esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 5 Simulation and Evaluation
### Simulation Results
#### 5.2.3 Statistical Results for Hops Based on Various Methods  
The performance of the routing methods is the main purpose of this section. Figure 9a,b show the equivalent results of the approaches in terms of the average number of hops. It is widely understood that the more prolonged the data transmission path is, the more extended the data transmission delay remains. As a result, reducing the number of hops is a standard network planning method.  
Figure 8: Average residual energy when fixing the number of sensors (N) and varying the number of sinks (M) from 5 to 15. (**a**)-N = 500, (**b**)-N = 1000.  
Figure 9: Average hops with a variable number when fixing the number of sensors (N) and varying the number of sinks (M) from 5 to 15. (**a**)-N = 500, (**b**)-N = 1000.  
This approach's average number of hops decreases as the total number of sinks grows, as seen in Figure 9a,b. When the overall number of sinks increases, the clusters' cover ranges decrease. The proposed model has a minimum number of hops in each situation, with an average number of hops less than two, as shown in Figure 9a. As the number of sinks increases to 11, the average number of hops is lowered to between 1.559 and 1.423. Since our suggested technique has established pathways from the sensors to the sinks using the Repacking_CH_Route algorithm, the sensors were connected to their associated CH and sinks. Even though other algorithms have used the clustering strategy, they have not produced superior outcomes to our proposed study achievement.
## 3 Results
Applying LSTM, however, led to F1 scores of 0.91-0.96 using feet, trunk-pelvis, and signal fusion. SD was best classified when subject-dependent training data was employed (F1 score = 0.85-1). Using LDA with subject-independent paradigms resulted in a very poor recognition of LWp (F1 score = 0.19-0.6). Even in subject-dependent training all signal sources except signal fusion demonstrated relatively low F1 scores (0.6-0.66) using LDA. LWp was mostly misclassified as RA/RD (Table 2). However, applying LSTM significantly improved the outcomes to 0.82-0.95 (\(p\) < 0.05). Higher F1 scores were obtained for LWf compared to LWp in most cases, with LSTM outperforming LDA in all training paradigms. Best outcomes were achieved when LSTM was used with subject-dependent training data where F1 scores of 0.98-1 were reported. Signals from the forearms had poor task generalization relative to that of trunk-pelvis and feet. The inferior performance of forearms signals was more noticeable when LDA was utilized (Table 1). This could be due to the significantly smaller degrees of forearms movement and decreased ranges of motion compared to feet and trunk-pelvis [26; 27]. A small amount of forearms movement presents low inter-class variability, posing a more difficult problem especially for a linear classifier [28].
###### Abstract  
Tooth biomaterial may be useful in bone regeneration for restoring peri-implant defects in vivo. The aim of this study was to compare bone regeneration capacity in peri-implant defects augmented with autogenous tooth biomaterial combined with platelet-rich fibrin (PRF), tooth biomaterial alone, or PRF alone. Two monocortical defects were generated on each tibia of 10 New Zealand white rabbits (\(n=10\) per group) with a trephine bur, and the dental implant was installed into the defects. In experimental groups 1, 2, and 3, the peri-implant defects were filled with tooth biomaterial and platelet-rich fibrin (PRF), tooth biomaterial only, and PRF only, respectively and the control was left empty. Micro computed tomography (CT), implant stability, and histomorphometric analysis were conducted eight weeks after operation. The mean regenerated bone areas were \(53.87\pm 7.60\%\), \(51.56\pm 6.45\%\), and \(18.45\pm 1.34\%\) in experimental groups 1, 2, and 3, respectively, and \(11.57\pm 1.12\%\) in the control. Mean bone-to-implant contact values were \(43.67\pm 2.50\%\), \(41.07\pm 2.59\%\), and \(21.45\pm 1.25\%\) in experimental groups 1, 2, and 3, respectively, and \(16.57\pm 2.83\%\) in the control. Tooth biomaterial enriched with platelet-rich fibrin (PRF) and tooth biomaterial alone showed more enhanced regeneration than PRF alone in our study.  
tooth biomaterial; platelet-rich fibrin; bone regeneration; graft material +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
This study experimentally investigates the nonlinear heat release response of a non-premixed methane-air flame to low-frequency acoustic excitations. The experiment measures the flame describing function (FDF) and analyzes the CH\({}^{*}\) chemiluminescence and schlieren images. It reveals the governing mechanisms of the nonlinear response. The FDF is characterized by low-pass filtering. When \(St_{f}>3\), the gain value is consistently below 0.25. The highest gain is observed at \(St_{f}=0.43\), and local valley and peak values of gain are observed at \(St_{f}\) ranges of 0.7-0.9 and 1.2-1.5, respectively. The local gain peaks and valleys of the FDF are related to the spatial distribution wavenumber of the heat release at different \(St_{f}\) values. As \(St_{f}\) increases, the oscillation zone of the flame shifts to higher wavenumbers, and the relative intensity of the positive and negative oscillation zones result in different cancellation effects. When the positive and negative oscillation zones are close to each other, the cancellation effect is at its maximum, leading to the minimum gain. In this condition, variations in frequency or amplitude can cause anomalous changes in the phase-lag of the flame, as the overall HRR becomes more sensitive to changes in a certain oscillation zone. Under relatively high-frequency excitation conditions, multiple oscillation zones of heat release along the flame are observed. The dispersion of multiple oscillation zones, as well as the cancellation effect of oscillation zones, are the primary causes of the low-pass filtering characteristic of the flame. The flame length significantly decreases under large amplitude and high-frequency excitation. Specifically, the normalized flame length (\(L_{f}/D\)) decreases from 3.79 to 2.37 when the excitation amplitude increases at an excitation frequency of 100 Hz. The delay time corresponding to the phase-lag exhibits the same as the convection time of disturbances under conditions of large-amplitude and high-frequency excitation. This behavior may be related to the accurate determination of the average convective velocity and the consistency of the oscillation region and the COM with short flame. These findings may provide insights into the stable design of non-premixed flame related to thermoacoustic instability.  
Figure 14: The evolution of the HRR and the normalized velocity fluctuation with respect to the phase angle at two different excitation amplitudes, namely 0.10 and 0.50, at an excitation frequency of 20 Hz.  
Author Contributions:Conceptualization, T.Z. and D.P.; methodology, C.J.; investigation, D.P.; resources, T.Z.; data curation, D.P.; writing--original draft preparation, D.P.; writing--review and editing, D.P. and C.J.; visualization, D.P.; supervision, T.Z.; funding acquisition, T.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51976140, and Science and Technology Commission of Shanghai Municipality, grant number 20 DZ1204902.  
Not applicable.  
The authors declare no conflict of interest.
## 6 Linear and Non-Linear Insulin Infusion Control Schemes
### Sliding Mode Control (SMC)  
The advantages of sliding mode control (SMC) are ultimate accuracy, insensitivity to the internal and external disturbances, robustness and convergence in finite time that are important characteristics of the SMC which are a suitable choice for the control algorithms related to the human body because it is important to obtain extreme precision [137]. Also, the robustness against the parameter variation is better in the SMC compared to the PID. The SMC is basically a simple and robust procedure to synthesize controllers for both the linear and non-linear processes. The design problem of the SMC consists of defining the switching logic and parameter tuning of each controller structure. The first step in SMC is to define a surface s(t), along which the process can slide to its desired final value. The sliding surface breaks the phase plane into regions where the switching function s(t) has different signs. The structure of the controller is intentionally altered as its state crosses the surface in accordance with a prescribed control law [138]. It was designed for T1 DM. As SMC is for the first-order, the higher-order sliding mode (HOSM) is a technique suitable to design the control function u(t) to stabilize the BG level. [PERSON] model (see Section 6.2) is considered for designing. State space is considered for designing, state space equations are:  
\[\dot{x_{1}}=-p1[x_{1}-G_{b}]-x_{1\times 2}+D(t) \tag{15}\]  
\[\dot{x_{2}}=-p2x_{2}+p3[x_{3}-I_{b}] \tag{16}\]  
\[\dot{x_{3}}=-n[x_{3}-I_{b}]+[x_{1}-h]^{+}t+u(t) \tag{17}\]  
Y : is the rate of the pancreatic \(\beta\)-cells release of insulin.  
D(t): rate at which glucose is absorbed to the blood following food intake (mg/dL/min).  
\[D(t)=0.5\ \mathrm{exp}(-0.05t) \tag{18}\]The output tracking error is defined as:  
\[\mathrm{e}=G_{\mathrm{b}}-G(t)=G_{\mathrm{b}}-x_{1} \tag{19}\]  
Relative degree was found to be r = 3. So, the desired sliding variable (\(\sigma\)) for Equation (18) is:  
\[\sigma=\mathrm{e}^{r-1}+\mathrm{c}_{r-2}\mathrm{e}^{r-2}+\ldots\ \ldots\ + \mathrm{c}_{\mathrm{0}}\mathrm{e} \tag{20}\]  
After the calculation in [44] we obtain the control function designed as:  
\[\mathrm{u}\ =-\alpha\,|\sigma|^{1/2}\mathrm{sign}(\sigma)-\ \beta\int sign(\sigma) \mathrm{d}\tau \tag{21}\]  
This control function is used with appropriate \(\alpha\) and \(\beta\) to stabilize sliding variable \(\sigma\) to zero in a finite time. This model is simulated in MATLAB to obtain the desirable responses.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 5 Measurement and Analysis
### Function and Precision Test
#### 5.2.1 Football Field Test  
A standard football field (with an actual area of 7140 m\({}^{2}\)) was selected as the reference test area. The area has no shelter and the elevation angle of the horizon is less than \(10^{\circ}\), which ensures the complete reception of the satellite signal.  
Table 4's data show that the relative error of each measurement is controlled within 0.67%, which verifies the basic performance of the system in an ideal environment. Figures 9 and 10 show the actual stepping point trajectory and the equipment drawing trajectory, respectively.
# AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
## 2 State of the Art: AI Algorithms Applied or Applicable in the Global Agrifood Industry
### Supervised Learning: A Subset of the Machine Learning Family
[10] devised an automated system employing machine vision to classify citrus fruits based on external defects, thereby enhancing efficiency within packing facilities. Advancements in supervised learning are also being leveraged in precision farming, where they support decision-making processes through the real-time analysis of sensor data. Models trained on soil and environmental conditions can furnish customized recommendations for irrigation, fertilizer application, and pesticide use, thereby ensuring optimal crop growth while minimizing environmental impacts [11]. Additionally, recent studies demonstrate that supervised models, when trained on environmental datasets, can be adapted to assess spatiotemporal contamination patterns in water bodies. By incorporating hydrological and ecological variables, these models offer refined insights into the distribution of heavy metal pollution and its ecological risks in impounded lakes [12]. An illustrative example of supervised learning in the agrifood sector is the detection of late blight disease in potato crops using image-based classification models. [PERSON] et al. [13] applied convolutional neural networks (CNN) and support vector machines (SVM) to identify infected potato plants based on field-acquired images. Through pre-processing and feature extraction, their model achieved high classification accuracy, with an AUC of 0.97 using CNN and 0.87 with SVM. While this study was conducted in Colombia, its methodological framework demonstrates a transferable potential for similar implementations in Spanish agricultural systems, emphasizing early disease detection through supervised learning models. These developments underscore the transformative potential of supervised learning in enhancing agricultural productivity and sustainability. By enabling precise monitoring, early disease detection, and optimized resource management, supervised learning significantly contributes to the advancement of modern agricultural practices.
## 2 Materials and Methods
### Remotely sensed data  
MODIS 250 m daily surface reflectance data acquired by the TERRA/AQUA platform (Product MOD09 GQ) were downloaded from the USGS Land Processes DAAC ftp download facility (Figure 1). The MODIS Reprojection Tool was used to reproject the downloaded MODIS tile to UTM zone N31 and a spatial subset to the extent of the borders of the Netherlands was made. From the original HDF file format as provided by USGS, band 2 (reflectance in red: 620-670 nm), band 3 (reflectance in NIR: 841-876 nm) and band 4 (band quality) were stored in geotiff file format. To facilitate further processing, the geotiff file format was converted to ASCII raster using the GDAL translator library ([[http://www.gdal.org](http://www.gdal.org)]([http://www.gdal.org](http://www.gdal.org))). To mask out the pixels which were completely covered or partly affected by clouds, information from the reflectance band quality was used. Only pixels which were flagged as being correctly produced with ideal quality for all bands (code 00) were further processes. Pixels with other quality codes were set to a no data value. During processing of the images, we found that not all clouded pixels were removed. Therefore, an additional masking step was applied: all pixels with a value smaller than 0 or larger than 1,500 for MODIS band 2 were set to a no data value. For the remaining pixels in the image, NDVI was calculated according to equation 2a and used as estimate for FPAR.  
Figure 1: Overview of different steps in the automated processing chain for the calculation of vegetation productivity over the Netherlands and the presentation of resulting maps for GPP in web mapping service.
## 5 Discussion  
### Major Findings  
The major findings are as follows. First, introducing ATs has dramatically altered the cost structure of truck operations. This analysis validated the overall TCO reductions for ATs from the standpoint of freight transport operators, albeit the magnitude of the savings varies depending on which of the five scenarios is considered. Second, the decrease in driver costs, which accounted for about half of TCO for an HDT, was the primary element behind the cost savings that will be realized. The new cost component for remote operation was the largest contributor to the overall cost increase across all five scenarios. TCO for introducing ATs increased because of rising acquisition charges, and the proportion of TCO for ATs increased as a result. Third, in terms of comparison with the findings of previous studies, the cost savings of this study across all scenarios were slightly higher than those in the few directly comparable publications. Specifically, previous literature suggested that the reduction in TCO of ATs was between 15% and 50%, depending on the types of ATs and the assumptions of scenarios [7; 8; 9]. However, our estimates fell anywhere between 25 and 65 percent. There are several possible explanations for these findings. For example, ATs can be more profitable for middle-mile operations since the first- and last-mile operations will require sophisticated technologies to perform well.
## CoAP-Based Mobility Management for the Internet of Things  
**[PERSON], [PERSON] and [PERSON] ***  
School of Electronics Engineering, College of IT Engineering, Kyungpook National University,  
Daegu 702-701, Korea; E-Mails: [EMAIL_ADDRESS] (S.-M.C.); [EMAIL_ADDRESS] (H.-S.K.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-05-3950-8643.  
Academic Editor: [PERSON]  
_Received: 12 May 2015 / Accepted: 29 June 2015 / Published: 3 July 2015_  
**Abstract:** Most of the current mobility management protocols such as Mobile IP and its variants standardized by the IETF may not be suitable to support mobility management for Web-based applications in an Internet of Things (IoT) environment. This is because the sensor nodes have limited power capacity, usually operating in sleep/wakeup mode in a constrained wireless network. In addition, sometimes the sensor nodes may act as the server using the CoAP protocol in an IoT environment. This makes it difficult for Web clients to properly retrieve the sensing data from the mobile sensor nodes in an IoT environment. In this article, we propose a mobility management protocol, named CoMP, which can effectively retrieve the sensing data of sensor nodes while they are moving. The salient feature of CoMP is that it makes use of the IETF CoAP protocol for mobility management, instead of using Mobile IP. Thus CoMP can eliminates the additional signaling overhead of Mobile IP, provides reliable mobility management, and prevents the packet loss. CoMP employs a separate location management server to keep track of the location of the mobile sensor nodes. In order to prevent the loss of important sensing data during movement, a holding mode of operation has been introduced. All the signaling procedures including discovery, registration, binding and holding have been designed by extending the IETF CoAP protocol. The numerical analysis and simulation have been done for performance evaluation in terms of the handover latency and packet loss. The results show that the proposed CoMP is superior to previous mobility management protocols, _i.e._, Mobile IPv4/v6 (MIPv4/v6), Hierarchical Mobile IPv4/v6 (HMIPv4/v6), in terms of the handover latency and packet loss.  
keywords:IoT mobility management; web-based mobility management; WoT mobility management
## 2 Materials and Methods
### Analytical Methods  
#### 2.2.1 Physical-Chemical Analysis  
All samples were homogenized using a knife mill Gridomix GM 200 (Retsh, Haan, Germany). The wet BSG was analyzed for water, protein, and ash content according to the American Association of Cereal Chemists (AACC) methods [16]. Also, the total dietary fiber was determined according to the AACC standard method (Method No. 32-07) [17].  
The pH of the sausage samples was measured with a ProfiLine pH 3210 pH meter (WTW Wissenschaftlich-Technische Werksattten GmbH, Weilheim in Oberbayern, Germany), according to the ISO 2917:1999. \(a_{w}\) was measured using a Rotronic Hygrolab 3 (Rotronic AG, Bassersdorf, Switzerland).  
A Hunter-Lab Mini ScanXE (A60-1010-615 Model Colorimeter, Hunter-Lab, Reston, VA, USA) was used for instrumental color (L* lightness; greenness (-a*) or redness (a*); and blueness (-b*) or yellowness (b*)) measurements.  
Determination of moisture content was performed according to ISO 1442:1997 [18] (Mermmert UFE 400, Memmert, GmbH Germany, Schwabach, Germany). The Kjeldal method (ISO 937:2023) [19] was used to determine total protein content. Total fat content was determined according to ISO 1443:1973 [20] (Soxlet method). The determination of collagen content was performed through the analysis of hydroxyproline according to ISO 3496:1994 [21], using a spectrophotometer ([PERSON], UV-1800., Tokyo, Japan).  
Texture profile analysis (TPA) was performed using a TA.XT2i SMS Stable Micro Systems Texture Analyzer (Stable Microsystems Ltd., Surrey, England) equipped with a P/75 aluminum cylindrical probe. This involved cutting the samples into 1.5 cm thick slices and their double compression so as to downsize them to 40% of their original thickness. The following parameters were quantified [22]: hardness (g), springiness (ratio), cohesiveness (ratio), and chewiness (g).
# Enabling Multi-Mission Interoperable UAS Using Data-Centric Communications
## 3 The Data Distribution Service Open Standard in UAS Environments
### Data Model Adaptability  
DDS allows very flexible ways to model both hierarchical GDS partitions and published/subscribed data. This is particularly useful, as a general consideration, to make the DDS middleware capable of easily supporting heterogeneous applications belonging to non-predefined application domains. In the specific targeted area of UAS, dynamic data model adaption can represent a relevant advantage, if compared with other lower-level communication solutions in the field, to support simplified extensibility and easy integration of new service components and applications, again along the direction of maximum openness and interoperability.
## 3 Results
### Model Performance on Specific Disease Target Test Set  
The model performance tests in the previous chapters were conducted on the dataset proposed by [PERSON] et al., which contains target proteins for multiple diseases. To better assess the DrugFinder's generalization ability for individual disease targets, we performed model testing on an additional test set consisting of druggable proteins related to tumors. The results showed that the model achieved an accuracy of 88.71%, precision of 93.72% and sensitivity of 81.68% on this test set. These results indicate that DrugFinder can maintain relatively high accuracy and precision in identifying druggable proteins corresponding to specific diseases. This will contribute to drug development in various disease domains.  
Figure 4 provides a radar chart for a more intuitive comparison of the model's performance on the two different test sets. From the test data and radar chart, it can be observed that the model's performance metrics on the specific disease (tumor) target test set are  
Figure 3: ROC curves of different machine learning classifiers.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Dimension** & **Classifier** & **Acc** & **Pre** & **Sen** & **Spe** & **F-Score** & **MCC** \\ \hline T5 & 1500D & SVM & 0.9226 & 0.9330 & 0.8945 & 0.9145 & 0.9133 & 0.8441 \\  & & RF & 0.8494 & 0.8042 & 0.8853 & 0.8950 & 0.8428 & 0.7018 \\  & & NB & 0.8493 & 0.8349 & 0.8849 & 0.8615 & 0.8349 & 0.6964 \\  & & **XGB** & **0.9498** & **0.9292** & **0.9633** & **0.9683** & **0.9460** & **0.8996** \\  & & KNN & 0.8765 & 0.8597 & 0.8716 & 0.8911 & 0.8656 & 0.7516 \\ PBD & 1500D & SVM & 0.9142 & 0.9078 & 0.9037 & 0.9195 & 0.9057 & 0.8271 \\  & & RF & 0.8724 & 0.8398 & 0.8899 & 0.9028 & 0.8641 & 0.7440 \\  & & NB & 0.8661 & 0.8598 & 0.8440 & 0.8712 & 0.8519 & 0.7298 \\  & & XGB & 0.9289 & 0.9220 & 0.9220 & 0.9346 & 0.9220 & 0.8566 \\  & & KNN & 0.8703 & 0.8482 & 0.8716 & 0.8898 & 0.8597 & 0.7394 \\ SeqVec & 1800D & SVM & 0.9100 & 0.9035 & 0.9122 & 0.9045 & 0.9031 & 0.8032 \\  & & RF & 0.8975 & 0.8658 & 0.9174 & 0.9271 & 0.8909 & 0.7956 \\  & & NB & 0.8410 & 0.8859 & 0.7477 & 0.8129 & 0.8109 & 0.6827 \\  & & XGB & 0.9456 & 0.9324 & 0.9495 & 0.9570 & 0.9409 & 0.8907 \\  & & KNN & 0.8494 & 0.8202 & 0.8578 & 0.8760 & 0.8386 & 0.6981 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of different classifiers on independent test set.  
slightly lower than the metrics on [PERSON]'s test set, which may be attributed to some differences in the data distribution between the two test sets. The tumor-related test set contains fewer sequences of druggable proteins, which could also introduce some randomness in the performance testing.