esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Compression with a Time Limit
## 3 Lossless Compression
### Lossless Compression of Two-Dimensional Data with a Time Limit  
We evaluated the performance of the algorithm by conducting experiments on different types of inputs. The following four cases were initially analyzed:  
* Compression of a set of animated images with similar scenes.
* Compression of a set of animated images with different scenes.
* Compression of a set of non-animated images with similar scenes.
* Compression of a set of non-animated images with different scenes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Compressed Size (Bytes)** &
\begin{tabular}{c} **Time to Compress** \\ **(milliseconds)** \\ \end{tabular} \\ \hline _Gzip_ & 7,700,660 & 1784 \\ \hline _XZ_ & 6,793,836 & 25,974 \\ \hline _Bzip2_ & 7,216,597 & 16,514 \\ \hline _Arithmetic Coding_ & 13,906,174 & 7928 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compression of 1000 html pages.  
The time budgets used are different for each experiment.  
The tests on the lossless compression of images with a time limit were conducted on a personal computer with the following specifications:  
* CPU: Intel Core i7-2630 QM CPU 2.00 GHz;
* RAM: 8.00 GB;
* Operating system: Windows 10 Pro;
* Mass storage: 128 GB Samsung Pro SSD.  
The following Table 2 lists all the compression tools used, their configuration adopted, and the download link for the compression libraries (accessed last time on 19 November 2024):  
The images were extracted from videos currently available at the following links (accessed last time on 19 November 2024):  
* [[https://www.youtube.com/watch?v=FmiRX9](https://www.youtube.com/watch?v=FmiRX9) Oh_0g]([https://www.youtube.com/watch?v=FmiRX9](https://www.youtube.com/watch?v=FmiRX9) Oh_0g);
* [[https://www.youtube.com/watch?v=5](https://www.youtube.com/watch?v=5) FNFBuguel]([https://www.youtube.com/watch?v=5](https://www.youtube.com/watch?v=5) FNFBuguel);
* [[https://www.youtube.com/watch?v=u5](https://www.youtube.com/watch?v=u5) CZs4y53 UE]([https://www.youtube.com/watch?v=u5](https://www.youtube.com/watch?v=u5) CZs4y53 UE);
* [[https://www.youtube.com/watch?v=CkEowml7a3w](https://www.youtube.com/watch?v=CkEowml7a3w)]([https://www.youtube.com/watch?v=CkEowml7a3w](https://www.youtube.com/watch?v=CkEowml7a3w)).  
Each video was downloaded by connecting to the portal [[http://it.savefrom.net/](http://it.savefrom.net/)]([http://it.savefrom.net/](http://it.savefrom.net/)) and then specifying the link and format (MP4 360p).  
To perform the subdivision into frames, we used a GOM Player tool (available at the link [[https://www.gomlab.com/](https://www.gomlab.com/)]([https://www.gomlab.com/](https://www.gomlab.com/))). The input images are in uncompressed TIFF format.
# Revolutionizing Palm Dates Harvesting with Multirotor Flying Vehicles
## 4 Quadrotor Performance  
The performance of the quadrotor during the cutting process is assessed through data extracted from the log files stored in the Pixhawk flight controller. The attitude and altitude angles, as shown in Figures 12-15, reveal only minimal variations throughout the operation. These minor deviations demonstrate that the system is able to maintain a stable orientation, which is crucial for ensuring precise control during the cutting process. The stability of the quadrotor is further confirmed by the velocity data, which fluctuates around zero with minimal magnitude. This indicates that the quadrotor experiences only slight oscillations, highlighting its ability to maintain steady flight despite the complexities of the task.  
Figure 11: Testing the system in the lab using the testbed.  
These small fluctuations in both attitude and velocity remain well within acceptable limits, particularly for precision tasks like branch cutting, where even slight movements can impact the outcome. The system's ability to control such minor variations is key to achieving a smooth and precise operation. The minimal deviation in orientation and velocity ensures that the quadrotor remains stable, thus allowing the cutting task to proceed with minimal interference.  
The robotic arm plays a crucial role in maintaining this stability, as it stabilizes the branch during the cutting process. Even with the slight movements of the quadrotor, the arm's firm grip on the branch prevents misalignment, vibrations, or other disturbances that could compromise the precision of the cut. The arm's stabilization function is particularly important in mitigating the impact of minor perturbations from the quadrotor's movement, ensuring that the branch remains steady and accurately positioned.  
Figure 12: Quadrotor attitude angles.  
Figure 13: Quadrotor speed in the longitudinal direction.  
The synergy between the quadrotor's controlled movements and the robotic arm's stabilizing mechanism contributes significantly to the overall success of the cutting operation. Despite the minor oscillations in the quadrotor's posture and velocity, the robotic arm ensures that the branch remains securely in place, preventing misalignment during the cutting process. This coordination between the quadrotor and the arm demonstrates the system's capacity to perform delicate tasks with high accuracy, even in the presence of minor perturbations. The ability to maintain this level of precision under such conditions highlights the robustness and effectiveness of the system, making it suitable for tasks requiring fine control and stability.
## 3 Methods
### In-Plane Heatwave Thermography
#### 3.1.2 Covariance of Temporal Pixel Behavior  
The second proposed method which is applied to the IPHWT video is based on the covariance matrix of the temporal behavior of each pixel and the average signal of the pixels with similar expected behavior. In the following, the calculation that results in a single image will be explained.  
As a first step, the heat source is localized by identifying the pixels with the total largest heating rate \((dT/dt)_{(x,y)}\). This identifies the area associated with the heat source. With this information, the shortest distance \(d_{(x,y)}\) between each pixel and the area of heat source is calculated in pixel values. With the assumption of a homogeneous plate and a point-like heat source, it can be assumed that all pixels with the same distance to the heat source show similar shapes in the temperature profile. However, if a crack, flaw or any other obstacle influences the in-plane heat flow, the temperature profile will differ from the other pixels with the same shortest distance \(d_{(x,y)}\). To minimize the influence of different surface emissivity, all temperature profiles are normalized so that the absolute value of the surface temperature becomes irrelevant but only the shape of the profile plays a role.  
To calculate the similarity of each pixel profile with the averaged profile in its group of pixels with similar \(d_{(x,y)}\), the normalized covariance matrix or correlation coefficient matrix \(R_{(x,y)}\) of the pixel profile and the averaged profile is calculated. The off-diagonal scalar elements \(r_{(x,y)}\leq 1\) of \(R_{(x,y)}\) indicate the similarity of the pixel profile and the averaged signals the respective group. Here, values near 1 represent a high similarity whereby a low similarity is indicated by \(r_{(x,y)}\ll 1\).
# Engineering a Combinatorial Laplacian Solver: Lessons Learned +
## Preliminaries
### Cycles, Spanning Trees and Stretch  
A cycle in a graph is usually defined as a simple path that returns to its starting point, and a graph is called Eulerian if there is a cycle that visits every edge exactly once. In this work, we will interpret cycles somewhat differently: We say that a cycle in \(G\) is a subgraph \(C\) of \(G\) such that every vertex in \(G\) is incident to an even number of edges in \(C\), i.e., a cycle is a union of Eulerian graphs. It is useful to define the addition \(C_{1}\oplus C_{2}\) of two cycles \(C_{1},C_{2}\) to be the set of edges that occurs in exactly one of the two cycles, i.e., \(C_{1}\oplus C_{2}:=(C_{1}\setminus C_{2})\cup(C_{2}\setminus C_{1})\). In algebraic terms, we can regard a cycle as a vector \(C\subseteq\mathbb{F}_{2}^{E}\) (\(\mathbb{F}_{2}\) is the finite field of order two), such that \(\sum_{v\in N_{C}(u)}1=0\) in \(\mathbb{F}_{2}\) for all \(u\in V\) and the cycle addition as the usual addition on \(\mathbb{F}_{2}^{E}\). We call the resulting linear space of cycles \(\mathcal{C}(G)\).  
In a spanning tree (ST) \(T=(V,E_{T})\) of \(G\), there is a unique path \(P_{T}(u,v)\) from every node \(u\) to every node \(v\). For any edge \(e=uv\in E\setminus E_{T}\) (an off-tree-edge with respect to \(T\)), the subgraph \(e\cup P_{T}(u,v)\) is a cycle, the basis cycle induced by \(e\). One can easily show that the basis cycles form a basis of \(\mathcal{C}(G)\). Thus, the basis cycles are very useful in algorithms that need to consider all of the cycles of a graph. Another notion we need is a measure of how well a spanning tree approximates the original graph. We capture this by the stretch \(\operatorname{st}(e)=\left(\sum_{e^{\prime}\in P_{T}(u,v)}w_{e^{\prime}}\right)/w_{e}\) of an edge \(e=uv\in E\). This stretch is the detour you need in order to get from one endpoint of the edge to the other if you stay in \(T\), compared to the length of the original edge. In the literature, the stretch is sometimes defined slightly differently, but we follow the definition in [PERSON] et al. (2012) using \(w_{e}\). The total stretch of the whole tree \(T\) is the sum of the individual stretches \(\operatorname{st}(T)=\sum_{e\in E}\operatorname{st}(e)\). Finally, we define the average stretch as the total stretch divided by the total edge weight. Finding a spanning tree with low stretch is crucial for proving the fast convergence of the KOSZ solver.
# Investigation of the Automatic Monitoring System of a Solar Power Plant with Flexible PV Modules
###### Abstract
In addition, the solar charging of smart devices such as mobile phones and tablets provides greater convenience to consumers as they can get power anywhere and anytime there is sunlight. Finally, combining these technologies with advanced monitoring systems allows for more efficient control of energy flow, the optimization of energy storage and improved overall system performance [1; 2; 3; 4; 5; 21]. The scientific novelty of this research lies in the creation of an automated monitoring system for a solar power plant with flexible modules, which allows the real-time monitoring and analysis of its operating parameters. The monitoring system consists of two monitoring stages based on an MCU (Microcontroller Unit) module and an industrial PLC. A number of variants of monitoring systems are described in the scientific literature, but the one considered in this study (intended for a solar power plant with flexible PV modules) has not yet been analyzed in the literature. The aim of the study is to develop and investigate an automatic system for monitoring the operating parameters of a solar power plant with flexible modules in order to optimize the battery charging process and evaluate the efficiency of different charging controllers.
## 2 Materials and Methods
### Data  
The adopted data were NPP data [18], CO\({}_{2}\) spatial grid data, meteorological data and data from statistical yearbooks. NPP data were acquired from MOD17A3H at a resolution of 500 m for the years 2000-2019 via the Google Earth Engine platform ([[https://code.earthegine.google.com/](https://code.earthegine.google.com/)]([https://code.earthegine.google.com/](https://code.earthegine.google.com/)), accessed on 1 February 2023) [19; 20; 21]. These data are derived from the sum of the 45 8-day Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference between the GPP and the Maintenance Respiration (MR) (GPP-MR). CO\({}_{2}\) spatial grid data were spatially gridded data for CO\({}_{2}\) emissions from fossil fuel combustion for 2000-2019 from the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) [22; 23] ([[https://db.cger.nies.go.jp/dataset/ODIAC/DL_odica2020b.html](https://db.cger.nies.go.jp/dataset/ODIAC/DL_odica2020b.html)]([https://db.cger.nies.go.jp/dataset/ODIAC/DL_odica2020b.html](https://db.cger.nies.go.jp/dataset/ODIAC/DL_odica2020b.html)), accessed on 1 February 2023) at a resolution of 1000 m. CO\({}_{2}\) spatial grid data called ODIAC2020b differ from ODIAC2020 in their improved representation of point source emissions, which is based on the latest country fossil fuel CO\({}_{2}\) estimates (2000-2017) made by the new Carbon Dioxide Information Analysis Center (CDIAC) team at Appalachian State University. The spatial decomposition of emissions was performed using a variety of spatial proxy data, such as the geographic location of point sources, satellite observations of night light and aircraft and fleet trajectories. In ODIAC 2020b, the geographic location of power plants and emission estimates for the rest of the world (USA, China and Eastern European countries) were also improved. The seasonality of emissions is taken from the CDIAC monthly gridded data product. Meteorological data were derived from the annual mean precipitation data and annual mean temperature data from the Re  
Figure 1: Location of Ordos City.  
source and Environmental Science and Data Center ([[https://www.resdc.cn/Default.aspx](https://www.resdc.cn/Default.aspx)]([https://www.resdc.cn/Default.aspx](https://www.resdc.cn/Default.aspx)), accessed on 1 February 2023) for the years 2000-2015. The meteorological data are based on daily observations from over 2400 meteorological stations across the country and are generated by collating, calculating and spatially interpolating the data. Among the interpolations applied is the Australian ANUSPLIN interpolation software, with annual average temperature units of 0.1 \({}^{\circ}\)C and annual precipitation units of 0.1 mm. The relevant driver data were obtained from the Ordos City Statistical Yearbook for the years 2010-2019.
# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
## 2 Related Work
### Transformer-Based Object Detection  
In recent years, due to the advantages of attention mechanisms, transformer-based object detection methods have gained substantial attention in remote sensing, leading to a swift increase in research findings. In 2021, [PERSON] et al. [31] proposed an improved model based on the Swin Transformer [32], incorporating a local perception Swin Transformer backbone network to enhance local feature perception and improve small-target detection accuracy. Furthermore, a spatial attention-interleaved execution cascade network further enhances detection and segmentation accuracy. In 2022, [PERSON] et al. [33] proposed an end-to-end transformer-based remote sensing object detection framework (TRD). This framework utilizes an attention-based transferring backbone network for feature extraction, which enhances detection performance. Additionally, during training, data augmentation methods, including sample expansion and multiple-sample fusion, are employed to mitigate overfitting. [PERSON] et al. [34] introduced an arbitrary-oriented object detection transformer (AO2-DETR), which removes the need for anchors and complex pre- or post-processing. This network utilizes an oriented proposal generation mechanism to generate rotating candidate boxes as object queries, which enhances positional priors for feature pooling. An adaptive oriented proposal refinement module is also introduced to adaptively adjust rotated boxes based on contextual information, ensuring one-to-one matching via a rotation-aware set matching loss. In 2023, [PERSON] et al. [35] proposed a network with separate feature refinement (SFRNet). The features processed by region of interest (ROI) align operation are input into two branches for separate refinement. A spatial and channel transformer and multi-ROI loss in the classification branch are employed to regularize fine-grained classification. The oriented response convolution with transformer is utilized in the localization branch to encode rotation information. This network can thus extract subtle differences between fine-grained categories in the classification branch and obtain rotation-sensitive features in the localization branch.
###### Abstract  
In recent decades, rice disease co-epidemics have caused tremendous damage to crop production in both China and Southeast Asia. A variety of remote sensing based approaches have been developed and applied to map diseases distribution using coarse- to moderate-resolution imagery. However, the detection and discrimination of various disease species infecting rice were seldom assessed using high spatial resolution data. The aims of this study were (1) to develop a set of normalized two-stage vegetation indices (VIs) for characterizing the progressive development of different diseases with rice; (2) to explore the performance of combined normalized two-stage VIs in partial least square discriminant analysis (PLS-DA); and (3) to map and evaluate the damage caused by rice diseases at fine spatial scales, for the first time using bi-temporal, high spatial resolution imagery from PlanetScope datasets at a 3 m spatial resolution. Our findings suggest that the primary biophysical parameters caused by different disease (e.g., changes in leaf area, pigment contents, or canopy morphology) can be captured using combined normalized two-stage VIs. PLS-DA was able to classify rice diseases at a sub-field scale, with an overall accuracy of 75.62% and a Kappa value of 0.47. The approach was successfully applied during a typical co-epidemic outbreak of rice dwarf (Rice dwarf virus, RDV), rice blast (_Magnaporthe oryzae_), and glume blight (_Phulloscita glumarum_) in Guangxi Province, China. Furthermore, our approach highlighted the feasibility of the method in capturing heterogeneous disease patterns at fine spatial scales over the large spatial extents.  
Keywords:high spatial resolution; PlanetScope; feature extraction; damage mapping; rice dwarf; rice blast; glume blight +
Footnote †: journal: Physics Letters B
## 4 Results
### Response Variables Distribution  
The boxplot shown in Figure 3 reports the distribution of the values that the three monitored response metrics (_SMAPE_, _RMSE_, _MAE_) assumed in each of the four considered experimental conditions. The chart thus represents the error distribution reported by the four predictive approaches when estimating the supplier delivery performance of each of the 134 examined components. The box for each group spans the interquartile range, with the bottom and top boundaries corresponding to the first quartile and third quartile, respectively. A line inside each box represents the median.  
As shown in the chart, the experimental condition corresponding to the proposed approach, identified as (LSTM, MULTIVARIATE), reported a median error of 53% expressed in terms of _SMAPE_, 5.5 days in terms of _RMSE_, and 4.1 days in terms of _MAE_. The proposed approach thus has a lower median error than the other benchmarks. However, the results show that for 50% of the forecasts provided by the proposed approach, the obtained prediction errors can vary from 25% to 88% in terms of _SMAPE_ and from 2.8 days to 6.9 days in terms of _MAE_. Furthermore, considerable errors of up to 161% can be made in terms of _SMAPE_. However, these peaks are lower than those of the other benchmarks.
# Ionic Liquid-Modified Porous Organic Polymers as Efficient Metallocene Catalyst Supports
## 3 Results and Discussion  
### Preparation of Ionic Liquid-Modified Porous Organic Polymer  
In the present work, [BVIM] [CI] was used as a functional comonomer (FC) for ionic liquid-modified mono-functional and dual-functional porous organic polymers synthesis. As shown in Figure 1, IL-POPs were synthesized by free radical polymerization, firstly, monomers were dispersed in the solvent and the polymerization was initiated by AIBN. In the ternary system, HEMA was used as the second functional comonomer, and the influence of HEMA on the IL-POPs' pore structure, MAO/metallocene immobilization and ethylene polymerization were also investigated.  
The porosity of IL-POPs was evaluated by nitrogen sorption analysis, the measured BET specific surface area (SSA) and the non-local density functional theory (NLDFT) simulated total pore volume (TPV) and average pore diameter (PD) results were listed in Table 1. Pore parameters of ionic liquid-modified POPs.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**POP No.** & **Solvent** & **FC** & \begin{tabular}{c} **DVB:FC** \\ **(Molar Ratio)** \\ \end{tabular} & \begin{tabular}{c} **FCs Content** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **SSA** \\ **(m\({}^{2}\)/g)** \\ \end{tabular} & \
\begin{tabular}{c} **TPV** \\ **(cm\({}^{2}\)/g)** \\ \end{tabular} &
\begin{tabular}{c} **PD** \\ **(nm)** \\ \end{tabular} \\ \hline POP-1 & EtOH & IL & 1:1 & 50 & 16.9 & 0.08 & 18.90 \\ POP-2 & EtOH & IL & 2:1 & 33 & 550.1 & 0.51 & 3.69 \\ POP-3 & EtOH & IL & 3:1 & 25 & 496.0 & 0.38 & 3.08 \\ POP-4 & EtOH & IL & 3:2 & 40 & 561.8 & 0.71 & 5.03 \\ POP-5 & EtOH & IL & 20:1 & 5 & 47.8 & 0.08 & 6.80 \\ POP-6 & EtOH & IL & 4:1 & 20 & 331.5 & 0.29 & 3.47 \\ POP-7 & EtOH:H\({}_{2}\)O/(9:1) & IL & 4:1 & 20 & 147.4 & 0.29 & 8.00 \\ POP-8 & EtOH & IL/HEMA (FC\({}_{1}\)/FC\({}_{2}\)) & DVB:FC\({}_{1}\):FC\({}_{2}\) = 4:1:1 & 43 & 139.7 & 0.34 & 9.83 \\ POP-9 & EtOH & IL/HEMA & 6:1:1 & 25 & 506.9 & 0.52 & 4.10 \\ POP-10 & EtOH & IL/HEMA & 4:1:1 & 33 & 492.7 & 0.47 & 3.80 \\ POP-11 & EtOH:H\({}_{2}\)O/(9:1) & IL/HEMA & 6:1:1 & 33 & 199.2 & 0.22 & 4.49 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pore parameters of ionic liquid-modified POPs.