esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Reliability of Measurements  
To ensure the precision and consistency of the data collected, reliability tests were incorporated into the study design. For strength metrics (e.g., 1 RM and associated parameters such as force, power, and velocity), inter-session reliability was evaluated during a preliminary phase involving a subset of five athletes who completed the same strength protocol on two separate occasions, one week apart. The intraclass correlation coefficient (ICC) values for the key metrics--1 RM load, relative power (W/kg), and velocity (m/s)--ranged between 0.91 and 0.95, indicating excellent reliability.  
Similarly, for speed and movement tests, test-retest reliability was assessed by having athletes complete two identical trials, separated by 48 h. The ICC values for time (ms), average velocity (m/s), and field-specific metrics (e.g., afv, aff) ranged from 0.88 to 0.93. These results confirm the consistency of the measurement tools and protocols used in the study.  
To further reduce measurement variability, all tests were conducted under standardized conditions by the same team of trained evaluators. For strength testing, the use of the Vitruye accelerometer ensured objective data collection, minimizing observer bias.
# Photon-In/Photon-Out X-ray Free-Electron Laser Studies of Radiolysis
## 2 Methods  
As a prototype of radiation-induced chemical reactions in more complex systems, we first consider pure water radiolysis as shown in Figure 1. The physico-chemical time regime is covered primarily by XFEL pump/probe (and optical pump/probe studies, as described in the introduction). The tabletop optical pump/probe studies have been limited to the study of the hydrated electron with those in the bulk liquid studies often employing solute anions to generate e\({}^{-}\)(aq), though extensions to the soft X-ray transient absorption studies are becoming feasible as high harmonic sources approach the oxygen K-edge [27; 28; 29]. Chemical phenomena occurring on longer timescales are addressed by pulse radiolysis, scavenger, and stopped flow techniques. The combination of these many methods promise a comprehensive view of ionization-induced phenomena in complex systems.  
Figure 1: Top: Various stages that occur in the radiolysis of water by fast ions or electrons or energetic photons. Energy loss by the incident radiation to produce ionized or excited states of the medium is considered to occur in the physical stage that is best described in terms of cross sections. The relaxation of the medium from a few femtoseconds to about a picosecond is considered the physico-chemical stage and represents the true beginning of the formation of chemically reactive species and that can be examined by XFELs. From top to bottom, these processes following ionization of the water molecule are the proton transfer to produce the OH radical, hydration of the electron, and dissociative electron attachment. Neutralization of the electron with the water cation or direct excitation will lead to the excited water molecule followed by two most probable reactions shown. Reaction scheme and time scales taken from Ref. [30]. Bottom: Techniques used to probe chemical phenomena at various timescales.  
The physico-chemical stage of the decomposition of pure water is independent of the radiation type. However, water in confined spaces or with high solute concentrations can have very different outcomes because of reactions within this time frame. Obviously, the transient species must be near its reaction site because mass diffusion does not occur on this timescale. Chemistry occurring on this timescale can be inferred by analysis of products at longer times, or, preferentially by examination of the variation in the temporal decay of the transient species using pump/probes technique with XFELs.
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 3 Microcontroller Design
### Related Work  
Over the time, several versions of TNODE design based on the same core architecture were implemented. The very first version (TNODE1) was a general purpose microcontroller enhanced by a communication accelerator (not designed for security applications) [46]. The second version (TNODE2) was the first that featured hardware crypto accelerators [26]. This design used an external flash memory accessed through a parallel data interface for its program memory. It introduced a large number of pads and significant area overhead and, consequently, increased the power dissipation. TNODE2 was fabricated in IHP's 250 nm BiCMOS technology. The third version (TNODE3) was implemented and fabricated in IHP's 130 nm BiCMOS technology [47]. This design relies on an external program memory as well, but contains four crypto accelerators and a communication accelerator to support the link layer operations defined by IEEE 802.15.4 standard. The recently developed fourth version (TNODE4) was the first with an internal non-volatile memory and an ADC convertor [48]. This design was fabricated in IHP's 250 nm BiCMOS technology and reaches the maximum frequency of 11.4 MHz. Our latest design (TNODE5) integrates the ECC crypto core optimized for better robustness against side channel attacks and a hardware accelerator ensuring reliable communication in noisy environments. The design also implements an additional 32-bit timer, which allows control of long sleep periods typical for WSN applications. The TNODE5 has a reduced number of pads (64) and an on-chip digitally controlled oscillator (DCO), which provide additional power savings and higher flexibility to application demands. In the following sections, the architecture, design, and implementation of TNODE5 are only discussed.
## 1 Introduction
### Comparison and Contribution  
Several researchers have separately presented engaging surveys on FL for vehicular networks [16; 18; 19; 20; 21] and blockchain for vehicular networks [22; 23; 24; 25; 26]. However, to the best of our knowledge, this is the first time that blockchain and FL have been combined for vehicle networks. Previously researchers examined IoT and blockchain applications in vehicular networks [22; 27; 28; 29; 30; 31; 32; 33; 34]. However, those surveys are not concerned with blockchain and FL applications in IoV networks. On the other hand, recently published research papers mainly focused on the blockchain applications in IoV networks [35; 36; 37; 38; 39; 40; 41; 42]. Therefore, comparing the proposed survey with the existing surveys, our proposed survey focuses on integrating blockchain and FL in the IoV environment from security, privacy, and energy efficiency perspectives.  
The main contributions of this survey are summarized as follows:  
* Elaborates VIoTs, blockchain, FL, and intelligent transportation infrastructure;
* Integrates blockchain and FL in a VIoT context focusing on privacy and security;
* Demonstrates various recent VIoT-related real-world projects;
* Addresses the role of state-of-the-art STI in VANET, such as vehicular networks, roadside infrastructure, and smart automobiles;
* Provides future blockchain and FL research in the VIoT space.
# Large Scale Implementations for Twitter Sentiment Classification
## 4 Sentiment Analysis Classification Framework
### Algorithmic Description
#### 4.4.1 Feature Extraction  
In this MapReduce job of Algorithm 1, we extract the features, as described in Subsection 3.1, of tweets in \(T\) and \(TT\) and calculate their weights. The output of the job is an inverted index, where the key is the feature itself and the value is a list of tweets that contain it. In the MapReduce Job 1 pseudo-code, we sum up the Map and Reduce functions of this process.  
```
1:Input:\(T\) and \(TT\) records
2:functionMap(\(k\),\(\
u\))
3:\(t\_id=\textbf{getId}(v1)\);\(class=\textbf{getClass}(v1)\);
4:\(feature=\textbf{getFeatures}(v1)\);
5:for all\(f\in features\)do// BF is BloomFilter
6:\(\textbf{output}(\textbf{BF}(f\_text{text}),<t\_id,f\_count,class>)\);
7:endfor
8:endfunction
9:functionReduce(\(k2,v2\))
10:\(feature\_freq=0\);
11:for all\(v\in v2\)do
12:\(feature\_freq=feature\_freq+v.count\);
13:endfor
14:\(l=\textit{List}(\{)\}\);
15:for all\(v\in v2\)do
16:\(weight=v.count/feature\_freq\);
17:\(\textit{add}(\textbf{newRecord}(v.t\_id,weight,v.class))\);
18:endfor
19:\(\textbf{output}(k2,l)\);
20:endfunction
```  
**Algorithm 1**MapReduce Job 1  
The _Map_ function takes as input the records from \(T\) and \(TT\) and extracts the features of tweets. Afterwards, for each feature, it outputs a key-value record, where the feature itself is the key and the value consists of the id of the tweet, the class of the tweet and the number of times the feature appears inside the sentence. The _Reduce_ function receives the key-value pairs from the Map function and calculates the weight of a feature in each sentence. Then, it forms a list \(l\) with the format \(<t_{1},w_{1},c_{1}:...:t_{x},w_{x},c_{x}>\), where \(t_{i}\) is the id of the \(i\)-th tweet, \(w_{i}\) is the weight of the feature for this tweet and \(c_{i}\) is its class. For each key-value pair, the Reduce function outputs a record where the feature is the key and the value is list \(l\).
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were done with SPSS (version 26.0, SPSS Inc., Chicago, IL, USA). Descriptive statistics are reported as mean \(\pm\) standard deviation. The normality of the data distribution was verified with Shapiro-Wilk tests. The intravisit reliability was assessed with two-way random intraclass correlation coefficient (ICC) for single measures (absolute agreement) and the typical error, which was expressed as percentage of the mean value. The reliability according to ICC was interpreted as poor (<0.5), moderate (0.5-0.75), good (0.75-0.9) and excellent (>0.9) [18]. Two-way mixed-model ANOVA was used to analyze the effect of the contraction type as within-subject factor (sustained vs. ballistic) and sex as between-subject factor (male vs. female), as well as the interaction between the two main effects. Effect sizes were expressed as partial eta-squared (\(\eta^{2}\)) and interpreted as trivial (<0.01), small (0.01-0.06), medium (0.06-0.14) and large (>0.14) [19]. Further separate comparisons between contractions types for each sex were done with pair-wise \(t\)-tests, with effects sizes calculated as Hedge's g and interpreted as trivial (<0.2), small (>0.2-0.6), moderate (>0.6-1.2), large (>1.2-2.0) and very large (>2.0-4.0) [20]. Since multiple outcome variables were separately analyzed, a Bonferroni-Holm correction was applied to control for the family-wise error rate [21]. Correlations between outcomes of the two contraction types were assessed with [PERSON]'s correlation coefficients and interpreted as negligible (<0.1), weak (0.1-0.39), moderate (0.4-0.69), strong (0.7-0.89) and very strong (>0.9) [22]. The threshold for statistical significance was set at \(\alpha\) < 0.05.
# Rock Crack Types Identification by Machine Learning on the Sound Signal+
## 3 Results and Discussion
### Identifying the Development of Tensile and Shear cracks during Rockburst
#### 3.2.2 Tensile and Shear Cracks Process during Rockburst
In the vicinity of the free surface, the \(\sigma_{y}\) value is close to 0, and the inhibition effect is low. Significant swelling appears near the free surface element, but, as the rock specimen element is further away from the free surface, \(\sigma_{y}\) also gradually increases, so the inhibitory effect becomes increasingly obvious. Under the applied forces, the relative friction and sliding between the cracks promote the increase in shear cracks, which creates conditions for the potential shear zone. As \(\sigma_{z}\) gradually increases until the rock specimen loses its bearing capacity, a large amount of strain energy is stored inside the rock specimen from loading to this stage. According to existing studies [39], most of the strain energy stored in a rock specimen that passes through an internal shear dislocation dissipates. At this time, many shear cracks, which are the main cause of the energy dissipation, form, and a macroscopic oblique shear zone that penetrates the rock specimen is formed. In addition, the remaining strain energy will be consumed by block ejection from the free surface. Therefore, with the loud noise of the macroscale destruction of the rock specimen, a large number of blocks from the free surface are ejected outward, and rockburst occurs. According to our recognition results during crack type development, tensile cracks dramatically decrease and shear cracks rapidly increase, which can be taken as an indicator of rockburst. Figure 25: Variation in the tensile and shear sound signals atg different stages using the proposed method under rockburst: (**a**) elastic–plastic stage; (**b**) plastic stage. In summary, based on the identification results using the proposed method, there is a transition from the early stage of tensile crack initiation, propagation, and coalescence to the late stage of shear crack occurrence during rockburst process (Figure 26), which is consistent with crack development during rockburst in previous studies [39; 40], which again indicates that the proposed method is feasible.
# Image Classification of Parcel Boxes under the Underground Logistics System Using CNN MobileNet
## 4 Experiments and Results
### Experimental Comparisons  
In order to compare the experimental results, we conducted an experiment using MobileNet, VGG16 and ResNet50, which are representative CNN algorithms, to classify whether the delivery box was damaged. In the case of the VGG16 and Resnet50 models, the classification accuracy could be higher than MobileNet, as shown in Table 5, but the disk space and parameter values occupied were significantly higher. Therefore, the experiments using VGG16 and Resnet50 take a relatively long time to test.  
The learning of VGG16 and Resnet50 was performed under the same conditions as MobileNet, and the results of the classification experiment for each algorithm are presented in Table 6. As a result of testing with 488 images, VGG16 showed the highest performance among the three models in prediction accuracy, sensitivity and specificity of 88.3%, 86.3% and 88.7%, respectively, as shown in Figure 10. In the case of ResNet50, the performances in prediction accuracy, sensitivity and specificity were 79.7%, 76.5% and 81.5%, respectively. The prediction accuracy showed the highest performance for VGG16, followed by MobileNet, and it showed the lowest performance for ResNet50.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Million Multi-Adds** & **Million Parameters** \\ \hline Full Conv. & 460 & 2.65 \\ Depthwise Separable Conv. & 53 & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The number of parameters for the layers.  
Figure 9: Accuracy and loss values in each epoch.  
In conclusion, when performing experiments for binary image classification, such as the classification of damaged parcel boxes, it was considered appropriate to use MobileNet, which shows a relatively high performance versus time compared to other complex models as shown in Table 7.
###### Abstract  
This article emphasizes the significance of investigating the nonlinear behavior and strength characteristics of polymer composite materials under various strain rates. The study utilizes test results of a unidirectional (UD) composite material subjected to compression at different angles relative to the reinforcement direction, using quasi-static, static, and dynamic strain rates. The analysis focused on a UD layer experiencing compressive stresses perpendicular to the fiber reinforcement and in-plane shear stresses. A novel model is presented, enabling the calculation and prediction of the strength of a UD composite under uniaxial loading at different angles to the fiber direction, considering various strain rates. The developed model facilitates the derivation of equations for the failure envelopes of UD Carbon Fiber-Reinforced Polymers (CFRPs) under quasi-static, static, and dynamic loading conditions. To construct the failure envelopes of CFRPs, it is necessary to acquire experimentally determined values of tensile and compressive strength in the direction perpendicular to the reinforcement, as well as the ultimate strength in uniaxial compression of a specimen with reinforcement at a 45\({}^{\circ}\) angle to the loading axis. The failure envelopes generated using the proposed model exhibit excellent agreement with experimental data, with coefficients of determination ranging from 0.864 to 0.957, depending on the deformation rate. Consequently, the developed model holds promise for predicting the strength of other UD polymer composite materials.  
strain rate; failure criterion; unidirectional composite materials; compression; shear; failure envelope +
Footnote †: journal: Applied Sciences
###### Abstract  
Research on next-point-of-interest (POI) recommendation has become a new focus in the field of POI recommendation in recent years. The goal of POI recommendation tasks is to predict a user's future movement trajectory based on their current state and historical behavioral information. Recent studies have shown the effectiveness of neural network-based next-POI recommendation engines. However, most existing models only consider the correlation between consecutive visits, neglecting the complex dependencies of the POIs in the area and category features, as well as the processing of unstructured time series. This paper presents a new Attention-Based Spatial-Temporal Point-of-Interest-Group (AST-PG) model for POI recommendation. The model consists of a spatial module and a temporal module combined with each other by a multiple-attention mechanism. The spatial module in the proposed model groups the POIs based on geographic and category features, while the temporal module develops a uniform-length time trajectory vector for the unstructured temporal features. Comprehensive experimental results on two real datasets demonstrate that the proposed model of this study is superior to the state-of-the-art POI recommendation models in terms of performance.  
real-time POI recommendation; category-regional features; time trajectory vectors +
Footnote †: journal: 2024, 14, 5337
## 1 Introduction  
The increasing adoption of big data and cloud computing has driven the need for secure outsourced computation. While cloud platforms enable efficient processing of large-scale data, they also introduce significant privacy risks, as sensitive data is often handled by untrusted servers. Fully Homomorphic Encryption (FHE), first proposed by Gentry in 2009 [1], provides a transformative cryptographic solution by allowing arbitrary computations on encrypted data without decryption. This powerful capability makes FHE particularly valuable in ciphertext retrieval [2], privacy-preserving data analytics [3], secure cloud computing, and confidential machine learning [4], where sensitive information must be processed without exposure. Since its inception, several highly influential single-key fully homomorphic encryption (SKFHE) schemes have been developed, including BGV [5], BFV [6, 7], GSW [8], CKKS [9], and TFHE [10]. These schemes are renowned for their distinct advantages in functionality, computational efficiency, and adaptability to various application scenarios.  
In cloud-based applications, especially in secure multi-party computation (MPC) protocols, multiple independent entities need to compute on their encrypted data collaboratively. However, SKFHE is not well-suited for multi-party settings, as it requires all plaintexts to be encrypted under the same secret key. This results in an authority concentration issue, where the entity holding the secret key can access and control all data, thereby potentially violating the privacy of other parties.  
To address this limitation, Multi-Key Fully Homomorphic Encryption (MKFHE) [11; 12; 13; 14; 15; 16; 17; 18; 19; 20] was introduced, enabling homomorphic operations on ciphertexts encrypted under different keys. Unlike SKFHE, MKFHE allows computations across independently encrypted data without requiring a shared secret key, making it a crucial cryptographic tool. Furthermore, MKFHE supports on-the-fly MPC [11], where the circuit to be evaluated can be dynamically chosen after the data providers upload their encrypted data. This feature allows computations to be carried out by a public server without requiring data owners to be online, significantly enhancing the flexibility and efficiency of real-time collaborative secure computing.
###### Abstract  
For reservoir structural models with obvious nonstationary and heterogeneous characteristics, traditional geostatistical simulation methods tend to produce suboptimal results. Additionally, these methods are computationally resource-intensive in consecutive simulation processes. Thanks to the feature extraction capability of deep learning, the generative adversarial network-based method can overcome the limitations of geostatistical simulation and effectively portray the structural attributes of the reservoir models. However, the fixed receptive fields may restrict the extraction of local geospatial multiscale features, while the gradient anomalies and mode collapse during the training process can cause poor reconstruction. Moreover, the sparsely distributed conditioning data lead to possible noise and artifacts in the simulation results due to its weak constraint ability. Therefore, this paper proposes an improved conditioning spectral normalization generation adversarial network (CSNGAN-ASPP) to achieve efficient and automatic reconstruction of reservoir geological bodies under sparse hard data constraints. Specifically, CSNGAN-ASPP features an encoder-decoder type generator with an atrous spatial pyramid pooling (ASPP) structure, which effectively identifies and extracts multi-scale geological features. A spectral normalization strategy is integrated into the discriminator to enhance the network stability. Attention mechanisms are incorporated to focus on the critical features. In addition, a joint loss function is defined to optimize the network parameters and thereby ensure the realism and accuracy of the simulation results. Three types of reservoir model were introduced to validate the reconstruction performance of CSNGAN-ASPP. The results show that they not only accurately conform to conditioning data