esa-sceva/satcom-chunk-collection huggingface dataset:
# Peak-Frequency Histogram Similarity of Bowel Sounds for the Evaluation of Intestinal Conditions
## 2 Materials and Methods
### Peak-Frequency Histogram of BSs  
Based on the mathematical model of BS generation [16], BSs can be expressed by the vibration of the intestinal wall. As shown in Equation (1), the resonance frequency of the vibration of the intestinal wall can be expressed using a spring and damper model [18].  
\[f_{0}=\frac{1}{2\pi}\sqrt{\frac{K}{M}-\frac{C^{2}}{4M^{2}}} \tag{1}\]  
Here, \(M\) is mass of the intestinal wall including the intestinal contents such as liquid and gas; \(K\) is the stiffness that is associated with resistance toward the distinction of the intestinal wall; \(C\) is a damping coefficient that is associated with fluid resistance.  
According to Equation (1), the resonance frequency of the vibration of the intestinal wall is thought to be changed by the variation of the intestinal wall distention and intestinal contents. Therefore, information concerning the intestinal conditions should be reflected in the PF of a BS. As a BS is generated multiple times in various locations in the intestine, the distribution that was created based on the PF of the BSs for a given time period may reflect a representative intestinal condition.  
Therefore, in this study, the PF and histogram of BSs obtained over a certain time length were generated using the following procedure:  
1. We considered the main frequency components of the BS episodes that were detected by using the BS automatic detection method described in Section 2.2, and applied a third-order Butterworth bandpass filter with cutoff frequencies of 100 and 1000 Hz
2. The BS episodes were segmented using a segment length of 64 ms (256 samples) and shift size of 16 ms (64 samples)
3. After conducting window processing using a Hamming window (duration: 128 ms, 512 samples) for each BS segment, the amplitude spectral frequency with the maximum amplitude was considered as the peak frequency
4. The signal to noise (SN) ratio was considered, and the frequency with the maximum amplitude (among the peak frequencies, which were detected for each segment) was treated as the maximum peak frequency of the BS episode
5. The maximum peak frequency of the BS episode that occurred over a certain time period T was used to generate a histogram with the probability of occurrence dis played on the vertical axis and the bin width (BW) presented in Hz. In this study, the generated histogram is called the \"peak-frequency histogram\" (PFH)
# Design of a Polarization-Insensitive and Wide-Angle Triple-Band Metamaterial Absorber
## 2 Modeling and Theoretical Analysis
The formula is as follows:  
\[\left\{ \begin{array}{l}n=\frac{1}{k\text{d}}\cos^{-1}\Bigl{[}\frac{1}{2S_{2 1}}\bigl{(}1-S_{11}^{2}+S_{21}^{2}\bigr{)}\Bigr{]}\\ Z=\sqrt{\bigl{(}1+S_{11}\bigr{)}^{2}-S_{21}^{2}/\bigl{(}1-S_{11}\bigr{)}^{2}-S_ {21}^{2}}\end{array} \right. \tag{3}\]  
where k is the wave vector of the incident wave and d is the total thickness of the MMA, represented as H + 2 \(\times\) H1(1.07 mm). At the same time, after obtaining the wave impedance and refractive index, the equivalent permittivity and equivalent permeability can be calculated as follows:  
\[\left\{ \begin{array}{l}e_{eff}=n/Z\\ \mu_{\text{eff}}=n\times Z\end{array} \right. \tag{4}\]  
Generally speaking, when the real part of the impedance is close to 1, the imaginary part is close to 0, and the real and imaginary parts of the equivalent dielectric constant and permeability are close in numerical value, the electromagnetic wave will be perfectly absorbed on the MMA structure.
# Jakarta Pandemic to Endemic Transition: Forecasting COVID-19 Using NNAR and LSTM
## 2 Materials and Methods
### Neural Network Auto-Regressive (NNAR)  
The Neural Network Auto-Regressive (NNAR) model is a feed-forward neural network with a single variable input, and lag 1, lag 2, etc., until lag to p; hence, it is called Neural Network Auto-Regressive (NNAR) [33]. This model is only for a feed-forward neural network with estimations of y and a single hidden layer with neurons and is denoted by NNAR (p,k), where k is the number of nodes in the hidden layer and p denotes lag-p as input [34]. The fitted model for data with a seasonal pattern is denoted as NNAR(p,P,k)\({}_{\rm m}\) model, where P is the seasonal lags and m is the seasonal period's length. NNAR fitted model for seasonal data is similar to ARIMA (p,0,0)(P,0,0)\({}_{\rm m}\). NNAR does not restrict on its parameters to achieve stationarity [35]. The mathematical representation of the relationship between the model output and the input is as follows:  
\[\rm y_{t}=w_{0}+\sum_{j=1}^{h}w_{j}g(w_{0j}+\sum_{i=1}^{n}w_{ij}y_{t-1})+ \varepsilon_{i}, \tag{1}\]  
where \(\rm y_{t}\) is the output, \(\rm y_{t-1}\);...; \(\rm y_{t-p}\) are the inputs, \(\rm w_{j}(j=0,\,1,\,\ldots,\,h)\), and \(\rm w_{ij}(i=0,\,1,\,\ldots,\,n;j=1,\,2,\,\ldots,\,h)\) are connection weights or parameters of the model, h is the number if nodes in the hidden layer, and n is the number of the input nodes. NNAR scales well to large p-orders, allowing long-range dependencies to be estimated (important in high-resolution monitoring applications) [36].
## 2 System Architecture, Spectrum Sharing Technique, and Interference Management
### Co-primary Shared Access  
For CoPSA, we consider another MNO termed MNO 2 collocated with the concerned MNO 1 as shown in Figure 4. Both MNO 1 and MNO 2 serve their outdoor macro UEs by their respective entire licensed spectrums. MNO 1 serves its indoor UEs by reusing its macrocell spectrum to small cells deployed within a number of buildings because of the high external wall penetration loss of a building. Assume that the spectrum of MNO 1 is not enough to serve all its indoor UEs and MNO 2 has surplus spectrum so that MNO 2 comes to an agreement with the MNO 1 to share its licensed spectrum. Like MNO 1, by exploring the high external wall penetration loss of each building, MNO 2 shares its macrocell spectrum as well with the small cells of MNO 1 within each building. Hence, each SBS operates at two transceivers where transceiver 1 operates at the spectrum of MNO 1 and transceiver 2 operates in the spectrum of MNO 2, both subject to the respective co-channel interference management strategies. Due to sharing between different MNOs, such kinds of sharing of the spectrum  
Figure 3: The system architecture of the concerned MNO1 and the use of the dedicated spectrum access technique to MNO 1.  
are termed as inter-operator spectrum sharing. We consider two usual cases of CoPSA [6], namely dynamic spectrum sharing by pooling (DySP) and static spectrum sharing by renting (StSR). In DySP, the TD resource scheduling for both MNOs is performed at a commonplace by a TD common resource scheduler (CoRS) whereas the frequency-domain (FD) resource scheduling is performed by local FD schedulers, i.e., one FD scheduler for all macro UEs and one FD scheduler for SBSs per building for both MNO 1 and MNO 2. However, for StSR, TD and FD schedulers of MNO 1 and MNO 2 are located at their respective macrocell base stations (MBSs). Both DySP and StSR are discussed in detail in what follows.
# Designing Multi-Agent System Organisations for Flexible Runtime Behaviour
## 2 Organisation-Oriented MAS Design and Development
### Organisation Meta-Models for MAS
#### 2.2.1 Comparison of Existing Organisation Meta-Models and Approaches to Coordination
The interaction artefact in JaCaMo+ is referenced based on protocols specified at design time and agents rely on the system to notify them rather than receiving direct communication from another agent. In JaCaMo+, it is not possible for agents to negotiate and create a coordinated plan at runtime in order to reach a goal involving interdependent activities. SharedPlans does provide for dynamic coordination of agent plans to enable dynamic planning. This level of coordination is not provided in the other meta-models under examination. SharedPlans incorporates an explicit level of interaction around intentions and sharing of intentions; however, SharedPlans does not incorporate any organisational knowledge. OperA+ and OMACS both use capabilities as a way of allocating agents to roles, but both assume that it will always be possible to find an agent possessing all the capabilities required to enact a role fully. Improvisation outside of a role description is not a feature of any of these models. It is common in organisation meta-models to find that the organisational objectives are described implicitly in the functional description of the system using roles. Effectively, if every role is fulfilled, the organisational objectives will be reached (e.g., [28; 52; 72]) so that within a group or organisational context, agents are associated with a role to determine the activities in which the agent may participate. In more flexible approaches, based on the particular context, roles may be selected or assigned to agents at runtime in order to gain some flexibility (e.g., [73]). In these cases, the organisational structure is derived to suit the situation. It is possible to achieve flexibility in both organisational roles adopted and in the way goals are decomposed so that potentially, multiple solutions can exist. Existing meta-models have much to offer, but do not provide for sufficient agent awareness and flexibility to addresses all our requirements. From the agent stories, we see the organisation meta-model should enable that:  
* Organisation agents can identify relevant other agents;
* Policies or norms specify obligations for sharing beliefs and intentions;
* The organisation is a first class entity with agents having awareness of organisation structure and membership;
* Agents can interact explicitly regarding intentions;
* Tasks can be aligned with the capabilities required to perform them;
* Agents can mutually adjust their individual behaviour and coordinate runtime behaviour to agree on plans;
* Agents reason with consideration of organisational objectives;
* Agents are not unnecessarily constrained by roles and can act based on their capabilities;
* Goals can be broken down into related tasks, so that agents can individually adopt tasks to contribute to the achievement of goals.
## 1 Introduction  
Rail transit constitutes a critical component of urban infrastructure, influencing the development and spatial organization of contemporary cities. With the rapid expansion of subway networks, tunnel systems experience numerous structural challenges, including water leakage [1], cracks [2], segment misalignment [3], cross-sectional deformation [4], and uneven settlement [5]. Among these issues, water leakage emerges as the most prevalent and consequential structural damage. This phenomenon accelerates weathering, lining spalling, and corrosion of tunnel lining, compromising structural integrity and posing significant threats to operational safety. Consequently, regular and effective inspection for tunnel leakage represents a critical priority for subway system maintenance [6]. Traditional inspection methods typically rely on manual photography, and entail a high level of subjectivity as well as the safety hazards inherent in confined and complex tunnel environments. These methods are widely criticized for their inefficiency, insufficient precision, and occupational risks [7; 8].  
Recent advancements in mobile laser scanning (MLS) technology have emerged as a promising alternative for tunnel leakage inspection. MLS systems, mounted on a rail trolley, integrate laser scanners, high-definition cameras, odometers, and computing units to automatically acquire high-precision and high-density 3D point cloud data [8]. This technology enables effective 3D reconstruction of tunnel environments and reduces reliance on manual inspection. However, several challenges remain. The irregular shapes and varying scales of leakages complicate detection. Additionally, tunnel point cloud data often contains interference from ancillary facilities, bolt holes, tracks, and noise, which hinder accurate identification. This study addresses these challenges by proposing a novel classification method that combines geometric and intensity features to automatically detect leakage in 3D space. The key contributions of this research are as follows:  
1. An automated framework was developed to accurately detect tunnel leakage in 3D point cloud data, which addresses challenges related to noise and the limited spatial representation of leakage patterns.
2. Geometric features were introduced as complementary characteristics, which resulted in improved accuracy for leakage detection.
3. The XGBoost classifier was introduced for leakage detection, and a comparative analysis demonstrated its superior accuracy and computational efficiency, thereby validating its applicability in this context.  
The remainder of this study is organized as follows: Section 2 reviews existing studies and summarizes limitations of the literature. Section 3 describes the proposed methodology in detail. Section 4 presents the experimental results and discussion. Finally, Section 5 concludes the study.
## 2 IA for Two-Unit Series System with Three States
### Numerical Simulation  
In this subsection, we will use two numerical examples to show the validity of our result and check whether there exist IA fluctuations for two-unit series system with three states.  
**Case 1**.: _We assume the fault, delay-repair and repair rates are as follows:_  
\[\lambda_{1}=1,\;\mu_{1}=1,\;\
u_{1}=1,\] \[\lambda_{2}=2,\;\mu_{2}=1,\;\
u_{2}=2.\]  
Then following the steps in Section 2.2, we can obtain the IA shown in the Figures 2 and 3:  
**Case 2.**: _We assume the fault, delay-repair and repair rates are as follows:_  
\[\lambda_{1}=1,\;\mu_{1}=1,\;\
u_{1}=1,\] \[\lambda_{2}=2,\;\mu_{2}=5,\;\
u_{2}=2.\]  
Then we can obtain the IA as follows:  
We can easily find from Figures 2 and 3 that there exists IA fluctuation in Case 1 but Case 2 does not. According to two cases, we can draw the conclusion that IA for the two-unit series system with three states has fluctuation under certain conditions.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 3 System Design and Scheduling Problem Definition
### Mathematical Formulation  
To formalize the problem, we define the following mathematical model:  
Decision Variables:  
* \(x_{i,j}\in\{0,1\}\): Indicates whether job \(J_{j}\) is assigned to machine \(M_{i}\).
* \(S_{j}\): Start time of job \(J_{j}\).
* \(E_{j}=S_{j}+d_{j}\): End time of job \(J_{j}\), where \(d_{j}\) is the duration of job \(J_{j}\).  
Objective Function:  
\[\min\sum_{i=1}^{n}\sum_{j=1}^{m}\sum_{t}P(M_{i},J_{j},t)\cdot x_{i,j},\]  
where \(P(M_{i},J_{j},t)\) represents the probability of the predicted fault for machine \(M_{i}\) executing job \(J_{j}\) at time \(t\).  
Constraints:  
* Job Assignment Constraint. Every job \(J_{j}\) must be assigned to exactly one capable machine \(M_{i}\): \[\sum_{i=1}^{n}x_{i,j}=1\quad\forall J_{j}\in J.\]
* Machine Capability Constraint. A job can only be assigned to a machine if it is capable of performing it, where \(C_{i,j}\) indicates machine \(M_{i}\)'s capability for job \(J_{j}\): \[x_{i,j}\leq C_{i,j}\quad\forall M_{i}\in M,\forall J_{j}\in J.\]
* Non-Overlapping Jobs Constraint. For any two distinct jobs \(J_{j}\) and \(J_{k}\) assigned to the same machine \(M_{i}\), where \(\delta\) is a small positive constant ensuring non-overlapping, \[S_{j}\geq E_{k}+\delta(1-x_{i,j})+M(1-x_{i,k}),\] \[S_{k}\geq E_{j}+\delta(1-x_{i,k})+M(1-x_{i,j}).\]
* Job Deadline Constraint. Each job \(J_{j}\) must be completed before its deadline \(D_{j}\): \[E_{j}\leq D_{j}\quad\forall J_{j}\in J.\]
* Task Precedence Constraint. For jobs with predefined order, job \(J_{i}\) must finish before job \(J_{j}\) starts: \[S_{j}\geq E_{i}\quad\text{if}\quad J_{i}\text{ precedes }J_{j}.\]
* Machine State Constraints. Operational parameters must remain within safe limits during job execution, where \(T_{\max}\), \(V_{\max}\), \(H_{\max}\), and \(W_{\max}\) are predefined safe thresholds for temperature, vibration, operating hours, and tool wear, respectively: \[T(M_{i},t)\leq T_{\max}\quad\forall t\in[S_{j},E_{j}],\forall J_{j}\text{ assigned to }M_{i},\] \[V(M_{i},t)\leq V_{\max}\quad\forall t\in[S_{j},E_{j}],\forall J_{j}\text{ assigned to }M_{i},\]\[H(M_{i},t) \leq H_{\max} \forall t\in[S_{j},E_{j}],\forall J_{j}\text{ assigned to }M_{i},\] \[W(M_{i},t) \leq W_{\max} \forall t\in[S_{j},E_{j}],\forall J_{j}\text{ assigned to }M_{i}.\]
## 1 Introduction
However, in the existing deep learning methods, signal features are generally extracted from Euclidean space directly, and then a simple neural network structure is used to identify the emitter. There is still a large room for improving the data representation and network structure. To effectively extract the features of the signal, we propose signal graph (a new data representation method) and ensemble neural network (ENN). The main innovations in this paper are as follows:  
1. By constructing a signal graph, the sequence signal is transformed from a Euclidean space to a non-Euclidean space. As a result, the graph convolution method can be used to extract a signal's non-Euclidean feature from its signal graph. Signal graph provides a new method, different from a signal sequence, for emitter signal representation. 2. The ENN is designed with a sequence feature extractor and a graph feature extractor. Hence, it can extract sequence features from Euclidean space and graph features from non-Euclidean space and fuse the two features together to enrich the feature information extracted from the signal and better identify the emitter. We conduct extensive experiments on ESP20 Dataset and RML2016a Dataset. Experimental results show that the proposed ENN outperforms the state-of-the-art methods for SEI tasks.
# Impact of Satellite Attitude on Altimetry Calibration with Microwave Transponders
## 3 Satellite Attitude and Transponder Calibration
### Range and Dutation Bias
The datation bias is defined as the difference between the two TCAs, as follows  
\[dt=\operatorname*{arg\,min}_{t\in\mathbb{R}_{+}}r(t)-\operatorname*{arg\,min }_{t\in\mathbb{R}_{+}}r_{0}(t), \tag{11}\]  
where the function \(\operatorname*{arg\,min}_{x}f(x)\) returns the value of \(x\) that minimizes \(f(x)\).
# Model-Based Methods to Produce Greener Metakaolin Composite Concrete
###### Abstract  
Metakaolin is reactive and is widely used in the modern concrete industry. This study presents an integrated strength-sustainability evaluation framework, which we employed in the context of metakaolin content in concrete. First, a composite hydration model was employed to calculate reactivity of metakaolin and cement. Furthermore, a hydration-based linear equation was designed to evaluate the compressive strength development of metakaolin composite concrete. The coefficients of the strength evaluation model are constants for different mixtures and ages. Second, the sustainability factors--CO\({}_{2}\) emissions, resource consumption, and energy consumption--were determined based on concrete mixtures. Moreover, the sustainability factors normalized for unit strength were obtained based on the ratios of total CO\({}_{2}\) emissions, energy consumption, and resource consumption to concrete strength. The results of our analysis showed the following: (1) As the metakaolin content increased, the normalized CO\({}_{2}\) emissions and resource consumption decreased, and the normalized energy first decreased and then slightly increased. (2) As the concrete aged from 28 days to three months, the normalized CO\({}_{2}\) emissions, resource consumption, and energy consumption decreased. (3) As the water/binder ratio decreased, the normalized CO\({}_{2}\) emissions, resource consumption, and energy consumption decreased. Summarily, the proposed integrated strength-sustainability evaluation framework is useful for finding greener metakaolin composite concrete.  
metakaolin; sustainability; strength; model; CO\({}_{2}\); energy; resource +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Research Process
#### 3.1.3 Data Annotation Stage  
The research team used the professional object detection and annotation tool Labe-IImg to perform brick-by-brick fine annotation on 375 standardized, high-quality images to ensure that the YOLOv8 model could learn and accurately identify various typical damage types on gray brick walls. The annotation work not only emphasizes the extraction of semantic information about damage in the image but also considers the true expression of the scale of building materials, reflecting the high-resolution and high-precision application capabilities of artificial intelligence technology in the field of architectural heritage protection. In terms of annotation strategy, the research team set eight annotation categories, namely crack, damage, missing, vandalism, moss, stain, plant, and intact. Each category uses a box to outline the area, and the annotation focuses on the exact part of the gray brick shown in the image to make sure that the results are consistent and can be traced back to the physical structure. In the case of overlapping or compound damage, the team prioritized labeling according to the preset damage severity hierarchy, namely missing > crack > damage > vandalism > plant > moss > stain. This priority sequence ensures that the model focuses on the key damage areas that have a greater impact on the stability of the building structure during training, thereby enhancing the practical application value of the model. In addition, since the goal of this study is not only damage identification but also includes the automatic counting function of the total number of gray bricks and the number of damages, it is required to fully label all gray bricks in the image, regardless of whether they are damaged, to achieve subsequent brick surface unit-level identification and metrological analysis based on the YOLOv8 model. To construct a well-structured dataset, 375 annotated images were divided into training sets and validation sets in proportion, including 303 training sets (train), 34 validation sets (val), 337 training and validation mixed sets (trainval), and 38 test sets (test). The annotated data formats were all converted into the YOLO-adapted XML file format and paired with the corresponding images one by one.