esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Field Measurements
#### 2.2.2 Tower-Based Solar-Induced Chlorophyll Fluorescence  
The measurement of canopy SIF was observed using a QE pro spectrometer (Ocean Optics, Inc., Dunedin, FL, USA) positioned on a tower station, situated 5 m above the ground and approximately 2.0-2.5 m away from the highest point of the vegetation. The spectrometer had a field of view of 25\({}^{\circ}\), a measurement range of 637-805 nm, a sampling interval of 0.15 nm, a spectral resolution of 0.38 nm, and a signal-to-noise ratio of 1000:1. A connector (FOS-1\(\times\)1-TTL, Ocean optics, Inc.) was used to link the spectrometer with two optical fibers. One device, a cosine corrector (CC-3, Ocean Optics, Inc.), was employed to measure incident irradiance (E) by pointing toward the sky. Another device was used to measure upwelling radiation (L) by pointing downwards toward the top of the vegetation, with a view zenith angle (VZA) of 0\({}^{\circ}\). Measurements were taken sequentially for E, L, and E from 8:00 to 18:00 at intervals of 2 min. The spectral fitting method (SFM) [43] and a fitting window that was adjusted within the range of 759.5-761.5 nm were used to obtain SIF\({}_{760}\) from in situ measurements. Moreover, quality control was performed for each spectrum [44; 45] to filter out poor-quality data. The retrieved SIF\({}_{760}\) data used in this study are consistent with those of [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [25].  
Figure 1: (**a**) Locations of the observation tower (red dot) utilized for (**b**,**c**) the eddy covariance (EC), bio-meteorology, and solar-induced chlorophyll fluorescence (SIF) measurements, the buoy station (yellow star), and the tide gauge station (orange triangle) that records the occurrence of tidal water flooding from 1 January to 31 December 2019.
# Reliable Application Layer Routing Using Decentralized Identifiers
## 7 Conclusions  
In this paper, a solution that leverages decentralized identifiers for secure application-layer routing is proposed. The proposed solution can be easily integrated into existing approaches since it does not require any modification to the SDN infrastructure. The proposed solution allows URLs to be used as DIDs by adding a trusted name registration service; the use of URLs as DIDs enables more user-friendly application-layer solutions, as well as greater interoperability with legacy systems. The proposed solution is lightweight since it requires only few ms to perform the necessary cryptographic operations even in IoT devices; it adds only a few bytes to the routing advertisement messages; it creates minimal additional routing states; it does not need auxiliary information to be stored per verifying entity; and it can be easily managed. Finally, the proposed solution has intriguing security properties: it protects the integrity and authenticity of advertisements, it is resilient to key breaches, and its dependence on a trust registrar is minimal.  
Future work in this area includes solutions for fast revocation so that key breaches can be handled in a secure and fast manner, as well as tools for automating the process of domain registration. Furthermore, the use of verifiable credentials as well as the application of different DID methods and ICN underlays will be further investigated.  
Writing--original draft preparation, K.A. and N.F.; writing--review and editing, B.A. and A.A.; supervision, M.A. All authors have read and agreed to the published version of the manuscript.  
Not applicable, the study does not report any data.  
The Deanship of Scientific Research (DSR) at King AbdulazizUniversity, Jeddah, Saudi Arabia, has funded this project, under grant no. RG-13-611-42.  
The authors declare no conflict of interest.
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 4 LoRa Network Performance Evaluation
### LoRa Network Performance Evaluation Model  
The calculation of the network performance evaluation score is shown in Equation (39), where \(\left(\overline{\Gamma},\overline{E_{avg}},\overline{T_{avg}}\right)\) represents the normalized values of the three performance indicators, and \(\left(v_{1},v_{2},v_{3}\right)\) represents the weight of each parameter. The LoRa network performance score is calculated based on the weights and normalized parameters, with a value range of \([0,100]\). The evaluation indicators include total network throughput \(\Gamma\), average power consumption of the EDs \(E_{avg}\), and average latency of the EDs \(T_{avg}\). These indicators are affected by the number of EDs using different data rates in the network. The weight of each network performance parameter is obtained using the combined weighting method introduced in Section 3.4. The specific calculation formula is given as Equation (39).  
\[S=\left(c_{1},c_{2},c_{3}\right)\cdot\left(\frac{\overline{\Gamma}}{E_{avg}} \right)\times 100 \tag{39}\]
# Second-Order Text Matching Algorithm for Agricultural Text
## 4 Proposed Method
### The Overall Framework of SOTM Algorithm  
Traditional text matching algorithms include TF-IDF algorithm, BM25 algorithm, etc. These algorithms typically focus on the word or character level, employing parameters such as word frequency and inverse document frequency to calculate the similarity scores of two sentences. It is characterized by simple implementation and fast matching speed. However, this type of algorithm cannot capture the meaning at the semantic level, resulting  
Figure 1: BERTBASE model architecture.  
in a relatively lower matching accuracy. Text matching algorithms based on deep learning include GRU, BERT and so on. This type of models can better understand the contextual semantics and has a relatively higher matching accuracy. However, the actual retrieval speed of this type of models is relatively slow.  
Therefore, this paper aims to combine traditional text matching algorithms and text matching algorithms based on deep learning. In the current work, BM25 algorithm in traditional text matching algorithm and BERT model based on deep learning are selected to construct a second-order text matching algorithm. The reasons for selecting BM25 algorithm and BERT model are explained in the following paragraphs.  
The overall framework of SOTM algorithm proposed by this paper is shown in Figure 2. The text is input by the user and subsequently segmented using the Jieba library. Jieba is an open source library for Chinese text segmentation library. It is based on the prefix dictionary to achieve efficient Chinese word separation function [39]. This is followed by the retrieval phase. The results of the word segmentation are compared with the text stored in the database using BM25 algorithm and the matching results are sorted. Some of the best matches are selected as the candidate set. The algorithm ends with a rearrangement phase. Match the candidate set again using BERT model to find out the text that has the highest degree of match. SOTM algorithm is shown on Algorithm 1.  
```
0: One query \(q_{0}\), text set \(T=\{q_{1},q_{2},\cdots,q_{n}\}\).
0: The optimal matched query \(q^{*}\in T\).
```  
**Algorithm 1** SOTM Algorithm Flow Based on BM25 Algorithm and BERT Model  
Figure 2: The overall framework of SOTM algorithm.
# Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling
###### Abstract
The challenges for nested CC are numerous, stemming from ATSSS proxy positioning, end-to-end RTT, tunnel reliability in the face of packet loss, bottleneck buffer size, flow size, and a combination of server and tunnel CCA. To illustrate the scale of the challenges, Figure 2 presents the download time when a multipath tunnel and the CPF scheduler are utilized, normalized against the download time when single-path end-to-end (E2E) transport is exclusively utilized. Ideally, the multipath solution should reduce the download time by 50%, as each path between the proxy and the UE is provided with the same capacity. A relative download time exceeding 100% demonstrates a worse throughput compared to using single path. The four bars in Figure 2 compare aggregation for far and near proxy deployments, when using either UDP or TCP end to end. The top two bars represent end-to-end UDP, showing a download time at approximately 50% for both proxy deployments. The bottom two bars depict end-to-end TCP, showing a download time which is cut in half when the proxy is situated close to the user but unchanged relative to single-path  
Figure 1: The topology used in our evaluation, with the corresponding TC queuing disciplines used. download when the proxy is far from the user. In other words, the addition of an end-to-end congestion control mechanism may have a very negative effect on the aggregation, contingent on some other parameter, such as the proxy deployment. Our findings in this paper demonstrate several key insights: (i) BBRv2 emerges as the preferred choice for a tunnel congestion control algorithm (CCA), demonstrating its ability to effectively circumvent congestion and buffer overflows; (ii) partial reliability for the tunnel framework may be useful to mask loss between the UE and the proxy as some unreliable traffic may still use a CCA that reacts to loss; (iii) CPF performs poorly and is often unable to achieve aggregation; (iv) while our primary focus is on CPF, we also highlight that the commonly used Shortest Round-Trip Time First (SRTT) scheduler encounters fewer challenges within our multipath tunnel framework with respect to throughput aggregation. The remainder of this paper is outlined as follows: Section 2 presents background information on MP-DCCP and our user-space multipath framework based on MP-DCCP. Section 3 describes the setup of our test environment and the evaluation scenarios used. Section 4 presents our results. Section 5 introduces related work. The paper concludes and outlines future work in Section 6.
## 3 Using LTE/5G-based Public Mobile Land Networks for Autarkic Microgrid Blackout Recovery
### Evaluation  
It has been shown that PLMN specific access barring feature can bar the Smartphone devices in emergency situations and give simultaneously access of the network to smart grid MTC devices, thus providing a resilient communications solution. It has also been shown that the LTE can provide latency of below 40 ms for smart grid devices. The results demonstrate that the PLMN specific Access Barring feature is directly applicable to real LTE networks. The lab tests demonstrate reliable MAS MTC communications during emergencies by preferential, differentiated use of an LTE mobile network. The reliable communications allowed the microgrid to autonomously perform black start in an islanded microgrid, while other communications network users lost service.
# Detecting the Speed Change Intention from EEG Signals:
## 2 Materials and Methods
### Offline BMI  
In this section, the methodology used to analyze the brain activity (recorded as the protocol methodology explained in Section 2.3) and create the model is explained. Previously to the recording, a high pass filter with a 0.1 Hz cutoff frequency and a notch filter at 50 Hz (remove power line interference) were applied by actionamp hardware. The preprocessing steps were applied sample by sample to the entirely signal. An adaptive artifact EOG removal algorithm \(H^{\infty}\)[19] was used, the algorithm parameters were: \(\gamma=1.15\), q = 1 \(\times\) e\({}^{-10}\), p0 = 0.5. Then, the EEG channel information was improved thanks to a Laplacian filter. Subsequently, a series of state variables butterworth band-pass filters of order 4 were applied to analyze different methods for the extraction of characteristics in these bands.  
Next, the two different classes were segmented thanks to the IMUs information (check the procedure explained in Section 2.4). The speed change class consisted of the EEG data previous to the detected change point by the IMUs (pink line in Figure 3) between \(-1.45\) to \(-0.2\) s (avoiding taking the right moment in which the change is made). The walking class was selected between \(-6\) to \(-4.75\) s, where the person walks at a constant speed (Figure 3). A repetition should be discarded if it did not comply any of the following premises:  
* Something goes wrong during the protocol: some strange noise, unexpected event of the subject or detachment of the IMUs.
* Failure of IMUs algorithm: the algorithm of the IMUs did not correctly detect the moment of change. This could be easily detected by an algorithm that aligns the signals with respect to the tagged point.  
Figure 3: Example of the selected processing windows used for offline validation and classification model creation. The figure shows a 15 seconds segment of the IMUs signal filtered and processed from Subject 2. Increase (+) shows the signal in a positive acceleration repetition. Decrease (\(-\)) shows the signal in a negative acceleration repetition. The pink dotted line marks the labeled point of change, the blue window marks the normal walking window class and the red window shows the class labeled as change of speed. The IMUs information was used to create the BMI prediction model for E1 and E2 labeling the different class windows.  
* Time failure: there was not enough time between the algorithm detection of the start of the walk (the third step) and the area of change (>6 s). With this rule it was avoided that in the walking class there was information close to the first step. On Table 1, the number of valid repetitions and total repetitions are specified by subject and each of the models considered: Increase, Decrease and Both.
## 4 Discussion
### Implications of Remote Sensing Products  
Detailed and accurate land cover maps are essential for capturing vegetation information over heterogeneous underlying surfaces [85]. The high-resolution remote sensing images obtained from unmanned aerial vehicles (UAV), airborne or satellite, are therefore particularly important in footprint analysis. However, remote sensing images with high spatial resolution usually have a lower temporal resolution. In fact, there is a trade-off between the grid resolution and revisit frequency for remote sensing images. The spatial and temporal resolution should be considered comprehensively. Hence, Landsat series images are still the most commonly used dataset to track the long-term changes in spatial representativeness and to compare the spatial representativeness across flux sites [25; 26; 31; 32].
## 3 System Description
### Mobile Data Acquisition System (MDAS)  
The procedure to acquire information about mobile network coverage and overall signal power in a space requires not only using any kind of tool capable of manoeuvring throughout all of its locations within the said space, but also a way to collect information and the suitable hardware to be used. This is what is provided by the MDAS, which, as had happened in the previous subsystem, was built using a collection of hardware and software components. A thorough description of the requirements that were conceived for the MDAS is included in Table 5, which were deemed of major importance in order to have an accurate perspective of the functionalities that the MDAS should fulfil. It can be inferred how portability and signal information were the most critical aspects of the MDAS.  
Figure 5: ArduPilot configuration details.
## 3 Mobility Support Scheme Based on Decision Tree-Based Pattern Classification and Regression
### Graph Construction and Resource Allocation Based on Predicted Mobility Pattern
This allows data to be transmitted through surrounding devices whenever and wherever the mobile device is present. However, it can be observed that a significant amount of resources are wasted in supporting the mobile device's communication when compared to Figure 9 or Figure 11. This waste occurs because resources are allocated to fixed devices independently of the actual location of the mobile device. Figure 11 shows that the proposed scheme allocates resources based on the movement patterns of mobile devices, allowing mobile device 6 to communicate with device 1 at TS 0, device 2 at TS 100, and device 3 at TS 200, as predicted in Section 3.1. Additionally, to prevent transmission failures due to prediction errors in the predictive model, additional resource allocation is performed at specific time slots, such as TS 1, TS 101, TS 102, and TS 201. Through this resource allocation process, it is possible to prevent transmission failures due to the movement of mobile devices, which could occur when resources are allocated, as  
Figure 8: The examples of constructed routing graph based on mobility pattern. shown in Figure 9. Additionally, the proposed scheme addresses the inefficiency problem of excessive resource use for mobility support, as depicted in Figure 10.
## 2 Problem Description  
### Application Scenario Description  
This paper mainly focuses on addressing the significant decline in collaborative positioning accuracy of intelligent unmanned clusters in high dynamic and strong interference environments. The actual application scenario is shown in Figure 1. In such environments, due to the refusal of satellite guidance, UAVs cannot use GPS to achieve autonomous positioning [20, 21, 22], so they need to be equipped with multi-source sensors such as UWB [23], IMU [24] and visual camera [25]. Due to the limited payload capacity of miniaturized UAVs, which results in generally inadequate sensor performance and susceptibility to obstacles and electromagnetic interference, individual UAVs face significant challenges in autonomous positioning, making collaborative positioning technology essential for accurate positioning and autonomous task execution in intelligent unmanned clusters [26, 27].  
Achieving accurate and reliable collaborative positioning of intelligent unmanned clusters depends on the interaction of position and status information among UAV nodes [28]. However, due to the high-speed movement of UAV nodes and interference from external environment factors, the frequent on-off of the unmanned cluster network link will cause the loss of transmission data [29], resulting in a significant decline in the collaborative positioning accuracy of the unmanned clusters [30]. Therefore, in order to ensure the real-time and accurate transmission of position and status information in the unmanned cluster network, it is necessary to design an intelligent unmanned cluster network routing protocol, which should be able to plan appropriate transmission links based on the network link status for positioning and navigation information transmission under conditions of high mobility and high load, reduce the number of information forwarding among nodes, minimize network routing overhead, and improve the overall information transmission quality of the unmanned cluster network [31, 32].  
Figure 1: Unmanned cluster collaborative positioning network.
# Estimation of the Prostate Volume from Abdominal Ultrasound Images by Image-Patch Voting
###### Abstract  
Estimation of the prostate volume with ultrasound offers many advantages such as portability, low cost, harmlessness, and suitability for real-time operation. Abdominal Ultrasound (AUS) is a practical procedure that deserves more attention in automated prostate-volume-estimation studies. As the experts usually consider automatic end-to-end volume-estimation procedures as non-transparent and uninterpretable systems, we proposed an expert-in-the-loop automatic system that follows the classical prostate-volume-estimation procedures. Our system directly estimates the diameter parameters of the standard ellipsoid formula to produce the prostate volume. To obtain the diameters, our system detects four diameter endpoints from the transverse and two diameter endpoints from the sagittal AUS images as defined by the classical procedure. These endpoints are estimated using a new image-patch voting method to address characteristic problems of AUS images. We formed a novel prostate AUS data set from 305 patients with both transverse and sagittal planes. The data set includes MRI images for 75 of these patients. At least one expert manually marked all the data. Extensive experiments performed on this data set showed that the proposed system results ranged among experts' volume estimations, and our system can be used in clinical practice.  
computer-aided diagnosis; medical-image analysis; automated prostate-volume estimation; abdominal ultrasound images; image-patch voting Article  
Estimation of the prostate volume with ultrasound offers many advantages such as portability, low cost, harmlessness, and suitability for real-time operation. Abdominal Ultrasound (AUS) is a practical procedure that deserves more attention in automated prostate-volume-estimation studies. As the experts usually consider automatic end-to-end volume-estimation procedures as non-transparent and uninterpretable systems, we proposed an expert-in-the-loop automatic system that follows the classical prostate-volume-estimation procedures. Our system directly estimates the diameter parameters of the standard ellipsoid formula to produce the prostate volume. To obtain the diameters, our system detects four diameter endpoints from the transverse and two diameter endpoints from the sagittal AUS images as defined by the classical procedure. These endpoints are estimated using a new image-patch voting method to address characteristic problems of AUS images. We formed a novel prostate AUS data set from 305 patients with both transverse and sagittal planes. The data set includes MRI images for 75 of these patients. At least one expert manually marked all the data. Extensive experiments performed on this data set showed that the proposed system results ranged among experts' volume estimations, and our system can be used in clinical practice.
# Repair Performance of Self-Healing Microcapsule/Epoxy Resin Insulating Composite to Physical Damage
###### Abstract  
Minor physical damage can reduce the insulation performance of epoxy resin, which seriously threatens the reliability of electrical equipment. In this paper, the epoxy resin insulating composite was prepared by a microcapsule system to achieve its self-healing goal. The repair performance to physical damage was analyzed by the tests of scratch, cross-section damage, electric tree, and breakdown strength. The results show that compared with pure epoxy resin, the composite has the obvious self-healing performance. For mechanical damage, the maximum repair rate of physical structure is 100%, and the breakdown strength can be restored to 83% of the original state. For electrical damage, microcapsule can not only attract the electrical tree and inhibit its propagation process, but also repair the tubules of electrical tree effectively. Moreover, the repair rate is fast, which meets the application requirements of epoxy resin insulating material. In addition, the repair behavior is dominated by capillarity and molecular diffusion on the defect surface. Furthermore, the electrical properties of repaired part are greatly affected by the characteristics of damage interface and repair product. In a word, the composite shows better repair performance to physical damage, which is conducive to improving the reliability of electrical insulating materials.  
self-healing; epoxy resin; microcapsule; insulating composite; breakdown strength; physical damage; electrical tree +
Footnote †: journal: Applied Physics Letters