizing these parameters. An ideal CO\({}_{2}\)/CH\({}_{4}\) permselectivity of up to 122 with CO\({}_{2}\) permeance of -3.72 \(\times\) 10\({}^{-6}\) [mol/(m\({}^{2}\)SPa)] and CO\({}_{2}\)/CH\({}_{4}\) selectivity of 111 with CO\({}_{2}\) permeance of 8.5 \(\times\) 10\({}^{-7}\) [mol/(m\({}^{2}\)SPa)] in an equimolar mixture at room temperature and pressure drop of 0.15 MPa was achieved. This is one of the highest performances compared to other publications for SSZ-13 or all-Si membranes.  
CO\({}_{2}\) separation; SSZ-13 membrane; synthesis parameters +
Footnote †: journal: Applied Sciences
## 4 Case Study
### Optimization Experiments
#### 4.3.3 Improvements in Pipeline Emergency Valves  
Another improvement that the company wished to test was the replacements of the actuators with the valve open. This replacement will facilitate the installation of a new actuator. In addition, the replacement of actuators is proposed, together with the installation of supports to avoid bending due to the weight of the component. With these improvements, the failures that affect the Pipeline Emergency Valves will tend to zero. A time of 10 years between failures is considered, and with this value, the new failure histogram is calculated. It is worth mentioning that as an analysis considering one year of simulation, the results will not largely affect the reliability and availability of the system.  
For calculation purposes, this is reflected in the change of the average failure value \"u\", which takes the value of 87,600 h. Taking this improvement into consideration, and after applying the new value of \"u\" in the Monte Carlo simulator, the frequency histogram and  
Taking into consideration the results of the sensitivity studies, through Monte Carlo experiments, it is demonstrated that the components with the greatest influence in terms of reliability and availability results are the ORV and SCV vaporizers. When applying the proposed improvement, the change of flow measurement system, it is estimated, through this tool, to have a reliability of 24.7% and an availability of 99.95% for the total time of 8760 h. This indicates that, in the case of suggesting and implementing an improvement in the plant based on the results obtained here, the most convenient improvement would be to maintain the focus on the vaporizers.  
The proposed case study where sensitivity analysis and a series of Monte Carlo simulations within the computational framework were performed allows the managers to effectively evaluate the impact on system-level reliability. By representing the outcomes as probabilistic distributions, this approach enables decision-makers to gain a clear understanding of the contributions of each design improvement and the system topology to the overall reliability of the system.