esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
#### 2.2.1 Overall Technical Route of the Study  
In this study, high-resolution satellite imagery of the study area is first subjected to a comprehensive preprocessing procedure to ensure the data is suitable for analysis. This preprocessing includes several critical steps, such as geometric correction, radiometric calibration, atmospheric correction, and noise reduction. Geometric correction ensures that the satellite images align accurately with real-world coordinates, which is essential for subsequent analysis. Radiometric calibration adjusts the pixel values to compensate for sensor-specific characteristics and atmospheric effects, thus enhancing the consistency of the data. Atmospheric correction removes the effects of atmospheric particles like clouds and aerosols, which can distort the image quality, ensuring that the surface reflectance is accurately represented. We obtain a multispectral color image with a resolution of 4 m and a panchromatic black-and-white image with a resolution of 1 m. Using the fusion tool in ENVI 5.6 software, we fuse the 4 m resolution multispectral color image with the 1 m resolution panchromatic black-and-white image. After fusion, we generate a 1 m resolution multispectral color image. The purpose of the fusion is to improve the resolution of the multispectral color image. We then use the vector data of the study area boundary to crop the fused image, resulting in a complete remote sensing image of the study area, as shown in Figure 2. The purpose of cropping is that after fusion, we obtain a rectangular image that covers areas outside the study area. Cropping out the irrelevant areas reduces the computational load for subsequent processing. Then, we input the remote sensing images from two different periods into ENVI 5.6 software, using the change detection function within the software. By applying the difference method, we generate change maps for cultivated land, forest land, orchard land, water bodies, buildings, and roads. We then compare and analyze the results, selecting abandoned cultivated land patches as the classification samples. There are 552 abandoned cultivated land samples, with a pixel size of 1 m \(\times\) 1 m. These samples are used to construct both a training sample set and a validation sample set. The Random Forest classification model is applied to classify the data. Compare the classification results with real testing data to further assess the accuracy of the Random Forest classification results and the rationality of sample selection. Following classification, monitoring and analysis were conducted to differentiate between seasonal and perennial abandoned farmland. The classification accuracy is then assessed using the validation sample set. Next, the slope map of the study area is derived from the DEM data. The Euclidean distance method is used to calculate buffer zones around key vector maps, including those for slopes, water systems, and residential areas. The abandoned farmland is then overlaid with these buffer zones to analyze the driving factors behind its abandonment. The overall technical process is illustrated in Figure 3.
# An Adaptive Hybrid Automatic Repeat Request (A-HARQ) Scheme Based on Reinforcement Learning
## 4 The Proposed Adaptive HARQ Q-Learning Model
### Model Description
#### 4.1.2 State Space  
The setting of _State_ space will determine whether the algorithm can converge and affect the analysis and design of reward. We employ innovative methods to categorize the data transmitted by 5G NR-V2X by _BLER_, _CQI_, _Distance_ and _NumRBs_.  
As is clear from the previous introduction to the HARQ process, the delay in processing by the receiver decoder has a great impact on the transmission RTT. More NACKs being returned means that more incorrect data are transmitted. It also means that the more times that retransmission is needed, the correspondingly greater the delay accumulation of the receiver decoder. Therefore, reducing the BLER can enhance the reliability of information transmission and reduce the delay. The BLER can be calculated as in Equation (2):  
\[BLER=\frac{NACK}{ACK+NACK} \tag{2}\]  
In addition to the important index of the downlink BLER(\(BLER\)), the design state space also takes the average downlink Channel Quality Indication (\(CQI\)), UE distance (_Distance_), and the number of resource blocks (_NumRBs_) into account. They all belong to discrete and relatively independent data, which together constitute the _State_ space of the Q-learning model.  
When the _Distance_ ranges from 0 to 2000 m, the _NumRB_ value ranges from 0 to 120. The UE distance is divided by a section every 100 m for a total of 20 sections. The number of resource blocks is divided into five intervals for a total of 20 intervals. The intervals of the UE distance (_Distance_) and the resource block numbers (_NumRBs_) are arranged andcombined, and the two state elements of the downlink BLER (\(BLER\)) and the downlink average CQI (\(CQI\)) are included to jointly establish the state space. Each divided state represents a class of transmission scenarios, including all possible transmission scenarios. Finally, the state space \(S_{i}\), \((i=1,\;2,\;\ldots,\;400)\). The state-space table is shown in Table 1.
## 4 Conclusions  
The results derived from this scientific work have demonstrated that monitoring the percentage of moisture and fat in pomace produced at the end of the olive oil extraction process is effectively achievable by using in-line NIR setups on the olive oil process line implemented at the solid outlet of the decanter.  
Despite some anomalous spectra being excluded from the database, the developed model demonstrated good and solid predictability in estimating the humidity of the pomace and the oil contained therein.  
The results obtained can be interpreted as a first step towards the large-scale application of multispectral devices capable of rapidly determining the characteristics of the pomace leaving the extraction plant. In-line monitoring of pomace characteristics would give the plant manager a valuable tool to control the process to achieve the lowest level of oil loss in pomace during the process, and to efficiently learn how different plant/process settings lead to variations in the oil discharged from the decanter and in the percentage of oil extractability.  
Our belief is that the integration of NIR tools and automatic plant setting systems combined with deep learning tools will actively contribute to increasing the efficiency of the extraction process, allowing for increased olive oil production with the same input raw materials. By increasing industrial extractability, it will be possible to avoid double oil extraction--which consists of virgin pomace reprocessing--with a consequent reduction in electricity and thermal energy and increase in the sustainability of the entire extraction process.  
Further advances in the technology developed could concern the application of NIR technology in line for the evaluation of the olive oil and moisture of the olive paste fed to the decanter or for the evaluation of the oil lost in the waste products of extraction processes of other oily substances.  
However, the ongoing and consistent efforts to enhance the NIR methodology represent an open-ended scope of research, also to other sectors not belonging to food processing, in order to achieve rapid and non-destructive analysis in real time. This will allow for quicker process intervention, improving efficiency.  
Conceptualization, A.L., G.A. and A.T.; methodology, A.L., A.B., C.D.D., G.A. and A.T.; software, A.L. and G.A.; validation, A.L. and G.A.; formal analysis, A.L., A.B., C.D.D., G.A. and A.T.; investigation, A.L., A.B., C.D.D., G.A. and A.T.; data curation, A.L., A.B., C.D.D., G.A. and A.T.; writing--original draft preparation, A.L., A.B., C.D.D., G.A. and A.T.; writing--review and editing, A.B.; visualization, A.L., A.B., C.D.D., G.A. and A.T.; supervision, A.L., G.A. and A.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors gratefully acknowledge Agricola Di Pietro, Andria-BT (Italy), for kindly providing the olives and industrial extraction plant necessary to accomplish the present study. The authors also thank [PERSON] for technical and laboratory support.  
The authors declare no conflict of interest.
## 3 Methods
### Low Rank and Sparse Frame Model  
Different values of singularities in the conventional convex kernel norm solve the imbalance penalty. Due to the equal treatment mechanism, if singular values are far from 1, the nuclear norm will have a considerable deviation. Each time the nuclear norm weight is determined, additional SVD will appear [26], which increases the running time of the method. [PERSON] proposed the \(\gamma\) norma which is a new rank of non-convex function [27]. The \(\gamma\) norm is unitarily invariant. The \(\gamma\) norm is almost in agreement with the true rank (\(\gamma=0.002\)), and the heuristic of the log-det performs poorly at minimal singular values [28], in particular when the value is close to 0; the \(\gamma\) norm of the matrix \(B\) is described as:  
\[\left|\left|B\right|\right|_{\gamma}=\sum_{i}\frac{(1+\gamma)\sigma_{i}(B)}{ \gamma+\sigma_{i}(B)} \tag{13}\]  
For the reason the \(l_{0}\) norm is NP-hard, the \(l_{1}\) norm [29] assigns the same weight to each single element. Therefore, many other methods use the \(l_{1}\)-norm to characterize the sparsity of the target patch-image [30; 31; 32], and the target T with the \(l_{1}\)-norm is described as follows:  
\[\left|\left|T\right|\right|_{1}=\sum_{i,j}W_{ij}|T_{ij} \tag{14}\]  
where \(W_{ij}=C/\left(\left|T_{ij}\right|+\varepsilon_{T}\right)\) is an element at position (\(i\), \(j\)), \(C\) is a compromise constant; moreover, \(\varepsilon_{T}\) is a small positive number.  
Infrared images also have a lot of strong edge noise, which makes many advanced methods [33; 34; 35] leave residual errors in the target image. The strong edge \(E\) is linearly sparse relative to the whole image, and each line (i.e., line vector) is described by the vector \(l_{2}\) norm, \(w_{i}=\sqrt{\frac{\Sigma\left|E_{i,j}\right|^{2}}{\gamma}}\), that is, the vector \(w=[w_{1},w_{,}\dots,w_{d}]^{T}\), and then the whole matrix \(E\) needs to be described by the norm. Therefore, the \(l_{1}\) norm is used to describe \(w\), that is, the \(l_{2,1}\) norm of the strong edge \(E\):  
\[\left|\left|E\right|\right|_{2,1}=\left|\left|\left|w\right|\right|_{1}=\sum _{i=1}^{d}\sqrt{\sum_{j=1}^{n}\left|E_{i,j}\right|^{2}} \tag{15}\]  
According to the foregoing discussion, the patch-tensor model for the infrared image sequences is proposed on account of the minimization of the non-convex spatial-temporal tensor low-rank approximation algorithm (MNSTLA), that is, Formula (10) is redefined as:  
\[B.T.E=\begin{array}{c}\underset{B.T.E}{arg\ min}\\ \underset{B.T.E}{\arg\ min}\\ \underset{B.T.E}{\arg\ min}\end{array}\]  
\[s.t.\ D=B+T+E \tag{16}\]
# MB-OFDM-UWB Based Wireless Multimedia Sensor Networks for Underground Coalmine: A Survey
## 4 Other Research Issues
### UWB Localization  
Besides the application sceneries mentioned above, WMSNs for underground coalmine will also plays an important role in production automation, people or asset tracking, security assisting or rescuing and underground coalmine robot navigation. Localization techniques for underground coalmine include the techniques for locating wireless sensor node itself and locating monitored targets, where the latter one is especially essential for underground coalmine applications such as target monitoring, robot navigation, and motion analysis for miners.  
For the self-localization problem of sensor nodes, since UWB technique is adopted in the physical layer of the communication stack, thus the problem turns to a UWB based localization problem of sensor nodes. As for the UWB localization technique which adopts impulse radio (IR), centimeter-level or even millimeter-level ranging accuracy is theoretically possible, due to the extremely short duration of transmitted pulses and the strong ability of time-resolving. Specifically, the UWB ranging system based on impulse radio technology regularly figures out the distance between sending and receiving end, by estimating the time of arrival (TOA) of the earliest component of received signals. In recent years, IR-UWB based TOA position estimation algorithms have been well studied [59; 60], including the coherent TOA estimation position algorithm in which high sampling rate and high-precision match filter (MF) are used, and the no-coherent energy-collection TOA position estimation algorithm which adopts low sampling rate and lower complexity.  
Nevertheless, the algorithms are mainly concentrating on IR-UWB technique, while sensor nodes in underground coalmine WMSNs use MB-UWB technique in their physical layer which is quite different from IR-UWB. Thus, the current IR-UWB localization techniques could not be applied in MB-UWB based system. The localization problem for the sensor nodes in underground coalmine should considering the structure of network and MB-UWB techniques in physical layer, leading to corresponding localization algorithms.
# Automated Tunnel Point Cloud Segmentation and Extraction Method
## 2 Materials and Methods
### Tunnel Cross-Section Extraction and Deformation Detection  
To improve the accuracy of tunnel cross-section extraction, this study proposes a cross-section extraction method based on fitted plane centering, which offers higher precision compared to traditional axis-based slicing methods. This approach, referred to as the ring-based cross-section extraction technique, involves the following steps:  
Assume the point cloud dataset is \(P=\left\{\mathbf{p}_{i}\right\}_{i=1}^{N}\), where each point \(\mathrm{P}_{i}\) has coordinates (\(\mathrm{x}_{i}\), \(\mathrm{y}_{i}\), \(\mathrm{z}_{i}\)). The general equation of a plane is expressed as Equation (1). First, the centroid \(c\) of the point cloud data is calculated as:  
\[c=(\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}x_{i},\frac{1}{N}\underset{i= 1}{\overset{N}{\sum}}y_{i},\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}z_{i}) \tag{12}\]  
Next, the point cloud data are centralized, resulting in a new point set. A covariance matrix SSS of the centralized point cloud data is then constructed as:  
Figure 11: Distribution pattern of the internal lining of the tunnel.  
Figure 10: Tunnel lining arrangement diagram.  
\[S=\frac{1}{N}\sum_{i=1}^{N}p_{i}^{\prime}p_{i}^{\prime T} \tag{13}\]  
\[S=U\Sigma V^{T} \tag{14}\]  
Here, \(\mathbf{V}\) contains the eigenvectors of the covariance matrix, and \(\Sigma\) is the singular value diagonal matrix.  
The eigenvectors and singular value decomposition (SVD) of the covariance matrix are used to obtain the normal vector of the plane, where the normal vector corresponds to the eigenvector associated with the smallest singular value (last column of \(U\)). The offset is calculated using the centroid. Subsequently, the vertical distance from each point in the point cloud to the single-ring plane is computed, yielding the centered cross-section of the tunnel. The output of this method is shown in Figure 12.  
This approach does not rely on the designed tunnel axis and eliminates the need to define slicing intervals, thus avoiding positional deviations during extraction. It enables precise extraction of cross-sections at any location within the tunnel. The output effect is illustrated in Figure 12.  
For tunnel structure deformation analysis, using B-spline curves or direct circular fitting often results in excessive errors and operational challenges. This study proposes an approach based on the geometric information of the tunnel to extract the point cloud set of each tunnel lining boundary point. The distance between the centroid of the point cloud set and the overall circle center is calculated. The misalignment of the tunnel is determined by analyzing the difference in distances between boundary points. The specific steps are illustrated in Figure 13.
## 3 Results  
### Example Application of the Methodology  
This paper showed a brief theoretical description of the evaluation methodology in the previous section. In this section, we describe the application of the methodology using a specific example, including a presentation of the results from this evaluation. Public transport vehicle crossing (PTVC) was chosen as an exemplary C-ITS use-case for presenting the results of the C-Road CZ evaluation at the pilot sites.  
The goal of the PTVC use-case is to notify road users in advance about terms (or other public transport vehicles) crossing the road on the expected route. This application is useful especially in problematic locations, where terms cross the roadway without the aid of traffic lights.
# Towards Understanding Driver Acceptance of C-ITS Services--A Multi-Use Case Field Study Approach
## 2 Methodology  
The aim is to evaluate the acceptance of C-ITS services by drivers. Therefore, two field studies were conducted (see Section 1.5), including an online questionnaire and test drives (see Section 2.1). Moreover, relevant literature was reviewed (see Section 2.2), being the foundation of a later discussion of the field study results. The focus of the user acceptance was on Infrastructure-to-Vehicle (I2V) C-ITS services. In other words, information is sent from a traffic management centre to vehicles on a motorway and displayed to the driver in the vehicle.  
The following C-ITS services are covered in the paper (ref. Figure 1):  
1. Road Works Warning (RWW), Lane Closure: warning information is given to the driver due to construction work. The right-most lane of a motorway is temporarily closed.
2. Hazardous Location Notification (HLN), warning for hazardous situations like Emergency of Rescue/Recovery Vehicle in Intervention, and Traffic Jam Ahead warning: warning information is given to the driver due to an accident or a broken-down vehicle. The left-most lane of a motorway is closed, and there is a traffic jam.
3. In-Vehicle Signage (IVS), Traffic Signage: information is given to the driver with the current speed limit on the motorway.
4. Routing recommendations within and between lanes based on the road wear map (LCR): information is given to the driver to change lane or lane offset due to a pothole on the lane of the motorway.  
While the C-ITS services 1-3 are specified at European level in C-Roads [4] and considered as Day 1 service, the fourth service is still under development in the EU-funded project ESERCOM-D to reach a higher Technical Readiness Level (TRL) [23].  
Figure 1 provides a summary of the user acceptance evaluation design. The participants are car and truck drivers. Two field studies apply the Technology Acceptance Model, while the literature review follows the PRISMA statement approach. User acceptance is evaluated through test drives and an online questionnaire. For the literature review, three categories with related keywords were used to identify relevant studies in different databases. Finally, the results from the field studies and the literature review are combined to form an overall picture of user acceptance of C-ITS services.
# Development of a New Rubber Buckling-Restrained Brace System for Structures
## 9 Results and Performance Evaluation
### Performance Evaluation
#### 9.4.1 Hysteresis Energy Dissipation  
Seismic energy is one of the important factors for the performance evaluation of a structure. With the assessment of the seismic response of the frames, the seismic energy content is affected by structural period and high amplitude pulse period for vibrations over a duration of 0.005 s to 0.02 s, and the vibration amplitude declines with time for a damping ratio of 0.05.  
By comparing all TH energy that was dissipated, it can be observed that the structural frame was elastically deformed at first, and there was no hysteresis energy generated by any bare and braced frames. After the first few steps, the structure started to dissipate the incoming energy gradually. In all TH records, the energy dissipated by RBRBFs was higher than bare frame and BRBF models. This demonstrates that RBRBFs' higher ductility caused the structure to consume incoming energy earlier than others. RBRBs as hysteretic dampers dissipated a significant portion of input energy with the aid of their rubber components. Therefore, the beams and columns were not damaged significantly.  
In order to make a comprehensive comparison, the Chi-Chi TH record under a MCE hazard level was selected to investigate the results. It can be clearly seen from Figure 56 that no hysteresis energy was governed until 0-29.2 s in the BRBF, RBRBF, and bare frame models. Subsequently, the energy started to dissipate between 29.2 and 32.525 s, which  
Figure 55: Top-story displacement of MCE TH Chi-Chi in X-direction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Structural Response**}} & \multicolumn{3}{c}{**Structural Configurations**} \\ \cline{2-4}  & **Bare Frame** & **BRB Frame** & **RBRB Frame** \\ \hline Story drift & 0.0379 & 0.00948 & 0.00574 \\ \hline Reduction story drift in (\%) & - & 74.9 & 84.8 \\ \hline RBRB improvement in comparison to BRB (\%) & \multicolumn{3}{c}{48.14} \\ \hline Base shear response, kN & 1460 & 2335 & 3046 \\ \hline Increase in base shear response (\%) & - & 59.93 & 108.63 \\ \hline RBRB improvement in comparison to BRB (\%) & \multicolumn{3}{c}{26.42} \\ \hline Top-story displacement response, mm & 392.93 & 108.2 & 76.217 \\ \hline Reduction top-story displacement response in (\%) & - & 72.46 & 80.6 \\ \hline RBRB improvement in comparison to BRB (\%) & \multicolumn{3}{c}{34.68} \\ \hline \hline \end{tabular}
\end{table}
Table 18: Maximum result differences for bare, BRBF, and RBRBF configurations.  
reached 59.01, 81.85, and 198.36 kN-m for the bare, BRB, and RBRB frames, respectively. This indicates a four times increase in the energy dissipated by the RBRB system. The maximum energy dissipated by the bare frame model was 347.43 kN-m, while BRBF reached 367.55 kN-mm. The RBRB system dissipated 505.14 kN-m, which is 37.43% more than the BRBF. It should be mentioned that this trend has been repeated for other records, similarly.
## 3 WDM-PON Network Architectures  
The WDM-PON network offers a scalability by supporting more wavelengths on the same optical fiber within its infrastructure and a transparency regarding the channel data rate and a power budget without passive optical losses. For fulfillment, the request for a flexibility in the bandwidth and a number of users, optical components and equipment must be economically acceptable for various architecture types [21]. WDM-PON network architectures can be classified according to the topology as follows:a Point-To-MultiPoint (P2 MP) architecture arising from the TDM-PON topology, a RING architecture.