esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Activities of Daily Living with Spatio-Temporal Information
###### Abstract  
The smart home has begun playing an important role in supporting independent living by monitoring the activities of daily living, typically for the elderly who live alone. Activity recognition in smart homes has been studied by many researchers with much effort spent on modeling user activities to predict behaviors. Most people, when performing their daily activities, interact with multiple objects both in space and through time. The interactions between user and objects in the home can provide rich contextual information in interpreting human activity. This paper shows the importance of spatial and temporal information for reasoning in smart homes and demonstrates how such information is represented for activity recognition. Evaluation was conducted on three publicly available smart-home datasets. Our method achieved an average recognition accuracy of more than 81% when predicting user activities given the spatial and temporal information.  
prediction by partial matching; spatio-temporal; activity recognition; smart homes +
Footnote †: journal: Pattern Recognition
## 4 Proposed Solution  
Figure 2 shows the geographically distributed water resources with partial or no cellular coverage for connecting the SNs with the IoT network. Here, the WSN is based on LoRa, and the IoT connectivity is achieved using a LoRa-IoT gateway. It also shows different types of WSN topologies, where a sensor node is acting as a repeater and communicating with a mobile data sink under partial cellular coverage. The mobile DS is mounted on an UAV to access the remote SNs when other means of vehicular movement may not be possible. A mobile DS, under partial cellular coverage, can also act as a LoRa-IoT gateway. On the other hand, an offline mobile DS (position A) stores the sensor data from the water resources with no cellular coverage and uploads the data to the IoT server when it comes to cellular coverage (position B). While designing the proposed dynamic protocol and the SN's auto self-reconfigurable capability for WARM, our primary focus is on the following:  
1. Selection of wireless communication technology; and
2. Lightweight dynamic communication protocol.
## 5 Conclusions  
In order to increase the effectiveness of monitoring the technical condition of a trainer aircraft airframe, two variants of implementations were proposed. The use of an unmanned aerial vehicle (UAV) to which a long-wave infrared camera would be attached would allow visual inspections of both surface and structural damage. In addition to the aircraft airframe composite structure, this solution also allows monitoring the technical condition of the propeller and engine components. By implementing this variant, it is possible to save time performing visual as well as structural inspections; in addition, when choosing the right variant of the IR camera, it could be possible to monitor the electronic elements of the aircraft. Ultimately, the proposed implementations can achieve reliable monitoring of the technical condition of the airframe, engine, propeller, and electronic elements of the aircraft, increase safety, and reduce the workload of technical personnel, reduce downtime, and ensure the reliable maintenance of maintenance records.  
RFID technology, specifically UHF RFID, is an excellent supporting tool for visual inspection by drone. In addition to monitoring of structural electrical failures, with the use of UHF, it is possible to identify any rivet or screw release of components equipped with it.  
New ways of condition monitoring could increase the effectiveness of the trainer aircraft maintenance as well as save time, costs, and manpower. For the small MRO, these suggestions present new possible ways of adapting to predictive maintenance strategy and replace time-consuming processes of preventive maintenance strategies.  
The future direction of our research would be experimental application of RFID tags for the detailed inspection of various rivet and nuts release monitoring. Testing their conductivity on various materials including metal, wood, and composite materials. The limitation of this technology include the read range and possible installation of an antenna to a UAV.  
[PERSON], [PERSON], and [PERSON] developed the conceptualization and investigation of this work. [PERSON], B.K., and B.B. conducted the experimental part--visual inspection of the aircraft airframe by unmanned aerial vehicle with camera at UNIZA. [PERSON] and [PERSON] were responsible for the calculation and validation of failure indexes. A.N. took part in verification of the image acquisition. All authors participated in the analysis of the results. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This publication was realized with support of the Operational Programme Integrated Infrastructure in frame of the project: Intelligent systems for UAV real-time operation and data processing, code ITMS2014+: 313011V422 and co-financed by the European Regional Development Found.  
The authors declare no conflict of interest.
## 3 Tracking Loops Error Characterization
### Dynamic Stress Error  
The dynamic stress error is associated to the motions that the receiver antenna suffers. It is inevitable in the PLL tracking and plays a major role in the tracking performance. The purpose of the ultra-tight integration is just to reduce the dynamic stress error by estimating and compensating the receiver antenna's LOS dynamics from the INS. The dynamic stress error (1-sigma) depends on loop order and described as follows [23]:  
\[\begin{split}\theta_{e}=&\frac{1}{3}\cdot\frac{d^{k }R/dt^{k}}{\omega_{PLL}^{k}}=&\frac{1}{3}\cdot K_{k}\,\frac{d^{k }R/dt^{k}}{\left(B_{PLL}\right)^{k}}\\ =&\frac{1}{3}\cdot K_{k}\,\frac{d^{k}R/dt^{k}}{ \left(B_{PLL}\right)^{k}}\cdot\frac{360\cdot L_{c}}{c}\,\end{split} \tag{2}\]  
where \(d^{k}R/dt^{k}\) is the relative dynamics between the satellite and the GNSS receiver antenna in its LOS (m/s\({}^{k}\)). \(B_{PLL}\) is the PLL bandwidth (Hz). \(L_{c}\) is the carrier frequency (Hz). \(c\) is the free space speed of electromagnetic propagation (m/s). \(K_{k}\) is the proportion coefficient. \(k=2,K_{2}=0.2809\) for a second-order PLL, \(k=3,K_{3}=0.4828\) for a third-order PLL.
## 8 Technical Challenges for 6G Development
### Terahertz Frequency
#### 8.1.3 Broad-Scale Fading Characteristics  
Terahertz signals are highly sensitive to shadows, significantly impacting coverage. For instance, the human body can cause a 20-35 dB signal attenuation with signal attenuation of this magnitude. However, humidity/rainfall fading has a relatively small effect on terahertz communication, particularly in frequency bands around 140 GHz, 220 GHz, and 340 GHz, which exhibit relatively low rain attenuation [42].
# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 3 Methodology
### ETL Process for Rail Asset Assessment
#### 3.2.2 Extraction, Transformation, and Load Process Phases (ETL)  
This section details the individual phases of the ETL process, including data extraction from multiple sources, transformations to normalize and enrich the data, and the final loading of this transformed data into a master table for use in criticality analysis.  
* SQL inventory databases (\(F1\)),
- Excel files with maintenance information (\(F2\)),
- IoT sensors (\(F3\)),
- GIS systems to locate assets (\(F4\)).
- Extraction is formalized as an operation: \(E(Di)=Di\) para \(i=1,2,\ldots\), \(n\)
- Here, (\(E(Di)\)) represents the raw data extracted from the source (\(i\)), which will then be transformed.
* Attribute Normalization: Normalize the raw data so all assets have a standard attribute format, (\(Di\)), so that all assets have a common attribute format. For example, if an attribute such as failure frequency is in different units or formats, it is transformed by a normalization function: \(T_{\text{norm}}(D_{i})=\text{normalize }(D_{i})\)
- Calculation of New Attributes: In many cases, the system must create new attributes that are not present in the raw data. An example is calculating the unavailability time (IT) and the number of circulations per hour (TT). These new attributes can be modeled mathematically as
\[T_{\text{new}}(Di)=\{\text{TI},\text{TT}\}\]  
Here, the function (\(T_{\text{new}}\)) applies calculations on (\(D_{i}\)) to derive new attributes.  
* Data Filtering: Irrelevant or redundant data are removed during filtering. For example, non-maintainable assets are filtered out if only information on maintainable assets is required. This task can be modeled as \[T_{\text{filter}}(D_{i})=\{x\in D_{i}\text{: }x\text{ is maintainable asset}\}\]
* Data Crossing: The ETL process also involves crossing different tables to generate a complete database. This cross can be formalized as \[T_{\text{cross}}\left(D_{i},D_{j}\right)=D_{i}\bowtie D_{j}\]  
where (\(\bowtie\)) represents a join between the tables (\(D_{i}\)) and (\(D_{j}\)) based on common keys (such as the asset identifier).  
* Load (L):
* Finally, the transformed and normalized data are loaded into a master table MASTER_TBL, which is the final result of the ETL process. This is expressed as \[L\left(T_{j}(D_{i})\right)=\text{MASTER\_TBL}\]
* The master table will contain all the calculated and transformed attributes, ready to be used in the criticality assessment. Each row in MASTER_TBL represents an asset, and each column represents one of the key attributes (failure frequency, impact, downtime, etc.).
## 3 Numerical Simulations
### Results and Analysis  
#### 3.3.1 Analysis of Temperature Field  
Figure 8 indicates that there are obvious low-temperature zones around the tunnel, and with the increase in time, the low-temperature zones are gradually expanding. This demonstrates that the heat exchange between water and the surrounding rock primarily occurs in the radial direction and that the heat conduction rate in the surrounding rock is lower than the heat exchange rate between the water and surrounding rock. The temperature distribution of the working fluid in SRV is shown in Figure 9. With the increase in production time, the cold front advances continuously along the direction of fluid injection. The low-temperature zone gradually increases and shows a clear temperature gradient along the radial direction. At the same time, the influence range of the cold front is the largest at the injection point and decreases along the injection direction.  
Figure 8: Temperature distribution of the near-field heat extraction zone.  
The thermal influence area radius is used to determine the influence scope and provide guidance for the spacing design of the structure. This value refers to the radial distance when the temperature closes to the initial temperature in the surrounding rock. In this paper, line ab (see Figures 8 and 9) is selected to investigate the temperature distribution along the radial direction and to determine the maximum influence radius. Figure 10 illustrates that the thermal influence area radius increases with time, while the increase rate of the thermal influence area radius decreases with the production time. For the near-field model, the thermal influence area radius increased by 82.12 m from year 0.1 to year 5, but only increased by 15.42 m from year 15 to year 20. Meanwhile, for the far-field model, the value increases by 60.79 m in the first 4.9 years but only by 23.82 m in the last 5 years.  
Figure 10: _Cont._  
Figure 9: Temperature distribution of the far-field heat extraction zone.  
The maximum thermal influence areas that the near- and far-field models determined are 156.60 m and 172.92 m in radius, respectively. This indicates that a distance of at least 172.92 m should be maintained between the near-field and far-field zones to avoid thermal interference, which also shows that the size of the computational model is sufficient.
## 3 Research Background and Hypotheses
### Government Regulation and Policies  
Several studies have explored the influence of government regulation on the adoption of green logistics practices [16; 17; 18; 19; 23; 25]. This variable examines the role of regulatory frameworks, environmental standards, and policy support in encouraging the implementation of green logistics technologies. The government plays a crucial role in shaping organizational behavior and raising public sector awareness about eco-industry development [67]. It enforces strict environmental regulations to ensure effective environmental protection controls by the domestic industry sector. Government intervention is often preferred over private sector control, which primarily focuses on profit maximization. Consequently, the government regulates private sector business operations through environmental regulations and incentives, such as promotional policies for cost reduction, low-interest loans for clean technology procurement, and support for research grants for green industry promotion [68]. The government's responsibility extends to enforcing environmental laws and rules, impacting environmental monitoring efficiency and public security and well-being. By enacting strict regulations regarding environmental standards for products, the government emphasizes environmental conservation and recovery for sustainable development. This regulatory framework supports the government's environmental policy, environmental quality management, and the promotion of sustainable efficiency [69]. Changes in the natural environment significantly influence business relationships, leading companies to adopt environmental management practices [70]. Organizational social responsibility and sustainable development policies have been examined in various studies, focusing on employee perceptions of internal environmental policies, innovation, and sustainable development, as well as top executives' intentions to encourage compliance with environmental practice standards. The literature also highlights the stimulation of voluntary compliance with green policies, the adoption of international certifiable environmental management standards like ISO 14001 and ISO 9000, and the promotion of service and process performance measurement and improvement policies [71; 72; 73; 74; 75]. The logistics business is influenced by both consumer pressure for green processes and government policies mandating green logistics implementation. Regulation guides companies to adopt eco-friendly production processes, and the development of green logistics policies with more environmentally friendly standardization is considered necessary [76]. The study investigates the impact of government regulation and policies on the adoption of green logistics practices, as detailed in Table 5. It presents **Hypothesis 4 (H4)**, proposing that government regulation and policies significantly shape green logistics
## 2 Materials and Methods
### Data and Methodology
#### 2.2.3 Spatial Autocorrelation Test  
A spatial autocorrelation test is the basis for constructing a spatial Dubin model. The geographic space distribution characteristics with variables of environmental regulation, financial support for agriculture and agricultural green development are analyzed by autocorrelation test [25]. The absolute value of the global Moran's \(I\) is denoted as the intensity of spatial correlation, in which the value range of Moran's \(I\) is [\(-1\), 0], indicating that there is a negative spatial correlation, while the value range of Moran's \(I\) is [0, 1] indicating that there is a positive spatial correlation. The formula of \(I\) is as follows:  
\[I=\frac{n\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij}(x_{i}-\overline{x})\big{(}x_{j}- \overline{x}\big{)}}{\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij}\sum_{i=1}^{n}(x_{i}- \overline{x})} \tag{2}\]  
In Equation (2), \(I\) is the global Moran's \(I\); \(n\) is the number of provinces; \(x_{i}\) and \(x_{j}\) represents variables' value of province \(i\) and \(j\), respectively, and \(\overline{x}\) is the average value of variables. \(w_{ij}\) represents the proximity of province \(i\) and \(j\), and if \(i\) and \(j\) is adjacent, then \(w_{ij}=1\), otherwise \(w_{ij}=0\).  
To further identify the spatial correlation among provinces, the scatter plot of the local Moran' \(I\) is adopted to test the spatial agglomeration of environmental regulation, financial support for agriculture and agricultural green development. The local Moran' \(I\) model is constructed as follows:  
\[I_{i}=Z_{i}\sum_{i=1}^{n}W_{ij}Z_{j} \tag{3}\]  
In Equation (3), \(Z_{i}\) and \(Z_{j}\) represents standardized forms of \(x_{i}\) and \(x_{j}\), respectively, and other variables are the same as in Equation (2).
# Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review
## 2 Fundamental Aspects of Microseismic Monitoring Technique
### Microseismic Parameters  
Microseismic parameters are closely related to coal/rock fracturing at the source and are used to characterize the development of microcracks, variation in the rock stress regime, and the risk of catastrophe instabilities. Energy and the event count are two basic microseismic parameters, which reflect the intensity and frequency of coal/rock fracturing [58]. However, utilizing the basic parameters from limited microseismic data makes it difficult to characterize rock fracture development and assess the risk of mining-induced instabilities. Other microseismic source parameters and statistical parameters are also employed to study the rock fracturing and failure process during coal mining. [PERSON] et al. [58] characterized the rockburst caused by a hard roof fall based on microseismic parameters, e.g., energy, event count, \(Z\) value, lack of shock \(b_{\text{L}}\), and dominant frequency. [PERSON] et al. [59] used source parameters and statistical parameters, e.g., energy, seismic potency, and \(b\) value, to study the potential precursory indicators of coal mining-induced water-conducting channels. [PERSON] et al. [60] integrated microseismic parameters, e.g., energy, the apparent volume, and the Schmidt number, to study the progressive failure process of the longwall working face. [PERSON] and [PERSON] [39] employed microseismic parameters, e.g., event rate, energy, and seismic viscosity to understand rock fracture development and identify the abnormal stress concentration prior to a roof fall. Table 2 lists the above-mentioned microseismic parameters in the published literature.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Description** \\ \hline Event count/rate & The number of microseismic events, and is used to reflect the frequency of rock fracture. \\  & The seismic potency represents the co-seismic inelastic deformation at the microseismic source, and is written as \(P_{p,S}=4\pi\rho v_{p,S}\int_{0}^{t_{s}}u_{\text{corr}}(t)dt\), where, \(v_{p,S}\) is P-wave or S-wave velocity, \(R\) is the distance from the microseismic source, \(u_{\text{corr}}\)(t) is the square of the seismic wave displacement pulse corrected for the far-field radiation pattern, and \(t_{s}\) is source duration [61]. \\ \hline \end{tabular}
\end{table}
Table 2: Microseismic parameters for characterization of coal mining-induced instabilities.  
Figure 3: A planar layout of microseismic sensors around a longwall working face of Xiashije coal mine, revised from [55].
## 4 Performance Analysis
### Simulations Results Corresponding to Network Radius
#### 4.4.3 Analysis of Handover Decision Errors Due to Network Radii  
The impact of changes in network radii on the variability indicator \(|\Delta D_{BA12}|\), caused by BS motion, has been confirmed in Section 4.4.2. This result was analyzed to determine its effect on handover decision errors.  
Figure 9: \(|\Delta D_{BA12}|\) according to time and sea state in the overlapping region when BS1 and BS2 move in the same direction as the AUV: (**a**) Network radius of 100 m. (**b**) Network radius of 300 m. (**c**) Network radius of 500 m. (**d**) Network radius of 1000 m.  
When the motion of BS1 and BS2 is in the same direction and the AUV passes through the overlapping region (i.e., when the handover decision error is most likely to occur), we derive the handover decision error based on the network radius and sea state code. It is expected that an increase in network radii decreases the variability in \(|\Delta D_{BA12}|\) and, consequently, reduces handover decision errors. As illustrated in Figure 10, errors occur at SS3, SS6, and SS8 when the network radius is 100 m. Moreover, as the previous analysis has shown, the greater magnitude of the BS motion, the more errors occur, and this pattern remains the same even as the network radius changes. However, as the network radius increases, the number of errors decreases, and it becomes almost error-free at 500 m or more. These results imply that the network radius is a parameter to be considered when applying handovers for B-UWANs suffering from BS motion. This also shows that the criteria for handover decisions can be set more leniently depending on the network radius.
# Fully Reconfigurable Photonic Filter for Flexible Payloads
###### Abstract  
Reconfigurable photonic filters represent cutting-edge technology that enhances the capabilities of space payloads. These advanced devices harness the unique properties of light to deliver superior performance in signal processing, filtering, and frequency selection. They offer broad filtering capabilities, allowing for the selection of specific frequency ranges while significantly reducing Size, Weight, and Power (SWaP). In scenarios where satellite communication channels are crowded with various signals sharing the same bandwidth, reconfigurable photonic filters enable efficient spectrum management and interference mitigation, ensuring reliable signal transmission. Furthermore, reconfigurable photonic filters demonstrate their ability to adapt to the dynamic space environment, withstanding extreme temperatures, radiation exposure, and mechanical stress while maintaining stable and reliable performance. Leveraging the inherent speed of light, these filters enable high-speed signal processing operations, paving the way to various space payload applications, such as agile frequency channelization. This capability allows for the simultaneous processing and analysis of different frequency bands. In this theoretical study, we introduce a fully reconfigurable filter comprising two decoupled ring resonators, each with the same radius. Each resonator can be independently thermally tuned to achieve reconfigurability in both central frequency and bandwidth. The precise reconfiguration of both central frequency and bandwidth is achieved by using the thermo-optic effect along the whole ring resonator path. A stopband rejection of 45 dB, with a reconfigurable bandwidth and central frequency of 20 MHz and 180 MHz, respectively, has been numerically achieved, with a maximum electrical power of 11.50 mW and a reconfiguration time of 9.20 us, by using the scattering matrix approach, where the elements have been calculated through Finite Element Method-based and Beam Propagation Method-based simulations. This performance makes the proposed device suitable as key building block of RF optical filters, useful in the next-generation telecommunication payload domain.  
photonic filter; tunable; space; payloads +
Footnote †: journal: Applied Sciences