esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Progress of Dynamic Measurement Technology of Atom Gravimeter
## 2 Dynamic Measuring Principle and Device of Atom Gravimeter  
### Basic Principles of Atom Gravimeter  
To measure the gravitational acceleration, the atoms are cooled and trapped by a three-dimensional magneto-optical trap in a vacuum environment. The atoms are further cooled to the order of \(\mu\)K by polarization gradient cooling, and then the light field is turned off so that the atoms fall freely under the action of gravity. As shown in Figure 2, the \(\pi\)/2-\(\pi\)-\(\pi\)/2 laser pulses formed by the reflecting mirror are used to split, reflect, and combine the falling cold atoms, and the M-Z interference is realized. The acceleration information of the atoms relative to the mirror is transferred to the interference fringes. The phase difference of the atom interference between the two paths is \(\Delta\phi=k_{\text{eff}}\cdot gT^{2}\), where \(k_{\text{eff}}\simeq 4\pi/\lambda\) is the effective wave vector of two backpropagating Raman beams, \(\lambda\) is the wavelength of the laser, and \(T\) is the time interval between the two Raman beams.  
Figure 1: Atom gravimeter for dynamic measurement from authors’ team.  
Figure 2: The actions of three Raman beams on atom cloud.  
In order to compensate for the Doppler shift during the free-fall of the atoms, a linear chirp sweep must be performed on the frequency difference between the two Raman beams. At this time, the interference phase difference can be expressed as  
\[\Delta\phi=(k_{\mathrm{eff}}\cdot g-2\pi\alpha)T^{2}. \tag{1}\]  
When the chirp rate \(\alpha=k_{\mathrm{eff}}\cdot g/2\pi\), the Raman optical phase shift is exactly the same as the gravitational phase shift.  
In the process of interference, the internal state of the atom changes. At the initial moment, the atoms are in the \(\ket{\mathrm{g}}\) state, and after the action of the first \(\pi/2\) Raman light, some of the atoms transition to the \(\ket{\mathrm{e}}\) state. The atoms in \(\ket{\mathrm{g}}\) transition to \(\ket{\mathrm{e}}\) and the atoms in \(\ket{\mathrm{e}}\) transition to \(\ket{\mathrm{g}}\) when the second \(\pi\) Raman beam actions. After the third Raman beam, the atomic wave packets combine and interfere. The population of atoms in \(\ket{\mathrm{e}}\) state after interference is measured by fluorescence signal, and its relationship with gravity phase shift is  
\[P=P_{0}-\frac{C}{2}\cos\Delta\phi, \tag{2}\]  
where \(P_{0}\) is fringe offset and \(C\) is contrast. Under static conditions, because the gravity value is basically unchanged, a number of interference fringes can be fitted by changing the chirp rate \(\alpha\) and Raman light interval time \(T\), and the corresponding gravity value at the center of the fringe is the local gravity value [1, 13, 40, 41], as shown in Figure 3.
## 3 Results
### Experimental Measurement
#### 3.2.2 STAGE 2 Dynamic Measurement  
As a Quantum MX410 is compatible with various measurement sensors, two accelerometers were added to the measurement chain as part of the second stage. The aim was to assess the vibrations transmitted to the flange caused by the vibration of the beam attached to the rigid arm and to verify the possibilities of using the applied strain gauges in the analysis of the manipulator vibration. A first uniaxial acceleration sensor type 4507B (Bruel & Kjaer) was installed on the flange at location A2, and a second uniaxial sensor type 4507B was placed on the beam at location A1 (see Figure 3).  
The measurement of the free vibration of the beam was carried out first. In Figure 10a,b, the time records of the strains and accelerations during the free vibration of the beam attached to the rigid arm are documented. The measurement was repeated three times. Figure 10c,d show the amplitude spectra corresponding to the first vibration cycle depicted in Figure 10a,b.  
Figure 10: _Cont._From Figure 10c,d, it is obvious that both strain gauges and accelerometers identified the same values of the natural frequencies of the analysed mechanical system.  
The measurement of the forced vibration of the beam at a known frequency using an MXG 9802 signal generator was carried out as the second one. Three regimes were simulated: forced vibration at 5 Hz (Figure 11 left) and 10 Hz (Figure 11 right) and measurements at varying excitation frequencies (Figure 12). For the evaluations of amplitude spectra in the first two regimes, the time intervals between 11-14.5 s (for 5 Hz) and 15-17.5 s (for 10 Hz) were chosen. The amplitude spectrum of the third regime was obtained using all of the samples registered during excitation.  
Figure 11: _Cont._  
Figure 10: Free vibration of the beam attached to the rigid arm. (**a**) Time record of measured strains; (**b**) time record of accelerations; (**c**) amplitude spectrum of strains; (**d**) amplitude spectrum of accelerations.  
Figure 11: Forced vibrations at 5 Hz (**left**) and 10 Hz (**right**). (**a**) Time record of strains registered by strain gauges; (**b**) time record of accelerations registered by accelerometers; (**c**) amplitude spectrum of strains; (**d**) amplitude spectrum of accelerations.  
Figure 12: _Cont._
# Novel Approach Sizing and Routing of Wireless Sensor Networks for Applications in Smart Cities
## 3 Problem Formulation
### Wireless Sensor Network Routing
#### 3.2.2 Multicast Routing  
This second model proposed for the routing of the wireless sensor network considers a set of sensors connected by bi-directional communication links. For example, if between sensor \(A\) and sensor \(B\), there is a link denoted as \(e_{A,B}\), then \(A\) can send data directly to \(B\) and \(B\) can send information directly to \(A\). Furthermore, the link \(e_{A,B}\) will have a cost, weight, or distance associated with it and is given by \(d_{A,B}\).  
It is important to note that the link \(e_{A,B}\) is arbitrary in the ordering of \(a\) and \(b\); that is, \(e_{A,B}\) represents the same as \(e_{B,A}\); therefore, the link can be named as \(e_{i}\), where \(i\) is the index of the link.  
Now, we define \(V\) as the set of sensors, \(E\) as the set of existing links, and \(G=(V,E)\) as an undirected graph; additionally, this represents a set of unordered pairs of elements of \(V\) and, thus, the network topology. An undirected graph indicates that the links are all bidirectional. For this stage, the optimization model seeks to find a minimum-cost spanning tree, which is defined as an undirected graph in which a single path connects any two vertices; that is, a tree is a connected graph with no loops.  
Hence, for this problem, we define the set \(V=\{V_{1},V_{2},\ldots,V_{N}\}\) as the set of wireless sensors and we define a tree as a set of links \(A=\{e_{1},e_{2},\ldots,e_{N-1}\}\) such that the links \(e_{i}\in E\). According to this, the cost of tree \(A\) can be defined as \(d_{A}\) and is represented by \(d_{A}=\sum_{i=1}^{N-1}d_{i}\).  
Thus, the minimum-cost tree is defined as tree \(A^{*}\) such that \"\(d_{A^{*}}<=d_{A}\)\" for any other possible tree. The problem started as the need to find a minimum-cost tree, which requires the definition of certain variables, such as \(X_{i,j}\), and the establishment of the link \(e_{i,j}\) that exists and where \(X_{i,j}\) has a value of \(1\) when the link \((i,j)\) belongs to the solution tree \(A\). Otherwise, the value is \(0\); furthermore, a subset \(B\) of any sensors belonging to the same set \(A\) is defined within the group of sensors \(V\).  
Thus, the problem is defined as the cost minimization of the chosen links and is subject to the constraint that the sensors are connected with \(N-1\) links, where \(N\) is the number of sensors belonging to \(V\).  
Objective function:  
\[\min\ \ \sum_{(i,j)\in E}d_{i,j}X_{i,j}, \tag{9}\]  
which is subject to:  
\[\sum_{e_{i,j}\in E}X_{i,j}=N-1; \tag{10}\]  
\[\sum_{e_{i,j}\in E,I\in B,j\in B}X_{i,j}\geq 1;\ \forall\ B\subset V \tag{11}\]
## 3 Results and Discussion
### COPRAS Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Price** \\ **(USD\(\cdot\)kg\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Density** \\ **(kg\(\cdot\)m\({}^{-3}\))** \\ \end{tabular} } & \begin{tabular}{c} **Young’s** \\ **Modulus** \\ **(GPa)** \\ \end{tabular} & \begin{tabular}{c} **Yield** \\ **Strength** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **Flexural** \\ **Modulus** \\ **(GPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Embodied** \\ **Energy** \\ **(GPa)** \\ \end{tabular} &
\begin{tabular}{c} **CO\({}_{2}\) \\ **(MJ\(\cdot\)kg\({}^{-1}\))** \\ \end{tabular} \\ \hline
0.36 & 0.07 & 0.10 & 0.26 & 0.11 & 0.41 & 0.21 \\
0.05 & 0.12 & 0.01 & 0.03 & 0.02 & 0.10 & 0.12 \\
0.04 & 0.16 & 0.17 & 0.17 & 0.17 & 0.06 & 0.16 \\
0.23 & 0.17 & 0.23 & 0.21 & 0.23 & 0.08 & 0.09 \\
0.08 & 0.13 & 0.17 & 0.06 & 0.16 & 0.07 & 0.10 \\
0.14 & 0.19 & 0.14 & 0.07 & 0.14 & 0.21 & 0.15 \\
0.10 & 0.16 & 0.17 & 0.21 & 0.17 & 0.07 & 0.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: COPRAS normalized matrix. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Material** \\ \end{tabular} } & \multirow{2}{*}{\(\mathbf{S+i}\)} & \multirow{2}{*}{\(\mathbf{S-i}\)} & \multirow{2}{*}{\(Q_{i}\)} & \multirow{2}{*}{\(\mathbf{U}_{i}\)} & \multirow{2}{*}{Rank} \\ \hline Bamboo & 0.04 & 0.08 & 0.17 & 100\% & 1 \\ Cement Bonded & & 0.00 & 0.08 & 0.12 & 74\% & 6 \\ Particle Board & & & & & \\ Coir (RC) & 0.03 & 0.10 & 0.13 & 74\% & 5 \\ Flax (RC) & 0.04 & 0.11 & 0.13 & 76\% & 2 \\ Palm 0.5 (RC) & 0.02 & 0.09 & 0.13 & 76\% & 3 \\ Polympropriate (RC) & 0.02 & 0.13 & 0.09 & 52\% & 7 \\ Sisal (RC) & 0.04 & 0.11 & 0.13 & 76\% & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: COPRAS weighted matrix. bamboo is that there are over 1250 species around the world [17], and it can be used from the first 4 to 6 months of life since it develops its full height, diameter and thickness [18], making it feasible to found, is sustainable and can be used in developing countries such as India [19] or Latin American countries.
# Magnetic Nanomaterials for Magnetically-Aided Drug Delivery and Hyperthermia
## 3 Synthesis of Magnetic Nanoparticles  
There are some main features that we need to consider when synthesizing magnetic nanoparticles. The size of the nanoparticles, the magnetic properties, and the ability to carry biomolecules are a few of those features. [PERSON] and co-workers have reported that the nanoparticles can flow with the bloodstream in blood vessels and penetrate through cell walls in order to avoid narrow blood vessels [66]. The ferromagnetic/superparamagnetic property of magnetic nanoparticles can lead them to the targeted organs/locations in the human body under the control of an external magnetic field [67]. Once the nanoparticles have reached the targeted area, they can release active biomolecules/drugs/genes, depending on the application. In order to meet the requirements, magnetic nanoparticles consist of the magnetic core, the protective coating, and the functional groups on the surface (Figure 5) [68].  
There are various types of magnetic nanoparticle synthesis methods. Each method has their own advantages and disadvantages. Therefore, depending on the availability of facilities, instrumentation, and regulations, the correct synthetic path for the synthesis of specific magnetic nanoparticles has to be optimized following diverse synthetic routes [68]. The following discusses a few synthetic routes to synthesize magnetic nanoparticles.
# Thermal Comfort and Sustainability in University Classrooms: A Study in Mediterranean Climate Zones
## 3 Results
### Impact of Thermal Discomfort on Educational Behaviour and Communicative Actions  
This section analyses students' responses to thermal discomfort in two respects: how it affects the following of lessons and the communicative actions they take in this respect, differentiating between AC and NV modes of operation.  
In terms of following the lessons (Figure 10), in AC mode, the majority of students (65%) reported that \"I don't give it a thought, I carry on as normal\". In comparison, in NV mode, only 45% of students reported this attitude, while a higher percentage (35%) stated: \"Make a greater effort to get the most out of the lesson\", suggesting that in hot thermal environments, NV may not be sufficient to reduce the impact of discomfort on their academic performance; students need to make a higher cognitive and physical effort to stay attentive [83]. It is important to note that, in NV mode, 20% of students indicated that they \"Do not get the most out of the lesson\" or that they \"Waste the class\", while in AC, these responses accounted for less than 5%.  
Figure 11 refers to the communicative actions taken by students if they feel thermal discomfort. The results show that a significant majority of students, both in AC mode (80%) and NV mode (70%), prefer not to express their discomfort explicitly (\"I don't show it in any way\"). However, in NV mode, an increase in complaints to teachers during class was observed (15% compared to 5% in AC). On the other hand, the use of formal channels, such as complaints to the class delegate or the student Management Team, was almost non-existent (<5% in both modes). This reflects a general tendency not to channel discomfort through structured means, which could limit the identification and resolution of problems related to the thermal environment in classrooms.  
Ultimately, this analysis reveals that the AC mode has more effectively mitigated the impact of thermal discomfort on class tracking, allowing students to continue their academic performance more consistently. In contrast, the NV mode has a higher proportion of students experiencing difficulty concentrating and requiring extra effort to stay focused. In addition, the differences in communicative actions between the two modes reflect that, although discomfort in NV is more evident, students prefer not to express it formally, which may reflect a perceived lack of expectation that significant changes in thermal conditions will not be implemented.
## 4 Experiments
### Ablation Experiment  
To investigate the contributions of the proposed PSGM and FEM to the performance of the model, we conducted ablation studies on the DIOR dataset with Split1 in 10/20 shot settings. We sequentially integrated these two modules onto the baseline and individually quantified the specific impact of each module on the model performance. The results are presented in Table 4. By incorporating PSGM alone, the mAP50 of the model in Split1 for the 10-shot and 20-shot scenarios increased by 1.4 and 3.6, respectively, with the performance enhancement ratios being 5.6% and 12.3%. This is because PSGM can effectively capture potential foreground information within the background, thereby assisting the model in accurately distinguishing between background and foreground features. Adding FEM  
Figure 5: Visualizations of detection results for novel classes in DIOR datasets on Split2 under the 20-shot settings. In both columns, the first row displays the ground truth labels, the second row presents the baseline results, and the third row showcases the results of PSGFE.  
alone led to increases in mAP50 by 0.7 and 2.7 for the 10-shot and 20-shot scenarios, respectively, with a performance improvement ratio of 2. 8% and 9. 2%. This demonstrates that our proposed FEM can effectively enhance the features of the ROI, allowing it to better represent the multi-scale information of the proposals. In general, by incorporating both PSGM and FEM, PSGFE achieved optimal performance, significantly outperforming the model with only PSGM or FEM. The ablation experiments demonstrate that the application of PSGM and FEM, either individually or in combination, enhances the model performance, thus validating the effectiveness of our proposed methods.  
During the few-shot fine-tuning phase, there are three possible positions for FEM to verify the impact of different implementation positions on the model performance. Therefore, we also conducted additional ablation experiments on the position of FEM in the DIOR dataset with Split1 in the 20-shot scenario, as shown in Table 5. Building on Case 1, FEM is added to Branch 2 and Branch 3, respectively, resulting in performance improvements of 2.9% and 2.0%, respectively. In Case 4, where FEM is added to all branches, mAP50 increased by 1.2, resulting in a performance improvement of 3.9%. The ablation experiments demonstrate the necessity and effectiveness of improving the ROI features of all modules through FEM.
# Design and Application of the BiVib Audio-Tactile Piano Sample Library
## 2 Creation of the BiVib library
### Recording Software  
Ten values of dynamics were chosen between MIDI key velocity 12 and 111 by evenly splitting this range into eleven intervals. This choice was motivated by a previous study by the present authors, which reported that both Disklaviers produced inconsistent dynamics outside this velocity range [12]. In general, the servo-mechanics of computer-controlled pianos fall short (to an extent that depends on the model) of providing a reliable response at extreme dynamic values [33]. Holding this assumption, two different software setups were used respectively for sampling sounds and vibrations.  
Binaural samples were recorded via an automatic procedure programmed in SuperCollider. (A programming environment for sound processing and algorithmic composition: [[http://supercollider.github.io/](http://supercollider.github.io/)]([http://supercollider.github.io/](http://supercollider.github.io/)).) The recording sessions took place at night time, thus minimizing unwanted noise coming from human activity in the building. On the grand piano, note lengths were determined algorithmically depending on their dynamics and pitch, ranging from 30 s when A0 was played at key velocity 111, to 10 s when C8 was played at key velocity 12. In fact, notes of increasing pitch and/or decreasing dynamics have shorter decay times. These durations allow each note to fade out completely, while minimizing silent recordings and the overall duration of the recording sessions--still, each session lasted approximately six hours.  
On the contrary, an undocumented protection mechanism on the upright piano prevents its electromechanical system from holding down the keys for more than about 17 s, thus making a complete decay impossible for some notes, especially at low pitches and high dynamics. In this case, for the sake of simplicity all tones were recorded for just as long as possible.  
Vibration samples were recorded through a less sophisticated procedure. A digital audio workstation (DAW) software was used to play back all notes in sequence at the same ten MIDI velocity values as those used for the binaural audio recordings, using a constant duration of 16 s. This duration in fact is certainly greater than the time taken by any key vibration to decay below sensitivity thresholds [12; 34].
## 2 Materials and Methods
### Study Region and Data
Figure 5 indicates, for each of the selected stations, the ratio of missing/complete data during the year 2007 as an example to describe the process. Another observed characteristic is that these missing data do not usually appear isolated, but rather appear in the form of blocks as shown in Figure 6. This is because when a sensor fault occurs, it may take some time for the fault to be corrected. For example, at the Sambrexio station shown in Figure 6g, the largest block has 1584 missing data items, which corresponds to 11 consecutive days without collecting rainfall data at that station; at the same station, you can see another block of 1424 missing data items that corresponds to almost 10 days. Other stations, such as Marco da Curra in Figure 6a, Mabegondo in Figure 6c or Punta Candieira in Figure 6e, contain a block of approximately 1000 missing data items, which corresponds to one week without data. Based on these results, it was decided to carry out a station-by-station analysis, thus noticing in which months missing data appeared and in which months data were complete. Figure 7 shows the monthly distribution of missing data for the stations indicated in Figure 6.  
Figure 4: Data missing in red from 2007 (a) to 2018 (l). Figure 5: Percentage of missing data (red color) in 2007. Figure 6: Data missing in blocks in several stations: (**a**) Marco da Curra, (**b**) Guitiriz, (**c**) Mabegondo, (**d**) CIS Ferrol, (**e**) Punta Candieira, (**f**) Fragavella, (**g**) Sambreixo, (**h**) Serra da Faladoira, (**i**) Corno do Boi, (**j**) Olas. Figure 7: _Cont._
## 2 Related Works
### Feature Extraction and Dimensionality Reduction  
Feature extraction and dimensionality reduction [25] are the preprocessing methods most commonly used in the analysis of signals such as those related to speech and activity recognition. Feature extraction refers to the identification of specific characteristics or patterns within a given signal. For example, the average value of a speech signal could be used to indicate the loudness of speech. The features used in the analysis of signals include mean values, correlations among axes, mean absolute deviation, root mean square, variance, standard deviation, signal magnitude area, and average energy. It should be noted however that the choice of feature set depends on the specifics of the case; that is, not all features are relevant, and irrelevant features can increase the execution time and/or undermine the precision of analysis [25]. Following feature extraction, a number of methods can be used to identify the features best suited to the analysis of a given case.  
Dimensionality reduction is the method most commonly used in the selection of features suitable for the analysis of a particular signal. For example, dimensionality reduction can be used to sort through a large number of features identified in the signals collected by automated sensors, such as those found on various forms of machinery. This makes it possible to identify the features best able to verify that a machine is working smoothly or identify cases of imminent failure. Dimensionality reduction is commonly implemented using principal component analysis (PCA) [26] or linear discriminant analysis (LDA) [25]. PCA is an unsupervised method, whereas LDA is a supervised method. PCA is used to identify the projections best suited to \"representing data\", whereas LDA is used for \"discriminating among data\" [27; 28]. This makes LDA more suitable than PCA for the monitoring of machinery status.