esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Model and Analysis  
Traditionally, numerical approaches have been used to solve models that describe biological systems since they are typically too complicated to be solved analytically. Once a mathematical model is developed, the biological system may simply mimic the reaction to various conditions attributable to the computational technology currently accessible for solving mathematical equations. However, it might not be possible to include all the microscopic features in the mathematical model created, given the broad biology theories already in existence. Determining the assumptions used to frame the mathematical model becomes necessary. In the present manuscript, a few assumptions concerning the problem are regarded to make it meaningful and also informative [36; 37].  
Although the activity of the heart is periodic, we consider the stationary study of the flow problem as the blood is considered to be Newtonian for the present investigation [38].  
* It is presumed that the walls of the arteries are elastic [39].
* The only driving force behind the flow is assumed to be the pressure gradient force.
* It is also assumed that the occurrence of either stenosis or aneurysm is only in the post-graphene region.  
The flow across the aneurysmal ballooning along the abdominal aortal wall is governed by the standard Navier-Stokes momentum equation conjugated with the continuity equation. The governing equations are given in Appendix A [40].  
Now, the model formulated is investigated mathematically for existence of a solution as well as its uniqueness. We convert the model to a weak form by using the standard results and principles of functional analysis. Weak formulations provide useful tools for analyzing mathematical equations because they allow the application of linear algebra principles to problems in other domains, such as partial differential equations. The principles of functional analysis utilized for the present study are tabulated in Appendix B.
# The Development of Piezo-Driven Tools for Cellular Piercing
## 1 Introduction  
Intracytoplasmic sperm injection (ICSI) is routinely used in the treatment of male infertility [1; 2; 3], husbandry such as in cows [4], and transgenic research using mice [5; 6; 7; 8]. To realize ICSI, many techniques have been developed such as conventional ICSI [2; 9; 10], piezo-assisted ICSI [4; 11; 12], and the Ros-drill technique [13]. Conventional ICSI is commonly implemented in humans and domestic animals where the rupture of the oolema is achieved by largely deforming the oocyte. In order to penetrate the zona pellucida without significantly deforming the oocyte, piezo-driven ICSI and the Ros-drill technique were developed. In the Ros-drill technique, the tip of the injection pipette is rotationally oscillated [13]. This technique produces a cleavage rate of approximately 70% in mouse oocytes. However, due to the eccentricity of pipettes from manufacturing, the pipette tip has a whirling motion resulting in undesirable lateral displacements, which causes damage to the oocyte membrane.  
In piezo-drilling, a small droplet of mercury is necessary for successful mouse ICSI [5]. Piezo-drilling employs impulsive signals to drive the injection pipette tip with precise and prompt motions. The use of mercury reduces the lateral oscillations of the injection pipette [14], without which piezo-drilling produces a low cleavage rate and a large lateral vibration. However, mercury causes concerns of toxicity and contamination to the manipulated oocytes [15; 16; 17]. Although FC-77 can be used as a substitute for mercury, its damping effect is not as satisfactory as mercury [9]. To avoid using mercury, a piezo-driven technique was reported for the puncture of zebrafish embryos [18].  
The piezo-drill design produces large lateral vibrations and is not suitable for piercing mouse oocytes that are at least 10 times smaller than zebrafish embryos. In addition, this technology utilizes a high frequency that may result in the agitation of the cytoplasm.  
Thus, the objective of this work was to develop new piezo-driven tools for piercing mouse oocytes with low lateral vibration magnitudes and without using mercury. This was achieved by two modified injectors. The first modified injector was developed by placing the piezoelectric actuator close to the injection pipette tip. The second modified injector was constructed by designing accessories to combine the injection pipette holder and piezoelectric actuator using friction force instead of a screw joint. Experimental results demonstrate that the piezo-driven tool produced low deformation on mouse oocytes and was efficient in penetrating the zona pellucida.
## 4 Numerical Implementation of a Magnetization Model Based on the Free Energy Hysteresis Model
### Kernel Function Implementation  
According to Equation (17), the piecewise linear equation of the local magnetization can be described by Equation (37) in the case of where thermal motion is ignored.  
\[\overline{M}\ =\ \frac{H}{\eta}+M_{R}\Delta, \tag{37}\]  
where, when \(\Delta\ =\ 1\), magnetization is located on the upper branch of the kernel function here, and when \(\Delta\ =\ -1\), magnetization is located on the lower branch of the kernel function here (as shown in Figure 6).  
Figure 6: Simulation curve of magnetization and magnetic field strength.  
In order to accurately represent the judgment conditions of the Equation (37), and the calculation speed is improved effectively, a matrix algorithm is used, as defined by Equation (38).  
\[\Delta_{\text{int}}\ =\ \left[\begin{array}{cccccc}&-1&\cdots&-1&1&\cdots&1\\ &\vdots&\vdots&&&\vdots&\vdots&\\ &-1&\cdots&-1&1&\cdots&1\end{array}\right]_{N_{i}\times N_{j}} \tag{38}\] \[H_{c}\ =\ \left[\begin{array}{ccccc}&H_{c1}&\cdots&H_{c1}\\ &\vdots&\vdots&&\\ &H_{cN_{i}}&\cdots&H_{cN_{i}}&\end{array}\right]_{N_{i}\times N_{j}}\] \[h_{k}\ =\ \left[\begin{array}{ccccc}H_{k}+{}_{1}&\cdots&H_{k}+{}_{N_{ j}}\\ &\vdots&\vdots&\\ &H_{k}+{}_{1}&\cdots&H_{k}+{}_{N_{j}}\end{array}\right]_{N_{i}\times N_{j}}\]  
The weight function vector is defined by Equation (39).  
\[V^{T}\ =\ \left[\begin{array}{cccc}v_{1}v_{1}(H_{c1})&\cdots&v_{N_{i}}v_{1 }(H_{cN_{i}})\end{array}\right]_{1\times N_{i}} \tag{39}\] \[W^{T}\ =\ \left[\begin{array}{cccc}w_{1}v_{2}({}_{1})&\cdots&w_{N_{ j}}v_{1}({}_{N_{j}})\end{array}\right]_{1\times N_{j}}\]  
According to the magnetic field strength value \(H_{k}\), magnetization \(M_{k}\ =\ M(H_{k})\), and it can be calculated by the following algorithm:  
\[\left\{\begin{array}{l}dH\ =\ H_{k}-H_{k-1}\\ h_{k}\ =\ h_{k}+dH\\ \Delta\ =\ \dot{s}\dot{s}\dot{s}\dot{s}(h_{k}+H_{c}\ast\Delta)\\ \overline{M}\ =\ \frac{h_{k}}{\eta}+M_{R}\Delta\\ M_{k}\ =\ V^{T}\overline{M}W\end{array}\right., \tag{40}\]  
where \(H_{k}\) is the value of the magnetic field strength corresponding to the \(k\) element after discretization, \(h_{k}\) is the effective field, the \(ij\) element in the \(\Delta\) constructed matrix indicates whether the \(j\) effective field just crosses the \(i\) critical magnetic field strength, and \(H_{c}\) is the critical magnetic field strength.
# Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic
## 7 Conclusions  
In this paper, we have tested five lightweight feature vectors used for network traffic classification and anomaly detection. These five vectors have been proposed for the last few years in the related literature, are designed to carry out flow-based analysis, and address scenarios that admit large networks and encryption (under the limitations described in this work). Conducted tests and comparisons studied vector performances in terms of preprocessing costs and classification accuracy. The used data belong to a 100 GB dataset (pcap format) recently proposed for the evaluation of intrusion detection systems. Applied algorithms were supervised learners based on: Naive Bayes classifiers, decision trees, random forests, neural networks (multilayer perceptron), support vector machines, and l2-logistic regression, from which random forests showed the best performances.The results of the conducted experiments endorse three main conclusions: (1) Profiling sources instead of flows can reveal relevant hints that improve detection rates. The combination of flow-based and source-based analysis is a strategy to test in future detection frameworks. (2) The feature vector obtained by meta-studies of past research offers a satisfactory trade-off proposal to be used as a basis benchmark in the field community for future research. (3) Packet length is still the determining feature for separating legitimate traffic from malware and for identifying malicious activities. TTLs, number of sent bytes, and number of sent packets are other relevant features to be kept in the analysis.  
Beyond these findings, a main contribution of this work is settling a basis for the discussion of the feature selection problem in network traffic analysis and network security. Here, we establish benchmarks oriented to build sound analysis methodologies and drive the future design of feature vectors, which should be supported by strong evidence about the discriminative power of the used features.  
Conceptualization and Methodology, F.I.; Formal Analysis and Investigation, F.M.; Validation, F.M. and F.I.; Writing--Original Draft Preparation, F.I.; Writing--Review & Editing, T.Z. and F.I.; Supervision, T.Z. and F.I.; Project Administration and Funding Acquisition, T.Z.  
This research was funded by Vienna Science and Technology Fund grant number ICT15-129.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Experimental Study of a Signal Modulation Method to Improve eLORAN Data Channel Communications
## 3 Results
### Experimental Results  
#### 3.3.1 Experiment Method  
Experiments were performed in BPL timing service system. The BPL adopts the standard LORAN-C signal system with a GRI of 60 ms. The pulse group is the main station pulse (nine pulses). The pulse group digital modulation adopts the Eurofix modulation method to provide users with time information data. The transmitter uses a half-wave power synthesis solid-state transmitter and the timer has a duplex transmission function.  
Figure 8: APM probability of error vs. SNR.  
Figure 7: Ninth pulse probability of error vs. SNR.  
Transmitter device improvement: Setting the transmitter timer to duplex working mode, the signal format, and data modulation method of the main station pulse group and its equipment remain unchanged, the repetition period of the sub-station pulse group is adjusted to 60 ms, and the substation pulse group delay is adjusted to 31 ms. According to the Eurofix frame structure and coding format, a set of time information data is encoded to generate 7-bit binary code information to perform on-off control of the second- to eighth-bit pulses of the substation pulse group.  
Improvement of receiving equipment: The original function of the receiver remains unchanged, and the modulation pulse waveform sampling, demodulation, and decoding functions need to be added only at 31 ms under normal reception conditions of the main station pulse signal.  
During the test, the content of the APM and 3S-PPM signal broadcasts was consistent, and the existing 3S-PPM receiver was modified to simultaneously demodulate APM signals to facilitate the demodulation success rate comparison between the two signal systems. The test site was chosen to be in the northwest of the BPL broadcast station to reduce cross-interference with the Changhe II system. The test sites are shown in Figure 9.  
In this experiment, the existing BPL receiver was modified to have the ability to simultaneously demodulate 3S-PPM and APM signals. By analyzing the correctness of the time code information output by the BPL receiver, the decoding success rate of 3S-PPM and APM signal systems was compared.  
By comparing the time difference between the 1 PPS output by the BPL receiver and the 1 PPS output by the GNSS receiver, the TOA error between the actual propagation delay value and the theoretical delay value is analyzed.  
\[TOA\ error=\overline{x}-\tau_{BPL}-\tau_{RCE} \tag{10}\]  
\(\overline{x}\)--mean value of time difference measurement data;  
\(\tau_{BPL}\)--the propagation delay of eLORAN ground wave signal;  
\(\tau_{RCE}\)--BPL receiver delay value.
## 3 Background
### Competitive Swarm Optimizer (CSO)  
This is a method that is based on a PSO but is significantly different. In a CSO, neither the particle's personal best position nor the global best position (or the neighborhood best positions) is used to update the particles. Instead, a pairwise competition mechanism is implemented in which the losing particle learns from the winner and updates its location. Despite its algorithmic simplicity, CSOs outperform the latest metaheuristic algorithms in terms of overall performance [6].  
In our CSO, we had \(P(t)\), which comprised a swarm of \(m\) particles, where \(m\) is the size of the swarm and \(t\) is the index of the generation. Each particle represented a candidate solution for the optimization problem. The CSO compared two particles that were randomly picked from \(P(t)\) in each generation until all particles had competed in at least one competition, providing that the swarm size was an even number. The comparison was made by calculating the fitness of each particle. The particle with the better fitness was considered the winner and was passed directly to the next generation \(P(t+1)\), while the particle that lost the competition was passed to the next generation after learning from the winner. The velocity of the losing particle was updated using Equation (3), where, \(x_{w,j}(t)\) is the position of the winning particle in the \(i\)-th round of competition in generation \(t\), \(x_{l,i}(t)\) is the position of the losing particle in the \(i\)-th round of competition in generation \(t\), \(v_{w,j}(t)\) is the velocity of the winning particle in the \(i\)-th round of competition in generation \(t\), \(v_{l,j}(t)\) is the velocity of the losing particle in the \(i\)-th round of competition in generation \(t\), \(i=1,2,\ldots,m/2\), \(m\) is the population size, \(r_{1}(i,t)\), \(r_{2}(i,t)\) and \(r_{3}(i,t)\in[0,1]\) are three vectors that were randomly generated after the \(i\)-th competition and learning process in generation \(t\), \(\bar{x}(t)\) is the mean position value of all particles (which can be regarded as the center of the swarm in generation \(t\)), and \(\varphi\) is the parameter that controlled the influences or effects of \(\bar{x}(t)\). Then, the position of the losing particle was updated using the newly calculated velocity, according to Equation (4) [6].  
\[v_{l,i}(t+1) =r_{1}(i,t)v_{l,i}(t)\] \[+r_{2}(i,t)(x_{w,i}(t)-x_{l,i}(t)) \tag{3}\] \[+\varphi\,r_{3}(i,t)(\bar{x}(t)-x_{l,i}(t))\]  
\[x_{l,i}(t+1)=x_{l,i}(t)+
# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 3 Analysis of the State of the Art
### Key Observations by Category: Methods, Diagnostic Applications, and Research Methodology
#### 3.5.3 Research Methodologies  
Among the analyzed works, experimental studies with real measurement data, e.g., from vibration, acoustic, optical, or electrical systems, prevailed. The paper [25] tested acoustic emission in various machines, achieving the best results in cutting depth prediction using the Fine kNN classifier. In [35], random forest (RF) was used in diagnosing WAAM welding joints by analyzing sound signals with high accuracy.  
Conceptual studies introduced new network architectures or atypical learning strategies, e.g., TFFNet in [24] for underwater signal classification, outperforming classical methods on real datasets. Meanwhile, case studies demonstrated the effectiveness of ML models in real conditions, e.g., tool wear assessment [27], engine and gearbox diagnostics [86], or industrial pipeline monitoring [88].  
In the future, balancing theoretical and practical research will be key. An increase in the number of validated benchmarks and open datasets is expected, which will allow for result replicability and objective model comparisons. At the same time, the importance of field studies in variable conditions will grow, with emphasis on model robustness to interference and data loss.
# Response of Global Terrestrial Carbon Fluxes to Drought from 1981 to 2016
## 4 Discussion
### Lagged Effects of Precipitation Anomalies on Carbon Fluxes  
Understanding the direct and lagged responses of terrestrial productivity to drought is crucial for assessing the effects of climate change on terrestrial ecosystems. The remotely sensed NDVI has been used as an indicator of terrestrial ecosystem productivity [81; 82], for investigating both direct and lagged effects of climatic change on vegetation activity and growth. [PERSON] and [PERSON] [83] found that the NDVI had the best correlation with the 3-month SPI, and the best NDVI-SPI relationship occurred in regions with low soil water-holding capacity. Using the NDVI and SPEI data, [PERSON] et al. [29] reported responses of global biomes to drought. They concluded that both arid and humid biomes responded to drought at short time scales and semi-arid and sub-humid biomes responded to drought at long time scales. The overall picture of our simulated lagged time scales was remarkably similar to this previous analysis.  
The lagged months at which terrestrial carbon fluxes respond to drought was also found to vary in different regions in this study. From the point of the world's climate type, the lagged time scales of drought impacting carbon fluxes are relatively short in tropical climate areas and long in cold climate regions. Similar findings have been reported in a study that used vegetation indices, tree-ring growth data, and GPP [29; 33]. Based on eddy covariance flux data, [PERSON] et al. [84] recently reported that temperate grassland ecosystems responded to climatic factors (i.e., net radiation, precipitation, and soil water content) at longer time scales while alpine ecosystems showed a less obvious time-lagged response to these factors.
## 3 Results and Discussion  
Outdoor SPD measurements in Zaragoza, Spain (latitude: 41.6370082\({}^{\circ}\), longitude: \(-0.9078846^{\circ}\)) are provided facing the four main cardinal points (north, east, south and west) 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\) from the horizontal plane. The seasonal irradiance (\(\upmu\text{W}/\text{cm}^{2}\)) from 380 to 780 nm, luminance (cd/m\({}^{2}\)), illuminance (lux), and CCT (K) were calculated from the SPD measurements to estimate variations depending on altitude and orientation. Calculations of nonvisual effects using the previously described metrics were performed to analyse values, compare current melanopic evaluation methods and estimate the variability ranges of daylight.
# A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations
## 5 Statistical Properties for the Proposed Model
### Time-Variant DPSD  
The Fourier transform of ACF \(r_{hh}(t,\tau)\) is called the DPSD, which denotes a continuous spectrum of the Doppler frequency. It describes how the power of a signal or time series is distributed over the frequency. The time-variant DPSD of the proposed model can be obtained as:  
\[S_{hh}(f,t)\ =\ \int_{-\infty}^{\infty}r_{hh}(t,\tau)\exp\{-j2\pi f\tau\}d\tau \tag{35}\]  
where time-variant DPSD \(S_{hh}(f,t)\) is established by the Fourier transform of \(r_{hh}(t,\tau)\) having a highly consistent trend to the time delay \(\tau\).
# On the Design of a Network Digital Twin for the Radio Access Network in 5G and Beyond
## 4 Results
### Performance Evaluation
To further assess the performance of the learnt policies, the _KPI metrics_ model of the implemented RAN NDT provides the per-tenant KPIs in Table 4 in terms of the average reward, the average SLA satisfaction (i.e., ratio between the tenant's throughput and the minimum between the aggregated offered load in all gNBs and the \(SAGBR_{k}\)) and the average assigned capacity utilization per gNB (i.e., ratio between the throughput and the assigned capacity per tenant). The values in Table 4 show that a similar average reward is obtained by both tenants. In addition, high average SLA satisfaction is achieved, with values above 0.85 for both tenants. The average assigned capacity utilization in all the gNBs is around 0.8, revealing that the learnt policies assign the capacities to both tenants with low overprovisioning. Overall, the obtained KPI values reflect that the implemented RAN NDT has allowed the learning of high-performance policies. Figure 5: Evolution of the aggregated reward by tenant 1 and tenant 2 during training. Figure 6: Comparison of the offered loads and the assigned capacity per tenant for (**a**) gNB 1, (**b**) gNB 2, (**c**) gNB 3, (**d**) gNB 4, (**e**) gNB 5 and (**f**) at system level. The required capacities are generally satisfied, showing that the policies have been successfully trained on the RAN NDT.
## 4 Discussion
This is because the isokinetic measurements are performed with voluntary dynamic concentric-eccentric muscle contractions at a constant speed of movement, whereas TMG measures contractile properties of the muscle during a brief isometric muscle contraction [27]. Therefore, it may be that the associations of TMG ratios are better when compared to the torque ratios of maximal voluntary isometric contraction (MVIC). However, because TMG measures the response of an involuntary muscle to a short-duration electrical stimulus (twitch) that does not mimic the complex intermuscular coordination required for isokinetic torque testing, it is unlikely that the associations would improve substantially. Another important limitation of our methodology is the small sample size and, consequently, the relatively low power of the statistical tests used to detect associations. Moreover, the observed correlations between TMG and isokinetic parameters were weak to moderate; thus, their physiological significance should be interpreted with caution. Interpreting contraction time (Tc) as a direct indicator of muscle fibre type composition may be overly simplistic, as Tc is also affected by factors such as neuromuscular coordination, fatigue, and subcutaneous tissue thickness. Additionally, we acknowledge that potential confounding variables, such as gender, BMI, and physical activity level, were not controlled for in our study. Future research employing samples matched for these variables may yield deeper insights into the relationship between muscle contractile characteristics as measured by TMG and isokinetic dynamometry. Finally, we recommend that subsequent studies more clearly differentiate between the technical limitations of measurement methodologies (e.g., inter-operator variability, electrode placement, and measurement sensitivity) and the inherent biological variability within the sample (e.g., individual differences in muscle architecture and activation patterns).
# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 2 Data-Driven Condition Monitoring of Industrial Equipment  
Data-driven condition monitoring technology relies on various sources and types of monitoring data collected during the operation of the equipment and employs various data mining techniques to extract the useful information implied therein, thereby enabling condition monitoring and fault diagnosis of the equipment [21; 22; 23]. As mentioned previously, the deployment of this technology consists of three steps: data collection; data processing; and fault detection, diagnosis and prediction. The methods and applications of data processing and condition monitoring and prediction are reviewed next in this section.