esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In summary, small-scale push tests were carried out to investigate the load slip behaviour of SCGC composite slabs compared to NC composite slabs with targeted compressive strengths equal to 40 and 60 MPa. The results obtained from the conducted push tests will help us to understand the composite interaction between re-entrant profiled steel sheets and SCGC and, subsequently, evaluate its efficiency compared to the normal concrete composite slab. The obtained results are summarised as follows:  
* SCGC has a better chemical adhesion bond with profiled steel sheets than normal concrete, confirmed by visual observation and the test results. SCGC showed a 131% better chemical adhesion bond than NC40 and 164% better chemical adhesion bond compared to NC60. It can also be concluded that the chemical adhesion bond between concrete and profiled steel sheets is influenced by neither the concrete compressive strength nor the tensile strength, but by the binder distribution at the concrete-profiled steel sheet interface.
* The SCGC composite slab has approximately 71% lower shear connection stiffness than the NC60 composite slab, but 46% higher than the NC40 composite slab.
* The SCGC composite slab developed 66% and 11% higher ultimate strength than NC40 and NC60. In addition, the relevant slip to the maximum load of the SCGC composite slab was higher than that for NC40 and NC60, indicating that the SCGC slab has better resilience than NC40 and NC60.
* The SCGC slab has higher toughness compared to NC40 and NC60, as a result of the low concrete hardness associated with SCGC, which allowed for better mechanical interlock and frictional resistance between the profiled steel sheet and SCGC, compared to NC.  
Conceptualization, M.H. and R.A.-A.; data curation, M.H.; investigation, M.H.; methodology, M.H.; supervision, B.K. and R.A.-A.; writing--original draft, M.H.; writing--review & editing, B.K. and R.A.-A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Available on request from authors.  
The authors would like to acknowledge the support of Deakin University in carrying out the present study. Moreover, the assistance of [PERSON], [PERSON] and [PERSON] during the experimental phase of the study is gratefully acknowledged.  
The authors declare no conflict of interest.
# A Novel Method for Tool Identification and Wear Condition Assessment Based on Multi-Sensor Data
## 4 Tool Wear Assessment  
### Elman and Adaboost  
Elman neural network is a typical dynamic recurrent neural network [22]. As shown in Figure 7, on the basis of the BP neural network, a support layer is added on the hidden layer. The receiving layer has output delay and storage for the hidden layer. The role, and the receiving layer then applies the output to the input of the hidden layer, making it sensitive to historical data, increasing the network's ability to process dynamic information.  
The Elman network nonlinear state space expression is:  
\[y(k)=g(w_{3}\cdot x(k)), \tag{1}\]  
\[x(k)=f(w_{1}\cdot x_{c}(k)+w_{2}\cdot(u(k-1))), \tag{2}\]  
\[x_{c}(k)=x(k-1), \tag{3}\]  
where \(y\) is the \(m\)-dimensional output node vector;\(x\) is the \(n\)-dimensional intermediate layer node unit vector; \(u\) is the \(r\)-dimensional input vector; \(x_{c}\) is the \(n\)-dimensional feedback state vector; \(w^{3}\) is the connection weight from the middle layer to the input layer; \(w^{2}\) is the connection weight from the input layer to the middle layer; \(w^{1}\) is the connection weight from the receiving layer to the middle layer;  
Figure 6: Long Short-Term Memory (LSTM) tool recognition network training process.  
Figure 7: Elman neural network structure.  
acquisition frequency is up to 50 khz, and the data of various wear conditions are comprehensive and rich, with high authenticity. Take the X-axis force data, X-axis vibration data, and AE-RMS of tool 1 as examples, compare the wear degree 1 with the wear degree 315, and the results are shown in Figure 8:  
It can be seen from Figure 8 that the \"amplitude\" and \"degree of oscillation\" of the data collected by the sensor increase significantly with the deepening of tool wear. Therefore, this paper uses the mean and variance of the data collected by the sensor in a time as the data characteristics. The variation curve of variance and mean value of seven sensor data with the degree of tool wear is shown  
315--which is abstract data--so there is no unit and the y-axis indicates the value after normalizing the variance and average value to the [-1,1] interval.  
It can be seen from Figure 9 that with the deepening of tool wear, the data of mean value and variance value are basically in an increasing state, and the variance is more obvious. PHM also provides three wear quantities (flute1, flute2, flute3) to characterize the wear degree. Figure 10 shows the change curve of tool 1 wear with wear degree--hereinafter referred to as wear curve.  
Figure 9: The characteristic value changes with the degree of wear. (**a**) Mean value curve. (**b**) Variance value curve.  
Based on the wear curves of the three tools, this paper sets the range of tool wear in three stages to determine the current wear status of the tools as shown in Table 1.
# Feasibility of a Patient-Specific Bolus Using the Life-Casting Method for Radiation Therapy
## 3 Results
### Dosimetric Evaluation  
Figure 5 and Table 1 present the DVH and dosimetric results for each plan, respectively. The DVH demonstrated small variations in the dosimetric results between the two types of boluses. Both 3D-CRT plans exhibited the same target conformity, whereas the use of a patient-specific bolus improved the homogeneity and target coverage. Regarding organ-at-risk (OAR) sparing, the delivery of radiation doses to the lungs and heart was higher when using a patient-specific bolus. However, both plans met the dose constraints, with the parameters of the lung V\({}_{\text{20 Gy}}\) < 30% and the mean lung dose < 17 Gy being satisfied for both plans, as well as the V\({}_{\text{25 Gy}}\) < 10% and the mean dose < 15 Gy for the heart.  
Figure 3: Optically stimulated luminescent dosimetry (OSLD) measurement points for in vivo dosimetry.  
Figure 4: Axial and sagittal views of the phantom’s computed tomography (CT) image using the patient-specific bolus and the SuperFlab bolus.  
Figure 5: Dose-volume histogram (DVH) of (**a**) three-dimensional conformal radiation therapy (3D-CRT) plans and (**b**) volumetric modulated arc therapy (VMAT) plans with the patient-specific bolus and the SuperFlab bolus.  
In the VMAT plan, both boluses exhibited the same target homogeneity, whereas the use of a patient-specific bolus improved the conformity and target coverage. For the OAR, higher doses were delivered using the patient-specific bolus. However, both plans met the dose constraints and satisfied the parameters of the lung at V\({}_{\rm 20 Gy}\) < 30% and the mean lung dose < 17 Gy, and a V\({}_{\rm 25 Gy}\) < 10% and the mean dose < 15 Gy for the heart.
## 3 Results and Discussion
### Experiments with Cancer Cells  
Next, the endocytosis of the leukemia cell line (HL-60, ATCC CCL-240) was analyzed and compared with the results for breast cancer cells [27]. Previously, we compared ESR spectra of magnetic iron(II, III) oxide functionalized with organic units bearing TEMPO or TEMPOL in different environments. The ESR measurements were carried out in the presence of human microvascular endothelial, breast cancer, and yeast cells. The measurement at room temperature resulted in very similar ESR spectra for the samples with and without cells. After temperature reduction, a wide triplet was observed for all cells studied. The spectra differed only in peak-to-peak line width. Therefore, in the present studies, the leukemia HL60 cells were used. These cells do not adhere to the bottom of the culture bottle, which distinguishes them from other solid tumor-derived ones. They can be used without trypsinization, which is stressful for the cells and may change their properties. Consequently, their standard physiological properties can be maintained. On this basis, we expected this might affect the structure of the EPR spectrum. The decrease in the spin probe concentration in leukemia cancer cells was swift, comparable to data obtained for breast cancer cells. It resulted from a high concentration of free radicals inside these cells. However, endocytosis in leukemia cells and breast cancer cells is of a different temporal nature. The ESR spectrum recorded immediately after mixing TEMPO with leukemia cells (240 K) is binary (Figure 12), consisting of a wide and narrow triplet.  
Figure 12: ESR spectrum (240 K) of TEMPO spin probe (WT: wide triplet, NT: narrow triplet), measured directly after adding HL-60 leukemia cells suspension (2.4 \(\times\) 10\({}^{5}\) cells/mL).  
The disappearance of both spin probes studied (TEMPO and TEMPOL) in leukemia cells was very fast; thus, after 15 min of incubation at 310 K, a wide triplet is observed in the ESR spectrum (Figure 13). The disappearance rate of the spin-probe signal inside the leukemia cells can be interpreted as the complete recombination of the spin probes with free radicals present in the cells. It is confirmed by an absence of a narrow triplet in the ESR spectrum for the sample of leukemia cells incubated for 2 h at 310 K (Figure 14). Moreover, it shows many free radicals outside the system controlling their contents in cells. Similar effects were observed for breast cancer cells [27].  
Figure 14: ESR spectrum (240 K) of TEMPO spin probe measured after 2 h of incubation with HL-60 leukemia cells.  
Figure 13: ESR spectrum (240 K) of TEMPOL spin probe (WT: wide triplet) measured after 15 min of incubation with HL-60 leukemia cells suspension (2.4 \(\times\) 10\({}^{5}\) cells/mL).  
Figure 15 shows the changes in spin probe concentration over time. It can be observed when the ratio of TEMPO molecule amount/yeast cell number is above 1.7 \(\times\) 10\({}^{9}\) and penetration inside a cell is relatively slow.
# Respiration Symptoms Monitoring in Body Area Networks
## 6 Tracking Person's Breathing in Typical Environment
### Analysis of Measured Data for Normal Breathing  
Figure 5 indicates the breathing pattern of a person lying in a straight position on the back. The measurements were taken for 60 s. Figure 5a shows the raw breathing pattern obtained using respiratory sensor and Figure 5b shows the filtered estimated breathing pattern. The person, in this case, completed nearly 16 breathing cycles. We further examine the breathing pattern obtained using S-Band sensing leveraging the wireless channel information.  
The frequency-selective fading poses various extents on the RF signal considering different frequencies. As shown in Figure 6, different frequencies have different variations with respect to time for different power levels. Thus we select frequency # 13 that best describes the breathing pattern of the subject as illustrated in Figure 6a. The data obtained for the particular frequency was then passed into the wavelet filter to remove the sharp edges and smooth the signal. Comparing the breathing pattern shown in Figure 6b with the one in Figure 5b, we observe that both the waves forms obtained are identical considering the number of cycles during the same time period.  
Figure 5: Estimated breathing pattern obtained using Digital Respiratory Sensor. (**a**) Raw breathing pattern obtained; (**b**) Filtered breathing pattern obtained.
# Presence of Cyanotoxins in a Mexican Subtropical Monometric Crater Lake
###### Abstract  
Microcystins (MCs) produced by cyanobacteria are a ubiquitous worldwide problem because some MCs can cause tumor formation and are hepatotoxic. In the Santa Maria del Oro crater lake, Mexico, plankton scums are recurrent during most of the year and are associated with cyanobacteria of the genera _Microcytis_ spp. and _Lyngbya_ spp. As some of these species are associated with the production of MCs and paralytic shellfish toxins (PSTs), samples from these scums and particulate matter were collected and analyzed for the main bloom species and toxins by a ultrahigh performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) and high performance liquid chromatography with fluorescence detection (HPLC-FLD). Results showed that the main bloom-forming species were _Limnoraphis robusta_ and _Microcytis aeruginosa_, the presence of at least seven MC congeners and the absence of PSTs in the algae scums. The MCs identified were MC-WR, MC-LR, MC-LA, MC-HiI8, MC-LF, MC-YR, and MC-LY. On a dry mass weight basis, MC concentrations were low and ranged between 0.15 and 6.84 ug/kg. Toxin profiles were dominated by MC-WR, MC-LR, and MC-LA, representing 94.5% of the total sample, with each analog contributing 39.8%, 38.1% and 16.5% by relative concentration, respectively. Two of the more hazardous congeners, MC-LR and MC-LA, represented 54.6% of the total MC concentration. MCs in particulate matter along the depth profile were not detected. The MC profile is linked to _M. aeruginosa_, and it represents the first quantitative MC congener description for this species from a Mexican water ecosystem. Since these mats are recurrent yearly, their effects on humans and wild fauna, and the possible role of anthropogenic activities that favor their presence and proliferation, need to be evaluated.  
cyanobacteria; microcystitis; lake; Mexico; paralytic toxins 2020
# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 2 Analysis of Modern-Day Smart Services  
In a first step, we look at seven real-world application scenarios, which require the comprehensive processing of highly sensitive data. This provides insights into the types of data that are involved and the requirements regarding data refinement. In addition, we discuss for each application scenario which opportunities are offered by it and which privacy threats it poses. The selection of the application scenarios is based on the IoT and smart service topics which are currently predominant in the literature. This includes _location-based services_ (Section 2.1), which originally had a primarily military background but have long since arrived in the private sector, _health services_ (Section 2.2), in which the IoT enables remote health monitoring, _voice-controlled digital assistants_ (Section 2.3), which are an important pillar of any smart home, and surveillance services driven by _image analysis_ (Section 2.4) [16]. Moreover, end-to-end food processing monitoring to ensure food safety is becoming increasingly common, which is enabled by IoT-supported _food analysis_ (Section 2.5) [17]. However, even long-established services, such as _recommender systems_ (Section 2.6) gain new capabilities due to the IoT [18]. As the available computing power is also steadily increasing, extensive analysis such as _DNA sequence classification_ (Section 2.7) can also be realized with the help of the IoT [19]. In those application domains in general, there are particularly severe privacy threats, such as data leakage or data tampering [20]. We summarize the findings from the study of these seven application scenarios in Section 2.8 and identify knowledge needs to be derived in each scenario as a result of the analysis and what adverse insights can be gained from the data in the process.
## 3 Results
### Analysis of Research Fields
#### 3.4.2 Keyword Co-Occurrence and Clustering Analysis  
Literature keywords serve as a rapid and accurate means of revealing research topics, technical methodologies, and application domains. To further explore the structural content and thematic evolution of UAV-RS research, this study employed CiteSpace to perform a co-occurrence clustering analysis of high-frequency keywords. The minimum occurrence threshold was set to 20, resulting in 10 thematic clusters. The modularity value of the clustering network was Q = 0.4394 (>0.3), and the silhouette score was S = 0.6018 (>0.5), both indicating that the clustering results are valid and structurally clear (Figure 6). The UAV-RS research was categorized into the following 10 co-clustering groups: #0-vegetation indices, #1-structure from motion, #2-object-based image analysis, #3-remote sensing, #4-deep learning, #5-hyperspectral imaging, #6-ECOSTRESS, #7-aerial photography, #8-soil surface, #9-land surface phenology. The analysis included a total of 742 keyword nodes and 6360 connecting edges. Due to its flexibility, cost-effectiveness, and high-resolution imaging capabilities, UAV-RS has emerged as a significant branch within the broader field of remote sensing. These clusters reflect the expanding application of UAV-RS across diverse research areas and its integration with advanced data processing technologies (e.g., deep learning), thereby enhancing the analytical and practical value of remote sensing data. Each connecting line represents the co-occurrence of two keywords within the same article, highlighting the thematic relationships between them. This study selected the most  
Figure 6: UAV-RS-related keyword cluster map. frequently occurring keywords within each cluster (Table 6). By analyzing the characteristics of the 10 clusters and their high-frequency keywords, the results indicate that UAV-RS is increasingly integrated with advanced technologies such as machine learning, deep learning, and hyperspectral imaging. This integration not only enhances the efficiency and accuracy of data acquisition, but also significantly improves the capabilities of data processing and analysis. UAV-RS is thus actively driving the advancement of remote sensing science and playing an increasingly vital role in domains such as precision agriculture, environmental protection, and climate change research. Notably, clusters directly associated with practical applications--such as #0 (vegetation indices), #7 (aerial photography), and #9 (land surface phenology)--dominate the field. This suggests that current research efforts are primarily focused on utilizing existing UAV platforms and sensors to address specific application-driven problems. However, the current research agenda appears heavily skewed toward short-term application value, with insufficient attention paid to addressing core bottlenecks that constrain the field's long-term development. These include issues such as UAV platform endurance, sensor performance, and data standardization--critical areas that require greater strategic focus and investment [132; 133].
# The Electric Vehicle Routing Problem with Time Windows, Partial Readers, and Parcel Lockers
## 4 Solution Method
### Removal and Insertion Algorithms
#### 4.4.2 Locker Removal and Insertion  
We adopt a procedure developed in the literature [40] for handling the self-pickup method. This procedure involves locker removal and re-insertion into the solution based on array 2 of the solution representation, denoted by _lockerRemovalInsertion()_. Note that the locker removal and insertion heuristics must be implemented together to effectively handle the self-pickup method. In particular, a parcel locker is first removed from the solution, which consists of one or more associated customers. We then obtain a list of removed customers. Subsequently, all customers with the designated parcel locker are re-inserted by using a locker insertion procedure. Locker removal and insertion mechanisms are presented as follows:  
* Locker removal as adopted by ref. [40] is employed to prevent unnecessary repetition of customer removal and insertion operators without yielding any improvement. The procedure begins by recording all utilized parcel lockers in the considered solution that can potentially be removed. Next, a parcel locker is selected based on the highest reduction in distance cost when it is removed from the solution. Note that the parcel locker is removed, resulting in the removal of one or more associated customers. Figure 3 provides an example illustrating the implementation of locker removal, as described in Figure 2. In particular, there are three parcel locker positions (i.e., P4 in route 1, P3 in route 2, and P1 in route 3), as indicated by the red boxes in Figure 2(a). Figure 2(b) shows the incomplete solution after removing parcel locker 3 in route 2.
* Locker insertion is similar to customer greedy insertion, but with the key distinction that it exclusively considers the self-pickup method for inserting customers into the solution. Notably, the set of absent customers is designated to a single parcel locker.
## 2 Materials and Methods
### Meshes for Capillary Flow Simulation  
We performed the 3-D flow simulations in the capillary dies (Figure 1, \(L/D=0.2\) and 30) with the viscous Cross-WLF model (Equations (5) and (6)). For the mesh arrangement, we chose a grid that progressively adds more elements toward the singularity in the entrance region of the capillary die, while the elements become bigger when going downstream of the inlet. A 3-D view and a partial view of the grids in the neighborhood of contraction are shown in Figure 1 for \(L/D=0.2\) and 30. The domains represent a 15:1 circular contraction at an entry angle \(2\phi=90\) or \(180^{\circ}\). Exponential biasing was used to generate elements in the axial and radial directions so as to reflect the large velocity gradients in the contraction regime and near the die wall. The grids were chosen after we confirmed that the subdivision of each element into four sub-elements to form denser grids gave virtually grid-independent results (less than 0.5% difference in the total pressure loss). Once the geometry was fixed, the only parameter left to vary was the apparent shear rate in the die \(\dot{\gamma}_{\rm a}=4Q/\pi R^{3}\), where \(Q\) is the volume flow rate and \(R\) is the die radius.
## 6 Simulation Results and Discussion
### Network Performance Parameters: Definitions  
* Stability period: In WBANs, the stability period is usually defined as a time interval between the start of a network and the time at which the first node dies.
* Residual energy: The average total remaining energy per second of a network is called the residual energy of the network.
* Network lifetime: The total time duration of a network operation, from the network establishment till the death of the last node, is called the network lifetime.
* Throughput: The total number of successfully-received packets per unit time at the sink is called the throughput.