esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Remote Sensing Platforms Used in Peatland Research
### Satellite-Based Remote Sensing
#### 4.1.5 Monitoring Peatland Fire Dynamics  
Peatland fires have severe environmental and socio-economic impacts. Satellite-based fire monitoring systems have been developed to understand fire dynamics and mitigate their effects. [PERSON] et al. (2022) investigated seasonally decomposed Sentinel-1 backscatter time series in Canada's boreal forest as indicators of wildfire vulnerability in peatland [79]. They emphasise the potential of SAR backscatter data as a nearly real-time tool for predicting fire vulnerability over extensive geographic areas. However, they also acknowledge certain limitations [79]. The method's effectiveness relies on the continuous acquisition of SAR data, making it susceptible to disruptions resulting in an incomplete time series dataset. Moreover, implementing this method necessitates substantial computing resources for generating spatial estimates. Nevertheless, the study highlights the potential of using seasonally decomposed Sentinel-1 backscatter time series to indicate peatland wildfire vulnerability, contributing to peatland fire occurrence [79].
## 1 Introduction  
Modern computer networks are expected to provide reliable high performance and end to end connectivity at any point in the world. They must also provide the ability to filter packets so that access to services is limited to trusted traffic defined in the security policy for the network. This must be achieved with a minimal delay without compromising the security policy. It can be a challenge for a network manager to meet these two conflicting requirements.  
Most networks contain one or multiple connections to external networks e.g., the Internet which is considered a great security risk. To mitigate this, trusted networks are created which perform stringent security checks on packets travelling across the network boundary in either direction. Such networks operate under a common security policy managed by a single authority and are known as domains. If network traffic is filtered at all ingress and egress points in the network then it should only contain traffic which is defined as trusted under the security policy (Figure 1).  
Infrastructure security within a domain is normally implemented in either firewalls or routers containing Access Control Lists (ACLs). ACLs have a common implementation across all platforms e.g., Cisco, Juniper and Linux [1]. Since every packet has to be tested significant delays can result from the introduction of such techniques due to the filtering requirement [2]. Attempts have been made to use various techniques to optimize the delay through routers caused by ACLs [3]. Optimization of packet filtering performance has been the subject of intense research for the past decade [4]. A number of studies have identified rule relations within an ACL which may result in redundant or conflicting rules.  
This paper investigates the significance of the delays encountered through the use of various ACL techniques. Factors which contribute to the delay incurred by packets passing through a router are identified and subsequently, a number of experiments were conducted to quantify these. Delays were investigated from a theoretical perspective which formed the basis of an equation which can be used to calculate the delay for a packet passing through a router running a particular router Operating System (OS). The equation was updated to reflect the packet delay experienced in a path across a domain. Recommendations were made to give guidance to network engineers that can be used during the network design phase. An argument for the use of an OS with appropriate functio  
Figure 1: Typical Domain allocation.  
is put forward based on experimental results. The authors recommend an optimal configuration using a worked example based on previous findings. Finally, a mechanism for the consolidation of distributed ACLs to a single ACL providing equivalent functionality is presented. Only delays through network equipment were considered in this paper. No account of link speeds was taken since these delays are easily quantifiable.
## Appendix A  
### Appendix A.1  
Rytov variances \(\sigma_{ht}^{2}\) and \(\sigma_{hs}^{2}\) are essential for atmospheric and coronal turbulence, respectively. Therefore, here we analyze and calculate the typical values for \(\sigma_{ht}^{2}\) and \(\sigma_{hs}^{2}\). Structure parameter \(C_{n}^{2}(H)\) in (4) is expressed as the Hufnagel-Valley model ([50], p. 481),  
\[C_{n}^{2}(H)=\left(C_{0}^{2}+594H^{10}\bigg{(}\frac{V_{d}}{27}\bigg{)}^{2} \right)\exp\biggl{(}-\frac{H}{1000}\biggr{)}+2.7\times 10^{-16}\exp\biggl{(}- \frac{H}{1500}\biggr{)}, \tag{1}\]  
where \(V_{d}\) denotes the root mean square of the wind speed, and \(C_{0}^{2}\) is the turbulence structure constant at the ground level. Solar wind density in (5) is given by [9]  
\[N_{e}=2.21\times 10^{14}\bigg{(}\frac{L_{sl}}{r_{s}}\bigg{)}^{-6}+1.55\times 10^{ 12}\bigg{(}\frac{L_{sl}}{r_{s}}\bigg{)}^{-2.3}, \tag{2}\]where \(r_{s}\) denotes the radius of the Sun, and \(L_{sl}\) is the vertical range between the Sun and the optical link. \(L_{sl}=L_{se}\sin(\theta_{sep})\), where \(L_{se}\) is the distance between the Sun and the Earth, and \(\theta_{sep}\) is the angle of Sun-Earth-probe (SEP).  
Consequently, based on parameters shown in Table A1[9; 44], results of \(\sigma_{ht}^{2}\) and \(\sigma_{hs}^{2}\) are calculated and shown in Figure A1a,b, respectively. It can be seen that, for atmospheric turbulence, \(\sigma_{ht}^{2}\) increases with the increase in wind speed \(v_{d}\) and structure constant at the ground level \(\mathcal{C}_{0}^{2}\). For coronal turbulence, when the angle of SEP \(\theta_{sep}\) increases or the spectral index decreases, \(\sigma_{hs}^{2}\) will decrease. In general, as the value of Rytov variance is much bigger than 1, turbulence is defined as strong turbulence.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 3 Algorithm Performance Test
### Accuracy Test in Dynamic Experiment  
In this paper, dynamic performance refers to the algorithm's measurement accuracy when the RGB-D sensor was approaching the pallet. In this experiment, the RGB-D sensor moved close to the pallet with a changeable distance and angle between the pallet and sensor to show the influence of changing distance and angle on the engaging process. To concisely show the parameters of the experiment, all results data were transformed into [W] space, as shown in Figure 1. The detection result was transformed from [C] space, with the RGB-D sensor position and angle in [W] provided by location sensor NAV350 (SICK, Germany, scanning frequency: 8 Hz, positioning accuracy \(\pm\)4 mm), a laser scanner sensor that could obtain the position and angle of the RGB-D sensor by detecting reflection marks fixed in the room.  
The position of the pallet was fixed at (0, 0, 75), and \(\theta\) was 90\({}^{\circ}\). The sensor moved in from 4 m to 1.5 m and changed angle in three different ranges: 0\(-\)10\({}^{\circ}\), 20\(-\)30\({}^{\circ}\), and 0\(-\)40\({}^{\circ}\). The pose of the sensor and the error of each parameter were recorded.
## 2 Preliminaries
### Cp-Abe  
[PERSON] et al. [36] first proposed attribute-based encryption (ABE). ABE facilitates the expression and enforcement of intricate access control policies in a manner that is both straightforward and adaptable, aiming to ensure data privacy protection. ciphertext policy attribute-based encryption (CP-ABE) [37] is a specialized form of ABE. It serves as an attribute-centric access control mechanism that encrypts and safeguards data by linking access rights with specific attribute conditions.  
In CP-ABE, the roles of individual participants are demarcated by attribute designations, thereby constructing an access architecture that delineates the authorized constellation of attributes. A set within the access structure is deemed authorized, while a set outside the access structure is considered unauthorized. The access policy is embedded within the ciphertext data and the user's private key is associated with a set of attributes. A user can decrypt the ciphertext only if the attributes within the user's private key satisfy the access policy of the encrypted data. Therefore, through the employment of the CP-ABE methodology, the data propriator is empowered to stipulate access permissions without necessitating intimate knowledge of the specific identities of all authorized entities.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
###### Abstract  
Object detection in remote sensing images has gained prominence alongside advancements in sensor technology and earth observation systems. Although current detection frameworks demonstrate remarkable achievements in natural imagery analysis, their performance degrades when applied to remote imaging scenarios due to two inherent limitations: (1) complex background interference, which causes object features to be easily obscured by noise, leading to reduced detection accuracy; (2) the variation in object scales leads to a decrease in the model's generalization ability. To address these issues, we propose a progressive semantic-aware fusion network (ProSAF-Net). First, we design a shallow detail aggregation module (SDAM), which adaptively integrates features across different channels and scales in the early Neck stage through dynamically adjusted fusion weights, fully exploiting shallow detail information to refine object edge and texture representation. Second, to effectively integrate shallow detail information and high-level semantic abstractions, we propose a deep semantic fusion module (DSFM), which employs a progressive feature fusion mechanism to incrementally integrate deep semantic information, strengthening the global representation of objects while effectively complementing the rich shallow details extracted by SDAM, enhancing the model's capability in distinguishing objects and refining spatial localization. Furthermore, we develop a spatial context-aware module (SCAM) to fully exploit both global and local contextual information, effectively distinguishing foreground from background and suppressing interference, thus improving detection robustness. Finally, we propose auxiliary dynamic loss (ADL), which adaptively adjusts loss weights based on object scales and utilizes supplementary anchor priors to expedite parameter convergence during coordinate regression, thereby improving the model's positioning accuracy for targets. Extensive experiments on the RSDO, DIOR, and NWPU VHR-10 datasets demonstrate that our method outperforms other state-of-the-art methods.  
feature fusion; contextual information; remote sensing images; object detection +
Footnote †: journal: Applied sciences
# Recursive Feature Elimination for Improving Learning Points on Hand-Sign Recognition
## 5 Conclusions  
This study compares models trained using several different datasets and a different number of features. Four datasets are used in this study; three datasets are used to train the model, while the fourth is used for evaluation purposes only. The original dataset has 21 features to train the model. Feature selection using RFE is made to reduce the number of features to 15 elements and 10 features. A novel hand-crafted feature, the distance from hand landmark to palm centroid, is proposed to address the limitation of the RFE implementation in python's sklearn library.  
Based on the experiments and results, we can conclude that not all 21 hand landmarks have the same importance in detecting ASL digit hand signs. Removing less important hand landmarks from the set of features can improve the model's detection accuracy. This conclusion is supported by the experiment results shown in Table 3, where the model's accuracy improved when the number of features was reduced to 15 features and 10 features.  
The quality of the dataset also has an impact on the model accuracy. Dataset 2 has the best performance compared to the two other datasets. Dataset 2 already provided the coordinates of the hand landmarks in CSV format. These detection results are of excellent quality and stability. The model has a better and more stable feature as an input in training. The other two datasets only provided hand images; feature extraction is performed using Mediapipe. During the feature extraction stage, some hand landmark detections are distorted when the images are rotated during image augmentation. The data quality becomes worse compared to Dataset 2, making the models less accurate.  
In the future, we will try to expand the number of classes by including the alphabet of the ASL hand sign. We will try to explore the possibility of using the palm centroid distance as the input to our classification network. Feature-reduction methods using neural networks, such as autoencoders, will also be explored to improve the hand-sign detection performance.  
Conceptualization, W.E.M.; methodology, R.-C.C. and W.E.M.; software, W.E.M.; investigation, W.E.M.; resources, R.-C.C.; writing--original draft preparation, W.E.M. and C.D.; writing--review and editing, R.-C.C. and C.D.; visualization, W.E.M.; supervision, R.-C.C. and C.D.; project administration, R.-C.C. and C.D.; funding acquisition, R.-C.C. and C.D.; All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the Ministry of Science and Technology, Taiwan. The Nos are MOST-110-2927-I-324-50, MOST-110-2221-E-324-010, and MOST-111-2221-E-324-020, Taiwan.  
Ehtical review and approval were waived for this study, due to the reason that we use the public and free datasets.  
All subjects gave their informed consent for inclusion before they participated in the study.
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 3 Reviewing Relevant Technologies and DT Components
### Notable Components of DT Implementations
#### 3.2.4 Devices  
Devices that continuously capture data concerning physiological and environmental factors information can serve DTs to gain a comprehensive understanding of the real twin's well-being and lifestyle. Such wearable devices, for example, smartwatches, fitness trackers, and other health monitoring devices have enabled individuals to collect and track various physiological parameters effortlessly. The abundance of such data presents a unique opportunity for digital twins to enhance their support for their real twins more efficiently [71]. By integrating the data from wearables into the DT model, real-time insights and personalized recommendations can be generated as feedback or alerts based on specific needs and goals. Furthermore, the continuous monitoring and analysis of wearable data by DTs enable proactive interventions whilst identifying patterns or deviations from normal physiological parameters and providing early warnings or interventions to address potential health risks or optimize performance. This proactive support can contribute to preventive healthcare, early detection of health issues, and overall well-being management.  
In summary, the DT is an advanced data management system that integrates 3D animation, modelling, IoT, AI, and other technologies such as ML. It enables the seamless connection and management of physical and virtual models within a digital environment [72]. As human civilization grows, agriculture remains crucial for our progress and well-being. Intelligent DT systems can analyze and digitize agricultural areas, enabling macro and micro-level visual processes. These systems can provide intelligent recommendations and suggestions that can be implemented to achieve multiple benefits in agriculture.
## 2 Materials and Methods
### Methods
#### 2.3.1 Data Preprocessing  
WUE was calculated with an 8-day time interval and 500 m spatial resolution during 2002-2021 using MODIS ET (MOD16A2.006) and MODIS GPP (MOD17A2H.006) products on the GEE platform. The monthly WUE was the average of the WUE at 8-day intervals throughout the month. If an 8-day synthetic data value occurred in two consecutive months, it was credited to the month in which more days appeared during those eight days. Similarly, the annual average WUE was determined. The WUE calculation formula was shown as follows:  
\[WUE=GPP/ET \tag{1}\]  
To address the problems of missing data and accuracy degradation of _GPP_ and _ET_ time series caused by cloud contamination, precipitation, snowfall, and sensor errors, we performed spatial interpolation and temporal filtering smoothing of _GPP_ and _ET_ time series using the inverse distance weight interpolation method and the S-G filter method, respectively. The S-G filter method is a least-squares local filtering method without strict restrictions on the sensor type [45]. The reconstructed data was of higher quality and was widely used for data smoothing and noise reduction.
## 2 Overall Methodology
### Friction Measurements  
The experiments were carried out with a Wedeven Associates Machine (WAM) 11, ball on disk test device. The lubricant is supplied at the center of the disk in an oil dispenser that distributes the lubricant across the disk surface. The lubricant is circulated in a closed loop from the oil bath, through a peristaltic pump to the oil dispenser at the center of the disk. The peristaltic pump is delivering approximately 180 mL/min. Three thermocouples are used in the test setup, one located in the oil bath, one in the outlet of the oil supply, and one trailing in the oil film close to the inlet region of the ball on disk contact. A more thorough description of the test rig and its features is presented in previous work [35].  
The ball on disk test device was used to generate friction data from a series of tests under different operating conditions. Two different entrainment speeds were used; 1 and 3 m/s. The slide to roll ratio (SRR) were varied between 0.002 and 1.05. SRR is defined as the ball surface speed minus the disk surface speed, divided by the entrainment speed. All tests in this investigation were hence conducted with the ball having a higher surface speed than the disk. Both ball and disk specimens were cleaned with heptane and ethyl alcohol before starting the experiments for each of the test cases. The tests were performed with two different loads; 100 and 300 N, which corresponds to a maximum Hertzian pressure of 1.25 and 1.95 GPa respectively. The tests were performed at a temperature of 40 \({}^{\circ}\)C. Before starting the experiments for each test case, the test device was warmed up to the desired operating temperature for approximately 60 min with lubricant circulation over both ball and disk to ensure thermal stability. When a stable temperature was reached, a 100 or 300 N load was applied and the machine was calibrated for pure rolling by adjusting spindle angle and positioning of the ball to ensure a condition of no spinning. These settings were then held constant for 20 min to ensure a mild run-in. Subsequently, the test cycle was started, wherein the load and entrainment speed were kept at constant values, and slide to roll ratio were varied from the lowest to the highest value. The temperature of the oil bulk and fluid adhered to the disk surface was typically deviating less than \(\pm 1\)\({}^{\circ}\)C from the target temperature of 40 \({}^{\circ}\)C during testing. A summary of the test conditions is found in Table 1. All tests were performed five times to ensure repeatability.
## 5 Experimental Results
### Experiment 4: Results Based on the Chest-XRay Dataset  
This experiment was based on the Chest-XRay dataset in order to train binary classifiers to differentiate between normal patients and those with pneumonia. The results are reported in Table 6. The resulting models with the extra FS step achieved better performance than the deep learning models without this step; see Table 3. Regarding the number of selected features, PSO selected the smallest number of features (79) compared to the other optimizers (MFO (91), WOA (98), AO (91), AHA (99), and AHA-AO (96)). Because it was based on the smallest number of features, the PSO-based LDA achieved the shortest time (0.1306). We discovered that the proposed AHA-AO optimizer achieved the best results compared to all other optimizers. These results were achieved by the SVM classifier (accuracy = 0.8686, BA = 0.8274, F1-score = 0.8617, recall = 0.8686, and precision = 0.8869). However, the SVM had a long processing time (0.6623). The fastest AHA-AO-based classifier was the LDA (i.e., time = 0.1783); even though it was not the best compared to the other optimizers' classifiers, this time was comparable to the best time of the PSO-based LDA (0.1306). The slowest AHA-AO-based classifier was the DT (0.5981), but it had a comparable time to that of the other DT classifiers with other FS optimizers. As a result, even though they did not achieve the best learning time, the AHA-AO-based classifiers achieved a better-than-average time. The slowest classifier in Table 6 was the SVM based on the MFO optimizer (0.7427). PSO selected the smallest number of features, but its performance suffered for all classifiers. The POS-based DT had the worst performance compared to all other classifiers (accuracy = 0.8013, BA = 0.7487, F1-score = 0.7875, recall = 0.8013, and precision = 0.8177). The PSO-based LDA classifier had the best time (0.1306), but its other performance metrics were not high (accuracy = 0.8446, BA = 0.7953, F1-score = 0.8339, recall = 0.8446, and precision = 0.87). We noticed that the SVM achieved the best results compared to those of DT and LDA for all optimizers (accuracy = 0.8478, BA = 0.8004, F1-score = 0.838, recall = 0.8478, and precision = 0.8706 for PSO, accuracy = 0.8574, BA = 0.8132, F1-score = 0.8492, recall = 0.8574, and precision = 0.8774 for MFO, accuracy = 0.8558, BA = 0.8103, F1-score = 0.847, recall = 0.8558, and precision = 0.8778 for WOA, accuracy = 0.8558, BA = 0.8111, F1-score = 0.8473, recall = 0.8558, and precision = 0.8763 for AO, and accuracy = 0.8542, BA = 0.8081, F1-score = 0.8451, recall = 0.8542, and precision
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## 4 Results  
### Experimental Setup  
#### 4.1.1 Simulated Dataset  
We used 5 cadaver CT scans from Massachusetts General Hospital [47] for simulating PAT dataset. These 5 cadavers were scanned on a GE Discovery 750 HD scanner under 120 kVp x-ray spectra. Noise index (NI) was used by GE to define the image quality, which is approximately equal to the standard deviation of the CT number in the central region of the image of a uniform phantom [48]. In this study, we used a noise index of 10, which represents a normal-dose scanning. Then, the images were reconstructed by the GE commercial iterative reconstruction algorithm, called adaptive statistical iterative reconstruction (ASIR-50%). To reduce the computational cost for simulating the data and training the network, we extracted image patches of size \(128\times 128\) from CT scans. More specifically, 64,000 image patches were randomly selected from 3 scans for training purpose. Then, 6400 image patches were randomly selected from 1 scan for validation. Finally, 200 image patches were randomly selected from 1 scan for testing the trained model. Please note that the patients for training, validation, and testing sets were randomly selected from 5 patients CT scans without replacement. Since the Hounsfield unit (HU) used in CT imaging ranges from \(-1000\) to \(\sim+2000\), we are interested in the complex tissue structure within \([-160,240]\) HU window for our PAT simulation. Thus, with this selected HU window, image patches were first normalized into \([0,1]\) serving as the initial source for PAT imaging, and the output of the first iteration of ATR serves as the input to the network.