esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research on the Influence of Track Structure Stiffness Change on Track Deformation
### Analysis of Sensitivity Indexes of Vehicle-Track System Dynamics Response
#### 5.2.5 Acceleration Analysis  
The change in curve of vehicle stability coefficient sensitivity indexes with different structural stiffness are shown in Figure 28, which shows:  
1. The lateral acceleration of the vehicle body is obviously affected by the change in the stiffness of the track structure. In particular, the increase in the lateral stiffness under block will cause the lateral acceleration of the vehicle body to increase significantly. In addition, it varies with the vertical stiffness under rail and the vertical stiffness under block. The lower lateral stiffness is the same, but the increase is small, and the change tendency of the lateral stiffness under rail is the opposite. Therefore, increasing the  
Figure 27: Sensitivity indexes of running safety coefficient. (**a**) Derailment coefficient and (**b**) Wheel load reduction rate.  
lateral stiffness under rail or reducing the lateral stiffness under block can improve the lateral train stability during operation;
2. The vertical acceleration of the vehicle body is significantly affected by the lateral stiffness under rail, while the stiffness of the rest of the track structure has no obvious effect on it.
## 3 Experimental Results and Analysis
### Calculation Results of Vowel Acoustic Deviation  
To further quantify the pronunciation differences among learners at different levels, this study calculated the deviation (Bark) of each group's vowels in the F1-F2 acoustic space. As shown in Table 4, elementary level learners exhibit significant acoustic deviations in complex vowels (such as the apical vowel \"z\"), while advanced learners have lower overall deviations and are close to the standard pronunciation group.  
Through the quantitative analysis of acoustic space deviation, this study reveals the systematic differences in vowel pronunciation among learners at different proficiency levels. As shown in Table 4, the acoustic deviation of advanced level learners is generally less than 0.5 Bark (e.g., the deviation of vowel \"a\" is only 0.09 Bark), indicating that their pronunciation features are highly similar to the standard group, reflecting the stability of advanced learners in tongue position and lip shape control. In contrast, the elementary group exhibits significant deviations in complex vowels, such as the apical vowel \"z\" with a deviation as high as 2.61 Bark (standard group F2 = 8.13; elementary group F2 = 10.74). The deviation of the intermediate group lies between the two (e.g., the deviation of vowel \"u\" is 0.38 Bark), suggesting that their pronunciation ability is in a transitional stage, with some phonemes approaching the standard, but systematic errors still exist in complex vowels.  
It is worth noting that the acoustic deviation is significantly positively correlated with pronunciation difficulty (r = 0.87, \(p\) < 0.001), further validating the sensitivity of formant parameters in pronunciation quality assessment. This finding is consistent with the acoustic-perceptual correlation theory proposed in [8], providing new empirical evidence for the mechanism analysis of cross-language pronunciation errors.
# A Novel Hierarchical Adaptive Feature Fusion Method for Meta-Learning
## 3 Method
### Meta-Learning Knowledge  
The meta-learning datasets are divided into meta-training, meta-evaluation, and meta-testing sets. The image types of each set are mutually exclusive. A meta-training set is defined as \(M=\{(\left.D_{i}^{train},D_{i}^{test}\right)\}_{i=1}^{I}\), and meta-evaluation set and meta-testing set are defined as \(C=\{(\left.D_{j}^{train},D_{j}^{test}\right)\}_{j=1}^{I}\) and \(D=\{\left(D_{k}^{train},D_{k}^{test}\right)\}_{k=1}^{K}\) respectively. The tuple \((D_{1}^{train},D_{1}^{test})\) describes a training task and a testing task. Training examples \(D^{train}=\{(\left.x_{t},y_{t}\right)\}_{t=1}^{Q}\) and testing examples \(D^{test}=\{(\left.x_{q},y_{q}\right)\}_{q=1}^{Q}\) are sampled from the same distribution.  
Firstly, the embedding model is trained. The meta-training set combines all individual tasks together and defines as:  
\[D^{combine}=\left\{D_{1}^{train},D_{2}^{train},\ldots\ldots D_{I}^{train}\right\} \tag{1}\]  
An effective embedding model \(\phi\) is defined as:  
\[\phi=\underset{\phi}{argmin}L^{ce}(D^{combine};\phi) \tag{2}\]  
\(L^{ce}\) represents the cross-entropy loss function (Figure 2). The cross-entropy loss function is a loss function for classification problems and is generally used in conjunction with the softmax function. We use \(p(x)\) to denote the true distribution of the input samples and \(q(x)\) to denote the distribution predicted by the model, the cross-entropy loss function can be expressed as:  
\[H(p,q) =D_{KL}(p||q)+H(p(x)) \tag{3}\] \[D_{KL}(p\mid\mid q) =\sum_{i=1}^{count}p(x_{i})\log(p(x_{i})q(x_{i}))\] (4) \[H(p(x)) =-\sum_{i=1}^{count}p(x_{i})\log(p(x_{i})) \tag{5}\]  
where \(count\) denotes the number of categories. \(D_{KL}(p||q)\) denotes the Kullback-Leibler divergence, which measures the difference between \(p(x)\) and \(q(x)\). \(H(p(x))\) denotes the information entropy. Since the input samples and their labels are deterministic, the information entropy is a constant, so minimizing the Kullback-Leibler divergence can be translated into minimizing the cross-entropy loss function. Therefore, the cross-entropy loss function is often used as a loss function in machine learning  
Figure 2: Schematic diagram of the training embedding model. Training the embedding model \(\phi\) using the merged meta-training set. \(L^{ce}\) represents the cross-entropy loss function.  
Then, a classifier \(A\) is given by \(y_{*}=f_{\theta}(\mathbf{x}_{*}),\mathbf{x}_{*}\) represents the feature extracted by \(\phi\). For a task \((D_{j}^{train},D_{j}^{test})\), train the classifier on \(D_{j}^{train}\) and test the classifier on \(D_{j}^{test}\). \(A\) can be represented as a multinomial logistic regression classifier, and its parameter \(\theta=\{\mathbf{W},\mathbf{b}\}\) includes weight term \(\mathbf{W}\) and deviation term \(\mathbf{b}\), which can be expressed by the following formula:  
\[\theta=arg\underset{\{\mathbf{W}\mathbf{b}\}}{min}\sum_{l=1}^{T}L_{t}^{\alpha}(\mathbf{W} f_{\phi}(\mathbf{x}_{t})+b,y_{t})+R(\mathbf{W},\mathbf{b}) \tag{6}\]  
where \(R\) is the regularization term.
# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 4 Business Impact Metrics
Study [16] recommended leveraging cloud storage and algorithmic optimizations to reduce environmental impact, while work [36] provided pseudocode for integrating real-time data streams with billing systems. Moreover, the work in [24] aligned predictive models with targeted re-engagement strategies for at-risk users, while [19] fine-tuned retention campaigns by balancing precision and recall, and designed hybrid models to ensure scalability for cloud deployment. Study [29] optimized conversion rates through strategic ad placements and precise timing of messaging. Study [17] ensured that key feature usage data is synced with CRM systems and flags low-usage accounts for proactive customer success outreach. Dashboards and interactive systems also play a critical supporting role. Study [15] presented a dashboard that visualizes churn risk metrics and integrates real-time alerts directly into CRM dashboards, ensuring that teams can act swiftly on critical issues. Study [15] offered a process mining toolkit that uncovers \"Aha!\" moments during onboarding, enhancing user activation efforts. Finally, authors in [35] proposed an automated feedback system that prioritizes critical user issues and provides dashboards that visualize feedback trends, helping teams monitor user sentiment and behavior patterns over time. \begin{table}
\begin{tabular}{c c} \hline
**Ref.** & **Business Metrics** \\ \hline
[18] & Cost savings per retained customer, revenue protection from churn reduction \\
[14] & Dynamic pricing ROI \\
[19] & Churn rates from early detection, acquisition cost reduction \\
[25] & Revenue boost from collaboration-based monetization \\
[8] & Negative MRR churn and CLV \\
[21] & Churn reduction and intervention acceptance in A/B tests. \\
[38] & Marginal ROI formulas \\
[30] & Improved retention and revenue \\
[26] & Quality investment costs against network-driven retention gains. \\
[16] & Predictive accuracy against carbon emissions \\
[36] & Customer satisfaction \\
[32] & Country-specific retention differences \\ \hline \end{tabular}
\end{table} 
Table 5: Business impact metrics (RQ4).
# Distinguishing Bovine Fecal Matter on Spinach Leaves Using Field Spectroscopy
## 1 Introduction
A limitation to using a reference panel during field spectroscopy is that illumination conditions will often change during the lag in time between reference panel measurement and target surface measurement, such as those brought on by changes in cloud cover [13]. \"Single-beam\" field spectroradiometers are more cost effective than \"dual beam\" because they work on the basis that the same spectrometer measures the reference panel and target, with the spectral reference taken before and after the target scan. The assumption with this technique is that the illumination changes in a predicable manner between scans, which is not likely due to episodic changes, such as the passage of sub-visual clouds through the direct solar radiance [13]. Dual-beam field spectroradiometers use two spectrometers to measure a reference panel and the target simultaneously, with the added complexity of requiring two well matched detectors [13]. Therefore, a field spectroscopy system which negates the need for reference panel measurements would eliminate these complexities. Passive optical methods require an understanding of the illumination conditions. Clear atmospheric conditions, including low atmospheric water vapour and aerosol content, are preferred [13]. For non-Lambertian surfaces, variations in solar zenith and atmospheric haze will also add to variability in field spectroscopy [15]. In this study, we assess the use of normalizing algorithms for detection of fecal matter on spinach leaves for passive field spectroscopy. Spectral responses to solar electromagnetic energy are measured for fecal contaminants on spinach leaves, soil on spinach leaves, and uncontaminated spinach leaf portions. First derivative and ratio normalizing techniques are used to take advantage of the differences in the spectral profiles among these sample types; this technique aims to negate the need for calibration using a reference panel, which is unreliable due to atmospheric or environmental changes during the lag time between reference panel and target scans.
## 3 Sector Capacity Evaluation Method Based on the DNN Algorithm
### DNN Algorithm  
Neural Network (NN) is made up of many human-neural connections that adaptively change internal node parameters based on input characteristics. Modern NNs are nonlinear statistical data modeling tools that are often used to model complex relationships between inputs and outputs or to explore patterns in data [21].  
A DNN can be regarded as a basic neural network model with multiple hidden layers and can be divided into the input layer, hidden layer, and output layer. The layers are interconnected, in that a neuron in layer \(i\) must be connected to a neuron in layer \(i+1\). The overall DNN model is generally complicated and difficult to understand, but splitting it up reveals that the whole structure comprises a single sensing mechanism; that is, the same linear function \(z=\sum w_{i}x_{i}+b\) plus an activation function \(\sigma(z)\). Additional hidden layers in the DNN model will result in a linear correlation coefficient \(w\) and the number of biases \(b\) in the linear relationship of the DNN model increasing.  
The 4-layer DNN model was introduced as follows in Figure 2.  
Where the number of input variables was set as 3, the node number at the hidden layer was set as 4, the connection weight between each two node \(w_{ij}^{k}\) at the \(k\) layer between the \(i\)-th node and the \(j\)-th node at the next layer  
To facilitate calculation, sigmoid is defined as the activation function  
\[H(x)=\frac{1}{1+e^{-x}} \tag{14}\]  
The entire model-building process is shown in the flow chart in Figure 3.  
The above analysis and description indicate that the DNN algorithm provides a good research model for studying the nonlinear relationship between multiple variables, and more accurate results can be obtained through error correction of the algorithm. This paper addresses the relationship between bad weather and the controller's workload, for which there is currently no perfect functional expression. Using the DNN algorithm model can solve this problem because the relationship between variables does not need to be preset, and the whole process from input variables to output results can be realized through the characteristics of its own algorithm. Using the controller workload trained by the model, the sector capacity under the influence of bad weather can be obtained by using linear regression.  
Input variables included sector flow (S), instruction times (N), instruction duration (I), optimized weather-influence coefficient (W) calculated using Gaussian mixture clustering,  
Figure 2: 4 layers DNN structure.  
and the number of diverted flights (C) calculated using the route-availability model. The training form was the DNN algorithm, the time update unit was 1 h, and the output data were the controller workload (L). The structure of the specific model is shown in Figure 4.
## 5 Experiments and Results
### Comparison with Other Integrity Protection Methods
[31] proposes a new hard-ware-assisted control-flow-checking method to resist CRAs, introducing a negligible 0.95% runtime overhead and 0.78% binary size overhead on average. The above-mentioned methods ignore the protection of data integrity, and sometimes, it is impossible to detect the tampering behavior of the data by the attacker. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  
These methods [12; 32] take into account both DI and CFI. They all use hash functions to monitor the integrity of program data. Ref. [32] proposes a Dynamic Sequence Checker (DSC) to check the CFI of BBs. A DSC can detect CRA gadgets, and the average performance overhead is 4.7%. However, Ref. [32] only uses the shadow stack to protect the return address of the program, ignoring the protection of indirect jumps. Ref. [12] protects the CFI of indirect jumps by limiting the target address range. The legal addresses are stored in the form of a secondary list. Despite the optimized search algorithm, they still need to perform multiple searches to determine whether the target address is in the secondary address list. Its performance overhead is 9.33% on average. In addition, the size of reference information in [12] is equal to the size of the protected program. Different from these methods, the proposed HIIMA can monitor the DI and CFI of the execution program at the same time. Once an attacker tampered with key data or manipulated the control flow, HMM would discover these abnormal behaviors in real time. The experimental results show that HIIMA satisfies the balance between security and practicability. However, it is undeniable that our proposed method also has limitations. The program may be interrupted when it is actually running. After the interrupt is completed, the embedded processor will return to the previous state to continue processing the program. In this case, our proposed CFI protection mechanism cannot identify whether the control flow change caused by the interrupt is legal. The CFI after the interruption can be protected by backing up and verifying the status parameters of the program. In addition, if external data participates in the program while it is running, HIIMA cannot monitor the integrity of the external data. In this case, the proposed method can be combined with dynamic information flow tracking technology to solve the problem.
# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 3 Methodology  
### Model Concept  
The conceptual foundation of SARAC4N is built upon the fusion of social awareness and resource awareness in Content-Centric Networking. The framework models each node in the clustered ICN environment as an intelligent agent with dynamic attributes--such as CPU, memory, and communication centrality--that influence its role in caching and forwarding decisions. The model assumes a heterogeneous network where content popularity, node connectivity, and load conditions vary over time. SARAC4N incorporates a weighted centrality-based ranking mechanism, which feeds into the cluster formation logic. Each cluster operates  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Caching Strategy** & **Resource Awareness** & **Social Metrics** & **Key Limitations** \\ \hline [PERSON] et al., 2024 [2] & [PERSON] Content Retrieval & Partial & No & Lacks social/contextual [PERSON] \\ [PERSON] et al., 2024 [6] & Segment-Based Caching & No & No & Not adaptive to node state or load \\ [PERSON] et al., 2024 [11] & Dynamic Clustering & No & Yes & No resource-aware node selection \\ [PERSON] et al., 2024 [12] & Edge RL. Caching & Yes & No & No social integration or clustering \\ [PERSON] et al., 2006 [15] & LCD Caching (Node-Based) & No & No & High duplication, no adaptability \\ Cisco, 2023 [19] & Network Load Trends & Indirect & No & Highlights need for scalable caching \\ [PERSON] et al., 2024 [21] & Socially Aware DRL + DTN & Yes & Yes & Domain-specific (vehicular IoV) \\
**SARAC4N (Proposed)** & Clustered Hybrid Caching & Yes & Yes (centrality metrics) & Needs real-time mobility extension \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative summary of existing caching methods in ICN.  
semi-autonomously under a dual-head (CH and CSH) structure, ensuring redundancy and fault tolerance. Load balancing is modeled via queue-based priority scheduling and workload reallocation policies, triggered upon exceeding an empirically defined resource utilization threshold (80%). This concept formalizes a distributed decision-making model that adapts in real time, allowing clusters to reconfigure under dynamic load and social interaction patterns. The model integrates five subsystems: (i) Social Metric Computation, (ii) Resource Profiling, (iii) Cluster Formation Logic, (iv) a Load Balancing Engine, and (v) a Cache Placement Strategy.  
Figure 5 illustrates the sequential process of SARAC4N cluster formation. It begins with assessing node-level resource availability (CPU, memory, and bandwidth), followed by evaluating social metrics such as centrality scores. Based on the combined Social Importance Score (SIS), the most optimal nodes are selected to serve as cluster heads (CHs), secondary heads (CSHs), or caching nodes, enabling efficient, distributed content placement across the network.
# Reliability Analysis of Military Vehicles Based on Censored Failures Data
## 2 Methodology  
Reliability of a technical object is commonly understood as the probability that the technical object, when used under specified conditions, will remain continuously in a state of operational condition until a given moment \(t\)[12; 31; 32; 33]. This can be described using the relation:  
\[R(t)=P(T\geq t)\text{ for }t\geq 0. \tag{1}\]  
The reliability function \(R(t)\) and the unreliability function \(F(t)\) constitute a complete system of events.  
The reliability function is non-increasing, which is interpreted in the literature as a decrease in the value of the probability of a correct operation of a technical object as the amount of work executed increases. Thus, relation (2) implies the property of the failure function, which is its non-decreasing monotonicity, which also results from the assumption that the probability of failure of a technical object increases with the increase in the service life [34].  
The characteristics that determine the probability of failure occurring at a given time \(t\) are the failure probability density function \(f(t)\) and the failure intensity function \(\lambda(t)\). The function \(f(t)\) represents the probability per unit of service life of a failure occurring at time \(t\). From the probability calculus, it follows that the failure density \(f(t)\) is a derivative of the failure function \(F(t)\), as shown in the following equation:  
\[f(t)=\frac{dF(t)}{dt}=F^{\prime}(t). \tag{2}\]  
The failure intensity function \(\lambda(t)\) defines the conditional probability of failure of the technical object at time \(t\) per unit of service life, provided that the object has not failed within the interval \((0,t)\). The value of the function \(\lambda(t)\) at time \(t\) is expressed by the relation:  
\[\lambda(t)=\frac{f(t)}{R(t)}. \tag{3}\]  
For a known course of the reliability function \(R(t)\), the expected time (mileage) of reliable operation without failure of the technical object is calculated according to the formula [35; 36]:  
\[ET=\int\limits_{0}^{\infty}R(t)dt. \tag{4}\]Figure 1 shows the author's algorithm for analysing and modelling the reliability of technical objects. According to the given assumptions, the reliability study of technical objects begins with the identification of a representative group of objects working under indistinguishable operating conditions and performing the same work. The starting point for modelling reliability characteristics is the estimation of the value of the reliability function using empirical data. This is followed by an approximation of the reliability function using analytical and neural models. On the basis of the created reliability function models, the remaining characteristics are created, i.e., the failure probability density function and the failure intensity function.
## 2 Determining Vehicle Position
### Position from ART flags
#### 2.2.5 Detecting Marker Frame and Reading Its Code  
Having determined picture from the previous step, it is possible to start reading the tag code. To do this, the image is divided into a grid of 9 \(\times\) 9 squares, then a count of the white pixels in each square is performed. If their number is higher than the defined threshold, then the square represents the binary number 1, oppositely the square is assigned to value 0. The resulting matrix containing binary numbers is compared with the code patterns. Each marker has a frame with two squares thickness.