esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Past and Ongoing Biomedical Activities at ISOLDE/Biomedical Research Performed at ISOLDE
### Past Activities
The recorded PET images were then reconstructed in 3D mode, revealing significantly better quality for \({}^{152}\)Tb as compared to \({}^{149}\)Tb. The first _in vivo_ experiments demonstrating the efficiency of alpha targeted therapy (TAT) using the only suitable lanthanide produced at ISOLDE, \({}^{149}\)Tb, were carried out by [PERSON] _et al_. in the summer of 2001 [21, 22, 23]. The radio-lanthanide was produced by the irradiation of the Ta-foil target with 1.4 GeV protons, implanted into a thin layer of KNO\({}_{3}\) on aluminum backings and separated from isotopic contamination using exchange chromatography column equilibrated with the \(\alpha\)-HIB eluent. The 5.5 MBq \({}^{149}\)Tb activity was then used for labelling the rituximab antibody linked to 2-(4-isothiocyanatobenzyl) diethylenetriamine pentaacetic acid (SCN-CHX-A-DTPA). Ready immunoconjugate was injected into a group of around 30 mice two days after xenotransplantation of Daudi cells used to simulate leukemia. The results of the study revealed a full sterilization of the tumor cells and complete protection of the xenografted mice over a period of at least four months. Furthermore, no toxic effects of \({}^{149}\)Tb were observed. In March, 2002, the first official initiative of running a specific medical isotope program at CERN was presented by [PERSON] at the Educational and Technology Transfer (ETT) Seminar at CERN [24]. This initiative was led by [PERSON], [PERSON], [PERSON] and [PERSON], already started in late 2001 and aiming at creating a radiochemistry laboratory at CERN for the on-site chemical purification of radioisotopes, the development of technologies for different ways of producing medical isotopes based on MegaWatt p-drivers, including off-line mass separation, launching a new international collaboration for bio-medical and nuclear medicine studies and maintaining a constant training of scientific personnel. This initiative lasted for a couple of years and, finally, came to a stand-by status.
## 3 Test Data Analysis  
### HNT/BG-HNT Surface Synthesis  
Analyses of SEM Images and FTIR Spectrum  
SEM images showing HNT and HNT with BG dye adsorbed are shown in Figure 2a,b. The dye adsorption onto the HNT surface can be depicted from these pictures. The pores in HNT texture resembles a honeycomb shape. The substrate adherence to the adsorbent is enhanced by this surface structure. Figure 2b affirms the adsorption of the color on the outside of HNT as the pores appear to be filled. The FTIR spectra for BG dye (Figure 3) exhibit distinctive peaks at 3500 and 3200, and 3100 cm\({}^{-1}\) refers to N-H and CH-aromatic elongation caused by periodic disturbances. The maximum vibration noticed at 1700/cm recognizes S=O of the sulphites group in dyed structures. The triplet peaks exist in the spectrum between 1620, 1590, and 1480/cm and C=C, related to the aromatic ring, which couples with N=N of the azo group in the dye structure. The multiple peaks that appear at 1460-1337/cm are orient by bowing, a periodic vibrational motion relevant to N-H and CH individually. Other than the range of the halloysite, 3660/cm addresses the extending movement on the inward surface OH gatherings, and 1010/cm handles the extension of SiO. The inward surface -OH bunches concerned the aluminum-focused sheets of octahedral stature and hydrogen-oxygen bonding in a bilayer structure.  
The FTIR spectrum showing complex BG-HNT in Figure 3 displays a significant change in the vibration modes compared to pure BG. It is noticed that the vibrational peaks at 3500-3200 cm\({}^{-1}\), which are referred to as the -OH and -NH groups in BG dye, tend to vanish. This indicates HNT's strong interactions with functional groups and modifies the morphology of the structure of the dyes in BG-HNT. Similar behaviors can also be witnessed at 3000 cm\({}^{-1}\), 1600-1450 cm\({}^{-1}\) and 1260-1030 cm\({}^{-1}\), confirming that HNT has high adsorption and good interaction with BG dye.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Variable** & \begin{tabular}{c} **Dimensional** \\ **Value** \\ \end{tabular} & \
\begin{tabular}{c} **Value\({}_{\text{min}}\)** \\ \end{tabular} &
\begin{tabular}{c} **Value\({}_{\text{max}}\)** \\ \end{tabular} \\ \hline A & Duration & minute & 0 & 180 \\ B & Temperature & \({}^{\circ}\)C & 27 & 50 \\ C & Concentration & mgL\({}^{-1}\) & 25 & 200 \\ D & Adsorbent quantity & gL\({}^{-1}\) & 0.5 & 6.0 \\ E & pH & - & 2 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test range for singular components [27] (adapted with permission from the publisher).
## 3 Geometry Modifications Considered  
The present study, is based on analyzing the effect of several geometry modifications on the (FO) outlet dynamic mass flow, frequency and amplitude. Four different modifications were evaluated, see Figure 1 and Table 2. Fluidic oscillator (MC) inlet width (a), was the first modification to be computed.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Reynolds Number** & 8711 & 11152 & 13593 & 16034 & 32068 \\ \hline
**(CFD) Mass Flow Output Frequency (Hz)** & 12.92 & 15.89 & 19.5 & 22.7 & 40.43 \\
**Experimental Output Frequency (Hz)**[1] & 12.9 & 15.5 & 18.7 & 21.8 & - \\ \hline
**Error in \%** & 0.15 & 2.5 & 4.2 & 4.1 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison experimental and simulated results.  
Figure 3: Fluidic oscillator output mass flow frequency (**a**) and peak to peak amplitude (**b**) as a function of the mixing chamber inlet width and for three different Reynolds numbers, 8711, 16034 and 32068.  
Eight different widths were analyzed, the maximum and minimum width ratio was respectively 2.14 and 0.35. Width ratio was defined as the generic inlet width divided by the original one. The inlet width was decreased by 64.4% and increased by 114.7%.  
The (MC) outlet width (b), was respectively increased and decreased by a ratio of 1.82 and 0.17. Outlet width ratio was defined as the generic outlet width divided by the original one. A total of 8 different outlet width ratios were analyzed. The outlet width increase and decrease was of 82.3%. The (MC) outlet angle (c), was progressively increased and decreased versus its original value until reaching an outlet angular ratio respectively of 2 and 0.63, the definition of the outlet angular ratio is similar to the previous definitions already given. A total of 8 different outlet angular ratios were studied. The outlet angle maximum increase and decrease was respectively of 100% and 36.6%. Finally, the (MC) inlet internal angle (d) was as well modified, two different angles which increase versus the original one was respectively of 74.3%, and 93% were evaluated. It is interesting to mention that the inlet angles are directly linked with the position, shape an intensity of the Coanda vortices generated alternatively at both sides of the (MC). This angles also modify the shape and dimension of the (MC) bubble generated alternatively on both sides of the main jet, and according to [5; 10; 11] among others, there is a direct link between the (MC) bubble volume increase and the feedback channels mass flow. Table 2 summarizes all the different internal geometry modifications performed. All geometry modifications presented were evaluated for three different Reynolds numbers, 8711, 16034 and 32068.
# Real-Time Geophysical Applications with Android GNSS Raw Measurements
## 2 Materials and Methods
### Varion  
The VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm can provide time series of real-time sTEC (slant total electron content) variations in a stand-alone operational mode starting from each dual-frequency GNSS receiver [11]. It was derived from VADASE: it is based on single time differences of geometry-free combinations of GNSS carrier-phase measurements, using a standalone GNSS receiver and standard GNSS broadcast products (orbits and clock corrections) that are available in real-time. sTEC estimates are referred to the ionospheric pierce point (IPP), which represents the intersection between the line of sight satellite-receiver and the single thin layer with which the ionosphere is approximated. Hence, the Klobuchar broadcast ionospheric model, based on an ionosphere thin shell (in our case, at the height of 350 km) approximation is used [17]: it enables the real-time computation of both the positions of the ionospheric pierce points and the sub-ionospheric points (SIP: IPP projection onto the ellipsoid) [11].  
VARION was successfully applied to the detection of traveling ionospheric disturbances (TIDs) induced by the 2012 Haida Gwai earthquake and tsunami [11] and by the Illapel earthquake and tsunami [18]. It was also efficaciously applied to ionospheric plasma density depletion caused by the Falcon 9 rocket launch [19]. All these applications prove VARION's extreme versatility and also explain the reasons for this preliminary analysis: using smartphones as a tool for increasingly accurate real-time ionospheric monitoring.  
VARION is implemented in a Python-based open-source software [20].
## 3 The Proposed HSTMAC Framework and System Model
### Optimal ACB Parameter  
For both S-ALOHA system and TDMA system, the resource utilization is a concave function with the number of active devices. In order to maximize the resource utilization, all the devices should be allowed to pass the ACB check if \(X\leq X^{*}\), while only \(X^{*}\) out of \(X\) devices can be allowed to pass the ACB check if \(X>X^{*}\). Therefore, the optimal ACB parameter for both S-ALOHA and TDMA systems is:  
\[p=\min(X^{*}/X,1) \tag{7}\]  
**Remark 2**. _According to the above discussions, we can derive the concept of saturation as follows. For a certain number of available resource blocks \(L\), the maximum number of supportable devices \(X^{*}\) can be computed by Equations (2) and (5). We define the system is in a saturation state if the actual number of active devices is \(X\geq X^{*}\), where some devices are barred to proceed the random access procedure. We define the system is in a non-saturation state if \(X<X^{*}\), where all the devices can participate in the contention of random access opportunities. Accordingly, the resource utilization of S-ALOHA and TDMA systems in different state can be computed as:_  
\[R_{S-ALOHA}=\left\{\begin{array}{ll}Le^{-1},&if\quad X\geq X^{*}_{S-ALOHA}\\ \delta Xe^{-\frac{X}{L\delta}},&if\quad X<X^{*}_{S-ALOHA}\end{array}\right. \tag{8}\]  
\[R_{TDMA}=\left\{\begin{array}{ll}L(1-\theta^{*}),&if\quad X\geq X^{*}_{TDMA} \\ \delta Xe^{-\frac{X}{L\delta}},&if\quad X<X^{*}_{TDMA}\end{array}\right. \tag{9}\]
# Delayed Combination of Feature Embedding in Bidirectional LSTM CRF for NER
## 2 Related Work
### Distributed Representations for Input
#### 2.1.3 Word-Level and Character-Level Representations  
The pre-trained word embeddings and contextual embeddings learn the representations from the context of a word in a sentence, but does not consider and learn the character composition of a word, which are very important especially in the NER task because we often can infer the named entity (NE) type of a word from its character composition. To make up for this weak point, two different wordrepresentation techniques have been developed: one is word-level pattern-based word encoding and the other is character-level CNN or LSTM-based word encoding.  
The former (word-level representation) classifies each word based on the following sub-criteria and learns the encoding during training the deep learning network:  
1. A case can be initialized in upper-case, all upper-case, all lower-case, and in a mixed case.
2. Punctuation
3. Digits including all digits, words with digits, cardinal and ordinal numbers
4. Characters, for instance, Greek letters
5. Morphology, e.g., prefixes and suffixes
6. Parts of speech - proper names, verbs, nouns
7. A function such as an n-gram, word, or feature pattern  
This technique makes it possible to learn word representation based on patterns of a word and encode words with different literals but same patterns with the same representation [2; 3; 5; 6; 22; 23; 24]. For example, [PERSON] et al. [22] used the capitalization information of a word, which was removed before training the word embedding. The method uses a lookup table to add a capitalization feature with the following options: AllCaps, UpperInitial, Lowercase, MixedCaps, Noninfo [5; 22]. [PERSON] et al., 2015 also used the spelling information as a word-level feature, which includes: start with a capital letter, all capital letter, all lower-case, mixed case, punctuation, prefixes and suffixes, has apostrophe end ('s), has initial capital letters, letter, non-letter and word pattern.  
The latter (character-level representation) is obtained by passing each character encodings within a word through Recurrent Neural Network (RNN) or CNN blocks. CNN-based one is good at extracting dominant character information in a word [3] while (RNN) (Gated Recurrent Unit (GRU) or LSTM)-based one is good at capturing prefixes and suffixes in a word [2; 24; 25]. These character-level representations also have an advantage in handling the Out-Of-the-Vocabulary (OOV) problem because it is possible to learn almost all character embedding from even small or moderate corpus. In other words, these representations are good at inferring unseen words and sharing information about morpheme-level regularities. To improve the model performance, the pre-trained word embedding have been actively combined with character-level CNN-based word encoding [3] or character-level LSTM-based word encoding [2; 24; 25].
## 4 Discussion
#### 4.2.5 Evaluating the VAOCMP Model in a Larger Set  
In this section, the VAOCMP model is evaluated using two algorithms for a larger data set, similar to Section 4.1.5. The results of location-allocation for existing stations are outlined in Table 21.  
One hundred sixty-three thousand eight hundred demands in the case study are without services. Because, taking into account the capacity of each station, each facility cannot allocate more than 50,000 persons, and surplus demands may not be allocated to any other station. Similar to Section 4.1.5, relocation-reallocations are done, but using the VAOCMP model. The results of relocation-reallocation are shown in Tables 22-26.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Fire Stations** & **Solving Time Based on Tabu Search** & **Solving Time Based on Simulated Annealing** \\ \hline
11 & 565.310 & 605.778 \\
12 & 613.585 & 632.112 \\
13 & 649.947 & 670.661 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Comparison of the average problem-solving time in seconds.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Tabu Search** & **Simulated Annealing** \\ \hline Runtime (sec) & 653.18 & 791.25 \\ Optimal value & 341,431,239.22 & 341,431,239.22 \\ Number of demands & 1,250,796 & 1,250,796 \\ Number of unallocated demands & 163,800 & 163,800 \\ \hline \hline \end{tabular}
\end{table}
Table 21: Runtime and the optimal value of the function by Tabu Search and Simulated Annealing algorithm for existing stations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Fire Stations** & **Normalized Standard Deviation** & **Normalized Standard Deviation** \\
**Number of Fire Stations** & **Solutions of Obtained Based on Tabu Search** & **of Solutions Obtained Based on Simulated Annealing** \\ \hline
26 & 0.0812 & 0.1008 \\
27 & 0.1258 & 0.1399 \\
28 & 0.1739 & 0.2073 \\ \hline \hline \end{tabular}
\end{table}
Table 22: The normalized standard deviation of solutions obtained using the VAOCMP model with both algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Fire Stations** & **Solving Time Based on Tabu Search** & **Solving Time Based on Simulated Annealing** \\ \hline
11 & 565.310 & 605.778 \\
12 & 613.585 & 632.112 \\
13 & 649.947 & 670.661 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Comparison of the average problem-solving time in seconds.  
So, the VAOCMP model can also solve the modest size problem efficiently. As it is known, the Tabu Search algorithm provides better solutions to this problem.
# Predicting the Colouration between Binaural Signals
## 3 Validation
### Test Scenarios
#### 3.1.2 Feature 2: Binaural Loudness Difference  
Second, to demonstrate the conversion from the Phon to some scale, which is used in this method to give a greater weight to louder sounds, two comparisons were made. The first assessed how a change in loudness at a lower amplitude should be less perceptually noticeable than one at a higher amplitude, a feature highly relevant in binaural signals with interaural level differences. In this scenario, the reference was a stereo signal with a flat response at 65 dB SPL (left ear) and 45 dB SPL (right ear). The first test signal had a 1 kHz +20 dB peak on the left ear signal and an unchanged right ear signal, and the second test signal had a 1 kHz +20 dB peak on the right ear signal (20 dB quieter) and an unchanged left ear signal. An illustration of the filters is shown in Figure 7 and calculated colouration values between the test signals and flat response reference signals are presented in Table 2.  
The BSD and PEAQ values are equal, regardless of whether the peak occurs on the louder or quieter side of the stereo signals. The CLL and PBC produce greater colouration values for the signal with a peak on the louder left ear signal. Due to the louder amplitudes, these signals have a higher perceptual relevance [30], so the CLL and PBC values are more in line with perceptual expectations.  
The second comparison looked at the colouration values of peaks and notches by comparing signals with a 1 kHz +20 dB peak and \(-\)20 dB notch at 65 dB SPL to flat response signals at the same level (see Figure 8 and Table 3). The same filter was applied to both left and right channels.  
The BSD calculation produces the same value of colouration and PEAQ and CLL give similar values, whereas the PBC method produces a value in line with what is expected from the human auditory system; a greater value for the peak than the notch, as peaks are more noticeable than notches [40].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **3 kHz** & **10 kHz** \\ \hline BSD (dB) & 0.48 & 1.01 \\ PEAQ (ODG) & 0.86 & 0.71 \\ CLL (Phons) & 0.62 & 0.48 \\ PBC (sones) & 1.12 & 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculated colouration values between 3 kHz and 10 kHz +20 dB peak filtered signals and flat response reference signals at the same level. Both filtered test signals used an equal filter bandwidth at 65 dB SPL.  
Figure 6: Frequency response of the 3 kHz (blue line) and 10 kHz (red line) +20 dB peak filters with equal ERB filter bandwidths, used to assess the use of equal loudness curves, for which a peak at 3 kHz should be weighted higher than 10 kHz.
# An Efficient Algorithm for De-Interleaving Staggered PRI Signals
## 3 Simulation and Analysis
### Semi-Physical Simulation Experiments  
This section validates the proposed algorithm through a semi-physical simulation experiment. The semi-physical simulation process is shown in Figure 6. First, the radar signal simulator simulates multiple radar signals arriving simultaneously. Then, the reconnais  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{Sequence Correlation} & Part1 & \(O(N^{2})\) \\ \cline{2-3}  & Part2 & \(O(KN)\) \\ \cline{2-3}  & Part3 & \(O(H^{2}legH)\) \\ \cline{2-3}  & Total & \(O(N^{2}+KN)\) \\ \hline \multirow{4}{*}{CMM} & Part1 & \(O(\beta N^{2})+O(L_{1}\sum_{i=-PRI_{min}}^{PRI_{max}}\phi(q))\) \\ \cline{2-3}  & Part2 & \(O(N)\) \\ \cline{2-3}  & Part3 & \(O(N)\) \\ \cline{2-3}  & Total & \(O((\beta N^{2})+O(L_{1}\sum_{i=-PRI_{min}}^{PRI_{max}}\phi(q))\) \\ \hline \multirow{4}{*}{Ours} & Part1 & \(O(L_{2}leg(L_{2}))\) \\ \cline{2-3}  & Part2 & \(O(N+Mlog(M))\) \\ \cline{2-3}  & Part3 & \(O(N)\) \\ \cline{2-3}  & Total & \(O(L_{2}leg(L_{2}))+O(N+Mlog(M))\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The computational complexity of the three de-interleaving algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**PRI Range**} & \multirow{2}{*}{**Radiation Sources**} &
\begin{tabular}{c} **Sequence** \\ **Correlation** \\ \end{tabular} & **CMM** & **Ours** \\ \hline
100-300 & 2 & 2.21 s & 4.15 s & 0.91 s \\
100-300 & 4 & 6.73 s & 7.22 s & 1.13 s \\
100-300 & 6 & 10.25 s & 13.57 s & 1.37 s \\ \hline
100-600 & 2 & 3.62 s & 8.62 s & 0.92 s \\
100-600 & 4 & 10.51 s & 12.51 s & 1.11 s \\
100-600 & 6 & 16.33 s & 19.25 s & 1.35 s \\ \hline
100-1000 & 2 & 5.51 s & 14.22 s & 0.98 s \\
100-1000 & 4 & 16.62 s & 17.52 s & 1.21 s \\
100-1000 & 6 & 24.33 s & 24.76 s & 1.39 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Execution time of the three de-interleaving algorithms.  
sance jammer measures the simulated radar signals' parameters. Finally, the measured TOA sequence is uploaded to the computer to verify the proposed de-interleaving algorithm.  
The parameter settings of the radar signal simulator are shown in Table 6, and the results are shown in Figure 7. The semi-physical simulation shows that the proposed algorithm can de-interleave staggered PRI signals and estimate the sub-PRIs of staggered signals.  
Figure 6: The semi-physical simulation process.  
Figure 7: The semi-physical simulation of the proposed algorithm: **(a)** The first round; **(b)** The second round; **(c)** The third round; **(d)** The fourth round; **(e)** The fifth round; **(f)** Sub-PRIs estimation result.