esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review
## 2 JPEG and JPEG2000 Compression Effects on Medical Images  
### Medical Images and Their Characteristics  
While natural images are primarily used for visual representation in everyday photography, digital media, and for aesthetic purposes, medical images are critical for diagnostic and therapeutic purposes, requiring higher fidelity and accuracy. Medical images differ significantly from natural images in terms of their formats and uses. Natural images are typically 2D, either in color or grayscale, and usually represented in the RGB color space. In contrast, medical images can come in various formats, including 2D grayscale, 2D with multiple channels, 3D volumetric, and even 4D, which includes an additional temporal dimension representing changes over time [15]. In a single slide 2D grayscale medical image, each pixel (8-, 12-, or 16-bit depth) represents a shade of gray, varying from black to white.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Modality**} & \multirow{2}{*}{**Compression Ratio Range**} & **Regions with Highest** & **Regions with Lowest** \\  & & **Accepted Compression** & **Accepted Compression** \\ \hline CR (chest, skeletal, body) & 10:1–30:1 & Canada (30:1) & UK, Germany (10:1) \\ \hline CR (pediatric) & Up to 30:1 & Canada & Not available for UK, Germany \\ \hline CR (mmmography) & 15:1–25:1 & Canada (25:1) & Germany (15:1), UK (20:1) \\ \hline CT (head) & 5:1–12:1 & Canada (12:1) & UK, Germany (5:1) \\ \hline CT (skeleton/chest/lung) & 5:1–15:1 & Canada (15:1) & UK (5:1) \\ \hline CT (body) & 10:1–15:1 & Canada, Germany (15:1) & Not available for UK \\ \hline CT (angio, pediatric) & Up to 15:1 & Canada & Not available for UK, Germany \\ \hline MR & 5:1–24:1 & Canada (24:1) & Germany (7:1), UK (5:1) \\ \hline NM & Up to 11:1 & Canada & Not available for UK, Germany \\ \hline US & 10:1–12:1 & Canada & Not available for Germany \\ \hline XA, XRF & 6:1 & Germany & Not available for UK, Canada \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Medically Accepted Compression Ratios by Modality and Region [6].  
Medical images, unlike natural images, are critical for diagnostic and therapeutic purposes, requiring high fidelity and accuracy. The special characteristics of 2D grayscale medical images, such as their specific variation in intensity ranges, location, scale, and the importance of fine details, make them uniquely challenging for image compression and ML applications. We examine each of these characteristics separately.
###### Abstract  
The production system, especially fertilisation has an important effect on yield and quality of strawberries. In the present study, plants were fertilized with different doses of nitrogen (0-100% recommended doses), calcium chelate, as well as nano fertilizer Lithovit. Strawberry cultivar 'Clery' yield and quality parameters (fruit color and firmness) including nutritional indicators (total soluble solids, sugars, organic acids, phenolic and volatile compounds) were monitored. Volatiles were identified and monitored using headspace solid phase microextraction and analysed using gas chromatograph-mass spectrometry (SPME/GC-MS) and sugars, organic acids, and phenolic compounds with high performance liquid chromatography. Organic acids and phenolic compounds were detected with mass spectrometer (HPLC/MS). Both nitrogen and calcium fertilisation had altered sugars, organic acids, volatile and phenolic contents in strawberry fruits. Fertilisation with higher doses of nitrogen and calcium increased the content of unpleasant aromas aldehydes hexanal (up to 3.8-fold) and (E)-2-hexen-1 (up to 3.7-fold). The content of fruits esters was uppermost in fruits fertilised with nano-fertilic Lithovit (up to 2.3-fold). Fertilisation with N and Ca decreased the strength of ketone and terpenoids fruity aroma. The highest content of total phenols, as well as all individual hydroxycinamic and hydroxybenzoic acid derivatives were obtained in the nano-fertilise Lithovit treatment. Fertilisation, especially with nitrogen, had mostly negative impact on strawberry flavour while nano-fertilization with Lithovit improved strawberry phenolic content and aroma.  
strawberry; fertilisation; nitrogen; calcium; lithovit; volatiles; phenolics Article
# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
## 4 Discussion
When conducting field measurements of the NEE, it is essential to be aware of potential overestimation and underestimation, both of which can impact the accuracy of the results. Factors such as nocturnal respiration and organic dissolved carbon contribute to uncertainties in NEE measurements. Of particular significance is the vulnerability of nighttime respiration to fluctuations in climatic conditions, leading to increased uncertainty. While daytime respiration measurements generally provide reliable data, errors in nighttime measurements further add to the uncertainty in estimating nocturnal NEE. To enhance the accuracy and dependability of NEE assessments near the site, it is crucial to address and comprehend these sources of uncertainty. This entails the careful consideration of a wide range of environmental variables and their interactions to obtain more precise and trustworthy NEE estimates. By doing so, we can gain a better understanding of the ecosystem dynamics, and ensure the robustness of field-based NEE evaluations [34]. The results of the study demonstrated that the SHAP model performed an effective interpretation of input features in the application of NEE estimat  
Figure 6: **(A,B)** are the time series of original and predicted NEE of CBF and HBW sites, respectively. model. The attribution analysis based on the SHAP model showed that the NDVI, LAI, and EVI are important factors affecting the model driving and dominating the NEE trends at several sites (Figure S1). The EVI is an indicator of vegetation canopy development, phenology, and greenness, and it indicates seasonal changes in the biomass of vegetation. In addition to the EVI, there is a significant relationship between solar-induced chlorophyll fluorescence (SIF) and the actual photosynthesis of vegetation [34, 40]. There were differences in the variables that played a dominant role in the different sites. Only 11 variables were used in this research for the study, and there may be other factors that play an important role in controlling the daily NEE, such as physiological processes [42], global radiation (Rg) [24], and management activities [43].
# In Situ Experimental Study of the Friction of Sea Ice and Steel on Sea Ice
## 4 Discussion
### Velocity Dependence  
From the viewpoint of ice navigation, velocity is a critical parameter for ice friction. To quantify the velocity dependence of \(\mu_{\text{k}}\), the rate- and state- dependent model developed from an engineering perspective was employed [35]. The model can be expressed as  
\[\mu_{\text{k}}=\mu_{0}+\theta+A\ln\frac{V}{V^{*}}, \tag{2}\]  
where \(V^{*}\) is a characteristic velocity for dimensional consistency, chosen here as \(10^{-2}\) m\(\cdot\)s\({}^{-1}\), and \(\mu_{0}\) and \(A\) are empirically determined constants. \(\theta\) is a state variable related to slip history, which can be expressed as  
\[\frac{\text{d}\theta}{\text{d}t}=-\frac{V}{L}(\theta+B\ \ln\frac{V}{V^{*}}), \tag{3}\]  
where \(t\) is time, \(L\) is a characteristic slip length, and \(B\) is an empirically determined constant. When sliding velocity is constant, \(\theta\) is constant, \(\frac{\text{d}\theta}{\text{d}t}=0\), and \(\theta=-B\ \ln\frac{V}{V^{*}}\). Then, \(\mu_{\text{k}}\) can be expressed as  
\[\mu_{\text{k}}=\mu_{0}+C\ \ln\frac{V}{V^{*}} \tag{4}\]  
where \(C=A-B\). Based on Equation (4), the velocity dependence was determined experimentally in this study. Table 2 gives the estimated empirical parameters and determination coefficients obtained by substituting the data in Figure 10 into Equation (4). The high determination coefficients demonstrate that the velocity dependence is well quantified, and Figure 12 shows comparison of the results.  
Figure 11: Variations of \(\mu_{\text{k}}\) with velocity under different air temperatures for: (**a**) CS-Lateral\(\bot\) and CS-Lateral\(//\); (**b**) Bottom-Bottom. The data were averaged over all normal stresses, and the error bars represent the standard deviation.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Exploiting Path Diversity
#### 4.2.3 LB-RPL  
[PERSON] et al. observed that uneven deployment of sensor nodes in large areas and heterogeneous traffic patterns in a network may cause workload imbalance which shortens network lifetime. They propose LB-RPL [35], which detects workload imbalance and optimizes the data forwarding path by considering both workload distribution and link-layer communication qualities. To alleviate workload imbalance, LB-RPL adopts two approaches. First, LB-RPL forwards data using multiple parent nodes. Each node considers top \(k\) parent nodes as potential next-hop nodes for data forwarding. The probability \(f_{ij}\) of node \(i\) forwarding a data packet to parent node \(j\) is calculated based on the packet drop probability \(p_{ij}^{c}\) between the two nodes due to the channel condition.  
\[f_{ij}=\frac{(1-p_{ij}^{c})}{\sum_{j=1}^{k}\left(1-p_{ij}^{c}\right)} \tag{16}\]  
Second, LB-RPL sets a timer for DIO message transmission in proportion to workload measured using a buffer utilization counter. Depending on the application and the underlying hardware, a buffer utilization counter is defined as the average number of packets in the queue over a certain time period or the total number of packets queued in the buffer. If a parent node has heavy workload in the current round, it will add more delay for DIO message transmission in the next round. Since the long delay lowers the probability that a parent with heavy workload is in top \(k\) list in its children's parent table, workload imbalance can be mitigated.  
Performance was evaluated via ns-2 simulation using IEEE 802.15.4 at the PHY and MAC layer. In the simulation, LB-RPL successfully spread out the workload among the nodes around the data collector and achieved a higher packet delivery ratio and a shorter average delay than RPL for all buffer sizes.  
However, [PERSON] et al. point out that DIO packet reception time may not exactly reflect the queue utilization due to DIO packet losses and that LB-PRL may cause slow recovery and the herding effect [33].
# Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers +
###### Abstract
As novel contributions, we introduce and optimize parametrized mappings that involve \(K\)-dimensional trees [24; 25]. We propose and investigate the design of a novel demodulation scheme for data transmission using non-binary low-density parity-check codes with binary phase-shift keying (BPSK) modulation which is based on nearest neighbor search in the \(K\)-dimensional trees as an entirely new contribution of this article. In summary, the contributions of this article are:  
* We develop and investigate the idea of applying genetic algorithms to maximize mutual information in a parametrized information bottleneck setup for communication receivers. * We design very powerful parametrized compression mappings that preserve large amounts of relevant information with very few parameters. These mappings are based on exact and approximate nearest neighbor search algorithms. * We illustrate enormous flexibility and generality of the considered approach. We present results on channel output quantization and demodulation in communication receivers. The article is structured as follows. The next section provides a brief overview of the required preliminaries. In Section 3, we propose different classes of parametrized mappings that can preserve significant amounts of relevant information. Moreover, we motivate and explain their genetic optimization. Section 4 then provides practical results on the proposed communication receiver design with maximum preservation of relevant information. Finally, Section 5 concludes the article.
# Estimating Recycling Return of Integrated Circuits Using Computer Vision on Printed Circuit Boards
###### Abstract  
The technological growth of the last decades has brought many improvements in daily life, but also concerns on how to deal with electronic waste. Electrical and electronic equipment waste is the fastest-growing rate in the industrialized world. One of the elements of electronic equipment is the printed circuit board (PCB) and almost every electronic equipment has a PCB inside it. While waste PCB (WPCB) recycling may result in the recovery of potentially precious materials and the reuse of some components, it is a challenging task because its composition diversity requires a cautious pre-processing stage to achieve optimal recycling outcomes. Our research focused on proposing a method to evaluate the economic feasibility of recycling integrated circuits (ICs) from WPCB. The proposed method can help decide whether to dismantle a separate WPCB before the physical or mechanical recycling process and consists of estimating the IC area from a WPCB, calculating the IC's weight using surface density, and estimating how much metal can be recovered by recycling those ICs. To estimate the IC area in a WPCB, we used a state-of-the-art object detection deep learning model (YOLO) and the PCB DSLR image dataset to detect the WPCB's ICs. Regarding IC detection, the best result was obtained with the partitioned analysis of each image through a sliding window, thus creating new images of smaller dimensions, reaching 86.77% mAP. As a final result, we estimate that the Deep PCB Dataset has a total of 1079.18 g of ICs, from which it would be possible to recover at least 909.94 g of metals and silicon elements from all WPCBs' ICs. Since there is a high variability in the compositions of WPCBs, it is possible to calculate the gross income for each WPCB and use it as a decision criterion for the type of pre-processing.  
waste PCB recycling; integrated circuit detection; YOLO +
Footnote †: journal: Applied Sciences
## 4 A Theoretical Framework of MTCP
### Concept and Annotation of MTCP  
Under the guidance of the Mining Information Modeling (MIM) concept, the concept, connotation and characteristics of the mining technology collaboration platform are proposed to solve the problems existing in the current digital mine construction process in China and to realize the mine's whole life cycle information integration, interoperability and high sharing and collaboration based on 3D data exchange.  
The mining technology collaboration platform (MTCP) is a process, standardized and integrated collaboration platform for the digitalized processing and management of the geology, surveying and mining technology business in the whole life cycle with the support of information technology. Based on the process and standardization management, with the help of the Internet, database technology, cloud computing and other information technologies and driven by business data and business processes, a unified digitized operation environment for mine production technology business processing and management is constructed, which enables all participants, departments and professional posts to work together on the same platform and realizes interoperability, highly sharing and integration of the geology, surveying and mining technology business data in the mine's whole life cycle and improves the team's across time and space multidisciplinary collaboration ability and work efficiency and improves the business processing and management efficiency. The core contents of the mining technology collaboration platform include the following:  
1. Centralized storage of business data to achieve accurate and effective management of various structured and unstructured data and data relationships and to provide a good security access mechanism.
2. Centralized management of the business work to realize centralized, unified and hierarchical management of the environmental parameters such as the design parameters, constraints, technical indicators and technical resources and the effective management and control of the parameters.
3. Centralized control of business processes to realize the transformation from \"what to do\" to \"how to do\" and \"how well to do it\" to achieve effective tracking, control and assessment of the business processes.  
Through the centralized storage of business data, centralized management of business work and centralized control of the business process, the operators of all participants, departments and professional positions collaborate efficiently on the same platform across time and space to complete the business operations of the mine's whole life cycle, such as exploration, infrastructure, planning and design, production management, survey and acceptance, mine closure and reclamation, to achieve interoperability, efficient flow and highly shared business data.
## 2 Materials and Methods  
### Satellite-Based WUE Datasets  
Datasets of MODIS monthly GPP and ET (MOD17A2 and MOD16A2, Version 055) at 1 km spatial resolution from 2001 to 2014 [30; 31] were used to calculate the ecosystem-scale WUE, which is defined as the ratio of GPP (g\(\cdot\)C\(\cdot\)m\({}^{-2}\)) to ET (mm) [4; 9; 10]. MODIS GPP and ET are derived from satellite-based vegetation information data along with meteorological data, using a light-use efficiency model [32; 33] and the Penman-Monteith approach [34], respectively. It is reported that MODIS WUEestimates compared favorably with tower-based measurements of WUE [8]. The monthly GPP and ET datasets were averaged to give seasonal mean for each year to a 0.5\({}^{\circ}\) grid [10]. In our analysis, the seasons concerned included spring, summer and autumn, defined as March-May, June-August and September-November, respectively.
# Calculation of Blasting Damage Zone Radius of Different Charge Structures in Burnt Rock
###### Abstract  
The radius of the failure area after a blasting fracture process of burnt rock is affected by joint fissures, does not conform to the existing theoretical calculation formula and the distribution law of the failure area also changes. The fracture area is large, and the fracture extension and expansion area are small. Therefore, in order to describe the damage of blasting to a fractured rock mass more objectively and accurately, on the basis of summarizing the previous research results, a damage variable was introduced to characterize the initial crushing degree of the fractured rock mass, and the corresponding rock failure criterion was used to derive the calculation formula of a blasting crushing circle and fracture circle radius of burnt rock with different charge structures. The results show that the blasting failure zone of fractured rock mass with different charge structures was not only related to the radius of the blast hole and the explosive and rock properties, but also had a strong relationship with the initial damage degree of the rock mass. Taking an open-pit coal mine in Xinjiang as an example, the radius of the fracture zone with different charge structures was obtained by using the obtained calculation formula, and it was applied to the determination of row spacing and hole spacing.  
charge structure; blast stress; damage factor; crushed area; crack area Article
# A Construction Method for the Random Factor-Based G Function
## 3 Analysis of the Principles and Limitations of Commonly Used G Functions
### G Function Based on State Grid  
The frequency state grid diagram is shown in Figure 2. The G function, based on the state grid [7], considers the current working frequency as a state. The transmitted data are processed using a linear and finite state shift register, resulting in log-N binary numbers. These binary numbers are then converted into N-base numbers, with each N-base number corresponding to N states of the frequency sequence. This creates a grid diagram of the system frequency, known as the frequency-hopping sequence. In the decoding process, the Viterbi decoding method is used, taking advantage of the correlation between frequencies. The more frequency points in the frequency set, the deeper the decoding depth, and the greater the difference in the metric value of each path. This allows for the identification of the path with the smallest path metric value, which can then be used to demodulate the data.  
While this method has shown some success in improving the bit error rate performance of the receiving end decoding, it requires a large amount of system calculation and does not  
Figure 2: Frequency state transition grid diagram.  
improve the fan-out coefficient. Additionally, the generated frequency-hopping sequence has limited variation and poor randomness performance.
## 2 Materials and Methods
### Data Processing  
Data were saved and then exported from the IMU built-in software (TK Motion Manager, NexGen Ergonomic, Montreal, QC, Canada), initially in.h5 format, then converted to.csv for analysis. A custom Python script was developed to automate data organization. This script extracted relevant data columns (time, acceleration, angular velocity), renamed files using a standardized convention (participant ID_sensor location_task_date), and organized them into a hierarchical folder structure.  
To ensure data quality and reliability before calculating the parameters of interest, we implemented several pre-processing steps. First, linear interpolation was applied to replace missing values in the IMU data. Then, a 6 th order Butterworth low-pass filter with a cutoff frequency of 3.667 Hz was applied to attenuate high-frequency noise while preserving significant movement components. This choice of the cutoff frequency came from its ability to remove noise in this dataset without altering the detection of movement, which was also explored through trial and error, and supported by a previous research study [26].  
Additionally, BBS scores in each of the fourteen tasks, originally collected in the range from 0 to 4, were transformed. Individual task scores were dichotomized such that the scores of 0-3, indicating potential balance difficulties, were re-coded as 1 (poor balance), while the score of 4, representing good balance, was re-coded as 0 (good balance).