esa-sceva/satcom-chunk-collection huggingface dataset:
# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
## 6 Conclusions  
We have proposed a fundamental \(2\times 2\) MIMO approach to analyze the radial velocity distribution and mean radial velocity for falling and walking activities. We confirmed the limitations of SISO RF sensing and emphasized the importance of a distributed MIMO RF  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Dist. of** & **Dist. of** & **Dist. of** & **Dist. of \(2\times 2\)** \\ \# & **Radar\({}_{1}\)** & **Radar\({}_{2}\)** & **SISO Radar** & **MIMO Radar** \\ \hline
1 & 19.6 & 81.2 & 19.6 & 19.6 \\
2 & 73.8 & 12.0 & 73.8 & 12.0 \\
3 & 7.3 & 9.1 & 7.3 & 7.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The DTW distance metric for the SISO and \(2\times 2\) MIMO radar systems.  
system in the context of different directions of human activities. Unlike the state-of-the-art monostatic SISO or MIMO radar systems, our proposed \(2\times 2\) distributed MIMO radar system enables the realization of a direction-independent HAR system using TV channel characteristics of human activities obtained from different aspect angles. A comparison with the performance of a radar-based passive step counter has been demonstrated for a SISO and a proposed \(2\times 2\) MIMO radar system. It is shown that the \(2\times 2\) MIMO radar-based step counter is able to accurately detect the number of walking steps in all scenarios, while the SISO radar-based step counter fails to perform. Moreover, the DTW distance metric is used to numerically assess the performance of the SISO and \(2\times 2\) MIMO radar systems.  
Although the fundamental approach presented in this paper may be adopted for various applications, we plan to extend this work to a direction-independent HAR system, where we intend to increase the overall performance of the RF-based HAR system by increasing the number of antennas. The proposed \(2\times 2\) MIMO radar system can straightforwardly be scaled to an \(N\times N\) MIMO radar system. Moreover, from the obtained multi-perspective channel characteristics, multiple features can be extracted for a classical machine learning-based HAR system. For a more complex HAR problem, a deep CNN can be adopted based on the data from the proposed distributed MIMO radar framework.  
Conceptualization and methodology, S.W., M.M., and M.P.; software, S.W. and M.M.; validation and formal analysis, S.W., M.M., and M.P.; investigation, resources, and data curation, S.W. and M.M.; writing--original draft preparation, S.W. and M.M.; writing--review and editing, S.W. and M.P.; visualization, S.W.; supervision and funding acquisition, M.P. All authors have read and agreed to the published version of the manuscript.  
This work has been carried out within the scope of the CareWell project funded by the Research Council of Norway under the grant number 300638.  
Not applicable.  
The authors may provide the presented measurement data upon request.  
The authors declare no conflict of interest.
## 4 Conclusions  
This paper presented an investigation of the accuracy of state-of-the-art machine-learning and quantum-behaved evolutionary algorithms in predicting the surface roughness of a face-milling machine. Ensemble learning based on least-square-gradient boosting and quantum-behaved particle-swarm optimization algorithm were utilized to predict the surface-roughness values, given the machining parameters of a face milling machine. The performance of these algorithms was compared with other existing methods in the literature, specifically the genetics algorithm and the particle swarm optimization, and have resulted in a better prediction performance. The accuracy of the algorithms was assessed based on the RMSE, MAPE, MAE, CVRMSE and R\({}^{2}\) values of each algorithm. QPSO achieved the best prediction accuracy in terms of its performance indicators with an RMSE of 2.17%, MAE of 1.59% and R2 of 0.95. LSBoost achieved the second-best prediction performance with RMSE of 3.74%, MAE of 2.92% and R\({}^{2}\) of 0.88. It is evident that QPSO and LSBoost have resulted in a high extent of accuracy in predicting the surface roughness in the face-milling machine. Moreover, utilizing such methods in predicting the surface roughness before the production would save a lot of resources in terms of expenses, labor and time.  
Conceptualization, M.S.A. and A.M.A.; methodology, M.S.A. and A.M.A.; software, M.S.A. and A.M.A.; validation, M.S.A. and A.M.A.; formal analysis, M.S.A. and A.M.A.; investigation, M.S.A. and A.M.A.; resources, M.S.A. and A.M.A.; data curation, M.S.A. and A.M.A.; writing--original draft preparation, M.S.A. and A.M.A.; writing--review and editing, M.S.A. and A.M.A.; visualization, M.S.A. and A.M.A. Both authors have read and agreed to the published version of the manuscript. 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.  
Figure 3: Absolute error percentage of each data point predicted by QPSO, LSBoost, PSO and GA algorithms.
## 6 Worst-Case Latency Analysis of the FWOS Model
### Maximum Waiting Time Consideration
As depicted in Figure 11, the earliest frame arrives at \((t_{k}^{h,arrive}\Big{|}_{arrival})\), which equals the starting time of the first \(Q_{k}^{h-1}\) contention-free window in the preceding node \((h-1)\)\(t_{k}^{h-1,B,i}\) scheduled after the previous \(Q_{k}^{h}\) contention-free interval added to the selection and propagation delays. The frame selection delay is the time required to select the largest size of the \(Q_{k}^{h-1}\) frame to the physical link (\(D_{k,select}^{h-1,max}=f_{k}^{h-1,max}/C_{h-1}\)). The propagation delay (\(D_{prop}^{h,h-1}\)) is the time required for a \(Q_{k}^{h-1}\) frame to transfer from \(h-1\) to \(h\). Note that when the frame arrives at the node, a switch processing delay (\(D_{proc}^{h}\)) is experienced through the switch input buffer, switching fabric, and priority filtering at node \(h\). This delay is needed even if the frame arrives through the contention-free interval in \(h\). Thus, it is worth noting that the lowest bound of the maximum waiting time should be limited by the propagation and switch processing delays, as follows:  
\[WT_{k}^{h,i}=\left\{ \begin{array}{ll}D_{k}^{h,h-1}&,D_{k}^{h,h-1}\geq D_{prop }^{h,h-1}+D_{proc}^{h}\\ D_{prop}^{h,h-1}+D_{proc}^{h}&,D_{k}^{h,h-1}<D_{prop}^{h,h-1}+D_{proc}^{h}\\ \end{array} \right. \tag{28}\]  
where \(D_{k}^{h,h-1}=t_{k}^{h,B,i}-\left(t_{k}^{h-1,B,i}+D_{k,select}^{h-1,max}\right)\). Note that the switch processing delay is counted even if the maximum waiting time is greater. Thus, this delay should be removed from the overall latency expression. Although the authors in [13] considered the frame arrival limitations in the non-first nodes, their latency calculation counted the switch processing delay twice, indirectly in the maximum waiting time calculations and overall latency expression. Even if this delay is a little small  
Figure 11: The maximum waiting time at the non-first node in the selected path. and constant, it can result in more pessimistic latencies when the maximum waiting time is larger.
## 4 Performance Analysis of the MA-SM  
### Performance Analysis of MA-SM with Maximum-Likelihood Receiver  
In the following, we derive the closed-form formula for the error probability of the MA-SM system. For the sake of simplicity, the upper bound on the error probability is derived assuming \(N_{P}=2\). The generalization is straightforward. Let  
\[\mathbf{g}_{m_{1}}=(\mathbf{h}_{i_{1}}s_{k_{1}}+\mathbf{h}_{i_{2}}s_{k_{2}})e^ {i\theta_{m_{1}}}\quad\text{and}\quad\mathbf{g}_{m_{2}}=(\mathbf{h}_{i_{1}}s_{ k_{1}}+\mathbf{h}_{i_{2}}s_{k_{2}})e^{i\theta_{m_{2}}} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Configuration I** & **Configuration II** & **Configuration III** \\ \hline \(N_{T}\) & 4 & 4 & 5 \\ \(N_{P}\) & 2 & 2 & 3 \\ Modulation & QPSK & 16-QAM & 16-QAM \\ \(\Gamma\) & (1,3), & (1,3), & (1,2,3), (1,2,4), \\  & (2,4), & (2,4), & (1,2,5), (1,3,4), \\  & (1,4), & (1,4), & (1,3,5), (1,4,5), \\  & (2,3) & (2,3) & (2,3,5), (2,4,5) \\ Rotation angle & \(\theta_{1}=0,\theta_{2}=\pi/8\), & \(\theta_{1}=0,\theta_be defined as two received codewords, where \(\theta_{m_{1}}\) and \(\theta_{m_{2}}\) are the rotation angles associated with the antenna combination \((i_{1},i_{2})\) and \((\hat{i_{1}},\hat{i_{2}})\), respectively. The PEP is defined as:  
\[Pr\left[\mathbf{g}_{m_{1}}\rightarrow\mathbf{g}_{m_{2}}\right]=Q\left(\sqrt{ \frac{\left\|\mathbf{g}_{m_{1}}-\mathbf{g}_{m_{2}}\right\|^{2}}{2\sigma_{n}^{ 2}}}\right), \tag{8}\]  
where \(Q(\cdot)\) is the Gaussian tail function, or simply the \(Q\)-function. The unconditional pairwise error probability (UPEP) is obtained by taking the expectation of (8) over the channel matrix \(\mathbf{H}\). A closed-form formula of (8) is given as follows [2]:  
\[Pr\left[\mathbf{g}_{m_{1}}\rightarrow\mathbf{g}_{m_{2}}\right]=\mu_{m_{1},m_ {2}}^{n_{R}}\sum_{l=0}^{n_{R}-1}\binom{n_{R}-1+l}{l}\left[1-\mu_{m_{1},m_{2}} \right]^{l}, \tag{9}\]  
where:  
\[\mu_{m_{1},m_{2}}=\frac{1}{2}\left(1-\sqrt{\frac{\rho\cdot d_{m_{1},m_{2}(tx) }^{2}}{4+\rho\cdot d_{m_{1},m_{2}(tx)}^{2}}}\right), \tag{10}\]  
and the square Euclidean distance between the two vector symbols is given by:  
\[d_{m_{1},m_{2}(tx)}^{2}=\left\|\mathbf{s}_{m_{1}}-\mathbf{s}_{m_{2}}\right\|^ {2}. \tag{11}\]  
In (10), \(\rho\) is the signal-to-noise ratio (SNR). At high SNR, the asymptotic pairwise error probability is approximated as follows:  
\[ \begin{split} Pr\left[\mathbf{g}_{m_{1}}\rightarrow\mathbf{g}_{m _{2}}\right]&\approx\frac{2^{n_{R}}\Gamma(n_{R}+0.5)}{\sqrt{\pi}( n_{R})!}\left(\frac{1}{\rho d_{m_{1},m_{2}(tx)}^{2}}\right)^{n_{R}}\\ &=\binom{2n_{R}-1}{n_{R}}\rho^{-n_{R}}\left(d_{m_{1},m_{2}(tx)}^ {2}\right)^{-n_{R}}\end{split}
## 4 Results and Discussions
### Comparison Methods  
[PERSON] et al. [28] combined the persistent model, K-nearest neighbor (kNN), linear regression, tree bagging, and support vector regression (SVR) models to develop an integrated model. The weight for each base model was defined by the prediction error obtained from the cross-validation, and the model that yielded better performance in the cross-validation was given a higher weight.  
[PERSON] et al. [38] employed a group method of data handling (GMDH)-type polynomial neural network for MRR prediction. The GMDH-type polynomial neural network was designed to effectively capture manufacturing information and address the problem of the high data dimensionality of the VM data. The final results indicated that their proposed method could further improve the prediction accuracy and outperform the random forest (RF), SVR, kNN, and logistic regression models.  
[PERSON] et al. [39] constructed the convolutional neural network (CNN) and the residual convolutional neural network (ResCNN) to implement automatic feature extraction and weight sharing through the convolution and pooling layers from which superior prediction performance was obtained. ResCNN achieved the best performance in their experiments.  
In addition, we further used three state-of-the-art ensemble methods as comparison methods. The first was RF, an ensemble method for classification, regression, and other tasks, which combines numerous decision trees. The key idea behind RF is to use a bootstrap sampling technique to generate enormous training sets, each of which is used to train a decision tree. To increase the diversity of the decision trees, the features of each decision tree are also obtained from a feature sampling set.  
The second one was the gradient-boosting regressor, based on the gradient-boosted decision tree (GBDT). It combines many weak learners to form a strong learner iteratively. At each iteration, a regression tree is fitted to the negative gradient of the loss function. The final one was extreme gradient boosting (XGBoost) [40], which is an extension of the GBDT. It has become one of the most popular algorithms in data science competitions. The objective function of XGBoost comprises training loss and regularization, in which regularization can help the model avoid overfitting. On the other hand, XGBoost has improved this problem by using the second-order Taylor expansion of the loss function, making the tree more complex and powerful in learning. Moreover, XGBoost uses \(\ell_{1}\) and \(\ell_{2}\) as the regularization terms to help prevent the model from overfitting.
## 5 Conclusions  
Data gathering schemes that can efficiently and effectively delivery data through intermittently connected networks are of critical importance to DTMSNs. In this paper, we propose a new data gathering scheme, called Distance-aware Replica Adaptive Data Gathering protocol (DRADG), which can provides better or comparable data gathering performance at the cost of much lower traffic overhead than current protocols. Extensive simulations have carried out for performance evaluation. The experimental results show that our proposed DRADG protocol not only achieves higher or comparable performance than several existing schemes in terms of data delivery ratio and transmission overhead, but also significantly outperforms them in the aspect of network traffic overhead.
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## 3 Experimental Measurements Conducted in a Cellular Network  
This section provides a comprehensive and systematic overview of the methodology used for the acquisition, validation, and preprocessing of cellular network data. Special attention is given to the procedures carried out within the selected urban and suburban study areas to guarantee the collection of high-quality and representative datasets. These datasets form the basis for subsequent analyses aimed at assessing network performance and behavior under real-world conditions. The methodology covers not only the measurement techniques and equipment employed but also the data cleaning and processing steps necessary to handle inconsistencies, outliers, and missing values, thereby ensuring the reliability and accuracy of the final dataset.  
The flowchart illustrates the process undertaken to collect, process, and analyze data related to signal quality and events in telecommunications networks during a _walk test_. The procedure begins with data acquisition, followed by monitoring key indicators such as **RSRP** (\(P_{\text{RSRP}}\)), which measures the reference signal received power; **RSSI** (\(P_{\text{RSSI}}\)), an indicator of the overall signal strength; **RSRQ** (\(Q_{\text{RSRQ}}\)), which assesses the quality of the received reference signal; and **CQI** (\(I_{\text{CQI}}\)), representing the channel quality. The collected data is then processed to derive additional parameters, including the **azimuth** (\(\phi\)), indicating the horizontal signal direction; **theta** (\(\theta\)), the elevation angle; **power derivatives** (\(\frac{\partial P}{\partial t}\)), which capture the power variation over time; and **power variation** (\(\Delta P\)), reflecting changes in power levels. Finally, a comparative analysis between the experimental and theoretical results is performed, calculating the relative error percentage to evaluate the accuracy of the model. The following sections provide a detailed description of each phase and the parameters involved.
# Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities
## 5 Evaluation
### Experiments
#### 5.5.2 Evaluation Results and Analysis
This is essentially in agreement with the statement that [PERSON] once made _\"A little semantics goes a long way\"_[50]. Regarding the precision metric, as expected, the XML-based approach derives exactly the same precision results as the OWL-based approach with the least complete ontology \(B_{5}\), regardless of the number of devices. The precision metric results of all batches are very high (more than 93%). Similarly to the recall metric results, a richer ontology provides better precision results. It can also be observed that the precision metric results increase with the number of devices until there are more than 3000. This is also as expected, since a query is more likely to have matching results when the datasets are large and diversified enough. As a combined metric of precision and recall, the F-measure metric results reflect the overall metric results of precision and recall. Thus, similar conclusions and observations apply. Figure 5d shows the time taken to register devices in the two approaches. As can be seen from the plots, the device registration time in the two approaches increases linearly with the number of devices. For each batch, the device registration time in the XML-based approach is always shorter than that in the OWL-based approach. For the OWL-based approach, apart from registering device descriptions, it takes additional time to run the inference engine and insert inferred facts into the triple store. The richness of the test ontologies affects the registration time; richer ontologies require the OWL reasoning to spend more time on the device registration process. The plot in Figure 5e shows the average time taken to answer a query by the matchers of the two approaches. We conclude that the query response time in the XML-based approach is exponentially dependent on the number of devices and is significantly longer than that in the OWL-based approach. However, we cannot conclude that XQuery is less efficient than SPARQL. Apart from the query language, the query response time also depends on other factors, such as the query processor, query optimization, dataset structure, etc. Research on these topics is beyond the scope of this paper. Regarding the OWL-based approach, the query response time increases linearly with the number of devices. For each batch in the plot, the query processing time increases with the richness of the test ontologies. The experimental results are reasonable, since richer test ontologies may derive more inferred facts, which results in the expansion of the search scope of each query to derive matching results.
## 2 Data and Methodology  
### Overview of the Research Region and Data Source  
#### 2.1.1 The Research Region  
The Three Northern Regions, as illustrated in Figure 1, encompass an expansive area, comprising 725 counties, including banners and districts, across 13 provinces, autonomous regions, and municipalities within China. This region, accounting for a considerable 42.4% of the country's total land surface, is of significant geographic and ecological relevance. The climatic conditions across these regions predominantly feature a temperate continental climate, with some areas exhibiting polar and high-altitude mountain climate characteristics presenting a diverse range of environmental conditions [25]. The topography is characterized by its complexity and diversity, with a variety of geographical features including plateaus, mountains, plains, and deserts. Such varied terrain fosters a diverse array of surface land cover types, such as grasslands, forests, wetlands, and deserts, each integral to the region's unique ecological tapestry [26].  
In terms of human geography, there is an uneven population distribution, with a majority residing in the more fertile and accessible eastern regions, contrasting with the sparser populations in the western areas. This disparity has profound implications for the region's economic activities and land-use patterns, predominantly based on agriculture and livestock farming, alongside notable industrial activities and an emerging tourism sector, illustrating a dynamic interaction between human activity and the natural environment. The study area's boundaries are defined based on the demarcations provided by the Chinese Academy of Sciences' Resource and Environment Data Center ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)), accessed on 10 November 2022). The delineation is strategically selected to analyze the carbon sequestration processes, the impacts of climate change, and human activities comprehensively, emphasizing the importance of this region in understanding the broader environmental conservation and ecological restoration efforts within China.
## 2 Materials and Methods
### Glucose Concentration in Supernatants and Glucose Consumption Calculation  
Following the _L. paracasei_ CFS treatment of mature 3T3-L1 adipocytes for 24/48 h, the extracellular glucose concentration (EG) in the cell supernatants was determined using a Mindray BS-120 automatic biochemical analyzer (Guangzhou, China) and Glucose GOD-PAD reagent (Biolab SAS, Maizy, France), according to the manufacturer's guidelines. Briefly, the method involved the oxidation of glucose by glucose oxidase to gluconic acid and hydrogen peroxide, followed by the formation of a red quinoneimine compound. The concentration of glucose in the supernatants was determined by measuring the absorbance of the colored complex at 500 nm, which was directly proportional to the concentration of glucose. Part of the initially prepared, cell-free culture media from each group (IG) was also kept in empty wells under the same conditions and analyzed in parallel with the experimental supernatants.  
Subsequently, the glucose consumption by the cells was calculated according to the following equation described by [PERSON] et al. [46]:  
Glucose consumption (mg/L) = IG - EG  
The final glucose consumption of adipocytes in each group was estimated as a percentage relative to the MRS_C.
## 1 Introduction  
The most common feature of cancer patients is the presence of a large number of cancers, including ovarian and lung cancers, but it often causes ovarian tissue damage and impairs reproductive health. Exosomes derived from mesenchymal stem cells are believed to possess reparative effects on such damage, as suggested by previous studies. This study aims to evaluate the reparative effects of cisplatin and exosome treatments on ovarian tissue damage through the analysis of histopathological images and machine learning (ML)-based classification techniques. Five experimental groups were examined: Control, cisplatin-treated (Cis), exosome-treated (Exo), exosome-before-cisplatin (ExoCis), and cisplatin-before-exosome (CisExo). A set of 177 Local Binary Pattern (LBP) features were extracted from histopathological images, followed by feature selection using Lasso regression. Classification was performed using ML algorithms, including decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and Artificial Neural Network (ANN). The CisExo group exhibited the most homogeneous texture, suggesting effective tissue recovery, whereas the ExoCis group demonstrated greater heterogeneity, possibly indicating incomplete recovery. KNN and ANN classifiers achieved the highest accuracy, particularly in comparisons between the Control and CisExo groups, reaching an accuracy of 87%. The highest classification accuracy was observed for the Control vs. Cis groups (approximately 91%), reflecting distinct features, whereas the Control vs. Exo groups demonstrated lower accuracy (around 68%) due to feature similarity. Exosome treatments, particularly when administered post-cisplatin, significantly improve ovarian tissue recovery. This study highlights the potential of ML-based classification as a robust tool for evaluating therapeutic outcomes. Additionally, it underscores the promise of exosome therapy in mitigating chemotherapy-induced ovarian damage and preserving reproductive health. Further research is warranted to validate these findings and optimize treatment protocols.