esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform
## 3 Materials and Methods
### Dataset  
The dataset employed in this study is public access data. The APTOS [16] and DDR [60] data were combined for training, testing, and validation. APTOS contained 3662 photos, each accompanied by its corresponding label. DDR had 12,524 images depicting various stages of DR. The two were merged, resulting in 16,186 images fed into the model with five labels (no DR, mild, moderate, severe, and PrDR). However, 16 images were excluded from the dataset due to missing valid image names in the file, so the total images were 16,170. Out of the total images, we employed 4333 images with 5 classes.  
The class label no DR (class 0) has 1014 images, mild DR (class 1) 999 images, moderate DR (class 2) 684 images, severe DR (class 3) 429 images, and PDR (class 4) 1207 images. Following a 70:15:15 data split, 3033 images were utilized for training, 650 for validation, and 650 for testing. The complete count for each class is depicted in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **Class 0** & **Class 1** & **Class 2** & **Class 3** & **Class 4** \\ \hline Training & 715 & 711 & 483 & 299 & 825 \\ Validation & 151 & 139 & 95 & 71 & 194 \\ Testing & 148 & 149 & 106 & 59 & 188 \\ \hline \end{tabular}
\end{table}
Table 2: The count of each class in training, validating, and testing datasets.  
Figure 2: The suggested study’s workflow.
## 2 Research Methodology  
The methodology adopted in the present research work can be summarized in the following phases (Figure 1), described with more details in the following paragraphs.  
* _Phase 1: Seismic vulnerability assessment of the case study._ Different statistical methods were selected and applied to Campi Alto di Norcia (Italy) with the aim of determining the corresponding IV and comparing their accuracy in predicting the damage scenarios. Possible limitations of selected methods were highlighted, finally selecting the most performing one for the considered case study. A database of information with data coming from in-situ surveys was elaborated.
* _Phase 2: Calibration of the IV method._ Based on the achievements of Phase 1, the calibration of the adopted vulnerability index method for the selected case study was performed, by comparing the \"observed\" damages caused by the 2016 earthquake with the \"analytical\" ones predicted through the previously selected method. The calibration was carried out through a statistical analysis within the context of Bayesian inference [24].
* _Phase 3: Development of an enhanced approach with the IQ index._ An additional IQ index was introduced in the calibrated procedure (Phase 2) accounting for uncertainties related to the lack of information due to limited access or inspections. The knowledge uncertainty was quantified through a parameter measuring its influence in the final vulnerability assessment; an iterative analysis involving the change of the uncertainty range of the parameters based on the observed damages was adopted.
# Logistics Service Provider Lifecycle Model in Industry 4.0: A Review
## 4 Concept of Logistic Service Provider Lifecycle Model (LSLM)
### The Middle of Life (MOL) Phase
#### 4.2.1 Operation Performance  
Operational performance is the leading part of logistics operations to lead to growth or decline in customer satisfaction to be globally competitive. Artificial intelligence, CPS, and autonomous manufacturing enable effective operational performance. This factor consists of three time aspects, namely, order lead time, on-time shipment delivery, and real-time information sharing. Time is a quantitative performance in which companies need to enhance their efficiency, reducing time and cost in the supply chain [103]. Reducing time is an excellent point to use in lean methodology to reduce NPV in the activities along the supply chain.  
Sub-Criteria of Order Lead Time  
Order lead time in logistics operations can be separated into two parts, the period of the order confirmation from sales acceptance, order confirmation by sales, and sending the product or service to the customer [68]. The order lead time is an intermediate criterion that relates to the improvement of the process, where time can have a direct impact on performance. An improved order lead time confirms the order and reduces planningalong the supply chain to analyze the stock of products sent directly to the customer [26]. Cooperation in the supply chain immensely impacts investigating the demand for products. Order lead time is a complexity of logistics operations that involve transport and warehouse management [76; 104], analyze the forecasting and planning of products in manufacturing, and manage information sharing through the same conditions along the supply chain [105]. Therefore, reducing cost and improving efficiency are the first strategies for companies to improve by reducing order lead time, thereby improving customer expectations.
## 4 Experimental Results and Discussion  
The proposed method is tested on a benchmark dataset about satellite images, the corresponding experimental results are analyzed thoroughly, and the proposed method is verified to be feasible and effective.
# EGSDK-Net: Edge-Guided Stepwise Dual Kernel Update Network for Panoptic Segmentation
## 4 Experiments
### The Dataset and Metrics  
We utilize the widely used Cityscapes dataset [14] to study EGSDK-Net. This dataset contains a total of 5000 images at a resolution of 1024 \(\times\) 2048, split into 2975 training images, 500 evaluation images, and 1525 test images. It offers fine-grained panoptic labels across 30 categories, with 19 (8 \"things\" and 11 \"stuff\" categories) allocated for evaluation. Similar to RT-K-Net [5], we apply data augmentation techniques such as random scaling, random cropping, random adjustments to color and brightness, and random horizontal flipping, where the random cropping size is fixed at 512 \(\times\) 1024. Following earlier methods [2; 5], we assess panoptic segmentation results using the standard metric: Panoptic Quality (PQ) [1]. PQ is a metric used to measure the similarity between panoptic segmentation predictions and ground truth. It unifies the evaluation criteria for both semantic segmentation and instance segmentation. By independently calculating the PQ for each category and averaging over all categories, PQ becomes insensitive to class imbalance, allowing for a fair evaluation of panoptic segmentation performance. For each category, the unique matching of predicted and ground truth segments is divided into three groups: true positives (TPs), false positives (FPs), and false negatives (FNs), representing matched segment pairs, unmatched predicted segments, and unmatched ground truth segments, respectively. Ref. [1] defines the use of intersection over union (IoU) to determine whether the ground truth segment and the predicted segment match. If \(IoU>0.5\), it is considered TP. For a given class \(c\), the formula for IoU is defined as follows:  
\[IoU_{c}=\left|\frac{P_{c}\cap G_{c}}{P_{c}\cup G_{c}}\right|, \tag{11}\]  
where \(P_{c}\) and \(G_{c}\) represent all the pixels labeled as class in the predicted and ground truth segments, respectively. Given these three groups, PQ is defined as follows:  
\[PQ=\frac{\sum_{(p,g)\in TP}IoU(p,g)}{|TP|+\frac{1}{2}|FP|+\frac{1}{2}|FP|}. \tag{12}\]  
Furthermore, to provide a complete evaluation of the model's performance, we report metrics for the \"thing\" and \"stuff\" classes, referred to as \(PQ_{th}\) and \(PQ_{st}\), respectively.
# Networking of Multi-Robot Systems: Architectures and Requirements
## 4 Communication Issues and Requirements for MRS Systems
### Robot-to-Infrastructure (R2I) Communication  
MRS networks very often need to exchange information with infrastructure networks or the Internet. Consequently, one of the important components in the communication architecture of MRS systems is the R2I link. In this case, one of the robots that are connected to the others through R2R links can be equipped with additional resources, which enable it to play the role of a gateway node provide connectivity with the backbone network and the Internet. The link from the gateway robot to the infrastructure network access point (R2I link) can use longer range access protocols such as IEEE 802.16 (WiMAX), cellular, or satellite depending on communication services available in the geographic area. This gateway node must be able to map the networking parameters associated with the data traffic in the data link layer header to the corresponding data link layer header in the infrastructure network.
## 5 LPWAN Application Requirements  
The extensive range of LPWAN applications correspondingly results in a wide variation in the characteristics mentioned in Section 2. Devices may span coverage ranging from very short to remote distances, from stationary to moving positions, from battery-based low power to commercial power-based connections, and a range of friendly to hostile environments. The coverage span for some applications is highly localized, for example, indoor stationary deployments. Applications involving device mobility, such as asset tracking, require global service coverage [7]. The massive IoT application segment requires connectivity for a large number of devices in contrast to critical IoT applications that require ultra-low latency and ultra-high reliability. A significant segment of low power wide area solutions typically sends small-sized messages infrequently, are delay-tolerant, do not need high data rates, and require low power consumption and low cost.  
Figure 2: Wireless access geographic coverage. RFID, radio frequency identification; NFC, near field communication; NB-IoT, Narrowband Internet of Things; LTE, long term evolution; GSM, global system for mobile communications; MTC, machine type communication; SUN, smart utility network; LE, low energy.  
The characteristics from Section 2 can be mapped into a set of requirements [4]. The key requirements relate to handling of M2M traffic, massive capacity, energy-efficiency, low power operations, extended coverage, security, and interworking.
# Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands
## 1 Introduction
Currently, several passive microwave radiometers operational in the L-band, including SMOS (Soil Moisture and Ocean Salinity) (Soil Moisture and Passive) (Soil Moisture and Passive) (Soil Moisture and Passive, 2018), provide SSM estimates at a very high revisit time (up to 1 day) and a very low spatial resolution (25 km-36 km). The low spatial resolution makes the available sensors in the L-band useless for local and regional hydrology applications and agricultural management. Currently, the only available SAR L-band data are provided by the Phased Array Synthetic Aperture Radar (PALSAR-2) onboard the Advanced Land Observing Satellite (ALOS-2). Research on the potential of ALOS-2/PALSAR-2 L-band SAR data at a high spatial resolution to estimate the SSM with the presence of crop cover is too limited (Saleitte Argentino de Observacion COn Microondas, launched on 08 October 2018) and the planned satellites SAOCOM-1A (launch planned on 2018) and NISAR (NASA-ISRO SAR, launch planned on 2021) illustrate the interest in the L-band for many applications in agricultural and hydrological domains. Finally, it is worth to mention that even when the SAR signal penetrates the vegetation cover, the SAR derived SSM could be not very accurate for very smooth and very rough plot ([PERSON] et al., 2019). Indeed, the backscattering from soil is a sum of SSM and surface roughness contributions ([PERSON] et al., 2018). For a given SSM condition and vegetation parameter, the soil backscattering increases as soil roughness increases. For instance, [PERSON] et al. (2017) reported that the SSM derived from C-band SAR data is highly overestimated and highly underestimated (by about 10 vol.%) for very rough (roughness > 3 cm) and very smooth (roughness < 1 cm) plots, respectively. The goal of this paper is to investigate the potential of SAR data in the C and L bands to penetrate into crop cover in all vegetation growth phases. In this paper, the ability of the L-band to penetrate into wheat and grassland canopies, as well as the potential of the C-band to penetrate into wheat, grassland, and maize canopies were investigated. The potential of the L-band to penetrate the maize canopy could not be analyzed because our SAR images in the L-band did not cover our maize plots. The penetration ability was assessed by analyzing the sensitivity of the SAR signal to soil moisture and by studying the temporal evolution of the SAR signal according to SSM and NDVI variations during one complete growth cycle. This paper includes four parts. The second part provides descriptions of the study site and the dataset used. The third part presents the data analysis. Finally, the fourth part gives the main conclusions.
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 4 Simulation Results and Analysis
### Papr
Comparison is made between the peak value and average value of the FTN waveform, which is shown in Figure 10. As the FTN acceleration factor \(\alpha\) decreases, the average value keeps increasing, whereas the peak value first decreases and then slowly increases and finally increases sharply. Therefore, the PAPR of FTN first decreases and then increases accordingly. This explains the observations of PAPR performance in Figures 4-6. Figure 8: Peak value of RRC filter-based signal with \(\alpha=0.64\). Figure 9: Peak value of RRC filter-based signal with \(\alpha=0.25\).
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 2 Localization Approaches
### Centralized Localization Approaches  
The centralized location approaches are based on powerful nodes for coordination among the nearby sensor nodes. The sensor nodes collect information such as received signal strength, details of adjacent nodes and then communicates it to the centralized node. The central coordinating node analyses the information and estimates positions of different sensor nodes using multi-dimensional and stochastic algorithms [7; 8] and then conveys it to the individual nodes. Centralized localization techniques overcome insufficient resources at the sensor nodes at the expense of higher communication costs. With the increase in the number of IoT nodes in the network, centralized approaches have become highly expensive. The nodes closer to the base station get exhausted due to more involvement  
Figure 1: Classification of localization techniques.  
Figure 2: Centralized and distributed localization.  
in the communication process. The complexity of the centralized algorithms is high, and hence the individual nodes are not allowed to do location estimates. Multi-dimensional scaling (MDS) [7], stochastic optimization algorithms [8] and linear programming [9] are the main techniques used in the designing of these algorithms.  
Centralized routing is appropriate for the networks in which the processing control trusts mainly on a single device. In these scenarios, the central device is accountable for processing, coordinating and managing the identified data activities. It also forwards the data to a sink node. The key advantages of the centralized routing technique are that it allows roaming inside the network and provides energy management and context information availability permits an improved application design in terms of nodes placement, application awareness, etc. Whereas, in distributed routing scheme, the information is managed by every node, and decisions are taken locally. The distributed routing included autonomous devices, and every node shares information to its adjacent node to have robust routing features. Therefore, it is good for larger networks and distributed applications such as multi-agent and self-organized systems.
# Research on Hybrid Vibration Sensor for Measuring Downhole Drilling Tool Vibrational Frequencies
## 3 Experiments  
### Experiment Platform  
A self-built vibration simulation device is used to simulate the vibration of the down-hole drilling tool, as shown in Figure 7. The sensor is installed on the top of the vibrator, and the controller is adjusted to make the vibrator vibrate at different frequencies. The vibration table has dimensions of 700 mm in both length and width, and it can simulate vibrations of up to 200k Hz. The HDV-TENG detects and produces a voltage signal, which is passed through the data acquisition card (USB5632, ART Technology Co., Ltd, Beijing, China), and a 6514 electrometer (Keithley Inc., Cleveland, OH, USA, 6514) was collected and sent to the PC software to display the signal waveform. In addition, the simulated environment is regulated by means of an incubator and a humidification device. Experiments include sensing performance experiments, power generation performance experiments, and stability experiments.  
Figure 6: The functions of each part of the sensor.  
Figure 7: Schematic diagram of experimental platform.
###### Abstract  
As technology advances, the services provided by domain servers require new innovative techniques that can be optimized for frequent changes. Man-in-the-Middle (MitM) attacks on Domain Name Servers (DNS) pose a security threat, enabling attackers to intercept, modify, and redirect network traffic to malicious sites or users. This study designed an anomaly-based detection scheme that identifies and mitigates MitM attacks on DNS. The proposed model utilizes machine learning algorithms and statistical analysis techniques to ensure that the analysis of DNS query patterns can efficiently detect anomalies associated with the MitM. By integrating the Cuckoo Search Algorithm, the scheme minimizes false positives while improving the detection rate. The Proposed scheme was evaluated using the Internet of Things Intrusion Detection (IoTID) and Intrusion Detection System (IDS) datasets, achieving a detection accuracy of 99.6% and demonstrating its effectiveness in minimizing the MitM attacks on DNS.  
Keywords:man-in-the-middle attacks; domain name system; machine learning; statistical analysis; cuckoo search algorithm; false positive Article
## 4 Performance Analysis
### Average Memory Size  
According to the protocol in this paper, when a conflict occurs, the receiver needs to buffer conflicting packets temporarily and then wait for a replica. Therefore, the receiver needs a buffer, and only in pseudo success scenarios does the system need to store conflicting packets. Therefore, the probability of pseudo success, \(P_{PS}\), is the probability of the packets stored in a buffer. \(R\) indicates the average memory size. The average number of packets stored in a buffer for each slot can be expressed as  
\[R_{in}=\sum_{N=0}^{M}\pi_{N}(p_{f},p_{r})\cdot P_{PS}. \tag{17}\]The average number of packets released from the buffer for each slot is  
\[R_{out}=\frac{R\cdot\sum_{N=0}^{M}\pi_{N}(p_{f},p_{r})\cdot P_{re}}{N_{a}}. \tag{18}\]  
Under a steady state, the average number of packets stored in a buffer is equal to the average number of packets released from a buffer, i.e., \(R_{in}=R_{out}\). From the Equations (17) and (18), the average memory size \(R\) of the system can be obtained as  
\[R=\frac{N_{a}\cdot\sum_{N=0}^{M}\pi_{N}(p_{f},p_{r})\cdot P_{PS}}{\sum_{N=0}^{ M}\pi_{N}(p_{f},p_{r})\cdot P_{re}}=\frac{\sum_{N=0}^{M}\pi_{N}(p_{f},p_{r}) \cdot N\sum_{N=0}^{M}\pi_{N}(p_{f},p_{r})\cdot P_{re}}{\sum_{N=0}^{M}\pi_{N}(p_ {f},p_{r})\cdot P_{re}}, \tag{19}\]  
where \(P_{re}=P_{f}(0,N)\cdot P_{r}(1,N)\).
## 2 Design
### Sensing, Control, Communication, and Localization
#### 2.4.2 Control  
The computing of different algorithms requires the corresponding hardware to balance performance with costs in terms of time and power consumption. A Raspberry main board (1 GHz CPU), acquired off the shelf, is used to run the main software to deal with docking, movement, and coordination tasks. The motor actuation, low-level sensor management, and chassis motion control is dealt with by an ARM-based motion controller board (168 MHz MCU) developed by ourselves. The motor and infrared sensors of the docking mechanism are managed by an ARM-based front board (72 MHz MCU) developed by ourselves. These ARM boards communicate with the main board via USART. The visual recognition using video in the docking system is processed by an ARM-based visual processing board (480 MHz MCU) which communicates with the main board via SPI bus because a high-speed interface is necessary to stream image data. The control hardware system is shown in Figure 6.  
Figure 5: Two degrees of freedom of the 2-SPS parallel mechanism. (**a**) The yaw motion is controlled via differential displacement of the two actuators. (**b**) The pitch motion is controlled via the displacement away from the initial position. (**c**) The orientation is achieved via simultaneous control of the two actuators.
## 3 Current Signal Processing and Feature Extraction
### Characteristic Processing of High-Frequency Current Signal
#### 3.1.2 Peak Value  
The peak index \(C_{f}\) is defined as the ratio of signal peak to root mean square value, and the calculation formula is:  
\[C_{f}=\frac{x_{\max}}{x_{rms}}=\frac{\max[x_{\tau}(\tau)]}{\sqrt{\frac{1}{N} \sum\limits_{i=1}^{N}x_{i}^{2}}} \tag{2}\]  
In the formula, \(x_{max}\) is the signal peak, \(x_{rms}\) is the signal root mean square value, \(x_{i}\) is the amplitude of the \(i\) discrete signal, and \(x_{\tau}\) is the processed discrete signal sequence.
## 4 Discussion  
The aim of the current study was to examine the progression of physiological responses and performance in a SIT set and the effect of SIT on swimming performance after a 6-week training period. It is worth noting that swimmers' critical speed improved after the 6-week SIT period. Comparison of Pre vs. Post values indicated that performance time in the 4 \(\times\) 50 m and 100 m tests as well as blood lactate concentration remained unchanged following the 6-week SIT period. Moreover, despite the fact that blood lactate in the 4 \(\times\) 50 m testing session did not change in Post- vs. Pre-training measurements, it progressively increased in the 4 \(\times\) 50 m front crawl training set applied in week 3 and week 5 compared to week 1. The performance time of the 4 \(\times\) 50 m front crawl training set was unchanged throughout the 6-week training period.  
Increased critical speed (4.4 \(\pm\) 5.2%) was observed after the 6-week SIT period. This finding is in agreement with the respective findings reported for aerobic indices in water polo players after 8 weeks of repeated sprint interval training [12]. It is possible that swimmers' aerobic endurance improvement can be attributed to an increase in oxidative enzyme activity [26; 27], muscle buffering capacity [16], and ionic regulation [16]. However, BL was similar in Pre vs. Post testing after the 4 \(\times\) 50 m testing session. The increased CS concomitant with the similar BL and HR may indicate that SIT led to the swimmer's aerobic endurance improvement after the 6-week period. Moreover, [PERSON] et al. [28] observed that the energy supply of SIT requires adenosine triphosphate resynthesis from all energy systems. Therefore, a wide range of metabolic, aerobic, and anaerobic adaptations (see Figure 5) can be expected to result from such training [29]. Such changes are probably connected to the training load [30]. In the current study, we recorded 7-21% increments in training load during the 6-week SIT period. It is possible that the 18 SIT sessions applied during the 6-week period led to improved aerobic endurance faster than previous works [31; 32], as reflected by the improvement in critical speed.