esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Despite diverse detection and prevention methods, human diseases have gradually increased for personal or environmental reasons. Cancer, the most common worldwide disease, is a condition in which cells grow irregularly and affect other body parts. Some of the cancers found are breast, prostate, lung, skin, and pancreas. Cancer causes many deaths globally [1].  
Breast cancer is the leading cause of death in women if not diagnosed early. The development of early detection and treatment methods for breast cancer has enhanced the possibility of survival. One of the procedures widely used in medicine to detect breast cancer is screening the patient using various imaging modalities, such as mammography, ultrasound, computed tomography imaging (CT), magnetic resonance imaging (MRI), and thermal imaging screening. These modalities have distinct approaches and tools; results depend on different factors, and using more than one method is recommended to verify the results. Mammography is the most common procedure for breast cancer screening; although it is considered the gold standard, it carries many risks for patients, so in recent years thermal imaging has gained attention because of its low risks, unlike mammography [2]. The use of digital infrared thermal imaging has not yet become the main tool for detecting early breast cancer since it relies on skin lesions and temperature [3]. However, promising results indicate that radiologists can use it with mammography to evaluate the state of the breast for accurate assessment and diagnosis of breast cancer.  
Recently, with the advances in machine learning (ML) and deep learning (DL) techniques, the detection and diagnosis of breast cancer using ML and DL are becoming more effective. Moreover, deep attention techniques have attracted more interest in this field [4] because of the promising results achieved when DL algorithms for breast cancer detection were integrated with deep AMs. This directs special attention to prominent areas of the image instead of treating all batches as equally important, which in turn increases the likelihood of improving the performance of the applied techniques.  
Although breast cancer detection is advanced, more research is needed to explore different modalities and enhance detection accuracy when using new ML algorithms with deep AMs.  
Therefore, this research aims to contribute to the field of breast cancer detection. The main objective of this study is to provide a robust and high-performance model based on DL and AMs to detect breast cancer using thermal imaging, helping physicians in making a more accurate and validated breast cancer tumor diagnosis.  
The remaining sections of this paper are organized as follows. Section 2 presents a background overview of the DL methods. Section 3 gives a brief overview of deep AMs. The following section reviews the related literature in breast cancer detection. The proposed method is discussed in Section 5, which includes the experiment design and evaluation methods. Section 6 discusses the experimental results. Finally, Section 7 concludes this paper with the main findings and future directions.
## 6 Practical Application  
GNSS and accelerometers seem similarly capable of quantifying distance while walking. This may have implications for sport-related training and competition, but future research should investigate whether both sensor types perform comparably well during sport related activities that include mixed movement types (i.e., changes of direction, running, jumping, etc.). While sport coaches and athletes should use caution when predicting distance from accelerometry derived metrics, accelerometers are also capable of identifying and quantifying a variety of locomotor events including steps, jumps, bounds, and impacts, among others. Since GNSS is limited to measures related to horizontal position (i.e., speed and distance), if accelerometers can also be used to estimate distance in mixed type activities such as sport, given their ability to quantify a variety of sport related movements, accelerometers may provide advantages compared to GNSS  
**Author Contributions:** Conceptualization, A.K.B., C.D.B., A.R.D., A.L.S. and J.A.G.; methodology, A.K.B., J.A.G., C.D.B., A.R.D. and A.L.S.; validation, A.K.B., C.D.B. and J.A.G.; formal analysis, A.K.B. and M.S.A.; investigation, A.K.B., A.A.A., M.M.A., A.I.A. and G.O.A.; resources, A.K.B., M.S.A., A.A.A., M.M.A., A.I.A. and G.O.A.; data curation, A.K.B., J.A.G., C.D.B., A.R.D. and A.L.S.; writing--original draft preparation, A.K.B. and J.A.G.; writing--review and editing, A.K.B., C.D.B., A.R.D., A.L.S., M.S.A., A.A.A., M.M.A., A.I.A., G.O.A. and J.A.G.; visualization, A.K.B., M.S.A., A.A.A., M.M.A., A.I.A. and G.O.A.; supervision, C.D.B., A.R.D., A.L.S. and J.A.G.; project administration, A.K.B. and J.A.G.; funding acquisition, A.K.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. GRANT114].  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of East Tennessee State University (protocol code 0719.16s-ETSU, approved on 22 August 2019).  
**Informed Consent Statement:** Informed consent was obtained from all participants involved in the study.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia for the financial support under Ambitious Researcher track [Project No. GRANT114].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
The analysis for the entire 20 min duration (Table 3) shows that all the time segments (PR, QT, RR, and ST) show significance (\(p<0.05\)) only between the normal and definite CAN group, and all the other group differences were insignificant. The \(p\)-value of the complexity analysis on all the four ECG segments shows that it is lowest for the normal (N) group when compared to all the other three (eCAN, dCAN, and sCAN) groups, and the mean value of FD was the highest in dCAN in all selected segments. The mean value variation between all four groups in PR, QT, RR, and ST segments seems to be the same; showing not much significance between the groups and hence failing to prove an early diagnostic for detection of CAN, which is evident from the statistical analysis of the ECG segments. Hence, to prove a significant variation, the time intervals of ECG segments at different instants of time were extracted from the data, and complexity in the time instants of all the segments was studied (Table 4). To make an early diagnosis, the groups of interest are normal vs. eCAN, which shows a significant \(p\)-value for all the four segments (PR, QT, RR, and ST). The complexity analysis in all the selected segments was found to be statistically significant for early-stage diagnosis of CAN in diabetic patients. In order to validate the results, the features were provided to different linear and nonlinear classifier models. However, all the analyzed segments are specific for CAN diagnosis; the idea is to identify which feature contributes more in categorizing the groups. This was accomplished by introducing feature ranking in the dataset. The results from Relieff ranking estimator clearly show that the most significant features were the fractal dimension of the time segments denoted as RR\({}_{\text{FD}}\), QT\({}_{\text{FD}}\), ST\({}_{\text{FD}}\), and PR\({}_{\text{FD}}\) time segments. Hence, the ranking estimation supports the statistical verification results that the complexity in time segments is more pronounced than analyzing the time segments. This also indicates that instead of analyzing complete 20 min data, ECG at short intervals of time can be computed to provide the complexity change that is more significant. Various linear and nonlinear measures have been developed [11] with HRV analysis that infers only the time segment data; however, to make an early diagnosis, time information alone is not sufficient to discriminate between the groups. Traditional methods of analysis used only one or two time segments of ECG to classify between groups [14]. It has also indicated that analysis of the entire 20 min data is not required, as small intervals of complexity analysis will provide more significant results. Hence, the technique in this study can be directly applied to ECG data being recorded, for complexity analysis, and can be a method for early prediction of CAN on subjective individuals.
# An Air Spring Vibration Isolator Based on a Negative-Stiffness Structure for Vehicle Seat
## 4 Simulation Results of the Air-Spring Vibration Isolator System
### Simulation Results
#### 4.2.2 ASVIS under Multi-Frequency Wave Excitation  
The multi-frequency wave suggested in [11] was employed as the input displacement excitation to evaluate the vibration isolation performance of the ASVIS according to  
\[z_{b}=5\sin(1.12\pi t)+3.5\sin(3.2\pi t)+2\cos(1.8\pi t)+4\cos(3.6\pi t) \tag{34}\]  
Figure 10 displays the displacement responses of the ASVIS with NSS and ASVIS without NSS under multi-frequency excitation. The displacement response curves in function of the time of the ASVIS with NSS and of the classical isolator without NSS were compared, as shown in Figure 10a. This result illustrates that the ASVIS with NSS performed well in isolating a multi-frequency wave excitation, and the general vibration of the proposed system was remarkably reduced. As can be seen from Figure 10b, the RMS value of the displacement response of the proposed system was significantly lower than the corresponding RMS values of the classical isolator without NSS and of the base excitation. On the contrary, the vibration isolator without NSS decreased the vibration displacement in a high-frequency band, and the vibration peak value after isolation was higher than the amplitude value of the base excitation.  
The vibration transmissibility of the ASVIS with NSS and of the ASVIS without NSS are compared in Figure 11. The proposed model showed a smaller resonance frequency than the ASVIS without the NSS; this was approximately 1.2 Hz for the ASVIS with NSS and 1.4 Hz for the ASVIS without NSS. As a result, the isolation region of the ASVIS with NSS expanded toward low frequencies in comparison that of with the system without NSS. In addition, the ASVIS with NSS showed lower vibration transmissibility than the ASVIS without NSS at the resonance frequency. This result proved that the vibration attenuation of the proposed model was greater than that of the ASVIS without NSS.  
Figure 9: Comparison between the ASVIS with NSS and the classical isolator without NSS for input frequency 1 Hz. (**a**) Displacement response; (**b**) RMS value of the displacement.  
From the above, we can conclude that the classical air-spring isolator without NSS only decreased the vibration displacement at high excitation frequency, while the ASVIS with NSS demonstrated excellent isolation performance at lower excitation frequencies. In particular:  
1. When the input frequency was lower than the natural frequency of the proposed system, the ASVIS with NSS reduced the excitation amplitude at each frequency component by more than 77.16%. On the contrary, the ASVIS without NSS increased the excitation amplitude to 102.41%.
2. When the input frequency was higher than the natural frequency of the proposed system, the displacement response of the classical isolator without the NSS decreased by up to 68.15%, while the displacement of the ASVIS with NSS remarkably was reduces by 89.59%.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 3 The DRL Algorithm
### The Double Rate Scheme  
Recently, most of the localization algorithms have a good performance only for one-hop UANs. However, one-hop UANs have a limitation on the detection range and other networks' applications. In this paper, we mainly focus on the multi-hop networks which can increase the network's scale, but introduce a challenge in terms of ranging accuracy which will ultimately have an effect on the localization performance.  
In this section, we propose a Double Rate Scheme by analyzing the relationship between bit duration and transmission range in an underwater environment. In this scheme, we separate the localization process into two procedures and each procedure uses a different transmission mode to increase the ranging accuracy in multi-hop UANs while maintaining the data transmission rate.
## 2 Materials and Methods
### Pullout Load and Strength of the Samples  
The maximum pullout load and strength for various types of joints, with standard error bars, are depicted in Figure 12. As can be seen in Figure 12, Gui bamboo sleeved joints had higher pullout loads and strength values than those of Moso bamboo joints. Joint strength was calculated by dividing the pullout load by bonding area. Having wood fittings could significantly increase the pullout load  
Figure 11: Load–displacement curves of steel-sleeved Gui and Moso bamboo joints, with and without wood fittings.  
Figure 10: Failure modes for various joints.  
and strength of the joints. The lower pullout load of Moso bamboo joints might have resulted from poor bonding, as previously explained.  
In a past study, the bonding strength of the interface between steel and Moso bamboo plywood, with epoxy of test samples, was determined as 0.78, 0.80, and 0.80 MPa [PERSON] (2014). For the other three samples with epoxy and self-tapping screws, it was 1.07, 1.10, and 1.11 MPa.  
Comparing those bonding strength values with the ones in Figure 12, the mean values of the strength of steel-sleeved Gui bamboo joints, with and without wood fittings, were 2.30 and 3.07 MPa, respectively, which are much higher than the bond strengths listed in [PERSON]'s study (2014) [24]. Steel-sleeved Moso bamboo joints, however, had only comparable mean values of 0.76 and 1.04 MPa. [PERSON]'s test (2014) [25] showed that Moso bamboo joints with wood fittings using 24 screws with a diameter of 6 mm under tension could withstand 57,600 N. Figure 12 shows that a steel-sleeved Moso bamboo joint with wood fittings, reinforced by 2 screws with a diameter of 3 mm, could have mean pullout load of 8462 N. From Figures 11 and 12, it can be observed that wood fittings substantially improve the joint strength and ductility by accurately filling the cavity of bamboo culms. The yield model theory used in the Wood Handbook (Forest Products Laboratory 2010) [26] to predict the lateral resistance of a screw joint might be employed to estimate the steel-sleeved bamboo joint, treating the steel sleeve as a side member and bamboo/wood as the main member. As such, the pullout load could be treated as lateral resistance. The joint strength could, therefore, be directly related to the dowel-bearing stresses of bamboo and steel and nail numbers and sizes.
## 2 Overall Design  
The submersible optically pumped cesium magnetometer contains an optically pumped probe, a flux gate probe, a magnetic signal processor, and a high-depth pressure hull. The front ends of the magnetic compensation probe and optically pumped probe are installed in the high-depth pressure hull without any magnetic probes, and the probe electronics unit and magnetic signal processor are installed inside the high-depth electronics pressure hull. The system block diagram is shown in Figure 1.  
Figure 1: System block diagram.  
The optically pumped probe is employed for high-precision scalar magnetic field measurements, achieving accuracy at the pT level. The fluxgate probe assists in selecting the optically pumped probe to circumvent dead zones and mitigate magnetic interference signals arising from the motion and attitude of the submersible platform. Additionally, a magnetic signal processor is utilized for signal processing and data communication with the upper computer.
# Historically Based Room Acoustic Analysis and Auralization of a Church in the 1470s
## 3 Digital and Historical Reconstruction
### Recording Liturgy  
This section describes the overall process of acquiring anechoic recordings of appropriate liturgical material for the auralization task. The full process of selecting material, recording material, and choosing performers will not be fully described here. Only the final choice in material will be presented, and the recordings themselves will be briefly discussed.  
The liturgical practices of the order located in this abbey are quite well documented. The daily services consisted of monophonic Gregorian chant, from the respective divine offices of the monks and nuns. The nuns followed their unique liturgical office, as mentioned in Section 2, while the monks observed the liturgy of the diocese where the abbey was located [37]. This information made it possible to recreate a plausible Friday sext, from which elements were chosen for recording. The selected material was chosen to include the important liturgical elements of the service: short responsories, antiphons, prayers, and psalms.  
The number of participants in the Friday sext was estimated to about 10 for each congregation. There were to be 60 nuns and 25 monks in the monastery, and only 13 of the monks were ordained and participated in all services. All members of the congregations had responsibilities which may excuse them from services and in particular they might be absent from the small hours (prime, terce, sext, and none). It was concluded that eight male voices and twelve female voices would suffice for the recordings.
###### Abstract  
Wireless sensor networks are appealing, largely because they do not need wired infrastructure, but it is precisely this feature that renders them energy-constrained. The duty cycle scheduling is perceived as a contributor to the energy efficiency of sensing. This paper developed a novel paradigm for modeling wireless sensor networks; in this context, an adaptive sensing scheduling strategy is proposed depending on event occurrence behavior, and the scheduling problem is framed as an optimization problem. The optimization objectives include reducing energy depletion and optimizing detection accuracy. We determine the explicit form of the objective function by numerical fitting and found that the objective function aggregated by the fitting functions is a bivariate multimodal function that favors the Fibonacci tree optimization algorithm. Then, with the optimal parameters optimized by the Fibonacci tree optimization algorithm, the scheduling scheme can be easily deployed, and it behaves consistently in the coming hours. The proposed \"Fibonacci Tree Optimization Strategy\" (\"FTOS\") outperforms lightweight deployment-aware scheduling (LDAS), balanced-energy scheduling (BS), distributed self-spreading algorithm (DSS) and probing environment and collaborating adaptive sleeping (PECAS) in achieving the aforementioned scheduling objectives. The Fibonacci tree optimization algorithm has attained a better optimistic effect than the artificial bee colony (ABC) algorithm, differential evolution (DE) algorithm, genetic algorithm (GA) algorithm, particle swarm optimization (PSO) algorithm, and comprehensive learning particle swarm optimization (CLPSO) algorithm in multiple runs.  
s 2021 21 July 2021 23 July 2021  
applications. Based on the belief of building up mathematical foundations and establishing synergy between detection accuracy and energy depletion (as the central theme of this paper), we suggest that the main contributions of this paper are:  
* A novel system model of wireless sensor network is introduced;
* An adaptive sensing scheduling strategy based on event occurrence behavior is proposed;
* The scheduling problem is formulated as an optimization problem and is solved by a Fibonacci tree optimization algorithm.  
The rest of the paper proceeds as follows: Section 2 reviews related work. Section 3 presents the system model. Section 4 introduces the Fibonacci tree optimization algorithm. Section 5 explains why the Fibonacci tree optimization algorithm is selected for parameter optimization and examines the advantages of the proposed FTOS strategy. Section 6 implements experiments. Section 7 discusses the deficiencies and unresolved issues. Finally, Section 8 draws the conclusion.
# A Novel Cloud Approach for Connected Vehicles
## 3 GeoVCDN Approach
### Discussion  
To summarize, vehicular cloud computing gives the opportunity to manipulate the data provided by a set of sensors in order to optimize the way they are delivered: the creation of metadata, adding a sequence number, and computing a hop limit. These three mechanisms are going to affect the network overhead and the data freshness. The next part of the paper partially consists of expressing and measuring these effects. Additionally,  
Figure 8: GeoVCDN Vehicle Sending CAM.  
Figure 7: GeoVCDN UBR Sending CAM.  
we present our ICN approach which consists of taking advantage of the CAMs' security data. Our strategy allows us to reduce the size of our ICN packets. The association of the cloud architecture and of the ICN approach gives a vehicular cloud networking (VCN [2]) method, which flawlessly fits the communications architecture and the European standards currently used in the C-ITS deployment projects in Europe.
## 4 Experimental Result
### Quantitative Analysis of CRPT Models  
Five consecutive sentence pairs (A/B) were selected from discharge summary notes to evaluate the similarity between sentence pairs in the latent space. As shown in Figure 3, the sentences were processed by separating them based on segment ID. Each sentence pair was input into the model, and the similarity-based distance between their representations was measured. This analysis compares the BERT baseline model [1] and our CRPT models, with the results summarized in Table 5.  
For qualitative analysis, similarity heatmaps were generated to visualize the degree of similarity between sentence pairs. These heatmaps are presented for both the BERT baseline model (see Figure 6a: Similarity heatmap between sentences using BERT model.) and the CRPT model (see Figure 6b: Three salient patterns emerge.). 1. Local coherence (main diagonal). In both models, the principal diagonal--corresponding to consecutive sentence pairs--displays the highest similarity. However, CRPT consistently yields darker cells and larger numeric values (e.g., 3.7 vs. 3.3), indicating that the contrastive objective tightens local semantic cohesion;
2. Global discourse (off-diagonal intra-document blocks). Cells linking non-adjacent sentences from the same summary retain moderate similarity under CRPT, whereas BERT often collapses these to near 0. This suggests that CRPT balances sentence-level specificity with broader document context;
3. True negatives (inter-document region). Rows and columns labeled '5-A' and '5-B' originate from different patient notes. In CRPT, these cross-document pairs cluster around 0 or even negative values (e.g., \(-\)3.7), forming a pale band that contrasts sharply with the intra-document area. The separation verifies that the mixed negative-sampling scheme effectively pushes unrelated sentences apart.