esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
## 3 Results and Discussion
### Reported Application of AI to RT
#### 3.2.5 Treatment Planning  
In the context of intensity-modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT), manual planning for complex cases is challenging, depending on the planner's experience and ability to manage the trial-and-error process. Planners, although very experienced, neither know \"a priori\" how much a plan can be optimized nor how to tailor all dosimetric constraints to the specific patient. During the optimization of the plan, the constraints depend on the site, stage of disease, and treatment schedule for given classes of patients. Knowledge-based treatment planning uses the ML technique to identify the patient-specific dose-volume constraints. This approach might have a limited  
Figure 6: The numbers of identified papers that aimed to generate synthetic images according to the input imaging modality for (**a**) DL- and (**b**) ML-based approaches.  
capability to guide the new treatment schedules [86] because it requires a large dataset of collected data to train the system and provide the best patient-tailored solutions [87].  
On the other hand, in a research study, [PERSON] et al. [88] implemented a DL method to find the optimal machine parameter solution for a VMAT plan. This method is based on reinforcement learning and a convolutional neural network, which are ML- and DL-based approaches, respectively. The paper employed a two-dimensional network design to optimize VMAT delivery. Its future extension to three-dimensional beam modeling could represent a fully DL-based time-saving solution that does not require previous plans.  
Another treatment planning optimization strategy is based on fully automated IO solutions that rely on computerized rules and reasoning methods [49]. The technique templates allow the automatic generation of plans without human intervention. The superiority of auto-planning versus manual VMAT optimization was reported using a blinded side-by-side plan comparison [89]. The full implementation of auto-planning in the clinical routine can allow one to develop consistent quality plans with minimal inter-planner variation in less than 30 min [90]. This will undoubtedly significantly reduce planning times in busy departments or help in finding a better solution for possible planning class optimization. With the advent of auto-planning technologies, a complete understanding of underlined methods and quality assurance procedures is necessary for the effective use of these tools.  
More research is needed to explore the full potential of auto-planning and its optimal clinical application by identifying ad hoc dose-volume constraints for generating novel treatments based on the available technologies in a multicenter context [89].
# Bounce: A High Performance Satellite-Based Blockchain System
## 5 Implementation
### Storage  
The blockchain and transactions are stored in a distributed manner across Broadcast Ground Stations, using RocksDB, a key-value store, to manage this data. The blockchain is structured as follows:  
* The head (most recent block) of the blockchain is represented by the key-value pair \"HEAD\" \(\to cr_{n}\), where \(cr_{n}\) is the latest commit record.
* Each commit record is stored as a linked key-value pair, such as \(cr_{i}\to cr_{i-1}\), where \(cr_{i}\) represents the \(i\)-th commit record.  
Transactions are stored based on their corresponding root hash, in the format root \(\rightarrow\) transactions. When a Broadcast Ground Station receives a new commit record, \(cr_{n+1}\), from the satellite and verifies it as positive, it prepends the block corresponding to \(cr_{n+1}\) to the blockchain. This entails updating the blockchain head to \"HEAD\" \(\rightarrow\)\(cr_{n+1}\) and adds a new key-value link \(cr_{n+1}\to cr_{n}\). The Broadcast Ground Station then stores the cached transactions associated with the root(s) in this commit record as root \(\rightarrow\) transactions.
# Subspace Detours Meet Gromov-Wasserstein
## 3 Subspace Detours for \(Gw\)
### Motivations  
First, we adapt the definition of subspace optimal plans for _different_ subspaces. Indeed, since the GW distance is adapted to distributions that have their own geometry, we argue that if we project on the _same_ subspace, then it is likely that the resulting coupling would not be coherent with that of GW. To illustrate this point, we use as a source distribution \(\mu\) one moon of the two moons dataset and obtain a target \(\
u\) by rotating \(\mu\) by an angle of \(\frac{\pi}{2}\) (see Figure 1). As the GW with \(c(x,x^{\prime})=\|x-x^{\prime}\|_{2}^{2}\) is invariant with respect to isometries, the optimal coupling is diagonal, as recovered on the left side of the figure. However, when choosing one subspace to project both the source and target distributions, we completely lose the optimal coupling between them. Nonetheless, by choosing one subspace for each measure more wisely (using here the first component of the principal component analysis (PCA) decomposition), we recover the diagonal coupling. This simple illustration underlines that the choice of both subspaces is important. A way of choosing the subspaces could be to project on the subspace containing the more information for each dataset using, e.g., PCA independently on each distribution. [PERSON] and [PERSON] [14] proposed to optimize the optimal transport cost with respect to an orthonormal matrix with a projected gradient descent, which could be extended to an optimization over two orthonormal matrices in our context.  
By allowing for different subspaces, we obtain the following definition of subspace optimal plans:  
**Definition 2**.: _Let \(\mu\in\mathcal{P}_{2}(\mathbb{R}^{p})\), \(\
u\in\mathcal{P}_{2}(\mathbb{R}^{q})\), \(E\) be a \(k\)-dimensional subspace of \(\mathbb{R}^{p}\) and \(F\) a \(k^{\prime}\)-dimensional subspace of \(\mathbb{R}^{q}\). Let \(\gamma_{E\times F}^{*}\) be an optimal transport plan for GW between \(\mu_{E}=\pi_{\#}^{E}\mu\) and \(v_{F}=\pi_{\#}^{F}\
u\) (with \(\pi^{E}\) (resp. \(\pi^{F}\)) the orthogonal projection on \(E\) (resp. \(F\))). Then, the set of \((E,F)\)-optimal plans between \(\mu\) and \(\
u\) is defined as \(\Pi_{E,F}(\mu,\
u)=\{\gamma\in\Pi(\mu,\
u)|\;(\pi^{E},\pi^{F})_{\#}\gamma= \gamma_{E\times F}^{*}\}\)._  
Analogously to [PERSON] and [PERSON] [14] (Section 2.2), we can obtain from \(\gamma_{E\times F}^{*}\) a coupling on the whole space by either defining the Monge-Independent plan \(\pi_{\text{MI}}=\gamma_{E\times F}^{*}\otimes(\mu_{E^{\perp}|E}\otimes v_{F^{ \perp}|F})\) or the Monge-Knothe plan \(\pi_{\text{MK}}=\gamma_{E\times F}^{*}\otimes\gamma_{E^{\perp}\times F^{ \perp}|E\times F}^{*}\) where OT plans are taken with some OT cost, e.g., \(GW\).  
Figure 1: From left to right: Data (moons); OT plan obtained with GW for \(c(x,x^{\prime})=\|x-x^{\prime}\|_{2}^{2}\); Data projected on the first axis; OT plan obtained between the projected measures; Data projected on their first PCA component; OT plan obtained between the the projected measures.
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 6 Conclusions  
In this article, for the detection task of aerial remote sensing images, we made a series of improvements to the YOLOX model, using three aerial remote sensing datasets for training and testing. We finally proposed an improved, high-performance remote sensing image detection model: RS-YOLOX.  
To enhance the features in the backbone network and the neck network and improve the performance of the model in detecting small targets, we used ECA and ASFF. To adjust the number of positive and negative samples, we used the VFL function. Finally, to improve the detection performance of the model for large-size images, we adapted the model to the SAHI framework. Although our model has high detection accuracy for remote sensing images, compared with the original YOLOX model, it increases the parameters and calculation as well as the detection time. Therefore, our next plan is to study the model in terms of making it lightweight and improving its speed.  
[PERSON] proposed the network architecture design and the framework of RS-YOLOX. [PERSON] and [PERSON] collected and preprocessed the datasets. [PERSON] performed the experiments. [PERSON], [PERSON] and [PERSON] analyzed and discussed the experimental data. [PERSON] and [PERSON] wrote the article. [PERSON], [PERSON] and [PERSON] revised the article and provided valuable advice for the experiments. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key R&D projects in the Yunnan Province (Grant No. 202202 AD08004), the Application and Foundation Project of the Yunnan Province (Grant No. 202001 BB050032), the Yunnan Provincial Department of Science and Technology--Yunnan University Joint Special Project for Double-Class Construction (Grant No. 202201 BF070001-005), and the Open Project of CAS Key Laboratory of Solar Activity, National Astronomical Observatories (Grant No. KISA202115).  
Not applicable.  
The three datasets that support this study are openly available online. DOTA-v1.5: [[https://captain-whu.github.io/DOTA/dataset.html](https://captain-whu.github.io/DOTA/dataset.html)]([https://captain-whu.github.io/DOTA/dataset.html](https://captain-whu.github.io/DOTA/dataset.html)) (accessed on 2 February 2022), TGRS-HRRSD: [[https://github.com/CrazyStoneonRoad/TGRS-HRRSD-Dataset](https://github.com/CrazyStoneonRoad/TGRS-HRRSD-Dataset)]([https://github.com/CrazyStoneonRoad/TGRS-HRRSD-Dataset](https://github.com/CrazyStoneonRoad/TGRS-HRRSD-Dataset)) (accessed on 6 February 2022), RSDD: [[https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-](https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-)]([https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-](https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-)) (accessed on 30 March 2022).  
We would like to thank the anonymous reviewers and the editor-in-chief for their comments to improve the article. Thanks also to the data sharer. We thank all the people involved in the study.  
The authors declare no conflict of interest.
# Robust Detection of Background Acoustic Scene in the Presence of Foreground Speech
## 3 DNN-Based ASC
### Multi-Condition Training  
To improve the robustness of ASC system, multi-condition training [15; 18] is performed. This is done consistently for the ResNet baseline and all proposed systems in this paper. Since the ResNet baseline is constructed with the setup of DCASE 2021, the training and test datasets exclusively consist of recordings of the acoustic scene, with no interruption of foreground speech. Multi-condition training with different levels of foreground speech can reduce the mismatch between training and test conditions and improves the robustness of the classification system. In the proposed multi-condition training, in addition to the usage of the original DCASE background noise, a supplementary dataset is created by mixing clean speech with background noise with SBRs randomly chosen from: [-5; 0, 5; 10; 15; 20] dB. This allows the model to learn from different combinations of foreground speech and background noise.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Paper** & **System** & **Contribution** \\ \hline
[15] & iVector & Noise-floor based features; Multi-condition training. \\
[16] & iVector & SoftVAD; Modified Baum-Welch statistics; Score-fusion. \\ This paper & ResNet & Noise-floor based features; CRUSE; Meta-learner. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The chief contributions of our research in previous papers and this paper.
###### Abstract
The measured signal is proportional to the force gradient, and the relationship can be written as  
\[\Delta\omega\approx-\frac{\omega}{2k}\frac{\partial F_{z}^{el}}{\partial z}=- \frac{\omega}{2k}\frac{\partial^{2}C}{\partial z^{2}}(V_{DC}-V_{CPD})V_{AC} \text{sin}\omega_{e}t=F_{e}\text{sin}\omega_{e}t \tag{2}\]  
\[F_{e}\equiv-\frac{\omega}{2k}\frac{\partial^{2}C}{\partial z^{2}}(V_{DC}-V_{ CPD})V_{AC} \tag{3}\]  
where \(\Delta\omega\) is the modulated resonance frequency, and \(F_{e}\) is the magnitude of the modulated frequency. The frequency-modulated cantilever is deflected by the external force and the motion of the probe can be described as  
\[ \begin{array}{l}x(t)=Asin\Big{(}\int_{0}^{t}(\omega+F_{e}\text{sin}(\omega_ {e}t))dt\Big{)}=Asin\Big{(}\omega t-\frac{F_{e}}{\omega_{e}}\text{cos}(\omega_ {e}t)\Big{)},\ \Big{(}if\ \frac{F_{e}}{\omega_{e}}\ \text{is small}\Big{)}\\ \approx Asin(\omega t)-A\frac{F_{e}}{\omega_{e}}\text{sin}(\omega_{e}t)cos(\omega t )\\ =Asin(\omega t)+\frac{A}{2}\frac{F_{e}}{\omega_{e}}\text{sin}((\omega-\omega_ {e})t)-\frac{A}{2}\frac{F_{e}}{\omega_{e}}\text{sin}((\omega+\omega_{e})t)\end{array}  \tag{4}\]  
where \(A\) is the oscillation amplitude [30; 31; 32]. In the former study, [PERSON] et al. showed that the side-band frequency sensitive to the force gradient can be generated by modulation frequency, and the lateral resolution of FM mode is higher than its of force-sensitive AM mode [30]. Moreover, [PERSON], et al. reported that the image resolution of Kelvin probe force microscopy could be enhanced by applying AC bias at a specific phase of oscillating force [31]. However, it is not yet well understood the relation between the degree of modulation and the side-band frequency. Thus, in this experiment, we described the convenient method to find the optimized side-band frequency, by tuning external frequency during in situ measurements. Thus, in this study, we utilize a simple, reliable, and precise EFM method using a direct digital synthesizer (DDS) that modifies the side-band frequency (\(f_{e}=\omega_{e}/2\pi\)) and the oscillation frequency (\(f_{e}=\omega_{o}/2\pi\)) of the tip. The side-band was formed near the natural frequency of cantilever by coupling with AC voltage of electrostatic force gradient [29; 30; 31]. In the case of conventional EFM method, two LIAs are used in a series to extract the side-band signals. The first lock-in amplifier (LIA) can produce a phase output, and the amplitude of the side-band frequency can be measured by feeding the phase output to the second LIA [32]. However, in current study, the force-gradient signals can be directly detected by feeding the side-band frequency to the external LIA of the EFM as a reference signal. Moreover, the advantage of the side-bands near the natural resonance frequency, lies in the fact that they are independent of the resonant frequency, noise bands, and other interfering resonances. Finally, we are able to produce an accurate side-band frequency using the DDS more easily than when using an analog method.
## 3 Results  
### Comparison of the HI Value for Infill Density and Infill Patterns  
CT images were transferred to the Eclipse treatment planning system to compare the HI values for 3D printed materials. The standard deviation of the average HI value of the ROI is shown in the CT voxel data, and the size of the ROI was measured to be 1.75 cm \(\times\) 1.75 cm, which is half the size of the internal insert. Figure 4 shows the results of comparing the 3D-printed materials, and the infill density was demonstrated by printing from 10% to 100%.  
As shown in Figure 4, it can be seen that the values of HI are different according to the infill density of PLA, ABS, TPU, steel, and copper. Since the density of PLA, ABS, and TPU is close to 1 (g/cm\({}^{3}\)), it was confirmed that the lower the infill density, the lower the HI value. On the other hand, since steel and copper are metallic materials, they have very high HI values compared to other materials even if the infill density is lowered. Among the filament materials, PLA had the highest HU value (110 \(\pm\) 20) and TPU had the lowest HU value (\(-\)113 \(\pm\) 12). It can be observed from Table 1 that ABS has the lowest physical density, but the HU value measured after outputting the actual ABS material is (0 \(\pm\) 20), which is a higher value. This is due to the effect of the ABS material on heat during the heat output process and the output characteristics due to heat shrinkage. The HU values of steel and copper, which are metal filament materials, can be obtained by printing from 30% to 100% of the infill density. At a low content of less than 30%, the inserts for low  
Figure 3: Irradiation of a clinical photon beam using a linear accelerator. **(a)** Photon beam irradiating the EBT3 film (PTW solid water layer with a thickness of 2 cm below). **(b)** Photon beam irradiating the EBT3 film (3D printed layer with a thickness of 2 cm below). Experiment consisting of inserting an EBT film between phantoms to evaluate the density uniformity of the phantom using a 6 MV photon beam. **(a)** Only commercially available solid water phantoms from PTW were used. **(b)** Solid water phantoms from PTW and 3D printed phantoms were used as substitutes in the experiment.  
filling output in the extrusion process of 3D printing filaments were not manufactured. In the case of steel and copper, the HU value with 30% infill is close to dense bone (about 800 HU); however, it is difficult to confirm the uncertainty of the material in the kVCT image acquired at 120 kVp owing to the artifact effect that occurs in the metal material [21].
# Variance-Triggered Two-Step GPS Acquisition
## 3 Variance-Triggered Two-Step Acquisition
### The Coarse Search Step
#### 3.1.1 Reduced Number of Samples  
In the search for the PRN phase, even a unitary phase misalignment would result in near-zero correlation values due to the auto-correlation properties of the PRN codes. So, it is impractical to reduce the number of phase shifts to be searched for [15]. Nonetheless, it is possible to reduce the number of accounted chip samples and still have useful correlation values. As a consequence of the use of Direct Sequence Spreading Spectrum (DSSS), GNSS signals can be expressed with fewer samples [7]. The signal can be folded and a correlation performed in the folded signal. Figure 3 illustrates this.  
The proposed VTTS approach uses interleaved samples to allow the correlation values to be composed using only one sample at a time from all prospective chips of the signal. Each code chip can be associated with \(P\) samples but only one is used per chip to accumulate convolution values to the whole search matrix. For that, every other \(P\) samples are chosen, where \(P\) is the number of samples per chip. The detection threshold is verified after 1023 interleaved samples. In Figure 4a-c it is possible to observe a peak rising gradually with the increase in the number of interleaved samples.  
In the proposed VTTS acquisition, when a peak raises sufficiently to be detected--see Section 3.3, a validation step is initiated to confirm the presence of a satellite signal with characteristics similar to the detected peak--see Section 3.2.  
Figure 3: An illustration of chip folding.
## 2 Electrical Resistivity/Impedance Measurement: Is It Really that Simple?
### DC Measurement of Electrical Resistivity
#### 2.1.2 4-Electrode Configuration
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Specimens** & **Electrodes** & **Material** & **Additions** \\ \hline [PERSON] et al. [48] & Prisms: \(40\times 40\times 160\) mm & Steel meshes (\(30\times 30\) mm immersed, 40 mm spacing) & Mortar & Graphene nanoplate filler and/or carbon fibers (virgin or recycled) \\ [PERSON] and [PERSON] [49] & Cylinders: 150 mm & Stainless steel nets & Concrete & Limestone powder, graphite powder, \\  & (diameter) \(\times\) 130 mm (height) & Titanium meshes (\(40\times 60\times 7.0\) mm) and copper wires (at \(30\) mm from meshes) & Mortar & - \\ [PERSON] et al. [22] & Prisms: \(40\times 40\times 160\) mm & & & \\ [PERSON] et al. [50] & Cylinders: 110 mm & Titanium-coated grids & Concrete & - \\  & (diameter) \(\times\) 220 mm (height) & & & \\  & Prisms & Gauzes (\(2.03\times 2.03\) mm opening) & Paste & Carbon fibers and carbon black \\  & & Cubes: 50 mm (side) & Copper wire meshes & Paste & Steel fibers \\ [PERSON] et al. [33] & Cubes: 50 mm (side) & Copper electrodes (\(20\times 75\times 0.3\) mm, 10 mm spacing) & Paste & Silica fume, carbon fibers, \\ [PERSON] and [PERSON] [56, 58, 59] & Dog bones: \(30\times 20\) mm (narrow part) & & & \\  & Cubes: 51 mm (side) & Silver paint and copper wires & Paste & - \\  & Cylinders: 125 mm & & & \\ [PERSON] and [PERSON] [60] & (diameter) \(\times\) 250 mm (height) & Silver paint and copper electrodes & Concrete & - \\  & & Prisms: \(40\times 40\times 160\) mm & Copper wires and silver paint & Concrete & - \\ [PERSON] and [PERSON] [62, 63] & Prisms: \(150\times 12\times 11\) mm & Silver paint & Paste & Fly ash or silica fume; coke powder \\  & & Prisms: \(160\times 40\times 40\) mm & Silver paint & Paste & Carbon fiber or carbon black \\  & & Prisms: \(150\times 12\times 11\) mm & & & \\ [PERSON] and [PERSON] [57] & Cubes: 51 mm (side) & Silver paint and copper wires & Paste & Carbon fibers \\ [PERSON] et al. [65, 66] & Prisms: \(25\times 25\times 70\) mm & Brass wires and carbon powder & Paste & Ground granulated blast-furnace slag \\  & & Mortar strips (\(90\times 140\times 133\) mm) & Copper wires fixed with silver paint & Mortar & - \\  & and \(95\times 140\times 142\times 13.9\) mm & Copper wires fixed with silver paint & Mortar & - \\  & overlapped at \(90^{\circ}\) & & & \\ [PERSON] et al. [53, 54] & Bell-shaped (\(25\times 50\) mm rectangular section) & Copper tapes and silver paste & Concrete & - \\ [PERSON] et al. [55] & Prisms: \(20\times 20\times 40\) mm & Copper loops & Paste & - \\  & & Cylinders: 150 mm & & \\ [PERSON] et al. [27] & (diameter) \(\times\) 300 mm (length) & Reinforcing steel (external electrodes) and Ag/AgCl reference (internal) electrodes & Concrete & - \\  & & & & \\ \end{tabular}
\end{table} 
Table 2: DC measurements with 4-point probe method.
## 1 Introduction
In this way, failure modes are ranked not only by their impact, probability of occurrence, and likelihood of detection, but also by the ability of those involved in the risk scenario to mitigate them. Considering this capability when prioritizing failure modes is extremely important, because failure modes with a low RPN resulting from a high detection capacity can become critical failure modes if there is not enough capacity to mitigate or even eliminate them.