esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Collection
### Data Collection and Annotation  
Acceleration dataset are collected in the smart lab located in Ulster University. Prior to data collection, the device is calibrated when placed in orthogonal orientations by using standard calibration techniques described in [32]. Raw acceleration dataset is collected with a sampling rate of 102.4 Hz and an amplitude range of \(\pm\)2.0 g (g = 9.8 m/s\({}^{2}\)). Previous work has shown that human activities can be captured by signals below 18 Hz [33]. Therefore, the sampling rate of 102.4 Hz is more than enough for capturing activities of daily living as well as transitional activities. The sensed data are stored in the format of {time, acceleration-\(x\), acceleration-\(y\), acceleration-\(z\)}. Consequently, the raw acceleration dataset is collected over ten hours, which contains 4,020,288 data samples.  
The acquired data are then annotated with activity class according to the voice instruction, then labeled with start time and stop time of each activity manually. Previous study has demonstrated that transitional activities usually occur during the time interval from 1.45 s to 3.49 s [34], thus, take the relatively short duration of the transitional activity and the possible delay in the activity execution when following the verbal command from the researcher into consideration, 4 s is used as activity duration to label the transitional activities that ensures to cover the whole transitional phase of an activity. As a result, 2160 s dataset is labeled with twelve activity classes, which can be used in activity classification tasks later.
## 7 Future Research Directions
### Frequency Regulations and Dynamic Spectrum Access  
Frequency regulations should support ship connectivity both in national and international waters. Spectrum-sharing techniques avoid interference and keep the QoS high enough. Hybrid networks integrating satellite and terrestrial technologies raise new questions. What are the best frequency bands for operation? How can the reliability of transmission be improved and the effects of intentional interference, both regarding the positioning system and connectivity links, be reduced? The use of licensed shared access techniques [63] seems useful here. National regulators play an important role by providing test licenses for technology development.  
Figure 13: Ship mega-constellation for global connectivity.
# Semi-Supervised Time Series Anomaly Detection Based on Statistics and Deep Learning
## 3 Proposed Anomaly Detection Framework
### Periodic Time Series Anomaly Detection  
Tri-CAD adopts the Pearson correlation coefficient for checking whether a time series is periodic. As shown in Figure 4, two consecutive equal-sized sub-time series \(Y\) and \(Z\) of size \(w\), \(w\leq D/2\), are first derived from the \(D\) data of the normal dataset. Next, the Pearson product-moment correlation coefficient \(Cor(Y,Z)\) is derived for any possible value of \(w\). The maximum \(Cor(Y,Z)\) value \(\rho\) and the corresponding value \(w\) are then obtained. If the value \(\rho\) is greater than a pre-specified threshold (e.g., 0.98), the time series is considered to be periodic with the period of \(w\).  
When the time series is determined to be a periodic time series with the period \(w\), a sliding time window of size \(w\) is applied to the \(D\) data of the normal dataset, and the skewness of every time window of data is derived. Skewness is a measure of the symmetry of a distribution. The skewness of a time series \(X\) of \(n\) data is defined according to (8).  
\[\gamma(X)=\frac{n}{(n-1)(n-2)}\sum_{x\in X}\biggl{(}\frac{x-\mu_{X}}{\sigma_{ X}}\biggr{)}^{3} \tag{8}\]  
where \(\mu_{X}\) and \(\sigma_{X}\) are the mean and standard deviation of \(X\), respectively. Tri-CAD adopts skewness for detecting anomaly because skewness is sensitive to the removal of outliers (anomalies), as shown in [52]. After obtaining the skewness values of all sliding windows for the \(D\) data of the normal dataset, the moving averages of \(k\) pervious skewness values are derived. Finally, the mean \(\mu\) and the standard deviation \(\sigma\) of the moving averages of skewness values are derived. Then, a similar process is applied to the test dataset. Specifically, a sliding time window of size \(w\) is also applied to all data in the test dataset, and the skewness value of every time window of data is derived. For a test data point \(x\), the moving average of \(k\) pervious skewness values is derived and is regarded as its anomaly score \(s_{x}\). Its standard anomaly score (SAS) \(\lambda_{x}\) is then calculated according to (9).  
\[\lambda_{x}=\frac{|s_{x}-\mu|}{\sigma} \tag{9}\]  
Note that the SAS \(\lambda_{x}\) of a data point \(x\) is the measurement of how far \(x\)'s anomaly score \(s_{x}\) deviates from \(\mu\) in terms of \(\sigma\). As shown earlier, a different \(\lambda_{x}\) value corresponds  
Figure 4: Illustration of using the Pearson correlation coefficient of two consecutive equal-sized time windows to check the periodicity of time series.  
to different occurrence probability of such a data point. For example, \(\lambda_{x}\) = 4.417173 corresponds to 0.001% of occurrence. If the SAS \(\lambda_{x}\) exceeds a pre-specified deviation threshold \(\tau\) and \(\lambda_{x}\) is increasing (i.e., \(\chi\)'s SAS is larger than the SAS of \(x\)'s previous data point), then the data point \(x\) is assumed to be anomalous.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 2 Literature Review
### Reliability-Based Routing Schemes  
The concept of an evolving graph was first applied by [PERSON] et al. [5] in the vehicular ad-hoc domain. Initially, the authors proposed a link reliability metric, which was later extended to a full-fledge routing scheme called evolving graph ad hoc on-demand distance vector (EG-AODV). The definition of link reliability is related to the probability of a link duration under varied speeds and transmission ranges. The proposed EG-AODV was a linear routing scheme based on VANET-oriented Evolving Graph (VoEG). The EG-RAODV iteratively discovers most reliable journey (MRJ) from the source vehicle to the destination by using Evolving Graph (EG) Dijkstra [5]. However, the performance of EG-RAODV was merely considered in sparse traffic scenario, where continuous arrival of vehicles was ignored. To improve the existing VoEG model, [PERSON] et al. [3] disclosed a novel and reliable cluster-based routing protocol, i.e., CEG-RAODV, where link reliability was used as a metric for cluster formation and CH selection. In [3], the authors suggested a reliable cluster-based routing protocol, i.e., CEG-RAODV, where link reliability was used for the first time in a cluster-based routing scheme [3]. The proposed CEG-RAODV not only reduces the computational cost but also has a scalability feature adoptable in any dynamic topological structure of VANETs. The reliability-based clustering protocol improved the performance considerably, but its proposed metric considered only the link lifetime while ignoring the node density and transmission range. From a literature perspective, the importance of node density and transmission range cannot be ignored [10].  
It is a challenging problem for VANETs to build and maintain stable clusters. The authors in [20] proposed a link reliability-based clustering algorithm (LRCA) that provided useful and reliable data transmission within VANETs. Furthermore, the authors gathered redundant unstable neighbors by link lifetime-based (LLT-based) strategy before clustering. Their proposed clustering scheme consisted of three components: selecting an appropriate cluster head, the construction of clusters, and the maintenance of groups. Moreover, LRCA appointed particular nodes at intersections to test the routing decisions for making routing decisions on different road zones. For the best data forwarding, routes with the lowest weights were then chosen.
## 5 Active Coating and its Control Algorithm  
We place immediately on the external surface of the protected body (_i.e._ on the high frequency dissipative polymer layer) the active coating of thickness \(D=2h_{c}<\lambda_{\min}\) in the form of two piezoelectric layers of thickness \(h_{c}\) (each layer), with identical polarization along the normal to \(\overline{S}_{{}_{V}}\). We assume that the longitudinal impedance \(Z_{c}=\rho_{c}c_{c}\) of the piezoelectric material is equal to the impedance \(Z_{w}=\rho_{w}c_{w}\) of the outside liquid (_i.e._\(Z_{c}=Z_{w}\) ), where \(\rho_{c}\)-mass density of piezoelectric material and \(\rho_{w}\)-mass density of outside liquid and correspondingly, \(c_{c}\)-speed of longitudinal sound in the piezoelectric material, \(c_{w}\)-speed of sound in the outside liquid. The total thickness of the body and coating cross sectional structure \(\mathrm{L}=h_{\bullet}+h_{r}+2h_{c}\) (see Figure 6c). For certainty we will assume \(c_{c}>c_{w}\).
# Wireless Sensor Networks for Enabling Smart Production Lines in Industry 4.0
## 4 Results and Discussion
### Performance Analysis  
#### 4.2.1 Network Robustness  
By not using the maximum TX power, more APs are needed to cover the complete floor plan. This results in an additional installation cost but has the advantage that the network can cope with layout changes by increasing TX powers after the network has been rolled out.  
Figure 8 shows the number of required WiFi APs for the two environments as a function of TX power. For the factory warehouse, we need 53 WiFi APs when using a TX power of 15 dBm, and the total number further increases up to 107 when the WiFi APs use a TX power of 10 dBm. Using a TX power of 15 dBm, we need 4 BLE APs, and 7 ZigBee APs. We need 10 SDR APs that have a TX power of 0 dBm. For the processing facility, 56 WiFi APs are required for full coverage when using a TX power of 15 dBm, and this number further increases to 147 when the APs transmit at 10 dBm. Even though the processing facility is much larger than the factory warehouse, a similar number of APs is required.  
To verify the network robustness, we calculate network coverage when racks are added and some of the existing racks are moved. Figure 9 shows the modifications made to the floor plan after the initial network-planning is performed, while Figure 10 shows the network coverage as a function of TX power for different levels of layout changes. Coverage is defined as the number of grid points on the floor plan for which the 54 Mbps throughput requirement is met, divided by the total number of grid points. In this figure, we see that for the factory warehouse environment, coverage is challenged by the presence of the large number of racks when the TX power is below 12 dBm, even when no changes to the floor plan occur. For the processing facility, coverage is above 98.5% for all floor plan modifications, if the TX power is in the range 10 to 14 dBm. For only three rack additions, the coverage stays above 99.6%. For (default) TX powers of 17 dBm or lower, the coverage for the most updated floor plan, that is, 10 added racks, is above 97.5%. A 1 dB increase in TX power results in a coverage of 96.0%. From this figure, the ideal TX power to use during the initial network-planning is 14 dBm.  
Figure 8: Number of required access points as a function of the maximum transmit power of the access points.
## 5 Hardware Implementation and Experimental Results
### FIR5 (Channel Selection Filter)  
FIR5 (CSF) has the same structure and operating principle as FIR4 in the preceding subsection and the only differences were that FIR5 had more filter coefficients and did not handle decimation. The largest number of coefficients was 43 (for case 11) among the ten LTE DFE frequency modes and if implemented in hardware, 22 in view of symmetry.  
Figure 29: FIR4 (carrier aggregation filter) block diagram.  
The critical path in the ASIC (180 nm CMOS) is highlighted in red in Figure 30, which includes a (15-bit \(\times\) 23-bit) signed multiplier and a 26-bit adder. In the case of FIR5-carrier 1, the critical path delay was the delay of (the first flip-flop + multiplier + adder) delay, which amounted to 17.11 ns while in case of FIR5-carrier 2, the critical path delay was 16.40 ns. The critical path in the XC6 VLX550T FPGA after the P&R is highlighted in green in Figure 30. Identical with FIR4, FIR5 in the FPGA also had a multiplier and an adder in the critical path, as with FIR5 in the ASIC. With FIR5-carrier 1, the overall critical path delay was the (multiplier + adder + route) delay, which equaled 3.441 ns + 0.961 ns + 1.792 ns = 6.194 ns while with FIR5-carrier 2, the overall critical path delay equaled 3.441 ns + 0.796 ns + 1.464 ns = 5.701 ns.  
As with the preceding building blocks, FIR5 was synthesized in a 180 nm CMOS ASIC and its required operating frequencies, which are the inverse of the FIR4 output period, were readily obtained as 39 MHz, 39 MHz, 19.5 MHz, 13 MHz, 3.25 MHz, and 3.25 MHz and 39 MHz, 39 MHz, 39 MHz, and 19.5 MHz with respect to cases 1-6 and 10-13, respectively. The FIR4 output period equaled its input period multiplied by its decimation factor and hence the required frequency of FIR5 was equal to the required frequency of FIR4 divided by the decimation factor of FIR4. As with FIR4, FIR5 in LTE DFE also had individual modules for carrier 1 and carrier 2 to support CA. The demanded critical path delay of FIR5 (CSF) with carrier 1 was 1/39 MHz or 25.64 ns, which was much looser than the critical path delay obtained in practice with a margin of 2 ns, which was 17.11 ns, and thus no speed constraint was necessary during the synthesis. The demanded critical path delay of FIR5 with carrier 2 was also 25.64 ns and without any speed optimization the FIR5 filter was synthesized with the critical path delay of 16.40 ns that was much shorter than the demanded one.  
The CSF lay at the back of the decimation chain and hence the speed requirement was relaxed while the hardware complexity was high. By contrast, the CIC filter lay at the head of the chain and hence the speed requirement was most stringent while the hardware complexity was low (since the CIC filter dispensed with any multiplier).
## 5 Conclusions  
This study demonstrates the potential of AI-based temperature control to significantly enhance indoor environmental quality and energy efficiency compared to conventional systems. The AI-based control system is designed to maintain stable indoor temperature and humidity levels while minimizing energy consumption, ensuring both efficiency and occupant comfort. Optimal control achieved through reinforcement learning in the main AI controller not only reduces unnecessary energy usage but also allows the system to predict and adapt dynamically to variations in user behavior and environmental conditions. By leveraging machine learning models such as LSTM and DQN, the system can continuously learn and refine its performance for further optimization.  
Figure 7: (**a**). Sensor and control status values. (**b**). DQN setting value and the average temperature sensor value.  
The AI main controller, utilizing Modbus TCP, can simultaneously manage multiple distributed sub-part units. In other words, the system can effectively control several HVAC units responsible for air temperature regulation across various rooms or zones, enabling centralized and efficient management. Moreover, by replacing the Raspberry Pi used in the experimental setup with a simpler microcontroller (MCU) and applying the Modbus TCP protocol, multiple rooms within a single building can be managed concurrently. This approach reduces overall costs, simplifies hardware requirements, and enhances system scalability, making it suitable for large-scale implementations.
## 4 Discussion  
This study proposes a closed-loop system combining the conventional feedback controller and the feedforward controller based on a compensation lookup table, in order to eliminate the periodic disturbances of the fluid gear pump. For the design of the compensation lookup table, the compensation  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Flowrate Command** & **Type** & **The Proposed** & **Open-Loop** & **Closed-Loop System Only** \\  & & **Control System** & **Control System** & **Using the PD Controller** \\ \hline \multirow{4}{*}{100} & Min & \(-0.8117\) & \(0.0012\) & \(-0.2283\) \\  & Max & \(1.4930\) & \(7.9981\) & \(3.9101\) \\  & Mean & \(0.5406\) & \(3.5921\) & \(2.1713\) \\  & RMS & \(0.7275\) & \(3.9604\) & \(2.3493\) \\  & MFA\({}^{1}\) & \(1.8790\) & \(5.0548\) & \(3.3110\) \\  & Min & \(-0.5256\) & \(3.1420\) & \(1.5237\) \\  & Max & \(2.3656\) & \(10.7120\) & \(5.6065\) \\  & Mean & \(0.9701\) & \(6.9149\) & \(3.5434\) \\  & RMS & \(1.1953\) & \(7.1526\) & \(3.6986\) \\  & MFA \({}^{1}\) & \(2.1891\) & \(5.1476\) & \(3.3740\) \\  & Min & \(-2.4770\) & \(20.0023\) & \(3.7901\) \\  & Max & \(3.8451\) & \(35.2313\) & \(11.2945\) \\  & Mean & \(0.9977\) & \(27.0950\) & \(7.9182\) \\  & RMS & \(1.6392\) & \(27.2863\) & \(8.0320\) \\  & MFA\({}^{1}\) & \(3.3696\) & \(6.8261\) & \(3.5198\) \\ \hline \end{tabular} \({}^{1}\) Mean of fluctuation amplitude.  
\end{table}
Table 3: The experimental results of the steady-state feedback error.  
Figure 13: Comparison of experimental results with flow feedback at the steady state; (**a**) 100 cc/min flowrate command; (**b**) 150 cc/min flowrate command; (**c**) 400 cc/min flowrate command.  
value according to the rotational angle and speed were experimentally obtained using the repetitive controller in the fluid gear pump. Experimental results have shown that the feedback controller based on the compensation lookup table improves the control performance, compared to the conventional feedback controllers such as the open-loop control system and the PD controller. For the low-speed flowrate command between 100-150 cc/min, which is widely used in the painting process, the proposed controller reduces the tracking error and fluctuation amplitude better than the conventional feedback controllers. From the result of this study, it is possible to improve the performance of the fluid gear pump-based painting system that controls the amount of paint sprayed in the automobile painting process. Based on this study, we will analyze the correlation with the compensation value according to the characteristics of the paint, such as the viscosity of the paint used in the fluid gear pump, and add it to the compensation lookup table so as to control the flowrate more precisely.  
Conceptualization and methodology, K.P. and D.J.; software, data curation, validation, formal analysis and writing, K.P. and M.C.; review and supervision, D.J.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 6 Conclusions  
In this paper, we investigate the resource scheduling and energy efficiency optimization problem of 5G and satellite converged networks for transmission and distribution scenarios. A Joint Device Association and Power Control Online Optimization (JDAPCOO) algorithm is proposed with the aim of maximizing the total long-term system energy efficiency while ensuring the minimum transmission rate requirement of PDs. The proposed JDAPCOO algorithm can make an asymptotically optimal device association strategy and power control scheme based on the current network state information, and the simulation results confirm the superior performance of the JDAPCOO algorithm. Although the algorithm proposed in this paper has been improved in terms of energy efficiency, it has not considered the delay of data transmission of PDs. Therefore, future work will focus on the problem of delay and jitter guarantee for data transmission of PDs in 5G and satellite fusion networks so as to further meet the needs of power service carrying.  
Figure 10: The relationship between the total system energy efficiency and the noise power.  
Figure 9: The relationship between the total system energy efficiency and the channel bandwidth.  
**Author Contributions:** Conceptualization, S.M., S.Z. and X.Z.; methodology, S.M., R.Z. and P.Q.; software, S.Z. and J.H.; validation, J.L., H.S. and P.Q.; formal analysis, J.L.; investigation, S.Z.; resources, Z.W.; data curation, J.L.; writing--original draft preparation, S.Z. and J.L.; writing--review and editing, S.M. and X.Z.; visualization, H.S.; supervision, Z.W.; project administration, S.Z.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the State Grid Corporation of China under the project \"Research on fusion architecture of 5G and Satellite Network for Transmission and Distribution Scenario (No.5700-202155325A-0-0-00)\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Single Wearable Sensor for Gait Analysis in Parkinson's Disease: A Preliminary Study
###### Abstract
Furthermore, in the literature, walking activity is not always correctly detected during free living [8; 20] and therefore it is necessary to develop reliable algorithms able to correctly identify walks and distinguish them from non-walking activities. Conversely, other recent papers have analyzed walking only in clinical environments, obtaining only time domain parameters [23; 24; 25]. Recent studies propose wearable sensor networks that are able to monitor the level of activity and the motor fluctuations of a PD subject during the day [26; 27]. However, the use of a single inertial measurement unit able to provide a complete analysis of quantity and quality gait in time and frequency domains has not yet been studied. In this work we propose a simple, non-invasive and low-cost system for home monitoring that does not involve changes in the common lives of the subjects, especially of the PD patients. Starting from data acquired by a wearable sensor placed at the lower back, we present an algorithm able to provide a complete gait analysis in terms of both qualitative and quantitative parameters of walking in the time and frequency domains. The goal of this study is to propose an algorithm to objectively assess various gait parameters, investigate treatment effects, and quantify the disease state in PD. With this aim we validated the proposed algorithm and tested it with PD subjects in an unsupervised environment.