esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analyses  
Statistical analyses were conducted using SPSS version 25.0 for Windows (IBM, Armonk, NY, USA). Normality was tested using the Kolmogorov-Smirnov test, and [PERSON]'s test determined the homogeneity of variance. Independent \(t\)-tests examined differences between the groups at the baseline. Training-related effects were assessed by two-way (group \(\times\) time) repeated measures analyses of variance (ANOVAs). Subsequently, [PERSON]'s post hoc correction was applied to locate pair-wise differences. Analysis of covariance (ANCOVA) was conducted for variables with baseline differences between groups. The effect size was calculated using [PERSON]'s \(d\), and it was classified as trivial (\(d<0.2\)), small (\(0.2\leq d\leq 0.6\)), moderate (\(0.6\leq d\leq 1.2\)), large (\(1.2\leq d\leq 2.0\)), or very large (\(d>2.0\)) [25]. The reliability of these measures was assessed using intraclass correlation coefficients (ICCs) [26] and the coefficients of variation (CVs) over consecutive pairs of intrasubject trials [10]. An ICC of more than 0.90 was considered as a high reliability, between 0.80 and 0.90 was a moderate reliability, and less than 0.80 was an insufficient reliability for physiological field tests [26]. The alpha level was reported as exact p values, as suggested by [PERSON] et al. [27]. Data were reported as means \(\pm\) standard deviation (SD).
# Shock Reduction through Opposing Jets--Aerodynamic Performance and Flight Stability Perspectives
## 2 Problem Formulation
### Validation of Computational Setup
#### 2.2.4 Validation of Stability Cases  
In this research, the longitudinal pitch damping derivatives of NACA-0012 airfoil (with and without opposing jet) were calculated by selecting suitable parameters such as amplitude of oscillation (\(\alpha_{A}\)), mean angles of attack (\(\alpha_{\infty}\)) and reduced frequency (k). The pivot point of rotation for NACA 0012 is at half of the chord (0.5\(c\)) of an airfoil for calculation of lift, drag and moment coefficients which calculate the pitch damping derivatives. The force and moment coefficients against time at different angles of attack were computed. The sinusoidal pitching motion of airfoil was achieved by applying forced pitch oscillation. A user defined function (\(UDF\)) generated the oscillatory motion. A circular domain was formed around the airfoil as an interface in Figure 2c and oscillates against its angular frequencies. The pitching oscillation and sinusoidal motion at different angles of attack, can be related as:  
\[\alpha(t)=\alpha_{o}+\alpha_{A}\sin(\omega t) \tag{6}\]  
\[\dot{\alpha}(t)=\dot{\alpha}_{A}\omega\cos(\omega t), \tag{7}\]  
where \(\alpha_{o}\) is the mean angle of attack while \(\alpha_{A}\) is oscillatory amplitude and \(\omega\) is the angular frequency. The reduced frequency (\(k_{c}\)) approach builds a relationship between the angular frequency (\(\omega\)) and free stream velocity (\(V\)). The forced pitching oscillatory motion (sinusoidal motion) of airfoil continuously change the free stream flow angle that brings the dynamic change in the moment coefficient of airfoil. Using the Taylor expansion [45], the moment coefficient can be expanded as:  
\[C_{m}(t)=C_{m_{o}}+C_{m_{a}}\alpha(t)+C_{m_{a}}\dot{\alpha}\frac{c}{2V_{\infty }}+C_{m_{q}}q\frac{c}{2V_{\infty}}+C_{m_{a}}\ddot{\alpha}\frac{c}{2V_{\infty} }+\cdots, \tag{8}\]  
neglecting higher order derivatives, Equation (8) will reduce to  
\[C_{m}(t)=C_{m_{o}}+C_{m_{a}}\alpha(t)+C_{m_{a}}\dot{\alpha}\frac{c}{2V_{\infty }}+C_{m_{q}}q\frac{c}{2V_{\infty}} \tag{9}\]  
or  
\[C_{m}=C_{m_{o}}+C_{m_{a}}\alpha+C_{m_{a}}\ddot{\alpha}+C_{m_{q}}\ddot{\alpha}+ C_{m_{q}}\ddot{q} \tag{10}\]  
as \(\ddot{q}=\frac{qc}{2V_{\infty}}\) and \(\ddot{\alpha}=\frac{\dot{\alpha}c}{2V_{\infty}}\). So, Equation (10) becomes  
\[(C_{m_{\ddot{\alpha}}}\ddot{\alpha}+C_{m_{\ddot{q}}}\ddot{q})=C_{m}-(C_{m_{o}} +C_{m_{\alpha}}\alpha), \tag{11}\]  
where \(C_{m_{\alpha}}\) and \((C_{m_{\alpha}}+C_{m_{q}})\) express the static and dynamic stability parameters for airfoil respectively. The values of normal force coefficient (\(C_{n}\)) over the range of angle of attack show a close agreement with experimental data [42] (having Mach number 0.57 and Reynolds number of 4 \(\times\) 10\({}^{6}\) for static case) as shown in Figure 6a. However, Figure 6b shows superimposed numerical and experimental results for dynamic analysis (perturbed airfoil) by the comparison of moment coefficient (\(C_{m}\)) at range of angles of attack.
## 3 Datasets for the Attack and Anomaly Detection  
An essential challenge of anomaly detection research is generating or finding a suitable dataset for the experiments. The authors analyzed existing datasets to select the dataset for further research.  
The authors specified the following requirements of the dataset based on the research goal of anomaly detection in cyber-physical systems:  
**R1:** the dataset should be gathered from the cyber-physical system;  
**R2:** the dataset should contain event logs;  
**R3:** the dataset should contain anomalies;  
**R4:** the dataset should be labelled (what is normal and what is abnormal);  
**R5:** the dataset should be close to real data (i.e., data from the real or semi-real system).  
Currently, there are a lot of datasets available for various purposes and systems; they represent the functioning of the computer networks and cyber-physical systems, including the Internet of Things, Industrial Internet of Things, and Industrial Control Systems (ICS), such as SCADA (Supervisory Control And Data Acquisition) system [41].  
[PERSON] et al. [8] present the comparative analysis of the available datasets for security purposes. Thus, there are datasets containing computer network traffic that was generated for attack detection purposes: KDDCUP99, NSL-KDD [37], UNSW-NB15 [39], and CICIDS2017 [38]. Such datasets do not contain sensors' data that is specific to CPS. Moreover, they do not include CPS network traffic, both normal and abnormal.  
There are also datasets generated for cyber-physical systems security research purposes. [PERSON] et al. [42] provide a comparison of the existing datasets generated for ICSs security research based on attack paths. [PERSON] and [PERSON] [43] generate the SCADA network datasets (Modbus dataset) for intrusion detection research. The SCADA network datasets by [PERSON] et al. [44] contain attacks on the S7 protocol. These datasets are SCADA specific and contain a limited set of protocol specific attacks.  
There are also multiple datasets for IoT and IIoT. [PERSON] et al. [45] propose the labelled wireless sensor network dataset (LWSNDR). It contains homogeneous data collected from a humidity-temperature sensor. The sensor is deployed in single-hop and multi-hop wireless sensor networks (WSNs). The dataset does not contain attack scenarios, but does contain anomalies introduced by the author using a hot water kettle. [PERSON] et al. [46] propose the datasets gathered from a smart home testbed. It contains network traffic characteristics of IoT devices. The dataset is generated for the IoT devices classification. The dataset does not contain attack scenarios.  
There are also multiple network-based IoT datasets [37; 38; 39; 46; 47; 48]. These datasets do not consider sensor data; thus, they do not allow for the detection of the attacks that manipulate sensors' data.  
The datasets that are suitable considering the set requirements, i.e., that contain labelled sensors and network data, are as follows: TON_IoT [8], SWaT [9], and HAI [10]. The authors conducted a more detailed analysis of these datasets.
# Enhanced Joint and Separable Reversible Data Hiding in Encrypted Images with High Payload
###### Abstract  
Recently, much attention has been paid to reversible data hiding (RDH) in encrypted images, since it preserves the data that the original image can be perfectly recovered after data extraction while protecting the confidentiality of image content. In this paper, we propose joint and separable RDH techniques using an improved embedding pattern and a new measurement function in encrypted images with a high payload. The first problem in recent joint data hiding is that the encrypted image is divided into blocks, and the spatial correlation in the block cannot fully reflect the smoothness of a natural image. The second problem is that half embedding is used to embed data and the prediction error is exploited to calculate the smoothness, which also fails to give good performance. To solve these problems, we divide the encrypted image into four sets, instead of blocks; the actual value of pixels is considered, rather than an estimated value, and the absolute difference between neighboring pixels is used in preference to prediction error to calculate the smoothness. Therefore, it is possible to use spatial correlation of the natural image perfectly. The experimental results show that the proposed joint and separable methods offer better performance over other works.  
Keywords:data hiding; reversible data hiding; encryption; embedding pattern; bit error rate; PSNR +
Footnote †: journal:
## 3 Results and Discussion  
### Preparation of the Protein Hydrolysate (KPH) of Kipjack Tuna Heads  
According to the chemical composition analysis, the major components in skipjack tuna heads were moisture, protein, fat, and ash, with contents of 67.1 \(\pm\) 3.4, 14.6 \(\pm\) 0.86, 10.2 \(\pm\) 0.25, and 6.2 \(\pm\) 0.93 g/100 g, respectively, on the wet weight basis. The data demonstrated that skipjack tuna heads were suitable for the production of bioactive peptides due to their high protein content.  
Proteins of skipjack tuna heads were hydrolyzed by the in vitro GI digestion system, and the prepared hydrolysate (KPH) with a DH of 25.76 \(\pm\) 1.68% can effectively scavenge DPPH radical with an EC\({}_{50}\) value of 5.36 mg protein/mL. The EC\({}_{50}\) value was lower than those of protein hydrolysates from skate cartilage (13.13 mg protein/mL) [3] and blueinf leatherjacket skin (5.23 mg protein/mL) [12], but higher than those of protein hydrolysates from _Tergillarca granosa_ muscle (3.55 mg protein/mL) [13], tilapia skin (3.66 mg/mL) [16], thornback ray (1.98 mg/mL) [27], and salmon pectoral fin (1.63 mg/mL) [28]. Therefore, the results indicated that the in vitro GI digestion model was effective in preparing bioactive peptides of skipjack tuna heads.
# Two-Level Congestion Control Mechanism (2 LCCM) for Information-Centric Networking
## 1 Introduction
However, if heavy congestion occurs, the replica node can be switched instead of just adjusting the request sending rate. To decide which level of congestion control to perform, we define heavy congestion and light congestion separately and describe the strategy of replica switching. We also design a replica selection method based on a Node State Table (NST) and a receiver-driven BBR congestion control algorithm, as well as a transport layer protocol that supports the implementation of the above 2 LCCM. The main contributions of this paper are as follows:  
* This paper is the first paper to explicitly propose a two-level congestion control mechanism for ICN, where replica nodes will be changed to avoid congestion paths under heavy congestion, and only the request sending rate will be adjusted under light congestion. Specific definitions of heavy congestion and light congestion that distinguish the two-level mechanism are provided, and the switching strategy is discussed to reduce the transmission time. * To implement the 2 LCCM proposed in this paper, we further designed an NST-based replica selection method for selecting the most appropriate replica, a receiver-driven BBR congestion control algorithm applicable to ICN, and a detailed transport layer protocol. * The experimental results show that switching replicas for successive transmission under heavy congestion can effectively shorten the transmission time, which verifies the effectiveness of 2 LCCM. In addition, the results also show that the Bandwidth-Delay Product (BDP)-based BBR algorithm has better bandwidth utilization than a loss-based algorithm. The remainder of this paper is organized as follows: We review the related work about congestion control mechanisms of ICN in Section 2; Section 3 describes the design of the two-level congestion control mechanism, defines heavy congestion and light congestion, and presents the switching strategy of the two-level mechanism; In Section 4, the replica selection method, congestion control algorithm, and transport layer protocol are described in detail, then we evaluate and discuss the performance in Section 5; Finally, Section 6 concludes our work.
# A Cognitive Anycast Routing Method for Delay-Tolerant Networks
## 3 Anycast Routing
### CSG Routing Method
#### 3.1.1 Spiking Neuron Model  
The CNC uses the leaky-integrate-and-fire (LIF) spiking neuron model, although other spiking neuron models are also suitable [41]. The LIF model describes the electrical properties of biological neurons as given by a resistor-capacitor (RC) circuit that is driven by a current \(I(t)\). The membrane potential is a function of time \(u(t)\) that can be determined with the expression: \(\tau\frac{d}{dt}u(t)=-u(t)+RI(t)\), where \(\tau=RC\) is the selected time constant of the circuit and \(I(t)\) the total stimulus.  
As in the biological case, neurons can communicate with each other via nerve impulses or _spikes_. One spike is emitted by a neuron when its membrane's potential reaches a given threshold \(\theta\). A neuron produces spikes at times \(t^{(f)}:u(t^{(f)})=\theta\). After emitting one spike, the membrane potential drops significantly, i.e., the neuron requires to rest for some time. The potential drops to the level \(u_{r}<\theta\) for a given time period \(\Delta\) (i.e., the refractory period). During that time, the neuron ignores the effect of new spike arrivals or any external stimulus. The SNN connections determine which neurons will be affected by the emission of spikes and by how much as given by the values of the synaptic strengths (or connection weights). From neuron \(j\) (the _pre synapse_) to neuron \(k\) (the _post synapse_) the synapse strength is denoted by \(w_{jk}\), with \(w_{jk}\leq 0\). Spiking neurons are either of excitatory or inhibitory nature. In the former case, the spikes emitted by such neurons tend to increase the membrane's potential of the post synapse neuron. In the latter case, the effect is the opposite.  
The stimulus \(I(t)\) consists of the aggregated effect of the potential gradients produced by all of the neuron's dendrites, which include the effect of any external stimulus \(i_{e}(t)\) and the spikes arriving from neuron \(j\). The spikes arriving at the post-synaptic neuron are modeled by the impulse train \(i_{jk}^{(f)}(t)=\delta(t-t_{j}^{(f)}-d_{jk})\), which occur at the firing times \(f\), delayed by \(d_{jk}\). Because more than one synapse may connect any two neurons, the notation emphasizes that the effect of the \(k\)-th synapse originated at neuron \(j\). Therefore, the resulting stimulus can be expressed as \(I(t)=i_{e}(t)+\sum_{f}\sum_{j}\sum_{k}w_{jk}\ i_{jk}^{(f)}(t)\).
## 2 Materials and Methods
### Statistical Analysis  
Data were statistically processed using IBM SPSS statistical analysis software (Version 25.0. IBM Corp., Armonk, NY, USA). The Kolmogorov-Smirnov test was used to assess the normality of data distribution to determine whether or not the values of a variable follow a normal distribution, and to determine the appropriate parametric or nonparametric test for each analysis. Levels of pain, inflammation/bleeding, and trismus were assessed for each group of patients before and after the irrigation sessions using the Wilcoxon test. The Kruskal-Wallis non-parametric test was also used to compare trismus rating and bleeding rating for the four solutions. The significance threshold was set, \(\alpha\) = 0.05 which corresponds to a confidence level of 95%.
# Experimental Study on the Effect of Wind Speed on Hail Impacts
## 1 Introduction
This approach integrated critical parameters, including hailstone size, impact velocity, panel type, and roof strength, to assess the failure risk of roofing systems under wind-driven hail scenarios. Additionally, natural hailstones exhibit complex layered structures. [PERSON] et al. [18; 19] compared the impact effects of homogeneous and parallel-layered hailstones, demonstrating that the hailstone microstructure significantly influences the impact force characteristics and target damage. Layered hailstones exhibited delayed fragmentation due to interlayer bonding, resulting in higher peak impact forces compared to their homogeneous counterparts [20]. These findings underscore the necessity of accounting for microstructural heterogeneity in hail impact modeling for accurate risk assessments. In summary, current research on structural wind-hail disasters, both domestically and internationally, predominantly focuses on the isolated impact of hailstones on structures while neglecting the synergistic effects of wind on hail impact dynamics [21]. However, wind speed is a critical parameter that cannot be overlooked in assessing the losses caused by wind-hail disasters [22; 23; 24; 25]. Furthermore, existing studies primarily investigate high-velocity hail impacts, with limited attention to low- and medium-velocity hailstones. This gap underscores the practical significance of exploring such scenarios. To fill this important knowledge gap, this study is the first to experimentally investigate the aerodynamic modulation of hail impact dynamics by wind speed, which greatly improves the validity compared to traditional vertical impact tests. This work establishes, for the first time, a quantitative framework for wind-speed-dependent hail impact forces, effectively reducing the gap between idealized laboratory conditions and real-world meteorological scenarios. This study leverages a self-developed integrated hail impact simulation device to conduct wind tunnel experiments under varying wind speeds. The experiments systematically examine the correlations among wind speed, hailstone diameter, hail velocity, and peak impact forces. Based on the experimental results, a predictive model for peak hail impact loads is proposed, tailored for wind-hail-disaster-resistant structural design. This model provides actionable insights for enhancing structural resilience against combined wind-hail hazards, offering a valuable reference for engineering applications.
## 4 Discussion  
This research studied the effect of deposition pattern on the temperature and global distortion of Direct Metal Tooling (DMT) based Additive Manufactured (AM) part through the experimental approach. The novelty of this study is that deposition patterns were designed combining the long raster pattern and the short raster pattern uniformly in various directions rather than single direction to prevent such anisotropy. The main conclusions are as follows:  
1. The deposition pattern highly affects the temperature gradient of each layer. For the case study, short raster pattern had higher temperature gradient for both heating and cooling compared with the long raster pattern.  
Figure 14: Deflection values at the center of the substrate along the length direction of pattern 1–pattern 6: (**a**) Overall patterns 1–6; (**b**) pattern 1, pattern 3 and pattern5; (**c**) pattern 2, pattern 4 and pattern 6.  
Figure 12: Out of plane (in Z axis) global distortion of Pattern 1–Pattern 6.  
Figure 13: Pattern 1 specimen result: (**a**)Top view; (**b**)Front view; (**c**)Side view.  
2. The peak temperature of each layer can increase or even decrease according to the sequence of the deposition pattern. This experimental result can be the evidence that appropriate design of the deposition pattern for each layer can be utilized to control the maximum temperature of the AM parts.
3. The resulting trends demonstrated that with the short raster pattern deposition, angular distortion is the main distortion and with the long raster pattern deposition, longitudinal bending is the main distortion of the substrate. The pattern of the first layer had a dominant influence on the longitudinal bending deflection that occurs.
4. Although the short raster pattern deposition showed higher temperature than the long raster pattern in the substrate, distortion in the longitudinal direction showed higher value with the long raster pattern than the short raster pattern. Based on these results, the expected dominant distortion type needs to be considered in advance of planning an appropriate tool path schedule.
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 1 Introduction
The selection of person re-identification for benchmarking is consistent with related work on uncertainty estimation for feature vectors [22; 23; 28], which made the same decision since person re-identification provides well-defined benchmarks while the task is very challenging. Furthermore, it is one of the few domains, where uncertainties for embedding vectors have been employed to enhance decision-making, thereby enabling a comparison of our results with multiple state-of-the-art approaches. While the state-of-the-art approaches that make use of uncertainties for person re-identification during inference only use data uncertainty to improve the single-shot person re-identification performance, in our experiments, we show that we can use the other two types of uncertainty to further improve the performance over the strong baseline and to quantify the extent to which an input image is out-of-distribution. Our contributions are as follows:  
1. In contrast to related work, we estimate three different types of uncertainty for feature vectors and, based on our experiments, we emphasize the individual value of diverse uncertainty types for improving the embedding and thereby the re-identification. 2. We show that our _distributional uncertainty_ estimate enables us to measure the degree to which an input is out-of-distribution, which, based on literature, is a strong indicator that the extracted values actually represent distributional uncertainty. 3. We utilize the estimated _model uncertainty_ vector to perform element-wise modifications to the feature vector, and thereby improve the person re-identification performance notably over an already strong baseline. This paper is organized as follows: In Section 2, we describe the state of the art on uncertainty quantification for feature vectors and the utilization of this uncertainty information. In Section 3, we describe the mathematics of uncertainty quantification and explain the algorithm for estimating three different types of uncertainty. In Section 4, we evaluate our experiments regarding the usefulness of the estimation and utilization of diverse types of uncertainty. Finally, in Section 5, we draw a conclusion and provide an outlook on future work.