esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Calibration Estimation for Robust Remote Pulse-Oximetry
## 4 Discussion
### Future Work  
This study showed the importance and ease of empirical, data-driven calibration. We recognize that our study also has limitations. Firstly, the current implementation guarantees accuracy, i.e., finding the global optimum, but is computationally expensive. As the number of calculations scales exponentially with the number of wavelengths, optimization of the processing time may become relevant if one would like to explore the use of APBV with more than three wavelengths. This can be achieved by narrowing the search range for each wavelength, using dedicated processing hardware and/or using computationally efficient search strategies.  
We demonstrated the importance of empirical calibration for one subject with an average \(\Delta\)SpO\({}_{2}\) of about 3%, and with SpO\({}_{2}\) values in the range 86-100%. We regard the results as proof-of-concept and do not claim that the estimated calibration models for our setup yield the best results for a population. This requires a calibration dataset with subjects with different skin-types and SpO\({}_{2}\) values over a larger range, which is difficult to realize with the current breathing protocol. Re-use of our previous calibration dataset was not possible as data was recorded with only two wavelengths [9]. Based on our earlier findings [9], RMSE \(<\)1.65% (\(n=26\)), we have no reason to assume that the calibration is highly subject-dependent.
## 4 Results and Discussion
### Effect of Vibration on the Pressure Drop  
#### 4.1.1 0.25 g Vibration at Different Frequency  
Figure 4 shows the time-domain internal pressure drop of the extrusion liquefier obtained by DMMFB and FEA without and with different-frequency vibration applied, of which the amplitude is 0.25 g and the frequency is, separately, 100, 200, 300, 400 and 500 Hz. When vibration is applied, the time-domain pressure drop changes periodically as the inlet velocity of the extrusion liquefier changes from a constant to a periodic variation. From the comparison, it can be seen that the analytical pressure drop (obtained by DMMFB) is in good agreement with the simulated one (obtained by FEA), both in value and trend.  
Table 3 summarizes the effective value of the pressure drop for each condition. It can be seen that the value of the pressure drop decreases with the application of vibration and further decreases with the increase of vibration frequency. For example, when no vibration is applied, the effective value of the pressure drop predicted by DMMFB is 16.1 MPa. When the frequency of the applied vibration is 200 Hz, the value is reduced down to 11.7 MPa, decreased by 27.3%. When the frequency is 500 Hz, the value is further reduced to 8.5 MPa, decreased by 47.2%. This can be explained by the fact that increasing vibration frequency will decrease the amplitude of inlet velocity and speed up the changes according to Equation (A4), the amplitude of the pressure drop is then reduced and the changes are fast, as referred to by Equation (A20). Therefore, the larger the introduced vibration frequency is, the smaller the magnitude in pressure drop will be.  
Figure 4: The internal pressure drop of the extrusion liquefier without and with 0.25 g vibration applied at different frequencies. (**a**) No vibration applied, (**b**) 100 Hz, (**c**) 200 Hz, (**d**) 300 Hz, (**e**) 400 Hz, (**f**) 500 Hz.
## 4 M2M-Based Division of MAC Layer Protocols  
The designing of MAC layers is a rich communication technology field. Existing MAC protocols that are devised into contention-based, contention-less, and hybrid protocols have their advantages and complications that are inherited by IoT. The taxonomy of MAC layer protocols is given by Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
Figure 1: MAC layer M2M protocols.  
To address the unique requirements and limitations of IoT, MAC layers for M2M protocols are studied. The most widespread MAC layer IoT protocols are explained in the upcoming subsections.
# OpenCare5G: O-RAN in Private Network for Digital Health Applications
## 4 Experimental Evaluation
### Definition of the Test Plan  
The test plan was divided into two main phases:  
* The first phase is related to the PoC-1 involving an active 5G private network with the architecture set up at the InRad of HCFMUSP in two rooms--the Intervention Sector Room and Ultrasound Exam Room--to use the portable ultrasound (USG) on a patient lying down on a stretcher. He was accompanied by Doctor 1, who performed the USG of the abdomen, and Doctor 2, a radiologist in the central room in front of a monitoring station who analyzed the data captured from the exams, and whose rooms are covered by the 5G network;
* The second phase is related to PoC-2 involving the same 5G private network remotely connected to the Computational Tomography MRI/CT.  
In these use cases, the focus is also on validating technical criteria involving latency and signal bandwidth in the 5G network of these PoCs, among other functional characteristics of the telecommunications engineering process in real time.
## 2 Model
### What Is the Super-Multi-Junction Solar Cell  
Although multi-junction cells have high efficiency, their performance ratio affected by spectrum variation is typically less than that of single-junction solar cells. This is due to spectrum-mismatching loss influenced by the variation of sun height [42; 92] and atmospheric parameters [92; 93]. The power output of conventional multi-junction solar cells constrained by spectrum-mismatching loss can be predicted, and we need a solution to minimize the damage.  
The super-multi-junction cell uses enhanced luminescence coupling [63]. Assuming the extreme and the best case that every junction in a solar cell can couple to each other in radiation energy by the radiative recombination, the excess carriers in one junction can be recycled and transfer to the bottle-necked junction [63]. Figure 1 indicates the configuration of the super-multi-junction cell [63]. Please note that the optical cap layer in the super-multi-junction solar cell is for confining recycled photons, specifically to reduce the angle of the escape cone from the solar cell. Using radiative coupling, we may carry the energy that was to be lost by the surplus current by spectrum mismatching [63]. However, an excessive number of junctions is sometimes harmful, and there is no advantage in having more than four junctions [61; 94]. The efficiency started to drop after more than 6 junctions in concentrator solar cells [61]. Calculation in the past was done in combination with the worst cases, such as a combination of worst-case atmospheric conditions, and perfect junctions (full absorption, no leakage) [61; 94]. There may be a chance of reasonable compromise. Therefore, we need to develop a new model that considers an individual variation of atmospheric conditions and spectrum.
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Coating  
A textile conductive coating is a material layer that adheres to a textile structure (fiber, yarn, or fabrics). These coatings are used to make the textile material conductive or to add functionality to the textiles. Coating methods are industrial technologies used to adapt the properties of textiles to those required for technical and specialty applications. The variety of methods allows for good adaption to end-use requirements [223].
# Radar Emitter Structure Inversion Method Based on Metric and Deep Learning
## 5 Experiments
### Hetero-Mode Operating Parameter Dataset  
In the modeling training dataset, there are RF signals of three modulation types relative to the DDS_CW, DDS_LFM, and DDS_BPSK models. More precisely, the DDS_CW signal is set to different frequencies, the DDS_LFM signal is set to different initial frequencies and bandwidths, the DDS_BPSK signal is set to different carrier frequencies, and the RF link model is set to different input powers. The operating parameters of the training dataset are shown in Table 2. The number of RF signal samples of each modulation type of each RES model is 2000. Gaussian noise with \(\text{SNR}=10\) dB is then added to each RF signal, and the simulation sample size is \(2000\times 3\times 3=18\),000 with respect to the hetero-mode operating parameters of the modeling training dataset as well as the corresponding simulation of the ideal training dataset. Each sample in the modeling training dataset is then supplemented with a corresponding label in the one-hot encoding mode, and the label is a \(1\times 3\) vector. To obtain the unbalanced dataset of hetero-mode operating parameters, these parameters will be randomly selected. For instance, there will be \(6\times 6\times 3=108\) combinations of operating parameters under the DDS_LFM model.  
The test dataset is divided into a closed-set test dataset and open-set test dataset. The operating parameters of the closed-set test dataset and the training dataset are the same, but these data and their operating parameters have not participated in the model's training, i.e., the network has not perceived these data. The operating parameters of the open-set test dataset are different from those of the training dataset; that is, the network has never seen these operating parameters nor participated in network training. As shown in Table 3, the test dataset is divided into a closed set and five different open-set test datasets, which are composed of F change; B change; P change; MT change; and MT, F, and P change. The number of RF signal samples for each modulation type under each class of RES models is 40. Gaussian noise with different \(\text{SNR}=(10,5,0,-5,-10,-15)\) dB is also added. For instance, there are \(40\times 3\times 3\times 6=2160\) samples in the closed-set test dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Operating Parameter** & **Ranges** \\ \hline DDS\_CW Model & Frequency (MHz) & 25\(\sim\)30 \\ \hline  & Initial Frequency (MHz) & 25\(\sim\)30 \\ \cline{2-3} DDS\_LFM Model & Bandwidth (MHz) & 10\(\sim\)15 \\ \hline DDS\_BPSK Model & Carrier Frequency (MHz) & 25\(\sim\)30 \\ \hline RF Link Model & Input Power (mW) & 0.1, 0.5, 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Operating parameters of the training dataset.  
Figure 11: Spectrum of DDS_CW signals of four different individuals in the RES2 model: (**a**) RES2_DDS_CW_REII; (**b**) RES2_DDS_CW_REI2; (**c**) RES2_DDS_CW_REI3; (**d**) RES2_DDS_CW_REI4.
## 3 Experiments and Analysis
### Qualitative Comparison
#### 3.4.3 SSTLCM  
The spatiotemporal contrast fusion method SSTLCM exhibits suboptimal performance in comparative experiments, with missed detections and occasional false alarms observed for targets in complex scenarios (Figure 7(d1-d6)). We attribute this deficiency to insufficient integration between temporal information analysis and spatial contrast extraction--failures in either aspect under complex scenarios directly lead to detection omissions.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 2 Study Area  
Freixeda is located in Zona de Galiza-Tras-os-Montes (ZGTM) in the municipality of Mirandela, north of Portugal (Figure 1). The study area has 83 km\({}^{2}\) of extension and encompasses several mineralizations of Au-Ag (silver)-As (arsenic) and As-Au-Ag with some mining activity from the Roman period [25,26]. The Freixeda Philonian field is also rich in tungsten, lead, zinc, and copper. Antimony can be found in small quantities and is always associated with gold deposits [27]. Among these mineralizations, we can give the example of the Freixeda mine, which encompasses two important veins (Sao Jeronimo and Santo Andre veins) with Sb mineralization [27]. The Covo vein is northeast of the Freixeda mine, a continuation of the Sao Jeronimo lode and, therefore, has great potential for Sb mineralization [27].  
Gold and silver deposits are generally associated with Paleozoic quartzites from the Ordovician period and black shales dating from the Ordovician and Silurian periods [28]. Occasionally, these mineralizations also occur in granitic rocks. Gold is found in veins, stockworks, and silicified masses within metamorphosed metasediments. The formation of these deposits was influenced by shear zones, tectonic pulses, and late fractures related to the Variscan Orogeny [26].  
The Freixeda mine operated as a gold mine from 1952 to 1955, ceasing its activities due to economic and organizational issues [27,29]. [PERSON] & [PERSON] [22] suggested a relationship between late granites and tungsten (W) and Au-Ag mineralization. [PERSON] & [PERSON] [29] described the mineral deposition sequence in two stages: the first is charac  
Figure 1: Study area location. (**a**) Study area location in Portugal. (**b**) Study area location in the Braganca district. (**c**) Geological map.  
terized by the presence of scheelite + wolframite + sulfides (such as arsenopyrite, pyrite, pyrrhotite, and molybden); the second stage includes the association of chalcopyrite + sphalerite + sulfides + galena + native elements, which is well represented in Freixeda.  
The main iron oxides present in the study area that can be mapped are Goethite and Hematite. Both Hematite and Goethite were identified through spectral analyses conducted on rock samples. The spectral analyses also identify maghemite, zincite, and ferrihydrite, but are less representative than Goethite and Hematite. For future intents, Goethite and Hematite will be called target minerals.
## 3 Materials and Methods
### Machine Learning Classifiers  
This study compares the performances of the various numbers of classifiers within the context of smoking cessation intervention, thereby assessing different imbalanced and balanced dataset. A brief explanation of each of the machine learning classifiers, such as LR [36], MLP [37], deep MLP [38], RF [39], GBT [40], k nearest neighbors (KNN) [41] and SVM [42], used in this paper is given below.  
LR is an extensively used statistical method for solving classification and regression problems. Therefore, we will be focusing on the binary response of whether a smoker can quit or not in this paper. Thus, LR can predict the probability of an outcome that only has two possible dichotomy values, which is limited to values between 0 and 1, from a set of independent features.  
MLP is the most typical type of neural network application using back propagation for training. Neural networks are inspired are composed of nodes. MLPs consist of at least three layers, such asinput, hidden and output. Nodes in neighboring layers are interconnected, but nodes in the same layer are not. Each connection between neurons is multiplied by the corresponding weight during training. Finally, the output of hidden nodes is estimated by applying an activation function and output layer makes decisions.  
Ensemble methods: ensemble methods can be categorized into two parts, such as parallel and sequential. RF is a parallel structured ensemble tree-based method that utilizes bagging to aggregate multiple decision tree classifiers. Each tree of the RF is trained on bootstrap samples of the training sets using randomly selected features in the tree generation process; after this, each tree votes for the most popular class. GBT is an effective method in sequential ensemble learning of either regression or classification tree models. It consists of a gradient boosting and regression decision tree, which uses an ensemble of classification, and regression trees by the base learner. Generally, GBT combines a series of decision trees sequentially; each tries to correct its predecessor in order to build the strongest one.  
KNN belongs to supervised learning, and instances are classified to the class most frequently occurring amongst the neighbors that measured by the distance function. In a sense, the number of k-neighbors and the number of distance metrics are important parameters in the KNN classification phase. In the case of the smallest k, the classifier may tend to overfit because of the noise in the training set. Conversely, too large values of k and the classifier may misclassify because the nearest neighbors include instances that even stayed far away from their neighborhood.  
Support Vector Machine: SVM is a powerful method for building classifiers. It finds a decision boundary known as the hyperplane. Here, optimal hyperplane separates instances correctly into each class, and the margin width between the optimal hyperplane and the training instances is maximized to fit the data. The function is used to map the instances.
## 5 Results and Analysis
### First-Stage Recognition  
In the first-stage recognition process, we needed to use the best trained Improved-YOLOv5 model on Enriched-NEU-DET dataset, so the recognition accuracy of the Improved-YOLOv5 model was related to the performance of the entire framework. To validate the performance of the proposed improved YOLOv5, we first used the public VOC2007 and Enriched-NEU-DET datasets to conduct comparison experiments with YOLOv5, YOLOv4 [26], YOLOv3 [27], SSD [15], then a further ablation test was done in Enriched-NEU-DET.
# Data Driven Leakage Detection and Classification of a Boiler Tube
## 2 Methods and Procedures for Leakage Classification of the Boiler Tube
### k-Nearest Neighbor (k-NN) Classifier  
k-NN is the simplest and most frequently used classifier in the classification problems related to the fault diagnosis domain. It is a non-parametric classifier with no specific training step required for the algorithm. It does not make an assumption for the classification of the data; however, classification depends on the structure of the data by itself. Moreover, during classification, whole data resides in the memory, hence increasing the computational complexity in case of large datasets. In this study, the k-NN classifier was used as one of the algorithms for comparison purposes. The value of k was selected arbitrarily and through the cross validation process and turned out to be 5.
# Experimental Evaluation of the Packet Reception Performance of LoRa
## 1 Introduction
We analyzed the influence of the physical layer parameter settings on the retransmission effect and proposed how to configure the physical layer parameters to minimize the energy consumption and maintain a high data rate, so as to achieve a balance between reliability, delay, and energy efficiency. We discuss related work in Section 2. In Section 3, we introduce LoRa technology. We created a LoRa transceiver based on USRP B200 software radios and carried out LoRa performance evaluation experiments under white Gaussian noise interference. We used this transceiver as a transmitter of LoRa signals, and two commercial devices as receivers. The specific experimental environment and methods are introduced in Section 4. In Section 5, the results and analysis of the LoRa performance evaluation experiment under different parameters are presented. Firstly, we studied the influence of LoRa physical layer parameters on the LoRa performance. Secondly, we considered the impact of the packet length on the LoRa performance. Thirdly, we compared the performance of two commonly used commercial transceivers. Finally, for the scene of noise interference, we propose what parameters to choose to reduce the communication time and power consumption. We discuss future work in Section 6 and present the conclusions of this work in Section 7.
# Recurrent Water Deficit and Epigenetic Memory in _Medicago sativa_ L. Varieties
## 3 Results
### MSAP Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**h Alleles**} & \multicolumn{3}{c}{**u Alleles**} & \multicolumn{3}{c}{**m Alleles**} & \multicolumn{3}{c}{**Total Methylation**} \\  & & **lepi** & **hepi** & **lepi** & **hepi** & **lepi** & **hepi** & **hepi** & **hepi** \\ \hline
**Lamia CC** & **Mean** & 0.168 & 0.109 & 0.194 & 0.127 & 0.168\({}^{\text{c}}\) & 0.111\({}^{\text{c}}\) & 0.204\({}^{\text{f}}\) & 0.142\({}^{\text{f}}\) \\ \hline  & **SE** & 0.013 & 0.009 & 0.016 & 0.010 & 0.014 & 0.010 & 0.011 & 0.008 \\ \hline
**Lamia CD2** & **Mean** & 0.125 & 0.081 & 0.213 & 0.143 & 0.128 & 0.083 & 0.157 & 0.110 \\ \hline  & **SE** & 0.012 & 0.008 & 0.017 & 0.012 & 0.013 & 0.008 & 0.011 & 0.007 \\ \hline
**Lamia D1D2** & **Mean** & 0.118 & 0.077 & 0.189 & 0.126 & 0.112\({}^{\text{c}}\) & 0.073\({}^{\text{c}}\) & 0.140\({}^{\text{f}}\) & 0.097\({}^{\text{f}}\) \\ \hline  & **SE** & 0.012 & 0.008 & 0.016 & 0.011 & 0.012 & 0.008 & 0.010 & 0.007 \\ \hline
**Chaironia CC** & **Mean** & 0.173\({}^{\text{a}}\) & 0.112\({}^{\text{a}}\) & 0.208 & 0.135 & 0.234d & 0.153d & 0.251\({}^{\text{c}}\) & 0.175\({}^{\text{k}}\) \\ \hline  & **SE** & 0.013 & 0.009 & 0.016 & 0.010 & 0.015 & 0.010 & 0.012 & 0.008 \\ \hline
**Chaironia CD2** & **Mean** & 0.114\({}^{\text{a,b}}\) & 0,073\({}^{\text{a,b}}\) & 0.212 & 0.140 & 0.105\({}^{\text{a,e}}\) & 0.068\({}^{\text{a,e}}\) & 0.1379\({}^{\text{h}}\) & 0095\({}^{\text{g,h}}\) \\ \hline  & **SE** & 0.011 & 0.007 & 0.017 & 0.011 & 0.012 & 0.008 & 0.010 & 0.007 \\ \hline
**Chaironia D1D2** & **Mean** & 0.188\({}^{\text{b}}\) & 0.120\({}^{\text{b}}\) & 0.207 & 0.136 & 0.210\({}^{\text{e}}\) & 0.136\({}^{\text{e}}\) & 0.252\({}^{\text{h}}\) & 0.176\({}^{\text{h}}\) \\ \hline  & **SE** & 0.013 & 0.008 & 0.016 & 0.011 & 0.014 & 0.009 & 0.012 & 0.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean epigenetic Shannon Information Index (lepi) and epigenetic diversity (hepi) and standard error (SE) for treatments for each cultivar. The significant differences that are provided from [PERSON]’s post hoc tests with Bonferroni correction among treatments for each variety are indicated with the same letters. Figure 7: The permuted data matrix is actually a heat map presentation of the six different treatments in a three-variable classification space (h-, m-, and u-methylation). The distance measure is the Euclidean distance, and the joining method is the single linkage. Three clusters are formed in the variety’s domain. Importantly, these clusters present the peculiarity of the Chaironia CD2 to be clustered together with Lamia CC and Lamia CD2 to form a cluster alone.