esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Positioning in IoT: A Look at Current and Future Trends
## 3 Designing an IoT Positioning System
### Comparing IoT Technologies and IoT Enablers
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Impact on (Battery, Device Cost per Domain 1**} \\ \hline
**Technology** & \begin{tabular}{c} **Network** \\ **Topology** \\ \end{tabular} & \begin{tabular}{c} **Network** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Time-Based** \\ **Positioning** \\ \end{tabular} & \begin{tabular}{c} **Power-Based** \\ **Positioning** \\ \end{tabular} & \begin{tabular}{c} **Space-Based** \\ **Positioning** \\ \end{tabular} & \begin{tabular}{c} **Achievable** \\ **Positioning** \\ **Accuracy 2** \\ \end{tabular} & \
\begin{tabular}{c} **Most** \\ **Suitable** \\ **Domain** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **Studies** \\ **Studies** \\ \end{tabular} \\ \hline \hline
5G & star & HR/HP-Short range & +,+ & +,+ & +,+ & High & Time & [14,15] \\ \hline ANT+ & mesh & LR/LP-Short range & +,++++ & +,+ & ++,++ & Low & Power & \\ \hline BLEmesh & mesh & LR/LP-Short range & +,++++ & +,+ & ++,+ & Medium & Power & [11,16,17] \\ \hline Dash7 & star & LR/LP-Long range & +,++++ & +,+ & ++,++ & Low & Power or Space & \\ \hline EC-GSM-IOT & star & HR/LP-Long range & +,++++ & +,+ & ++,++ & Low & Power & \\ \hline EnOcean & mesh & LR/LP-Long range & +,++++ & +,+ & ++,++ & Low & Power or Space & \\ \hline Ingenu /RPMA & star & LR/LP-Long range & +,++++ & +,+ & ++,++ & Medium & Power or Space & \\ \hline ISA101.11a & mesh & LR/LP-Short range & +,++++ & +,+ & ++,++ & Medium & Power or Space & \\ \hline LoRa & star & LR/LP-Long range & +,++ & +,+ & ++,++ & Medium & Power & [18] \\ \hline LTE-M & star & LR/LP-Long range & +,+ & +,+ & ++,++ & Medium & Time & [2] \\ \hline MiWi & mesh & LR/LP-Long range & +,++++ & +,+ & ++,++ & Medium & Power & \\ \hline NB-IoT & star & LR/LP-Long range & +,+ & +,+ & ++,++ & Medium & Time & [2] \\ \hline RFID & star & LR/LP-Short range & +,++++ & +,+ & ++,++ & Medium & Power & [19,20,21,22,23] \\ \hline Sigfox & star & LR/LP-Long range & +,++++ & +,+ & ++,++ & Medium & Power & [24,25] \\ \hline Telensa & star & LR/LP-Long range & +,++++ & +,+ & ++,++ & Low & Power or Space & \\ \hline Thread & mesh & LR/LP-Short range & +,++++ & +,+ & ++,++ & Medium & Power \\ \hline Weightless-N & star & LR/LP-Long range & +,++++ & +,+ & ++,++ & Medium & Power or Space & \\ \hline Weightless-P & star & LR/LP-Long range & +,++++ & +,+ & ++,++ & Low & Power or Space & \\ \hline Weightless-W & star & LR/LP-Long range & +,++++ & +,+ & ++,++ & Medium & Power \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of key positioning related aspects for several IoT protocols and IEEE 802.11\(\ast\) family protocols.
# Polarimetric Detection of Chemotherapy-Induced Cancer Cell Death
## 2 Theory  
### The Mueller Matrix Formalism  
The polarization state of a light beam can be described by the Stokes vector **S**:  
\[\textbf{S}=\begin{bmatrix}I\\ Q\\ U\\ V\end{bmatrix}=\begin{bmatrix}I_{H}+I_{V}\\ I_{H}-I_{V}\\ I_{45}-I_{135}\\ I_{R}-I_{L}\end{bmatrix}=\begin{bmatrix}(E_{\parallel}E_{\parallel}^{*}+E_{ \perp}E_{\perp}^{*})\\ (E_{\parallel}E_{\parallel}^{*}-E_{\perp}E_{\perp}^{*})\\ (E_{\parallel}E_{\parallel}^{*}+E_{\perp}E_{\perp}^{*})\\ i(E_{\parallel}E_{\parallel}^{*}-E_{\perp}E_{\perp}^{*})\end{bmatrix}\quad, \tag{1}\]  
where \(I_{\parallel}\), \(I_{\perp}\), \(I_{45}\), and \(I_{-45}\) are the intensity measurements with linear polarizers oriented in the corresponding directions and \(I_{L}\) and \(I_{R}\) are the intensities measured with circular polarizers.  
When a light beam passes through a medium, its polarization changes and so does its corresponding Stokes vector. The output and input Stokes vectors are related by the equation \(\textbf{S}^{\prime}=\textbf{MM}\ \textbf{S}\), where **MM** is the \(4\times 4\) Mueller matrix [1]:  
\[\begin{bmatrix}s_{1}^{\prime}\\ s_{2}^{\prime}\\ s_{3}^{\prime}\\ s_{4}^{\prime}\end{bmatrix}=\begin{bmatrix}m_{11}&m_{12}&m_{13}&m_{14}\\ m_{21}&m_{22}&m_{23}&m_{24}\\ m_{31}&m_{32}&m_{33}&m_{34}\\ m_{41}&m_{42}&m_{43}&m_{44}\\ \end{bmatrix}\quad. \tag{2}\]  
The 16 Mueller matrix elements contain all the polarimetric information about the sample under study for the wavelength and configuration considered, but no direct interpretation can be initially given for its adequate analysis.  
Moreover, prior to performing any analysis method, it is necessary to check that the experimental **MM** is physically realizable. The **MM** of a real system satisfies [PERSON]'s criterion [28], i.e., its corresponding coherency matrix \(H\) must have four real non-negative eigenvalues. By means of this test, we would find any deviation (like a systematic error in the experiment) able to transform our **MM** into a non-physical one.
## 3 Experiment and Results Analysis  
### Experimental Data and Pre-Processing  
The self-constructed dataset in the field of audit presented in this paper is derived from the audit text data available on the official website of Xixiangtang People's Government. The initial dataset comprises a substantial amount of unstructured textual information, which has been subjected to cleansing using regular expressions. This process primarily involves eliminating stop words, special symbols, and irrelevant details. Additionally, based on expert recommendations within the audit domain, six distinct audit entities have been identified and annotated sequentially using the BIO annotation method. In this approach, B-X denotes the initiation of entity X, I-X represents its middle or concluding segment, while O signifies other characters unrelated to any specific entity. Consequently, we obtain a standardized dataset specifically tailored for auditing purposes (Audit). Table 1 illustrates the categories associated with these audit entities while Table 2 provides detailed data annotations.  
The self-constructed audit domain dataset is supplemented with the public dataset Resume [14] as experimental data to further evaluate the model's generalization ability across different domains, encompassing eight entity types. In this study, the experimental dataset is partitioned into training set, validation set, and test set in an 8:1:1 ratio. Table 3 presents statistical information regarding the dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entity** & **Entity** & **Entity** & **Entity Category** \\ \hline Name & Name & Specific title of audit project & \begin{tabular}{c} (Green intelligent manufacturing \\ environmental protection equipment \\ production project) \\ \end{tabular} \\ Address & Loc & \begin{tabular}{c} Audit project- \\ specific address \\ \end{tabular} & \begin{tabular}{c} (XiXiangtang \\ District Xixiang Avenue 36) \\ \end{tabular} \\ Willfulness & Prp & Main Project Manager & \begin{tabular}{c} Investment Group limited \\ liability company \\ 28.05 \\ \end{tabular} \\ Amount & Fee & \
\begin{tabular}{c} Amount to be audited \\ for projects \\ \end{tabular} &
\begin{tabular}{c} 28.05 \\ (280,500 yuan) \\ 17.04 hm\({}^{2}\) \\ \end{tabular} \\ Area & Sco & Audit project area & 10.04 hm\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Audit domain entity type.
## 6 Evaluation and Result Analysis
### Simulation Results
In short, a larger temporal correlation coefficient means stronger resistance to additive noise, however, the hyperparameter estimation error, such as the error of \(\gamma\), is often larger than that of small temporal correlation coefficients in the high SNR region. Figure 6a,b show the MSE of the \(\gamma\) estimated by the proposed PB-CSBL and NC-CSCE algorithms, respectively. The MSE is evaluated under different SNRs and temporal correlation coefficients. As can be seen, a larger temporal correlation coefficient achieves a smaller MSE in the low SNR region, while a smaller temporal correlation coefficient reduces the MSE value in the high SNR region. Finally, to evaluate the estimation performance of the proposed NC-CSCE algorithm under colored noise, we compared the NC-CSCE algorithm with the TMSBL and PB  
Figure 5: The MSE performance of the proposed algorithms and their theoretical lower bounds: (**a**) the temporal correlation coefficient \(\beta=0\); and (**b**) the temporal correlation coefficient \(\beta=1\). Figure 6: The MSE of the estimated hyperparameter \(\gamma\) by the proposed PB-CSBL and NC-CSCE algorithms under different SNRs and temporal correlation coefficients: (**a**) the PB-CSBL algorithm; and (**b**) the NC-CSCE algorithm. CSBL algorithms. The group number of subcarriers in NC-CSCE is adjusted to evaluate the channel estimation performance. In the simulation, all paths are distributed in three non-overlapping clusters. The clusters are set with three different temporal correlation coefficients ranges over multiple OFDM blocks, which are \([0.1,0.3]\), \([0.5,0.7]\), \([0.8,1.0]\). All subcarriers are divided into eight groups and each group is set with a different SNR. As shown in Figure 7b, the reference SNR ranges are from 5 to 20 dB. With QPSK mapping, the MSE and BER performance curves are shown in Figure 7a, and the results prove that the proposed NC-CSCE algorithm is superior to the TMSBL and PB-CSBL algorithms. The NC-CSCE-8 denotes the NC-CSCE algorithm with eight groups, and it performs better than the NC-CSCE algorithm with other groups. Therefore, the setting of group number is critical for the NC-CSCE algorithm. In one instance, the actual distribution of colored noise can hardly be reflected with a limited group number; yet in another, a small symbol number will result in low measurement accuracy in one group. Figure 7b also shows the statistical results of the estimated SNRs based on 500 simulations. Similarly, NC-CSCE-8 matches the reference best among all compared methods.
## 5 Conclusions  
This research proposed an effective, component substitution-based image Pan sharpening method that uses CIELab color space for Pan sharpening of the VHR Pleiades satellite images. Ten test sites with different landscape characteristics were selected to evaluate the performance of the proposed method in conjunction with six common Pan sharpening algorithms; namely, GS, HCS, IHS, EHLERS, NNDiffuse and GIHS. The comparative evaluation results from Pleiades VHR images supports that the proposed CS algorithm is powerful and ensures better performance compared to the other Pan sharpening methods according to the spectral and spatial accuracy assessment procedures and the visual interpretation. In addition, results indicated that proposed method provided comparatively consistent results, while the performance of other methods varyied with respect to land surface characteristics of the region. As an example for RMSE metric, the best values among the all ten sites were obtained for forest and vegetated areas. Pan sharpening in urban areas resulted in coarser metric values, which illustrate the impact of different land characteristics on the performance of Pan sharpening algorithms. Characteristics of unique CIELab color space, led to producing similar brightness characteristics in Pan sharpened images compared to original MS image. The nature of L* component of MS image helps to preserve spectral and spatial information of original MS and Pan images, respectively. Further improvement of the CIELab-based method could be the implementation of this approach for Pan sharpening of satellite images with more than three bands. In addition, further studies are planned to evaluate the performance of CIELab in fusions of satellite images from different sources. Lastly, other accuracy assessment approaches, such as comparisons of classification and segmentation results of Pan sharpened images, could also help future investigations.  
conceptualization, A.R., U.A. and C.G.; methodology, A.R., U.A. and C.G.; formal analysis, A.R.; investigation, A.R.; data curation, A.R.; writing--original draft preparation, A.R., U.A. and C.G.; writing--review and editing, A.R. and U.A.; visualization, A.R.; supervision, U.A. and C.G.; project administration, C.G.  
This research received no external funding.  
**Acknowledgments:** Great appreciation to ITU CSCRS for providing VHR \"Pleiades\" images. The authors would also like to acknowledge the many useful contributions of [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Simulation Results and Discussion
### Constellation Size Issue  
Since \(\text{Energy efficiency}~{}=~{}\{E_{SISO}-E_{C-MIMO}\}/E_{SISO}\), positive energy efficiency indicates \(\{E_{SISO}>E_{C-MIMO}\}\). In Figure 8, energy efficiency is simulated over distance for different  
Figure 7: Delay difference over distance for code rate \(r=\frac{3}{4}\).  
constellation sizes. The simulation results show that for rate \(r=1/2\), cooperative MIMO outperforms SISO after 80 meters for constellation size \(b=1\) whereas it takes 10 meters for constellation size \(b=8\).
# Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems
## 2 Circuit Design of the PPG-Based Wearable System
### Energy Harvesting Circuit  
The EH circuit is connected to a small 80 \(\times\) 100 mm, 1 W solar PV cell, with a typical energy transformation efficiency of 35% [29]. The mono-crystalline panel has an open circuit voltage of 8.2 V, with a voltage and current at the maximum power point of 6.4 V and 170 mA, respectively. The EH circuit is based on the LT3652 (Analog Device, Wilmington, MA, USA) monolithic step-down converter that operates with a minimum input voltage of 4.95 V. It is compatible with low-power systems, i.e., once charging is terminated, it enters standby mode with a low-power consumption of 85 \(\upmu\)A. The LT3652 maximizes the transfer of energy from the PV panel to an 8 F supercapacitor array, with an energy capacity of 54.76 J for the IoWT system operation. The LT3652 uses a charge-current scheme, which ends a charge cycle when the supercapacitor charge current falls to one-tenth of the programmed 1.0 A maximum charge current. On the other hand, when the supercapacitor voltage reaches 60% of the fully charged float voltage, the IC automatically increases the maximum charge current to the full programmed value. Once charging is terminated, the LT3652 automatically enters a low-current standby mode where supply bias currents are reduced to 85 \(\upmu\)A.
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 2 Probabilistic Forecasting Models  
### Examples of Parametric Probabilistic Forecasting Models  
Each forecast has an error. This error was yielded by subtracting the forecasted value from the actual value in the post-processing stage. At the time of the forecast, a point forecast does not give forecasters any information about the error. Consider a linear regression model as an example. It is a forecasting model that aims to predict the response variable \(Y_{t}\) based on some explanatory variables \((x_{t,1},\ \ldots,\ x_{t,m})\) as follows:  
\[Y_{t}=\hat{Y}_{t}+e_{t} \tag{1}\]  
\[\hat{Y}_{t}=b_{1}x_{t,1}+\ldots+b_{m}x_{t,m} \tag{2}\]  
where \(\hat{Y}_{t}\) is the point forecast in the deterministic model and \(b\) denotes the weight associated with each explanatory variable. Unfortunately, as can be seen in (2), the deterministic linear regression model does not consider the forecast error. Probabilistic forecasts, on the contrary, will give forecasters different sets of forecasts that follow a known specific probability distribution function (PDF). They will then forecast the parameters of the distribution function. For instance, a probabilistic linear regression forecast assumes that the error term \(e_{t}\) is normally distributed with a zero mean and a constant standard deviation, \(\sigma^{2}\). Thus, the forecasted value is defined as follows [12]:  
\[\hat{Y}_{t}\sim N\Big{(}\mu_{t},\sigma^{2}\Big{)} \tag{3}\]  
\[\mu_{t}=b_{1}x_{t,1}+\ldots+b_{m}x_{t,m} \tag{4}\]  
where (3) states that the forecasted values are normally distributed in this model. \(\mu\) denotes the mean and \(\sigma\) is the variance. The generalized linear models, however, consider exponential family distributions (\(Exp\)) of the response variables and utilize a link function \(g(.)\) to model the mean value in terms of explanatory variables [12]:  
\[\hat{Y}_{t}\sim Exp(\mu_{t},\phi_{t}) \tag{5}\]  
\[g(\mu_{t})=b_{1}x_{t,1}+\ldots+b_{m}x_{t,m} \tag{6}\]  
where \(\phi_{t}\) denotes the variance associated with the exponential family distributions.  
There is a more advanced model, called the Generalized Additive Model for Location Scale and Shape (GAMLSS), that considers a huge set of distributions and is able to model all of the scale parameters related to the distributions [12]:  
\[\hat{Y}_{t}\sim D(\mu_{t},\sigma_{t},\
u_{t},\tau_{t}) \tag{7}\]  
\[g_{1}(\mu_{t})=b_{1}x_{t,1}+\ldots+b_{m}x_{t,m} \tag{8}\]  
\[g_{2}(\sigma_{t})={b_{1}}^{\prime}x_{t,1}+\ldots+{b_{m}}^{\prime}x_{t,m} \tag{9}\]  
\[g_{3}(\
u_{t})={b_{1}}^{\prime\prime}x_{t,1}+\ldots+{b_{m}}^{\prime\prime}x_{t,m} \tag{10}\]  
\[g_{4}(\tau_{t})={b_{1}}^{\prime\prime}x_{t,1}+\ldots+{b_{m}}^{\prime\prime}x_{ t,m} \tag{11}\]  
where \(v\) demonstrates the skewness and \(\tau\) shows the kurtosis of the distribution function. This means that the GAMLSS model is able to give us the exact shape of the distributions of our forecast.
## 2 Communication Middleware Technology Trend for Smart Factory
### Dds  
DDS [8] is a network communication middleware standard proposed by Object Management Group (OMG). It supports scalability, real-time, reliability, high performance, and mutual data exchange. A programming model related to data-centric publishers/subscribers for a distributed environment is standardized. To simplify complex network programming, DDS is a network communication middleware that implements a publisher/subscriber model that exchanges data, events, and commands between nodes (heterogeneous embedded devices). The publisher creates a topic that is the basis for generating information. It is a structure in which data samples are published. It then distributes the data sample to all the subscribers interested in the topic.  
DDS manages message addressing, data marshaling, demarshalling, distribution, flow control, and retries. In addition, DDS has the characteristics of a message-based connectionless service. It provides a real-time communication environment that enables access to data regardless of service information (location, time, synchronization).
# A Boundary Distance-Based Symbolic Aggregate Approximation Method for Time Series Data
## 3 SAX-BD: Boundary Distance-Based Method For Time Series
### Difference from ESAX  
In the ESAX method, the maximum, minimum, and mean values in each time segment are mapped and arranged according to the following formula:  
\[<S_{1},S_{2},S_{3}>=\left\{\begin{array}{l}<S_{\max},S_{mid},S_{\min}>if\ P_{\max}>\ P_{mid}>\ P_{\min}\\ <S_{\max},S_{\min},S_{mid}>if\ P_{\max}>\ P_{\min}>\ P_{mid}\\ <S_{mid},S_{\min},S_{\max}>if\ P_{mid}>\ P_{\min}>\ P_{\max}\\ <S_{mid},S_{\max},S_{\min}>if\ P_{\min}>\ P_{\max}>\ P_{\min}\\ <S_{\min},S_{\min},S_{\max}>if\ P_{\min}>\ P_{\min}>\ P_{\max}\\ <S_{\min},S_{\max},S_{mid}>\text{ }Otherwise\end{array}\right. \tag{11}\]  
However, for the same feature points, we did not directly map these points in the same way as the ESAX method, mainly due to the following two reasons:  
Firstly, in Figure 5, if you follow these points in the ESAX method diagram, for example, for A, B, C, D, E, they will all be mapped to the same character 'f' for \(\alpha=6\) and F, G, H will be mapped to 'a'.  
Figure 4: Several typical segments with the same average value but boundary distance. Segment **a** and **d**, **b** and **e**, **c** and **f** are in opposite directions while all in same mean value. The trend distance is replaced by boundary distance.  
We directly retain these feature points and calculate the boundary distance. At this time, the specific values of A, B, C, D, E, and F, G, H can have a better discrimination.  
Secondly, if we follow the ESAX method, we can see from Equation (11) that there may be a total of 6 comparisons. In fact, according to our method, only two comparisons are needed. Since our distance measurement is consistent with SXA-TD, the low correlation between Equation (13) and Euclidean distance has also been proven in the SAX-TD paper.  
\[<\Delta S_{1},\Delta S_{2}>=\left\{\begin{array}{l}<\Delta S_{\min},\Delta S _{\max}>if\ P_{\max}>\ P_{\min}\\ <\Delta S_{\max},\Delta S_{\min}>if\ P_{\min}>\ P_{\min}\end{array}\right. \tag{12}\]  
Finally, time series Q and C can be expressed as follows according to our method SAX-BD:  
\[\text{Q}:\hat{q}_{1}\Delta S_{1}^{1}\Delta S_{2}^{1}\ \hat{q}_{2}\ S_{1}^{2} \ \ldots\hat{q_{w}}\ \Delta S_{1}^{w}\Delta S_{2}^{w}\text{C}:\ \hat{c_{1}} \Delta C_{1}^{1}\Delta C_{2}^{1}\ \hat{c_{2}}\ \Delta C_{1}^{2}\Delta C_{2}^{2}\ \ldots\ \hat{c_{w}} \Delta C_{1}^{w}\Delta C_{2}^{w}\]  
The equation for calculating the distance between Q and C can be expressed as follows:  
\[\text{bd}(\text{q},\text{c})=\sqrt{\left(\Delta q(t)_{1}-\Delta C(t)_{1} \right)^{2}+\left(\Delta q(t)_{2}-\Delta C(t)_{2}\right)^{2}} \tag{13}\]  
\[BDIST\left(\hat{Q},\hat{C}\right)=\sqrt{\frac{n}{w}}\sqrt{\sum_{i=1}^{w}\left( \left(dist(\hat{q},\hat{c})\right)^{2}+\frac{w}{n}\left(bd(q_{i},c_{i})\right) ^{2}}\right. \tag{14}\]
## 6 Open Challenges, Future Prospects and Research Directions
### Future Directions  
This section explores future directions in optical communication technologies, focusing on several key advancements. It delves into novel modulation schemes, adaptive transmission techniques, and aperture averaging methods to enhance signal quality. The discussion extends to spatial diversity and cooperative relaying strategies aimed at improving communication reliability. Additionally, the integration of AO and ADTs is examined for their potential to mitigate atmospheric distortions. The potential of hybrid FSO systems, along with advanced ECC and modulated signal optimization, is also highlighted, underscoring the continuous evolution of this field. The future directions are highlighted in Figure 16 and explored in detail in the following subsections.
## 1 Introduction
### Scientific Contributions  
The present work attempts to address the existing gap in the scientific literature, regarding an extensive summary/review of the studies comparing the application of ARIMA and machine learning techniques in time series forecasting applications. This is the only such review, according to the authors' best knowledge.  
Our work compares time series forecasting studies across multiple applications and data sources, and it consists of mostly recent comparative studies published after 2018 (with the exception of the works of [PERSON] et al. (2018) and [PERSON] et al. (2018)). Due to the multitude of different data sources and forecasting challenges, we attempt a multilateral, sampled view of the existing literature, presenting studies from five basic data categories (financial, medical, weather, utilities, and network characteristics). For the above reasons, our work constitutes a contemporary, multifaceted, review of the existing literature comparing the ARIMA statistical approach with its machine learning counterparts. Our study also summarizes the main performance metrics of the algorithms presented in each study, thus enabling an intuitive review and comparison of the methods in each forecasting application. A practical evaluation (Section 5) of the studies in which the ARIMA models outperform their machine learning counterparts results in an enumeration of the application, dataset, and model-dependent characteristics that drive the choice of the optimal forecasting model in any particular application.