esa-sceva/satcom-chunk-collection huggingface dataset:
# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
###### Abstract  
Clustering-based routing techniques are key to significantly extending the lifetime of wireless sensor networks (WSNs). However, these approaches often do not address the common hotspot issue in multi-hop WSNs. To overcome this challenge and enhance network lifespan, this study presents FQ-UCR, a hybrid approach that merges unequal clustering based on fuzzy logic (FL) with routing optimized through Q-learning. In FQ-UCR, a tentative CH employs a fuzzy inference system (FIS) to compute its probability of being selected as the final CH. By using the Q-learning algorithm, the best forwarding cluster head (CH) is chosen to construct the data transmission route between the CHs and the base station (BS). The approach is extensively evaluated and compared with protocols like EEUC and CHEF. Simulation results demonstrate that FQ-UCR improves energy efficiency across all nodes, significantly extends network lifetime, and effectively alleviates the hotspot issue.  
WSNs; fuzzy logic; Q-learning; unequal clustering Article
# Monitoring and Calibrating Building Materials Drying Kinetics with Capacitance Sensors
## 4 Conclusions  
The capacitance sensing system tested in this work is a very promising and effective tool for structural health monitoring of buildings and components. This is due to the impracticality and destructive nature of performing gravimetric testing to determine the moisture content of materials, as well as the unpredictable conductivity of various building materials. This system facilitates in situ measurements and real-time data processing from the cloud for a variety of different construction types, from historic to modern structures.  
The calibration process encompassed the entire spectrum of moisture content during the drying process. By simultaneously measuring capacitance and material weight, as expressed by the dimensionless moisture content and voltage signal, it was evident that the relationship between moisture content and capacitance (derived from the voltage signal) can be described by a linear model for all materials under investigation, with high accuracy. These linear equations offer high accuracy in predicting moisture content throughout the entire range of the dynamic drying process and not only at specific water content levels, with a very high degree of reliability.  
In future works, the effect of environmental variations could be evaluated to supply an integrated model covering diverse environmental conditions. This research could yield even more reliable and accurate results, enabling the full exploitation of this valuable tool for monitoring the health of buildings and infrastructure.  
Conceptualization, M.K. and C.M.; methodology, M.K., C.M. and A.B.; software, G.M.; validation, M.K., C.M. and A.B.; formal analysis, M.K. and A.B.; investigation, M.K. and A.B.; resources, G.M. and S.A.; data curation, M.K.; writing--original draft preparation, M.K.; writing--review and editing, C.M., A.B. and G.M.; visualization, M.K. and C.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data are contained within the article.  
Author [PERSON] was employed by the company Delta Controls Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 3 Evaluation Method
### Typical Application Benchmarks in Space
#### 3.4.2 AES Encryption Application Benchmark  
The AES (Advanced Encryption Standard) encryption algorithm has many applications in the field of aerospace and satellite, and is usually used to protect the security of communication data, storage data, and key exchange. For example, spacecraft and satellites  
Figure 3: Image calibration processing flowchart.  
will regularly send remote test data, such as posture information, temperature, and battery status. Using the AES encryption algorithm can ensure the security of these data during the transmission process, prevent data from being eavesdropped or tampered with, at the same time protecting the communication between the ground station and the spacecraft or satellites, and prevent unauthorized access and attacks. In addition, some spacecraft and storage devices on satellites include sensitive information, such as navigation data, images, and videos, using the AES encryption algorithm to ensure that these data are protected during the storage process.  
The standard AES algorithm is a symmetric-key encryption algorithm that encrypts the grouped plaintext by executing the same polling function 10 times, ultimately generating a ciphertext, which requires the same key for encrypting and decrypting the plaintext. The unit of processing in AES is the byte, and the input 128-bit plaintext (\(P\)) and the key (K) are divided into 16 bytes. In general, the grouped plaintext is described by a square matrix in bytes, called the state matrix. In each round of the algorithm, the content of the state matrix keeps changing and the final result is output as the ciphertext. The bytes in this matrix are arranged in order from top to bottom and left to right. The flow is shown in Figure 4 below. When executing the benchmark experiment, we need to input two basic parameters, \"DATA_LENGTH and KEY_LENGTH\", which are the length of data to be encrypted and the length of the key.
# Prototype to Increase Crosswalk Safety by Integrating Computer Vision with ITS-G5 Technologies
## 3 Prototype Specification  
The implemented prototype aims to provide better road information to its users and, therefore, to make the public road network safer for pedestrians and drivers, based on pedestrian pattern detection technologies and the ITS-G5 protocols.  
The prototype has two stations, one installed in the road network infrastructure (RSU) and another inside a vehicle (OBU) connected to an infotainment system. The RSU unit incorporates a pedestrian detection (PD) module to detect and recognize pedestrians on a crosswalk and an ITS-G5 module for the creation and sending of DENM messages. The PD module contains a motion sensor, a video camera and deep leaning pedestrian detection and recognition algorithm (YOLO v3 and OpenCV), running on a personal computer (PC).
## 3 Collapse Performance of Seismically Isolated Buildings
### Analysis Models of Isolated Buildings
Similarity on the base shear and story drift relations obtained from both models can be found in the figure. In addition, Figure 6b illustrates the comparison between the analysis results of the SLMM and the detailed model excited by MCE ground acceleration records. The elastic and effective periods under MCEs have a difference less than 10% for both the analysis models. In addition, the mean values of maximum floor accelerations and story displacements obtained from the SLMM are well-matched with those of the detailed model. Figure 5: Description of simplified lumped mass model. Incremental dynamic analyses (IDAs) were performed with 40 ground motion records, which had been chosen from a far-field ground motion database provided by FEMA P695 [17]. The ground motion database includes a sufficient number of ground shaking records to permit evaluations of record-to-record variability that should be required for the calculations of median collapse intensity of a structure. A total of 40 ground motion records obtained from 20 earthquakes (two records for a single earthquake), as shown in Table 4, was used for uni-directional excitations. Records in the database had been already calibrated by normalizing their values of peak ground velocity (PGV) by the median PGV to reduce the inappropriate variability. Figure 6: Comparison of analysis results of the simplified lumped mass model (SLMM) and the detailed model for 10L\({}_{\text{E}}\)30B building: (**a**) Superstructures; (**b**) Isolated buildings. According to the criteria in ASCE/SEI 7 [14], response spectral accelerations of the selected 40 accelerograms were scaled until the horizontal response spectra over the period range of \(0.75T_{M,LIB}\) to \(1.25T_{M,LIB}\) were equal to or greater than those of the MCE seismic hazard levels where \(T_{M,LIB}\) and \(T_{M,LIB}\) are the effective periods at maximum displacements which are determined using upper and lower bound properties. Figure 7 presents the mean response spectrum of scaled ground motions and target MCE spectrum of the prototype isolated buildings located at high and low-to-moderate seismicity regions. In this study, an isolated building is considered in the collapse state when they reach the limit states of both the superstructure and the isolation system. The limit states of the isolated buildings are determined according to the predefined criteria: (1) the maximum story drift of the superstructure exceeds the limit value of 0.04, (2) the maximum shear strain of the isolation system exceeds the limit value of 250%, or (3) the tangential slope calculated in the current step is less than 20% of the initial slop in a corresponding IDA curve. The collapse criterion in terms of a story drift ratio of 0.04 is
## 3 Results and Discussion
### Characterization of the Nanoparticles Containing Zinc Complexes
#### 3.2.3 Fourier Transform Infrared Spectroscopy (FTIR)  
IR spectroscopy provides information concerning the interactions of the functional groups of the ligands from the vine leaves extract with the zinc ions [35; 36; 37; 38]. The main absorption bands that occurred in the mid-infrared spectra of the VLSENPs and ZnNPs  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pos.** & **Intensity** & **FWHM** & **d-Spacing** & **Rel. Int.** & **Matched** & **Crystallites Size** \\ (\({}^{\circ}\)**20)** & **(cts)** & **(\({}^{\circ}\)**20)** & **(Å)** & **(\%)** & **(\%)** & **(nm)** \\ \hline
12.6920 & 12,169.39 &  
samples (Figure 3) are assigned to the vibrational modes of the following groups of atoms: stretching vibrations of the O-H bonds from the phenolic groups (3404 cm\({}^{-1}\) and 3401 cm\({}^{-1}\)); asymmetrical stretching vibrations of the C-H bonds from the methline and methylene groups (2926 cm\({}^{-1}\) and 2931 cm\({}^{-1}\)); stretching vibrations of the C=O bonds belonging to the carbonyl group of the aromatic ring (1617 cm\({}^{-1}\) and 1579 cm\({}^{-1}\)); overlapping of the stretching vibrations of the C-C and C=C bonds present in the aromatic ring with the out of plane bending vibrations of the O-H bonds from polyphenols (1414 cm\({}^{-1}\) and 1419 cm\({}^{-1}\)); overlapping of the stretching vibrations of the phenolic C-O bonds with the stretching vibrations of the C-O bonds from the carboxylic groups of the phenolic acids (1077 cm\({}^{-1}\) and 1049 cm\({}^{-1}\)).  
The peaks detected at 674 cm\({}^{-1}\) and respectively to 618 cm\({}^{-1}\) in the FTIR spectrum of ZnNPs are attributed to the asymmetrical stretching vibrations and, respectively, to the symmetrical stretching vibrations of Zn-O covalent bonds from ZnNPs. The absorption band attributed to the stretching vibrations of the O-H bonds from the hydroxyl groups of the polyphenols is more intense in the FTIR spectrum of the ZnNPs sample (3401 cm\({}^{-1}\)) compared to the one from the FTIR spectrum of the VLSENPs sample (3404 cm\({}^{-1}\)), what it indicates the involvement of the hydroxyl group in the formation of the Zn-O covalent bond. The shifting of the absorption band assigned to the stretching vibrations of the C=O bonds from 1617 cm\({}^{-1}\) in the FTIR spectrum of VLSENPs to 1579 cm\({}^{-1}\) in the FTIR spectrum of ZnNPs indicates the involvement of the C=O functional group in the formation of a coordinative covalent bond with zinc ions. The formation of coordinative covalent bonds between the zinc ions and the carbonylic oxygen atoms and the formation of covalent Zn-O bonds with phenolic oxygen atoms are proved by the modification of the absorption bands from 1414 cm\({}^{-1}\) to 1419 cm\({}^{-1}\) and from 1077 cm\({}^{-1}\) to 1049 cm\({}^{-1}\).
## 4 Results
### Case Study of PLS-MARS Method  
In this subsection, the PLS-MARS method was used to construct a prediction model between VNIR spectra and SOM content based on the representative calibration set in the study area introduced in Section 2. The study area was utilized as a demonstration zone to  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Illustrative** & **Explanatory** & **Prediction** & **Coefficient of** & **Root of Mean** & **Coefficient of** & **Root of Mean** & **Relative** \\
**Func-** & **Variable** & **Prediction** & **Determination** & **Square** & **Determination** & **Square** & **Percent** \\
**Functtions** & **Size** & **Model** & **of Simulation** & **Error (RMSE)** & **of Prediction** & **Error (RMSE)** & **Deviation** \\  & & & **Analysis (\(R_{c}^{2}\))** & **(g kg\({}^{-1}\))** & **Analysis (\(R_{p}^{2}\))** & **(g kg\({}^{-1}\))** & **(g kg\({}^{-1}\))** \\ \hline \hline \multirow{8}{*}{\(f\)} & \multirow{8}{*}{100} & Multiple & \multirow{8}{*}{0.92} & \multirow{8}{*}{1.08} & \multirow{8}{*}{0.93} & \multirow{8}{*}{0.99} & \multirow{8}{*}{3.9} \\  & & linear & & & & & \\ \cline{1-1} \cline{4-7}  & & regression & & & & & \\ \cline{1-1} \cline{4-7}  & & (MLR) & & & & & \\ \cline{1-1} \cline{4-7}  & & partial least & & & & & \\ \cline{1-1} \cline{4-7}  & & squares & & & & & \\ \cline{1-1} \cline{4-7}  & & regression & & & & & \\ \cline{1-1} \cline{4-7}  & & (PLS) & & & & & \\ \cline{1-1} \cline{4-7}  & & Support & & & & & \\ \cline{1-1} \cline{4-7}  & & vector & & & & & \\ \cline{1-1} \cline{4-7}  & & machine & & & & & \\ \cline{1-1} \cline{4-7}  & & (SVM) & & & & & \\ \cline{1-1} \cline{4-7}  & & PLS–MARS & 0.94 & 0.89 & 0.96 & 0.69 & 5.7 \\ \hline \hline \multirow{8}{*}{500} & MLR & 0.56 & 4.29 & 0.02 & 31.2 & 0.2 \\ \cline{1-1} \cline{4-7}  & PLS & 0.64 & 3.33 & 0.75 & 2.98 & 1.9 \\ \cline{1-1} \cline{4-7}  & SVM & 0.69 & 3.09 & 0.78 & 2.79 & 2.2 \\ \cline{1-1} \cline{4-7}  & PLS–MARS & 0.65 & 3.21 & 0.76 & 2.93 & 2.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results of typical prediction models for the illustrative function.  
validate the effectiveness and accuracies of the PLS-MARS method. Three missions were carried out as follows.  
Mission 1 selected the representative calibration set by utilizing the MVARC-R-KS method [3], which is specified in Section 4.2.1. Mission 2 (in Section 4.2.2) constructed the PLS-MARS method, which was calibrated by the selected calibration set using the MVARC-R-KS method [3]. Typical prediction methods based on typical calibration sample selection methods were utilized to predict SOM content in the research area to verify the effectiveness of the proposed PLS-MARS method and the influence of the calibration set. These comparison experiments are elaborated in Section 4.2.3.
## 3 Results and Discussion
In fact, when the sample is highly p-doped (n-doped), the bulk valence (bulk conduction) band might intercept the Fermi level, providing additional states that could enhance the relaxation process, in the spirit of [PERSON]'s golden rule. Therefore, a more isolated (i.e., bulk-free) TSS shows a longer decay. However, when the DN is at the the Fermi level, an exceptionally long electron persistence is observed in the TSS. This is the case of the Bi\({}_{0.8}\)Sb\({}_{1.2}\)Te\({}_{3}\) sample (see the corresponding dark cyan curve in Figure 4c), where the relaxation time is much longer than the investigated time windows (i.e., \(\gg\)7 ps). In this particular case, not only the absence of bulk contributions, but also the vanishing DOS in the TSS as the FE is approached is important. Under equilibrium conditions, the bulk conduction bands and the upper TTS branch are empty, while the bulk valence bands and the lower TSS branch are fully occupied. Upon laser pumping, electrons are excited to BB1 and subsequently decay to lower energy states. Since bulk diffusion is strongly hindered, electrons eventually accumulate on the lowest available energy levels that is the upper branch of the TSS. The extremely limited number of available states as the Fermi level is approached (keeping in mind that the DOS at the Fermi level is vanishingly small), leads to the extraordinarily long fermions lifetime, which can be interpreted as a strongly enhanced bottleneck effect. It should be mentioned that some authors have claimed an additional effect that could increase the electronic lifetime in TSS; a photo-induced surface band bending (also known as surface photovoltage effect) [23; 33]. This occurs when the TSS is well isolated from bulk states at the FE and leads to a different transient chemical potential of electrons and holes which creates a barrier for relaxation. However, its relevance to the observed enhancement of the electronic lifetime in TSS is still debated [36]. Figure 5: (**a**) Typical phenomenological fit of TSS electronic dynamics (data of Sb\({}_{2}\)Te\({}_{3}\) sample): exponential growth with rise-time \(\tau_{r}\), followed by an exponential decay with time constant \(\tau_{d}\). The function is convoluted with the TARPES response time of \(\approx\) 80 fs. TSS rise times (**b**) and decay times (**c**) of the different BSTS samples as a function of the DN energy relative to the Fermi level. The extra-long decay dynamics of Bi\({}_{0.8}\)Sb\({}_{1.2}\)Te\({}_{3}\) (DN = 0 eV) cannot be fit within our experimental time window and the corresponding region is marked by the dashed area.
## 2 Theory and Method  
### Reasons and Analysis of Missing Data  
#### 2.1.1 Reasons of Missing Data  
The reasons for the missing of landslide monitoring data are complex, which may be caused by the process of data collection, transmission, storage and analysis. Time series with missing data typically share the same characteristics, such as noisy, incomplete and abnormally abrupt data in the time series. Missing data in the time series can have a significant impact on future landslide data analysis, monitoring and early warning. The data should thus be preprocessed in the process of data analysis, i.e., data cleaning, and the processing of missing data is one of the key elements in data cleaning.  
Data deficiencies in landslide monitoring time series are broadly characterized as follows. (1) Long time span and large amount of data: Landslide monitoring requires the data collection of the different factors affecting landslide displacement and deformation, and the continuous extension of monitoring time leads to the increasing amount of data. (2) Randomness: The data acquisition and transmission in the landslide monitoring system are completed automatically by the field equipment, and the components and modules used for acquisition and transmission are all electronic components. It is highly susceptible to rainfall, hail, electromagnetic interference and other natural factors, and the above natural phenomena have randomness. (3) Spatial correlation: The mechanism of landslide disasters is complex. Corresponding monitoring devices will be deployed in different areas of the landslide, and there will be some correlation between different sensors. Therefore, missing data completion needs to consider the spatial correlation among the sensors in the time series.  
Due to the diverse properties of the landslide time series and the different types of monitoring data, there are missing data in the time series for a variety of causes, which can be roughly summarized as follows. (1) Data are not available: The landslide monitoring process is affected by the harsh natural environment. Problems with the power supply of the equipment and the transient failure of the sensors can lead to a certain period of time or a certain moment of failure to complete the data acquisition, thus leading to the lack of data. (2) Data transmission failure: The transmission of landslide monitoring data mainly relies on a wireless communication network; when the wireless signal is interrupted, the data for a certain period of time or a certain moment cannot be transmitted to the background monitoring center. (3) Human interference: When technicians perform data preprocessing operations, the relevant data may be transmitted incorrectly for various reasons, which may also lead to data loss.
## 3 Proposed Method
### Data Partitioning  
The purpose of data partitioning is to extract training, validation, and test data from the available HSI dataset. Most existing methods use sliding blocks by center pixel to obtain learnable data suitable for the CNN framework. However, such approaches result in overlap between the image blocks, which may lead to information leakages as exampled in Section 2.2. Recently, [PERSON] et al. [29] proposed a data partitioning strategy without block overlapping. But their strategy leads to an unbalanced number of different categories in the training and test dataset because the partitioning process is random. Therefore, a reasonable partitioning strategy is needed to make the partitioned data independent of each other and to have a balanced number of categories.  
To solve the problem of information leakage and unbalanced partitioning, we continue to utilize the information leakage-free strategy that we proposed in literature [19].  
Firstly, we divide the whole HSI image into several blocks of the same size.1 Then, these blocks are divided into three categories: (1) the blocks consisting of background pixels entirely; (2) the blocks consisting of the same labeled pixels; (3) the blocks consisting of the different labeled pixels. We discard the first type of blocks and regard the second type of blocks as Testing set-1. The third type of blocks is divided into K groups according to the order of positions, and then the K groups are further divided into the training set, the testing set, and the validation set. For example, in the PU dataset, we divide the third type of blocks into ten groups in order, and then the order of blocks in the Mth group is [M,10 + M,..., 10n + M],  
Figure 3: The framework of the proposed method.  
where n denotes the number of blocks in the group. Then, we randomly select one group as the training set, one of the remaining nine groups as the validation set, and the remaining eight groups as Testing set-2. Testing set-1 (the second type of blocks) is combined with Testing set-2 as the whole testing set. Figure 4 shows that the number, class, and location of the selected datasets are not the same for different K groups at the same spatial location. The white patches represent the data that are fed into the network. This method ensures balanced data partitioning.  
Moreover, since the number of HSI samples is limited, we obtain more samples by applying sliding windows on these larger HSI image blocks. And we also utilize data augmentation [30; 31; 32] to increase the number of samples in the training and validation sets, such as flipping, panning, etc.
## 2 Problem Description and Model Formulation
### Map Modeling
#### 2.3.1 Classification of Obstacles in Manufacturing Environment  
Manufacturing environments contain various types of obstacles beyond pre-mapped static objects. These include workers, other AGVs, offline AGVs (stopped or disconnected vehicles), unregistered shelves and equipment. The obstacles are categorized in Table 1 along two dimensions.  
Based on these two dimensions, obstacles are classified into the following four types:  
1. Known, Movable: Other online AGVs, automated doors;
2. Unknown, Movable: Workers, forklifts;
3. Known, Fixed: Factory layout, pre-registered shelves;
4. Unknown, Fixed: Unregistered equipment, offline AGVs.