esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting the Aqueductal Cerebrospinal Fluid Pulse: A Statistical Approach
## 1 Introduction  
The cerebrospinal fluid (CSF) pulse in the Aqueduct of Sylvius (AoS) (the aqueductal CSF (aCSF) pulse) is closely linked with the motion of the lateral ventricles [1] and can be used as a surrogate measure of brain tissue'stiffness' [2]. As such, the aCSF pulse is often measured in order to evaluate structural changes that may occur in the brain parenchyma. Numerous studies have found increased pulsatility of the aCSF to be associated with multiple sclerosis (MS) [3; 4; 5; 6; 7], normal pressure hydrocephalus (NPH) [8; 9; 10; 11; 12; 13], and white matter (WM) alterations [14; 15], suggesting that increases in the amplitude of the aqueductal pulse may be linked to pathological changes.  
It has been shown that the dynamics of the aCSF pulse are influenced by the patency of the cerebral venous drainage vessels [16], with compression of the internal jugular veins (I)Vs leading to: (i) accumulation of blood in the cortical veins [17]; (ii) increased brain tissue'stiffness' [2]; and increased aCSF pulse amplitude [2]. As such, a relationship exists between the intracranial fluid dynamics and blood flow in the neck. This is because the cranium is a rigid container and the parenchymal tissue and fluids enclosed within it are incompressible [18]. Consequently, it is possible to characterize the behaviour of the various intracranial fluids by interpreting fluid flow signals acquired from the arterial, venous and CSF vessels in the neck [19; 20; 21; 22]. Given this, we hypothesized that it should be possible to predict aCSF motion from the extracranial arterial, venous and CSF flow rate signals. Therefore, we designed the 'proof-of-concept' pilot study presented here, in which we used the generallinear model (GLM) to predict the motion of the aCSF pulse. The primary aim of the study was to test whether or not a statistical linear model could accurately predict the motion of the aCSF pulse using only data acquired from the vessels in the neck. As a secondary aim, we also wanted to identify the cervical flow signals (i.e., arterial, venous or CSF) that best predicted the motion of the aCSF, as this might assist in better understanding the factors that influence the motion of the CSF in the AoS. As such, we hoped to gain new insights into the physiology of the intracranial fluidic system in healthy individuals. Furthermore, we anticipated that the model might be a useful tool for identifying changes in the dynamics of the intracranial fluidic system that could be of clinical importance.
## 2 Background
### ISO/IEC 25010:2011 Standard
* Maintainability: The standard defines it as the \"degree of effectiveness and efficiency with which a product or system can be modified to improve it, correct it, or adapt it to changes in the environment, and in requirements\". This characteristic ensures that edge hardware and AI models can be easily updated or optimized, which is essential for adapting to evolving requirements and ensuring long-term system viability. * Compatibility: The standard defines it as the \"degree to which a product, system, or component can exchange information with other products, systems, or components, and perform its required functions while sharing the same hardware or software environment\". Ensuring compatibility ensures that edge devices and AI models can work cohesively within a larger ecosystem, enabling seamless data exchange, integration with other services, and interoperability across different technological stacks. * Security: The standard defines it as the \"degree to which a product or system protects information and data so that persons or other products or systems have the degree of data access appropriate to their types and levels of authorization\". Edge devices and AI models often process personal, medical, financial, or other confidential data, which makes robust security mechanisms essential. The security characteristic ensures that the system protects data integrity, confidentiality, and availability by preventing unauthorized access or tampering. From the business perspective, adopting ISO/IEC 25010:2011 provides several advantages: First, it is a globally recognized standard for software product evaluation, ensuring that the proposed methodology aligns with industry best practices. This supports confidence in the evaluation process among stakeholders. Second, the standard provides a flexible framework that can be applied to a wide range of edge hardware (e.g., microcontrollers, single-board computers) and AI model types, and across various domains (e.g., autonomous vehicles, smart homes, MAVs). This flexibility offers significant business value,  
Figure 1: ISO/IEC 25010 quality attributes and sub-attributes. as the methodology can be generalized for a variety of use cases, enabling adaptable Edge AI solutions.
###### Abstract  
Background: Despite that photophobia and phonophobia are well-known symptoms related to migraine, it is unclear whether they affect daily life activities during the headache-free period. Objective: To evaluate the interictal photophobia/phonophobia intensity during daily activities in migraineurs and non-headache individuals. Methods: Women with migraine without aura (MoA, n = 30), migraine with aura (MA, n = 30), chronic migraine (CM, n = 30) and without headache (CG, n = 30) reported the photophobia and phonophobia intensity during daily activities using a Likert scale ranging from 0 (no discomfort) to 10 (maximum discomfort). Results: The migraine groups reported higher intensity of interictal photophobia and phonophobia than CG during \"driving\" and \"social situations\", respectively (\(p<0.05\)). MA and CM groups presented higher intensity of phonophobia than CG, hearing sounds in everyday situations and listening to conversations in noisy places (\(p<0.05\)). Also, the MA group presented higher interictal phonophobia than the CG to keep concentration in noisy places (\(p<0.05\)). Weak positive correlations were observed between the intensity of both photophobia and phonophobia with migraine intensity, frequency of migraine and frequency of aura (\(p<0.05\)). Conclusion: Interictaly, the intensity of photophobia and phonophobia reported during daily activities is higher in patients with migraine, especially those with aura and chronic migraine, than in non-headache subjects.  
hypersensitivity; visual discomfort; auditory discomfort; headache +
Footnote †: journal: Appl. Sci.
# An Open-Source R Package for Building and Using US Environmentally-Extended Input-Output Models
## 2 Materials & Methods
### Economic Input-Output Data  
In _useeior_, the most recent IO data are the form of \"Make\" (showing the production of commodities by industries) and \"Use\" (showing the consumption of commodities by industries and by final demand) tables compiled by BEA [20], saved in native R data formats,.rda, via automated downloading and writing functions. These tables are available at three levels of sector resolution: \"Detail\" (405 commodities by 405 industries), \"Summary\" (73 commodities by 71 industries), and \"Sector\" (17 commodities by 15 industries). The summary and sector levels tables are released annually by BEA, while the detailed tables are produced roughly every five years, with 2012 representing the most recent release [20]. Therefore, in _useeior v1.0.0_, the summary and sector make and use tables are available for years 2010-2018, while the detail tables are only available for the year 2012. The Make and Use tables compiled by BEA were available \"before redefinition\" and \"after redefinition\". Redefinition adjusts secondary products \"from the industry that produced it to the industry in which it is primary\". [25] In _useeior_, \"before redefinition\" tables are used, as they are more aligned with the majority of environmental data that reflect the original industry activities that occurred [1]. Additionally, the use tables are available in producer price and purchaser price. Existing model configuration files in _useeior_ used the use tables in producer price. _useeior_ provides the option to convert the model from producer to purchaser price, with additions of trade and transportation margins [26]. In EEIO modeling, the direct requirements matrix \(A\) and domestic direct requirements matrix \(A_{d}\) can be derived from the Make and Use tables to build EEIO models in two forms: industry-by-industry or commodity-by-commodity. _useeior_ is capable of building industry (industry-by-industry) and commodity (commodity-by-commodity) models. The former was the most suitable for EEIO models, with a focus on industries and environmental impacts from related producing processes; the latter was most relevant for EEIO models concerned with products and services and their associated materials. A direct requirements matrix derived based on two distinct assumptions, industry-technology and commodity-technology, yields different results. The former assumes that all commodities produced by the same industry have the same input structure, while the latter assumes that each commodity has a unique input structure, regardless of the industry that produced it [25]. _useeior_ v1.0.0 implements the industry-technology model. Other economic data, including multi-year economic gross output, gross output chained price index, the margins table, and the import matrix, are also prepared and made available for immediate use in _useeior_. Model IO data are loaded upon model initialization and with paths to configuration files if provided.
## 2 Materials and Methods
### Classification Stage  
To guarantee optimal performance, we preprocess the data before training the integrated classifier. During this preprocessing phase, the data are normalized, the superfluous information is eliminated, and the missing values are filled in. We start by deleting any duplicate data from the dataset. We then ensure that the data are complete for an additional analysis by adding 0 to any missing values.  
We employ Min-Max normalization to address the issue of features with a large range of values dominating the effect. This normalization method scales each feature to a value between 0 and 1, ensuring that every feature contributes proportionately to the overall classification process. The ith characteristic is denoted by \(x_{i}\), as indicated in Equation (6).  
\[x_{i}=\frac{x_{i}-(x_{i})_{\min}}{(x_{i})_{\max}-(x_{i})_{\min}} \tag{6}\]  
The preprocessed data are used to train the integrated classifier after the data preprocessing step. Equation (7) defines the loss function for the classification stage. In this equation, the true value of the ith sample is denoted by \(y_{i}\), the predicted value of the ith sample is denoted by \(h_{\theta}(x)_{i}\), and the number of samples is represented by \(n\).  
\[L_{\text{Classification}}=-\frac{\sum\limits_{i=1}^{n}y_{i}\log h_{\theta}(x)_{i} +(1-y_{i})\log(1-h_{\theta}(x)_{i})}{n} \tag{7}\]  
Algorithm 2 describes the training steps for the integrated classifier. The number of classifiers in the integrated classifier is denoted by C. In the rebuilt dataset, \(\overline{x}_{i}\) represents the \(i\)th sample, and \(h_{\theta}(\overline{x})_{i}\) represents the corresponding anticipated value. \(R_{i}\) (\(i=1,2,3,4,5,6,7,8\)) represents the classification result for each classifier, while \(R_{i}\) represents the best classification result. Now, let us delve into the methods used to train integrated classifiers. The reconstructed dataset is first fed into the integrated classifier, which undergoes data preprocessing operations such as removing the redundant information, filling in the missing values, and normalization. Next, the dataset is fed into each classifier of the integrated classifier, which undergoes forward and backpropagation to minimize the cost function and converge. The prediction results of each classifier are then output, and the integrated classifier selects the best classification result based on these outputs.
# Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority
## 3 System Model
### Cost Model
\(\alpha\), \(\beta\in[0,1]\), and \(\alpha+\beta=1\), where \(\alpha\) and \(\beta\) represent the balance ratio parameters for the average completion time and average energy consumption, respectively. \(c_{i,j}\) represents the communication cost from the subtask \(v_{i}\) to the successor subtask \(v_{j}\). A higher weight for \(rank_{k}(v_{i})\) indicates a higher priority for scheduling, where \(k\) represents the service request's identifier. For the above variables, normalization is required when performing summation. The above design is specifically intended for regular DAG subtasks. Public subtasks, due to their uniqueness, are uniformly identified and executed by the public task management module within the GEO satellite. When executing the batch task set \(a=\{application_{1},application_{2},\ldots,application_{n}\}\), preprocessing is uniformly carried out by the public task management module of the SDN controller. If a subtask is executed more than two times, it will be marked, and the execution results of the subtask will be cached in the GEO satellite for public use by all tasks. Setting a time parameter \(T\), if a public subtask has not been executed beyond the time \(T\), the record for that subtask is removed from the public task management module. We assume that the public subtasks are represented by \(v_{i}^{common}\), and the set of public subtasks is represented by \(Common\).
# Adaptive IDS for Cooperative Intelligent Transportation Systems Using Deep Belief Networks
###### Abstract  
The adoption of cooperative intelligent transportation systems (cITSs) improves road safety and traffic efficiency. Vehicles connected to cITS form vehicular ad hoc networks (VANET) to exchange messages. Like other networks and systems, cITSs are targeted by attackers intent on compromising and disrupting system integrity and availability. They can repeatedly spoof false information causing bottlenecks, traffic jams and even road accidents. The existing security infrastructure assumes that the network topology and/or attack behavior is static. However, the cITS is inherently dynamic in nature. Moreover, attackers may have the ability and resources to change their behavior continuously. Assuming a static IDS security model for VANETs is not suitable and can lead to low detection accuracy and high false alarms. Therefore, this paper proposes an adaptive security solution based on deep learning and contextual references that can cope with the dynamic nature of the cITS topologies and increasingly common attack behaviors. In this study, deep belief networks (DBN) modeling was used to train the detection model. Binary cross entropy was used as a loss function to measure the prediction error. Two activation functions were used, Relu and Softmax, for input-output mapping. The Relu was used in the hidden layers, while the Sigmoid was used in the last layer to map the real vector to output between 0 and 1. The adaptation mechanism was incorporated into the detection model using a moving average that monitors predicted values within a time window. In this way, the model can readjust the classification thresholds on-the-fly as appropriate. The proposed model was evaluated using the Next Generation Simulation (NGSIM) dataset, which is commonly used in such related works. The result is improved accuracy, demonstrating that the adaptation mechanism used in this study was effective.  
coperative intelligent transportation systems (cITSs); IDS; vehicular ad-hoc networks (VANET); adaptive model; deep belief network (DBN) Article
## 3 Improved Firefly Algorithm-Wavelet Support Vector Machine
### Firefly Algorithm  
Firefly algorithm is a biological-inspired metaheuristic optimization algorithm using the flashing characteristic of fireflies, developed by [PERSON]. In nature, each firefly searches for prey, communicates and finds mates with the help of luminance produced by other fireflies. Accordingly, the concept of luminance production is provided to solve optimization problems.  
To ensure the implementation of algorithm, some idealized rules are used as follows: (1) each firefly attracts other fireflies regardless of their sex, making exploring the search space more efficient; (2) attractiveness of a firefly is directly proportional to the brightness. The entire swarm of fireflies move towards the brightest one, which moves randomly in the space; (3) the brightness of a firefly is determined by a fitness function.  
In the firefly algorithm, the two critical issues are the variation of brightness and formulation of the attractiveness. For minimum optimization problems, brightness at the particular location \(x\) should increase as the fitness function value decreases. In addition, brightness of one to another firefly is relative to the distance between the firefly and beholder, decreasing with the distance from the source. Consequently, the brightness can be represented by:  
\[I(\mathbf{x})\propto\frac{1}{f(\mathbf{x})} \tag{21}\]\[I\big{(}r_{ij}\big{)}=I_{0}\exp\Bigl{(}-\gamma r_{ij}^{2}\Bigr{)} \tag{22}\]  
where \(f(x)\) is the value of fitness function; \(I_{0}\) is the original brightness; \(\gamma\) is the coefficient of light absorption in the range of \([0.1,10]\); \(r_{ij}\) is the Cartesian distance between any two fireflies \(i\) and \(j\), which is denoted by:  
\[r_{ij}=\|\mathbf{x}_{i}-\mathbf{x}_{j}\|=\sqrt{\sum_{k=1}^{D}\Bigl{(}x_{i,k}-x_{j,k} \Bigr{)}^{2}} \tag{23}\]  
where \(\mathbf{x}_{i}=\bigl{\{}x_{i,k}\bigr{\}}\) is the location of firefly \(i\) in the \(D\)-dimension future space.  
Similarly, due to that attractiveness is proportional to the brightness seen by adjacent fireflies, it can be defined by:  
\[\beta\big{(}r_{ij}\big{)}=\beta_{0}\exp\Bigl{(}-\gamma r_{ij}^{2}\Bigr{)} \tag{24}\]  
where \(\beta_{0}\) is the maximum attractiveness, presenting the attractiveness at \(r=0\).  
Finally, the movement of a firefly \(i\) towards to another firefly \(j\) with a higher brightness is determined by:  
\[\mathbf{x}_{i}(t+1)=\mathbf{x}_{i}(t)+\beta\big{(}r_{ij}\big{)}\bigl{[}\mathbf{x}_{j}(t)- \mathbf{x}_{i}(t)\bigr{]}+\alpha\big{(}rand-0.5\big{)} \tag{25}\]  
where \(t\) is the number of iterations; \(\alpha\) is the randomization parameter; \(rand\) is a pseudo-random number uniformly distributed in \([0,1]\).
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## 3 Noise Techniques
### Causes of Noise and the Necessity of Its Reflection  
Industrial noise arises from various factors, each impacting system behavior in unique ways. This section discusses the causes of three types of noise--electrical interference noise, harmonic noise, and random shock noise--and highlights the necessity of reflecting them in fault detection systems.  
* Electrical Interference Noise Electrical interference noise is caused by uneven fluctuations in current flow due to bearing failures such as pitting or wear. These failures alter the electrical contact area and resistance, leading to inconsistent contact between the bearing race and rolling elements. This inconsistency increases the load required for operation, impacting the entire system and altering its vibration pattern. Simulating these conditions using electrical interference noise techniques aids in developing early fault detection and classification systems, offering insights into system anomalies.
* Harmonic Noise Harmonic noise arises from factors such as component wear, cracks, or misalignment, often triggered by excessive loads, thermal expansion in high-temperature environments, or prolonged usage. These conditions produce not only fundamental frequencies but also harmonic frequencies (e.g., doubles, triples) of the fundamental frequency. By utilizing harmonic noise techniques, early abnormal conditions can be detected, and harmonic component analysis can help predict failures, enabling proactive maintenance in industrial settings.
* Random Shock Noise Random shock noise results from sudden shocks or collisions between machine parts due to abnormal forces. These impacts often precede machine failures, creating strong disturbances in sensor data. Simulating random shock noise facilitates the design of systems capable of preventing accidents or mitigating the impact of such events. This noise type is particularly useful for triggering emergency responses, such as system shutdowns, to prevent large-scale damage.  
Although these noise types may originate from similar sources, they exhibit distinct signal characteristics, such as varying frequency bands and patterns. Electrical interference noise enables early warning systems by identifying initial anomalies. Harmonic noise supports fault progression monitoring and maintenance scheduling to optimize system responses. Random shock noise allows for immediate emergency measures, such as halting system operations to prevent extensive damage. By analyzing these noise types in a multi-dimensional manner, a fine-grained response system can be built, enhancing the ability to address industrial site failures effectively and efficiently.
# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
## 2 Research Method
### Step 1. Spectral Transformation Methods for Speech Processing
#### 2.1.3 Log-Wavelet Spectrogram Features  
The continuous log-wavelet spectrum utilizes the concept of multi-resolution analysis to achieve non-uniform partitioning in the time-frequency domain [33]. This enables the  
Figure 4: Spectral feature map used in this article. (**a**) Log-power spectrogram; (**b**) log-Mel spectrogram; (**c**) log-wavelet spectrogram.  
adaptation of time-frequency resolutions based on the characteristics of different signals. Such flexibility makes wavelets particularly well suited for speech signal processing. For speech signals, high-frequency components demand finer time resolution to detect rapidly changing transient features, whereas low-frequency components require finer frequency resolution to accurately capture slowly varying features, such as formants, over time.  
The calculation is shown below (Equation (3)), where \(\log-\) wavelet spectrogram\((t,f)\) represents the log-wavelet spectrogram at time t and frequency w. An example of the extracted birdsong audio features is shown in Figure 4c. In Equation (3), y represents the input signal, typically a time domain signal such as audio, speech or biological data. The parameter \(t\) refers to the time shift, indicating the temporal position of the wavelet in the signal, while \(\omega\) is the frequency (or scale) parameter, which controls the width of the wavelet. \(cwt(y,t,\omega)\) denotes the continuous wavelet transform (CWT) of the signal y at time t and frequency \(\omega\), providing a time-frequency representation of the signal. The term \(\left|cwt(y,t,\omega)\right|^{2}\) is the squared magnitude of the CWT result, representing the energy of the signal at time t and frequency \(\omega\), while \(\epsilon\) is a small positive constant added to avoid numerical instability when calculating the logarithm, ensuring that a logarithm of zero is never encountered. This process generates a log-scaled representation of the wavelet transform, capturing the signal's local energy in both the time and frequency domains.  
\[\log-\text{wavelet spectrogram}(t,f)=\log\Bigl{(}\left|cwt(y,t,\omega)\right|^{ 2}+\epsilon\Bigr{)} \tag{3}\]