esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Results of PCG and PPG Detection  
Figure 10 shows an example of heart sounds and PPG signals measured by both the manufactured finger-ring-shaped hybrid personal smart stethoscope prototype and the MP160 for one subject. Heart sounds measured and detected as a PCG signal by the prototype device and the MP160 are shown in Figure 10a,b. Because the PCG signal was processed with digital data directly and preprocessed to suppress environment noise and S3 and S4 heart sounds in the prototype device, S1 and S2 heart sounds were more prominent, with large signal variation, and could be distinguished better than the signals measured by the MP160. Using [PERSON]'s cross-correlation, similarities between the two devices' detected PCG signals were observed to evaluate the performance of the prototype device in heart-sound measurement. As Figure 10c shows, two signals with a smaller difference in amplitude and time variation were observed.  
For PPG-signal detection, the sample signals measured by the prototype device and the MP160 are shown in Figure 10d,e, respectively. Because the baseline noise and residual noise were suppressed by preprocessing after the data acquisition in the prototype device, the PPG signals acquired by the prototype device were more distinct and stable than the signals acquired by the MP160. Therefore, as Figure 10f shows, a smaller difference between two signals was observed after the two PPG signals were also treated with the same cross-correlation algorithm to evaluate the performance of the prototype device in PPG-signal measurement. Table 1 shows the calculated cross-correlation value of the heart sounds and PPG signals measured by the prototype device and of the BIOPAC system for 20 subjects, with means of 0.98 \(\pm\) 0.02 and 0.96 \(\pm\) 0.03, respectively. Both values were approximately 1, meaning the heart sound and PPG signal measured by the prototype device and the MP160 were similar and positively correlated. Therefore, the experimental  
Figure 10: An example of the heart sounds and PPG signals measured by the prototype device and the MP160: (**a**) PCG signal detected by the prototype device; (**b**) PCG signal detected by the MP160; (**c**) cross-correlation results of the PCG signal comparison; (**d**) PPG signal detected by the prototype device; (**e**) PPG signal detected by the MP160; (**f**) cross-correlation results of the PPG signals comparison.  
results demonstrated that the PCG signals and PPG signals detected by the prototype were credible.
# Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios
## 2 AUV Model
### Sensors  
The AUV is equipped with a set of INS sensors to measure the pose and velocities. The sensors measure position and orientation of the vehicle with respect to the inertial reference frame, and the instantaneous linear velocities with respect to the body-fixed reference frame. A delay of 20 ms affects the sensor measurements. The accuracy of the linear position sensors is 0.005 m and the linear velocities accuracy is 0.1 m/s. The angular position measurements have an accuracy of \(5.42\times 10^{-5}\) rad., and the angular velocities accuracy is \(5.42\times 10^{-3}\) rad/s.
# WUE and CO2 Estimations by Eddy Covariance and Remote Sensing in Different Tropical Biomes
## 2 Materials and Methods
### Description of Study Area  
#### 2.2.1 Cerrado Site  
Measurements were conducted on an experimental site located on an inundation plain at Cantao State Park, 260 km west of the city of Palmas (Tocantins state, Figure 1) as a part of the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA). A micrometeorological tower equipped with an eddy covariance system and instruments for meteorological variable measurements were used. The tower was located 2 km east of the Araguaia River (9\({}^{\circ}\)49\({}^{\prime}\)27.9\({}^{\circ}\)S, 50\({}^{\circ}\)08\({}^{\prime}\)92.8\({}^{\prime\prime}\)W, 120 m in altitude), 1 km east of the Javaezinho River, at the northern border of the Bananal Island, and south of Cantao State Park. The Araguaia plains, where the Bananal Island is located, is characterized by an exuberant landscape comprising Cerrado and Amazon biomes extending throughout three conservation units: Araguaia National Park, Cantao State Park and the Cantao Environmental Protection Area. The Bananal Island encompasses a total area of 21,000 km\({}^{2}\) (approximately 80 km \(\times\) 260 km) and is the largest fluvial island in the world, covered mainly by savannas and pastures with seasonal inundations occurring from February to June [3]. The instrumental fetch area covers different types of physiognonies: cerradao, semi deciduous forests (trees with an average height of 20 m), cerrado s.s. and bare field areas with isolated lagoons. The climate in the region is warm and wet, with mean annual precipitation of approximately 1466 mm year\({}^{-1}\) and 90% of the total precipitation occurring in the wet season between October and April [3]. Measurements comprise the period from January 2004 to December 2006.
## 5 Mechanical Properties of the Natural Fibers Reinforced Epoxy Composites
### Hardness  
Hardness represents the resistance of any material against scratch. Hardness becomes significant for NFRCs when used in automobile window liners, building walls, furniture, etc. The effects of thermal ageing on the scratch resistance of natural fiber-reinforced epoxy composites were studied by [PERSON] et al. [119]. The thermal ageing of NFRCs was carried out for 7, 15, and 30 days at a temperature of 90 \({}^{\circ}\)C. As the thermal ageing period increased, the scratch resistance of the composites decreased. [PERSON] et al. [26] investigated bagasse fiber epoxy composites' physical and mechanical behaviour with four varieties of bagasse fiber and fiber lengths (5, 10, and 15 mm). They observed that hard fibers significantly increased the hardness compared to soft fibers. The effect of fiber length (10, 20, and 30 mm) of coir fiber epoxy composite was investigated by [PERSON] et al. [92]. They found that the hardness decreased with fiber length up to 20 mm due to the formation of voids during the fabrication of composites. It was also seen that when fiber loading increased, the hardness decreased. This might be because the tiny voids formed during composite fabrication were likely caused by inadequate wettability or bonding between matrix and fiber [120]. [PERSON] et al. [94] investigated the effect of treated banana fiber loading (5, 10, and 15 wt.%) on the hardness of composites. They observed that the hardness increased with the fiber loading, attributed to the hard and brittle nature of the treated banana fiber after treatment. In general, the composites with hard and brittle fiber exhibited higher hardness. Further, fiber hardness can also be improved with chemical treatment. The fiber loading and length increase the hardness up to a certain limit. After that, it declines as the further addition of fibers causes the formation of voids during the fabrication of composites.
## 2 Background Theories
### Sentiment Analysis  
Sentiment Analysis is a method for defining whether data are positive, negative, or neutral by using Natural Language Processing (NLP). Sentiment analysis is commonly used on textual data to examine the attentions, feelings, behaviors, decisions and emotions of persons who are either the speaker or writer concerning the target topics. The basic task in sentiment analysis is grouping texts in sentences or documents. The grouping of texts are determined by the opinions of people which are either positive, negative, or neutral.  
Sentiment analysis techniques can be categorized into three approaches: lexicon-based approaches, machine learning-based approaches and hybrid approaches. First, lexicon-based approaches are a method of using a lexicon to perform sentiment classification by calculating the weighting of labeled words and counting. Second, machine learning-based approaches are a method of using machine learning techniques, for example, Naive Bayesian and Support Vector Machine, which are considered as standard machine learning techniques. The input of the model includes lexical features, sentiment lexicon-based features and parts of speech [30]. Last, hybrid approaches are methods that use the aggregation of both lexicon-based and machine learning techniques [31]. In addition, sentiment analysis can generate profits for investors because it can help to make decisions [32].  
Financial Sentiment Analysis with Bidirectional Encoder Representations from Transformers (FinBERT) proposed by [33] is a language model based on Bidirectional Encoder Representations from Transformers (BERT) for financial NLP tasks. The FinBERT model includes two phases: pre-training and fine-tuning. During the pre-training phase, the FinBERT model constructs a large variety of pre-training objectives to help better capture language knowledge and semantic information. This phase trains the BERT language model in the finance domain, using a large financial corpus and a general corpus. During the fine-tuning phase, datasets for financial sentiment classification are labeled. The main sentiment analysis dataset is Financial PhraseBank. Researchers extracted 4845 sentences from the dataset with financial terms. Then, 16 experts and master students with finance backgrounds labeled the data with sentiments including positive, negative and neutral. The FinBERT model will provide a polarity score for a given text and SoftMax outputs for one of three labels: positive, negative, or neutral.
## 3 JTAG Exploitations
### Exploitation of Modern Game Console  
[PERSON] et al. [16] listed past successful attacks on the game consoles such as XBOX 360 and Sony PS3. The authors reviewed three significant XBOX 360 exploits so far, i.e., the King Kong glitch/JTAG exploit, Timing Attack, and the Glitch Attack in this report. The King Kong glitch, also called JTAG exploit, was exposed in late November 2006. A Kernal update (4532) was exposed to a privilege escalation vulnerability [17] in which an unchecked memory exports in some games has been done by unsigned shader memory. Generally, Xbox 360's security system is built around a hypervisor concept. The hypervisor is responsible not only for memory access but also offers encryption and decryption services. It manages policies for code that will be executed in either privileged or unprivileged mode. Normally application executable code interacts with hypervisor via \"sc\" (syscall) instruction which switches machine into hypervisor mode. The authors explained \"cmpwi\" instruction which caused problem for memory management. XBOX contains basically two types of memory, i.e., encrypted and unencrypted. Here, \"syscall jump table\" is kept in encrypted format in the memory. When \"cmplwi\" is executed, it is responsible for memory jump which compares only the lower 32 bits of the system call number. The upper 32 bits are being ignored where other instructions operate on 64 bit values. As a result, hackers intentionally injected hypervisor syscall with preloaded register values via unprivileged application code. Moreover, these memory operations are used to write the result of pixel shader (graphic card memory valus) into main memory while execution of graphics related operation. However, the hacker used this shader to hold some register values and instructs kernel to jump on certain location in memory. Due to this, the hacker could get full control access to hypervisor and run unsigned code. This exploit is also called DMA (Direct media access) attack since hacking can be done using the authorized memory access tool such as JTAG interface. In the computer architecture industry, it is well known that the SMC (System Management Controller) could trigger a DMA because SMC cannot write into NAND registers which are required to enable correct DMA. A group of hackers reverse engineered GPU JTAG to see if DMA can be triggered by hardware or not. JTAG could not be used to trigger the DMA since generally interface gets disabled in early boot process. The smart hackers tried to get JTAG access with DMA address and SMC is used to initiate DMA. This attack was well spread in hacker community till Microsoft has patched in all new Xboxes and kernel update. Due to this, Microsoft as well as game design companies have faced significant loss.
# Randomized Feature and Bootstrapped Naive Bayes Classification
## 5 Results
### Stability and Robustness Evaluation  
Although the stability of RFB-NB varies across datasets, certain cases clearly highlight its advantages. For instance, Figure 2 (Breast Cancer dataset) and Figure 5 (Secondary Mushroom and Rice) demonstrate narrower performance ranges for RFB-NB compared to RF and KNN, indicating improved prediction stability. Similarly, in Figure 3 (Telecom Churm) and Figure 4 (Room Occupancy), RFB-NB exhibits relatively tighter boxplots than RF. However, for datasets such as Hepatitis C Patients (Figure 2) and German Credit (Figure 3), the performance variability between RFB-NB and other classifiers like RF and KNN is comparable, suggesting no substantial robustness advantage. Thus, while RFB-NB generally achieves robust predictive performance, the extent of its stability benefits noticeably depends on specific dataset characteristics.
## 3 Crack Propagation and Dual-Crack Model
In the equation, \(K\) is the stress intensity factor; \(Y\) is the crack geometry correction factor; \(\sigma\) is the far-field stress; \(a_{f}\) is the critical crack length; and \(K_{c}\) is the material's fracture toughness. Reference [1] determined the fracture toughness of corroded steel wire experimentally as \(K_{c}=1944.12\) MPa\(\cdot\)mm\({}^{0.5}\), and [PERSON] fitted the expression for the geometry correction factor of semicircular cracks in steel wires [29; 30; 31], as shown in Equation (4), where a represents the crack depth and \(D\) denotes the diameter of the wire. \[Y\left(\frac{a}{D}\right)=0.5964-0.2686\left(\frac{a}{D}\right)+4.0122\left( \frac{a}{D}\right)^{2} \tag{4}\]  
Combining Equations (3) and (4), the critical crack length is calculated to be 3.27 mm, which can be approximated as 3.3 mm. At the same time, assuming the crack extends by 0.1 mm per increment, the steel wire containing a corrosion pit with a depth of 0.2 mm, after the initiation of a 0.1 mm initial crack, would reach the critical crack length after 31 steps of crack propagation, resulting in the fracture failure of the steel wire. Through fatigue testing, the fracture sections of steel wires reveal significant effects of coplanar damage mechanisms on wire performance. The expansion behavior of coplanar elliptical cracks with equal spacing and axis dimensions between adjacent crack tips is compared. The dimensions of the wire model are significantly larger than the crack opening dimensions. Two coplanar semi-elliptical cracks are included in the wire model perpendicular to the axis of the cylinder (i.e., the direction of the applied load), as shown in Figure 6a. The parameters of the dual-crack system are identified in Figure 6b. The crack size is controlled by the lengths of two semi-axes, \(a\) and \(b\). Parameter \(l\) represents the spacing between adjacent crack tips. In fatigue loading conditions, the actual crack shape is often assumed to be elliptical. Point A represents the deepest point of the crack, \(B\) and \(B^{\prime}\) denote the endpoints of the elliptical major axis, and \(C\) and \(C^{\prime}\) represent the actual crack tip endpoints on the wire. When the wire is subjected to axial loading \(\sigma\), it remains in a state of linear elasticity. Interaction between adjacent cracks leads to damage different from that caused by a single crack. By combining coplanar crack dimensions, the equivalent crack shape after interaction is obtained for fatigue assessment, as shown in Figure 6c. The combination of two cracks results in an equivalent crack depth \(a\) equal to the depth of a single coplanar crack. The equivalent crack width equals the sum of the major axis length of the single crack and the spacing between adjacent crack tips.
# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 2 Principles and Methods  
Satellite optical-IR and microwave sensors (Supplementary Table S1) have provided complementary observations of cold regions since the 1970s. In general, optical-IR sensors are well suited for mapping environmental variables over heterogeneous landscapes due to their relatively high-resolution (sub-meter to 1 km) imaging capability, though the signal-to-noise ratio of the observations may be degraded by cloud-atmosphere aerosol contamination, low solar elevation, and long periods of seasonal darkness at higher latitudes. Microwave remote sensing is less affected by atmospheric conditions and provides earth observations day-or-night under nearly all-weather conditions [(43)]. The microwave penetration ability is generally superior to optical-IR wavelengths and depends on sensor frequency and landscape conditions. Lower-frequency (e.g. 1-2 GHz or L-band, 0.3-1 GHz or P-band) observations provide better measurements of forest biomass and enhanced soil sensitivity under low to moderate vegetation and snow cover, while higher frequency (e.g., 12-18 GHz or Ku-, 26.5-40 GHz or Ka-band) signals are more suitable to detect sparsely vegetated soil, snow properties (e.g., snow depth, surface roughness, stratification, and microstructures) [(44; 45)] and ephemeral surface freeze-thaw (FT) conditions [(46; 38; 47; 48)]. Among microwave sensors, satellite synthetic aperture radar (SAR) measures backscatter signals at relatively high spatial resolution (1-100s m), though the utility of operational SARs has been constrained by limited data access, incomplete global coverage and low temporal sampling. Alternatively, satellite microwave scatterometers and radiometers provide global coverage and frequent sampling (i.e., every 1-3 days) valuable for monitoring environmental dynamics over large regions, but at relatively coarse spatial resolution (-5-36 km).  
A variety of sensor configurations and remote sensing techniques have been applied for monitoring cold regions, based on radiative transfer theory and the unique spectral signatures of various target variables. These approaches are summarized in the following subsections for selected variables where remote sensing has been used to document significant environmental changes attributed to global warming.
# Measurement Vertex Position of a Large Aspheric Mirror
###### Abstract  
We present a novel method to accurately measure the vertex position of a large aspheric mirror with a phasing measuring interferometer, a laser tracker, and a micro alignment telescope. The method consists of rotating the mirror by 180\({}^{\circ}\) about its mechanical axis and measuring the change in displacement. Use of a micro alignment telescope eliminates the need to adjust the mirror during the measurement, eases the alignment of the testing system, and improves the vertex position measurement accuracy. Using this method, we measured the vertex position of an aspheric mirror 1 m in diameter and 2 m in radius of curvature. The vertex position measurement uncertainty is 88 \(\upmu\)m.  
optical measurement; dimensional measurement; aspheric mirror; vertex position
## 2 Methodologies and Dataset
### Dataset  
The Indian Institute of Tropical Meteorology (IITM) website ([[https://www.tropmet.res.in/data/data-archival/rain/iitm-regionrf.txt](https://www.tropmet.res.in/data/data-archival/rain/iitm-regionrf.txt)]([https://www.tropmet.res.in/data/data-archival/rain/iitm-regionrf.txt](https://www.tropmet.res.in/data/data-archival/rain/iitm-regionrf.txt)), accessed on 20 May 2021) [25] has annual rainfall data for the Kerala state from 1871 to 2016. This rainfall data are based on a fixed network of 10 rain gauge stations located throughout Kerala. One rain gauge station is placed for every 3886.4 square kilometers. The yearly rainfall statistics report of the Indian Meteorological Department provided rain data from 2017 to 2020. Thus, the total data acquired span a period of 150 years. The entire data from 1871 to 2020 were partitioned  
Figure 2: RF–IMF model.  
into training and testing datasets. In this work, the training dataset ranges from 1871 to 2000, whereas the testing dataset was rainfall data from 2001 to 2020, which formed approximately 12% of the total dataset.  
The model was prepared on the training dataset while the forecasting was made for the test dataset. Using the trained model, a one-step forecast of annual rainfall was created based on the '\(L\)' previous data. The actual observation from the test set was then added to the training dataset and the model was refitted. This process was repeated for the entire test dataset. This model is called the walk forward testing. Since the training window was a sliding one, this model is also called a rolling forecast [26]. Figure 3 depicts the rolling forecast model that was used in random forest-based prediction. Previous '\(L\)' years rain data were used to predict the '\(L\)+1'th year rain and the actual rain of '\(L\)+1' year was used to predict the rain for the subsequent year, and this was continued for the entire testing dataset.
# Taguchi Method-Based Synthesis of a Circular Antenna Array for Enhanced IoT Applications
## 2 Synthesis of Radiation Patterns of a Concentric Ring Antenna Array Using the Taguchi Method
### Design and Validation of Concentric Ring Antenna Arrays Using CST Microwave Studio
#### 2.2.3 30-Element Antenna Array (\(N_{1}=8\), \(N_{2}=10\), and \(N_{3}=12\))
Additionally, we applied the Taguchi method to optimize the design parameters of the antenna array, including the ring spacing and feed mechanism optimization. This approach allowed us to reduce the number of trials needed to achieve optimal efficiency while minimizing losses, thereby improving the overall efficiency of the array for IoT applications. Figure 20: Synthesis of 3D radiation patterns for a 30-element array (\(N_{1}=8\), \(N_{2}=10\), and \(N_{3}=12\)) at 2.45 GHz. (\(N_{1}=8\), \(N_{2}=10\), and \(N_{3}=12\)), (**a**) Structure of the concentric ring array and (**b**) Uniform excitations. (**c**) Excitations with Evolutionary Programming (EP). (**d**) Excitations with the Firefly Algorithm (FA). (**e**) Excitations with Taguchi.