esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Monitoring and Control Systems Based on Internet of Things
## 3 Monitoring and Control Systems Based on IoT
### IoT-Based Predictive Maintenance Systems
Analyzing large datasets generated by sensors enables quick decision-making. In recent years, energy efficiency issues have become a production priority. Energy consumption significantly impacts costs and production quality. The \"cold chain\" encompasses equipment and processes that ensure proper conditions for perishable goods from production to consumption. Refrigeration and Cold Storage Systems (ROSS) play a crucial role in preserving the freshness of goods but consume over 50% of supermarket energy. Therefore, monitoring data is essential for optimizing ROSS operation and preventing breakdowns. Existing literature presents various approaches to detecting anomalies in cooling systems. Approaches based on energy analysis, machine learning methods, and fault diagnosis systems have been mentioned. There are also tools for monitoring energy consumption and detecting refrigerant leaks. Scientific work has proposed monitoring and predictive maintenance systems that leverage IoT technologies and artificial intelligence. The proposed system architecture is based on sensor networks and cloud computing. Components include sensor nodes, a network application, and a user interface. Communication protocols such as HTTP, WebSocket, and TCP are utilized. Deployment involves integrating databases, servers, and software to ensure data flow between them. Sensor nodes have various sensors and communication modules such as Particle Device OS and XBee Wi-Fi. The user interface enables data visualization and the receiving of alerts about failures. The software is developed usingNodeJS, MongoDB, and WebSocket technologies. Implementation includes deploying a sensor network management system that handles communication between sensor nodes and the network application. A prototype device for data acquisition has been developed using microcontrollers and communication modules. This device integrates various sensors, enabling monitoring conditions in refrigeration chambers.
## 4 Research Methodology
### Calculation of Effective Thermal Transmittance
\[U=\frac{1}{\frac{1}{h_{si}}+\sum_{i}^{n}\frac{d}{\lambda}+\frac{1}{h_{se}}} \tag{4}\]  
where \(h_{si}\) and \(h_{se}\) are internal and external surface heat transfer coefficients, taken according to Table 3, and \(d\) and \(\lambda\) are thickness and thermal conductivity of the each of the \(n\) specific layers, respectively, taken according to Table 2.  
The inverse of the thermal transmittance is the thermal resistance \(R\). Thermal resistance is a measurement of a material's or a building element's resistance to heat flow. \(R\) is calculated according to Equation (5) [34]. \[R=\frac{1}{U} \tag{5}\]  
Numerical analysis of heat transfer and calculation of \(L_{2D}\) is executed using CRORAL software [40], the in-house developed software based on the calculation procedures described in the EN ISO 10211 [35] and EN ISO 10077-2 [41]. CRORAL is validated by the current versions of these standards [40]. After calculating the \(L_{2D}\), one can determine the linear thermal transmittance \(\psi\)-value using Equation 6 [35] for the evaluation of the contribution of the steel studs to the thermal transmittance and the evaluation of the quality of construction details from the thermal point of view. \[\psi=L_{2D}-\sum_{i=1}^{n}U_{i}\times l_{i} \tag{6}\]  
where \(U_{i}\) is the one-directional thermal transmittance of the \(i\)-th building element separating the two environments, and \(l_{i}\) is the length of the 2D geometrical model over which the \(U_{i}\) value is applied. The \(\psi\)-value was determined using the external dimensions. The temperature factor \(f_{Rsi}\), calculated according to Equation (7) [35], is of importance for well-being and comfort reasons. The \(f_{Rsi}\) is defined as the ratio between the minimum internal surface temperature (\(T_{si,min}\)) and the external air temperature (\(T_{e}\)) difference, and the average internal (\(T_{i}\)) and external (\(T_{e}\)) air temperature difference:  
\[f_{Rsi}=\frac{T_{si,min}-T_{e}}{T_{i}-T_{e}} \tag{7}\]  
The smaller \(\psi\)-value means that the additional heat losses through thermal brides are smaller, and the risk of construction damage such as mould growth is reduced. At the same time, a value of \(f_{Rsi}\) closer to 1 indicates a more uniform temperature distribution on the internal surface, i.e., that internal surface temperature is very close to indoor air temperature.
# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 4 Experiments
### Dataset and Evaluation Metric  
Experiments were conducted using the AMI meeting corpus [31], a audio dataset commonly used for speech and audio processing research. The AMI corpus provided a diverse set of audio recordings that allowed us to evaluate the performance of these distance metrics under various conditions and scenarios. In the experiments, we used the standard validation and test split of the AMI corpus. The test data includes 16 meeting's audios with a total duration of around 9 h, and an average duration of 33.38 min per meeting. The full data statistics are given in Table 2. It can be observed that the change points, overlaps, total number of speakers, and audio length are provided. For this statistic, 40% of the change points are overlapped.  
In the preprocessing step, 1-second chunk presegmentation was applied before the experiments. All audio files were then resampled to 16 kHz, and the following parameter settings were used for feature extraction: \(hop\_length=512\) and \(n\_fft=2048\).  
For evaluation, the following metrics were chosen for the comparative analysis. False alarm rate (FAR) indicates the frequency of incorrect detections of speaker changes. It is calculated as the proportion of false positives (FP) among all non-change points:  
\[\text{FAR}=\frac{\text{False positives (FP)}}{\text{False positives (FP)}+\text{True negatives (TN)}} \tag{5}\]  
Missed detection rate (MDR) measures how often the system fails to detect an actual speaker change. It is the proportion of false negatives (FN) among all true change points:  
\[\text{MDR}=\frac{\text{False negatives (FN)}}{\text{True positives (TP)}+\text{False negatives (FN)}} \tag{6}\]  
\[\text{Performance Score}=1-\frac{\text{FAR}+\text{MDR}}{2} \tag{7}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Meeting ID** & **\# Change Points** & **\# Overlaps** & **\% Overlaps** & **\# Speakers** & **Length (min)** \\ \hline EN2002a & 1366 & 678 & 49.63 & 4 & 43.90 \\ EN2002b & 954 & 444 & 46.55 & 4 & 33.52 \\ EN2002c & 1468 & 681 & 46.39 & 3 & 56.46 \\ EN2002d  
Performance score reflects both types of errors (false alarm rate and missed detection rate), this score is only used for threshold optimization.
###### Abstract  
This paper is focused on the performance analysis of protection mechanisms utilized in common wavelength division multiplexing-based passive optical networks. The main aim of the proposed research is providing an option of comparing different traffic protection scenarios for advanced optical network designs, evaluating their possible realizations from a viewpoint of the total optical power budget and analyzing their network availabilities with accommodation to concrete optical access infrastructures. First, a short basic classification of passive optical network architectures utilizing advanced wavelength division multiplexing techniques is introduced. Second, considered presumptive protection scenarios deployed to this kind of passive optical networks involved in the performance analysis are presented. For the performance analysis, corresponding reliability diagrams, relations and formulas used for the calculation of the total network availability are prepared for any specific scenario. In addition, the optical power budget is considered and subsequently evaluated for each protection scheme. For evaluating possible migration scenarios related to considered protection schemes and for comparing protection possibilities of various passive optical networks, a realization of the appropriate simulation tool must be executed. The simulation program utilizes specific parameters of particular optical components utilized in a selected protection scheme and presents its resultant network availability. Values of considered specific parameters can be changed according to the well-known data resources or specific network operator's data. Subsequently, relevant simulation results obtained by the Network Availability Evaluator that can provide insights into relationships between a number of protection schemes are used for the performance analysis. Finally, an evaluation of the total network availability for considered traffic protection scenarios utilized in passive optical networks with advanced wavelength division multiplexing is included.  
passive optical networks; wavelength division multiplexing; traffic protection schemes; reliability block diagrams; power budget; Network Availability Evaluator; performance analysis
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 3 Unific-Measurement-Based Beam Switching and Handover Scheme
### Proposed Beam Switching and Handover Procedure  
As described in the 3 GPP specification, each beam in a single-gNB can have its own mobility parameters. However, each value has to be set by the same configuration of the layer-3 (network layer) including the radio-resource-control (RRC) sub-layer. Thus, the beam switching in the same cell is performed in a simpler manner than the handover procedure as shown in Figure 2. There is only one process that needs to be updated by a new control information: \"target beam sends new measurement control information.\" Therefore, the overall switching process is much shorter than that of the handover process. For conventional handover, the measurement control information includes parameters such as \(O_{cn}(k,l,l_{s})\), \(O_{ff}(k_{s})\), and time-to-trigger (TTT). Concerning the beam-switching, the serving beam also sets the measurement control information for a user. After the parameters are set, the user can measure the signals from the neighbor cells and beams. If the user recognizes the event A3 entering state between two beams, it measures the time of the met-condition of the event for a TTT parameter. If TTT expires, the user sends an MR for a BS or HO. After the serving cell receives an MR, BS, or HO is started based on the following condition:  
\[k\in\mathcal{K},k\
eq k_{s},l\in\mathcal{L}, \tag{21}\]  
\[k=k_{s},l\
eq l_{s},l\in\mathcal{L}, \tag{22}\]  
where (21) implies that the target beam (\(l\)) is from a cell (\(k\)) is different from the serving cell, \(k_{s}\). Equation (22) implies that the target beam (\(l\)) is not the serving beam (\(l_{s}\)) but is from the same serving cell, \(k_{s}\). The serving cell initiates a handover procedure when the condition (21) is satisfied while a beam switching is initiated when condition (22) is satisfied. The beam switching process is shown in Figure 2. The handover process is the same as shown in the 3 GPP specification [42].
## 2 Initial Data, Their Preliminary Analysis and Preprocessing
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Data Source**} & \multirow{2}{*}{**\(T_{W}\)**} & \multicolumn{2}{c}{**\(T_{F}\)**} & \multirow{2}{*}{**\(N_{F}\) = \(N_{W}\)**} & \textless{**\(T2R\)**} & \textless{**\(T2F\)**} \\ \cline{3-8}  & & **[min]** & **[\%]** & **[min]** & **[\%]** & & **[min]** & **[min]** \\ \hline
1 & VKH & 511,200 & 97.26 & 14,400 & 2.74 & 7 & 2057.1 & 73,028.6 \\
2 & IVA & 478,290 & 91.00 & 47,310 & 9.00 & 13 & 3639.2 & 36,791.5 \\
3 & KEV & 525,521 & 99.98 & 79 & 0.02 & 8 & 9.9 & 65,690.1 \\
4 & SOD & 521,160 & 99.16 & 4440 & 0.84 & 5 & 888.0 & 104,232.0 \\
5 & LOZ & 501,623 & 95.44 & 23,977 & 4.56 & 21 & 1141.8 & 23,886.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Assessment of completeness of time series and indicators of data source reliability. ranges. Thus, the size of the final sample includes -35,040 values (including gaps) for each data source, which is enough to identify statistical relationships. In magnetometer data analysis, the main attention has been paid to the east-west component \(Y\), because this component corresponds to the north-south (\(X\)) telluric electric field \(Ex\), driving GIC along the latitudinally extended power line [16]. Figure 2 shows an example of variations of magnetic field (Y-component), field variability (\(\,\mathrm{|dY/dt|}\,\)), and GIC during the period from 2015-03-16 00:00 (UT) to 2015-03-18 23:59 (UT). This time interval includes one of the powerful magnetic storms during recent years--St. Patrick's Day Geomagnetic Storm (17 March 2015). Data are averaged over 15 min intervals.
# Design and Demonstration of a Flying-Squirrel-Inspired Jumping Robot with Two Modes
## 5 Pitching Requirement and Adjustment
### Pitching Adjustment
#### 5.2.2 Numerical Simulation  
The above dynamics model is simulated using MATLAB to verify the feasibility of adjusting the pitching posture by the forelimbs in this section. Assuming that the additional angular velocity is 95 deg/s when the robot takes off at t = 0 in the mode of jumping for gliding. The simulation parameters are tabulated in Table 1, and the simulation results are shown in Figure 20. It can be seen from Figure 20 that when the forelimbs are not actuated and remain stationary relative to the body, the pitch angle of the body increases linearly under the action of additional angular velocity, reaching 14\({}^{\circ}\) at 0.15 s (dotted line in Figure 20); when the forelimbs swing away from the body driven by the forelimbs motor with a stall torque of 0.15 N\(\cdot\)m and a no-load angular speed of 600 deg/s, the pitch angle of the body first increases from 0\({}^{\circ}\) to 3\({}^{\circ}\), and then basically stops changing (solid line in Figure 20).  
With the same other simulation parameters, the changes of the pitch angle of the body under different no-load angular speeds of the forelimbs motor are shown in Figure 21. It can be seen from Figure 21 that when the no-load angular speed decreases to 580 deg/s, the pitch angle of the body increases from 0\({}^{\circ}\) to 4.3\({}^{\circ}\) within 0\(-\)0.15 s; when the no-load angular speed increases to 620 deg/s, the pitch angle of the body first increases from 0\({}^{\circ}\) to 2.2\({}^{\circ}\), and then gradually decreases to 1.4\({}^{\circ}\) within 0\(-\)0.15 s.  
The above simulation results show that the pitching posture of the body can be adjusted by driving the forelimbs to swing relative to the body after the robot takeoff. In the case of jumping for gliding with initial angular momentum (counterclockwise rotation), the change of pitching attitude caused by the additional angular velocity can be inhibited by the forelimbs swinging away from the body, and the inhibition effect is related to the no-load angular speed of the forelimbs motor: The greater the angular speed is, the more significant the inhibition effect is.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline \(m_{B}\) & 0.07 kg & \(m_{L}\) & 0.01 kg \\ \(l_{BC}\) & 0.03 m & \(l_{AC}\) & 0.06 m \\ \(J_{B}\) & \(6.3\times 10^{5}\) kg\(\cdot\)m\({}^{2}\) & \(l_{L}\) & \(3.6\times 10^{-5}\) kg\(\cdot\)m\({}^{2}\) \\ \(\tau_{s}\) & 0.15 N\(\cdot\)m & \(\omega_{0}\) & 600 deg/s \\ \(L_{O}\) & \(8.5\times 10^{-4}\) kg\(\cdot\)m\({}^{2}\)/s & \(\phi_{B}(0)\) & 0\({}^{\circ}\) \\ \(\theta_{BL}(0)\) & 90\({}^{\circ}\) & \(\phi_{L}(0)\) & \(-90^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: MATLAB simulation parameters.  
Figure 20: Simulation results for pitching adjustment under different states of forelimbs.
# Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities
## 3 Affordable Broadband for Rural Communities
### Dedicated Rural Broadband Policy  
In particular, considering the rural areas of Nepal, adequate policy intervention is required to develop a sustainable, digital, rural society. A dedicated and community focused policy helps to address the subsidization schemes mentioned in the above section. The recent broadband policy of Nepal [7] requires additional amendment for the effective mobilization of the RTDF to provide subsidies to communities and service providers to run the broadband infrastructure and services in a PPP model. Clear policy vision is to be derived to address the sustainability issues of disadvantaged schools, public libraries, community health centers, and many more [3]. For example, low income households with their children enrolled in community schools can be provided broadband internet for as little as possible or free of cost [8]. Broadband services will have significant impact on the economic growth of rural communities in their organizations and small businesses [18]. With a favorable environment created by the government, if guaranteed via policy means, affordable services and operational sustainability can be achieved with broadband networks in rural and remote areas [19].
## 7 Conclusions  
In this study, our primary aim was not to juxtapose two distinct versions of AR interfaces but to validate the feasibility and efficacy of the proposed audio and non-visual AR interfaces designed for enhancing accessibility, particularly for users with visual impairments. Through comprehensive user evaluations involving university students, we sought to assess how these innovative interface modalities could foster user engagement and improve usability within AR environments.  
The feedback and data gathered from the SUS and the UES, supplemented by qualitative insights, underscored the significant potential of audio-enhanced and non-visual interactions in AR. Participants' experiences with the NAI demonstrated a clear appreciation for the immersive and intuitive nature of the interaction, highlighting the value of integrating auditory feedback and natural interaction techniques to convey spatial and contextual information effectively.  
The findings from this investigation offer several actionable insights for developers and researchers aiming to advance AR technology. Firstly, the positive reception of the audio and non-visual interfaces among users reinforces the importance of accessibility in AR design, suggesting that future developments should prioritize inclusivity. Secondly, the study illustrates the utility of natural user interfaces in creating more engaging and intuitive AR experiences, advocating for a user-centered approach in the design process. Lastly, the research highlights the need for ongoing exploration into multisensory feedback mechanisms, including not just audio but potentially haptic and olfactory cues, to further enhance the immersiveness of AR applications.  
In conclusion, this research affirms the viability of audio and non-visual AR interfaces as effective means to extend the accessibility and appeal of AR technologies. By demonstrating the positive impact of these interfaces on user engagement and usability, the study provides a foundational basis for future work in this area. It is our hope that the insights gleaned will inspire other developers and researchers to pursue innovative approaches in creating AR experiences that are not only accessible to a wider audience, including those with visual impairments, but also more immersive and intuitive for all users.  
This research was funded by Beijing Jiaotong University grant number [A24 JBRCW00010]. And the APC was funded by [A24 JBRCW00010].  
Informed Consent Statement:Informed consent was obtained from all subjects involved in the study.  
The original contributions presented in the study are included in the article, AND further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The author would like to extend their sincere gratitude to all the participants who generously dedicated their time and effort to contribute to this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 SPS: Grid-Based Single Phantom Node Source Location Privacy Protection Scheme  
In this section, we first introduce the proposed grid-based single phantom node source location privacy protection scheme (SPS) in WSN. In our proposed schemes, the PNCS is firstly determined by the sink node, and then the source node randomly selects a PN from the PNCS. After the SoN transmits the data packets to the selected PN via multi-hop routing, the PN sends the data packets to the sink node via the single path routing. The method that the sink node helps the SoN to select the PNCS, not only avoids a large amount of energy consumed by the ordinary sensor nodes when determining the PN, but also distributes the PNs randomly throughout the network. Our proposed new method greatly increases the security of the SoN and provides better privacy preservation for them. In our latter description, the sensor node is simply referred to as nodes.  
Specifically, our proposed scheme is divided into three phases: The initialization phase, the phantom node determination phase and the routing phase. For ease of understanding, the notations used herein are listed in Table 1.
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 4 Underwater Acoustic OTFS System Design
### Frame Design
#### 4.1.2 Design of \(l_{g}\)  
Zero-padding can completely avoid inter-block interference when \(l_{g}>l_{max}\) but comes at the cost of a very low communication rate. Thus, the means by which to balance the communication rate and robustness need to be carefully considered. As shown in Figure 2, which displays an example of the UWA channel power response, we can define a maximum additional delay \(\tau_{g}\) as the time at which the multi-path power decays from its initial value by a factor \(\beta\) dB, defined as follows:  
\[10\lg\frac{P_{\max}}{P_{\tau_{\beta dB}}}=\beta, \tag{10}\]  
where \(l_{\beta\text{dB}}\) is the delay tap based on \(\tau_{\beta\text{dB}}\). Later lower-power paths can be ignored, due to their minimal impact.  
Figure 2: The UWA channel power response.  
In brief, we can set \(l_{g}=l_{\delta\text{dB}}\) to enhance the UWA communication rate and ensure communication robustness.