esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of PCI Express Protocol-Based Systems in Response to 5G Application Demand
## 3 Technology Analysis Using PCI Express, CXL, USB, NVMe, and Security
### PCI Express  
According to the PCIe industry-standard roadmap, PCIe 5.0 will provide more efficiency in 5G, AI, and network computing requirements, since its bandwidth is increased to 32.0 Gb/s. The I/O bandwidth doubles every three years in PCI Express [25; 26]. This drives PCIe fabric topology, as it is the main architecture in the current PC industry [17; 27].  
In 2021, PCIe 5.0 products were introduced to the market. Intel(tm) introduced PCIe 4.0 and PCIe 5.0 solutions to the PC industry, which can provide systems with better performance, as shown in Figure 4[27]. The PCIe 5.0 and PCIe 6.0 technology roadmaps have been verified in other high-speed serial bus protocols [17; 28].  
Figure 4: PCIe layers [17].  
Figure 3: Serial bus [17].
## 6 Data Transmission Using XOR Encoding and Decoding
### Network Coding  
In this phase, the packet is encoded at the source and forwarded to the CH that is within its range, and the sink decodes the packet. At the source, XOR encoding is employed for encoding data packets, and XOR decoding is performed at the sink for decoding data packets. The process of XOR encoding and decoding is explained below.  
Network coding refers to the process of linear encoding and decoding in which the intermediary nodes perform encoding of data packets obtained from neighboring nodes in the underlying network.
## 6 Discussion
### Proposed CNN-Based Models  
From Figure 10, it can be observed that the performance of the CNN-based models had improved over the years. To date, the model by [PERSON] et al. [71] achieved the best overall accuracy (96%) for sleep stage classification. However, this accuracy was achieved using a private clinical dataset. When they evaluated the performance of their model using the Sleep-EDF dataset, an overall accuracy score of 86.4% was achieved. This was lower than the accuracy obtained by [PERSON] et al. [63] (93.7%) who used single channel EEG signals from the same database. The unique feature of the model proposed by [PERSON] et al. was the attention mechanism that they incorporated into the CNN's learning framework. This attention mechanism improved the feature extraction performance of the model through intra- and inter-epoch feature learning. However, the same model achieved a lower accuracy of 82.8% when it was tested on EEG signals from the expanded Sleep-EDF database. Another unique CNN model was proposed by [PERSON] et al. [91] wherein fine-grained methods were used to assist the CNN model to find the best time segment in the EEG signals. Fine-grained methods construct time series from the EEG signals. Basically, if the time window in fine-grained method is set to 3, every 3 time-steps along the EEG signals will be combined together as one-time segment, hence reducing the complexity of EEG signals. The layers in their proposed CNN were shallow-7 layers, including 2 convolutional layers. Yet, they were able to achieve a high overall accuracy of 92%.  
A more versatile and consistent CNN model was proposed by [PERSON] et al. [65], which was a 19-layer 1D-CNN model with 10 convolutional layers. It achieved an accuracy higher than 90% in both Sleep-EDF dataset and its expanded version, as seen in Figure 10. The peak accuracy was achieved (91.2%) when a mixture of PSG signals was used as input (EEG and EOG), but when single channel EOG signals were used, the accuracy of the model decreased to below 90% (88.8% and 89.8%), (Figure 11).  
Both [PERSON] et al. [63] and [PERSON] et al. [91] showed that CNN models with a small number of layers can achieve a high classification performance of sleep stages by improving the feature extraction ability through additional tools, such as attention mechanism or fine-graining. On the other hand, [PERSON] et al. [65] showed that a deeper CNN model can achieve high accuracy classifications across different inputs of PSG recordings (EEG, EOG, EEG + EOG).  
Figure 11: Sleep stage classification accuracy of CNN-based models based on stand-alone EOG signals or a mixture of EEG, EOG, and/or EMG signals. Various sleep datasets are represented by different colors and the type of signals described in the bar chart. * Summary statistics: Using EEG+ other signals/EOG in 2018–2019.  
Figure 10: Performance of CNN-based models analyzing only EEG signals. Sleep datasets are presented in different colors. * Summary statistics: Various sleep databases used to develop CNN models for sleep stage detection.
# The Spotting Distribution of Wildfires
## 3 Examples of the Spotting Distribution
### Case (W2,V1): Logarithmic Profile for \(w\), Constant Vertical Velocity  
In this subsection, we again assume a constant vertical velocity \(v<0\), and we introduce the logarithmic horizontal wind profile (first employed by [PERSON] in spot fire modelling [1]):  
\[w(z)=\frac{u_{*}}{\kappa}\ln\left(\frac{z-d}{z_{0}}\right). \tag{43}\]  
where \(z\geq H+d\approx H\), \(u_{*}\) is the friction velocity, \(\kappa\) is [PERSON]'s constant, \(z_{0}\) is the zero-datum displacement, where \(H\) is the canopy height and \(d\) is the distance above the canopy where horizontal winds begin [49]. We will approximate \(d\approx 0\).  
As in the preceding subsections, we may determine the landing time \(t^{*}(x)\). After some work (see [29] for details), we arrive at an implicit expression for the landing time:  
\[\frac{u_{*}}{\kappa}H\ln H-x+\frac{t^{*}u_{*}}{\kappa}(\ln H-1)=\frac{u_{*}}{ \kappa\kappa}\left((H+|v|t^{*})\ln\left(H+|v|t^{*}\right)\right). \tag{44}\]  
For given values of the parameters \((u_{*},H,v)\) we can then use a numerical method, like [PERSON]'s iterative root-finding method, to compute the landing time as a function of \(x\) to any desired precision, since we cannot obtain an explicit expression for the landing time. Because the landing time \(t^{*}\) must be increasing with \(x\) by uniqueness of firebrand trajectories, with some more work we obtain once more a very similar expression for the the asymptotic landing distribution,  
\[\mathbb{L}(x,m)=N\phi\Big{(}H+|v|t^{*}(x),C^{-1}(m,t^{*}(x))\Big{)}, \tag{45}\]  
though the formula is slightly less attractive since \(t^{*}\) must be solved implicitly from Equation (44).
## 2 Materials and Methods
### Data and Processing
#### 2.2.4 Analysis of Glacier Processes  
To analyze glacier surface changes, dDEMs were calculated by subtracting the older DEM from the newer DEM of the respective period. Since gaps pose a problem for the analysis, they had to be filled. This was done in the dDEMs to avoid creating very smooth areas in the DEMs, which would lead to erroneous difference values in rugged mountain terrain. The gaps in the dDEMs were filled by using the mean value of the next 20 cells containing data.  
Firstly, the outlines of Hochjochferner and the Khumbu Himal region were determined. On Hochjochferner, this was done by manually adjusting the data from the Austrian glacier inventory [41] based on the 2014 Pleiades orthoimages, while for the Khumbu Himal region, outlines  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acquisition** & **Shift in x [m]** & **Shift in y [m]** & **Shift in z [m]** \\ \hline Alps 2014\(-\)ALS 2013 & 0.1 & \(-\)3.2 & 0.6 \\ Alps 2015\(-\)ALS 2013 & 0.2 & \(-\)2.4 & 0.5 \\ Khumbu Spring 2015\(-\)Khumbu 2016 East & 1.0 & \(-\)0.2 & \(-\)0.1 \\ Khumbu Spring 2015\(-\)Khumbu 2016 Northwest & 4.0 & \(-\)0.1 & 0.7 \\ Khumbu Spring 2015\(-\)Khumbu 2016 Southwest & 1.5 & \(-\)0.6 & 0.3 \\ Khumbu Autumn 2015\(-\)Khumbu 2016 East & 0.0 & \(-\)0.2 & 0.5 \\ Khumbu Autumn 2015\(-\)Khumbu 2016 Northwest & 1.1 & 1.8 & \(-\)3.9 \\ Khumbu Autumn 2015\(-\)Khumbu 2016 Northwest & \(-\)3.9 & \(-\)1.4 & 4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Shifts of the slave DEM in relation to the master DEM calculated during the co-registration process. Multiple values for the Khumbu Himal study area are due to the three triplets needed to cover the whole study area. ALS: airborne laser scanning.  
for the five study glaciers (Figure 2) were manually mapped on the basis of the orthoimages and DEM from spring 2016.  
Multitemporal orthophotos were generated from the available Pleiades scenes. Those can then be used to determine the glacier movement by using image correlation software. In our case, we used IMCORR [43] to obtain surface displacement rates. The IMCORR software determines the surface displacement rates by using a reference block of the raster cell values of the first image and correlating the values within a determined search block. The output consists of surface displacement vectors.
## 3 Empirical Results and Analysis
### Comparison of Vertical and Horizontal Peak Ground Motion Attenuation
#### 3.2.2 Comparison of Vertical EPA and Horizontal EPA  
The average ratio of the vertical EPA to NS EPA for all strong-motion records was 0.6075, while the average ratio of the vertical EPA to EW EPA was 0.6029. The NS EPA was roughly 1.0089 times the EW EPA. The computed values of the vertical EPA from the prediction equation were 0.590 times the computed values of the horizontal EPA. Figure 6 suggests that within a fault distance of 0-300 km, the V/H ratio of the computed values from the EPA prediction equation is consistently below 2/3, ranging from 0.42 to 0.69. The V/H values of EPA in the strong-ground-motion records at each station exhibited a substantial dispersion, with a maximum value of 1.37 and a minimum value of 0.25.  
Figure 5: Scatter plot of vertical PGA to horizontal PGA ratio.  
Records with V/H ratios smaller than 2/3 accounted for over 68% of the total records. It is evident from the comparison of Figures 5 and 6 that the V/H curves for PGA and EPA computed from the prediction equations differ significantly. The V/H curve for EPA gradually increased with an increasing fault distance, while the PGA exhibited a pronounced vertical effect near the fault. Further research is required to scrutinize the influence of near-field vertical effects on structural damage in Turkish earthquakes.
###### Abstract  
In a manufacturing system, lot sizing and maintenance are interdependent and interact with each other. Few studies jointly investigated production lot sizing and maintenance management considering system degradation. However, during the production process, the system and critical component performance will undergo inevitable degradation over time. For example, equipment wears out due to both its own internal causes and the external environment. To monitor the degradation process, interval inspection is usually performed to obtain information about the system degradation and nonlinear degradation is more general. Thus, based on the nonlinear degradation of the production system, this study developed a joint optimization model of production lot sizing and preventive maintenance (PM) thresholds with the goal of maximizing profit per unit of time. The maintenance decision follows the control limit principle, i.e., the choice between preventive maintenance (PM), corrective maintenance (CM), or neither (do nothing) is based on the magnitude of degradation. A simulation algorithm is proposed to obtain the optimal lot-sizing allocation and PM thresholds. The effectiveness of this joint optimization model algorithm is illustrated by numerical examples and the results show that the maximum profit per unit time can be obtained by reasonably formulating PM thresholds and production lot sizing.  
Keywords:lot sizing; preventive maintenance; degradation modeling; joint optimization +
Footnote †: journal: Applied Sciences
# An Energy-Efficient Fail Recovery Routing in TDMA MAC Protocol-Based Wireless Sensor Network
## 1 Introduction
The authors of this paper believe that there is no related research proposing an energy-efficient fail recovery routing algorithm for this specific type of network model. In most environmental monitoring, facility diagnosis and wireless metering applications employing energy-harvesting devices like solar cells are not effective solutions since sensor devices are usually installed in an indoor environment or the dark basements of buildings. Using a larger battery is not acceptable due to the cost and size constraints on the sensor devices, since these applications are often deployed throughout the entire city to monitor temperature, air pollution or toxic gas level. The remainder of this paper is organized as follows. In Section 2, we introduce our network topology and energy model for tree-structured WSN. Section 3 elaborates the proposed fault recovery re-routing algorithm. The definition of NE-MCR algorithm's cost (objective) function is explained in Section 4. Performance evaluation of the NE-MCR algorithm is provided in Section 5, followed by the conclusion in Section 6.
## 2 Materials and Methods
### Image Acquisition Protocols  
We collected the PLR signals via the Corneal Analyser CA-800 corneal topography from Topcon Europe Medical B.V. and the related software, equipped with white LEDs for dynamic and static pupillometry examination. We applied two acquisition protocolsfor each patient, varying the exposition time to the lights from 0.5 s to 1 s. The entire acquisition protocol could be summarized as follows: 3 s in scotopic conditions (dark), 0.5 s/1 s of photopic vision (white LEDs source), and finally 12.5 s/12 s of scotopic vision again. In the following, we refer to Protocol 1 as the acquisition related to 0.5 s-12.5 s of photopic/scotopic visions and to Protocol 2 as the acquisition with 1 s-12 s ones. Between each patient and each different measure, the eye was at rest for at least 1 min in a dark room. A representation of the PLR signals analyzed in this work is shown in Figure 1a.  
Figure 1: (**a**) Representation of the PLR signal acquired and analyzed in this work, with the highlight of the scotopic and photopic regions. In orange, we show the raw signal obtained by the pupillometer, and in blue the signal obtained by the application of a smoothing algorithm. (**b–m**) PLR features extracted and analyzed in this work. In detail, each subplot refers to: the baseline of the raw pupil  
diameter (\(D_{0}\)) (**b**); the latency time of constriction (\(T_{P}\)) (**c**) the minimal diameter (\(min[D]\)) (**d**); the maximal contraction velocity (\(max[v_{c}]\)) (**e**); the latency time (\(T_{s}\)) between light off and minimum diameter (**f**); the maximum re-dilation velocity (\(max[v_{d}]\)) (**g**); the exponential re-dilation time constant (\(\lambda_{t}\)) (**h**); the number of oscillations (crosses, \(N_{p\text{o}ks}\)), (**i**); and the average (\(\mu_{prv}\)) and standard deviation (\(\sigma_{prv}\)) of the time distances between peaks (**j**); the ratio of the two highest peaks (the two crosses) detected from the proportion between the signal and the envelope, (\(R_{H}\)) (**k**); the total area between the signal and the baseline (\(A_{tot}\)) (**l**); and the exponent (\(\alpha\)) of mean squared displacement (\(MSD\)) as a function of time of the pupil center (**m**).
## 7 Network Performance Analysis: NB-IoT vs. LoRaWAN
### Latency  
As expected, NB-IoT experiences a higher delay than the LTE network due to the transmission pattern of the downlink control information and data being sent via the downlink control and shared channels, respectively. The coverage capacity of the NB-IoT network depends on several factors such as the RF link budget, antenna gain, transmission power, and the target is to achieve a high data rate at 164 dB MCL through an adequate performance of LTE or GSM physical channels. For instance, this would mean a 90% narrowband primary synchronisation signal (NPSS) and narrowband secondary synchronisation signal (NSSS) detection rates with high synchronisation accuracy to meet the latency requirement of 10 s, and also, the NPRACH timing advance of 3 us. The latency target of the NB-IoT network performance during RRC connection procedure and data rate transmission is to deliver 10 s. The stateful nature of the LTE uplink and downlink transmissions increases the data overhead. The TCP retransmission timeout (RTO) of 244 ms occurred following a 44 bytes transmission segment. The latency in the uplink and downlink channels, as presented, is the sum of the time spent in the synchronisation, resources reservation, data transmission, and reception processes. The latency between the eNB and MME clients, as shown in Figure 9 varies from 6 us to 50 ms. As observed, the message size does not directly impact the delay, although it does increase the energy consumed as the UE has to wait for message acknowledgement in successful transmission. In the LoRaWAN platform, we considered latency as the duration between when the data are sent by the Fipy and received by the web server. For sending 16 bytes of data in a custom format to the web server per day, for every experiment, between 2 s and 3.5 s latency was measured between when the data were sent and received by the web application. In the LoRaWAN, with a packet length of 20 bytes in class A mode, 2 s was needed for packet re-transmission. However, the success rate of every transmission was above 50%.
## 4 Validation of the STAR COSMIC-2 Temperature and Water Vapor Profiles
### Comparisons with UCAR COSMIC-2 WetPrf2 Retrievals  
This section compares the STAR COSMIC-2 retrievals with collocated UCAR COSMIC-2 (wetprf2). Although not shown, the STAR dry pressure and dry temperature are almost identical to those from UCAR outputs. Figure 12 presents the COSMIC-2 water vapor mixing ratio and temperature difference between STAR and UCAR retrievals for the entire October 2019 data (\(\sim\)100,000 profiles). The mean water vapor differences between STAR and UCAR (Figure 12a) are less than a few tenths of a g/kg near the surface, and temperature differences (Figure 12b) have a peak value of \(-\)2 K at an altitude of 1 km and less than a few tenths of 1 K above and up to 30 km. Both datasets are in excellent agreement, considering the different approaches applied.
# A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode
## 5 Discussion
### Analysis of Limitations of the Proposed Method  
Benefiting from vehicle detection under two color modes and the setting with multiple loops, the counting accuracy of vehicles greatly improved, but there are still some false results. There are two types of counting errors, namely missed counting (\(FN\)) and redundant counting (\(FP\)). We start from these two aspects to analyze the causes of the errors and the limitations of the proposed method.
* _Missed Counting_ Incomplete vehicle detection and unreasonable ROI settings could both lead to the generation of missed counting, while redundant counting is mainly caused by unreasonable ROI settings. Therefore, in previous studies, a reduction in counting accuracy was mainly due to errors missing counts. Benefiting from vehicle detection under two color modes and the setting with multiple loops proposed in this paper, the number of errors due to missing counts was greatly reduced and the counting accuracy significantly improved. However, as shown in Table 15, there are still a small number of missing counts (\(FN=6\)) in the experiments, which is mainly affected by incomplete detection of the vehicle and has little to do with the setting with multiple loops. Here is an example. As shown in Figure 22a, the color of the incomplete part is very similar to the color of the road background. Therefore, it is difficult to separate them no matter whether under gray mode or RGB mode, resulting in incomplete vehicle detection. As a result, no matter using the primary loops or the secondary loops, this vehicle does not meet the activation conditions, leading to a missed count.  
* _Redundant Counting_  
The error of redundant counting mainly occurs for large vehicles, that is, counting are carried out simultaneously in two adjacent lanes. As shown in Figure 23a, this large vehicle spanned two lanes, so it was counted both in \(lane^{1}\) and \(lane^{2}\), resulting in redundant counting. This phenomenon could also be proven by the experimental results. As shown in Table 15, the error of redundant counting (\(FP\)) is more likely to occur in Video IV, Video V, and Video VI, mainly because the proportion of large vehicles on urban roads and expressways is higher than on highways.
## 7 Conclusions  
This study presents the design process of a multi-suspension unit equipped with the SPCR to minimize vibrations on PV panels resulting from SPCR oscillations during operation. The proposed approach offers customization, allowing flexibility in design to accommodate different types of PV panels, wheeled track mounting spaces, SPCR weight, and PV array structures. Furthermore, the design's advantage lies in its compatibility with commercial solar panel cleaning robots, as it can be installed on mobile platforms, facilitating the ease of upgrades and maintenance. The experimental results unequivocally demonstrate the effectiveness of the proposed multi-suspension unit in reducing or eliminating substantial deflections, particularly when dealing with vibrations solely from rotating brushes. However, the proposed design was found to reduce the number of deflection deviations while the vibration frequency remained constant.  
Figure 23: Experimental results for the dynamic state of SPCR with the PV array installed in landscape orientation: (**a**) SPCR passes over the module along the center of the PV panels; (**b**) SPCR passes over the modules along the side of the PV panels.  
Figure 24: Variation of deflection deviation under SPCR operation on the portrait PV module: (**a**) SPCR passes over the module along the center of the PV panels; (**b**) SPCR passes over the modules along the side of the PV panels.  
For future work, this study will incorporate a mass-spring-damper system, integrating a shock absorber alongside the spring. This enhancement is expected not only to reduce vibration amplitude but to also reduce the vibration frequency of the PV panel. To achieve this, the dynamics based on the Lagrange equation for the multi-suspension unit will be considered to determine the necessary damper coefficient for effective vibration suppression. Alternatively, a novel mechanical design for the shock absorber, capable of regulating the damper coefficient, will be explored to adapt to varying vibrations as the SPCR changes motion. These advancements aim to further enhance the overall vibration mitigation capability of the multi-suspension unit.  
Conceptualization, C.T.T. and N.T.L.; methodology, N.T.L. and V.T.D.; software, N.T.L.; validation, T.D.P., M.D.N. and V.T.D.; formal analysis, T.D.P. and N.T.L.; investigation, H.H.N.; resources, C.T.T.; data curation, T.D.P. and M.D.N.; writing--original draft preparation, N.T.L., T.D.P. and C.T.T.; writing--review and editing, V.T.D.; visualization, M.D.N. and T.D.P.; supervision, H.H.N.; project administration, T.T.N.; funding acquisition, T.T.N. All authors have read and agreed to the published version of the manuscript. This research was funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number TX2023-20b-01.  
Not applicable.  
The authors declare no conflict of interest.
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 4 Applications of Deep Learning in Agriculture
### Identification/Classification of Plant Disease
They employed off-the-shelf ConvNet representations to evaluate the plant growth of a maize crop. SVM classifier is proposed in [24], which involves an autonomous plant disease identification using image analysis with a 94 % accuracy. The experiment was carried out with a dataset of 500 plant leaf images taken from thirty different native plant species. The other deep learning studies such as [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35] developed automatic plant detection and recognition systems to classify nine different diseases by defining a single healthy class. All these studies employed the PlanVillage dataset for training and testing purposes and achieved an accuracy of >91%.