esa-sceva/satcom-chunk-collection huggingface dataset:
# Similarity Measurement and Classification of Temporal Data Based on Double Mean Representation
## 3 Our Method, SAX-DM  
### SAX-DM Design and Analysis  
In the SAX algorithm, expressing the information of a time series segment solely based on its mean would overlook some important features, resulting in limited expressive power. The SAX-TD method represents trend distance using the distance from the endpoints to the mean, which has certain limitations for sequences with complex variations. The SAX-BD method represents trend distance using the distance from the maximum and minimum values to the mean, requiring more symbols for representation and resulting in poor dimensionality reduction.  
In this paper, we propose a new approach that utilizes the mean and trend of time series subsegments as key information. To better quantify the trend of the subsegments, we further divide the segments obtained through the PAA algorithm into two parts. The distance from the mean of the left part to the overall mean is used as the trend distance, which intuitively represents the trend of the subsegment. However, considering the issue of positive and negative values, we use the difference between the left mean and the time average mean to represent the trend. Since the PAA algorithm already normalizes the time series data, the trend distance range is \(\Delta q\in[-1,1]\). Figure 4 illustrates some examples of determining the trend distance. When the left mean in the segment is smaller than the overall mean, the trend is considered increasing, and the trend distance is positive, as shown in example in Figure 4a. When the left mean in the segment is larger than the overall mean, the trend is considered decreasing, and the trend distance is negative, as shown in example in Figure 4b.  
Figure 3: Some cases of SAX-BD. (**a**–**d**) represent four sequences with the same size mean, where the left and right endpoints of (**a,b**) are the same, while the maximum and minimum values of (**c,d**) are the same, respectively.  
Compared to SAX-TD and SAX-BD, SAX-DM can better represent the overall trend of time series data segments with complex variations, while requiring fewer symbols. For a segment with the mean value \(\hat{q}\), its trend distance \(\Delta q\) can be represented within the range \([-1,\,1]\). Therefore, when dividing time series data into \(n\) parts, it can be expressed as follows:  
\[\Delta q_{1}\hat{q}_{1}\Delta q_{2}\hat{q}_{2}\Delta q_{3}\hat{q}_{3}\ldots \Delta q_{n}\hat{q}_{n}\]
## 3 Data and Methodology  
### Applied Techniques  
#### 3.1.1 Horizontal-to-Vertical Spectral Ratio Technique  
The horizontal-to-vertical spectral ratio, HVSR, or H/V technique enables retrieving the fundamental frequency of the soil and the corresponding amplitude using ambient noise recordings. This method was initially proposed by [46] and became widely used after some improvements carried out by [PERSON] [3]. It is now widely acknowledged for its efficiency in estimating soil fundamental frequency [3]. Nevertheless, the scientific community maintains a degree of skepticism regarding the accuracy of the technique in estimating amplification factors, primarily due to the complex contributions of various  
Figure 2: The geological map of the Ain T\(\acute{\text{e}}\)mouchent region, inspired from [13]. The blue circles indicate the single-station ambient noise measurement points, the green circles indicate the array measurement points, and the red circles indicate the boreholes (as detailed in Table 1). The red numbered lines (1 to 6) correspond to cross-sections, and the black discontinuous line delineates the industrial zone. The bottom right panel illustrates the geometry used in the array measurements.  
seismic waves to the wave field. At present, the quantification of the ratio between body and surface waves remains a challenging undertaking. A previous study [20] demonstrated that when there are significant impedance contrasts between bedrock and sediments, the HVSR curve is predominantly influenced by surface waves. The relatively low amplitude of body waves renders them insufficient for providing precise estimation of the amplification factor. Furthermore, the authors of [47] demonstrated that the HVSR peak amplitude can fluctuate over time. It was therefore recommended that the amplitude of the frequency peak obtained using the HVSR technique in this study be regarded as a relative indicator rather than an exact representation of the amplification factor.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Holographic Metasurfaces
#### 3.3.6 Integration with Front-End Circuitry  
By integrating holographic metasurfaces with front-end circuitry, many possibilities exist for improving overall system performance. RHS technology (ref. [74; 75]) can facilitate multi-user communication systems by optimizing digital precoding and holographic beamforming. By integrating advanced algorithms for configuration and control, metasurfaces achieve high precision in electromagnetic manipulation, thus becoming irreplaceable in next-generation networks. Moreover, tensor holographic metasurfaces (ref. [77]) illustrate low-cost, scalable designs that can be integrated well into the existing communicationinfrastructure and have a considerable advantage for indoor and outdoor applications (refs. [70; 72]).
## 2 Fundamentals of Aerial Wireless Sensor Networks for Oceanographic Monitoring.
### Wireless Communications
maximum distances that a communications link will have to cover, as this will determine the choice of RF antenna. There are several types of antenna (omnidirectional, sector type, _etc._) which are chosen on the basis of characteristics such as the radiation diagram, the bandwidth needed, directionality, gain, efficiency, beamwidth and the desired polarization. In the case of sensor nodes communication is more effective with omnidirectional antennas so that the radiated power is the same in all directions. This is necessary in that the movement of the sea can cause the sensor node to move rotationally, vertically or horizontally, thus altering the original position of the buoy. The drawback of this kind of antenna is that the radiated power is more dispersed and hence the range is smaller than with more directional antennas. Directional antennas need to be properly aligned and the power channelled in a single direction; this assures more range in that direction and in some cases avoids interference with other services. One important factor that must be taken into account is the height of the antenna with respect to the flotation device supporting the node, since over long distances, visual line-of-sight is not sufficient for propagation due to attenuation and so RF line-of-sight is required. The range of these nodes is affected by that height according to Fresnel Zone theory, which tells us that for true RF line-of-sight, the Fresnel Zone radius must be less than the combined height of the antennas [20].
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 3 Tracking Error Analysis of Delta Doppler Aided PLL  
In the case of harsh environments with high dynamics and signal attenuation, the dynamic stress error and thermal noise are the most critical INS-aided PLL error sources, and oscillator phase error will also influence the tracking performance [19]. Here we analyze these three major error sources. In order to accurately analyze the effect of the coherent integration time on the tracking error of INS-aided PLL, it is necessary to consider the discrete nature of the loop [6].
## 1 Introduction  
Attempting to reduce the number of surgical interventions, implant placement simultaneous with sinus lifting [1; 2; 3] and tooth extractions [4; 5] has been advocated. Both procedures are considered being technically sensitive requiring experience from the implant surgeon [4; 5; 6; 7].  
Proper positioning seems to be the key factor in immediately placed implants [8] as concerns regarding bone healing leading to greater bone loss still exist [9]. In the anterior maxilla for instance, the implant osteotomy has to leave the alveolus palatally [10; 11] resulting in a rather bone driven implant position in order not to perforate the concave alveolar process [12] and for obtaining a stable buccal bone thickness of at least 1 mm [13]. Given the mismatch between socket dimensions and implant diameter, implant selection [14; 15] is important. Wider and tapered implants are frequently employed for increasing primary stability [16; 17], which often collides with space limitations in single tooth gaps [18; 19]. Two main sources for gaining primary implant stability [7] are available in extraction sites. Circumferential support can be achieved by using wider implant diameters or bone apical to the alveolus can be engaged by extending the osteotomy apically [1]. This however may be limited by anatomical factors [20; 21]. Despite these approaches, primary implant stability in general has been shown to be lower as compared to healed sites [22; 23; 24].  
In an attempt to optimize the use of the host bone present in extraction sites, a dental implant with an \"inverted body shift\" has been introduced and described [25]. This implant is characterized by an increase in its outer diameter aimed at engaging the socket walls [3; 19; 21; 26] rendering it unsuitable for healed implant sites.  
In sinuslift situations [27] primary stability has to be derived from engaging residual bone underneath the sinus cavity with cervical areas of the implant. Residual bone heights have been reported to reach low values of 2 mm [28], 2.57 mm [29], 3.1 mm [30] and 3.86 mm [31]. Engaging cortical bone has been shown to greatly affect primary stability [32], thereby questioning the benefit of implant designs characterized by a cervical region with a back taper [33].  
An implant macrodesign characterized by modulation in core diameter coinciding with a change in thread geometry starting with sharp threads in the apical region, followed by bone condensing threads in the middle portion and sharp threads in the cervical portion of the implant has recently been described [34]. It was the goal of this in vitro experiment to compare this novel implant design (Figure 1a) to an established tapered implant design (Figure 1b; [33]) when being placed in simulated sinus lift situations and extraction sockets.
## 4 Proposed System Design
### FSO Channel
#### 4.2.3 Weather Conditions of Alexandria and Srinagar Cities  
In this study, we consider the effect of weather on two different cities in two different continents that are Alexandria, located in Egypt, Africa, and Srinagar, located in India, Asia.  
According to www.weatherspark.com (accessed on 1 May 2022), the weather of Alexandria city in summer is warm, arid, and clear, while in winter, it is cool, windy, and mostly clear. As for Srinagar city, the weather in summer is warm and clear, while in winter, it is very cold and partly cloudy.  
As for visibilities, the meteorological data for daily visibility for Alexandria city are obtained from www.worldweatheronline.com (accessed on 1 May 2022) for the years 2014 to 2018, while the daily visibility for Srinagar is obtained from the Indian Meteorological Department for the years 2014 to 2018 [40].  
Alexandria city has a recorded average \(R\) of 9.8 km, while for Srinagar city, the recorded average \(R\) is 2.9 km. According to Equations (9) and (10), the attenuation of Alexandria and Srinagar cities are calculated and tabulated in Table 4.
## 3 Results and Discussion
### Frost Growth Behavior Analysis  
The eight pictures in Figure 3 show the frost growth characteristic of the two heat exchangers at different time points. The experiment was carried out at an inlet temperature of 2 \({}^{\circ}\)C, an inlet velocity of 1.5 m/s, an RH of 90%, and an inlet coolant temperature of \(-7\)\({}^{\circ}\)C. At 2 min, the frost first formed at the base of the fin due to the low temperature of the fin base. Over time, the fin tips became colder, and the frost gradually spread from the base of the fin to the tip; at 15 min, the windward-lengthened fins were covered with frost. By comparing these pictures, we can see that the frost blockage of the MCHX-A was not as severe as that of the MCHX-B. At 30 min, the frost-blocking situation of the MCHX-B was much more severe than that of the MCHX-A. At 60 min, the extended fins captured a large amount of frost, and the airflow passage was not completely blocked. However, the airflow passage of the MCHX-B was nearly blocked by the frost.  
Figure 4 depicts the frost growth of the two heat exchangers at the early and late stages based on frosting photos. In the early stages of frosting, frost usually forms where the fin and the flat tube are in contact because the temperature is lower there. As the frosting progresses, thermal resistance develops where the fin and the flat tube are in contact, and heat begins to transfer more to the fin away from the flat tube. Therefore, the extended portion of the windward side fins begins to capture frost. During the late stage of frosting,  
Figure 3: Images of frost accumulation on two tested heat exchangers.  
extended fins capture large amounts of frost to a degree that makes the frost distribution more even, thus inhibiting rapid blocking on the front side. However, for the MCHX-B, the frost layer quickly forms on the front side and blocks the airflow passage, resulting in the deterioration of the heat transfer rate.
## 4 Experiments & Analysis  
### Dataset  
In this study, a dataset (Mining Equipment Nameplates Dataset, MEND) was created for detecting and capturing text on industrial equipment nameplates. The images in the dataset were collected from photographs of industrial equipment nameplates from various mining companies in China, containing more than 700 photographs. The images are split into 600 training set items and 142 test set items, but the images in the dataset are inconsistent and present challenges such as excessive brightness or poor lighting, partial occlusion, distorted shapes, contamination, rotation, and uneven shapes. This makes the data set more challenging than others.  
The purpose of MEND is to track the text content and recognize all text occurrences in the images. It serves as a fruitful dataset for testing text recognition models and character recognition for industrial nameplates, offering opportunities for end-to-end research in text detection and recognition. An example is shown in Figure 3. Moreover, this dataset can be leveraged for further research in this domain.  
Several high quality datasets were used for model pre-training. The use of large, publicly available datasets improves the accuracy and quality of deep learning algorithms. The synthetic dataset SynthText, which consists of 800,000 images generated by combining 8 k background images, was used with the ResNet18 and ResNet50 pre-training models [28]. The bilingual MSRA-TD500 dataset [29] also used the ResNet18 and ResNet50 pre-training models and consisted of 200 test images and 300 training images annotated with line-level text instances in both English and Chinese. The ICDAR2015 dataset [30], with 1000 training images and 500 test images, each with a resolution of 720 \(\times\) 1280, was annotated with word-level text instances and used the ResNet50-D pre-training model. Finally, the TOTALTEXT dataset [31], consisting of 300 test images and 1255 training images annotated with word-level text occurrences, was used with the Res-Net50-D pre-training model and contained text in various forms, such as horizontal, multidirectional, and curvilinear.
## 3 Results
### Simulation Study  
This paper adopts the basic tenet of using synthetic tensors with added noise to test the robustness of segmentation methods as in [20,21], for example. Adding noise directly to diffusion tensors can cause the resulting matrix to not be positive semidefinite, to ensure the matrix is positive semidefinite noise is added to the Cholesky decomposition (see Appendix A) of each tensor instead, following [6]; that is, the noise is added to the lower triangular matrix first using Cholesky decomposition. Therefore, in terms of experimental design, we will select three values of noise to be added to the Cholesky decomposition of a tensor, and compare the effects on the segmentation of the CC. To compare the quality of segmentation methods (K-means or sFCM), together with a choice of distance metric (the Euclidean, log Euclidean, and root Euclidean), the standard performance measures (with contextual interpretations to follow) of the accuracy, sensitivity, specificity, precision, F-measure, and Gmean are computed. These measures are computed at each noise level to enable an evaluation of robustness. Let \(\Delta_{j}=\text{chol}(D_{j})\), be the Cholesky decomposition of tensor \(D_{j}\), with \(j\in\{1\dots n\}\), and let \(X_{j}\) be a random matrix with an independent and identically distributed (i.i.d.) normal distribution with expected value \(E[(X_{j})_{ls}]=0\) and standard deviation \(sd((X_{j})_{ls})\), for each \(l\in\{1,2,3\}\) and \(s\in\{1,2,3\}\). Thus, we have [6]:  
\[D_{j}=(\Delta_{j}+X_{j})(\Delta_{j}+X_{j})^{T}\text{ for }j\in\{1\dots n\}. \tag{10}\]  
To create three levels of noisy tensors, three values of \(sd((X_{j})_{ls})\) are selected for each simulation study. The number of simulated tensors is \(n=1491\) (from using a \(2D\) image with size \(71\times 21\)). The region of interest is then clustered into five clusters with the CC being one of the clusters ([17] found that the best cluster size for segmentation of the CC was 5). In order to segment the CC, cluster label 1 is assigned to the CC, whilst 0 is assigned to the other four clusters (i.e., we take the logical image with 1 as the CC and 0 as the background). To evaluate the performance of the Euclidean, Log Euclidean, and Root Euclidean metrics and the segmentation methods (K-means or sFCM) for the segmentation of the CC, we use the performance measures of accuracy, sensitivity, specificity, precision, F-measure, and Gmean. These are standard performances measures used in prediction [22], that are recalled here, but to use them, the concepts of True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) need to be interpreted suitably in our context. Take TP and TN to be the numbers of voxels in the CC and in the background (i.e., segmented as any other cluster except the CC), respectively, that are segmented correctly. Then, FP and FN are the numbers of tensors in the background and in the CC that are incorrectly segmented, respectively.
## 6 Results and Discussion
### Computational Load Analysis  
In this subsection, we discuss the computational load of GCLTE. Table 8 presents the RNN (complexity \(O(n)\)) structure of NRT and the transformer (complexity \(O(n^{2})\)) structure of PETER, along with the computational load of our GCLTE (complexity \(O(n^{2})\)) on an NVIDIA RTX4090 when processing the REASONER dataset. As shown in Table 8, although our GCLTE takes longer and requires more GPU memory than NRT on a single batch (due to its computational complexity), it requires significantly fewer training rounds, indicating that our model converges faster during training. Compared to PETER, GCLTE requires roughly the same training time and GPU memory on a single batch, but it also requires significantly fewer training rounds, resulting in a substantially shorter total training time.
## 4 Conclusions  
In summary, we have successfully fabricated Cu/PTFE composite targets with various Cu weight percentages for sputtering and deposited Cu/PPFC nanocomposite thin films. This was followed by analysis of the properties of the Cu/PPFC nanocomposite thin films with respect to the Cu composition ratio. The Cu nanoclusters were well distributed in a spherical shape with a size of 10-20 nm examined by a FE-TEM image. The Cu/PPFC nanocomposite films prepared by RF sputtering with Cu 20 and 50 targets showed very similar transmittance and color characteristics to those of pure PTFE. However, the water contact angle gradually decreased owing to the Cu on the film surface. The effect of Cu content was more pronounced in the Cu 80 target. The Cu 80 target has excellent conductivity and can be sputtered not only in RF but also in MF and DC power sources; moreover, the physical properties of the thin film change significantly depending on the power source. Interestingly, the Cu 80 MF and Cu 80 DC thin films exhibited absorption peaks at 618 and 678 nm, respectively, indicating that the Cu nanoclusters were well formed. Further, from the red shift of the absorption peak, it can be assumed that the size of the nanoclusters prepared using DC was larger than that using MF, which is also why the nanocomposite films prepared by MF and DC exhibit sheet resistances of 2 \(\times\) 10\({}^{4}\) and 34.55 \(\Omega\)/sq, respectively, thus explaining why the electrical properties of the thin films prepared using DC are superior.  
This study describes a novel method to fabricate metal-polymer nanocomposites. This method can be expanded and diversified to easily fabricate not only various metal nanocomposites but also  
Figure 6: Normalized XPS spectra of (**a**) C 1s with increasing Cu content, (**b**) C 1s with RF, MF, and DC power sources, and (**c**) Cu 2p for the Cu/PPFC nanocomposite thin films with varying Cu contents and power sources. (**d**) Calculated carbon, fluorine, and copper ratio for the nanocomposite Cu/PPFC nanocomposite thin films.  
ceramics and semiconductor nanocomposites. Therefore, this method is industrially promising, as it enables fabrication over a large area and in a continuous manner.  
[PERSON]-[PERSON] designed the study and the experiments. [PERSON] fabricated the Cu/PPFC thin films using a test sputter system. [PERSON], [PERSON], and [PERSON] analyzed the properties of the Cu/PPFC thin films. [PERSON]-[PERSON] and [PERSON] wrote the manuscript. All of the authors discussed the results and commented on the manuscript.  
This study was supported by the Core Research Project at Korea Research Institute of Chemical Technology (KRICT) (KK-1806-00) funded by the Ministry of Science and ICT.  
The authors declare no conflict of interest.
# Autonomous Load Regulation Based Energy Balanced Routing in Reshargeable Wireless Sensor Networks
## 3 System Model
### Traffic Load Model  
The load of each node is quantified by the traffic it relays. It is assumed that every node generates \(\lambda_{i}^{s}\) data packets per round and the load of a node can be calculated by Formula (2).  
\[l_{i}=\lambda_{i}^{s}+\sum_{j\in BF(i)}l_{j} \tag{2}\]  
where \(l_{i}\) and \(l_{j}\) are the traffic loads of node \(v_{i}\) and node \(v_{j}\), respectively. \(BF(i)\) is the subset of node \(v_{i}\) and the nodes in \(BF(i)\) send the data they need upload to node \(v_{i}\) for relay. \(l_{i}\) reflects the load of the node through the network traffic distribution, which refers specifically to the number of data packets needed to be uploaded in each round. A data packet is the traffic corresponding to the unit load. The nodes in the areas where traffic converges are heavily loaded.