esa-sceva/satcom-chunk-collection huggingface dataset:
# Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization
## 6 Conclusions  
In this paper, we aim to improve the localization accuracy of the trilateration technique based on pairwise distances between sensor nodes. For this, we explore the problem of missing data in sensor network localization. Technically, we formulate the information distance from RSSI measurements using a propagation model. We obtain a squared Euclidean distance matrix with several unknown entries. Then, based on the matrix completion process, the complete EDM is efficiently generated by exploring only the available distances. For this, Gradient descent based advanced methods, some of which use an adaptative learning rate, are introduced. To perform trilateration, all pairwise distances between the node to localize and different sensor nodes are explored. To validate the merits of the proposed framework, an extensive set of simulations was carried out in noiseless and noisy environments using a real propagation model. Simulation results suggest that the proposed Gradient descent variants based matrix completion reliably estimate the complete EDM exploring partial known information. Trilateration combined with matrix completion outperforms the traditional localization system. Our simulation results indicate that trilateration combined with Adam which is used to solve the matrix completion problem outperforms the other combinations. This approach does not require a high computational complexity. Real experiments have been done in order to support simulation results and show that our approach outperforms the classic trilateration.  
Since there are pairwise distances that are better estimated than others, we are interested in using weighted distances when applying trilateration in future work. The accorded weight increases when the distance is well estimated. New localization system improvements in terms of complexity can be provided using machine learning methods, since these aim to shift the prediction complexity to an offline phase.  
[PERSON] and [PERSON] conceived the presented idea and developed the closed-form of each algorithm. Simulations and results presentation were conducted by [PERSON] [PERSON], [PERSON] and [PERSON] verified the analytical methods and discussed the results contributed to the final manuscript. All authors provided critical feedback and helped the research, analysis and manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results
### Monitoring Results
#### 3.2.2 Monitoring of CO\({}_{2}\) Concentration through 2.16% WW\({}_{\rm eq}\)R with SNVP  
A new monitoring was carried out on the 17 th of December using a 2.16% WW\({}_{\rm eq}\)R, as suggested by the simulations and the previous monitoring processes. The results are shown in Figure 12.  
During the whole process, the SNVP was not activated because the CO\({}_{2}\) concentration remained all the time below 800 ppm, except for a 10 min period from 13:20 to 13:30. Therefore the WW\({}_{\rm eq}\)R selected (2.16%), achieved the maintaining of adequate levels of CO\({}_{2}\) concentration. There were some modifications in the key environmental parameters: the occupancy was 21 instead of 26 and the wind speed remain constant, between 2 and 3 m/s. Both parameters have a major influence on CO\({}_{2}\) concentration, as described in Equation (2).  
Figure 13 shows a comparison of occupancy and wind conditions between the 18 th and 26 th of November and the 17 th of December 2021. In all of them, the wind direction trend is quite similar, a northwest direction, but the wind speed is quite different. The first graph in Figure 13a shows peak speed values over 20 m/s, while the graph in Figure 13b shows lower wind speeds. Results in Figure 13c are similar to those average values described in Section 2.3.4., as used in the simulations. Figure 13a-c also includes the outdoor temperature, which is also relevant in comparing the TC with the climatic conditions. Figure 13a,c show outdoor temperatures from 10 \({}^{\circ}\)C to 20 \({}^{\circ}\)C, while Figure 13b shows a 5 \({}^{\circ}\)C-lower outdoor temperature.  
Table 6 includes a summary of the average values for each 30 min time frame for the three different days monitored. Each day starts with a specific WW\({}_{\rm eq}\)R, from 0.23% to 2.16%. The table also includes the peak value for 2.16%. The table also includes the peak value for each period. During all the time frames, the concentrations of CO\({}_{2}\) remain below the suggested limits when the DNVP activates a WW\({}_{\rm eq}\)R bigger than 2.16%, for a typical windy winter day in Seville.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**WW\({}_{\rm eq}\)R**} & **Protocol** & **09:10** & **09:30** & **10:00** & **10:30** & **11:00** & **11:30** & **12:00** & **12:30** & **13:00** & **13:30** & **Peak** \\  & **09:30** & **10:00** & **10:30** & **11:00** & **11:30** & **12:00** & **12:30** & **13:00** & **13:30** & **14:00** & **Peak** \\ \hline
0.23–1.07 18 th-Nov. & DNVP & 964 * & 880 * & 1106 * & 570 & 1139 * & 1201 * & 607 & 666 & 896 * & 915 * & 1201 * \\ \hline
1.07–1.82 26 th-Nov. & DNVP & 880 * & 1114 * & 839 * & 538 & 562 & 888 * & 655 & 767 & 877 * & 912 * & 1114 * \\ \hline
2.16 17 th-Dec. & DNVP & 529 & 503 & 652 & 684 & 661 & 603 & 552 & 588 & 787 & 711 & 787 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Monitorization summary of average CO\({}_{2}\) concentration in ppm for several WW\({}_{\rm eq}\)Rs.  
Figure 13: Wind conditions on 18 th and 26 th of November (**a**,**b**) and 17 th of December 2021 (**c**).
## 5 Conclusions  
A novel method for landslide displacement data completion and prediction based on MLATC model is proposed for missing landslide displacement data and time series prediction. The tensor structure in the MLATC model well perpetuates the structural information of landslide spatiotemporal data in the time dimension, makes full use of the correlation of landslide displacement time series in different time scales, and solves the problem of serious data loss caused by the destruction of the original matrix structure. The data completion and prediction models are implemented in a tensor structure framework, combining VAR models and autoregressive paradigms, and different proportions of random and non-random missing cases are selected for experimental analysis. The experimental results of the Shuizhuyuan landslide prove that the model can also achieve the effective completion of missing data and displacement trend prediction of landslide displacement time series without the need of complete original landslide displacement data. The reliability and accuracy of the model data completion and prediction are verified, further improving the feasibility of the model, which likewise helps to issue timely and accurate early warning forecast signals to remind people who are in the danger area to evacuate and avoid casualties and property damage. The method can be extended and applied in data completion and in the prediction of such landslides.  
Conceptualization, C.W. and Y.Z.; methodology, C.W.; validation, C.W.; formal analysis, C.W.; investigation, C.W.; resources, C.W.; data curation, C.W.; writing--original draft preparation, C.W.; writing--review and editing, C.W. and Y.Z.; supervision, Y.Z.; project administration, C.W. and Y.Z.; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Geological Survey Projects of China Geological Survey (No. DD20230442), the National Key Research and Development Program of China (No. 2019 YFC150960101 and No. 2018 YFC150480502) and the Young Scientific and Technological Talents Program of the Ministry of Natural Resources.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Parameter Identification Method and Process
### Particle Swarm Optimization Algorithm Based on Random Perturbation
Check parameters under various conditions based on sub-module and the integrated model. Figure 7a shows the nuclear power parameter identification process diagram. Figure 7b shows the parameter identification process diagram with the RP-PSO and Simulink. Big sensitivity is beneficial for parameter identification in both time domain and frequency domain analysis. Apart from parameter influence analysis on model dynamic simulation, the trajectory sensitivity analysis [25] could provide a reference for parameter identifiability and reference for the perturbed parameter selection in the RP-PSO algorithm (see Figure 7a). Figure 7: Nuclear power parameter identification process. (**a**) Nuclear power parameter identification process diagram; and (**b**) parameter identification process diagram with the random perturbation particle swarm optimization (RP-PSO) and Simulink.
## 3 System-Level Configuration
For the smart city scenario, the power transmitted by IoT devices was set as 10 mW (10 dBm) both for 3.6 GHz and 28 GHz. Two types of links are considered, namely, a direct link between BS and IoT devices or users and an indirect link through the RIS. For all scenarios, because of the C-RAN, there are always double-links, where IoT devices or users are served simultaneously by an RIS and a BS. The noise power is \(N_{0}=-85\) dBm for the bandwidth \(B\) = 0.4 GHz and \(N_{0}=-94\) dBm for the bandwidth\(B\) = 50 MHz. The receiver noise figure F = 3 dB. The spacing between each element of the RIS panel is \(d_{RIS}\) = \(\lambda/2\) = 42 mm (3.6 GHz), \(d_{RIS}\) = \(\lambda/2\) = 5.4 mm (28 GHz), and \(d_{RIS}\) = 1.5 mm (100 GHz), resulting, respectively, in areas of A = 1764 mm\({}^{2}\), A = 29.16 mm\({}^{2}\), and A = 2.25 mm\({}^{2}\) per element. The gains of the individual antenna elements of the arrays are 0 dBi for both the transmitter and receiver. Next, we present the parameters considered for these scenarios in Tables 1-3.
# Fluctuation-Enhanced Sensing (FES): A Promising Sensing Technique
## Fundamental FES Resolution Limits
### Information Channel Capacity in Classical (Deterministic) Gas Sensors  
[PERSON] and coworkers have shown [73] that in the case of homogeneous probing current density in the sensor and if the sensor resistance fluctuations in the reference gas have 1/\(f\) spectrum, by using [PERSON]'s formula of information channel capacity in analog channels, classical resistive sensors have the following upper limit of information flow rate:  
\[C\approx\frac{1}{2t_{m}}ln\left[1+\frac{8\pi^{2}V(R-R_{0})^{2}}{AR^{2}}\right] =\frac{1}{2t_{m}}ln\left[1+\frac{8\pi^{2}A_{S}d(R-R_{0})^{2}}{AR^{2}}\right] \text{ (bit/s) s} \tag{2}\]  
where \(t_{m}\) is the measurement time window, \(R\) and \(R_{0}\) are the resistance response in the test gas and in the reference gas, respectively, \(V\) is the volume of the sensor film, \(A_{S}\) is the surface of the sensor film, and \(d\) is its thickness. The factor \(A\) characterizes the strength of 1/\(f\) noise of the conductance noise spectrum [73] in the specific sensor by the simplified Hooge formula:  
\[\frac{S_{u}(f)}{U^{2}}=\frac{A}{Vf} \tag{3}\]  
where \(U\) is the DC voltage drop across the resistive sensor and \(f\) is the frequency.  
According to Equation (2), in the practical (1/\(f\)-noise-dominated) limit and at fixed measurement time and film thickness, the larger the surface of the classical resistive sensor the greater the information channel capacity. However, in the limit of a sufficiently large agent concentration, the saturation time is controlled by the underlying diffusion processes taking place through the thickness of the film; therefore, in this case, the shortest measurement time is also controlled by diffusion according to  
\[t_{m,min}\approx\left(\frac{d}{D}\right)^{2} \tag{4}\]  
where \(D\) is the diffusion coefficient of the agent and/or its fragments through the film. Therefore, the thinner and larger the film, the greater the information channel capacity. This fact indicates that, in classical films, small thickness and large surface are preferable, with the thickness being the dominant control parameter.
## 3 The Inverse Problem
### Decentralized Fuzzy Adaptive PID Control Method
#### 3.3.2 Fuzzy Adaptive Control PID Units  
Each independent measurement point is a single FACU. The FACU model is shown in Figure 3.  
The measurement and inverted information in the heat conduction system have certain spatial distribution characteristics. Therefore, the inverse heat conduction problem can also be regarded as a kind of feedback control problem, that is, a closed-loop system, where the observation results (measured temperature) of the system are the setting value, the parameters to be solved are the controlled object, and the calculated temperature of the positive problem is the sensor measurement information. \(e=\Delta T\) is obtained according to Equation (10); if \(\Delta T>0\), in order to eliminate or reduce the deviation, it is necessary to increase \(y\); if \(\Delta T<0\), to eliminate or reduce the deviation, it is necessary to decrease \(y\). Then, \(\Delta u\) is defined as follows:  
\[\Delta u=k_{p}\ \Delta T+k_{i}\Delta\sum_{h=0}^{it}\Delta T \tag{12}\]  
Figure 3: Fuzzy adaptive PID control system model.  
Figure 2: Decentralized fuzzy adaptive Proportion Integral Differential (PID) control inversion system.  
where \(k_{p}\) and \(k_{i}\) are the feedback factor, and \(k_{p}\) and \(k_{i}\) are adaptive updates by fuzzy inference.
## 4 Experiment Setup and Result Analysis  
### Experiment Setup  
GNSS IF data and MEMS (Micro-Electro-Mechanical System) data are simultaneously input into the Matlab based software receiver to complete GNSS signal tracking and the navigation solution calculation. Taking into account both the number of satellites and computational complexity, the software receiver is designed to have eight receiving channels. Figure 6 shows the composition of experimental system. The parameters of MEMS are shown in Table 2. GPS L1 signals are sampled as GNSS data with the IF signal sampler SPL-SIS800, of which the IF is 4.02 MHz and the sampling frequency is 20 MHz. The integration time of correlator is set to 10 ms, and the output rate of navigation solution result  
Figure 4: The structure of deep integration assisted by CNN.  
Figure 5: Flowchart of the proposed method.  
is 10 Hz. To identify faults, the false alarm probability is set to 0.02, and the corresponding alarm threshold is 2.58.  
In practical application, there are many reasons for the fault of GNSS and INS. Some types of fault data are easy to be obtained, such as occlusion leading to temporary loss of GNSS signals, etc. However, it is difficult for most fault types. Thus, the commonly used method is to artificially inject abnormal values into the collected data to simulate faults. In this study, the experiment includes two parts: one is simulation test, and the other is experimental test. The simulation test is to simulate different fault modes by injecting various types of abnormal values, and obtain the accuracy of fault identification by constructing a large number of fault data, while the experimental test adopts the real fault data which are collected from outdoors during multiple satellites are occluded.
## 4 Case Study--Life Cycle Sustainability Assessment Procedure
### Sustainability Assessment Methodology
#### 4.1.1 Environmental Life Cycle Assessment ((e)-LCA) Method  
In the case of the environmental life cycle assessment ((e)-LCA) the reference methodologies applied were ISO 14040:2006 [35] together with ISO 14044:2006 [36].  
The environmental impact categories assessed have been those defined in the product category rules of the EPD international system for highways, streets and roads, for asphalt mixtures and for construction products [37], which are the following:  
* GWP100: Emission of greenhouse gases (expressed as the sum of global warming potential, GWP, 100 years, in carbon dioxide equivalents, CO\({}_{2}\) equivalent),
* AP: Emission of acidifying gases (expressed as the sum of acidifying potential in sulphur dioxide equivalents, SO\({}_{2}\) eq.),
* EP: Emission of substances to water contributing to oxygen depletion, \" eutrophication\" (expressed as phosphate ion equivalents, PO\({}_{4}\)\({}^{3}\)-eq.),
* POFP: Emission of gases that contribute to the creation of ground-level ozone, \"photochemical oxygen creation potential\" (expressed as the sum of ozone-creating potential, in ethylene equivalents, C\({}_{2}\)H\({}_{4}\) eq.)
* ODP: Emission of ozone-depleting gases (expressed as the depletion potential of the stratospheric ozone layer, ODP kg CFC\({}^{11}\) eq.)
* ADP: Depletion of abiotic resources-elements (expressed as abiotic depletion potential (ADP-elements) for non-fossil resources, kg Sb eq.)
* ADP-FF: Depletion of abiotic resources-fossil fuels (expressed as Abiotic depletion potential (ADP-fossil fuels) for fossil resources, MJ, net calorific value)  
The characterisation models and factors used to calculate each environmental impact category were those developed in the CML-IA (baseline) methodology released in 2013. Likewise, another two additional environmental impacts have been assessed:  
* CED: Cumulative energy demand (MJ)
* WU: Water resources used (m\({}^{3}\))
# Fast and Improved Real-Time Vehicle Anti-Tracking System
## 3 Implementation and Results
### Recognition System  
The tracking results are also very accurate (Figure 18). As stated, in the tracking phase we used the SURF method on our detected vehicles database to recognize a newly detected vehicle. What sets our approach apart from other techniques, is that the detected database is made up completely of already cropped vehicle images. There is no need to remove any other unwanted object in the picture. This leads to less time in computation, and less error in the recognition accuracy.
# Predicting Activities of Daily Living with Spatio-Temporal Information
## 7 Conclusions  
This paper shows the importance of spatial and temporal information in interpreting human activity and how such information can be represented for activity recognition. The prediction by partial matching method is extended to capture spatio-temporal information by exploiting the repetitions from activity events. Evaluation was performed on three publicly available smart-home datasets. Our method can achieve an average accuracy of more than 84% for \(\mathit{time}\rightarrow\mathit{location}\) prediction. For \(\mathit{location}\rightarrow\mathit{activity}\) prediction, our method achieved more than 98% average accuracy across all the three datasets. Although the \(\mathit{(time,location)}\rightarrow\mathit{activity}\) has a lower recognition performance, our method can achieve an average accuracy of more than 81%. The results showed that the size of training data has an impact on the recognition performance for \(\mathit{time}\rightarrow\mathit{location}\) and \(\mathit{(time,location)}\rightarrow\mathit{activity}\) predictions. Compression tends to be more effective when trained with more data and the training time grows approximately linearly with the number of instances. The results from the experiments showed that location provides useful context information for inferring user activity. As future work, the plan is to extend our approach for abnormality detection. The learned PPM can be used to identify inputs that do not fit into the contexts.  
Conceptualization, S.-L.C. and H.W.G.; Data curation, S.-L.C. and L.K.F.; Formal analysis, S.-L.C. and L.K.F.; Methodology, S.-L.C. and L.K.F.; Writing--original draft, S.-L.C. and L.K.F.; Writing--review and editing, L.K.F., H.W.G. and S.-L.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Experimental Results
### Noise Attacks
#### 5.2.3 Mean Filtering Attack  
Mean filtering with a size of 3 \(\times\) 3 is applied, producing an average value of their neighbors, including themselves. Average or mean filtering also reduces the intensity disparity between adjacent pixels.
# SMS: A Secure Healthcare Model for Smart Cities
## 3 Related Works
With issues like network latency, traffic bottleneck, central node dependency, etc., the existing solutions using the \"cloud-computing paradigm\" fail to address the crucial need of \"real-time data processing\" and \"decision-making\" [12; 13; 14]. The time taken for the data to travel from the source sensor node to the cloud and return the results to the users is significantly high. The existing cloud-based solutions do not provide any reliable and significant solution to address this issue. Considering the above-mentioned insufficiencies of the existing cloud computing paradigm, it cannot provide a feasible solution for the critical real-time healthcare monitoring use-cases [12; 13]. As the IoT devices are strictly power-constrained systems, the implementation of exhaustive and complex cryptographic techniques is not practically possible, because of the limited processing capabilities of the sensor devices. In the past, researchers have shown that cloud computing offers a large number of facilities and advantages compared to the legacy systems; however, in cases of real-time processing and data management, they somehow fail to keep up with the increasing demands for efficiency and satisfy the needs of the users [12; 13; 14]. With all these limitations of the classical cloud computing paradigm as motivation, this paper proposed a new framework that combines the capabilities of both cloud and edge computing paradigms to provide real-time, cost-effective, and energy-efficient data processing and management. Blockchain technology is integrated in the proposed framework to ensure the security and integrity of the sensitive and personal data like patient health records [58; 59; 60]. Edge computing helps in processing and managing critical use-cases at the edge nodes rather than centralized processing at the cloud. This can facilitate a near-real-time decision-making and delivery of the services. In addition, as the data captured by the sensors are handled on the edge only, a very short distance has to be travelled for the data to reach the edge nodes, which makes it faster, energy efficient, and cost-effective with very few chances of data losses [17; 18; 19; 20; 9; 10; 39].