esa-sceva/satcom-chunk-collection huggingface dataset:
# The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems
## 3 Results and Discussion
### BER Analysis
SLM \(U=128\) and clipping ratio \(\gamma=1.5\) are adopted in the simulation. We can observe that the proposed scheme can even adapt to the ISI with only smaller performance degradation than the curve without ISI. That is because trainable parameters of the network can be used to compensate the multiple effects of the optical channel. Moreover, in the presence of ISI, the performance of the proposed ESLM-AE is still superior as compared to SLM and Clipping methods in the whole SNR range. From the performance results, we can conclude that the ESLM-AE scheme outperforms conventional PAPR reduction methods in terms of both the BER and PAPR. In addition, the decrease in the PAPR transforms to a BER performance gain both in the LOS channel and the DOW channel due to the mitigation of nonlinear distortion caused by the nonlinearities of the LED.
# Linear Time Non-Local Cost Aggregation on Complementary Spatial Tree Structures
## 2 Methods
### Aggregated Cost on Complementary Spatial Trees  
We perform cost aggregation on two complementary spatial tree structures to balance the supports from pixels in different directions, and the final aggregated cost \(C^{A}\) for pixel \((i,j)\) at disparity \(l\) can be expressed as  
\[C^{A}_{i,j}(l)=\hat{\mathbf{C}}^{\bar{\mathbf{r}}1}_{i,j}(l)+\hat{\mathbf{C}}^{\bar{\mathbf{r} }2}_{i,j}(l). \tag{7}\]  
We present a recursive strategy to compute the filtering result along each sub-tree, since only the weights among eight-connected neighborhoods are needed. We use both intensity similarity and spatial affinity to measure the distance between neighboring pixels. The distance in the range space is the maximum absolute difference between color vectors, and the spatial affinity is defined by the Euclidean distance. Thus, the weight between neighboring pixels \(u\) and \(v\) can be obtained by  
\[\omega=\exp(-\alpha||u-v||_{2}-\beta\cdot\max(\cup_{c\in(r,g,b)}|l^{c}(u)-l^{c }(v)|)), \tag{8}\]where \(||\cdot||_{2}\) is the Euclidean distance between pixel \(u\) and pixel \(v\); \(\alpha\) and \(\beta\) correspond to the support ranges in both spatial and range spaces.  
Table 1 presents a comparison of typical non-local filters. Both NL and ST need extra computation to build the tree structures, while DT and our method utilize the spatial relationship among pixels to propagate information across the entire image. All these non-local filters are implemented recursively to achieve high efficiency. The spatial distance between any two pixels for NL and ST is evaluated along the corresponding tree structure, while it is the Manhattan distance for DT. The spatial distance is composed of two parts in our method. They are along row/column and diagonal directions, as shown in Figure 1. Moreover, our method propagates information along the edges in the eight-connected neighborhood. NL, ST, and DT only utilize a section of edges in eight-connected neighbors. Figure 2 shows the results of filtering on different tree structures. It turns out that filtering along the MST tends to generate piece-wise constant results. The reason is that sub-trees in the MST are loosely connected since the informative message in each sub-tree has little impact on pixels in other sub-trees. Most pixels contribute to the output of the root node along diagonal directions on _Tree 1_, which are horizontal and vertical directions on _Tree 2_, resulting in stripe artifacts stretching along these two directions, denoted by red arrows in Figure 2c,d. We perform filtering along both _Tree 1_ and _Tree 2_ to balance the information propagated along all directions, which makes stripe artifacts visually unnoticeable.
## 7 Current Challenges and Problems
### Adopting the Proper Method to Choose the Suitable Modulation Used for the Wireless Telemetry  
Adopting aproper method to choose the suitable modulation to be used for the wireless telemetry depends on the nature and type of the implanted device. Some implanted devices such as brain, retinal, cochlear implants, and wireless capsule endoscopy require high data rate transmission [35,38,39]. Thus, the advantages and disadvantages of the suitable modulations used in biomedical devices and biotelemetry systems need to be focused. For example, the BPSK modulation offers a practical circuit implementation by transmitting 1-bit binary data by the influence of shifted carrier frequency, which does not change the phase carrier of the sine wave. However, there is a change in the carrier phase to a negative sine wave for 0-bit. Thus, the BPSK is only able to modulate 1-bit/symbol, which may not provide a high enough data rate due to the bandwidth limitations and and the demodulator power consumption [27]. These problems can be solved by techniques offering high data rates like QPSK and n-PSK modulation. However, these suffer from complicated structures and high power consumption, as explained in Section 6. The FSK is general uses two signals with different frequencies to represent binary 1 and 0. These modulations offer high date rates and suffered from complications and size issues [22,26] as explained in Section 6. The PSK and FSK are used for implanted devices which need high data rates such as retinal and cochlear implants. We have seen that ASK modulation is more suitable than FSK and PSK for implant devices and telemetry systems within a short range. Thus, modulation with simple hardware implementation and small in size, fast and reliable transmission, less power consumption and constant RF signal would be the best choice for a wireless telemetry system.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 4 Proposed Push-Based Data Forwarding Scheme with Fuzzy Logic
### Critical Data Forwarding Procedure in the Proposed Scheme  
Figure 5 demonstrates the critical data flow from the producer to all other vehicles. We assume that the critical data packet, such as an emergency alert, is necessary for all vehicles moving towards the location of the critical situation. Before any critical condition, all the vehicles are grouped into different clusters by employing the K-means clusters, as discussed in Section 4.1. Subsequently, different roles are assigned to the vehicles in each cluster by using the fuzzy logic method, presented in Section 4.2.  
Figure 5: Example of a critical data forwarding scenario in the proposed push-based forwarding scheme using fuzzy logic method.  
When a critical situation occurs, the producer generates a critical data packet and transmits it to the CH using Algorithm 1. The producer sends the alert type, location, and timer to the critical data packet. Once the CH receives the data, the critical data packet is broadcast to all the member vehicles of the clusters. When the critical data packet is received at a member vehicle of the cluster, it drops the packet. On the other hand, the GW forwards the critical data packet to the next GW of the nearest cluster.
###### Abstract  
The C+L band elastic optical networks (C+L-EONs) increase the network capacity significantly. However, the introduction of an L band enhances the inter-channel stimulated Raman scattering effect (ISRS), consequently deteriorating the quality of transmission (QoT) of the signal. Furthermore, spectrum allocation leads to spectrum fragmentation inevitably, which escalates the bandwidth blocking rate. In addition, in C+L-EONs, a single fiber carries more services, and once one of the links fails, a huge number of requests will be interrupted, resulting in huge economic losses. Therefore, this paper proposes a survivability routing, band, modulation, and spectrum allocation (RBMSA) algorithm that effectively guarantees service survivability and reduces the impact of ISRS and spectrum fragmentation. The algorithm employs shared backup path protection and a band partitioning method, whereby the spectrum resource of the primary path is assigned in the L band and the backup path is assigned in the C band in order to minimize the impact of ISRS on the QoT of the request while ensuring the survivability of the network. Furthermore, a fragmentation metric accounting for both the free and shared spectrum resource is proposed to mitigate both free and shared spectrum fragmentation. The simulation results reveal that the proposed RBMSA algorithm reduces the bandwidth blocking probability (BBP) and the fragmentation rate (FR) by 47.7% and 21.3%, respectively, and improves the optical signal-to-noise ratio (OSNR) by 4.17 dB in NSFNET. In COST239, the BBP, FR, and OSNR are 22.1%, 21.5%, and 4.71 dB, respectively.  
C+L-bands elastic optical networks (C+L-EONs); routing, band, modulation, and spectrum allocation (RBMSA); survivability; fragmentation; inter-channel stimulated Raman scattering (ISRS)
## 2 Materials and Methods
### Statistical Analysis  
The bias of MACS-Countess against FACS was calculated as the ratio between the difference in cell count from MACS-Countess compared with that from FACS to the average cell count of the two methods as a percentage [23]. The correlation coefficient was used to assess the strength of the relationship between MACS-Countess and FACS, whereas the Bland-Altman plot was used to assess the agreement between the two methods; for each pair of cell count values, the average was calculated and then plotted on a graph as the mean against bias [23,24]. Mean bias and standard deviation (SD) were determined. The limit of agreement (LoA) (upper and lower) was referred by adding or subtracting the mean bias with 1.96 SD (mean bias \(\pm\) 1.96 \(\times\) SD).  
Percentage similarity (PS) analysis was generated to directly compare both methods, and was calculated as the ratio between the average cell counts from the two methods and the reference method to percentages [25]. The mean percentage similarity (MPS) and SD were estimated, and the coefficient of variance (CV) was calculated as the ratio of SD to MPS in percentage.  
The sensitivity, specificity, and misclassification rate [26] were also calculated at the threshold \(\leq\)350 cells/\(\upmu\)L and \(\leq\)500 cells/\(\upmu\)L, as at these cut-off values, initiation of ART is recommended for the treatment of HIV-infected patients [27]. The number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) were determined. The TP at a particular cut-off, for instance, <350 (cells/\(\upmu\)L), is defined as the case when FACS returns the cell count value of <350 (therefore eligible for ART treatment or \"positive\") whereas MACS-Countess also returned a cell count <350 (true prediction of positive case). Similarly (at cut-off 350), TN, FP, and FN are defined as the case when FACS and MACS-Countess return cell count values of >350 and >350, >350 and <350, and >350 and >350 (cells/\(\upmu\)L), respectively. Sensitivity was then calculated as the ratio of TP to the sum of TP and FN, whereas specificity was the ratio between TN and the sum of TN and FP. The upward misclassification rate was calculated as 1 minus sensitivity, whereas the downward misclassification rate was calculated as 1 minus specificity. Positive Predictive Value (PPV) was calculated as the ratio between TP and the sum of TP and FP; Negative Predictive Value (NPV) was the ratio between TN and the sum of TN and FN. The same was carried out for the cut-off 500 cells/\(\upmu\)L.
# Evaluation and Improvement of Lidar Performance Based on Temporal and Spatial Variance Calculation
## 3 Data Distribution Characteristics of the Lidar Probing Traces
Compared with variance values from the sample distributions of the normal lidar return in the PC data along the line of \(\mu=\sigma^{2}\), the spatial variance values from the sample distributions of the normal lidar return signal in the analog data are parallel to the line of \(\mu=\sigma^{2}\), showing the potential of analog data to be converted to photon-like data using a linear transformation. If we count the number of sample distributions displayed in the scatter plots of the temporal variance and spatial variance, it is found that not all of the sample distributions are confined within the theoretical number of the sample distributions in the temporal variance and spatial variance calculations (Figure 5). As the analog data are the summed results of the lidar returns and the electrical noises are from A/D cards, the effective number of sample distributions displayed in the scatter plots is coincident with the theoretical number. However, the effective number of sample distributions for both the temporal variance and spatial variance of PC data is less than the theoretical number, because in the far field, the PC system cannot detect extremely weak signals and return a PC data record with  
Figure 3: Example of the calculation results of temporal variance for both (**a**) analog data and (**b**) photon-counting data. Fourteen successive probing profiles with a fixed elevation angle of 15\({}^{\circ}\) from the measurements of the ultraviolet elastic scanning lidar were selected as the study objects (profile in Figure 2 is one of the successive 14 events). A green line \(\mu=\sigma^{2}\) is plotted to show the necessary condition of Poisson property. zero value. The effective number of the lidar data is measured using variance calculation results, with the consideration of avoiding the random effect of zero value. Based on this phenomenon displayed on the scatter plots of spatial variance and temporal variance, especially for the PC data, optimization of data acquisition parameters and calibration of lidar data were performed, including evaluation of the linear working range of the photo-electronic device, the estimations of the optimal discriminator level and dead time of PC system, and a novel technique of data gluing between analog data and PC data, which are described in detail in the following sections.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 3 SAR-Based Studies and Methods to Detect Snow
### Employed Methods to Monitor Snow Cover with SAR Data
#### 3.3.2 Total SCE Detection  
Based on the phase information of two SAR images recorded for the same location but at different observation times, the similarity of surface conditions (coherence) can be revealed based on interferometric SAR (InSAR) technique. The underlying theory of InSAR-based total snow detection is that, in comparison to snow-covered areas, the snow-free area can preserve a high coherence between two sensed dates. The reason for the decorrelation between snow-covered observations (in both dry and wet snow) is the alteration of the SAR penetrating depth and the scattering mechanism [106; 145]. Hence, in contrast to the backscattering-based approach presented in Section 3.3.1 that can only detect wet SCE, the InSAR-based approach can detect both wet and dry snow.  
The first attempt of using repeat-pass InSAR techniques was conducted by [PERSON] et al. [146], utilizing space shuttle-based SIR-C images to create coherence maps. Later, [PERSON] et al. [147] pursued this approach, applying it to ERS images; they found that the coherence helps distinguish the SCE better than conventional backscattering-based methods. [PERSON] et al. [148] employed two pairs of InSAR observations sensed before and after a snowfall event, and classified the total SCE with coherence thresholds. [PERSON] et al. [144] proposed a more elaborate workflow to track the total SCE change in different periods. First, they used the normalized difference snow index (NDSI) derived from optical images as well as land cover information to initially estimate the tree and snow line elevation. Additionally, temperature information was incorporated to decide the actual snow condition. Finally, they applied a coherence threshold to derive the type of surface and snow cover change. Figure 7 illustrates the general workflow of InSAR-based algorithms to detect total snow cover.  
The definition of an appropriate coherence threshold for the snow cover classification is critical. [PERSON] et al. [144] utilized an NDSI-based snow cover classification result to determine final coherence threshold for each area. However, using a hard threshold to classify snow cover may lead to misclassification, since the coherence values of snow-covered and snow-free areas largely overlap [149]. To the authors' knowledge, this problem has not been addressed in any study so far.  
Another challenge for InSAR-based snow cover detection is the influence of various additional factors on the coherence value. According to [PERSON] and [PERSON] [150], temporal, spatial and thermal factors influence the coherence value. Essentially, [PERSON] et al. (2019) discovered that the coherence value is related to the polarization mode, land cover type, and LIA. Those factors need to be considered to improve the reliability of InSAR-based snow detection approaches.
# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 3 Data Acquisition Setup  
### Setup Specifics  
In order to investigate the discrimination possibility of essential and [PERSON]'s tremor, as well as the effect of utilizing tremor data from different parts of the hand, a signal acquisition device was developed based on the use of 4 accelerometers for enabling the extraction of hand tremor information from multiple measurement points.  
Specifically, the index and thumb were selected for their tactile dominance and kinematic difference, while the metacarpal and forearm were chosen to further analyze the tremor origin and its propagating properties. Thus, the accelerometers are placed each on the index, the thumb, the outer side of the metacarpal region and on the lower part of the volunteer's forearm, as graphically shown in Figure 1, while their placement is properly stabilized via a protective glove and the use of Velcro straps. The standard coordinate systems are defined as presented in Figure 1, where the accelerometers are placed on the outer side of the hand and parallel to the transverse plane formed when the hand is in the extended position.  
To be able to extract the necessary data for classifying and discriminating PT and ET, an acquisition device was developed for implementing the aforementioned conceptual approach. The device (Figure 2) consists of 4 ADXL345 3-axes accelerometers connected to two Arduino Mega Boards via SPI protocol. The reason for utilizing two boards was for ensuring the highest possible sampling rate during the tremor recording sessions, enough for safely measuring the maximum PT and ET frequencies. Double-sided Velcro straps are used for easy and comfortable adjustment of the accelerometers on the volunteer's hand, while minimizing the effect on the measurements during hand motions and maintaining the tactile ability of the person's fingertips.
## 3 Quantitative Analysis of Condition Monitoring and Predictive Maintenance
Blue Cluster--Hydraulic Systems and Pumps: The blue cluster focuses on specific applications such as _\"centrifugal pump\", \"hydraulic system\"_, and _\"vibration\"_, indicating the relevance of predictive maintenance to specialized mechanical systems. Terms like _\"classification accuracy\"_ and _\"support vector machine\"_ reveal ongoing efforts to refine model accuracy and efficiency in these applications. Yellow Cluster--Emerging Techniques and Methodologies: The yellow cluster represents the application of advanced computational techniques and novel methodologies in predictive maintenance and condition monitoring. Terms such as _\"deep learning\"_, _\"convolutional neural network\"_, _\"fault detection\"_, _\"physics\"_, and _\"proposed method\"_ are prominent, highlighting the focus on implementing state-of-the-art approaches to address complex challenges in the field. Connections between _\"robustness\"_, _\"experimental result\"_, and _\"CNN-model\"_ suggest a strong emphasis on validating these methodologies through empirical studies and practical implementations. This cluster bridges foundational topics from the green cluster and application-driven insights from the red cluster, serving as a pivot for innovation in predictive maintenance technologies. The analysis underscores the interconnected nature of the research landscape, with each cluster addressing a critical aspect of predictive maintenance and condition monitoring while contributing to the overall development of the field. In summary, the bibliometric analysis confirms the significant growth of research in predictive maintenance and condition monitoring, highlighting the convergence of machine learning, deep learning, and signal processing techniques to address failures in industrial equipment. The findings reveal a high level of international collaboration, with China, India, and the United States as the leading contributors. Influential publications and emerging approaches are concentrated in areas such as fault diagnosis using convolutional neural  
Figure 9: Concept network analysis of predictive maintenance and condition monitoring, illustrating thematic clusters and key term relationships. networks, remaining-useful-life estimation, and the integration of IoT sensors. Building on this quantitative overview, the next section delves into a qualitative analysis of the most representative articles, discussing the applied methods, identified limitations, and future research prospects within each relevant topic.
# An Investigation of CTP Use for Wireless Structural Health Monitoring of Infrastructure
## 1 Introduction
In particular, we show a function that can be used to differentiate the nodes operating under CTP between buyers and sellers. These two sets of nodes cooperate, in order to forward packets to specific nodes using the Expected Transmission Count (ETX) metric. As such, SHM can be performed using IEEE 802.15.4 communication with high success in data transmission, and potential defectfull packets will not be lost. The civil infrastructure will be monitored for a very long time without human intervention. Moreover, note that the terminal computer that all the packets of the network will be destined to, is in a particular geographic region of the infrastructure; thus, a cooperative scheme with geographic parameters may be used in the case that the direct transmission is weak. The main contributions of this paper are the following: The CTP is suggested with a cooperative mechanism based on ETX, which provides a geographical essence to the routing protocol in SHM applications. Examples of direct and cooperative transmissions are given to strengthen the authors' point of view. A practical mechanism is suggested, whereby an extra field in the packet struct is proposed, which will provide the CTP with further robustness to changing conditions and direct communication loss.