esa-sceva/satcom-chunk-collection huggingface dataset:
# Narrowband Internet of Things (NB-IoT):
## 5 Open Research Questions and Discussion
### Timing Advance (TA)  
When the base station responds to NB-IoT UEs about RRC connection request, it incorporates the TA command to be used for NB-IoT UE terminal data uplink transmission timing (i.e., to time-synchronize the UEs to the base station and help to compensate the propagation delays). However, for NB-IoT UE, the TA adjustment accuracy of the signaled timing advance with respect to the prior uplink transmission may highly be affected by the massive number of NB-IoT devices contending for the access. This is because the base station may need to correct some UE timing while for other NB-IoT UEs that had already transmitted NPRACH could receive the random access response which is not intended for them. Some works have addressed the receiver algorithms for NPRACH TA estimation as well as detection timing advance adjustment decoding schemes to improve theestimation but the NB-IoT receiver sensitivity and weak channel estimation quality still negatively affect the TA adjustment.
# Outcome Prediction for Patients with Bipolar Disorder Using Prodromal and Onset Data +
###### Abstract  
Background: Predicting the outcomes of serious mental illnesses including bipolar disorder (BD) is clinically beneficial, yet difficult. Objectives: This study aimed to predict hospitalization and mortality for patients with incident BD using a deep neural network approach. Methods: We randomly sampled 20,000 US Veterans with BD. Data on patients' prior hospitalizations, diagnoses, procedures, medications, note types, vital signs, lab results, and BD symptoms that occurred within 1 year before and at the onset of the incident BD were extracted as features. We then created novel temporal images of patient clinical features both during the prodromal period and at the time of the disease onset. Using each temporal image as a feature, we trained and tested deep neural network learning models to predict the 1-year combined outcome of hospitalization and mortality. Results: The models achieved accuracies of 0.766-0.949 and AUCs of 0.745-0.806 for the combined outcomes. The AUC for predicting mortality was 0.814, while its highest and lowest values for predicting different types of hospitalization were 90.4% and 70.1%, suggesting that some outcomes were more difficult to predict than others. Conclusion: Deep learning using temporal graphics of clinical history is a new and promising analytical approach for mental health outcome prediction.  
prediction; bipolar disorder; deep neural network; support vector machine
## 2 Literature Review
### Concrete Formwork Applications in BIM  
In the construction industry, BIM has recently been used to facilitate the integration of the design and construction processes, saving both cost and time [14; 40]. BIM can store and manage geometric and semantic information for design components and integrate all descriptions of visualized objects and related data during a construction project. Many BIM applications have focused on pre-construction planning activities [41]. A few studies have tried to develop BIM applications for decision support systems facilitating construction project control and operation [42; 43]. Particularly for concrete formwork, [PERSON] et al. [44] recently proposed a BIM-based decision support system for concrete formwork design that managed all information needed and collected during the concrete formwork construction process. BIM has been implemented to help the design and planning process, as well as simulate the designed formwork. [PERSON] et al. [45] provided a conceptual overview of a knowledge repository for concrete formworks via BIM. The initial concept was to connect 3 Dmodels and associated information, saving on parameters related to cost and concrete formwork materials such as unit price, specifications, and quantity [45]. [PERSON] and [PERSON] [46] developed a BIM-based automated concrete formwork layout and simulation system that could predict construction schedules based on a BIM formwork simulation. Another study in this domain developed a BIM-based concrete formwork management system for improving the efficiency of formwork use, such as by optimizing reuse and maximizing return-on-investment [47]. The current state of knowledge has contributed significantly to developing BIM-based management systems, especially in terms of concrete formwork design and planning during the pre-construction step [44; 46; 47]. However, the end-users of such systems employ static data instead of real-time information collected by sensors or other information technologies during the construction process [48]. Real-time data collected in BIM can support project stakeholders' decision-making by controlling and interpreting data pattern changes and trends [44; 47; 48; 49]. Current research has focused on using BIM for concrete formwork planning and design rather than monitoring and calculating the maturity of concrete during the formwork process [44; 47; 48; 49; 50].  
Therefore, the present study expanded the interoperability of BIM for real-time monitoring of the maturity of concrete by using sensor data and calculations conducted through the API interface, in addition to conveying the possible formwork opening time that does not risk damage to the concrete's structural elements. The interoperability of exchanging BIM models and sensor data can also extract all computed outcomes to an IFC data format in the BIM environment.
# Enhanced Pet Behavior Prediction via S2 GAN-Based Heterogeneous Data Synthesis
## 3 Pet Behavior Prediction via S2 GAN-based Data Synthesis
### Data Preprocessing
#### 3.2.2 Sensor Preprocessing  
Sensor data were preprocessed via outlier removal, data normalization, interpolation, and the creation of sensor data sequences.  
Outlier removal involves excluding values that significantly deviate from the observed range. Including such values in training can lead to suboptimal performance, for which the interquartile range was employed.  
To ensure uniform scaling of the data, normalization was performed. When conducting deep-learning-based training using numerical data, the data must be normalized because a wide distribution and range can make predictions difficult. Noise may be present after outlier removal; therefore, the Z-Score normalization method was used as it was less affected by outliers.  
Next, interpolation was performed for some missing values that occurred for various reasons. Interpolation was performed when less than 20% of the data were missing in the sequence length based on the 3 s standard in the entire collected data. A cubic interpolation method was used for this purpose. The method interpolates the data using a cubic polynomial between two data points, better reflecting the trend of the time series than linear or quadratic methods.  
Finally, similar to video preprocessing, sequence generation also applied a sliding window with 3 s intervals and 50% overlapping. Consequently, the dimension of the sequence was configured as (sequence length, 3, 3), where the first \"3\" denotes the three sensors: accelerometer, gyroscope, and magnetometer, and the last \"3\" represents the \(x,y\), and \(z\) axes.
## 4 Analysis of Experimental Results
### Comparative Analysis of Fine Matching Algorithms  
For the fine alignment stage, on the basis of 4 PCS algorithm based on ISS feature points, the point cloud alignment effect of model 60 rails obtained by using the NDT algorithm, the traditional ICP algorithm, the GICP algorithm, and the KD-ICP algorithm in this paper is shown in Figure 10. From the figure, it can be seen that the four algorithms used have achieved a good overlap between the source point cloud and the target point cloud.  
The average values of the results obtained by the four algorithms for 10 runs are shown in Table 4. From the data in Figure 10 and Table 4, it can be seen that the RMSE values of the four algorithms are basically the same. however, the KD-ICP algorithm in this paper is 22.309 ms in terms of runtime, which is 71.39% higher than that of the traditional ICP algorithm, 79.02% higher than that of the GICP algorithm, and 83.11% higher than that of the NDT algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Run Time/s** & **RMSE/mm** \\ \hline PCA & 1.1 & 9.316 \\ SAC & 35.58 & 3.827 \\
4 PCS & 10.21 & 1.655 \\ K-4 PCS & 8.05 & 1.648 \\ Algorithm in this paper & 6.57 & 1.387 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of coarse alignment algorithms.  
Figure 9: Rough alignment effect. (**a**) Initial position before alignment; (**b**) PCA algorithm; (**c**) SAC algorithm; (**d**) 4 PCS algorithm; (**e**) K-4 PCS algorithm; (**f**) the algorithms in this paper.
###### Abstract  
We report a fiber-optic 10 GHz frequency transfer technique based on an optical-electronic joint phase compensator. A highly stable frequency signal at 10 GHz was transferred in a 50-km long fiber link by using this technique. Two key parameters of the frequency dissemination, the timing fluctuation and frequency stability were both measured. The experimental results show the root-mean-square timing fluctuation of the transferred microwave is about 103 fs within 10,000 s, and the frequency stability for the transmission link is 2.2 \(\times\) 10\({}^{-14}\) at 1 s and 8.5 \(\times\) 10\({}^{-17}\) at 2000 s. The technique proposed in this paper provides a powerful tool which can be used to transfer atomic clocks (e.g., commercial H-master and Cs clocks) in a long fiber link.  
frequency transfer; fiber link; optical-electronic joint compensation; timing fluctuation +
Footnote †: journal:  
stretcher can reach nanometers. In this case, the stretcher-based compensator can provide extremely high precision timing jitter suppression, and achieve ultralow residual phase noise [30]. However, the adjustable range of the fiber stretcher is limited by its physical size, resulting in a limited range of phase compensation [31]. Although the electronic phase shifter has a large range of the tunable timing delay, its resolution is a bit worse than that of the fiber stretcher [32]. Therefore, it is difficult to rely solely on optical or electric compensation to improve both the resolution and range of timing suppression.  
In this paper, we report a novel fiber-optic 10 GHz frequency transfer based an optical-electronic joint phase compensator which consists of a fiber stretcher and an electronic phase shifter. The joint compensator is able to provide not only an ultra-high resolution but also a very wide range. We built a fiber-based frequency transfer experiment, and a stable 10 GHz microwave signal was transferred in a 50-km long fiber link, where the RMS timing fluctuation is 103 fs in 10,000 s, and the relative fractional frequency stability of the transmission link is 2.2 \(\times\) 10\({}^{-14}\) at 1 s and 8.5 \(\times\) 10\({}^{-17}\) at 2000 s. In our experiment, the 10 GHz microwave was synthesized from an atomic Rb clock, and the stability of the transmission link is much better than even a commercial H-master clock [33]. This indicates that the proposed frequency transfer technique with the joint phase compensation can be directly applied to the dissemination of most atomic clock signals in a fiber link.
## 5 Energy Detection Process
### Energy Detection with NU and DT Adaptation
In addition, the second case, where \(\rho^{\prime}>1.00\) and \(\rho=1.00\), means that the ED process excludes the impact of NU and the only DT adaptation is simulated in the ED process for sensing PU signals. For the same channel characteristics (e.g., the same SNR at the location of SU) impacted by NU and the same sample quantity \(N^{NUDT}=N^{NUD}\), the detection probability and false alarm probability, expressed with relations (22) and (23), converge to relations (19) and (20) for the first case and relations (16) and (17) for the second case. In the third case, the influence of NU and DT adaptation on the ED process can be simulated when \(\rho>1.00\) and \(\rho^{\prime}>1.00\). In order to illustrate the impact of these specific factors, Figure 5 visualises two signals with equal average received powers (\(\left.\right|P=\int_{0}^{t}y(t)dt\)) but different NUs (variations). The received signal presented on the left side in Figure 5 has a lower noise variance, which is in the range \(\sigma_{11 NUDT}^{2}i\epsilon\left[\frac{\sigma_{12i}^{2}}{\rho_{1}},\ \rho_{1}\sigma_{1 2i}^{2}\right]\), while the received signal presented on the right side has significantly higher noise variance spanning the range \(\sigma_{2 NUDT}^{2}i\epsilon\left[\sigma_{2n_{i}}^{2}/\rho_{2},\ \rho_{2}\ \sigma_{2n_{i}}^{2}\right]\), where \(\rho^{\prime}_{2}>\rho^{\prime}_{1}\). Hypotheses \(H_{0}\) or \(H_{1}\) from relations (1) or (9-10) are satisfied if the signals from Figure 5 fall below or above the dynamically set threshold, respectively. The detection threshold is dynamically adjusted according to the signal level and noise variations (NUs), and it can be in the range \(\lambda_{1i}^{NUDT}\epsilon\left[\frac{\lambda_{1i}}{\rho^{\prime}_{1}},\ \lambda_{1i}\rho^{\prime}_{1}\right]\) for the left side signal in Figure 5 and \(\lambda_{2i}^{\prime}\lambda_{1i}^{NUDT}\epsilon\left[\frac{\lambda_{2i}}{\rho ^{\prime}_{2}},\ \lambda_{2i}\ \rho^{\prime}_{2}\right]\) for the right side signal where \(\rho^{\prime}_{2}>\rho^{\prime}_{1}\). In essence, the detection threshold has to be set based on the worst-case noise level uncertainty. In order to maximise the detection probability, the threshold value has to be adjusted appropriately. This means that an increase in NU must be followed by an appropriate change in DT adaptation (Figure 5). Figure 5: DT and NU ranges in the ED process of signals with equal average received power.
## 3 Results and Discussion  
### CleanSeq Processes WGS Raw Data for Contamination Detection, Decontamination, and Calling Variants  
As shown in Figure 1 and described in the Materials and Methods, the CleanSeq pipeline incorporated routine WGS analytical tools, such as Trimmomatic [24], BLAST [9], SAMtools [11], GATK4 [12], BWA [10], and Clustal Omega [25], to process raw reads directly. To date, some existing pipelines for microbial WGS, such as Bactopia [37], TORMES [38], ASA3P [8], and BacPipe [39], often only contain similar modules like taxonomic identification and genome assembly. To the best of our knowledge, CleanSeq is the first attempt to integrate four modules of taxonomic classification, decontamination, and variant calling and verification in one Python-based pipeline. It detects and removes contaminated reads based on BLASTn mapping and generates clean data for subsequent analysis of variant calling via GATK. All the reads containing called variants are extracted and verified by local re-alignment and visualization via Clustal Omega [25].
## 4 Experiments
### Comparison  
In this subsection, we show the main results of dynamic anchor. We equipped the previous state-of-art detector RetinaNet with dynamic anchor and used ResNet-50 and ResNet-101 as the backbones. To verify the efficiency of our method, we also compared dynamic anchor with guided anchoring. As presented in Table 4, dynamic anchor with ResNet-50 and Res-Net101 as the backbone networks achieved an AP of 38.0% and 40.0% on COCO test-dev 2017, respectively, which outperformed RetinaNet and GA-RetinaNet. Compared to the baseline and the counterpart, our model achieved 2.4% and 1.2% AP improvement, and the AP metric of DA-RetinaNet outperformed the other detectors on all size of objects. DA-RetinaNet also achieved a stable improvement for detection with Res-Net101, while the AP\({}_{\text{S}}\) metric was slightly lower than RetinaNet (21.5% vs. 21.8%). The improvement of our model comes from medium and large object detection, which is the strength of dynamic anchor, as dynamic anchor can generate anchors with the theoretical maximum IoU scores.  
Based on semantic features, Guided Anchor predicts the locations where the center of objects of interest are likely to exist, as well as the scales and aspect ratios at different locations. In training, Guided Anchor uses IoU scores of the pre-defined anchors and the ground-truth boxes to determine the positive sample points in the feature maps, and also sets an initial reference box for each position to regress the anchor of the object. Guided Anchor employs the pre-defined anchors and the initial reference boxes to predict anchors; its performance is severely affected by these boxes, which does not avoid the defect of the pre-defined anchor. Guided Anchor does not break away from the frame of the manual anchor in the anchor-based detectors. However, our dynamic anchor does not introduce any pre-defined boxes, and instead achieves better detection performance.  
Moreover, compared with RetinaNet and GA-RetinaNet, the DA-RetinaNet shares the heads between different feature levels and predicts only one anchor box at each point on the feature map, making the detector more parameter-efficient. In the GFLOPs metric, our method with ResNet-50 only reached 141.79, which is 29.6% and 28.1% lower than RetinaNet and GA-RetinaNet, respectively. Similar results were obtained on ResNet-101. To sum up, sufficient experiments have proved that our method is not only superior to other methods in detection performance, but also has significant advantages in reasoning speed.
## 2 Background
### Ratio Based Approach  
The ratio based approach is an approach to find DMU targets of radial FDH models without solving any mathematical programming models. To the best of our knowledge, [PERSON] et al. (2006) [13] were the first researchers to propose RBA to find targets of radial FDH models. They considered a set of n peer DMUs (DMUj, j = 1, \(\ldots\), n), such that each DMUj produces multiple outputs \(y_{ij}>0\) (r = 1, \(\ldots\), s) by utilizing multiple inputs \(x_{ij}>0\) (I = 1, \(\ldots\), m). Considering DMUj(xo,yo) (o = 1, \(\ldots\), n) as the unit under assessment, the basic input-oriented and output-oriented linear mixed-integer radial FDH model under variable returns-to-scale technology are shown by models 7 and 8, respectively [13,20].  
\[\theta_{o}=Min\ \ \theta,\] s.t \[\sum_{j=1}^{n}\ \mu_{j}x_{ij}\leq\theta x_{io},\,i=1\ldots m,\] \[\sum_{j=1}^{n}\ \mu_{j}Y_{ij}\geq Y_{ro^{\prime}},\,r=1\ldots s,\] \[\mu_{j}=\delta\omega_{j},\,\,\omega_{j}\in\{0,1\},\,j=1,\ldots,n,\] \[\theta_{o}=Max\ \ \varphi,\] s.t \[\sum_{j=1}^{n}\ \mu_{j}x_{ij}\leq x_{io},\,i=1\ldots m,\] \[\sum_{j=1}^{n}\ \mu_{j}Y_{ij}\geq\varphi y_{ro^{\prime}},\,r=1 \ldots s,\] \[\mu_{j}=\delta\omega_{j},\,\,\omega_{j}\in\{0,1\},\,j=1,\ldots,n,\] \[\delta=1,\,\sum_{j=1}^{n}\ \omega_{j}=1.\]  
In model 7, DMUo is called radial input-oriented FDH-efficient if \(\theta_{o}=1\). Moreover, in model 8, DMUo is called radial output-oriented FDH-efficient if \(\varphi_{o}=1\). The targets of radial models 7 and 8 can be found by computing some simple ratios using RBA. After considering DMUo (o = 1, \(\ldots\), n) as an under assessment DMU, for j = 1, \(\ldots\), n, \(\lambda_{jo}=\max_{r}\left\{y_{ro}/y_{ij}\right\}\) and \(\lambda^{jo}=\min_{i}\left\{x_{io}/x_{ii}\right\}\) are defined. The optimized objective function in radial models 7 and 8 is then calculated as \(\theta_{o}=\min_{j=1,\ldots,n,y_{j}\geq y_{o}}\{\max_{i}\left\{x_{ii}\left\{x_ {ii}/x_{io}\right\}\right\}\) and \(\varphi_{o}=\max_{j=1,\ldots,n,y_{j}\leq x_{o}}\{\min_{i}\left\{y_{ii}/y_{ro} \right\}\}\), respectively [13]. The details on RBA validity have been reported previously [13,14,19]. The targets of radial FDH models 7 and 8 can be found by RBA.
# Photon Enhanced Interaction and Entanglement in Semiconductor Position-Based Qubits
## 3 Position-Based Semiconductor Qubits in the Frame of Semiconductor Photon Communication
### Representation of the System in a Position Basis  
Qualitatively, we can write the Hamiltonian in the tight binding approximation:  
\[H=\left|\begin{array}{cc}E_{p}&t_{s,0\to 1}\\ t_{s,1\to 0}&E_{p}\end{array}\right| \tag{6}\]  
where \(t_{s,i\to j}=\left\langle i\right|\left.\hat{H}\left|j\right\rangle,i\
eq j= \left\{0,1\right\}\) are the tunneling (also known as hoping) terms in the tight-binding formalism which express the tunneling probability between the two neighboring quantum dots. Now, if we set \(E_{g}=E-\Delta\) and \(E_{e}=E+\Delta\), then \(E_{e}-E_{g}=2\Delta\) and \(\omega_{0}=\frac{2\Delta}{\hbar}\), so (6) can be written as [29; 30]:  
\[H=\left|\begin{array}{cc}E&-\Delta\\ -\Delta&E\end{array}\right| \tag{7}\]  
with eigen-values \(\lambda=E\pm\Delta\). The wave-function, in a position basis can be written as:  
\[\left|\phi\right\rangle=c_{0}\left|0\right\rangle+c_{1}\left|1\right\rangle \tag{8}\]  
where \(c_{0}\) and \(c_{1}\) are the occupancy coefficients in the Wannier-position basis \(\left\{\left|0\right\rangle\),\(\left|1\right\rangle\right\}\), with  
\[\left|0\right\rangle\left(t\!=\!0\right) =\frac{1}{\sqrt{2}}(\left|\psi_{g}\right\rangle+\left|\psi_{e}\right\rangle)\] \[\left|1\right\rangle\left(t\!=\!0\right) =\frac{1}{\sqrt{2}}(\left|\psi_{g}\right\rangle-\left|\psi_{e}\right\rangle) \tag{9}\]  
The time evolution of the system, starting from state \(\left|0\right\rangle\left(t\!=\!0\right)\) is:  
\[\left|\phi\right\rangle\left(t\right)=\frac{1}{\sqrt{2}}(\left| \psi_{g}\right\rangle e^{-i(E-\Delta)t/\hbar}+\left|\psi_{e}\right\rangle e^{ -i(E+\Delta)t/\hbar}=\] \[\frac{1}{\sqrt{2}}\left(\frac{1}{\sqrt{2}}\Big{(}\left|0\right\rangle +\left|1\right\rangle\Big{)}e^{-i(E-\Delta)t/\hbar}+\frac{1}{\sqrt{2}}\Big{(} \left|0\right\rangle-\left|1\right\rangle\Big{)}e^{-i(E+\Delta)t/\hbar}\right) =\] \[\frac{1}{\sqrt{2}}e^{-iEt/\hbar}\left(e^{i\Delta t/\hbar}\Big{(} \left|0\right\rangle+\left|1\right\rangle\Big{)}+(e^{-i\Delta t/\hbar}\Big{(} \left|0\right\rangle-\left|1\right\rangle\Big{)}\right)=e^{-iEt/\hbar}\left( \cos(\frac{\Delta t}{\hbar})\left|0\right\rangle+i\cdot\sin(\frac{\Delta t}{ \hbar})\left|1\right\rangle\right) \tag{10}\]  
The above time-dependent oscillations have frequency probabilities:  
\[P_{\left|0\right\rangle}(t) =|\left\langle 0|0\right\rangle(t)\rangle|^{2}=\cos^{2}\left(\frac{ \Delta t}{\hbar}\right)\] \[P_{\left|1\right\rangle}(t) =|\left\langle 1|1\right\rangle(t)\rangle|^{2}=\sin^{2}\left(\frac{ \Delta t}{\hbar}\right) \tag{11}\]  
Notice that we can write:  
\[\left|\phi(t)\right\rangle=c_{0}\left|0\right\rangle e^{i\omega_{0}t}+c_{1} \left|1\right\rangle e^{-i\omega_{0}t} \tag{12}\]  
Photon Emission Due to Transitions in a Semiconductor Position-Based Qubit--Description Based on a Jaynes-Cummings-Hubbard Formalism