esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight YOLOv8 Model for Apple Leaf Disease Detection
## 1 Introduction
These models are integrated for inference tasks, and transfer learning accelerates model training. The model achieved a [EMAIL_ADDRESS] of 85.2% for detecting 37 pests and 8 diseases. [PERSON] et al. [17] introduced the faster DR-IACNN model, enhancing its feature-extraction capabilities through inception and inception-reset modules. The model achieves a [EMAIL_ADDRESS] of 81.1%, albeit operating at only 15.01 FPS. [PERSON] et al. [18] utilized the AlexNet model to extract disease features from leaves with complex backgrounds using the parallel technique. Inflated convolution helped to reduce the model's parameters, realizing a model volume of 5.87 MB and a recognition accuracy of 97.36%. These target detection models usually use complex and huge neural network structures requiring powerful computing resources, such as GPU and TPU [19; 20]. However, due to hardware limitations, mobile and embedded devices may not perform well in terms of performance and responsiveness. Current research into identifying apple leaf diseases is constrained by the numerous parameters in traditional target detection algorithm models and the compromised accuracy of lightweight models. To address these issues, the YOLOv8n-GGi algorithm has been proposed as follows:  
1. In the backbone network, GhostConv is substituted for the traditional Conv layer, and the C3 Ghost module replaces the original C2f structure. It effectively realizes the lightweight of the network model and significantly reduces the number of parameters and computational burden of the model. 2. A global attention mechanism (GAM) is implemented to enable the network to identify and localize regions of interest more precisely and improve the accuracy of detection. 3. The enhanced weighted bi-directional feature pyramid network (BiFPN) has been designed to detect better small target spots on apple leaves in complex environments and improve detection capabilities in the field. We have successfully incorporated different weights for features at various scales and implemented cross-scale weight suppression and expression to enhance feature fusion and improve target detection capabilities.
# Subcooled Flow Boiling Heat Flux Enhancement Using High Porosity Sintered Fiber
## 2 Experimental Setup and Procedure
### High Porosity Sintered Fiber  
High porosity sintered fiber attached to the surface could both enhance the maximum heat flux and reduce the wall superheat temperature in comparison with the bare surface [24]. Microfibers were attached to the bare surface using the same material by a sintering process, thus, the contact resistance was reduced significantly. The attached sintered fiber created a porous height (hp), as shown in Figure 3b. The bare surface had a roughness of Ra = 0.4 \(\mu\)m. The microfibers had a diameter of 0.06 mm, and the porosity of the porous material was 86%, the highest porosity for this mach  
Figure 2: Test section details: (**a**) heating cooper block; (**b**) thermocouples distribution on the heating surface; (**c**) flow channel, (**d**) distance between each pair of thermocouples.  
of 4 different porous height hp, were fabricated (0.2, 0.5, 1, and 2 mm) and 1 bare surface hp = 0 mm. The intertwined fibers increased the wetting area and promoted the nucleation sites. Both the surface and microscopic view are shown in Figure 3.  
### High Porosity Sintered Fiber  
After 2 h of degassing, the magnetic pump was activated. Deionized water flowed into the channel, then the preheater was activated, and it controlled the inlet subcooling temperature to 50 K and 70 K as parameters. The mass flux was regulated by the control valve and confirmed with the oval Coriolis flow meter. After the mass flux and the inlet temperature became stable, the PID controller was activated, and the group of cartridges provided the heat flux. The copper block B temperature was initially adjusted to 100 \({}^{\circ}\)C. Once the temperature measured by the thermocouples became stable, the data were recorded for around 3 min, and a video using the high-speed camera was recorded. Then, the copper block was increased to 40 \({}^{\circ}\)C, and the process was repeated until the heat flux limit (around 2.5 MW/m\({}^{2}\)) provided by the cartridge heaters was reached or the critical heat flux was reached. If the temperature of the heating surface increased abruptly, the CHF conditions were assumed. Detailed information of the experimental parameters is shown in Table 1.
## 4 The Proposed Algorithm-TCEB
### Intra-Cluster Topology Construction
If there is existing an equally largest \(u_{i}\) from different cluster-heads, then go to **Step 4**, otherwise, reply an _ACK_ message and perform **Step 6** directly. * **Step 4. ** The non-cluster-head randomly chooses one of the clusters which have the same largest \(u_{i}\), and then it responses the _ACK_ reply. * **Step 5. ** Once the non-cluster-head does not receive any _INVITATION_ message from any cluster-head, it has to select the relay node according to Equation (16). Afterwards, the non-cluster-head will establish the communication link with the relay node. Figure 1: Multipath propagation in shallow water. * **Step 6**. The intra-cluster topology keeps going on constructing until all non-cluster-heads join one of the \(k_{opt}\) clusters. ```
1:for\(k\gets 1\)to\(k_{opt}\)do
2:\(chs_{k}\) broadcasts the INVITATION and set a timeout
3:endfor
4:for\(i\gets 1\)to\(n\)do
5:if\(n[i]!=\) 'C' then
6:if\(n_{i}\) receives the INVITATION within timeout then
7:if\(Num(INVITATION)>1\)then
8:if there is no equal \(u_{i}\)then
9:\(n_{i}\) becomes the cluster member of the cluster-head with the largest \(u_{i}\)
10:else
11:\(n_{i}\) randomly chooses one of clusters to join
12:endif
13:else
14:\(n_{i}\) becomes the cluster member of the cluster-head which sends INVITATION
15:endif
16:\(n_{i}\) replies an ACK
17:else
18:\(n_{i}\) selects the relay node \(j\) according to Equation (16)
19:\(comm[i,j]\gets 1\)
20:endif
21:endif
22:endfor
```  
**Algorithm 2** Intra-cluster topology construction
## 4 Simulation and Experimental Analysis  
To verify the effectiveness of the self-adaptive-coefficient double-power sliding mode control method proposed in this paper, we conducted a simulation and an experimental analysis. First, a joint simulation model of the lower limb rehabilitation exoskeleton robot and the patient was established using MATLAB/Simulink and RecurDyn dynamics software. Then, the simulation analysis of the robot trajectory tracking control was carried out under interference from the patient. We compared the control accuracy and anti-interference ability of the three control methods (i.e., the self-adaptive-coefficient double-power sliding mode control method, the double-power sliding mode control method, and the traditional computed torque method). Finally, an experiment was conducted on the lower limb rehabilitation exoskeleton robot prototype with selected healthy testers.
# MobiDabet: Mobile Telemonitoring System for Patients with Type 2 Diabetes Mellitus (T2 DM)
## 5 Discussion
However, this system lacks two important services: laboratory analysis and health education records. Therefore, our work is characterized by providing all desirable services that a telemonitoring system of patients with T2 DM should consider for achieving adequate monitoring and continuous control of nutritional therapy based on the Mexican Official Standard NOM-015-SSA2-2018. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Related Works** & **PR** & **CD** & **CH** & **AG** & **LR** & **N** & **R** & **H** \\ \hline MRPDUTMI [5] & ✓ & ✓ & ✓ & ✓ & & & & & \\ Proyecto PITES T-YUDA [6] & ✓ & ✓ & ✓ & ✓ & & & & & ✓ \\ PALANTE [7] & ✓ & ✓ & ✓ & ✓ & ✓ & & & & ✓ \\ VITADAT [8] & ✓ & ✓ & ✓ & ✓ & & & ✓ & ✓ & \\ Diabetes conet [9] & ✓ & & & & & & & & \\ Diabetes diario [10] & ✓ & & & & & & ✓ & & \\ Diabetes M [11] & ✓ & & ✓ & & & ✓ & & & \\ MyD DiabeticAlert [12] & ✓ & & ✓ & ✓ & & ✓ & & & \\ MySugr [13] & ✓ & & & & ✓ & ✓ & & & ✓ \\ Social Diabetes [20] & ✓ & ✓ & & ✓ & & ✓ & & ✓ \\ USKAYSMS [21] & ✓ & ✓ & & & & ✓ & & & ✓ \\ S.M.R.P.D. [22] & ✓ & ✓ & & ✓ & & & ✓ & & ✓ \\ Our proposed & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Telemonitoring system comparisons. On the other hand, a limited number of related works [20; 21; 22] have carried out usability assessments (see Table 13). These works evaluated their systems mostly based on empirical methods and inquiry methods such as interviews or questionnaires applied to a group of users, to measure the attributes of effectiveness, efficiency, and satisfaction based on [PERSON]'s heuristics, resulting in a percentage of usability and know if users recommend using the application. Among these related works, the one proposed in a study [22] stands out. However, this work did not use usability standards that allowed measuring the quality in use of the system that they propose. Finally, and according to the analysis carried out in a previous study [23], the number of users who participated in our study allowed us to identify probably around 85% of the usability problems in both applications of our system. Some usability problems that were identified by users are the following:  
* The system must show clear messages to users when an error occurs. * Lack of contextual help to the user in some services of medical staff and patients. * The system must show help messages to the user for entering some data. All the usability problems identified in our study were corrected.
## 3 Methodology
### Establishment of Spatial Proximity  
Spatial proximity is the distance relationship between two units in space. GWR model establishes a model for each unit according to the spatial proximity, and the areas within a unit are considered homogeneous. Therefore, it is essential to determine the spatial adjacency relationship between units, i.e., the boundary of map units. Administrative boundaries [25] and grid boundaries [26] were commonly used as the spatial proximity expressions of GWR in previous studies, but they are inconsistent with the neighborhood characteristics of landslides. These boundaries could not perform well to express the heterogeneity between units and the homogeneity within units, affecting the modeling ability of GWR model.  
Therefore, we incorporate the methodology of slope units to express spatial proximity. Slope unit is the basic unit of geological disasters. It divides the terrain into mapping units with similar hydrological and geomorphological conditions, and is shaped by similar processes occurring in the natural landscape under the same geo-environmental conditions [37]. A slope unit division map is formed by the GIS-based hydrologic analysis tool [38]. Firstly, reverse DEM is generated by subtracting the elevation value from the highest elevation value in each unit. Secondly, fill the DEM and reverse DEM, and the flow direction can be obtained by these filled DEMs. Then, by setting the minimum number of cells that flow to the calculating point, the watershed can be calculated. Eventually, by combining the watershed by DEM and the watershed by reverse DEM, slope units can be obtained.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria** & **Former Studies Using the Same Criterion for Landslide Susceptibility Assessment** \\ \hline Elevation & [PERSON] et al. (2009) [32], [PERSON] et al. (2012) [33], [PERSON] et al. (2014) [26], [PERSON]. (2013) [34], [PERSON] et al. (2019) [35] \\ Slope & [PERSON] et al. (2009) [32], [PERSON] et al. (2012) [33], [PERSON] et al. (2014) [26], [PERSON]. (2013) [34], [PERSON] et al. (2019) [35], [PERSON] et al. (2019) [36], [PERSON] et al. (2012) [37] \\ Slope height & [PERSON] et al. (2012) [33], [PERSON] et al. (2019) [36], [PERSON] et al. (2012) [37] \\ Aspect & [PERSON] et al. (2009) [32], [PERSON] et al. (2012) [33], [PERSON] et al. (2014) [26], [PERSON] et al. (2019) [35], [PERSON] et al. (2012) [37] \\ Distance-to-stream & [PERSON] et al. (2012) [33], [PERSON] et al. (2014) [26], [PERSON]. (2013) [34], \\ Distance-to-fault & [PERSON] et al. (2012) [33], [PERSON] et al. (2014) [26], [PERSON] et al. (2019) [35], [PERSON] et al. (2019) [36] \\ Lithology & [PERSON] et al. (2009) [32], [PERSON] et al. (2012) [33], [PERSON] et al. (2014) [26], [PERSON]. (2013) [34], [PERSON] et al. (2019) [35], [PERSON] et al. (2019) [36] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Criteria for a rational selection of landslide factors on the basis of literature review.
# Preliminary Field Results of a Dedicated 5G Positioning Network for Enhanced Hybrid Positioning +
## 5 Conclusions  
This paper presents the first field ranging measurements with a dedicated fifth generation (5G) flying base station (BS) for positioning purposes. This field experimentation is performed within the Hybrid Overlay Positioning with 5G and GNSS (HOP-5G) testbed, which exploits 80 MHz downlink positioning reference signals (PRS) to achieve real-time ranging measurements at the user equipment (UE). Considering hovering and optimal conditions for a static UE, a ranging precision is achieved below 1 m in 95% of cases, when considering the pseudorange variation over periods of 1 s within test points of 50 s. Future work is envisaged to further demonstrate the positioning capabilities of dedicated 5G positioning networks under static and dynamic UE conditions.  
Conceptualization, J.A.d.P.-R., A.Y.Y., N.C.K., P.N., S.M.R., S.P., R.M., F.K., N.S., S.G., L.C., J.T. and D.F.; experimentation, J.A.d.P.-R., A.Y.Y., P.N. and D.F.; data post-processing, J.A.d.P.-R., N.C.K. and P.N.; writing, J.A.d.P.-R.; project administration, J.A.d.P.-R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work from the Hybrid Overlay Positioning with 5G and GNSS (HOP-5G) project has been supported under NAVISP-EL1-050 activity by the European Space Agency Navigation Innovation and Support Programme (NAVISIP), which aims to generate innovative concepts, technologies and system solutions in the wide-field of Positioning, Navigation and Time.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing is not applicable to this article.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Point Group**} & \multirow{2}{*}{**Point**} & \multirow{2}{*}{\(d\)} & \multirow{2}{*}{\(h\)} & \multicolumn{2}{c}{**Ranging**} \\  & & & & **Precision @** & **Outlier Rate** \\ \hline \(d<10\) m, \(h<3\) m & P\({}_{4}\) & 7.1 m & 1.10 m & 0.35 m & 0.18\% \\
10 m \(<d<\) & P\({}_{2}\) & 10.8 m & 2.23 m & 0.42 m & 0.28\% \\
20 m, \(h<3\) m & P\({}_{3}\) & 16.3 m & 1.43 m & 0.42 m & 1.14\% \\ \(d>20\) m, \(h<3\) m & P\({}_{1}\) & 22.2 m & 0.39 m & 0.72 m & 1.14\% \\ \(d>20\) m, \(h>3\) m & P\({}_{5}\) & 21.1 m & 9.35 m & 0.53 m & 0.32\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ranging precision at 95% of cases and overall outlier rate of 5G ranging measurements for each group of UAV hovering points.  
Figure 3: Preliminary field results of the downlink UE ranging measurements from a flying 5G BS: (a) CDF of the pseudorange variation over 1 s; (b) CDF of the RSRP.
# Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods
## 3 Chinese Document-Level Emergency Event Extraction Dataset
### Dataset Features  
We not only annotated the DEE task with the unique problem of argument scattering and multiple events but also addressed the issue of role overlapping, which is often overlooked in the DEE task. Specifically, first, we annotated the event elements not only in a single sentence but also in the whole document. Figure 4 details the number of sentences that contain event element information. The Y-axis represents the number of events. We can see from the figure that although some events can find all event elements in a single sentence, for most events, the event elements need to be found in multiple sentences. This further highlights the prevalence of argument scattering in the DEE task.  
Figure 3: Statistics on the number of event types in the CDFEE dataset.  
Second, we marked several different events in the same document, and Figure 5 shows the number of events contained in the document, where the Y-axis represents the number of documents. We can see that most of the documents contained two or three events, except for a few documents that contained only one event. This is a good reflection of the fact that there are often multiple events in a document.  
Third, we labeled the role overlapping problem where a candidate entity plays different roles in one or more events, and Figure 6 shows the number of roles played by the candidate entities, with the Y-axis representing the number of events. We can see from the figure that although in most cases a candidate entity plays only one role, there are still a significant number of candidate entities that play two or three different roles at the same time.  
Figure 4: Statistics for the number of sentences involved in event arguments.  
Figure 5: Statistics for event count of documents.
## 5 Case Studies
### Step Change in Solar Irradiation with Constant Temperature  
As shown in Figure 6, the core process is to impose a set of solar irradiation steps on the PV array, where the step change is applied every second. The temperature is maintained to be constant at 25 \({}^{\circ}\)C during the whole test. The online optimization outcomes of various approaches for MPPT with step change solar irradiations are illustrated in Figure 7. It can be found that the obtained results are similar to those of the start-up test. The output power and voltage derived by those meta-heuristic algorithms, except TRL, are relatively prone to volatility if the solar irradiation is not always steady and varies at a dramatic pace. This also verifies that the knowledge transfer can effectively guarantee the convergence stability of TRL, i.e., the control strategies of adjacent optimization tasks only have a slight difference under the same weather conditions.  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline Typical peak power & 51.716 W &
\begin{tabular}{c} Nominal operation cell \\ temperature (\(T_{\text{ref}}\)) \\ \end{tabular} & 25 \({}^{\circ}\)C \\ \hline Voltage at peak power & 18.47 V & Factor of PV technology (A) & 1.5 \\ \hline Current at peak power & 2.8 A & Switching frequency (\(f\)) & 100 kHz \\ \hline Short-circuit current (\(I_{\text{sc}}\)) & 1.5 A & Inductor (\(L\)) & 500 mH \\ \hline Open-circuit voltage (\(V_{\text{oc}}\)) & 23.36 V & Resistive load (\(R\)) & 200 \(\Omega\) \\ \hline Temperature coefficient of \(I_{\text{sc}}\) (\(k_{1}\)) & 3 mA/\({}^{\circ}\)C & Capacitor (C\({}_{1}\), C\({}_{2}\)) & 1 μF \\ \hline \end{tabular}
\end{table}
Table 2: The photovoltaic (PV) system parameters.  
Figure 5: PV system responses of seven methods obtained on the start-up test. (**a**) Voltage; (**b**) Power.
# The Influence of Processing on the Bioactive Compounds of Small Berries
## 1 Types of Berries  
The botanical term \"berries\" refers to fleshy fruits resulting from a single flower ovary. In agreement with this definition, true berries include some fruits not commonly known as berries, namely tomatoes, kiwifruit, grapes, bananas, and coffee beans [1]. However, the common commercial term \"berries\" differs from the scientific botanical one; in addition to the small simple fruits like blueberries and cranberries, it also includes small aggregated and multiple fruits, such as strawberries, raspberries, currants, or mulberries, which have many seeds, and are developed from many ovaries or a cluster of flowers [2]. In this review, we will focus on the common small berries, comprising a variety of purple-, blue-, or red-colored juicy fruits, appreciated by consumers for their particular flavor, delicate texture, and numerous health-related properties.  
Raspberries (_Rubus ideas_), strawberries (_Fragaria \(\times\) ananssa_), and blackberries (_Rubus_ spp.), which belong to the Rosaceae family, together with blueberries (_Vaccinium_ spp.) and cranberries (_Vaccinium_ _exycococos_), from the Ericaceae family, and currants (_Ribes_ sp.) from the Grossularaceae family, are of high economic importance, being the most widely known and consumed, either fresh or after processing [2, 3]. Because of their high content of bioactive compounds, they are often used to obtain functional ingredients [3]. Other berries, such as chokecherries (_Prunus virginiana_), elderberries (_Sambucus_ spp.), bilberries (_Vaccinium_ _myrtillus_), gooseberries (_Ribes_ _wva-crispa_), sea buckthorn (_Hippolae_ _hramnoides_), and honeybeerries (_Lonicera caeulea_), have lower levels of bioactive compounds but still hold limited nutraceuticaltical potential [3].  
Berry fruits are rich in nutrients and phytochemicals. They are low in calories, but are excellent sources of both micro- and macronutrients, including dietary fiber. They havea high content of dietary fibers, trace amounts of minerals (calcium, iron, magnesium, phosphorus, and potassium) and vitamins (vitamins C, E, K, and A, niacin, riboflavin, and thiamine) [2]. The antioxidant activity and health-promoting properties of berries are particularly attributed to phenolic compounds, which exert their metabolic activity mainly through scavenging reactive oxygen species (ROS) [4], but also to carotenoids and vitamins [4; 5].  
The objective of this paper is to provide an overview of the phytochemicals contributing to the antioxidant and health-related properties of berry fruits and to summarize the influence of the processing methods on the bioactive compounds of the resulting berry-based products.
# First Evaluation of Topography on GNSS-R: An Empirical Study Based on a Digital Elevation Model
## 4 Understanding the Effects of Rough Topography on GNSS-R Space-Borne Data
### Global Scale
TWI accounted for saturation of SMC in local topographic converging areas and was used to predict zones of surface saturation and therefore organized spatial fields of SMC. It could be expected that inland water bodies, i.e., rivers, are predominant in regions with high TWI, which could justify the specular nature of the scattering [29]. On the other hand, topographic heterogeneity (TPI, TRI, and VRM), \(\beta\), and curvature parameters (TPI, TRI) are high over the Andes and Himalayan mountains, where TE is high because of the effect of diffuse scattering \(\left\langle\left|Y_{\tau,\text{t,colon}}(\tau,\text{f})\right|^{2}\right\rangle\).