esa-sceva/satcom-chunk-collection huggingface dataset:
# DANet: Temporal Action Localization with Double Attention
## 5 Conclusions and Discussion  
In this paper, we present our one-stage, anchor-free network model DANet, which leverages a dual-attention mechanism. The core component of DANet is DABlock, which is composed of MLSA and MA. MLSA is utilized for modeling local temporal context. In MA, the max-pooling operation is employed to extract the most significant information from adjacent clip embeddings selectively, while average pooling is used for aggregating global features. Our experimental results on the THUMOS14 and ActivityNet1.3 datasets demonstrate that our approach achieves excellent performance in TAL. While our proposed method has shown good performance in TAL, one of the main limitations is the requirement for fully annotated data, which may not be available in some cases. Further work is to explore weakly supervised learning methods to reduce the dependence on fully annotated data.  
Conceptualization, [PERSON]; methodology, [PERSON] and X.W.; software, [PERSON] and Y.X.; validation, [PERSON] and C.W.; formal analysis, [PERSON], Y.L. ([PERSON]) and Y.L. ([PERSON]); investigation, [PERSON]; resources, L.W.; data curation, J.S.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, Y.Z.; project administration, [PERSON]; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China (2021 YFF1201200), the Jilin Provincial Department of Science and Technology Project (20230201083 GX, 20220201145 GX, 20230201065 GX), the National Natural Science Foundation of China (62072212, 61972175 and 12205114), the Guangdong Universities' Innovation Team Project (2021 KCXTD 015), and Key Disciplines (2021 ZDJS138) Projects.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 5: In a video, adjacent frames are typically highly similar. This figure presents the correlation matrix of adjacent frames, with stronger correlation indicated by darker colors and weaker correlation indicated by lighter colors.
## 2 Materials and Methods  
### Study Site  
Two experiments were carried out during 2018 in Murcia (SE Spain). Plot 1 (from 29 June to 1 October, 180-274 DOY) located at the \"Tomas Ferro\" Experimental Agro-food Station of the Technical University of Cartagena (37\({}^{\circ}\)41\({}^{\prime}\) N, 0\({}^{\circ}\)57\({}^{\prime}\) W, 32 m elevation, La Palma). The plant material consisted of three-year-old sweet cherry trees (_P. avium_ L.), \"Lapins' grafted on 'Mirabolano' rootstock. The trees, planted at a spacing of 3.5 m \(\times\) 2.25 m, were drip-irrigated by three on-line pressure-compensated emitters per tree, each with a discharge of 2.2 L h\({}^{-1}\) and fitted on a single lateral per tree row. The irrigation water, with an electrical conductivity (EC\({}_{25}\) \({}^{\circ}\)C) of 1.1 dS m\({}^{-1}\) and pH of 8, was from the Tajo-Segura Water Transfer System. The soil was deep and well-drained, had a sandy-clay-loam texture (34.5% clay, 21.3% silt and 44.2% sand), with an available water capacity of about 0.18 mm\({}^{-1}\) and bulk density of 1.4 \(\pm\) 0.1 Mg m\({}^{-3}\) and a low organic matter content (1.5%). Plot 2 (from 27 April to 7 November, 117-311 DOY) is located in a commercial orchard (38\({}^{\circ}\)8\({}^{\prime}\) N; 1\({}^{\circ}\)22\({}^{\prime}\) W, 680 m elevation, Jumilla) and consisted of sweet cherry trees (_P. avium_ L.) 'Prime Giant' that were fifteen years old grafted onto 'SL64' rootstock, and with 'Brooks' and 'Early Lory' as pollinizers. The tree spacing was 5 m between rows and 3 m within rows. The soil was moderately stony and presented a sandy loam texture (67.5% clay, 17.5% silt and 15% sand), with high available phosphorus (108.67 mg kg\({}^{-1}\)), low potassium (0.32 meg 100 g\({}^{-1}\)) and a normal active limestone (2.7%) content. The irrigation water was drawn from a well and it had an average electrical conductivity EC\({}_{25}\) \({}^{\circ}\)C of 0.8 dS m\({}^{-1}\). Water was applied using a single lateral with three pressure-compensated emitters (4 L h\({}^{-1}\)) per tree.
# Low Radiation Dose Implications in Obese Abdominal Computed Tomography Imaging
## 2 Materials and Methods
### Dosimetry  
Abdominal organ and skin dosing procedures were performed using a metal oxide semiconductor field effect transistor (MOSFET) system (model TN-RD-60, Thomson-Nielsen) with high-sensitivity radiology dosimeters (TN-1002 RD, Thomson-Nielsen). Cal  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Scan range & Lung bases to symphysis pubis \\ Detector Collimation (mm) & 128 \(\times\) 0.6 \\ Slice thickness (mm) & 5 \\ Rotation Time (s) & 0.5 \\ Pitch & 0.6 \\ Kernel & Medium smooth \\ Window & Window width: 350, window center: 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Constant scanning parameters for abdominal CT used to scan the phantom.  
Figure 1: RANDO anthropomorphic phantom (**a**) with and (**b**) without fat layers and MOSFET dosimeters in situ. (**c**) Axial CT image for the phantoms, showing the AP diameter measurements for normal-weighted (22 cm) and obese (28 cm) phantoms.  
Figure 2: Schematic diagram demonstrating the 12 abdominal CT protocols used per each phantom setting.  
ibration of the dosimeters was carried out at 120 kVp alongside a Radcal(r) ionization chamber (model 9095, Radcal, Monrovia, CA, USA) on a calibration jig on the CT table (Figure 3). The jig allows convenient positioning at the isocenter of the beam, thereby allowing a reproducible calibration procedure.  
In order to compute the calibration factor (mV/cGy), the MOSFET readings (mV) and the dose delivered (cGy) were used. Tube voltages other than 120 kVp were not used because MOSFET has been shown to be energy-independent [14].  
Twelve detectors were inserted into different organ locations for every scan performed (Table 2). On the outward most surfaces of the phantoms, two dosimeters were positioned posteriorly and anteriorly to measure skin doses. To account for the doses absorbed by female breast tissue, a female breast phantom was placed on the thoracic area and was stabilized with nylon screws. The organs where doses were measured have the highest radiosensitivity in the scanned area as per the International Commission on Radiological Protection (ICRP) publication 103 (2007) [15].  
Delivered dose metrics, including dose length product (DLP), volume CT dose index (CTDivol), and tube current (mA) values, were collected at the end of each scan. Each protocoled exam was performed three times in order to average the MOSFET dose measurements, as well as the delivered dose metrics. Additionally, the effective dose in milliSievert (mSv) was calculated by multiplying each individual organ by its ICRP tissue-weighting factor and adding all organ doses together.
## 3 Methods
\right., \tag{8}\]  
(1) Fixing \(\mathbf{P}_{k},\mathbf{Q}_{k},\mathbf{D}_{k},\mathbf{x}_{k+1}\) is obtained by solving:  
\[\underset{\mathbf{x}}{min}\frac{\lambda}{2}\|\mathbf{y}-\mathcal{U}_{\mathbf{ x}}\|_{2}^{2}-\text{Tr}\Big{(}\mathbf{A}_{r}^{H}\mathcal{R}_{\mathbf{x}} \mathbf{B}_{r}\Big{)}+\left\langle\mathbf{D},\mathcal{R}_{\mathbf{x}}- \mathbf{PQ}^{H}\right\rangle+\frac{\beta}{2}\|\mathcal{R}_{\mathbf{x}}- \mathbf{PQ}^{H}\|_{F}^{2}. \tag{9}\]  
The solution is:  
\[\mathbf{x}_{k+1}=\left(\lambda\mathcal{U}^{H}\mathcal{U}+\beta\mathcal{R}^{H} \mathcal{R}\right)^{-1}\Bigg{[}\lambda\mathcal{U}^{H}\mathbf{y}+\beta\mathcal{ R}^{H}\Bigg{(}\mathbf{P}_{k}\mathbf{Q}_{k}^{H}+\frac{\mathbf{A}_{\Omega,J}^{H} \mathcal{R}_{\mathbf{x}}\mathbf{B}_{\Omega,J}-\mathbf{D}_{k}}{\beta}\Bigg{)} \Bigg{]}. \tag{10}\]  
(2) Fixing \(\mathbf{x}_{k+1},\mathbf{Q}_{k},\mathbf{D}_{k}\), \(\mathbf{P}_{k+1}\) is obtained by solving:  
\[\underset{\mathbf{P}}{min}\frac{1}{2}\|\mathbf{P}\|_{F}^{2}+\left\langle \mathbf{D},\mathcal{R}_{\mathbf{x}}-\mathbf{PQ}^{H}\right\rangle+\frac{\beta} {2}\|\mathcal{R}_{\mathbf{x}}-\mathbf{PQ}^{H}\|_{F}^{2}. \tag{11}\]  
The solution is:  
\[\mathbf{P}_{k+1}=\left(\beta\mathcal{R}_{\mathbf{x}_{k+1}}+\mathbf{D}_{k} \right)\mathbf{Q}_{k}\Big{(}\beta\mathbf{Q}_{k}^{H}\mathbf{Q}_{k}+\mathbf{I} \Big{)}^{-1}. \tag{12}\]  
(3) Fixing \(\mathbf{x}_{k+1},\mathbf{P}_{k+1},\mathbf{D}_{k}\), \(\mathbf{Q}_{k+1}\) is obtained by solving:  
\[\underset{\mathbf{Q}}{min}\frac{1}{2}\|\mathbf{Q}\|_{F}^{2}+\left\langle \mathbf{D},\mathcal{R}_{\mathbf{x}}-\mathbf{PQ}^{H}\right\rangle+\frac{\beta}{ 2}\|\mathcal{R}_{\mathbf{x}}-\mathbf{PQ}^{H}\|_{F}^{2}. \tag{13}\]  
The solution is:  
\[\mathbf{Q}_{k+1}=\left(\beta\mathcal{R}_{\mathbf{x}_{k+1}}+\mathbf{D}_{k} \right)^{H}\mathbf{P}_{k+1}\Big{(}\beta\mathbf{P}_{k+1}^{H}\mathbf{P}_{k+1}+ \mathbf{I}\Big{)}^{-1}. \tag{14}\]  
(4) Fixing \(\mathbf{x}_{k+1},\mathbf{P}_{k+1},\mathbf{Q}_{k+1}\), the solution of \(\mathbf{D}_{k+1}\) is:  
\[\mathbf{D}_{k+1}=\mathbf{D}_{k}+\Big{(}\mathcal{R}_{\mathbf{x}_{k+1}}-\mathbf{ P}_{k+1}\mathbf{Q}_{k+1}^{H}\Big{)}. \tag{15}\]  
The alternating iterations in Equation (8) stop if the number of iterations \(k\) reaches the maximal number \(K\), or the normalized successive difference \(\eta_{k+1}=\|\mathbf{x}_{k+1}-\mathbf{x}_{k}\|/\|\mathbf{x}_{k}\|\) is smaller than that of a given tolerance \(\eta_{\text{tol}}\). The pseudocode of this reconstruction algorithm is shown in Table 1.  
A 2D NMR spectrum contains two dimensions, one of which is the direct dimension \(t_{2}\), and the other is the indirect dimension \(t_{1}\) on which almost all the experiment time is spent. In the case of the 2D NMR spectrum, reconstruction can be performed on the 1D reconstruction of each slice one by one. Thus, it is possible to accelerate the computation by making use of the proposed parallel architecture. In detail, the reconstruction tasks for different slices are assigned to multiple processing cores [36] and can be computed simultaneously (Figure 1).
## 3 Methodology
### Speed Prediction  
#### 3.1.1 Gcn  
This research mainly aims at the traffic speed prediction problem. The essence is to learn function \(f\) to predict the traffic state of all nodes in the future time period according to the historical data and the corresponding road structure.  
Since the target road segments of this study are all unidirectional, an undirected unweighted graph \(G(V,E)\) is constructed to describe the topological structure of the road network. The position of sensors is treated as nodes set \(V\) in the graph, and the connection relationship between the sensors are treated as edges \(E\). Assuming that the current time is \(t\), the traffic information \(X\) at time \(t+1\) can be expressed as:  
\[X_{t+1}=f(G;X_{t-T},\ldots,X_{t-1},X_{t}), \tag{1}\]  
where \(T\) represents the length of the input historical time series; \(X_{t}\) represents the traffic information of each sensor at time \(t\).  
The GCN is a neural network that extends the convolution operation to the graph structure [46]. The core idea is that the central node performs information aggregation on its neighbor nodes. The node feature is regarded as a signal, which is transformed into the frequency domain space through Fourier transform, and finally the graph domain convolution is obtained through inverse Fourier transform. Each layer of convolution only processes the first-order neighborhood information, and the information transfer of multi-order neighborhoods can be realized by stacking several convolutional layers [47]. The propagation rules for each convolutional layer are as follows:  
\[GCN(X)=\bar{D}^{-\frac{1}{2}}\bar{A}\bar{D}^{-\frac{1}{2}}X\theta, \tag{2}\]  
where \(\bar{A}=A+I_{N}\) represents adjacency matrix \(A\) with a self-connection structure, \(I\) is the identity matrix, and \(\bar{D}=\sum_{j}\bar{A}_{ij}\) represents the degree matrix of \(\bar{A}\). \(X\) represents the input, and \(\theta\) is the convolution kernel. The adjacency matrix \(A\), which contains elements of 0 and 1, is used to describe the connection between nodes. The element is 1 if the sensors are connected, and 0 otherwise. Traffic speed is treated as a feature of nodes.
## 1 Introduction
The system mixes multisensors with IP cameras in a wireless mesh network in order to detect and verify fire thus minimizing the reaction time of the fire fighters and, therefore, the effects of the fire in rural and forest areas of Spain. When a fire is detected by a wireless multisensor, the sensor alarm is sent through the wireless network to a central server. The central server runs a software application that selects the closest wireless cameras to the multisensory and gives them coordinates to rotate to the multisensor that raised the alarm, and sends them a message in order to receive real-time images from the zone. It will let the fire fighters corroborate the detected fire. We will also research the power consumption of the devices involved in this deployment in order to demonstrate that it is sustainable. The paper is organized as follows. Section 2 presents some related works with the use of wireless sensors for fire detection that we have found in the literature. Section 3 describes the main features of a rural area and the research we have done to perform the deployment. The radio design, the analytical considerations to know the number of devices needed, and the channel distribution plan is shown in section 4. Section 5 shows the hardware deployed that has been used in this work. The system design and protocol operation is shown in Section 6. Section 7 shows the user interface for the firefighters. In Section 8, the performance test and the power consumption measurements are presented. Finally, Section 9 shows the conclusion and future works.
# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Fingerprinting Localization Algorithm
In [179], the RADAR system is concerned with the deterministic location approach, and proved competent to determine the user's location with the nearest neighbour of Euclidian distance by using scalar values. The classification method of nearest neighbour (NN), K-nearest neighbour (KNN), and weighted K-nearest neighbour (WKNN) are implemented for the matching of nearest locations in the online phase [187]. These mathematical equations are able to compute the mobile device's actual position. In addition to this, the support vector machine (SVM) is used as an advanced deterministic approach for the WLAN standard which can also give better accuracy on type location [188]. The probabilistic approach is based on the conditional probability distribution function (PDF) of unknown variables by providing more accurate results with statistical framework [184]. It can guess the position of dimension between reference points (RPs) of FP and target measurement depending on the statistical conditions [189]. In [180], the Horus WLAN system can perform the location determination with high accuracy and low computation by using a probabilistic approach in order to improve the RADAR system. Then, the authors in [184] described the increased accuracy of about more than 64% by implementing with a linear autoregressive model in a Horus WLAN system in which the process uses the correlation of sequential sample values received from the same APs. In the Horus system, the Bayesian model is applied to obtain the probability distribution of random variables by addressing the noisy signal strength characteristics in wireless networks [180; 190; 191]. In [192], the authors introduce batteries of sufficient energy with a probabilistic fingerprinting method on the application of a heterogeneous mobile device platform. This approach is able to solve the energy inefficiency of a smartphone due to the long time consumption of collection and the computation of the measurement data from apps, as well as increasing the smartphone battery lifetime. The probabilistic FP approach is not similar to the conventional one, as it extends the battery lifetime without a deterioration in its position accuracy. As a whole, the deterministic method can estimate the user's position by using the classical time-based methods and angle-based methods like TOA, TDOA and AOA. A deterministic method can deduce the probability of the user's position more closely within a little distance. The probabilistic method can decrease the noisy characteristic of random signals received. In spite of the probabilistic method requiring more information, it can  
Figure 13: Illustration of the fingerprinting algorithm. estimate the location more precisely than the deterministic method, for example, where the object is located presently. However, the low capacity of integrated electronic devices such as sensors and track tags are not effective for the probabilistic approach as these devices do not have a suitable ability for computing purposes.
## 5 Conclusions  
Using calculations and simulations, we show that, in the presence of double-pulsing, the SPIDER technique cannot see a second pulse at all when its relative phase varies from 0 to \(2\pi\). This conclusion holds regardless of whether the temporal separation of the two pulses varies. Thus, SPIDER is utterly incapable of guaranteeing that the output of a laser is free of double-pulsing since multi-pulse averaged measurements are nearly always used.  
In addition, even very small variations in the pulse relative phase result in SPIDER significantly underestimating the intensity of any satellite pulses. Thus, it will be extremely difficult to see even a slightly random satellite pulse if SPIDER is used for the measurement.  
While SHG FROG tends to underestimate their amplitude, satellite pulses are clearly visible in SHG FROG measurements. Such satellites retrieved from SHG FROG traces accurately yield the correct average of the pulse separation if and when it varies. There are also clear structural differences between measured and retrieved FROG traces when pulse-train instability is present. Specifically, their average discrepancy (error) is large compared to that of a quality measurement. Such a large discrepancy is a clear indication of instability, as is also the case for other types of pulse instability. (Interestingly, for many years, such discrepancies were erroneously blamed on \"non-convergence\" of the FROG algorithm).  
We also find that simple observation of the measured and retrieved FROG traces provides clear indications of the measured pulses. For example, the three-lobed trace structure is a clear indication of double pulsing, and missing center-lobe fringes is also a clear indication of relative-phase instability. As an example, we demonstrate that the relative energy of the two pulses in the double pulse can be retrieved from the trace directly, even when the temporal separation of the two pulses is not stable. We conclude that comparing the measured and retrieved traces allows FROG users to have much greater confidence in whether instability is present in their lasers, and therefore to have greater confidence in the correctness of their measurement.  
Some of the results in this manuscript have been published in the SPIE Proceedings 9732, and text and figures from that article have been reproduced herein. Financial support is from the US National Science Foundation Grants #ECCS-1307817 and #ECCS-1609808 and the Georgia Research Alliance. This work was performed in part under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07 NA27344.  
**Author Contributions**: [PERSON] conceived the research and suggested and oversaw the simulations. [PERSON] and [PERSON] performed the simulations. [PERSON] wrote the paper with help from [PERSON] and [PERSON].  
**Conflicts of Interest**: [PERSON] owns a company that manufactures pulse-measurement devices.
## 3 Results and Discussion
### Mechanism of Photocatalytic Activity  
As shown in Figure 9, Zr\({}_{0.15}\)Cd\({}_{0.85}\)S nanoflowers and P25 with different band structures have a good compatibility with the absorbance of visible light. It leads to improve the transfer of photo-excited electron-hole pairs and reduce the recombination of photo-generated electrons and holes then improve the efficiency of photocatalytic degradation [43]. The more efficient charge-carrier separation and increased radical formation lead to the enhanced photocatalytic activity and improved stability of the P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite. The use of P25 with high specific surface area and Zn\({}_{0.15}\)Cd\({}_{0.85}\)S with flower-like structure during hydrothermal preparation of P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite is responsible for increasing the surface area and may improve absorption properties and then increase of the active sites, on which organic molecules and intermediate products are adsorbed. Based on the above discussion, in the reaction system under visible light irradiation, charge carriers are produced on the semiconductor of Zn\({}_{0.15}\)Cd\({}_{0.85}\)S to P25, the photo-excited electrons on the valence band (VB) of Zn\({}_{0.15}\)Cd\({}_{0.85}\)S can be promoted to the conduction band (CB). Then, the excited electrons transfer to the CB of TiO\({}_{2}\) due to the fact that CB of TiO\({}_{2}\) is slightly more positive than that of CdS. The electrons on the TiO\({}_{2}\) would be trapped by adsorbed molecular O\({}_{2}\) to form super oxide radicals \({}^{\bullet}\)O\({}^{2-}\), which has the ability to degrade molecules of Rh-B. The holes will transfer from VB of TiO\({}_{2}\) to CdS because VB band of TiO\({}_{2}\) has lower energy than VB of CdS. Meanwhile, the holes that transferred from the VB of TiO\({}_{2}\) could be consumed by Rh-B molecules adsorbed on the surface of P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite and react with H\({}_{2}\)O to form \({}^{\bullet}\)OH radicals. Thus, recombination of electron-hole pairs can be efficiently inhibited  
Figure 8: XPS survey scan spectra of fresh and used of P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite with P25.Cd ratio of 15 (**a**) and high resolution XPS spectra of used P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite 1-5, (**b**) Cd 3d, (**c**) Ti 2p, and (**d**) Zn 2p.  
by the reactive species, which prolong the catalyst lifetimes. P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite system can induce a large light-harvesting efficiency and supply more reaction active sites to enhance the overall photocatalytic efficiency of the catalyst.