esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Discussion
### Experimental Methods
The last group of results compares the proposed method with other methods using ShipsEar in recent years. All the networks are trained by random gradient descent. Adam is used as the optimizer. The training minibatch is set to 32, the initial learning rate is set to 0.005, and the learning rate is decreased once every 5000 steps. The decline factor is 0.1, and a total of 15,000 steps are trained. The extraction methods of FBank, MFCC and LFCC refer to torchaudio [43]. FBank extracted with window size 2048 and hop length 1024. For FBank, the number of Mel filters is 128, followed by a logarithmic operation to extract input features. STFTs also utilize a logarithmic operation to extract input features in the training stage. For MFCC and LFCC, the number of mfc coefficients is set to 40, the number of linear filters is set to 128 and the number of lfc coefficients is set to 40. The convolution kernel size of TFCK is set to 63 as a low-pass FIR filter with a 1500 Hz cutoff frequency, and the parameters are locked in the training stage. The kernel size and strides of FEM-ATNN are set to 1024 and 512, respectively. Finally, we use recognition accuracy, recall rate, accuracy, and F1-score to evaluate the performance of the network.
## 4 Proposed Non-Intrusive Monitoring Hardware for Measuring the Performance of Isolated PVPSs  
The performance of the isolated PVPS (Figures 1 and 2) was determined by executing a monitoring campaign (data acquisition and logging) [27] with the proposed non-intrusive monitoring hardware presented in Figure 3.  
The monitoring campaign (data acquisition and log) was carried out according to IEC 62253 [26], adapted to the acquisition of sensor data. The same parameters recommended by the IEC 62253 standard [26] were monitored. However, instead of measuring their nominal values at different operating points using classical measurement tools, these parameters were acquired using sensors, with data logged every second throughout the measurement campaign. The selection of sensors and data acquisition equipment followed standard engineering practices to ensure accurate, reliable and continuous monitoring of the PV pumping system. Devices were chosen based on measurement accuracy, environmental durability, electrical compatibility and non-intrusiveness, meeting industrial standards for precision and reliability. The non-intrusive monitoring hardware (data acquisition and logging) consists of a calibrated photovoltaic cell [29] to measure the irradiance and cell temperature, a DC voltage transducer [30], a pressure transducer [31], a volume flow transducer [32] and a temperature sensor [33] to measure the water temperature.
# Signal Design and Compatibility Assessment for LEO Navigation Augmentation System +
## 2 Signal Design Requirements
### Dual-Frequency Broadcasting Requirement  
PPP usually uses an ionosphere-free combination observation model, which means that dual-frequency navigation augmentation signals need to be broadcasted. This requires LEO navigation augmentation satellites to broadcast the dual-frequency navigation augmentation signals to support the ionosphere-free combination. The ionosphere-free combination would amplify the observation noise, and the frequency of the dual-frequency signal cannot be too close.
# Wavelength Selection for Detection of Slight Bruises on Pears Based on Hyperspectral Imaging
## 2 Materials and Methods  
### Pear Samples  
Korla pears were purchased from a local market in Hangzhou in October 2015. One hundred twenty years free from damage on surface and similar in size, were picked out as trial samples. Sixty samples were dropped from the height of 25 cm to the horizontal ground by artificial simulation and slight bruises were caused near the equator line of pears. These areas were difficult to be recognized by naked eyes but softer than normal areas. As shown in Figure 1a, the bruised area (in black frame) was similar to normal area around it. However, Figure 1b shows that when the peel of bruised area was cut off, there were obvious differences between normal and bruised area in color.  
After bruising, all pears were left in the laboratory at room temperature (18-20 \({}^{\circ}\)C and 60%-70% Relative Humidity (RH)) for 24 h to allow the bruises to develop. Sixty hyperspectral images of bruised peers were collected as bruised samples and 60 hyperspectral images of normal peers were collected as normal samples. Forty-five bruised samples and 45 normal samples were chosen randomly as the model set and the others as the prediction set.
# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 4 Secure WiFi Fingerprint Localization
### Offline Testing Phase
#### 4.1.2 Construct the Standard Deviation  
Compared with the traditional fingerprint localization method, the algorithm in this paper records the standard deviation of the RSSI of each AP in the fingerprint database, according to the size and standard deviation of RSSI voting rules. When the fingerprint database is updated, we need to determine whether the fingerprint in the fluctuations is in the normal range; if so, the fingerprint is trusted; otherwise, it is not trusted. In this way, we can obtain the trusted value of APs and determine the contribution value of APs according to the trust value in the localization process, reducing the localization error caused by the abnormal RSSI of APs.  
Now that the total number of grids is \(N\), the standard deviation of \(AP_{i}\) at the \(j\)-th grid is \(\text{b}_{i,j}\), and the RSSI standard deviation matrix \(B\) is as follows:  
\[B=\left\{\begin{array}{cccc}\text{b}_{1,1}&b_{1,2}&\cdots&b_{1,N}\\ b_{2,1}&b_{2,2}&\cdots&b_{2,N}\\ \vdots&\vdots&\ddots&\vdots\\ b_{L,1}&b_{L,2}&\cdots&b_{L,N}\\ \end{array}\right\}. \tag{3}\]where \(b_{i,j}=\sqrt{\sum\limits_{\tau=1}^{q}\left(\varphi_{i,j}\left(\tau\right)-\sum \limits_{\tau=1}^{q}\varphi_{i,j}\left(\tau\right)/q\ \right)^{2}/q}\,i=1,2,...,L,j=1,2,...,N\) is the degree of volatility of the RSSI from \(AP_{i}\) at the \(j\)-th grid. \(q\) is the number of RSSI samples received by the reference anchor node. \(L\) is the number of APs.
# Properties of Foamed Lightweight High-Performance Phosphogypsum-Based Ternary System Binder
## 3 Results and Discussion
### Properties of High-Performance Ternary System Binder and Mortar
#### 3.1.2 Chemical and Mineralogical Characterization  
XRD results are given in Figure 4. The mineralogical composition of a ternary binder powder was investigated before hydration (Figure 4c,d). Bassanite (CaSO4-0.5H2O, ref 33-0310), calcium silicate oxide (Ca3(SiO4)O, ref 73-0599), and quartz (SiO2, ref 78-1252) were identified from the source dry materials of GCP-PG. GCP-BG had the same three minerals as for GCP-PG, while additionally a dolomite (CaMg (CO3)2, ref 36-0426) and an anhydrite Ca(SO4), ref 72-0916) were also detected, which is associated with the composition of commercial BG.  
The phase change after binder hydration was detected (Figure 4a,b). Moreover, ternary binder contained gypsum, cement, and pozzolan, the only phases which were identified in a hydrated GCP-PG mixture where gypsum (Ca(SO4)(H2O)2 (74-1433), quartz (SiO2, ref 78-1252), and ettrin96 (Ca6(Al(OH)6)2(SO4)3(H2O)26, ref 72-0646) were present. The formation and presence of the ettrin96 in such a late hydration stage are associated with high gypsum content in the mixture. In such a way, expansions can occur from excessive calcium sulfoaluminate formation after hardening and continue until the gypsum becomes depleted, that is why pozzolan was added and, according to previous studies, addition of pozzolan could even eliminate delayed ettrin96 formation [18]. In mixture composition, GCP-BG ettrin96 was not detected. Besides minerals coming from raw materials (dolomite, quartz), gypsum (Ca(SO4)(H2O)2 (74-1433) and also gypsum hemihydrate Ca(SO4)(H2O)0.5 were detected, which could be formed slowly from the hydration of the anhydrite.  
The EDX analysis of the GCP-PG binder is given in Figure 5. Large amount of Ca element was identified in most EDX points analyzed, which is associated with the fact that Ca is present in all binder  
Figure 4: Mineralogical characterization of raw material mixture and hydrated paste of ternary binder: (**a**) Hydrated GCP-PG; (**b**) hydrated GCP-BG, (**c**) raw mixture of GCP-PG, (**d**) raw mixture of GCP-BG. B—Bassanite (CaSO4-0.5H2O, ref 33-0310), C—calcium silicate oxide (Ca3(SiO4)O, ref 73-0599), Q—quartz (SiO2, ref 78-1252), D—dolomite (CaMg (CO3)2, ref 36-0426), A—anhydrite Ca(SO4), ref 72-0916), G—gypsum (Ca(SO4)(H2O)2 (74-1433), E—ettrin96 (Ca6(Al(OH)6)2(SO4)3(H2O)26, ref 72-0646).  
components (phosphogypsum, cement, and metakaolin). The gypsum source in the structure can be identified through element S, which comes from PG binder (CaSO4 0.5H2O). Small quantities of Al and Si were identified as elements coming from metakaolin and cement. This explains the fact that no cement minerals were identified by XRD. The XRD analysis of raw material mixture and hardened binder was investigated while no strong new mineralogical peaks were identified. Mostly transition between gypsum hemihydrate and gypsum dihydrate was observed.
# Considering the Optimization Design of Urban Bus Network Scheduling
## 2 Synchronized Optimization Model for Network and Scheduling
### Model Construction
#### 2.3.2 Constraint Conditions  
To provide a detailed definition of the conditions set in the model, the following constraints are imposed to further restrict the model:  
(1) Route length  
In bus network optimization, if bus routes are too short, operational costs increase, while excessively long routes reduce passenger comfort. Therefore, to better meet passenger needs, routes should not be excessively short or long. The length of each route needs to satisfy the following constraint:  
\[l_{min}\leq L_{r}<l_{max} \tag{9}\]  
(2) Headway  
Setting the headway correctly is crucial. Too long a headway increases passenger waiting time, leading to higher passenger time costs. On the other hand, too short a headway results in a higher number of required vehicles, leading to increased operational costs for bus operators. Therefore, the headway needs to satisfy the following constraint:  
\[f_{min}\leq f_{r}<f_{max} \tag{10}\]  
(3) Non-repeating route stations  
In the designed bus network, to avoid bus routes forming loops which increase passenger travel costs and lead to resource waste, a constraint is set that each bus route should contain non-repeating bus stations. This means that each bus station should only appear once along each bus route.  
\[O_{i}^{r}\
eq O_{j}^{r}\ \ \ ;\ i,j=1,...,S;\ i\
eq j;\ r=1,...,R \tag{11}\]  
(4) Non-linear coefficient  
To avoid excessive detours in the established routes, reduce passenger travel time costs, and improve the quality of bus services, the non-linear coefficient of routes is constrained based on the \"Urban Road Traffic Planning and Design Code\" in this paper's synchronous optimization model.  
\[\left[\frac{L_{r}}{d_{r}}\right]\leq 1.4 \tag{12}\]
###### Abstract  
The occurrence and fate of polyfluorinated compounds (PFCs) in the aquatic environment resulting from anthropogenic activities has become an emerging issue of environmental chemistry. PFCs have been detected in drinking water samples, aquatic life, human tissue, and blood serum. This is attributed to their xenobiotic attributes making them environmentally persistent, bio-accumulative, and globally distributed in water receiving bodies, posing serious health problems to aquatic life and human health. This is ascribed to PFCs' peculiar physicochemical properties of being hydrophobic and oleophobic and their removal process from wastewater streams is different from any other organic pollutants. Therefore, this review summarizes the environmental occurrence and recent developments on microbial degradation of the most detected PFCs, i.e., perfluorooctanoic acid (PFOA), and perfluorooctane sulfonic acid (PFOS) in water bodies. The available literature suggests that PFOA and PFOS are susceptible to biodegradation by _Acidimicrobium_ sp. strain A6, _Pseudomonas parafillux_ strain YAB1, _Pseudomonas picoglossicidia_ 2.4-D, and _Pseudomonas aeruginosa_ strain HJ4. Moreover, the current study presents a summary on phytoremediation of PFOA and PFOS as a sustainable green technology. Despite the extensive work undertaken on bioremediation of PFOA and PFOS by biological processes, the available literature suggests that a lot of work still needs to be carried out aimed at investigating the biodegradation pathway of PFOA and PFOS by both microbial species and plants.  
polyfluorinated compounds; perfluorooctanoic acid; perfluorooctane sulfonic acid; phytoremediation; microbial species +
Footnote †: journal: Applied Sciences
## 7 Nanomedicine to Enhance the Therapeutic Effectiveness of BC
### Nanoparticle-Mediated Drug Delivery
#### 7.1.3 Other Polymeric Nanoparticles  
Doxorubicin and cisplatin loaded with polylactic-co-glycolic acid (PLGA)-based nanosystems are currently being employed for breast cancer treatment, demonstrating controlled delivery in both in vitro and in vivo studies [104]. Likewise, methotrexate combined with chitosan biopolymer nanoparticles has also been utilized for breast cancer treatment. The serum biomarkers were evaluated with controlled and treated biofluids to confirm the controlled release and biodistribution cytotoxic effect [105]. Hence, a novel approach was used to synthesize MSN-COOH and encapsulate Dox within its pores to address the nontargeted adverse effects of doxorubicin. Subsequently, [PERSON] et al. modified thesurface of MSN-COOH with polyethyleneimine (PEI) and amino acids (AAs) using amide bonds [106]. This system not only ensured Dox retention until it reached tumor sites but also facilitated efficient Dox release, leveraging the inherent pH sensitivity of tumor cells. Furthermore, AA-enhanced nanoparticles improved uptake in tumor cells and their spread within breast tumors by specifically binding to sigma receptors. This system achieved a coordinated antitumor effect by combining a pH-driven controlled release with targeted delivery, both in lab tests and live subjects, to minimize harm to healthy tissues [106]. Similarly, paclitaxel-loaded PLGA polymeric nanoparticles were used to treat TNBC. The drug-loaded nanoparticles were employed to assess adenosine receptor expression (a TNBC marker) in both controlled and treated breast cancer cell lines, revealing higher levels of apoptosis [107]. Another research group, [PERSON] et al. developed a double-network hydrogel polymer infused with paclitaxel nanoparticles, targeting sustained local drug delivery for the treatment of BC. This formulation demonstrated enhanced stability, tolerability, and effectiveness [108]. Polic acid-PEGylated nanoliposomes were engineered to codeliver both water-soluble (cisplatin/CIS) and water-insoluble (epirubicin/EPI) chemotherapy drugs for breast cancer treatment. These FA-PEGylated noisome nanocarriers, optimized for the codelivery of CIS and EPI, showed increased stability over a two-month period and sustained delivery at a physiological pH. Cellular tests revealed anticancer effects on SKBR3 and 4T1 cancer cells, with reduced toxicity to healthy cells. Additionally, the FA-PEGylated noisome CIS and EPI (FPNCE) and epirubicin-loaded noisome groups were more effective in preventing the migration and division of cancer cells compared to free drugs [109].
# A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
## 3 Proposed Solution
### Detection Algorithm  
The amplitude of the frequency spectrum \(|X(f)|\), which was evaluated in the previous step, has to be compared against a set of given thresholds for the frequency ranges to be monitored. More precisely, each range \(i=0,\ldots,M-1\) is characterized by the frequency interval \([f_{s,1},f_{e,i}]\) and by a threshold \(TH_{i}\), as shown in Figure 4. The detection algorithm works as follows; for each range \(i=0,\ldots,M-1\), if \(\forall f\in[f_{s,1},f_{e,i}],|X(f)|<TH_{i}\), then no defect occurs; otherwise, such range is pointed one. The monitored ranges are centered around a specific defect fundamental frequency (\(BPFO\), \(BPFI\), etc.), and provide a sufficiently large frequency interval to account for uncertainties. As the frequency ranges are affected by the geometry of the bearing and on its operating condition, whereas the thresholds are related to the noise floor, which is strictly related to the particular environment, they can be entirely configured using the wireless link, thus giving the maximum flexibility to the designed system.  
Figure 3: Scheme of the hardware blocks required by the proposed solution.
# Analysis of Permanent Magnet Motors in High Frequency--A Review
## 6 Conclusions
### Modeling  
The high-frequency models of electrical machines can be classified based on their topology, parameter extraction methods and their main characteristics as well as on their working domain. Distributed parameter models tend to be more accurate, but they are difficult to integrate in the whole electric drive due to the intensive computation required. Lumped parameter models are more used as they are more practical. The values of the LPM are usually calculated from measurements, but there are some DPM obtained from FEM values that are converted to LPM with matrix reduction techniques.  
With respect to the simulation domain, some models are working in the frequency domain, for example, to obtain the CM and DM impedance versus frequency. However, to simulate the over-voltages and currents, the time domain is needed. In this domain, the frequency dependency of the parameters is usually considered using RL parallel branches or foster networks, as varying the value for each frequency may not be practical. If frequency-dependent values are obtained from FEM, the equivalent RL branches can be obtained using data fitting methods.  
Finally, depending on the objective of the simulation, the developed model may highlight or neglect some parts of the machine. On the basis, all models are RLC circuits, with different number of segments and different physical meanings, but in origin, all refer to winding self and mutual inductances, resistance and parasitic coupling capacitances. The inter-turn effects in the winding are essential to obtain accurate results, whereas the bearing capacitance is just included when the bearing currents are analyzed. The iron losses produced by Eddy currents in the stator sometimes are included as a resistor in parallel with the winding, whereas other times the losses are just implicit in the circuit values.  
Concerning the rotor, its influence may be only significant when analyzing bearing currents, shaft voltages, or terminal over-voltage. The rotor position is also important in the low-frequency range for salient pole permanent magnet machines as the inductance changes with the rotor position. Hence, if a full frequency range (0 Hz-30 MHz) model is required, the rotor should be considered.
## 2 Methods
### Establishment of the Color Matching Network  
#### 2.4.1 Color Conversion  
The color space will directly affect the description of color features. Red (R), green (G), and blue (B) are the fundamental colors of the RGB color space; however, human eyes have different sensitivities to these colors. Since human eyes are least sensitive to red and most sensitive to blue among the monochromatic hues, the RGB color space is uneven for human eyes. Since RGB is computer coded and unsuitable for a visual color analysis, the extracted color values in RGB space needs to be converted into HSV color space for the network model, including a visual representation of color tones, brightness, and lightness. H stands for hue, which denotes the hue attribute and is connected to wavelength. S represents color intensity and stands for saturation. Value, abbreviated as V, refers to color brightness. Formulas (4)-(6) are used to convert the RGB parameters of the extracted representative colors into HSV parameters [23]:  
\[H=\left\{\begin{array}{ll}0^{\circ}&\text{if max}=\text{min}\\ 60^{\circ}\times\frac{G-B}{\text{max}-\text{min}}+0^{\circ}&\text{if max}= \text{R},\text{G}\geq\text{B}\\ 60^{\circ}\times\frac{\frac{G-B}{\text{max}-\text{min}}}{60^{\circ}\times\frac {B-B}{\text{max}-\text{min}}}+360^{\circ}&\text{if max}=\text{R},\text{G}< \text{B}\\ 60^{\circ}\times\frac{B-B}{\text{max}-\text{min}}+120^{\circ}&\text{if max}= \text{R}\\ 60^{\circ}\times\frac{B-B}{\text{max}-\text{min}}+240^{\circ}&\text{if max}= \text{B}\\ \end{array}\right. \tag{4}\]  
\[S=\left\{\begin{array}{ll}0&\text{if max}=0\\ 1-\frac{\text{min}}{\text{max}}&\text{otherwise}\\ \end{array}\right. \tag{5}\]  
\[V=\frac{\text{max}}{255} \tag{6}\]  
In Equations (4)-(6), max and min stand for the \(R\), \(G\), and \(B\) maximum and minimum values. H \(\in[0,360^{\circ}]\), \(S\in[0,1]\), and V \(\in[0,1]\).  
After the conversion to HSV space, the Apriori algorithm was used to excavate frequently recurring datasets according to support and confidence, and the collocation correlations could be analyzed to establish the matching models. Support represents the percentage of various related data in the dataset, and confidence reflects the possibility that one data point will appear after another.
# A Critical Review of Flood Risk Management and the Selection of Suitable Measures
## 2 Flood Management Practices
### Measures, Approaches, and Practices
#### 2.2.1 Measures  
Understanding the role of numerous measures is a requirement for designing an efficient approach. Flood measures can broadly be divided into three classes, namely: thwarting flood peak formation (flood abatement), regulating the inundation (flood control), and reducing adverse effects (flood alleviation) [15]. When selecting a flood management measure, it is important to consider what the targeted risk parameter is. Table 1 describes the various flood management options/measures related to target risk factors. Understanding the role of a selected flood management measure in reducing risk by reducing one or more components of risk helps facilitate choosing an appropriate flood management option under site constraints. The constraints could be economic, social, topographical, or environmental.  
Often, it is crucial to verify that measures include some structural activity that may alter a river's flow. Non-structural measures to control floods involve practices to mitigate or alleviate damage caused by floods without constructing a flow-changing infrastructure [16]. Although non-structural measures were initially suggested as part of an integrated floodplain management plan [17], the non-structural possibilities were also presented as an alternative to conventional engineering solutions.  
Some practices limit risk by decreasing the chance and/or magnitude, although other measures aim to decrease susceptibility and/or damage by reducing vulnerability. Focusing on the mechanism of risk, measures are being divided into two main categories--direct and indirect measures. It is necessary to remember which criteria a flood control measure aims at during the selection of a measure. The adoption of structural measures and risk mechanics together with the target risk parameters is critical for achieving desired outcomes in the consideration of flood management solutions based on classification. Considering flood management options based on classification, the involvement of structural measures and risk mechanics, along with the target risk parameters, is important in order to obtain desirable results.  
Figure 1: Schematics of flood measures, approaches, design criteria, and projects.  
Some measures strive to improve society's ability to rebuild after floods. Examples include flood insurance, rehabilitation, reimbursement, and voluntary assistance, which can be categorized as recovery/indirect intervention, as they do not explicitly mitigate losses [18]. Relief efforts do less to mitigate potential casualties in floods [19], and likewise, the damage can also be raised by compensating.