esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery
## Materials and Methods
### Change Detection
This uses the Google Earth Engine function'reducer' to perform the compositing from the images in the collection, generating a single output image. We chose to use this method of preparing the imagery for analysis because using the median value reduces the effects of cloud (high values) and shadow (low values), and the resulting outputs are seamless mosaics appropriate for visualisation purposes required by a variety of users from the heritage community. The algorithm then uses a per-pixel, layer arithmetic change detection algorithm which computes the amount of difference in reflectance values between the composites, across all bands at each pixel \(i\). This method was selected because it is simple, unsupervised and fast to use and to understand for a variety of stakeholders, enabling them to modify the code to meet their needs. It does not require the preparation of training data. It is a well-established method in remote sensing studies of change using medium-resolution data [24; 26]. The Google Earth Engine script sums the difference between each spectral band of the satellite image composites and generates a new 'change' layer, with values between 0 and 1;  
\[\sqrt{[\Sigma 1\,(\mathrm{image}1\,-\,\mathrm{image}2i)^{2}]} \tag{1}\]  
The user must then decide on a threshold value above which to retain information about the change, based on a manual examination of the values present in the change layer. The user also defines the radius of a buffer area around each site point in which to append the information from the change layer, and the scale. We used relatively'sensitive' settings, taking into account even a single pixel, 'any non-zero' where change had been detected (see Table 2). Using Google Earth Engine, huge collections of data can be processed. Downloading this imagery separately is time-consuming and processing it requires access to expensive software licenses and high-performance computing power out of reach of most archaeologists within the MENA region. Given this, Google Earth Engine is increasingly being used for archaeological research, including by EAMENA [1]. Other research has focused on using it for detection of sites and landscape features [27; 28], with its potential for heritage management first raised by [PERSON] [18]. There are limitations to Google Earth Engine, including restrictions on asset and download sizes, but at present, it offers the best option for heritage professionals to access data and analysis-power for change detection in a standardised and open-source way. EAMENA is the only project to utilise Google Earth and Google Earth Engine to actively protect a wide-ranging, cross-regional dataset comprising sites of all types and periods. This is facilitated through our network of cultural heritage professionals from Libya and Tunisia who attended EAMENA CPF-funded courses during 2017-2020 [29] and who have been trained in the use of Google Earth Engine. Planned future courses will continue to develop this in line with updates to our workflow.
# Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos
## 3 Methodology  
To mitigate the issues identified in our previous weapon detector [4], this work solves those problems by having an increased number of frames per second for real-time scenarios and a reduced number of false positives. The methodology provided herein making this work successful is divided into multiple steps listed as follows:  
1. Dataset selection;
2. Preprocessing operations;
3. Model selection;
4. Model training and tuning;
5. Model optimization using TensorRT network.
## 3 Results
### Changes in Indicators of Biological Stability  
During the composting process, the biological stability of the waste was assessed at five-day intervals using the change in four-day respirometric activity (AT4), the change in loss on ignition (LOI) values, and the change in total organic carbon (TOC) content. The change in AT4 served as the primary indicator of the biological activity of the waste, with the commonly accepted threshold value of \(<\)10 g O\({}_{2}\cdot\)kg\({}^{-1}\) DM set as the target for the end of the composting process. The initial AT4 value of the research substrate was 79.27 g O\({}_{2}\cdot\)kg\({}^{-1}\) DM, nearly eight times higher than the permissible level. Over the following days, this value declined relatively rapidly, reaching 15.01 g O\({}_{2}\cdot\)kg\({}^{-1}\) DM on the 10 th day of the experiment and remaining relatively stable for the next 15 days. Subsequently, the AT4 value temporarily increased, reaching 20.84 g O\({}_{2}\cdot\)kg\({}^{-1}\) DM. This AT4 increase seemed to correlate with rises in the moisture (r = 0.78) and temperature (r = 0.73) of the waste. Finally, the target AT4 threshold was passed on the 45 th day of the process when a value of 9.82 g O\({}_{2}\cdot\)kg\({}^{-1}\) DM was achieved (Figure 3A).  
LOI and TOC were additionally used for insight into the kinetics of organic matter mineralization. The proportion of organic matter, as measured by the LOI value, decreased linearly from 81% DM to 68% DM, at an average rate of approximately 0.28% DM per day, corresponding to an organic matter decrease of 21.1 kg at a rate of approximately 0.5 kg DMO per day (Figure 3B). The percentage of TOC content decreased linearly from 44.1% DM to 38.2% DM, at an average rate of approximately 0.16% DM per day, corresponding to a loss of 10.9 kg.  
Figure 3: (**A**) Loss on ignition (LOI), (**B**) mass of organic substances, (**C**) total organic carbon (TOC), and (**D**) respirometric activity (AT4) of the substrate material at sampling points during the 45 days of composting in the passively aerated bioreactor.
## 1 Introduction
With the advent of new GNSS constellations such as BeiDou, Galileo, Quasi-Zenith Satellite System (QZSS), and Indian Regional Navigation Satellite System (IRNSS), the IGS has initiated the Multi-GNSS Experiment (MGEX) project to pave the way for the provision of high-quality data and products for all GNSS constellations ([[http://igs.org/mgex/products](http://igs.org/mgex/products)]([http://igs.org/mgex/products](http://igs.org/mgex/products))) [16]. At present, there are three MGEX Analysis Centers (ACs) providing the final satellite orbit and clock products of BDS (i.e., Center for Orbit Determination in Europe (CODE), GeoForschungsZentrum Potsdam (GFZ), and Wuhan University (WHU)) [17]. It is worth noting that CODE only provides the final products of IGSO and MEO. GFZ has been providing 5 min orbit and 30 s clock products instead of 15 min orbit and 5 min clock products since 3 May 2015. As shown in Table 1, BDS precise orbit products released by MGEX ACs are updated every 15 min or 5 min, which is similar to GPS precise orbit products. However, the BDS clock products are only provided at 5 min/30 s sampling intervals, while those of GPS are provided at 30 s/5 s intervals. When processing high-rate (up to 1 Hz) GNSS measurements at epochs that do not coincide with the sampling epochs of the clock products, the clock bias of the observed GNSS satellites has to be obtained through interpolation [18; 19]. Due to the stochastic characteristic of the clock variations, [PERSON] et al. [20] mentioned that the interpolation error for 5 min clock products would not satisfy the requirement to achieve centimeter-level accuracy for high-rate (up to 1 Hz) GNSS positioning. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Institution** & **ID** & **Orbit** & **Clock** & **Remarks** \\ \hline CODE & COM & 15 min & 5 min & Since October 2013 \\ GFZ & GBM & 5 min & 30 s & Since 3 May 2015 \\ WHU & WUM & 15 min & 5 min & Since January 2013 \\ \hline \hline \end{tabular}  
* MGEX: Multi-GNSS Experiment; BDS: BeiDou Navigation Satellite System CODE: Center for Orbit Determination in Europe; COM: the GNSS products from CODE; GFZ: GeoForschungsZentrum Potsdam; GBM: the GNSS products from GFZ; WHU: Wuhan University; WUM: the GNSS products from WHU.  
\end{table} 
Table 1: An overview of the MGEX BDS precise orbit and clock products. In this contribution, first we generated and evaluated 1 Hz BDS clock corrections based on the precise orbit products. The precision of BDS PPP with 1 Hz clock products was analyzed and applied to retrieve seismic displacements induced by the 2015 Mw 7.8 Gorkha, Nepal, earthquake. Finally, the benefits of high-rate satellite clock corrections to BDS PPP were investigated.
# Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks
## 4 Probabilistic Information Dissemination
At the initialization stage, every node (variable \(u\)) apart from the initiator (variable \(s\)) sets its status (variable isCovered) as uncovered (Line 9). The initiator node, randomly (according to the forwarding probability \(q\)) selects a fraction of its neighbors (i.e., function randomSelect) and constructs the set \(R_{s}\) (\(\equiv R_{u}\)). Subsequently, (Lines 6-8) forward the information message to each of the operating nodes in this set (i.e., \(R_{s}\cap L_{s}\)) and remove the particular node from \(R_{s}\). After the initialization stage, every node that receives the information message for the first time updates its status as covered (Line 12), constructs the set \(R_{u}\) (Line 13) and sends the particular message to each of the operating nodes in this set (Lines 14-16). The corresponding node periodically checks whether the non-operating neighbors in the set \(R_{u}\) have been revived and sends the information message to them. The proposed algorithm can be applied to energy harvesting environments since it takes into account the nodes' operational states. It is shown in the simulations section that the coverage under RPF outperforms PF coverage at the expense of increased termination time. It is observed from Algorithm 1 that the number of messages is the same as in the case of the PF under non-energy harvesting environments. Another observation is that, when all the nodes of the network are operational, the proposed algorithm reduces to PF. Under RPF, every node has to maintain the set of nodes that need to be covered after they become operational again (i.e., \(R_{u}\)), thus an extra memory overhead is introduced. However, this overhead is not expected to be prohibitive for the node's operation since it is bounded by the number of neighbors. Figure 2: Network topology with a bottleneck link \((u,v)\). Nodes within the dotted line will not receive the information message if either \(u\) or \(v\) are not operating. ```
1:Var \(u\): current node; isCovered: Boolean flag;
2:Initialize:
3:if\(u==s\)then\(\triangleright\)\(s\) is the initiator node
4:isCovered:=TRUE;
5:\(R_{u}:=\textsc{randomSelect}(S_{u},q)\);
6:for\(\forall\;v\in R_{u}\cap L_{u}\)do
7:send \(<\)\(message,u>\to v\);
8:\(R_{u}:=R_{u}\setminus\{v\}\);\(\triangleright\) Remove node \(u\) from \(R_{u}\)
9:elseisCovered:=FALSE;
10:Operate:
11:if receive \(u\) =\(<\)\(message,x>\)&& recovered then
12:isCovered:=TRUE;
13:\(R_{u}:=\textsc{randomSelect}(S_{u}\setminus\{x\},q)\);
14:for\(\forall\;v\in R_{u}\cap L_{u}\)do
15:send \(<\)\(message,u>\to v\);
16:\(R_{u}:=R_{u}\setminus\{v\}\);
17:functionrandomSelect(\(K,q\))
18:Var \(K:=\emptyset\);
19:for\(\forall\;v\in V\)do
20:if\(\textsc{uniform}([0,1])<q\)then
21:\(K:=K\cup\{v\}\);
22:return\(K\);
```  
**Algorithm 1** Robust Probabilistic Flooding RPF(\(s\),\(q\))
# Enhancing the Integration of Protein-Rich Oat Waste Material into Meat Formulations
## 3 Results
### Characterisation of Oat Protein Hydrolysate Properties  
Raw material obtained from \(\beta\)-glucan extraction and the hydrolys material were analysed. Fat, protein, ash, moisture, and carbohydrate content, as well as the antioxidant properties, are presented in Table 1. The molecular weight distribution of oat by-product protein hydrolysates was monitored via tricine-SDS-PAGE (Figure 1). A relatively low level of protein hydrolysis was observed in the electrophoretic pattern.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Moisture** & **Fat** & **Protein** & **Ash** & **Carohydrates** \\ \hline Raw material & 5.00 \(\pm\) 0.08 & 10.64 \(\pm\) 1.17 & 42.18 \(\pm\) 1.48 & 6.87 \(\pm\) 0.90 & 35.07 \(\pm\) 3.67 \\ Hydrolyzed & 0.50 \(\pm\) 0.02 & 6.11 \(\pm\) 0.79 & 52.00 \(\pm\) 3.29 & 1.82 \(\pm\) 0.16 & 40.31 \(\pm\) 1.06 \\ \hline Antioxidant properties & & & & \\ \hline \multicolumn{4}{c}{FRAP \(\mu\)M Trolox/mg liophlyaste} & Metal chelating ability [\%] & DPPH [\%] \\ \hline Hydrolyzed & 18.26 \(\pm\) 0.15 & 59.15 \(\pm\) 2.78 & 1.23 \(\pm\) 0.47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proximate composition of the oat sample and hydrolysate and antioxidant properties of the hydrolysate (mean values \(\pm\) standard errors).  
In Table 2, the amino acid profile and free amino acid content in protein hydrolysates from oat by-products are shown. The fundamental amino acids in the oat protein hydrolysate were found to be glutamic acid and leucine, constituting approximately 23.77% and 8.46% of the overall amino acids, respectively.  
Figure 1: SDS-PAGE protein profile of oat by-product protein hydrolysates obtained using Alcalase. The first line displays the protein standard, while the second line represents the sample applied to the gel (OPH).
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 5 Simulation Results and Analyses  
An assessment of three standard test systems--Garver 6-bus test system, Egyptian WDN, and 93-bus Colombian system--were carried out to verify the proposed optimization technique's capability to solve the TNEP problem. Static plan results, using LSHADE-SPACMA, were compared to results obtained in previous studies [4; 13; 20; 21; 28]. Finally, MSDTNEP of WDN up to 2040 with embedded N-1 security constraints is employed based on two scenarios. The following case studies are conducted on MATLAB r2017a on a DELL PC, and its model name is 'OptiPlex7050', including an 'Intel(r) Core(tm) i7' CPU at 2.6 GHz and 16 GB RAM.
## 2 Experiments and Results
As evident from Figure 3b, the LHB intensity only partly recovers at longer delays (\(\Delta t=5\) ps) because the quasi-equilibrated state after a few ps is different from the state before excitation, since a finite amount of energy was deposited in the system. Similar reduction of the LHB intensity can also be found from temperature-dependent photoemission data recorded under equilibrium conditions [12]. The dynamic pathway between the initial and the quasi-equilibrated state can; however, not be explained by pure thermal effects as the LHB recovery (\(\tau_{LHB}\approx 1\) ps) does not occur on the electron-phonon energy transfer time scale (\(\tau_{phB}\approx\) 200-350 fs). In fact, the recovery occurs slower than any other process observed in our experiment which implies that either non-local relaxation phenomena (mapped via \(\mathbf{k}\)-dependent studies not performed here) or non-thermalized states in the lattice system are responsible for the slow re-equilibration of the electronic subsystem. In fact, such long-living non-equilibrium situations in the lattice system have been reported earlier [35] and are also present here. As can be seen from the data in Figure 2a, the excitation of the CDW amplitude mode results in spectral shifts of the photoemission spectra that that can be analyzed best in the vicinity of the Fermi level \(E_{F}\), where the photoemission intensity abruptly changes as a function of energy. Figure 4a shows the integrated photoemission intensity in this spectral range (\(E-E_{F}=-65\)... \(-45\) meV) for several pump fluences. Long-living oscillations with damping times of the order of 10-20 ps are found and the photonic subsystem is still in a non-equilibrium situation after the majority of electronic energy is transferred to the lattice. Since these damping times are substantially longer than the recovery times of the LHB signature, the population of the amplitude mode phonon does not limit but potentially drives the electronic re-equilibration of the system. The amplitude of the observed oscillations, as extracted from the transients in Figure 4a, shows a similar saturating behavior as the LHB quench (Figure 4b), indicating that the local excitation of polaron clusters and the CDW amplitude mode oscillations are inherently linked.
## 2 Modeling and Numerical Methods
### Numerical Model for Body Force Generation  
The plasma active region of the phenomenological model [14] with a local Cartesian coordinate system is illustrated in Figure 1. The wedge region AOBB'A'O' composed of points A, O, B, B', A' and O' in the figure is depicted by the dashed line. A high-frequency voltage is applied on the upper and lower electrodes of the plasma actuator to ionize the air and generate the plasma. The plasma is assumed by the phenomenological model to exist only in the wedge region with width \(b\) and height \(a\); namely, the wedge region is the plasma active region after the actuator is ignited. Under the influence of the electric field, the plasma can collide with surrounding neutral molecules to cause directional movement of the surrounding fluid. Therefore, the phenomenological model recognizes that the plasma actuators can apply the body force to the surrounding fluid. The phenomenological model assumes that the body force exists only in the wedge region, and the body force is parallel to the plane ABB'A' (the inclined surface of the wedge region AOBB'A'O') and points to the lower right. The body force \(F\) is obtained by  
\[F=\omega\alpha\rho_{c}e_{c}\Delta tE \tag{3}\]  
where \(\omega\), \(\alpha\), \(\rho_{c}\), \(e_{c}\), and \(\Delta t\) represent the voltage frequency, collision factor, charge density, electronic charge, and charge time, respectively. The linear distribution of the electric field strength \(E\) is defined as \(E=E_{0}-k_{1}x^{\prime}-k_{2}y^{\prime}\), where \(E_{0}=U_{0}/d\), \(k_{1}=(E_{0}-E_{0})/b\), and \(k_{2}=(E_{0}-E_{0})/a\). Moreover, \(E_{o}\), \(U_{o}\), \(d\), and \(E_{b}\) represent the maximum electric field strength at the boundary OO', peak voltage, horizontal distance, and minimum electric field strength at the boundary ABB'A', respectively. Here, \(k_{1}\) and \(k_{2}\) are the intermediate variables for the linearization. According to the research [19], the values of the parameters are chosen as follows: \(b=8\) mm, \(a=4\) mm, \(\omega=6.5\) kHz, \(U_{o}=7.5\) kV, \(\alpha=0.35\), \(\rho_{c}=10^{11}\) cm\({}^{-3}\), \(\Delta t=6.7\) us, \(d=0.15\) cm, \(E_{b}=30\) kV/cm. The detailed methods for determining these parameters can be found in the literature [14]. Aiming to scale the actuation strength, a normalized scaling parameter \(D_{c}\) is defined by [PERSON] et al. [15; 17] to show the ratio of body force to inertial force. This can be written as  
\[D_{c}=\frac{\rho_{c}e_{c}E_{0}L}{\rho U_{\infty}^{2}} \tag{4}\]  
where \(\rho\) is the fluid density, \(L\) is the reference length, and \(U_{\infty}\) is the free stream velocity. In the present simulation, the scaling parameter is set as 0.6.  
Figure 1: Plasma active region in the phenomenological model.
## 3 Results and Discussion
### ANOVA Analysis  
ANOVA was performed for each influencing factor regarding the R derived from the MSCR test. The results are shown in Table 3. The \(p\)-values of all influential factors, except for different development times (without sulfur), were less than 0.05. As a result, the performance of SBS-modified asphalt is greatly affected by the preparation process.  
The \(p\)-values for different development times (with sulfur) were the lowest, indicating that the swelling of SBS during high-temperature development and the cross-linking effect of sulfur have a significant effect on the performance of SBS-modified asphalt. Shear rate, shear time, and shear temperature also have a significant effect on modified asphalt properties. This is attributed to the influence of these three factors on the particle size and morphology of the modifiers in asphalt.  
The \(p\)-values of different development times (without sulfur) are more than 0.05, indicating that high temperature development has no significant effect on the performance of modified asphalt without sulfur. The contrast of different development time (with/without sulfur) highlights the significance of sulfur addition during preparation.
## 4 Discussion
### Forest Classification in Northeast China
The aforementioned comparison implies that accurately mapping forest is very difficult ([PERSON] et al., 2018), and there is still great uncertainty in the spatial extent, distribution, and subcategories of forest in Northeast China. Since 2008, the Landsat data freely released by the United States Geological Survey (USGS) has been the major data source for forest mapping with moderate resolution, but HJ-1 images also showed a reliable alternative or supplementary in forest mapping and subcategories identifying in this study. Similar free and open data policies would enable greater use of these data for public good and foster greater transparency of the development, implementation, and reactions to policy initiatives that affect the world's forests ([PERSON] et al., 2018). In the future, how to integrate spectral data from Landsat-like images and structural data from radar observations (e.g., PALSAR and Sentinel-1) to improve the accuracy of forest maps should be considered a priority ([PERSON] et al., 2018). On the other hand, to reduce uncertainties in estimating the forest cover in large areas, fine-scale estimates are needed because the rapid land-cover changes related to forest can be observed at a small-scale. UAVs (unmanned aerial vehicles), LiDAR (light detection and ranging), and hyperspectral digital image data sources can provide accurate estimations of many key forest parameters. Results based on the integration of multi-source data have been shown to be superior to results obtained using a single data source ([PERSON] et al., 2018), and they could provide important validation data for large scale estimation, but the costs of those data sources are higher.
## 4 Discussion
### Limitations and Future Research Perspectives  
Although this study offers valuable insights for handball coaches and practitioners, a few limitations must be noted. First, this study utilized a very specific sample (i.e., a Spanish 2 nd Division team) that was not particularly large (\(n=14\)). Second, internal match load and recovery status were measured only via self-reported subjective ratings. Third, the well-being status was only collected during the two days after the match. Fourth, the playing position of each player was determined solely based on her position in the offensive phase, disregarding her defensive position. Fifth, since this was an observational study, player rotations could not be regulated or affected by researchers. Lastly, there was large variability in the well-being responses and in match RPE values, resulting in an inconsistent patten of response, as can be seen in the data dispersion presented in the figures. Therefore, coaches and practitioners should apply the results with caution when generalizing and extrapolating.  
Future lines of research could investigate elite handball teams and a larger number of players to verify the conclusions. Likewise, future studies should employ objective monitoring tools to analyze the internal match load (e.g., heart rate or lactate) and post-match recovery (e.g., heart rate variability, serum creatine kinase, or countermovement jump). Also, it would be appropriate to examine the internal match load and well-being responses reported by players during microcycles with multiple matches (two or three) or in fixture congestion across a competitive season. In addition, future studies should examine the recovery time course for at least three days after the match. Lastly, future research should explore the interplay between the external load experienced by players (e.g., high-speed running, accelerations, or decelerations) and the internal load and well-being responses assessed with objective and subjective markers.