esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Because piezoelectric impact drive mechanisms (PIDMs) have a compact structure, high positioning accuracy, and a long stroke, they have been widely used in the fields of precision positioning [1, 2]. Multiple-degrees-of-freedom (DOF) motions of stages and manipulators with PIDMs can achieve flexible and wide control in actuators. Multi-DOF motions of PIDM can be designed in two structures. The first kind of structure is the complex structure in series [3, 4, 5], for which the motions are independent and their corresponding controls are relatively simple. The second kind of structure is the compact structure in parallel [6, 7, 8, 9], for which the motions often share a common friction interface [6, 8] that leads to a coupled motion when the motions are operated at the same time [8, 10]. Recent studies on parallel PIDMs have attained multi-DOF motions by using different modes of vibration under ultrasonic driving [8, 11, 12, 13, 14, 15], and studies on series PIDMs have achieved multi-DOF motions by avoiding coupled motions [4, 5, 16]. However, there are few studies on the coupled motion characteristics of the parallel impact PIDM [17, 18].  
Establishing the dynamic model of parallel multi-DOF PIDMs is foundational to understanding coupled motion interactions and estimating the influences on motion performances. PIDMs are usually combined by three basic elements: piezoelectric actuator (PAs), a stage, and a slider. The working process is a stick-slip motion with friction actuation under a sawtooth driving voltage signal [19]. In the dynamic analysis, the PA and stage are simplified as a spring-mass-damper lumped model, and the slider is considered as a mass. The motion of the slider relies on the friction between the stage and slider [20]. The friction force is nonlinear, so an accurate friction model is important to study coupled motion interactions. Many friction models, such as the Coulomb, Karnopp, LuGre, Leuven, and elastoplastic friction models, have been widely used in the friction analysis of PIDMs [21; 22; 23; 24; 25]. The Coulomb model is a basic static friction model that cannot describe friction when the relative speed is zero. The Karnopp model is an improved static friction model that provides a simple manner to avoid the detection of zero relative velocity by defining a zero velocity interval. Dynamic friction models, such as such as the LuGre, Leuven, and elastoplastic models, have high accuracy and can explain the presliding, Stribeck, and hysteretic effects, although the definitions of the model parameters are difficult [26; 27; 28; 29; 30]. The Karnopp friction model is used for analysis when the precision is low.  
In this paper, a 2-DOF parallel linear PIDM is considered as the research object. A simplified model for the orthogonal motions of the PIDM is established and a two-dimensional extended Karnopp friction model is introduced. Interactions of the orthogonal motions are investigated numerically by simulations [31].
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 1 Introduction
The proposed approach was evaluated on an enterprise dataset collected from SOC operations in a network and the UNSW-NB15 benchmark dataset. The proposed method had a recall rate of 99.519% and an FPR of 0.119% on the enterprise dataset and a recall rate of 96.772% and an FPR of 0.232% on the benchmark dataset. Hence, it can be concluded that it outperformed conventional oversampling approaches by a large margin. This finding casts light on solving highly skewed class distribution problems commonly encountered in cybersecurity scenarios and paves the way for more effective and efficient artificial-intelligence (AI)-assisted security operations. The main contributions of this study are summarized as follows:  
* We propose a bidirectional approach to address imbalanced classification problems with extremely high skewness in the class distribution. * We present a highly reproducible implementation of the bidirectional approach with conventional oversampling and data subsampling methods and a benchmark dataset. * As proof of concept, we show the effectiveness of the proposed approach in detecting critical alerts from security alert logs issued by multiple security appliances. The remainder of this paper is organized as follows. Section 2 discusses the current literature on the topic. Section 3 details the proposed methodology for the security alert data analysis. Section 4 presents the experimental setup and comparison of the results. Finally, Section 5 concludes the study and highlights ideas for future work.
# Unsupervised Deep Embedded Clustering for High-Dimensional Visual Features of Fashion Images
## 1 Introduction
In our previous study, we enhanced the joint learning approach, which motivated us to perform further experiments [19]. This paper introduced the fashion images deep clustering (FIDC) method, which jointly performs input dimension reduction and clustering assignments simultaneously. We inherit a deep-stacked denoising auto-encoder to achieve dimension reduction and discover optimal parameters for our real-world fashion dataset. Further, we improve visual feature representation using Kullback-Leibler divergence training loss and employ auxiliary target distribution for data distribution. Figure 1: An instance of item representation in different feature spaces. (**a**) Shows data points are comparatively closely packed on one axis. (**b**) Demonstrates data points in a two-dimensional space stretched in the direction of the longitudinal axis so that the distances between points become larger. (**c**) Represents data points are further stretched in a three-dimensional space. To evaluate the validity of the FIDC method, labeled data is not used to train the model. However, labels are used later on to evaluate the model's performance. This approach allows the model to learn patterns and relationships in the data without bias toward any particular outcome. The significant contributions in this paper are summarized as follows:  
* This study is the first research that uses real-world data from Amazon Fashion images for a deep clustering task without any prior supervision
* In building the Amazon-Fashion dataset for unsupervised learning, we extracted a subset of clothing items and split them into distinct category labels based on tags. Further, we reproduced the experimental results on the Amazon-Fashion dataset for baseline methods. * We used an under-complete autoencoder to extract embedded features while maintaining the local structure of the data distribution. This process facilitates the simultaneous execution of dimension reduction and clustering assignments. * Our results prove that the machine learning model's performance can be improved by concentrating more on the data rather than constantly creating new algorithms. It demonstrates the effectiveness of our method in comparison to highly effective baseline methods. The paper is divided into several sections. Section 2 discusses previous research and baseline methods. In Section 3, the theory of the proposed method is explained in detail. The results are presented in Section 4. The performance of the proposed model (FIDC) is evaluated and discussed in Section 5 through various experiments. Finally, in Section 6, the paper is concluded, and the symbols and terms used throughout the paper are defined in Table 1.
## 5 Discussion
### Complexity Analysis  
To assess the resource requirements of our proposed model, we conducted a complexity analysis inspired by the approach presented in [60] for HAR. This analysis evaluated the memory usage, average prediction time, and number of parameters that can be adjusted during training. All models, including the baseline versions, were tested on the same benchmark datasets (Opportunity, PAMAP2, and REALWORLD16).
## 3 Results
### A Hotspot Analysis on PV Wastes' EoL
On the other hand, the recycling of thin-film PVs requires energy-intensive crushing and hummermilling processes in the beginning of the recycling, then glass cullet and metal components are recovered in multiple steps of chemical treatment methods [22; 48]. The chemicals and electricity contribute approximately 20% and 80% of impacts, respectively, for thin film CIGS and CdTe PVs. The recycling processes are the major contributors of GWP impacts from EoL of thin-films while the impact of transportation varies between 28%-40% of total GWP of thin-films EoL impacts. The impacts of recycling processes of thin-film solar modules have been briefly analyzed in the literature but, due to data uncertainty, a range of results have been published. When comparing disposal, recycling, and transportation of PV panels, one of the most common conclusions is that recycling processes (even including disposal of materials that cannot be recycled) have higher GWP impacts than landfilling the whole PV panel [49]. This conclusion is because recycling processes use considerable quantities of electricity, which are normally from non-renewable sources, which increases the GWP effect compared to landfilling. Additionally, recycling plants are still not as numerous as landfills, so possible additional transportation is considered, also increasing GWP impacts as well [20]. Table 5 provides a comparison between the CO\({}_{\text{2-eq}}\) emissions from EoL management and cradle-to-gate life cycle phases of PV panels. The results show that the EoL scenario for recycling of 1 m\({}^{2}\) of a c-Si PV panel resulted in 7.79 kg of CO\({}_{\text{2-eq}}\) emissions. When the upstream CO\({}_{2}\) emissions of c-Si PV modules (~130 kg of CO\({}_{\text{2-eq}}\)) are considered, it is seen that recycling c-Si PV panels at their EoL will contribute less than 10% of the CO\({}_{\text{2-eq}}\) emissions of the panels' entire life cycle (Table 5). However, the impacts of the EoL phase on the entire impacts of thin film PVs are found to be more significant. It can be seen in Table 5 that EoL management of CdTe and CIGS PV modules contributes 13% and 21% of the entire life cycle GWP emissions, respectively. Therefore, the thin film PV recycling should be improved with development of more environmentally friendly material recovery methods.
# Triaxial Experimental Study of Zinc Contaminated Red Clay under Different Temperature Conditions
## 3 Results and Discussion
### Changes in Shear Strength of Zn-Contaminated Red Clay under Different Temperature Conditions  
#### 3.2.1 Effect of Temperature on the Shear Strength of Zinc-Contaminated Red Clay  
In order to better study the effect of temperature on the shear strength of Zn-contaminated red clay, the shear strength variation of red clay with contamination concentration of 0% and 0.5% under different temperature conditions were selected in this section. Figure 5 illustrates the shear strength variation curves of Zn-contaminated red clay at various ambient temperatures.  
It can be seen in Figure 5 that the effect of temperature on the shear strength of red clay is relatively obvious, that with an increase in temperature, the shear strength of red clay under different contamination concentrations exhibits an upward trend, and that the relationship between the two is essentially linear. In addition, when the red clay is subjected to the same zinc contamination concentration is at the same temperature, its shear strength increases as the surrounding pressure rises, demonstrating a clear positive correlation.  
This is because, when the ambient temperature of the red clay soil increases, the molecular movement inside the soil becomes more active, and the particles and pore water expand in volume due to the effect of thermal expansion and contraction. In the consolidation stage, due to the effect of the surrounding pressure, the expansion of both causes the pore water within the soil to leave the soil, the volume of the soil decreases, and the distance between the particles shrinks, bringing a specific soil particle into contact with more soil particles and increasing the effective stress of the soil. Therefore, the stress required to produce axial strain in red clay during shear likewise increases as ambient temperature  
Figure 5: Influence of temperature on the shear strength of red clay. (**a**) The zinc ion concentration is 0%; (**b**) The zinc ion concentration is 0.5%.  
rises, and the macroscopic expression is that the shear strength of zinc-contaminated red clay increases with the increase of ambient temperature.
### Related Works  
The hybrid channel access model was first proposed in [1] and has since been adopted in the domain of CRN. A hybrid channel access model for classified SUs with channel bonding mechanism was presented in [3] for CRN, while the hybrid spectrum sharing scheme-based power allocation methods for SUs' data rate maximization under PUs' QoS constraints was investigated by [PERSON] et al. [4]. When transmitting under the overlay channel access scheme, power is allocated to any SU in proportion to its payment without additional constraints by the relay, hence, achieving a higher data rate and throughput. Under the underlay channel access scheme, however, relay assigns power to SUs based on PUs' QoS constraints. The analyses for the hybrid spectrum access model were provided in [1,5,17,18].  
Similarly, a hybrid mode strategy for dynamic spectrum access using double threshold energy detection and Markov model was considered in [19], while a hybrid mode-based resource allocation method was proposed in [20], where the secondary base station allocates sub-channels and total power to SUs based on the received channel state information. In a similar work, the achievable throughput was maximized by [21]. [PERSON] et al. [22] proposed the hybrid and modified hybrid modes-based CRN schemes with the aim of improving throughput while reducing outage probability. A modified hybrid model was defined as one in which SU transmits in both sensing and detection cycles. The switching rate also knows as the hybrid rate was optimized in [5] by maximizing SUs' throughput while satisfying PUs' QoS constraints. The analysis for departure rate [1,17], outage probability [1,4,17,18,23], bit error rate [18] and achievable rate [4] were subsequently obtained.  
While most of the preliminary works considered the relationship between the underlay and overlay modes in hybrid spectrum access mode, most of the analysis presented in such works did not capture the mutual interference that can result due to the transmissions of multiple PUs, SUs as well as sensing error. This was considered in the analyses presented in [1,5,17,18]. However, these analyses did not capture the spatial distribution of users--an essential requirement to obtain accurate analysis when modeling interference in wireless networks, while intra-network and inter-network interference were largely ignored. These spatial distributions are well captured in this paper through the use of point process--an important element of SG. We investigate users' performance in the hybrid mode-based CRN, while capturing intra-network and inter-network interference in the analysis. We obtained tractable analyses for the success probability, network throughput and average AoI for the hybrid CRN and compared the network performance under the hybrid mode with the conventional overlay and underlay modes.
If the transactions pass the validation process undertaken by the majority of the nodes, then the transactions will be added to the ledger [6; 7]. This core feature of blockchain ensures transparency. It is impossible to reverse or change the hash. If a single change is made to the input, then the hash is generated completely differently. In order for a malicious node to corrupt the data in the network it must change the data stored in the ledger on every node. This is highly complex if the network consists of millions of nodes and each node has the same digital copy of the ledger. Each transaction in a blockchain network is stored and a hash of the block is recorded in the next block to trace the transaction and ensure transparency. The main existing privacy-preserving strategies and solutions for blockchain are identified in this paper, to provide insight into the different cryptographic primitives and privacy-preserving approaches, methods, and techniques used in blockchain. This paper proposes an efficient, secure, decentralized Conditional Privacy-Preserving and Authentication (CPPA) scheme for IoV networks. The proposed scheme is based on Hyperledger Fabric for the selection of leaders in the consensus algorithm. It also provides traceability and anonymity ensure that authorities can trace the vehicle nodes in case of disputes. Hyperledger Fabric is an open source blockchain developed by Linux foundation. Hyperledger is a permissioned blockchain technology in which all participants are identified and authenticated. Hyperledger allows the execution of smart contracts which are called chaincodes. Most importantly, it ensures privacy by facilitating confidential transactions. The main contributions of this paper are as follows:  
* The proposed scheme handles multiple transactions at once and provides scalability by using blockchain technology. * The scheme provides multiple decentralized trusted authorities and avoids the issue of a single point of failure in traditional networks. * The scheme provides feature traceability for malicious node detection  
The remainder of this paper is organized as follows: Section 2 presents a review of the relevant literature. Section 3 presents an efficient, secure, decentralized, and conditional privacy and authentication scheme for IoV. Section 4 illustrates the results and provides a discussion. The last section concludes the paper with possible future directions.
# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
## 3 Convergence Analysis and Problem Formulation
### Assumptions  
As a preliminary step for our convergence analysis, we introduce several foundational assumptions, which are in line with those commonly utilized in prior studies [19; 25; 26; 27; 28; 29]. These include the smoothness of local loss functions, bounded variance of stochastic gradients, and Polyak-Lojasiewicz condition to ensure the stability of gradient-based updates and allow the analytical derivation of learning dynamics under communication constraints.  
**Assumption 1** (Lipschitz Smoothness).: _For any cell \(m\in\mathcal{M}\), the global loss function \(H_{m}(\mathbf{e})\) is assumed to be differentiable and \(L_{m}\)-smooth. That is, there exists a non-negative constant \(L_{m}>0\) such that for all \(\mathbf{e},\mathbf{v}\in\mathbb{R}^{C}\), the following condition holds [19; 25]:_  
\[\|\
abla H_{m}(\mathbf{e})-\
abla H_{m}(\mathbf{v})\|\leq L_{m}\|\mathbf{e}-\mathbf{v}\|, \tag{13}\]  
_where \(\
abla H_{m}(\mathbf{e})\) denotes the gradient of the loss function. Equivalently, the following upper bound holds for the loss function:_  
\[H_{m}(\mathbf{e})\leq H_{m}(\mathbf{v})+\
abla H_{m}(\mathbf{v})^{T}(\mathbf{e}-\mathbf{v})+\frac {L_{m}}{2}\|\mathbf{e}-\mathbf{v}\|^{2}. \tag{14}\]  
**Assumption 2** (Bounded Variance).: _The local gradient estimates \(\mathbf{a}_{k}^{(n)}\) are assumed to be independent and unbiased estimates of the true gradient \(\
abla H_{k}(\mathbf{e}_{k})\) and possess bounded variance, i.e., [26; 27]_  
\[\mathbb{E}[\mathbf{a}_{k}^{(n)}]=\
abla H_{k}(\mathbf{e}_{k}^{(n)}),\forall k\in \mathcal{K},\forall n\in\mathcal{N}, \tag{15}\]  
\[\mathbb{E}\bigg{(}\Big{\|}\mathbf{a}_{k}^{(n)}-\
abla H_{k}(\mathbf{e}_{k}^{(n)}) \Big{\|}^{2}\bigg{)}\leq\frac{\phi_{k}^{2}}{n_{b}},\forall k\in\mathcal{K}, \forall n\in\mathcal{N}, \tag{16}\]  
_where \(\phi_{k}\geq 0\) denotes the variance of stochastic gradients at each device, and \(n_{b}\) denotes the mini-batch size used for gradient computation. (While we assume uniform mini-batch sizes and dataset sizes across devices for analytical tractability, the stochastic gradient variance term \(\phi_{k}\) captures statistical heterogeneity arising from non-identically distributed (non-IID) local data distributions. Extensions to support heterogeneous dataset sizes and computation budgets can be incorporated by adjusting the weighting factors in the loss function and optimization problem.)_  
**Assumption 3** (Polyak-Lojasiewicz Condition).: _Let \(H_{m}^{\star}\) denote the global optimal loss function value for cell \(m\). The Polyak-Lojasiewicz condition asserts that there exists a constant \(\mu_{m}\geq 0\) such that \(\
abla H_{m}(\mathbf{e})\) satisfies [28; 29]_  
\[\|\
abla H_{m}(\mathbf{e})\|^{2}\geq 2\mu_{m}(H_{m}(\mathbf{e})-H_{m}^{\star}). \tag{17}\]
## 5 Experiments and Results  
The first release of the INSTEAD system, without the sensors and the alarm management system, was deployed at Anagnina during October 2021. This early installation allowed for tuning the deep learning methods used by the video components with real local data and for testing the system also in a non-controlled environment. The modular architecture of the C&C facilitates both upgrading the existing software and extending the architecture with new components without affecting the previous behavior. The testing period lasted until the BCT, which started in May 2022, when the MIC, EXTRAS, and the Alarm management system were integrated, and the controlled environment was introduced. The corridor was protected by barriers to allow entrance and walk only to the project participants and to pre-identified volunteers.  
The BCT test runs consisted of tracks, which mimic real-world human walking at a metro station, including various speeds, stops, curved, and wobbling gait patterns. Figure 12 depicts real-life examples on these cases as captured during the BCT experiments by the 3D Video sensor and illustrated in X-Y plane in real-world coordinates. During the BCT test runs, the average walking speed was 0.8 m/s and 75% of the tracks had their variation within the \(\pm\)0.3 m/s range.  
Figure 11: RNN data model of 3D Video system.  
Data captured at Anagnina using the 3D Video system on 1-19 April 2022 demonstrates that BCT test runs have had commuter speeds close enough to real-life conditions.  
In the following, the INSTEAD results on the BCT trials are briefly presented. The system validation was performed in real time by supervisors, including experts appointed by NATO who had prepared the trials, and they were verified after an accurate ex post analysis of the system logs. In particular, the INSTEAD system accuracy and that of the INSTEAD-sensor integration have been evaluated.  
The INSTEAD system accuracy has been computed as the number of correctly detected commuters, i.e., in this case, correctly (re)identified by the 2D Video at init, reid, and exit line and correctly tracked by 3D Video over the number of commuters.  
The INSTEAD-sensor integration accuracy has been computed as the number of false positive and false negative threat results, considering the messages sent by the sensors, over the number of correctly detected commuters. The details of the system assessment are discussed in [7].  
Here, the focus is on the analysis of the results of the two data-fusion methods described in Section 4.1 and on lessons learned for further improvement. Additional performance results of INSTEAD when operated in the same environment without the scenario constraints imposed by the two sensors are described in this paper.