esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
The presence of formaldehyde in human body can be associated with several sources, such as folate derivatives breakdown, protein and nucleic acid demethylation, glycine and serine metabolism or exogenous sources (e.g., smoking and diet) [13]. In contrast with our study, formaldehyde was found elevated in the headspace of urines collected from patients diagnosed with BC and prostate cancer in another study [40]. Aromatic compounds, such as 1-methyllnaphthalene, 2-methyllnaphthalene, 1,2,4-trimethylbenzene and \(p\)-cresol, were found in significantly higher levels in urine of BC patients. Methyllnaphthalenes may have an exogenous source (e.g., cigarette smoke) [41]. The reactive metabolites generated in vivo from the metabolization of these molecules may damage body organs by modification of proteins [14; 41]. \(p\)-Cresol (4-methylphenol) may be associated with a dysregulation in tyrosine and phenylalanine metabolism [15] and has been found increased in urine of patients diagnosed with lung, colorectal and breast cancers, as well as leukemia and lymphoma [36; 38; 42]. The presence of ketones in human body may be related to \(\beta\)-oxidation of fatty acids [12]. The decrease in 4-heptanone levels corroborate a previous study performed in urine of BC patients [11]. Moreover, 2-butanone and 4-heptanone have been found altered in other cancer types but the direction of the variation differed among studies [31; 32; 37; 39; 43; 44; 45; 46]. Finally, lower levels of two monoterenes (carvone and piperitone) were observed in urine of BC patients. Terpenes are present in herbal and dietary plants and can participate in lipid and carbohydrate metabolisms [16; 17]. Interestingly, decreased levels of piperitone in urine of BC patients were also previously reported [11]. Overall, our study unveiled the presence of distinct volatile profiles in urine of BC patients with potential for tumor detection and staging. Future studies should include validation of these results in a larger cohort of samples, and improvement of sensitivity and specificity rates for overall BC detection as well as better diagnosis of NMIBC cases. In addition, it is very important to consider other concomitant conditions (e.g., hematuria, urinary tract infection) that may influence the urinary volatilome. Furthermore, the si multaneous analysis of urine by GC-MS and electronic sensors may offer new insights into chemical composition of odor signatures that can boost urinary volatilome analysis to clinical practice.
## 4 Limitations  
According to the conditions of execution of the experiment and the analyses carried out, the research considers the following limitations:  
* GCP and checkpoints were not considered for the reconstruction and verification for the 3D model; therefore, there could be uncertainty in the model's location concerning the road points. However, the IRI obtained in this case study through the proposed methodology is similar to the real certified value of the road (provided by the government agency). It is recommended to replicate the experiment in other roads, where certified IRI values (obtained with standardized equipment) are available to generalize the proposed method based on statistical evidence.
* Only Bentley Context Capture software was used since other software licenses were not available at the university where the study was conducted. It is recommended to compare the results of models in other software as they may be different. This is because each software uses its own SfM algorithm for the reconstruction of a 3D model.
* The methodological proposal restricts its application to roads without traffic due to restrictions on flight height and the non-consideration of the elimination of vehicles in the model. The adaptation of the method to perform flights with the road in operation could be evaluated, providing the safety conditions and regulations of the country are considered.
## 2 The FNA Method
### The Prediction of Machining Trajectory
#### 2.2.1 The Solution of Difference Equation Coefficients  
For the coefficients \(a_{0},a_{1},a_{2},b_{0},\) and \(b_{1}\) in the difference Equation (3), the input and output data of the X-axis servo system can be collected first, and then those coefficients can be obtained through parameter identification. The specific process is as follows.  
Taking the interpolation period \(T_{\rm s}\) as the sampling period, \(M\) points are sampled from the input and output position data of the servo system to obtain the output vector  
\[\mathbf{q}=\left[\begin{array}{c}q_{x}(0)\\ q_{x}(1)\\ \vdots\\ q_{x}(M-1)\end{array}\right] \tag{4}\]  
and the observation matrix  
\[\mathbf{\Phi}=\left[\begin{array}{cccc}-q_{x}(-1)&-q_{x}(-2)&p_{x}(0)&p_{x}(-1) &p_{x}(-2)\\ -q_{x}(0)&-q_{x}(-1)&p_{x}(1)&p_{x}(0)&p_{x}(-1)\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ -q_{x}(M-2)&-q_{x}(M-3)&p_{x}(M-1)&p_{x}(M-2)&p_{x}(M-3)\end{array}\right] \tag{5}\]  
Figure 2: The function and control structure of the servo system.  
Let the combined observation noise of the input and output sequence be  
\[\boldsymbol{\xi}=\left[\begin{array}{c}\xi_{0}\\ \xi_{1}\\ \vdots\\ \xi_{M-1}\end{array}\right] \tag{6}\]  
Then, substituting Equations (4)-(6) into (3), the following equation can be obtained:  
\[\boldsymbol{q}=\boldsymbol{\Phi r}+\boldsymbol{\xi} \tag{7}\]  
in which  
\[\boldsymbol{r}=\left[\begin{array}{cccc}b_{0}&b_{1}&a_{0}&a_{1}&a_{2}\end{array} \right]^{\mathrm{T}}\]  
The \(\boldsymbol{\xi}\) in the above formula is usually not 0 and unknown. In this case, the least square method can be used to calculate the optimal estimate \(\hat{\boldsymbol{r}}\) of the coefficient vector \(\boldsymbol{r}\), which means that the sum of squares of the difference between the observed value \(\boldsymbol{q}\) and its estimate \(\hat{\boldsymbol{q}}=\boldsymbol{\Phi r}\) of the output sequence (Equation (8)) reaches the minimum.  
\[e(\boldsymbol{r})=(\boldsymbol{q}-\hat{\boldsymbol{q}})^{\mathrm{T}}( \boldsymbol{q}-\hat{\boldsymbol{q}})=(\boldsymbol{q}-\boldsymbol{\Phi r})^{ \mathrm{T}}(\boldsymbol{q}-\boldsymbol{\Phi r}) \tag{8}\]  
Due to the fact that the derivative of a continuously derivable function reaches 0 at the extreme point, the following equation can be achieved:  
\[\left.\frac{\partial e(\boldsymbol{r})}{\partial\boldsymbol{r}}\right|_{r= \hat{r}}=-2\boldsymbol{\Phi}^{\mathrm{T}}(\boldsymbol{q}-\boldsymbol{\Phi \hat{r}})=0 \tag{9}\]  
Thus, the optimal estimate of \(\boldsymbol{r}\) is  
\[\hat{\boldsymbol{r}}=\left(\boldsymbol{\Phi}^{\mathrm{T}}\boldsymbol{\Phi} \right)^{-1}\boldsymbol{\Phi}^{\mathrm{T}}\boldsymbol{q} \tag{10}\]  
So far, the coefficients of the difference Equation (3) have been obtained.
###### Abstract  
Easily available and detailed area-related information is very valuable for the optimization of crop production processes in terms of, e.g., documentation and invoicing or detection of inefficiencies. The present study dealt with the development of algorithms to gain sophisticated information about different area-related parameters in a preferably automated way. Rear hitch position and wheel-based machine speed were recorded from ISO 11783 communication data during plowing with a mounted reversible moldboard plow. The data were georeferenced using the position information from a low-cost differential global navigation satellite system (D-GNSS) receiver. After the exclusion of non-work sequences from continuous data logs, single cultivated tracks were reconstructed, which represented as a whole the cultivated area of a field. Based on that, the boundary of the field and the included area were automatically detected with a slight overestimation of 1.4%. Different field parts were distinguished and single overlaps between the cultivated tracks were detected, which allowed a distinct assessment of the lateral and headland overlapping (2.05% and 3.96%, respectively). Incomplete information about the work state of the implement was identified as the main challenge to get precise results. With a few adaptions, the used methodology could be transferred to a wide range of mounted implements.  
Data-driven agriculture; ISOBUS; Rear hitch position; Wheel-based machine speed Article
###### Abstract  
The unavailability of a constant power supply has been a major problem in remote communities in Africa as it impedes the proper operation of healthcare facilities in these locations. This has deprived inhabitants of free access to good healthcare services, thereby resulting in an increase in maternal and child mortality rates in rural communities in Africa. Therefore, in order to address this problem and render a life-saving intervention for rural dwellers and to also improve their healthcare service delivery, this paper focuses on the optimal configuration of a hybrid energy system for the rural electrification of community healthcare facilities. It presents an analysis of an off-grid hybrid energy system comprised of diesel generators, wind turbines and solar PV with a battery storage system to meet the energy demand of healthcare facilities in a remote community in Nigeria. In this study, hybrid energy systems are considered owing to the high reliability and availability of the intensity of solar radiation and wind speeds in Nigeria. An optimization model was developed which seeks to minimize the operational cost of hybrid energy systems. The proposed model was implemented using four case studies and solved using algebraic modeling language. The results obtained from the sensitivity analysis indicate that the configuration that includes solar PV, wind turbines, a battery storage system and a diesel generator provides the optimum power required for a rural healthcare center with a suitable energy cost and emission reduction from the system of diesel generators.  
hybrid energy system; renewable energy; healthcare facilities; optimization; rural community +
Footnote †: journal: Applied Sciences
# Noise Prediction Using Machine Learning with Measurements Analysis
###### Abstract  
The noise prediction using machine learning is a special study that has recently received increased attention. This is particularly true in workplaces with noise pollution, which increases noise exposure for general laborers. This study attempts to analyze the noise equivalent level (Leq) at the National Synchrotron Radiation Research Center (NSRRC) facility and establish a machine learning model for noise prediction. This study utilized the gradient boosting model (GBM) as the learning model in which past noise measurement records and many other features are integrated as the proposed model makes a prediction. This study analyzed the time duration and frequency of the collected Leq and also investigated the impact of training data selection. The results presented in this paper indicate that the proposed prediction model works well in almost noise sensors and frequencies. Moreover, the model performed especially well in sensor 8 (125 Hz), which was determined to be a serious noise zone in the past noise measurements. The results also show that the root-mean-square-error (RMSE) of the predicted harmful noise was less than 1 dBA and the coefficient of determination (R\({}^{2}\)) value was greater than 0.7. That is, the working field showed a favorable noise prediction performance using the proposed method. This positive result shows the ability of the proposed approach in noise prediction, thus providing a notification to the laborer to prevent long-term exposure. In addition, the proposed model accurately predicts noise future pollution, which is essential for laborers in high-noise environments. This would keep employees healthy in avoiding noise harmful positions to prevent people from working in that environment.  
noise prediction; machine learning; noise equivalent level (Leq); gradient boosting model (GBM); harmful noise +
Footnote †: journal: Applied Physics Letters  
1
## 3 Method  
### Overall Architecture  
The proposed adaptive shadow compensation method involving multi-exposure fusion and edge fusion in hyperspectral images, shown in Figure 1, consists of shadow detection and two-stage shadow compensation. The process of shadow detection is depicted in Figure 1a. First, the RGB channels from the hyperspectral image are extracted. Next, a conversion to the HSI color space is carried out, followed by utilizing differences in hue and intensity values to obtain a shadow feature map. Finally, the shadow mask is adaptively obtained using the Otsu threshold segmentation method. The process for spectral compensation of shadow areas using the multi-exposure fusion method is depicted in Figure 1b, which utilizes the shadow detection results combined with spectral intensity to identify the direction of shadow component attenuation, which contributes to determining the coefficients to obtain an exposure sequence. For each exposure image, the fusion weight is composed of the exposure, contrast, and spectral variance. Fusion weights are constructed into the Gaussian pyramid to perform weighted averaging on the Laplacian pyramid of exposure images, and this is combined with using shadow detection results to obtain the shadow-compensated image. The process of the edge fusion method is depicted in Figure 1c. The shadow edge mask is generated based on the detection result. By iteratively applying Laplacian filters, edge pixels are continuously selected for processing. The obtained edge pixels are processed according to their priority, which is composed of confidence and data items, where the data item refers to the edge feature map obtained by applying the p-Laplace operator to the original image. To process each pixel, we select the best-matching pixel from a fixed-size patch centered on that pixel to perform spectralcorrection on that pixel. After processing each pixel in the shadow edge mask, the final shadow compensation image is obtained.  
Figure 1: Overall flowchart of shadow detection and compensation in hyperspectral image. (**a**) Shadow detection. (**b**) Shadow compensation stage one: multi-exposure fusion. (**c**) Shadow compensation stage two: edge fusion.
## 6 Discussion
### Project Challenges  
Any large and ambitious project is likely to face challenges, despite, in this case, the detailed planning and communication between the fifteen commercial and academic partners. One of the issues that the project faced was the COVID-19 pandemic, which impacted resourcing and travelling. However, the project adapted, and many project collaborations were carried out via online meetings. COVID-19 contributed to the global semiconductor shortage, which delayed the procurement of some parts and equipment.  
Another issue encountered by project partners was the retention and recruitment of skilled personnel. Access to technical specialists is sometimes an issue that affects the wider business and academic communities. The project experienced some minor delays due to engineers and scientists not being available at times.  
Commissioning and debugging of prototype equipment and systems are unlikely to proceed without issues occurring. This was the case with the AirQKD project. Examples of issues that require overcoming include the following:  
* The Cohda RSUs and OBUs are reliable units and work well; however, we had problems with certain units not transmitting the CPM messages. It seems that an earlier version of the MK5 hardware did not work with the later SDK.
* Adhesive failure in an optic module (for the optical assemblies) occurred when shipped between organisations. This resulted in a redesign of the module to reduce the reliance on the strength of the adhesive.
* Failure of a photon-emitting component. The photon semiconductors are sensitive to temperature and are electrically cooled. The control system can stress the components to the point of failure if the cooling is not performed correctly. While it is not known if this was the cause of a component failure in the AirQKD project, it required further engineering of the control system to improve the safety margin in the operation of the semiconductors.
## 2 Materials and Methods  
### Television Background  
The principal television over-the-top (OTT) platforms provide at least live channel services. The adaptive streaming television might employ the MPEG-DASH [22], Apple HLS [23], or Microsoft Smooth Streaming [24] protocols to deliver multimedia content.  
Additional features include content playback in the given time (catchup), timeshifting, personal video recording in device storage (PVR) or the cloud (nPVR), and finally, video-on-demand content (VOD). Set-top-boxes (STB) offered by telecoms frequently run the Linux or Android-based software that serves additional options, such as content recommenders, profile management, and third-party applicants (Netflix, YouTube, Disney, and others). However, out of all the listed capabilities of the STB, the most important ones from the TV provider's perspective are those related to living content.
# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 3 Materials and Methods
### Inference-Time Re-Parameterization of MRAB
#### 3.2.2 Details of RRA-Net's Lightweight Structure  
In RRA-Net, as shown in Figure 2, a \(3\times 3\) convolution is firstly applied to the hazy image, outputting a feature map of the shape \(64\times H\times W\) where \(H\) and \(W\) are the height and width of the input image. Then, a \(3\times 3\), stride 2 convolution is used to downsample the feature map, which doubles the receptive fields of the subsequent convolutions. After that, 6 MRABs are applied subsequently to gradually extract features, followed by a pixel shuffle layer, which upscales the feature map back to the original spatial size \(H\times W\) and reduces the number of channels to 16. To avoid the loss of shallow features, before the final fusion tail, a long skip connection with attention is used to introduce shallow features. Specifically, the feature map after the pixel shuffle passes through a \(5\times 5\) convolution, a spatial attention layer, and a channel attention layer. In the meantime, the shallow features from the first \(3\times 3\) convolution are channel shrunken and long skip connected. The final fusion tail consists of a reflection padding, a \(7\times 7\) convolution, and a residual connection. The reflection padding operation alleviates the image boundary distortion problem caused by the following convolution of the large kernel size. The \(7\times 7\) convolution outputs the difference \(Y_{\text{res}}\) between the input hazy image \(I_{\text{hazy}}\) and the recovered clean image \(RRA(I_{\text{hazy}})\). Thus, the output of the proposed RRA-Net is  
\[RRA(I_{\text{hazy}})=Y_{\text{res}}+I_{\text{hazy}}. \tag{7}\]  
Such a multi-stage fusion strategy has the advantages of combining shallow and deep features and avoiding the instability caused by the upsampling process. The structure of RRA-Net achieves a good balance between the dehazing performance and model complexity. Overall, it only has about 0.3M parameters.
# Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
## 5 Conclusions  
Integrating multi-temporal imagery data from Sentinel-2 and the RF regression method enabled the development of predictive yield models for commercial sugarcane fields. The use of spectral bands outperformed derived-VIs. In addition, the RF regression showed greater accuracy (lowest RMSE and higher R\({}^{2}\)) when compared with the MLR. Overall, spatial patterns were successfully verified at the field level using high-resolution RS data with time-series analysis and the ML technique.  
Conceptualization, T.F.C. and J.P.M. Methodology, T.F.C., M.C.F.W. and L.F.M. Software, T.F.C., M.C.F.W. and L.F.M. Formal Analysis, T.F.C. and M.C.F.W. Writing--Original Draft Preparation, T.F.C., M.C.F.W., L.F.M. and J.P.M. Writing--Review & Editing, T.F.C., M.C.F.W., L.F.M. and J.P.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** For the operational support of Solinftec and Sao Manoel sugar mill. This study was financed in part by scholarships from the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES-Brazil-Finance Code 001). We would also like to thank [PERSON] for his translation services and review of this text. We are grateful to the suggestions of reviewers and editors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Methodology  
In this section, we will provide a detailed description of the proposed methodology incorporating the RFR and Gaussian model to predict human mobility traces.  
Prior to the brief explanation of the proposed methodology, it is highly important to understand the significant potential of predicting human mobility. The core benefits of such prediction are resource optimization, such as the efficient utilization of constrained communication resources, and energy efficiency. Furthermore, the proposed methodology leverages the time-space association of the users over different time spans, which enables the robust understanding of the mobility patterns. Finally, the training of the dataset, which comprises various locations, such as a dataset of a university campus, could reflect the contextual analysis of the environment, resulting in the enhanced scope of the trained model [24; 25; 26]. This study incorporates human mobility patterns while prioritizing resource optimization and energy efficiency, as shown in Figure 2. We utilized the RFR, a reliable technique that works well with big datasets for encounter prediction. By comparing our method with a benchmark Gaussian model, we are able to illustrate a potential efficacy in reducing energy usage in smart connected buildings upon incorporating these predicted mobility encounters. To reduce energy consumption, the current system first utilizes the mobility traces of a user's mobile phone, which can be conceptualized as a node in a communication network [27]. These traces provide time-space associations between the sender and receiver nodes, which are then used to measure the similarity rankings. Based on these similar profiles, the system predicts the future location and encounter time intervals for different users. It is also important to clarify that a trained model can be deployed on the network's devices to predict a mobility encounter, forwarding the data to the next node.  
In the final phase, an energy consumption model based on the opportunistic model is employed to reduce overall network energy consumption. Figure 2 illustrates the flowchart that depicts the RFEPM process for encounter prediction. When a user visits a new location, the system checks whether this location is included in the mobility preference of any other user. If no other user's mobility preference aligns with this location, it is added to the user's mobility history. The location ID and the duration of the visit are recorded in a new sub-decision tree to update the mobility history. Conversely, if a user with a mobility history that includes this location is identified, the visit time is compared to the visiting time of the first user. An encounter between these users is predicted at the specified location and time if a match happens [11]. Let us understand the basics of the RFR to clearly understand the proposed model.