esa-sceva/satcom-chunk-collection huggingface dataset:
# May the Extensive Farming System of Small Ruminants Be Smart?
## 3 Results
### Wearable Sensors
#### 3.2.3 Sensors to Measure Physiological Activity  
Sheep exposed to high ambient temperatures experience heat stress, and their homeostasis, reproductive function, and performance are compromised. Body temperature is a reliable measure of heat stress and, hence, a good indicator of an animal's health and well-being, reflecting the physiological activity of the animal body [5; 6]. Non-invasive temperature-sensing technologies have made substantial progress over the past decade. Types of measuring devices, such as surgical devices, have been developed: implanted devices, infrared devices, and intraluminal boluses equipped with temperature sensors [7; 48]. [PERSON] et al. [49] monitored the body temperature and heart rate of domestic sheep grazing freely on unfenced mountain pastures with surgically implanted devices. Moreover, subcutaneously implanted sensors could provide reliable measurements for heart rate and body temperature, two indicators that can be used for the early detection of diseases and stress. Instead, caudal data loggers programmed to record temperature could also be applied for lambing time identification in sheep [50]. Such systems require an accessible transmission network. Sensors inside ear tags are unreliable and less commonly used [4]. Rumen boluses sense changes in the ruminal microflora for temperature and pH parameters, which are used to monitor rumen conditions. Furthermore, pasture access and composition changes can be detected if they influence the reticulum pH [51]. Sensors for recording heart rate or ECG are currently used only in the research field because, to become commercial, they require an accurate and constant transfer of data and algorithms that can simultaneously transform and process the data [6]. [PERSON] et al. [8] reported using a multisensor (accelerometer, magnetometer, temperature sensor, and GPS) tag to assess feeding patterns, energy expenditure, and environmental factors. [PERSON] et al. [76] examined the effects of climatic conditions on the grazing behaviour of Mongolian sheep. Through studiesabout eating, ruminating, standing, and lying behaviours, machine learning algorithms can achieve classification accuracy rates ranging from over 70% using motion sensors embedded in collars [77].  
[PERSON] et al., 2021 [78] used collar multisensors to estimate urination volume, considering the ewe's squatting time from accelerometer data.
## 4 Case Study
### Debutanizer Column
#### 4.2.2 Performance Test
The results in the Figure 7 are the same as those in Table 6. The ensemble pruning method based on the multi-objective optimization algorithm achieves optimal performance at a smaller base model size. In many experiments, the maximum RMSE generated is still smaller than the minimum RMSE of the full ensemble soft sensor model. In detail, the prediction results of MLV-VSEPLS and MOSE-MLV-VSPLS in the test set are given separately in Figure 8. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Ensemble** & **Method** & **Criterion** & **Number of Subspaces** & **Size of Subensemble Model** & **RMSE** & **R2** \\ \hline ALL & MLV-VSEPLS & - & 45 & 45 & 0.0503 & 0.9195 \\ SEL & SE-MLV-VSPLS & Accuracy & 45 & 12 & 0.0518 & 0.9051 \\ SEL & SE-MLV-VSPLS & Diversity & 45 & 13 & 0.0550 & 0.8942 \\ SEL & MOSE-MLV-VSPLS & Accuracy and diversity & **45** & **16** & **0.0453** & **0.9302** \\ \hline \end{tabular}
\end{table} 
Table 6: Performance of fully ensemble and selective ensemble under diversity enhancement mechanism. Figure 7: Comparison of RMSE with different ensemble soft sensor model algorithms. The curves in Figure 8 show that for the MOSE-MLV-VSPLS method proposed in this study, by using the ensemble pruning method, the predicted output values match well with the actual values in data locations with large changes especially and achieve the most satisfactory prediction performance.
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 4 Results and Discussion
### Throughput  
Throughput is the total number of biological data packets sent to a specific destination in time. It can also be defined as the relationship between packet size and transmission time.  
Figure 6 depicts the throughput efficiency of this approach. The MT-MAC method achieves a lower throughput of approximately 3.9433 in this case. Packet loss analysis reveals that the number of packet losses in the MT-MAC method is relatively high. The MT-MAC approach's extensive packet loss reduces performance efficiency. Similarly, the DHCO method has a performance of about 3.8548, which is higher than the MT-MAC  
Figure 5: Number of Alive nodes analysis.  
method but lower than the ALOC method. The participation support performance of the M-GWO-based routing algorithm is 4.3991. Because the number of packets reaching the destination increases over time, the proposed ABBSOA-MSSOA approach increases throughput with the number of rounds.
# A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography +
## 2 Materials and Methods
### Performance Metrics  
The overall performance of the multiclass classification was evaluated in terms of accuracy, with its 95% confidence interval (CI) and Cohen's kappa. The classification evaluation performance for the individual four-density classes was evaluated in terms of sensitivity and specificity. In particular, sensitivity is the portion of true positives correctly classified, specificity is the portion of true negatives correctly classified, accuracy is the parameter that summarizes how well the classification went, evaluating the proportion of correct predictions (both positive and negative) on the total number of cases examined while the Cohen's kappa coefficient is calculated by comparing the observed agreement (true positives and true negatives) between the model inferences and the ground truths with the randomly expected classifications based on the marginal frequencies of each class. The latter is a particularly robust metric particularly when there is a significant class imbalance as in the present case.
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 4 Experimental Setup
### Performance Metrics
#### 4.3.3 Reconnaissance Packet Delivery Ratio  
How well reconnaissance is performed can be interpreted as how evenly UAVs scout through the reconnaissance area. The reconnaissance performance and packet delivery ratio have a trade-off relationship. If nodes are spread over an area, the reconnaissance performance increases, but the packet delivery ratio decreases. If nodes are concentrated in one place, the packet delivery ratio increases, but the reconnaissance performance decreases. Therefore, it is important to consider both the reconnaissance performance and the packet delivery ratio for a reconnaissance scenario of UAVs in FANETs.  
We evaluated FANETs in a reconnaissance scenario using the reconnaissance rate \(R_{t}\) over time \(T\) = 10 min, 30 min, and 60 min. The reconnaissance rate per time refers to the probability of finding an event during a specific time period, and it is one of the evaluation criteria of reconnaissance. The reconnaissance packet delivery ratio is obtained by multiplying the reconnaissance rate per time by the packet delivery ratio.  
\[R_{t}=\frac{\textit{area covered by the mobility model per time }T}{\textit{total operational area}} \tag{5}\]  
\[\textit{Reconnaissance Packet Delivery Ratio}=R_{t}\times\textit{PDR} \tag{6}\]
## 3 Materials and Methods
### Methodology
#### 3.2.8 Evaluation Measures  
The performance of supervised machine learning models has been assessed using four evaluation parameters: sensitivity score, precision score, F1 measure, and accuracy score. The maximum and minimum accuracy ratings are 1 and 0, respectively. For measuring the values of these performance evaluation metrics, TP (True Positive), TN (True Negative), FP (False Positive), and FN (False Negative) are used. A prediction is TP when the model predicts the positive class correctly while a TN is a result in which the model correctly predicts the negative class. On the other hand, FP is the prediction when the model incorrectly predicts the negative sample as positive, and FN is the sample of positive class predicted as negative. Accuracy shows the ratio of correct predictions to total predictions. Sensitivity refers to the capability of a model to correctly predict a sample of positive class while precision is used to evaluate the exactness of a classifier. Precision and recall alone may not be appropriate to evaluate the model, so an F1 score is used that incorporates both precision and recall:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+ \text{FP}+\text{FN}} \tag{4}\] \[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (5) \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\] (6) \[\text{F1 score}=2\times\frac{\text{Precision}\times\text{Recall}}{ \text{Precision}+\text{Recall}} \tag{7}\] \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Terms** & **TF (doc1)** & **TF (doc2)** & **IDF** & **TF-IDF (doc1)** & **TF-IDF (doc2)** \\ \hline england & 1/6 & 0 & 0.3010 & 0.050 & 0 \\ \hline high & 1/6 & 0 & 0.3010 & 0.050 & 0 \\ \hline school & 1/6 & 0 & 0.3010 & 0.050 & 0 \\ \hline face & 1/6 & 0 & 0.3010 & 0.050 & 0 \\ \hline mask & 1/6 & 1/4 & 0 & 0 & 0 \\ \hline may & 1/6 & 0 & 0.3010 & 0.050 & 0 \\ \hline lift & 1/6 & 0 & 0.3010 & 0.050 & 0 \\ \hline wear & 0 & 1/4 & 0.3010 & 0 & 0.07525 \\ \hline right & 0 & 1/4 & 0.3010 & 0 & 0.07525 \\ \hline way & 0 & 1/4 & 0.3010 & 0 & 0.07525 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: TF-IDF features on preprocessed data taken from the dataset.
## 4 WBANs: The Key M2M Area Network Technology for mHealth Applications
### PHY Technologies for WBANs  
PHY layer technologies determine the way in which the source data is converted into electrical signal for transmission. In order to increase the energy and bandwidth efficiency, as well as the transmission reliability, a series of signal processing functions must be carried out, including channel coding and decoding, modulation and demodulation, filtering and frequency conversion, synchronization, channel estimation and equalization. All these operations are executed at both the transmitter and the receiver of a digital communication system, which are typically implemented together in a single device, referred to as the transceiver. This section aims to provide a survey of the main challenges in the optimization of different transceiver parts in WBANs for mHealth applications, filling the respective gap that, to our knowledge, exists in the literature.  
In accordance to the PHY layer technologies supported by the IEEE 802.15.6 specification, as mentioned in Section 4.1, we divide this section into three parts, devoted to NB, UWB and HBC transceivers, respectively. Particular emphasis will be placed on UWB transceivers, since it is a promising technology for WBAN applications, due to its high energy efficiency and low complexity.
## 4 Next Forwarding Node Selection in UWSNs
### Taxonomy of Next Forwarding Node Selection in UWSNs  
In this paper, we focus on location information and forwarding techniques as selected criteria to introduce a classification of forwarding node algorithms in UWSNs. In Figure 1, routing protocols have been divided into two categories: localization and non-localization routing protocols. Each category has been divided into different subcategories based on the selection mechanism of forwarding nodes. Some of the protocols belong to a different category. The next sections describe the features of each category and some existing well-known routing protocols belonging to each category in detail, along with techniques used in these protocols to handle forwarding node selection.
## 3 Results and Discussion
### Simulation and Measured Evaluations in Free Space
Apart from a bit of discrepancy, a consistent tendency in the working BW and isolation occurred between their simulated and measured outcomes. Nevertheless, the measurement was repeated until less discrepancy was observed. Before evaluating the antenna's radiation pattern for both the simulation and measurement assessment, it was important to investigate the antenna's beamsteering and directions [38]. These bands are not independently switched or isolated via filtering; instead, the antenna maintains continuous, wideband impedance matching and radiation performance across all three bands. It also shows that the antenna can offer a wideband behavior and also stable radiation characteristics, like directive gains, efficiency, and lower levels of sidelobes at working BWs. However, a simultaneous multi-band operation (e.g., transmitting in one band while receiving in another) would require the integration of multi-band RF front-end circuitry or filtering at the system level to prevent mutual interference and ensure signal separation. The antenna's beamsteering capability is depicted in Figure 10, where ports 1 and 2 are active separately. It shows that the antenna can steer the beams of 18\({}^{\circ}\)-55\({}^{\circ}\) and 206\({}^{\circ}\)-341\({}^{\circ}\) when port 1 (Figure 10a) and port 2 (Figure 10b) are active, respectively. After verifying the simulated and measured S-parameters of the antennas and confirming their perfect agreement, the antenna's radiation pattern was measured to demonstrate that it radiates properly and matches the simulated radiation pattern. Figure 11 depicts the simulated and measured radiation patterns of the E and H fields at the desired frequency bands. Perfect agreement is depicted between the simulated and measured E and H field patterns. It also shows that they are bidirectional at lower bands and become more directional at higher bands. Figure 10: A simulated evaluation of the beamsteering capability of the proposed antenna (**a**) Port 1 is active and (**b**) Port 2 is active. Figure 9: The simulated and experimentally obtained S-parameter data for the designed antenna in an open environment.
# Pharmacokinetics of Marine-Derived Drugs
## 3 Pharmacokinetics Studies in Animals
### Absorption  
#### 3.1.1 Algal-Derived Compounds  
Fucoidan (Figure 1, **(1)**) is a fucose-rich sulfated heteropolysaccharide found in various algae. It shows a broad spectrum of pharmacological activities including anticancer, anti-coagulant, anti-diabetic, anti-inflammatory, anti-viral, immunoassayJuvant, treatment of renal diseases activities [20; 21; 34; 61; 62; 63; 64; 65; 66]. The pharmacokinetics of fucoidans from different sources has been reported in rabbits, rats, and mice and after different routes of administration. A low molecular weight (MW) of fucoidan (7.1 kDa) from _Laminaria japonica_ rapidly appears in the plasma (C\({}_{\rm max}\) = 110.53 \(\upmu\)g/mL; T\({}_{\rm max}\) = 5 min) after intravenous (i/v) injection in rabbits (50 mg/kg). The serum concentration-time function was two-exponential with t\({}_{1/2~{}\alpha}\) = 11.24 \(\pm\) 2.93 min and t\({}_{1/2~{}\beta}\) = 98.20 \(\pm\) 25.78 min. Due to the low sensitivity of the analytical method, fucoidan was only detected at 2 h, but not quantified in the serum of rabbits after peroral administration (200 mg/kg) [36]. The pharmacokinetics of fluorescein isothiamate (FITC)-labeled commercially-available fucoidan (MW = 107.8 kDa) from _Fucus vesicolusus_ was investigated after i/v injection (50 mg/kg) in mice. The C\({}_{\rm max}\) in the blood was 66.37 \(\upmu\)g/mg, and AUC was 198.11 \(\upmu\)g/g-h [32]. The pharmacokinetics of another fucoidan (MW = 100 kDa) from _L. japonica_ was reported in rats after i/v injection (6 mg/kg) and peroral administration (20 mg/kg). The C\({}_{\rm max}\) was 75.59 \(\upmu\)g/mL, and AUC was 479.07 g-h/mL after i/v injection. C\({}_{\rm max}\) was 7.33 \(\upmu\)g/mL, T\({}_{\rm max}\) was 2 h, and AUC was 42.69 g-h/mL after peroral administration [35]. Low- (7.6 kDa) and medium-MW (35 kDa) fucoidans from _L. japonica_ were administered perorally to rats at doses of 200, 400, and 800 mg/kg. The absorption of medium-MW fucoidan was lower than the absorption of low-MW fucoidan. The C\({}_{\rm max}\) levels of fucoidan in blood after administration of 800 mg/kg were 151.7 \(\upmu\)g/mL for low MW at T\({}_{\rm max}\) = 15 h and 131.81 \(\upmu\)g/mL for medium MW at T\({}_{\rm max}\) = 25 h [34]. Recently, the pharmacokinetics of fucoidan (MW = 735 kDa) from _Fucus vesicolusus_ was investigated in rats after peroral administration, j/v injection, and transdermal application at the dose of 100 mg/kg. After i/v injection, C\({}_{\rm max}\) was 9.15 \(\pm\) 0.60 \(\upmu\)g/mL [28]. Fucoidan was detected in plasma 30 min after peroral administration with C\({}_{\rm max}\) of 0.125 \(\upmu\)g/mL and T\({}_{\rm max}\) was 4 h [38], whereas after transdermal application of ointment, C\({}_{\rm max}\) was 0.12 \(\upmu\)g/mL at 1.2 h [28]. [PERSON] et al. [40] demonstrated that fucoidan from _Cladosiphon okamuraus_
## 3 Research Method
### Generation of Audio Spectrograms
#### 3.2.1 Mel Spectrograms  
The Mel spectrogram is a visual representation that captures how the frequency and amplitude of an audio signal change over time and is designed to reflect the human ear's perception of sound. The audio signal is divided into overlapping frames, each containing a certain number of sampling points. Fast Fourier Transform (FFT) is applied to each frame, converting the signal from the time domain to the frequency domain, revealing the amplitude of each frequency component. The frequency axis is then converted to the Mel scale, a non-linear scale that reflects the different sensitivities of the human ear to  
Figure 3: Data preprocessing process.  
Figure 2: Framework diagram of this paper.  
low and high frequencies. The Mel scale can be obtained from the Hertz scale using the following formula:  
\[\text{m}=2595\log_{10}(1+\frac{f}{700}) \tag{1}\]  
where \(f\) represents the frequency in Hertz and m is the corresponding frequency in Mel.  
The frequencies on the Mel scale are segmented into several triangular filters, each covering a certain frequency range. Each frame is multiplied with the filter bank in the frequency domain to obtain the energy output of each filter. These energy values form a column of the Mel spectrogram, and this process is repeated to obtain all columns of the Mel spectrogram.
## 3 Threshold Segmentation  
During the extraction of features for color image segmentation, the non-uniformity of the RGB color model can often lead to confusion and loss of color information [9]. In contrast, the HSV color model has stronger uniformity and more distinct components than the RGB color model, making it better suited to reflect specific color information. It visually expresses the hue, brightness, and saturation between colors, making it more suitable for color image segmentation [10].  
Image noise refers to unwanted interference or random disturbances present in digital images. It can be introduced during the processes of image acquisition, transmission, storage, and processing, resulting in a degradation of image quality and loss of information. Therefore, prior to performing feature segmentation on the ROI image, it is usually necessary to apply image-filtering techniques to reduce the interference caused by noise points and improve the accuracy of subsequent image segmentation. The filtering method used is the cv2.gaussian blur function in Python-OpenCV.  
\(H\), \(S\), and \(V\), respectively, represent hue, saturation, and value. As OpenCV reads the initial image in the BGR color space, it is necessary to convert it to the HSV color space.  
The most commonly used method for image segmentation based on the HSV color model is threshold segmentation, which usually involves using a global threshold to divide the image into a binary one. For color images with complex content, the concept of threshold segmentation can be extended to 3D space. Since the three components of the HSV color space are independent from each other, the problem of segmenting a three-dimensional space can be transformed into three independent one-dimensional segmentation problems. The \(H\), \(S\), and \(V\) components of the packaging image for the arterial blood sample collector are shown in Figure 3.  
By utilizing upper and lower threshold values for segmenting features in each of the three components of HSV separately, the cv2.createTrackbar function module can be employed. This module serves as a tool for dynamically adjusting parameters associated with an image window. Through continuous adjustment of the upper and lower limits of the H, \(S\), and V components, while observing the dynamic segmentation results of the cv2.inRange function module, the upper and lower limits are determined based on the saliency of the segmentation features. The intersection of the segmentation results from each channel is then taken to obtain the feature region, as shown in Equation (1).  
\[\text{Region}=\text{region\_S}\ \cap\ \text{region\_V}\ \cap\ \text{region\_H} \tag{1}\]
# New Security Improvements in Next-Generation Passive Optical Networks Stage 2 +
## 1 Introduction
Authentication and confidentiality can be performed by algorithms from symmetric or asymmetric cryptography. Asymmetric cryptography is generally more computationally and resource demanding than symmetric cryptography. Integrity ensures checking that data were not modified during the transmission between communicating entities. Integrity can be ensured by a hash function. Nonrepudiation ensures that an entity cannot deny a fact that was previously performed. Nonrepudiation can be ensured by asymmetric cryptography and symmetric cryptography using a trusted third party; see [8]. The rest of this paper is structured as follows. Section 2 introduces related works. Section 3 provides an overview of the security in NG-PON2 networks. Section 4 presents our proposal of security improvements in the NG-PON2 based on physical unclonable functions, correction codes, and robust cryptographic algorithms. Section 5 presents the security analysis of our proposal of security improvements in the NG-PON2. Finally, Section 6 concludes the paper.
# Vector Signals and Invariant Systems: Re-Tooling Linear Systems Theory
## 4 Time Domain Analysis for LRITI Systems
### Summary of Time-Domain Analysis for VV Signals and LRITI Systems  
In Section 4, we provided the properties of LRITI systems, including linearity, time invariance, and rotational invariance. Additionally, we introduced the sifting property of the Dirac delta and the impulse response for LRITI systems. We highlighted both the fundamental similarities and the distinctive differences of VV LRTII systems theory compared to traditional CV LTI systems theory. Furthermore, we pointed out that there are two aspects of the commutative property, and using the generalization of the second aspect is sufficient to derive the eigenfunction property of LRITI systems. Finally, we provide an answer to the open question posed by [PERSON] in [PERSON] (1977) regarding the number of impulse responses necessary to characterize a system. In particular, if the system is LRITI, then a single impulse response suffices. Furthermore, a key advantage of rotational invariance, in the context of the first fundamental theorem of the LRITI system, is that it simplifies system analysis in a manner similar to how time invariance benefits traditional LTI systems---by reducing the number of impulse responses required for convolution.