esa-sceva/satcom-chunk-collection huggingface dataset:
# An Alternative Procedure for the Description of Seismic Intensity Parameter-Based Damage Potential
## 2 Methods  
### Seismic Intensity Measures  
Characterizing seismic excitations is a challenging and complex task, given the random nature of the phenomenon and the limited understanding surrounding earthquake generation and propagation. Therefore, it is obvious that no single Intensity Measure (IM) perfectly describes and quantifies the destructive potential of a seismic event. For this reason, a series of IMs have been proposed in the past decades and the field remains open to research. IMs are typically divided into four categories: peak, duration, spectral, and energy-related [3]. Peak IMs, such as the Peak Ground Acceleration (PGA) [3], refer to the absolute maximum amplitude of the measured variable observed in a time-history record.  
On the other hand, duration-related IMs identify the portions of a strong motion relevant to seismic engineering. For example, Significant Duration (or Strong Motion Duration) [26] pinpoints the significant phase of an earthquake event. Both of these IM types are often easily calculated, yet they provide little information about the overall characteristics of the strong motion. This problem is tackled by using the more sophisticated Spectral and Energy IMs. The former reveals essential information on the frequency content of the excitation and, simultaneously, allows engineers to relate strong motion records with the response of a structural system. One such IM is the Spectral Acceleration (S\({}_{\text{a}}\)) of an equivalent single-degree-of-freedom oscillator [3]. The latter, i.e., the Energy IMs, characterize an earthquake's energy output in a lumped manner. One of the most used energy IMs is the Arias Intensity [27].  
In this study, a selection of 34 Intensity Measures is utilized to characterize each seismic event, as shown in Table 1. The selected 34 IMs are well-known representatives of peak, spectral-based, and energy-based seismic parameter categories. Initially, no IM is excluded, which could be at the expense of the model's reliability. Thus, at the start of the proposed procedure, all the numerical values of the 34 IMs are evaluated and included in the present study. After that, a selection is performed based on the rank correlation of each IM with the considered DIs to provide an appropriate technique to rule out IMs that are not highly interrelated with the considered DIs. The numerical computation of the listed Intensity Measures is conducted using the proprietary software SeismoSignal v2024 [28]. As an extensive presentation for each employed intensity parameter is a vast topic thoroughly discussed in the literature [3;4] and is beyond the scope of the current study, interested readers are encouraged to refer to the cited bibliography.
## 2 Methods  
### Principal Component Analysis (PCA)  
In practical applications, researchers are often faced with datasets containing multiple correlated indicators. This correlation makes the analysis of these datasets more complex and difficult. Principal component analysis (PCA) is a statistical method that can be used to address this problem by reducing the dimensionality of the data.  
PCA involves the linear transformation of high-dimensional datasets into a smaller number of comprehensive indicators, which are known as principal components. These principal components are ranked according to the variance that they explain in the data [12; 13]. The principal component with a larger variance contains more information and contributes more to explaining the variability in the data. This makes PCA a useful tool for identifying the most important variables in a dataset and reducing the dimensionality of the data to enhance analysis.
## 3 Results and Discussion  
### Oxidation Status  
Fat content and oxidation status parameters are shown in Table 2. Fat content was not affected significantly (\(p>0.05\)) by either the sumac treatment or the storage time. Lipid oxidation of fish and fish products can lead to changes in their sensory properties (off-flavour and colour change) and texture deterioration. The TBARS value is widely used as an indicator for the evaluation of the level of lipid oxidation [26]. The limit value of TBA for fish is 1-2 mg MDA kg\({}^{-1}\), and exceeding this limit usually leads to development of an objectionable odour in fish [27]. The TBARS values of samples treated with sumac (T1, T2 and T3) decreased significantly (\(p<0.05\)) after 24 h of chilled storage and remained constant until 72 h of the storage period. [PERSON] et al. [28] also found on the first day of storage a significant impact of sumac on the TBARS values of silver carp fillets treated with sumac extract (1%, 2.5% and 5%). This impact may be attributed to the presence of flavonoid in the sumac, which has an antioxidant effect. The role played by sumac in delaying the lipid oxidation of trout fillets was also reported by [PERSON] and [PERSON] [12]. These authors found that using a combination of chitosan with 2% sumac reduced lipid oxidation significantly during the storage period. It has been demonstrated that sumac extract has the ability to reduce TBARS levels due to its radical-scavenging activities [29]. Our results showed that gradual elevation of the percentage of sumac (T1, T2 and T3) applied did not significantly affect the TBARS values of carp samples. In contrast, [PERSON] et al. [29] found that increasing the concentration of sumac extract led to a decrease in the TBARS values of beef meat in a concentration-dependent manner during 20 days of refrigerated storage. Similarly, [PERSON] et al. [28] observed that increasing sumac percentage (1%, 2.5% and 5%) led to a slow elevation in TBARS values of silver carp fillets during 18 days of storage. However, the length of the chilled storage period may be sufficient to demonstrate the effect of sumac on secondary oxidation.
## 3 Results and Discussion
### Cytotoxic Activity  
The cytotoxic effects of the fir seed and fir cone EOs in the range of 0.012-0.2 \(\mu\)L/mL were analyzed using the MTT assay in skin fibroblasts, keratinocytes, and two melanoma cell lines: A375 and C32. As shown in Figure 2, both EOs induced a decrease in the cell viability in a concentration-dependent manner. However, the cone essential oil was slightly more cytotoxic than the seed EO. This is consistent with our previous studies on _Pinaceae_ family members, which showed that the EO from the cones of _Abies concolor_ is slightly more cytotoxic than the EO from its seeds [18]. The cone EO of fir is also almost two times more cytotoxic to fibroblasts than EOs from _Pica pungens_, _Pica orientalis_, and _Abies concolor_ cones [18; 26]. Neither the cone nor seed EOs had selective effects on melanoma cells when compared with normal cells. The lack of selective reduction in the cancer cell viability was shown previously in a study on EOs from _Abies alba_ and _Abies koreanu_ seeds and cones in breast cancer [24; 25].
# Variable Delayed Dual-Core Lockstep (VDCLS) Processor for Safety and Security Applications
## 3 Design Architecture
### Cache Synchronization  
In the described approach we decided to leave both master and slave cores connected independently to the main memory system busses. In comparison with classic DCLS implementation, this step increased the SoR area with the instruction and data cache memories. Each core fetches an instruction to its cache memories using separate requests on the system bus. This introduces additional protection against single point of failures (SPOF) resulting from the single data fetch that propagates to both cores. In general, in such a system, caches are protected using parity or Error Correction Codes (ECC), but this additional layer significantly hinders side channel manipulation. The attacker would need to intervene in two bus transactions taking place in two hard-to-predict time points. The same mechanism is used for data caches with one exception, that only the master core is capable of issuing store transactions to the system bus to update main memory or change peripherals state. Data caches are implemented as write-through with a write-no-allocate policy. This means that a cache miss during write does not require line refill. However, already allocated lines need updates in both master and slave cores. As the result, data dependency hazards such as read-after-write (RAW) can occur between cores during VDCLS execution. To resolve this issue, data caches were modified to mutually monitor their read and write attempts and if necessary postpone their action to prevent fetching incorrect data.  
Figure 6 shows the exemplary situation when the master core performed write operation (W(B)) before the slave core could read the older value (R(B)). The system behavior in such a situation depends on whether the read value is in cache or has to be loaded from the main memory. After a cache hit, the slave core will use the value already existing in the cache memory. Otherwise, the bus transaction will occur causing a load of the incorrect value. To prevent such a situation, the master core is forced to wait for the slave core to finish the previous read operation. The importance of the cache transaction monitoring and synchronization mechanisms is shown in the Figure 7. It depicts the hypothetical situation when master and slave cores cause an entire system deadlock. The improperly implemented synchronization between both cores can lead to a situation when master and slave cores will indefinitely wait for each other to complete the operation. The implemented hardware mechanisms were extensively verified through PC simulation to ensure non-deadlock operation and maintain data coherency between master and slave cores. As a result, no external assistance from user software or compiler is needed. The VDCLS processor executes the same binary files as the original CCRV32 ST processor core.  
Figure 5: VDCLS Controller Module simplified block diagram.
## 3 Proposed Converter Topology
### Single-Stage Amplification  
It is divided into two sections, one for inductor loading and one for the loading portion. During the on condition of a switch, a DC-link voltage is applied across the inductor, causing its current to grow linearly and, therefore, energizing the inductor. Throughout this time, the load capacitance reverse-biases the diode. When the toggle is switched off, the electricity flows to the loading section via diode \(D\), exchanging energy between the inductor and the loading area. The energy contained in the resonance is partially transmitted to the capacitance charging. In contrast, the load capacitance is discharged via the load impedance during the inductor's energizing stage, and \(D\) stands for the duty cycle. The duty cycle is denoted in Equation (1)  
\[D=\frac{V_{o}-V_{s}}{V_{o}}\textit{ and }V_{o}=\frac{V_{s}}{1-D} \tag{1}\]  
The input voltage is denoted \(V_{s}\), the output voltage is denoted \(V_{0}\), and the duty cycle is denoted as \(D\). The denominator is one, so the power output is greater than the input signal; thus, the step-up chop is named. When the exchange stage is opened, the inductor is de-energized, resulting in a drop in flow \(i_{L}\), which creates a voltage spike whose size can be calculated using Equation (2).  
\[V_{l}(x)=-\mathrm{L}\frac{di_{L}}{dx} \tag{2}\]  
The inductor is denoted as L, the current across the inductor is denoted \(i_{L}\), and the time is denoted \(x\). Equation (3) can be used to assess the value of inductance.  
\[L=\frac{D(1+D)^{2}}{2f_{r}}\times R \tag{3}\]  
Here, \(D\) is duty cycle, \(f_{r}\) is frequency, and resistance is denoted by \(R\).
## 3 Experimental Results and Discussion
### Energy Dissipation Behavior  
The energy dissipation capacity of the structure can be reflected by the energy dissipation coefficient E and can be obtained by the calculation diagram for energy dissipation coefficient E, as shown in Figure 10.  
With the help of Figure 10, \(E\) can be calculated according to the following equation:  
\[E=\frac{S_{1}}{S_{\Delta AOB+\Delta COD}}, \tag{4}\]  
where \(S_{1}\) is the area enclosed by the hysteresis loop of the specimen. Table 3 gives the dissipation coefficient of each specimen. It can be seen from the table that the dissipation coefficient of L3, L4, L5, L7, and L8 with welding access holes was between 1.59 and 1.98, which is higher than those without welding access holes such as Specimens L1, L2, and L6. This indicates that the welding access hole effectively improves the energy dissipation behavior of the link and relieves the stress concentration at the weld. From Table 4, the dissipation coefficient of L3 is 1.27% higher than that of L5, and L4 is 17.85% higher than that of L7 as the lengths of the specimens and the stiffener spacing are the same. The links with relatively smaller cross-sections control the energy dissipation behavior of the structure better, because the links ensure the force transmission on the same frame beam. Under the same length ratio, the dissipation coefficient of L4 is 24.53% higher than that of L3 and L7 is 7% higher than that of L5. The closer the stiffener arrangement of the link, the better the energy dissipation capacity; however, it should not be smaller than 0.5 times the depth of section of the links.
# Development of Screen-Printable Nafion Dispersion for Electrochemical Sensor
## 3 Results
### Rheology Properties
#### 3.1.2. Thixotropy  
Thixotropy describes the time-dependent shear thinning in which the viscosity decreases with time with applied shear stress and recovers when stress is removed. Thixotropic properties and associated recovery rate are also important characteristics of screen-printing inks. During the screen-printing process, the viscosity of the ink is greatly reduced when high shear stress is applied by a squeeze bar. The ink is expected to recover its viscosity after being deposited onto the substrate. A slow thixotropic recovery rate may lead to misprints or defects, while a leveling issue could happen if the recovery rate is too fast. The thixotropic property of the modified Nafion ink was characterized by a three-level flow method, similar to the three-interval thixotropy test (3 ITT) [22,24]. Viscosities of the samples were measured at three different levels of shear rate for different time periods. The shear rate begins at 0.1 s\({}^{-1}\) as if the sample is at rest on the screen. In the second step, the shear rate increases to 100 s\({}^{-1}\) for 10 s, then 500 s\({}^{-1}\) for 5 s, to represent movements of the flood bar and squeeze bar. The shear rate is lowered to the rest condition (0.1 s\({}^{-1}\)) afterwards to monitor the thixotropic recovery rate. The three-level flow was consecutively measured three times to check the repeatability of sample thixotropic recovery.  
The results of the thixotropy analysis of the modified Nafion ink by the three-level flow method are summarized in Figure 2. It was found that the viscosity of the sample recovered to within 92% of the corresponded value at the rest condition within 5 min. Good repeatability was observed among three consecutive measurements for both viscosity and recovery rate. The results further show the reversible recovery of the ink's microstructure even after exposure to high shear. The thixotropic index was calculated based on the ratio between low and high shear rate at 0.1 and 100 s\({}^{-1}\). The thixotropic index of the ink is above 28, again demonstrating its excellent shear-thinning property.
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 1 Introduction
[31] investigated the performance of machine learning models in forecasting soil temperature at a single station in Turkey and found an optimal combination of input meteorological factors. Banglades faces significant challenges in adapting to the impact of climate change and is often cited as one of the countries most vulnerable to climate change [32; 33]. Soil temperature patterns are of great importance for an agro-based economy like Bangladesh because their changes threaten national economic and social development. Unfortunately, research on the soil temperature in Bangladesh is limited. Only [PERSON] et al. [34] established a simple linear regression between soil temperature and air temperature. In this study, five tree-based models (decision tree, random forest, gradient boosting tree, a hybrid of decision tree and gradient boosting tree, and a hybrid of random forest and gradient boosting tree) were used to determine strong links among different meteorological factors and soil temperature at different time window sizes. We explored the optimal combination of model, input scenario, and time window for soil temperature forecast at depths of 10 cm, 30 cm, and 50 cm in Bangladesh.
# Hybrid Clustering Algorithm Based on Improved Density Peak Clustering
## 3 Theoretical Basis
### DPC-MS Clustering Algorithm  
In the DPC algorithm (Algorithm 1), the parameter setting for the cutoff distance, often denoted as \(d_{c}\), significantly impacts the algorithm's accuracy. Currently, the selection of the parameter \(d_{c}\) largely relies on empirical values, typically falling within a range of 1% to 2% of the dataset size, lacking a scientific foundation or formulaic derivation. The mean-shift algorithm's advantage lies in its capacity to identify all existing local extreme points and its underlying theoretical knowledge. This article deviates from the DPC algorithm's reliance on the \(d_{c}\) parameter for locating density extremum points. Instead, it enhances the mean-shift algorithm's concept to find all local density extremum points. The stepwise procedure is as follows:  
1. Randomly select an unmarked data point as the initial center point.
2. Utilize a Gaussian kernel function with a dynamic bandwidth: With an initial bandwidth of \(h=2\), calculate the number of data points with weights exceeding 10% within the range of the kernel function centered on the current point, denoted as \(n\).
3. Compute the weight vectors for all data points falling within the range of the Gaussian kernel function centered on the current point. Aggregate these weight vectors to obtain the shift vector \(shift\) and label the corresponding data points.
4. Update the center position by moving it in the direction of the shift vector \(||shift||\) by an amount proportional to its magnitude: \(Center=Center+shift\).
5. Repeat steps 2, 3, and 4 until the magnitude of the shift vector \(||shift||\) becomes very small (indicating convergence), and mark the current _center_ as a _core_ point.
6. Iterate steps 1 through 5 until all data points have been marked as accessed, and record all _core_ points.
7. Calculate the adaptive distance from each data point to each _core_ point and assign it to the closest _core_'s class.
8. Compute the weighted Euclidean distance between each pair of _core_ points. If this distance is less than a predefined threshold, merge the two _core_ classes, and merge the data points within these classes. The class of the current point set is determined based on which _core_ class has a larger number of \(n\) in its neighborhood.  
To address the limitations of the DPC algorithm, including the absence of a theoretical foundation for the parameter \(d_{c}\) setting and its propensity to trigger cascading effects, as well as to mitigate the potential misallocation issues associated with the mean-shift algorithm during the shifting process, this paper introduces the DPC-MS algorithm. The DPC-MS algorithm is developed by integrating the initial phase of the mean-shift algorithm with the latter portion of the DPC algorithm, thereby harnessing the strengths of both approaches. Additionally, the concept of weighted Euclidean distance is introduced to quantify the separation between two points, facilitating an iterative process. Figure 2 illustrates the iterative flowchart of the DPC-MS algorithm.
###### Abstract  
The use of machine learning (ML) in soccer allows for the management of a large amount of data deriving from the monitoring of sessions and matches. Although the rate of perceived exertion (RPE), training load (S-RPE), and global position system (GPS) are standard methodologies used in team sports to assess the internal and external workload; how the external workload affects RPE and S-RPE remains still unclear. This study explores the relationship between both RPE and S-RPE and the training workload through ML. Data were recorded from 22 elite soccer players, in 160 training sessions and 35 matches during the 2015/2016 season, by using GPS tracking technology. A feature selection process was applied to understand which workload features influence RPE and S-RPE the most. Our results show that the training workloads performed in the previous week have a strong effect on perceived exertion and training load. On the other hand, the analysis of our predictions shows higher accuracy for medium RPE and S-RPE values compared with the extremes. These results provide further evidence of the usefulness of ML as a support to athletic trainers and coaches in understanding the relationship between training load and individual-response in team sports.  
Keywords:sports analytics; external workload; training volume; internal workload; sports data science +
Footnote †: journal: Applied Sciences
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 6 Open Challenges and Future Research Directions
### NDN Privacy  
In NDN, the encryption or obfuscation of content names enhances privacy by preventing eavesdroppers and even routers from discerning requested content. However, this practice complicates the detection of IFAs, as traditional defense mechanisms rely on identifying specific name prefixes associated with malicious interests. When content names are encrypted or hashed, routers perceive only opaque identifiers, hindering their ability to distinguish between legitimate and malicious requests. This challenge is further exacerbated in IoT environments where gateways aggregate requests or function as proxies, resulting in composite traffic that obscures the attribution of requests to individual devices [8].  
Addressing these detection challenges requires content-agnostic methodologies. An approach involves analyzing traffic patterns by monitoring metrics such as PIT utilization and satisfaction ratios; significant deviations in these metrics may signal the presence of an IFA, enabling detection without reliance on content name semantics [23]. Cryptographic techniques like zero-knowledge proofs could allow routers to verify the validity of interest packets without accessing plaintext content names, thereby preserving privacy while authenticating requests. Similarly, employing data structures like Bloom filters enables  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Challenge** & **Why It Matters** & **What’s Needed** & **Future Direction** \\ \hline High False Positives & Can block legitimate users & Adaptive thresholding, behavior modeling & Adaptive baselines \\ \hline NDN Privacy vs. Detection & Encrypted names hide attacker behavior & Content-agnostic or zero-knowledge detection & Cryptographic techniques \\ \hline Real-Time IoT & Slow methods can not run on microcontrollers & RTOS-compatible lightweight algorithms & Interrupt-driven detection mechanisms \\ \hline Lack of Evaluation & No uniform metrics across studies & Shared testbeds, benchmark datasets & Edge-assisted NDN-based IoT framework \\ \hline \hline \end{tabular}
\end{table}
Table 4: Open research challenges in IFA detection.  
producers to disseminate representations of valid content names, allowing routers to probabilistically determine the legitimacy of content requests without decrypting them. These strategies aim to balance the imperative for robust security measures with the preservation of user privacy in NDN architectures [23].