esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Cellular Basis of Laser-driven Radiation Therapy
### Queens University Belfast Consortium  
UK activities aimed to develop laser-driven ion sources for medical therapy are currently carried out within the framework of the A-SAIL consortium, which involves researchers at Queen's University Belfast (QUB), University of Strathclyde, Imperial College and Rutherford Appleton Laboratory. This project, started in May 2013 aims to focus more closely on medically-related objectives. The main aim of the initiative is to unlock, within the next 6 years, the potential of the laser-driven ion approach as an alternative source for cancer therapy, advancing laser-ion sources to the point at which they can be a competitive alternative to conventional RF accelerators for medical therapy. The project aims to demonstrate controlled, all-optical acceleration of dense bunches of protons and other low-Z ion species in the 60-300 MeV/nucleon range of interest for therapy of deep-seated cancer. A number of different laser acceleration protocols will be studied to increase the energy of the ion beams e.g., Radiation Pressure Acceleration. Achieving this objective will require a coordinated effort involving development of new target media, understanding and controlling the physical processes of the relevant interaction regimes, and developing innovative solutions to known difficulties. A unique property of laser-driven ion beams is their ultrashort duration since ions are emitted in bursts of ps or fs duration at the source and their therapeutic use may result in dose rates up to 9 orders of magnitude higher than normally used in therapy. Dose rate can be a critical parameter affecting radiation biological responses. The biological effects of ions at such extremes are virtually unknown, and need to be carefully assessed. In parallel to the source development, the consortium is pursuing, by employing the unique capabilities of current and developing laser systems in terms of ion production and delivery, a program of investigations of the dynamics of cellular response to ion irradiation and damage at these unprecedented dose rates. The first experiments exploring the biological effects on cells at dose rates \(>\)10\({}^{9}\) Gy/s have been carried out on QUB's TARANIS laser, and recently on the GEMINI laser at RAL [108].
# Compensation of Measurement Uncertainty in a Remote Fetal Monitor
## 4 Results and Discussion
### Stochastic Model  
A probabilistic model for each patient was calculated from the experimental data set (data for modeling) acquired from clinical studies carried out on the 19 patients under study. From the fetal heart rate measurements, the perturbations \(\xi_{k}\) was obtained, using the dynamic model of the fetal monitor applying Equation (18), for each patient. Data for ten clinical studies per patient were used to obtain the probabilistic model. From perturbations histogram, estimations of probability density functions \(f_{\Xi_{n}}\) and distribution functions \(F_{\Xi_{n}}\) were obtained, finally a nonparametric model of the inverse function \(F_{\Xi_{n}}^{-1}\) was available for each patient. The direct and inverse distribution functions were estimated by using a linear interpolation by parts [26]. Using the Monte-Carlo method with a large data number of the amount of \(N=10^{5}\) and \(K=10^{3}\), the probabilistic models \(F_{\Xi_{n}}^{-1}\) were simulated applying the procedure described in Figure 4 and \(s_{K}^{2}\) was estimated to calculate the variance using \(\sigma^{2}=E\!\left[s_{K}^{2}\right]\). The probability distribution function models of the patients used in the simulation are shown in Figure 9, and Table 2 shows the estimated standard deviations and data number \(n\) used in the \(F_{\Xi_{n}}\) modeling.  
Figure 8: Fetal monitor model simulation results.  
Finally, 100 variance correction coefficients were calculated for each patient, using the relationship defined in Equation (25).
# Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities
###### Abstract  
Radio frequency (RF) technologies are often used to track assets in indoor environments. Among others, ultra-wideband (UWB) has constantly gained interest thanks to its capability to obtain typical errors of 30 cm or lower, making it more accurate than other wireless technologies such as WiFi, which normally can predict the location with several meters accuracy. However, mainly due to technical requirements that are part of the standard, conventional medium access strategies such as clear channel assessment, are not straightforward to implement. Since most scientific papers focus on UWB accuracy improvements of a single user, it is not clear to which extend this limitation and other design choices impact the scalability of UWB indoor positioning systems. We investigated the scalability of indoor localization solutions, to prove that UWB can be used when hundreds of tags are active in the same system. This paper provides mathematical models that calculate the theoretical supported user density for multiple localization approaches, namely Time Difference of Arrival (TDoA) and Two-Way Ranging (TWR) with different MAC protocol combinations, i.e., ALOHA and TDMA. Moreover, this paper applies these formulas to a number of realistic UWB configurations to study the impact of different UWB schemes and settings. When applied to the 802.15.4a compliant Decavave DW1000 chip, the scalability dramatically degrades if the system operates with uncoordinated protocols and two-way communication schemes. In the best case scenario, UWB DW1000 chips can actively support up to 6171 tags in a single domain cell (no handover) with well-selected settings and choices, i.e., when adopting the combination of TDoA (one-way link) and TDMA. As a consequence, UWB can be used to simultaneously localize thousands of nodes in a dense network. However, we also show that the number of supported devices varies greatly depending on the MAC and PHY configuration choices.  
UWB; TDoA; TWR; TDMA; ALOHA; scalability; IPS; user density; Decawave DW1000
## 2 Methods
### Design and Fabrication of the Combiner
#### 2.3.1 The 4 to 1 Combiner Design
a \(\sim\)30% microstrip length reduction via capacitor C, which is a high-Q-value ceramic capacitor such as the ATC 100C, resulting in a compact two-way combiner with a size of 150 \(\times\) 100 \(\times\) 10 mm and the four-way input (P2-P5), with the P1 as the combiner output.
# Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers
## 5 Computational Complexities
The orthogonal and quasi-orthogonal systems are not illustrated for the entire simulation range in Figure 25 due to the absence of coding matrices and the simplification of the ML metric for configurations with \(M_{T}=M_{R}>8\)[11]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Decoder** & **Multiplications** & **Additions** & \(\mathbf{exp(\cdot)}\) \\ \hline OSTBC \({}^{\dagger}\) & \(3.33\times 10^{4}+ch\) & \(4.95\times 10^{4}+ch\) & \(ch\) \\ QOSTBC \({}^{\dagger}\) & Not applicable & Not applicable & Not applicable \\ ML for R = 1 \({}^{\dagger}\) & \(3.36\times 10^{7}+ch\) & \(2.94\times 10^{7}+ch\) & \(ch\) \\ MIMO-PTRBF (train) & \(9.30\times 10^{4}\) & \(7.23\times 10^{4}\) & \(300\) \\ MIMO-PTRBF (test) & \(2.43\times 10^{4}\) & \(4.29\times 10^{4}\) & \(300\) \\ \hline \hline \end{tabular} \({}^{\dagger}\)_ch_ refers to the additional complexity of channel estimation.  
\end{table} 
Table 4: Computational complexities for \(M_{T}=M_{R}=8\) and 2 bits/s/Hz. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Decoder** & **Multiplications** & **Additions** & \(\mathbf{exp(\cdot)}\) \\ \hline OSTBC \({}^{\dagger}\) & Not applicable & Not applicable & Not applicable \\ QOSTBC \({}^{\dagger}\) & Not applicable & Not applicable & Not applicable \\ ML for R = 1 \({}^{\dagger}\) & Not applicable & Not applicable & Not applicable \\ MIMO-PTRBF (test) & \(1.31\times 10^{6}\) & \(2.53\times 10^{6}\) & \(1200\) \\ \hline \hline \end{tabular} \({}^{\dagger}\)_ch_ refers to the additional complexity of channel estimation.  
\end{table} 
Table 5: Computational complexities for \(M_{T}=M_{R}=32\) and 2 bits/s/Hz.
## 2 Dispersion-Managed Link and WDM System Modelling
### WDM Transmitters, Receivers, and Midway-OPC  
The configuration and specification of 24 transmitters/receivers for the WDM channel and midway OPC shown in Figure 1 are the same as in the previous study by [15]. The WDM transmitter and receiver modeled in this study are based on intensity modulation/direct detection (IM/DD). First, we assume that the center wavelengths of the 24 WDM channels are 1550 nm to 1568.4 nm, spaced at 100 GHz (0.8 nm), according to ITU-T recommendation G.694.1. An independently generated 127 (=2\({}^{7}-1\)) PRBS (pseudo-random bit sequence) intensity-modulates the optical continuous wave emitted from each distributed feedback laser diode (DFB-LD). The output of the modulator is assumed to be return-to-zero (RZ) pulses. The output electric field of the RZ format is assumed to be a second-order super-Gaussian pulse with a 10-dB extinction ratio (ER), duty cycle of 0.5, and chirp-free.  
The specific type of midway-OPC required to build the MSSI system uses highly nonlinear dispersion shifted fiber (HNL-DSF) OPC, as in the previous study by [15]. The characteristics of HNL-DSF are assumed as follows: the attenuation coefficient of 0.61 dB/km, the nonlinear coefficient of 20.4 W\({}^{-1}\) km\({}^{-1}\), the zero-dispersion wavelength of 1550.0 nm, the dispersion slope of 0.032 ps/nm\({}^{2}\)/km, and the length of 0.75 km. The 24 optical signals incident to the midway-OPC are phase conjugated through a four-wavelength mixing (FWM) process with the pump light. The power and wavelength of the pump light in the midway-OPC are assumed to be 18.5 dBm and 1549.75 nm, respectively. Consequently, each wavelength of the conjugated 24 channels in the midway-OPC is then arranged from 1549.5 nm to 1528.5 nm (\(-\)0.8 nm interval).  
The receiving part of each WDM channel consists of a fiber amplifier, optical filter, photodetector, pulse-shaping filter, and decision circuit. The assumptions for simulation are as follows: receiver bandwidth of 0.65 times 40 Gb/s, erbium-doped fiber amplifier (EDFA) with 5 dB noise figure, PIN diode as photodetector, and Butterworth filter as pulse shaping filter.
## 6 Intrusion Detection System
### Anomaly Detection  
Different types of anomalies of WSN are node anomaly, network anomaly and data anomaly as described below.  
* Node Anomaly: This types of anomalies can be detected during failure of WSN node or power problems. Failure of solar panel, or fluctuations in power of different components can cause this type of anomaly. Node anomalies can be due to hardware or software issues in the WSN nodes [82].
* Network Anomaly: Unexpected fluctuations in the signal strength and connection problems can be used to detect network anomaly. Complete loss of connectivity or episodic connectivity can be used to detect intrusions in the network.
* Data Anomaly: An intrusion attempt can be detected from chaotic or disordered data communication.  
Detection of a specific type of anomaly is very useful to decide the type and explication of cyber-attack. To detect the above-mentioned anomalies, different types of approaches are applied [86]. These anomaly detection approaches can be classified as game theoretic, statistical, machine learning, artificial immune system and data mining based approach [85; 86; 87; 88].
## 3 Proposed Dehazing Method
### Atmospheric Light Estimation
To assess the performance of \"A-Est\" algorithm, we present a visual comparison with existing dehazing methods. Table 1 and Figure 5 summarize the _AL_ estimation principle used in each of the concerned methods. The visual comparison employs the physical degradation model in Equation (1), where the transmission map is generated by using the conventional DCP approach [14]. As shown in Figure 6f, the proposed algorithm estimates the _AL_ value accurately from a hazy image, where it achieves an estimation result similar to the ground truth. Some color distortions appear on dehazing results when using [PERSON]'s [20] and [PERSON]'s [35] methods (as shown in \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**[PERSON] 2015** & **[PERSON] 2016** & **Sulami 2018** & **Salazar-Colores 2018** & **[PERSON] 2019** & **Proposed** \\ \cline{2-5}
[20] & [28] & [38] & [35] & [36] & \\ \hline \hline \end{tabular} \begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} Top 0.1\% \\ brightest pixels \\ in depth map \\ \end{tabular} & Fixed value (A = 1) & \begin{tabular}{c} Twice PCA on all \\ haze patches \\ \end{tabular} & \
\begin{tabular}{c} Top 0.1\% brightest \\ pixels in \(I_{dark}\) \\ pixels in \(I_{dark}\) \\ pixels in \(I_{dark}\) \\ min energy \\ \end{tabular} &
\begin{tabular}{c} Top 0.1\% \\ blurred pixels \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of _AL_ estimation methods used in existing and proposed dehazing approaches. Figure 4: A practical example of proposed algorithm “A-Est”. The red spot indicates the region of interest (quadtree with maximum blur amount). The last row shows blur maps of each quadtree with the blur measure. Figure 5: Parts of interest for estimating _AL_ in different existing dehazing methods and the proposed one: (**a**) [PERSON]’s method [20]; (**b**) [PERSON]’s method [38]; (**c**) [PERSON]’s method [35]; (**d**) [PERSON]’s method [36]; and (**e**) the proposed method. Figure 6a,d, the white color of the window becomes blue), which is caused by the inaccuracy of \(AL\) estimation. In addition, [PERSON]'s method [28] provides a dim scene radiance, because the estimated \(A\) is brighter than that of ground truth (A = 1). Conversely, [PERSON]'s method [38] produces a bright scene radiance since the estimated \(A\) is darker than that of the ground truth. On the other hand, we observe that [PERSON]'s method provides an estimation of \(AL\) somewhat similar to the ground truth.
## 3 Results  
### Summer/Autumn Trial  
Acaricide efficacy of VarroMed(r) recorded during the summer/autumn trial ranged from 71.2%, when applied three times (Croatia), to a maximum of 89.4% (Belgium) when applied five times (Table 2; Figure 4). All treated groups efficacies differed from control groups (Kruskal-Wallis test: H(7) = 59.14, \(p\) < 0.0001; [PERSON]'s tests: \(p\) < 0.05). There were no statistically significant differences recorded between the locations, and the number of applied treatments (three or five) was not related to the natural mite fall rates before treatment. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Statistics** & **VARROMED** & **VARROMED** \(\times\)**5 & **VARROMED** \(\times\)**5 & **VARROMED** & **CONTROL** & **CONTROL** & **CONTROL** & **CONTROL** \\  & \(\times\)**(BELGIUM)** & **(ITAL)** & **(LSI)** & **(SLOVENIA)** & \(\times\)**(CROTA)** & **(BELGIUM)** & **(ITAL)** & **(LSI)** & **(SLOVENIA)** & **(CROTA)** \\ \hline Minimum & 15 & 73.9 & 83.4 & 13.3 & 6.8 & 28.2 & 21.4 & 0.4 \\ Maximum & 100 & 99.2 & 94.8 & 80.7 & 32.8 & 75.3 & 69.2 & 1.5 \\ Its Quarter & 80.6 & 82.9 & 84.5 & 76.1 & 9.8 & 31.1 & 28.8 & 0.5 \\ Median & 89.2 & 89.7 & 88.6 & 77.3 & 12.9 & 39.8 & 46.7 & 0.6 \\
3 rd Quantile & 94.1 & 98.2 & 91.3 & 78.9 & 13.5 & 53.7 & 48.7 & 0.7 \\ Mean & 89.4 & 88.2 & 88.3 & 71.2 & 15.3 & 44.6 & 42.7 & 0.7 \\ Variance (m\({}^{-1}\)) & 0.5 & 0.9 & 0.2 & 4.2 & 0.9 & 2.7 & 3.2 & 0 \\ Standard & 7.2 & 9.3 & 4.0 & 20.4 & 9.7 & 16.3 & 17.9 & 0.4 \\ deviation (m\({}^{-1}\)) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Acaricide efficacy recorded during the summer/autumn trial in the apiaries and relative mite fall during the same period in the control group (%). The number of adult bees was reduced in treated groups, compared to the control ones, in Belgium, Italy and Slovenia, ranging from \(-130.1\%\) to \(-12.1\%\), and increased in Croatia +8.8% (Table 3 and Figure 5). These differences were statistically significant only in one apiary (Belgium) ([PERSON]'s tests \(p\) value: 0.0001), where colonies were smaller and three of them died after the treatment. Figure 4: Box plot of acaricide efficacies recorded during the summer/autumn trial and relative mite fall during the same period in the control group (%). The horizontal line in boxes shows the median. The box shows the first and third quartile. Whiskers extend to 1.5 \(\times\) interquartile range (IQR). Diamonds mark the outliers outside the 1.5 \(\times\) IQR. Additionally, the pluses show mean values. Figure 5: Box plot of adult bee coverage in all groups recorded during the summer/autumn trial compared to the beginning of the treatment (%). The horizontal line in boxes shows the median. The box shows the first and third quartile. Whiskers extend to 1.5 \(\times\) interquartile range (IQR). Diamonds mark the outliers outside the 1.5 \(\times\) IQR. Additionally, the pluses show mean values.
# Development of a Stock Price Prediction Framework for Intelligent Media and Technical Analysis
## 2 Materials and Methods
### Methodology
#### 2.3.2 Fundamental Analysis  
Fundamental analysis studies the stock market by analysing new headlines, economic and social reports, and political forces that may affect the price movement [24]. This paper employs a similar strategy through the use of fundamental analysis by looking at news headlines and social media reports in its research. The fundamental analysis experiment is split into three different sub-experiments using four different models to evaluate performance: linear discriminant analysis, support vector machine, decision tree, and random forest. To assess the performance of the models, the paper uses accuracy, precision, recall, the F-measure, and a confusion matrix. The data is split into 80/20 for training and testing; three datasets for fundamental analysis were constructed, considering dates from 2012 to 2019. The first dataset was the news headlines dataset, scraped off the Money web site. This dataset consisted of news headlines linked to the Vodacom Group Limited Company. The second dataset was composed of tweets collected using the GetOldTweets API with the hashtag #vodacom. The total length of the combined dataset was 893 rows. The combined dataset was then labelled according to price movement on the closing price for that particular day, with +1 indicating an increase and \(-1\) indicating a decrease.  
The data collected from the scraped Moneyweb site and the #vodacom tweets were processed using the same python function mentioned above. This function removes all URLs, usernames, dates, whitespace, and the hashtag sign (#) to prepare the dataset for the experiments. The last stage to take the dataset through is the transformation stage. Here, categorical encoding and scaling were applied to the data to transform the dataset. Thesetwo techniques convert text into numerical representations and scale the data into a specific range, respectively [25]. Categorical encoding on the fundamental analysis experiment was applied to the target column. This column contains the variables 'UP' and 'Down', which categorises whether Vodacom's stock price went up or down on a given day. These up-down variables were replaced with 1 and \(-1\). The dataset was also scaled using the sklearn MinMaxScaler Python library [26]; this technique scales all the numerical data between 0 and 1 but does not scale the target and sentiment column.  
Feature engineering is the process of extracting new features by transforming the current features [25]. The sentiment was extracted from all news headlines and tweets in the collected dataset, and this sentiment feature determined whether the polarity of the text was negative, positive, or neutral.
# Artificial Intelligence, Sensors and Vital Health Signs: A Review
## 5 Artificial Intelligence (AI) for Healthcare Delivery  
### Application of AI in Vital Health Signs Monitoring  
Diagnosis is the identification of the nature of an illness or other problem by examination of the symptoms with the aid of clinical devices. For instance, the excessive rate of occurrences of heart disease has resulted in the development of smart devices and systems that can detect/recognize early warning symptoms of anomalies. More recent findings have shown that heart diseases are the topmost leading cause of death; thus, we need to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interconnection Protocol** & **Range** & **Data Rate** & **Spectrum** \\ \hline WiFi & 200–100 m & 50–90 Mbps & 2.4/5 GHz \\ \hline WiMax & 50 Km & 10–376 Mbps & 2–11 GHz \\ \hline GSM/EDGE & \textless{}35 Km & 270 kbps & 900–1800 MHz \\ \hline UMTS/CDMA & \textless{}30 Km & 2 Mbps & 1885–2200 MHz \\ \hline LTE & \textless{}100 Km & \textless{}300 Mbps & 700–2500 MHz \\ \hline Infrared & 1–10 m & 2.4 kbps–1 Gbps & 300 GHz-430 THz \\ \hline ZigBee & 10–20 m & 20–256 kbps & 2.4 GHz/84–915 MHz \\ \hline UWB & 2–30 m & 110 Mbps & \textgreater{}500 MHz \\ \hline Bluetooth & \textless{}100 m & 2.1 Mbps & 2.4 GHz \\ \hline BLE & \textless{}50 m & 1 Mbps & 2.4 GHz \\ \hline NFC & \textless{}10 cm & 106–424 kbps & 13.56 MHz \\ \hline RFID & 5 cm–2 m & 40–640 kbps & 120–150 kHz \\ \hline NFC & \textless{}10 cm & 106–424 kbps & 13.56 MHz \\ \hline \multicolumn{3}{c}{6 LoWPAN} & \textless{}50 m & 250 kbps & 868 Hz/902 MHz/2.4 GHz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of interconnection technologies commonly used in IoHT devices, partially obtained from [19; 47; 48; 73; 74].  
detect symptoms of these diseases before they get worse [79]. A non-contact HR measuring device was proposed that operates based on a sparse reconstruction algorithm via a smartphone video camera. The integration of AI, IoT, and vital signs into a single platform has demonstrated a remarkable way to monitor patients' health and diagnose anomalies. The most commonly used devices in clinics are ECG, pulse oximeter, wearable wristwatches, and BT sensors. ECG signals represent the electrical activity of the heart at rest. It can be used to create conclusions about heart rate activity and can be applied for diagnosing the enlargement of the heart due to high blood pressure, elevated heart rate, and dysrhythmia or heart attacks [80]. [PERSON] et al. [81] proposed an AI-based model to diagnose heart diseases by monitoring several parameters in real-time, using wearable IoT technology. According to their findings, wearable ECG devices, pulse, and temperature sensors with a trained prediction model were used to classify risk for heart disease or arrhythmia.
## 5 Performance Comparison and Validation
### Results and Discussion
#### 5.2.2 Latency Time  
Figure 6 shows the real-time performance of DQAAR and the three protocols mentioned above. MTERR achieves the best real-time performance with an average latency of 1780 ms, but this is due to its advantage of reducing hop count at the expense of lifetime. In the initial stage of the network, DQAAR is in the parameter adjustment stage with significant delays. After a period of learning, its real-time performance is greatly improved, which is not much different from MTERR. AUMPR and MVECR excessively pursue the balance of energy consumption between nodes and achieve node energy balance by increasing the number of link hops. Long link hops greatly increase system latency.  
Figure 5: Protocol life cycle, FND and HND performance.  
Compared with the other three routing protocols, the DQAAR delay in the stable operation stage has better stability performance. That is to say, the initial high delay is caused by the exploration behavior of DQAAR. In the stable operation stage, the real-time performance of DQAAR far exceeds that of MVECR and AUMRP, achieving good real-time performance.