esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Outdoor Positioning in the IoT Ecosystem
### Location-Enabled IoT Technologies
#### 2.3.5 5G
\begin{table}
\begin{tabular}{c c c c c} \hline
**Technology** & **LTE-M** & **NB-IoT** & **Sigfox** & **LoRaWAN** \\ \hline Frequency & Licensed LTE & Licensed LTE & \begin{tabular}{c} License-free channel of 868 MHz \\ (in Europe), 902 MHz (in US), \\ and 433 MHz (in Asia) \\ \end{tabular} & \begin{tabular}{c} License-free channel of 868 MHz \\ (in Europe), 902 MHz (in US), \\ and 433 MHz (in Asia) \\ \end{tabular} \\ \hline Bandwidth & 200 kHz & 200 kHz & 100 Hz (in Europe) and 600 Hz (in US) for uplink and 1.5 kHz for downlink & Range from 7.8 kHz to 500 kHz. \\ \hline Modulation & QAM & QPSK & \begin{tabular}{c} DBPSK modulation for uplink and \\ GFSK modulation for downlink \\ \end{tabular} & CSS (Fsk is defined too) \\ \hline Maximum & 1 Mbps & 200 kbps & 100 bps & 37.5 kbps \\ \hline Maximum & 5 km & \begin{tabular}{c} 1 km (in urban) \\ and 10 km (in rural) \\ \end{tabular} & 10 km (in urban) and 40 km (in rural) & 5 km (in urban) and 20 km (in rural) \\ \hline Packet & 100 bytes & 1600 bytes & \begin{tabular}{c} 12 (uplink packet) and 8 (downlink \\ packet) bytes \\ \end{tabular} & 243 bytes \\ \hline Battery life & \begin{tabular}{c} 9 month \\ \end{tabular} & 3.5 years & 4.5 years & 5 years \\ \hline \begin{tabular}{c} Maximum \\ message per day \\ \end{tabular} & Unlimited & Unlimited & \
\begin{tabular}{c} 140 message (uplink) and \\ 4 message (downlink) \\ \end{tabular} & Unlimited \\ \hline Topology & Cellular Network & Cellular Network & Star & Star \\ \hline Authentication & LTE encryption & LTE encryption & Not supported &
\begin{tabular}{c} Over-The-Air Activation and \\ Activation by personalization \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of main technical features of LPWAN technologies. \begin{table}
\begin{tabular}{c c c c c} \hline
**Technology** & **LTE-M** & **NB-IoT** & **Sigfox** & **LoRaWAN** \\ \hline \multirow{3}{*}{Advantage} & Higher traffic & \multirow{3}{*}{Higher traffic reliability with low delay} & \multirow{3}{*}{Higher traffic reliability} & \multirow{3}{*}{Wide communication range} & \multirow{3}{*}{Low private network construction,} \\  & & & & & \\ \cline{1-1} \cline{5-5}  & Higher energy & & & & \\ \cline{1-1} \cline{5-5}  & consumption & & & & \\ \cline{1-1} \cline{5-5}  & (more than & & & & \\ \cline{1-1} \cline{5-5}  & NB-IoT) & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Conf.
# Interferometry of Gas-Phase Flows during Selective Laser Melting
## 5 Conclusions  
In this work, a method for studying the vapor-gas jet that arises during SLM process based on interferometry with subsequent digital processing was applied. Registration of spatial changes in the refractive index made it possible to evaluate the geometry of the jet and the manner of its propagation. No jet was observed above the laser zone, and the observation zone changed slightly with time at scanning speed of 50 mm/s and laser power of 50 W. The increase in power leads to the emergence of a stable gas-phase flow, a significant part of which is vaporized metal. From the obtained interferograms, the volume fraction of iron vapor was calculated, which at 130 W was \(V_{Fe}\) = 0.545.  
The flow rate estimated from the tracking movement of inhomogeneities inside the plume also increases with increasing specific power due to more intense evaporation. The maximum velocity of 2.6 \(\pm\) 0.5 m/s was achieved at 170 W. This provokes the movement of the surrounding gas in the direction of the jet, which explains the appearance of denudation zones during SLM. Qualitatively, the experimental data correspond to the simulated data on the pressure change and the resulting gas flow velocity.  
**Supplementary Materials:** The following are available online at [[https://doi.org/10.5281/zenodo.3540948](https://doi.org/10.5281/zenodo.3540948)]([https://doi.org/10.5281/zenodo.3540948](https://doi.org/10.5281/zenodo.3540948)). Video S1: Interferogram of a gas-phase flow at 50 W. Video S2: Interferogram of a gas-phase flow at 90 W. Video S3: Interferogram of a gas-phase flow at 130 W. Video S4: Interferogram of a gas-phase flow at 170 W.  
**Author Contributions:** Conceptualization, A.V.G.; methodology, A.V.G. and A.E.S.; software, A.V.G. and A.E.S.; validation, P.A.P. and A.V.G; formal analysis, P.A.P. and R.S.K.; investigation, P.A.P., R.S.K. and A.D.K.; resources, P.A.P. and R.S.K.; data curation, A.V.G. and A.E.S.; writing--original draft preparation, P.A.P.; writing--review and editing, A.V.G. and A.E.S.; visualization, A.D.K. and P.A.P.; supervision, P.A.P.; project administration, A.V.G.; funding acquisition, A.V.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Russian Science Foundation (grant agreement 15-19-00254-P dated 19 April 2018).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Find You: Multi-View-Based Location Inference for Twitter Users
## 3 Proposed Methodology
### Tweet-View Construction  
In our work, the entire corpus is represented as a heterogeneous graph based on the co-occurrence relationships between words in users' tweets and users' mentions of words in tweets, as shown in Figure 1's tweet view \(tv\), to exploit the local and global correlations between words to extract the characteristics of users' tweets. The weights of the edges between user nodes and word nodes are computed using TF-IDE, which represents the frequency of a word in a user's tweet, and IDF, which is the Inverse Document Frequency, i.e., the IDF of a particular word is calculated by dividing the total number of users by the number of users whose tweets contain that word and then dividing the quotient by the total number of users. The main idea behind TF-IDF is that if a word appears frequently in one user's tweets but infrequently in the tweets of other users, it is deemed to have good category differentiation and to be suitable for classification. To exploit the global word co-occurrence information, a sliding window of fixed size was used on all tweets in the corpus to collect co-occurrence information for word pairs [32], and Positive Point Mutual Information (PPMI) was used to calculate the correlation between two-word nodes. The weights of the edges between any two nodes i and j in the tweet text view are defined as shown in Equation (5).  
\[A_{i,j}=\left\{\begin{array}{ccc}PPMI(i,j)&i,j\text{ are words and }i\
eq j\\ TF-IDF_{(i,j)}&i\text{ is user },j\text{ is word}\\ 1&i=j\\ 0&\text{ otherwise}\end{array}\right. \tag{5}\]  
The PPMI values between word nodes \(i\) and \(j\) are computed according to Equations (6)-(8):  
\[PPMI(i,j)=\log\frac{P(i,j)}{P(i)P(j)} \tag{6}\] \[P(i,j)=\frac{W(i,j)}{W}\] (7) \[P(i)=\frac{W(i)}{W} \tag{8}\]  
where \(W\) is the total number of sliding windows on the corpus and \(W(i)\) is the number of sliding windows that contain the word i. \(W(i,j)\) is the number of sliding windows that contain both the words \(i\) and \(j\).
## 2 Materials and Methods
### Materials  
Ethical approval was obtained from patients involved in the study. The dataset considered to experimentally evaluate the proposed method was collected by two different and independent research teams located in two countries: Portugal and Greece. The dataset includes 920 annotated respiratory audio recordings of varying length (i.e., from 10 s to 90 s). The audio was obtained from 126 different patients (namely, 46 women and 80 men) with 5.5 h of sound recordings related to 6898 respiratory cycles. The audio samples are related to clean breaths and also noisy audio simulating real-world situations with the related annotation about healthy or lung disease cases. The patients' ages are categorised as children, adults, and the elderly [7], by considering patients ranging from 1 to 83 years. Indetail, of the 126 patients considered, 1 patient was affected by asthma, 7 by bronchiectasis, 6 by bronchiolitis, 64 by chronic obstructive pulmonary disease (i.e., COPD), 2 by infection of the lower respiratory tract (i.e., LRTI), 6 by pneumonia, and 14 by infection of the upper respiratory tract (i.e., URTI), for a total of 100 patients affected by lung disease, and the remaining 26 were healthy patients. Annotation of sounds by respiratory experts is the considered the most common and reliable method for evaluating the robustness of algorithms for detecting adventitious respiratory sounds [8].  
Two respiratory physiotherapists and a doctor, with experience in recognizing visual-auditory crackles and wheezing, independently annotated the sound files in terms of the presence (or the absence) of adventitious sounds and identification of respiratory phases [7]. In the case of divergent judgments, the diagnosis was decided by a majority vote.  
The dataset is freely available for research purposes ([[https://www.kaggle.com/vbookshelf/respiratory-sound-database](https://www.kaggle.com/vbookshelf/respiratory-sound-database)]([https://www.kaggle.com/vbookshelf/respiratory-sound-database](https://www.kaggle.com/vbookshelf/respiratory-sound-database)) accessed on 8 February 2022).
## 4 Results  
Supervised learning techniques with a deep learning model, such as CNN and MLP, are widely used for classification purposes. We have used the Aurora 2 dataset with multiple files required for the training of data, and classified it in MATLABr2018b. There are many reasons for the selection of Aurora 2. It is a publicly available dataset and provides the best identification and recognition results when used with the CNN. It also provides parameter flexibility that performs better in selected models.  
The results are derived using the methodology of the research. The training data is imported into the system. The given trained data is applied as input to the deep learning approaches. The cloud service model is implemented using supervised learning techniques. All experiments are performed using SAAS with cloud. The trained algorithms are tested with different deep learning approaches. Classification based on the security parameter is performed to select the best RE model among the selected models. Confidentiality, integrity and interoperability are the selected security parameters. The selected parameter is provided as input to the deep learning approaches for classification of the best RE model. The results of all deep learning techniques are ranked with regard to RE models for cloud based on security parameter. The best RE model is selected on the basis of the ranked results generated by different classification techniques. All the models are selected based on different aspects for the choice of the best RE model and classifications factors, as shown in Tables 1 and 2.  
The evaluation of the results is presented using the most popular evaluation measure available on the market.  
As we have to classify the requirement engineering models for the performance and security parameters, we classify and predict the performance using Accuracy, F-value and Recall.  
\[\text{Precision (\%)}=\text{True Positive/(True Positive + False Positive)} \tag{3}\]  
\[\text{Recall (\%)}=\text{True Positive/(False Negative + True Positive)} \tag{4}\]  
\[\text{F - measure (\%)}=\text{(2 * Recall * Precision)/Recall + Precision} \tag{5}\]  
Classification results for the four requirement engineering models selected for security and performance evaluation are shown in Table 3.  
The formulas for the Accuracy, Recall and F-value have been applied to cost, maintainability, portability, reliability, reusability, time and security. Figure 5 shows pie graphs, with the yellow color representing F-value, green for the Accuracy and blue for Recall.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Requirement Engineering Models** & **Accuracy** & **F-value** & **Recall** \\ \hline MLRE & 0.745 & 0.658 & 0.689 \\ \hline LKIRE & 0.945 & 0.845 & 0.810 \\ \hline LPRE & 0.784 & 0.678 & 0.578 \\ \hline SMRE & 0.987 & 0.457 & 0.745 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification results of selected 4 requirement engineering models.  
Figure 5: Recall and F-value results of selected parameters.
# Advancing Sustainable Textile Metrology: Reflectivity Measurement with Controlled Light Sources
## 3 Results and Discussion
### Measurement Repeatability and Uncertainty  
The present study proposes a methodological approach that, to the best of the authors' knowledge, has not been described in the existing literature. The measurement setup combines several well-known components into a configuration specifically tailored to flexible textiles. This approach includes the following:  
* The use of a lux meter inside a photometric chamber with controlled lighting sources;
* Measurements conducted against a fixed black background;
* Calculation of the reflectance ratio relative to the reference black surface.  
The application of the approach to deformable knitted structures presents additional challenges due to their open geometry and surface irregularity.  
While each element is individually known, their integration into a coherent and repeatable system for characterizing reflective performance in knitted fabrics represents an applied novelty and fills a methodological gap in current textile research.  
To evaluate the consistency of the proposed method, each reflectance ratio was calculated based on three independent measurements. The coefficient of variation (CV%) was computed for each combination of sample and lighting condition to indicate relative variability. All CV values remained below 5%, which is considered acceptable for textile research.  
Although the setup is not calibrated against an international photometric standard, it provides internally consistent measurements under strictly controlled conditions. Potential sources of uncertainty include minor sample positioning shifts, ambient light leakage (minimized using a black background and partially enclosed lighting chamber), and structural irregularities in the knitted surface. These factors will be addressed in future work by increasing the number of repetitions and validating results against standardized photometric methods.
## 5 Numerical Evaluation Results
### Evaluation Setup
#### 5.1.4 Traffic Model  
We model the upstream packet traffic generation at a given eNB (which is due to upstream packet arrivals from associated user end devices [91]) as an independent Poisson process. We set the eNB Poisson process rates such that the aggregate load from the eNBs at a given operator \(o\) results in a base packet traffic load of 5 Mbps, whereby each of the 30 eNBs at a given operator \(o\) contributes equally to the aggregate operator load. We conduct simulations of 100 s of backhaul network operation, whereby the Poisson traffic generation occurs over time increments of 0.1 ms, i.e., one simulation run of 100 s corresponds to one Million Poisson packet traffic generation instantiations.  
We consider dynamic upstream traffic variations, which can, for instance, be caused by new temporary connection establishment or data connection handovers, e.g., due to user mobility. Specifically, we initially simulate a peak load of 20 Mbps occurring by default at operator 1 from 10 to 20 s of a simulation run and at operator 2 from 50 to 60 s of a simulation run.
# An Overview of Transcranial Magnetic Stimulation and Its Application in Multiple Sclerosis
## 3 TMS Measures
### Short-Interval Intracortical Inhibition and Facilitation  
Using pulse-paired TMS, intracortical inhibition and intracortical facilitation can be examined at varying interstimulus intervals (ISIs). In these assessments, a conditioning stimulus (CS) is first delivered, followed by a test stimulus (TS) at varying interstimulus intervals (ISIs) using a target muscle that is typically at rest. The MEP amplitude from pulse-paired assessments is then compared to that of single-pulse assessments. To assess short-interval intracortical inhibition (ICI), first a subthreshold conditioning stimulus (CS, typically 80% of the RMT) is delivered, followed by a suprathreshold stimulus (typically 120% of the RMT), which is delivered with ISIs of 1-5 ms. This results in an inhibited test response that elicits a lower MEP amplitude. At longer ISIs (7-30 ms), intracortical facilitation (ICF) occurs instead, and the MEP amplitude of the test response increases [7]. ICI is thought to arise from the activation of a low-threshold inhibitory system, leading to a hyperpolarizing inhibitory postsynaptic potential [29]. On the other hand, ICF is thought to arise from the summation of excitatory postsynaptic potentials [29]. ICI and ICF measures have been found to be abnormal in some MS patients [30; 31; 32], as well as those with other neurodegenerative conditions such as Parkinson's disease [33]. This is thought to reflect changes in cortical excitability [30]. However, previous studies have reported mixed results regarding the potential association between disability, ICI, and ICF measures [31; 34; 35].
## 3 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Accuracy** & **Specificity** & **F1-Score** & **G-Mean** & **AUC** \\ \hline A0 & 50 & 0 & 66.67 & 0 & 50 \\ A1 & 59.35 & 49.15 & 63.77 & 58.13 & 60.03 \\ A2 & 88 & 91.3 & 87.5 & 87.9 & 94.72 \\ A3 & 66.94 & 58.62 & 65.81 & 68.22 & 70.7 \\ A4 & 72.22 & 65.03 & 71.77 & 72.87 & 74.12 \\ A5 & 60.75 & 57.66 & 58.87 & 61.11 & 61.55 \\ A6 & 76 & 68 & 77.78 & 75.58 & 77.6 \\ A7 & 93.57 & 94.37 & 93.33 & 93.5 & 94.37 \\ \hline \hline \end{tabular} A0 is not use any oversampling algorithm, A1 is random oversampling algorithm, A2 is classic SMOTE algorithm, A3 is Borderline SMOTE algorithm, A4 is K-Means SMOTE algorithm, A5 is SVM SMOTE algorithm, A6 is ADASYN algorithm, A7 is based on IOWA operator SMOTE algorithm.  
\end{table} 
Table 2: Comparison of results of different oversampling methods. Figure 7: The result of different classification algorithms of raw data and oversampled data.
## 2 Materials and Methods
### Processing of RGB Color Sensors
#### 2.2.2 Determination of the Spectral Sensitivity Curves of an RGB Color Sensor
Thus, in order to improve system performance, the color sensor's measured sensitivity curves, which give a flawed spectral sensitivity (SS) model, should be corrected accordingly by introducing suitable wavelength-specific correction factors to minimize for each sensor channel the differences in output between the virtual and the real color sensor for a set of representative test light sources, whose SPDs are either known or determined spectroradiometrically. In the present case, a four-channel luminaire prototype [120] consisting of two narrowband monochromatic (red and blue) and two broadband phosphor-converted (cyan and mint-green) LEDs was used to generate \(108\) different white light test spectra with \(R_{\mathrm{a}}>85\) and CCTs in the range from \(2700\,\mathrm{K}\) to \(6500\,\mathrm{K}\). The corresponding optimization pipeline is illustrated in Figure 5, where the differences between virtual and real channel responses calculated for the different test light  
Figure 4: Image representation of the monochromator setup used in this work. spectra serve as feedback for running an interior-point optimization algorithm [121; 122; 123] to iteratively converge on an optimal set of corrected spectral sensitivities \(R^{\prime}(\lambda)\), \(G^{\prime}(\lambda)\), and \(B^{\prime}(\lambda)\) that yield the same channel outputs as the real color sensor. For the RGB color sensor used in this work, the corresponding results are summarized in Figure 6 together with the CIE \(\overline{x}(\lambda)\), \(\overline{y}(\lambda)\), and \(\overline{z}(\lambda)\) color matching functions (CMFs) representing the chromatic response sensitivities of the standardized CIE \(2^{\circ}\) observer [124]. As can be seen, the color sensor's sensitivity curves considerably deviate from the CMFs of the standard observer. Thus, in order to be able to perform colorimetric measurements, the sensor's \(RGB\) outputs in response to an arbitrary color stimulus need to be mapped onto its corresponding \(XYZ\) values as calculated from CIE colorimetry. As is shown in the next section, this mapping can be implemented in the form of a matrix transformation.
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 2 Related Works  
There is a growth of activities exploring aquatic environments such as ponds, lakes, rivers, and oceans. An underwater sensor network (USN) is an important technology enabling us to monitor various underwater environments [8; 9; 10; 11; 12; 13]. USNs consist of sensor nodes that are connected through wired or wireless manners. Compared with wired networks, wireless connection of sensor nodes can greatly reduce the deployment cost and offer the flexibility to different environments [14]. Due to the high attenuation of EM waves in underwater environments, acoustic communications are more scalable in various aquatic environments due to the high penetration of acoustic signals and the long communication range.  
There is a growing interest in investigating cognitive radio technology in UASNs since the acoustic spectrum has become a scarce resource due to the proliferation of human aquatic activities.  
[PERSON] et al. proposed a channel allocation mechanism to efficiently manage acoustic spectrum in UASNs in [15]. A spectrum signaling approach was proposed for distributed channel allocation for the underwater acoustic networks in [16]. [PERSON] et al. proposed a dynamic spectrum sharing mechanism in cognitive radio (in terrestrial wireless networks) in underwater acoustic networks in [17]. In [1], research challenges in applying cognitive radio technology in UASNs are summarized. Meanwhile, a spectrum management mechanism with the integration of physical layer schemes and Media Access Control (MAC) layer schemes was proposed in [4]. Moreover, [PERSON] et al. proposed a dynamic spectrum borrowing algorithm in [6]. In [18], a novel statistical power adaptation over multiple-input and multiple-output (MIMO) generalized frequency division multiplexing (GFDM) was proposed for underwater acoustic networks. [PERSON] et al. developed a receiver-initiated spectrum management system for underwater acoustic networks in [7]. In [5], a spectrum detecting scheme was developed to avoid the interference to PUs (especially for marine animals like dolphins).  
However, few studies have concentrated on quality-of-service (QoS) in cognitive acoustic underwater networks. Since PUs have higher priority to access acoustic spectrum than SUs, the QoS of PUs is often guaranteed. In contrast, SUs are spectrum-constrained. Therefore, the QoS of SUs is difficult to ensure, which nevertheless has been ignored in most of studies on cognitive acoustic underwater networks. Therefore, the goal of this paper is to investigate two of the most important QoS metrics--the link connectivity and the probability of coverage.