esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology of the Proposed Concept
### Equalization of a Histogram  
The histogram equalization in this paper allows these areas to display a higher contrast. The histogram accomplishes this by effectively distributing the most frequent intensity values. The histogram shows a graphical interpretation of the tonal intensity distributed in a digital image [41]. Usually, the distribution of its discrete intensity levels is within the range of 0 to \(L-1\), where \(L\) is the total number of gray levels in the image (typically 256). The tonal intensity distribution is a discrete function \(h\) that is associated with each intensity level: the number of pixels (\(r_{k}\)) with this intensity (\(n_{k}\)). Histogram equalization is a method to improve the contrast in images by modifying the intensity distribution of the histogram. In [42] the authors demonstrated the enhancement of an image that improved its visual effect and overall brightness and clarity. The objective of histogram equalization is to provide linearity to an image's cumulative distribution function (CDF). As shown in Equation (3), the CDF of the histogram is defined as the cumulative sum of all the probabilities within its domain [34,43]:  
\[\text{CDF }(x)=P\left(X\leq x\right) \tag{3}\]  
Equation (2) shows a value less than or equal to \(x\) of the random variable \(X\) (e.g., _image_), where \(n_{kn}\), is the variation in the intensity level of the image. Figure 7 shows the effect of histogram equalization.  
Histogram equalization is an essential technique for enhancing low-contrast images. In this work, the _'histogram equalization'_ function is utilized on the thermal solar cell images. The equalized histogram image \(q\) is defined by:  
\[q_{\text{ij}}=floor\left(\text{L}-1\right)\sum_{n=0}^{f_{ij}}p_{n} \tag{4}\]  
At the normalized histogram of \(f\) with a bin given below:  
\[p_{n}=\frac{number\ of\ pixels\ with\ intensity\ n}{total\ number\ of\ pixels}\ n=0,\ 1,\ldots\text{L}-1 \tag{5}\]  
where _floor_() is rounded off to the nearest integer, which is equivalent to transforming the pixel intensities, \(k\), of \(f\) using the function below:  
\[\text{HE}(k)=floor\left(\left(\text{L}-1\right)\sum_{n=0}^{k}p_{n}\right) \tag{6}\]
# Effect of IoT Capabilities and Energy Consumption behavior on Green Supply Chain Integration
## 3 Methodology  
### Sample and Data Collection Procedure  
In this study, partial least square--structure equation modeling (PLS--SEM) has been used to analyze the data. It is a new methodology on the exploratory stage relationships which can work on a small sample size. Considering this fact, this study analyzes the conceptual framework with modern statistical techniques to obtain greater accuracy in the results.  
The target population of this study is the retail industry in Pakistan. The sample was taken from the utility, metro, and modern retail stores from five major cities, i.e., Faisalabad, Karachi, Peshawar,  
Figure 1: Proposed research model.  
Lahore, and Islamabad. The justification of the population is that, in modern trade too many customers and suppliers are involved in trading on a regular basis. Modern retail stores have a number of products and brands because they have more space and larger investments compared to other local stores. They are actively involved with customers and suppliers at the same time to create a balance between the demand and supply of products. Therefore, they need to manage their stores with the help of new technologies such as computers, scanners, and radio frequency identification (RFID) to increase their operational performance. This study focuses on IoT, SI, CI, ECB, GT, and GSCP; all these concepts are practically implemented in modern retail stores [76]. Hence, modern retail stores are a suitable population for this study. In the previous literature, the retail sector was considered as the targeted population for IoT, supply chain integration, and performance [11].  
The sample is the subset of the population. In this study, 400 employees from modern trade stores have been selected through a simple random sampling technique; a probability sampling method. The demographics of the respondents are mentioned in Table 1. Both offline and online data collection methods were used. A questionnaire was designed based on the Likert Scale in this study. The offline questionnaire is distributed physically among employees while the online questionnaire link is forwarded through emails. A face to face survey has a better response rate compared to the online method, but the online survey method is free and the data collection is quick this way. Data were collected from February 2018 to May 2018. For an online questionnaire, one gentle reminder was sent to increase the response rate. In this study, a total of 400 questionnaires were distributed using both methods. The total number of completed filled questionnaires that were received was 250, that resulted in a total response rate of 62.5%.
## 2 Description of the THz Wireless Communication Setup
### THz Receiver Setup
It is well known that GaAs-based mixers typically have relatively higher LO power demands, from 1 to 3 mW for narrow-band and 4 to 10 mW for full-band mixers. In contrast, InGaAs-based mixers show significantly lower LO power requirements, needing 0.1 to 0.5 mW for this particular design. A picture of the mixers under study is shown in Figure 2. Independently of the diode technology, both mixers have the same block. The NF of the GaAs- and InGaAs-based Schottky diode mixers as a function of LO power is depicted in Figure 3. These results were obtained under electrical pumping conditions. As seen in Figure 3a, the GaAs-based mixer exhibited superior performance, reaching a minimum NF of approximately 6 dB. The noise figure decreased sharply as the LO power increased, stabilizing at 1.5 mW, where it reached a plateau. Beyond this power level, the NF remained constant even at LO power levels, exceeding 3 mW. However, at LO powers below 1 mW, the NF deteriorated significantly, indicating that the mixer operates inefficiently in this regime and would not be suitable for practical applications at such low LO drive levels. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mixer**} & **Diode Technology** & **Frequency** & **IF** & **DSB CL** & **P LO (mW)** \\  & **nology** & **(GHz)** & **(GHz)** & **(dB)** & \\ \hline Narrow Band & GaAs & 270–320 & 0–25 & 6 & 1–3 \\ \hline Narrow Band & InGaAs & 270–320 & 0–25 & 13 & 0.1–0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main characteristics of the mixers under study, where diode technology, frequency, bandwidth, conversion loss, and LO power requirements are compared. Figure 2: The 300 GHz sub-harmonic mixer mechanical block used in this study. The mixer presents a WR 6 and a WR 3.4 rectangular waveguide for the LO and RF ports, respectively. The narrow-band mixer employs a high-performance SMA female connector enabling IF frequencies above to 25 GHz. In contrast, Figure 3b illustrates the performance of the InGaAs-based mixer, which achieved acceptable NF values with substantially lower LO power. The NF improved rapidly as the LO power increased from 0.05 mW to 0.15 mW, where it reached a local minimum and stabilized. Unlike the GaAs mixer however, the InGaAs device exhibited a degradation in NF when the LO power continued to increase. Beyond 0.4 mW, the NF rose noticeably, indicating a power-dependent degradation mechanism. This happens due to exceeding the opimal operational point of the diodes, resulting in noise figure degradation. These results highlight that the GaAs mixer demonstrates lower NF at optimal LO drive levels and requires significantly higher LO power to achieve this performance. The InGaAs mixer, on the other hand, offers acceptable NF with minimal LO drive but suffers from performance degradation at higher LO powers.
## 4 Experimental Results and Discussion
### The Datasets  
We have conducted the experiment using the IoTID20 [53] and the NSL-KDD [61] datasets to evaluate the performance of the hybrid feature selection approach. The IoTID20 dataset consists of various types of IoT attacks (i.e., DDoS, DoS, Mirai, ARP Spoofing, etc.) as well as normal (benign traffic). The IoTID20 dataset was collected from the IoT ecosystem of a smart home. The smart home was designed to incorporate multiple interconnected components including AI Speakers (SKINGU), Wi-Fi cameras (EZVIZ), laptops, smartphones, tablets, a wireless access point (Wi-Fi), and a Wi-Fi router. The cameras and AI speakers were represented as the IoT victim equipment, and the other equipment were represented as the attacking devices. The testbed has been implemented to simulate different actual attacks in the IoT ecosystem using the Network Mapper (Nmap) tool. Figure 2 represents the testbed environment where the IoTID20 dataset [62] was generated and collected. Figure 3 shows a taxonomy of the dataset and the number of instances for each target of the dataset. The IoTID20 dataset has 86 features and contains 625,784 instances.  
NSL-KDD dataset is the second dataset that we have utilized for the validation of this work. It is the improvement of the KDD99 by removing the redundant and duplicate data from the original KDD99 dataset. It contains 41 features (32 continuous and 9 nominal attributes) to describe each activity in an IoT system and the targets (classes), converting five types of Normal, Probe, DoS, R2L, U2R. The advantages of NSL-KDD are focused on minimizing the level of difficulty that exists in the original dataset KDDcup99. However, it has the same problems regarding the real network representation. The NSL-KDD dataset is widely used in realm of intrusion detection, and other related areas. The description of NSL-KDD dataset is shown in Figure 4.  
Figure 2: The IoTID20 dataset testbed environment.
# Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features
## 4 Experimental Results and Analysis
### Training Data  
It is normally difficult to acquire a large dataset of paired fog-free images and synthetic datasets are usually used (i.e., images with fog are synthesized based on atmospheric scattering models on the real dataset). RESIDE [19] is a large-scale synthetic dataset consisting of five subsets: the indoor training set (ITS), outdoor training set (OTS), comprehensive objective test set (SOTS), real test set (RTTS), and hybrid subjective test set (HSTS). Among them, ITS and OTS are used for training on indoor and outdoor environments, respectively, SOTS is used for testing on both indoor and outdoor scenes, and RTTS is used for testing on real fogged images. The fog removal models in this paper were trained on outdoor datasets including the outdoor training set (OTS) of RESIDE and the NTIRE2020-Dhaze dataset for uniform haze and non-uniform haze removal, respectively. For the outdoor training set (OTS), a sub-dataset of RESIDE, 41,240 samples were randomly selected from this paper for training, and 500 outdoor synthetic haze images were randomly selected from the synthetic target test dataset (SOTS) for testing. For the NTIRE2020-Dhaze dataset, 4800 samples were synthesized from the first 50 original high-resolution samples of the dataset by random cropping, flipping and rotating for training, and the last five samples of the dataset were used for testing.
## 3 Results and Discussion
### Metric of Confidence  
The decisions made by the classifier are based on probabilities that are estimated (through the ANN) for each species. The predicted label is then assigned to the species with the highest probability. Here, we analyze the effect of introducing a confidence threshold requiring the assigned probability to be above the threshold in order to accept the classification. In Figure 6, one can see that the precision (cTPS/cPs), i.e., the ratio of true positives to all classified positives changes as the confidence threshold is varied between 0.5 and 1.0.  
We see that the precision increases as the confidence threshold is increased. For instance, for \(n=30\) species, the precision for a confidence threshold in range of 0.9-1.0 is more than 0.8. Similar results can be seen for other \(n\). This basically shows that when the model is assigning high confidence to its predictions, the predictions are mostly correct, which should be expected from a good classifier.
## 2 Results
### Microbiological Parameters in Inoculated Samples  
_Staphylococcus aureus_. In milk inoculated with 7 log CFU of _S. aureus_ ATCC 13565 _per_ mL, pathogen counts in curd and serum were 7.8 log MPN/mL and 7.1 log MPN/mL, respectively, while in the 24 h cheese, 8.7 log MPN/mL were recovered. After seven days of maturation of the inoculated cheese, microorganisms from samples taken from the core survived until day 45 (0.15 log MPN/g), unlike the main matrix samples where it was only possible to detect them after 15 days (2.55 log MPN/g). The area where the longest survival time of _S. aureus_ was observed was the surface, since counts were detected until day 90 with 3.6 log MPN/g. The statistical analysis of the inoculated cheese showed no significant differences regarding the recovery of the microorganisms on day seven (_p_ = 0.326). From the second sampling time (15 days), a statistically significant difference was observed in the distribution of _S. aureus_ (_p_ = 0.001) in cheese. All the samples, regardless of the sampling area, were negative for staphylococcal enterotoxin (\(\geq\)1 ng/mL) in inoculated cheese after 24 h. Initially was detected in milk, rennet, and whey (Table 2). _Salmonella Typhimurium._ In the inoculated milk, _S. Typhimurium_ ATCC 14028 showed a gradual and continuous decrease in all samples from the 7.2 log MPN/mL initial inoculum. After 24 h of storage, cheese samples had a reduction of 2.5 log (4.8 log MPN/g of cheese). In the following samplings, the concentration of the pathogen gradually decreased until it stopped recovering on day 45 (core) and 60 (main matrix and surface) (Table 2). _Listeria monocytogenes._ Regarding cheese made with inoculated milk (7.3 log of MPN/mL), a similar behavior was observed compared to the uninoculated milk. In cheese, a pathogen concentration increase was observed (8.7 log of MPN/g) after 24 h of storage, then its presence decreased. In the core and the main matrix, only 5.3 and 5.9 log of MPN/g were recovered until day 15, respectively, while on the surface, _L. monocytogenes_ ATCC BAA-751 survived longer since its last recovery was on day 45 with a 4.5 log of MPN/g. Table 2 shows the behavior of each pathogen in the different parts of Cotija cheese that were analyzed during its elaboration and maturation process. Indicator groups. Lactic acid bacteria (LAB), aerobic mesophilic bacteria (AMB), coliform organisms (CO), molds and yeasts (M/Y) were quantified. Table 3 shows the microbiota content in milk, curd, whey, and cheese with 24 h of maturation, without inoculation. No significant differences were observed in LAB, CO, M/Y of milk, curd, and
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 3 Results and Discussion
### Energy Awareness and Wind Resilience  
#### 3.3.1 Throttle Modulation Simulation  
The effect of wind for throttle modulation was simulated for a fixed ground speed of 15 mph, a wind speed of 5 mph flowing from the west, and a maximum UAV TAS  
Figure 12: Blockage map path in the presence of hazardous areas shown in green. The white area denotes the zones within the visibility region, whilst the grey one denotes the blockage regions.  
Figure 13: Cumulative links between UAV and points of interest through the paths under hazardous weather.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Path Type** & **Distance (m)** & **UAV Elevation** & **No. of Links** & **Links/Distance** \\ \hline Blockage map & 76,581 & 100 & 1,011,607 & 13.20963 \\ \hline Blockage map + hazards & 81,241 & 100 & 1,081,354 & 13.31045 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average number of successful links per meter of flight under hazardous weather.  
of 40 mph. Specifically, the heading angle was constantly changed throughout the path to model the influence of wind. Figure 14 shows the throttle and true air speed of the UAV to ensure a fixed ground speed. Notably, a required TAS above 15 mph implied that the UAV was flying in the opposite direction to the wind and required an increase in throttle. Conversely, when the TAS was lower than 15 mph, the UAV was flying in the same direction as the wind, such that the throttle could be reduced.
# Telehealth Secure Solution to Provide Childhood Obesity Monitoring
## 6 Security Analysis
### Key Space Analysis  
An exhaustive search attack could be implemented by using super computers, where each possible key is used until the correct plain text is retrieved. The key space must be at least \(2^{128}\). to resist this attack. In the proposed scheme, each secret key is based on two control parameters and two initial conditions (\(a\), \(b\), \(x_{0}\), and \(y_{0}\)), with 64-bit floating-point arithmetic (IEEE 754 standard ([PERSON] et al., 2016)), i.e., with 15 decimal precision for each number, the key space is \(10^{15}+10^{15}+10^{15}+10^{15}\approx 2^{199}\).  
Thus, the key space is enough to resist an exhaustive search attack using super computers. In addition, the floating-point computation under 64-bit double precision allows a huge key space and avoids digital degradation of chaos.
## 6 Simulation Results
### First Stage: One Day Evaluation  
The solar radiation profile used for the one-day evaluation is shown in Figure 9. This profile corresponds to 18 May 2020. This day was selected since it combines rough and smooth variations with high radiation intervals.  
The same radiation profile was introduced to the modeled PV arrays shown in Figure 7. If the SOC was above 100% or below 30%, the battery banks stopped absorbing or injecting energy, respectively. The initial SOC was set to 60% to reduce the probability of reaching the SOC limits. The sampling period of all the simulations was set to 1 min.
# Monitoring of Ground Movement and Groundwater Changes in London Using InSAR and GRACE
## 4 Results
### InSAR Results
The standard deviation of average velocity for all the Persistent Scatterers (PSs) obtained from PSInSAR range from 0.45 to 10.5 mm/year. The statistical analysis suggests that the standard deviation is less than 3 mm/year for 75.34% of the monitoring points and less than 5 mm/year for 98.22% of the monitoring points (Figure 5), which suggests that the land displacement monitoring accuracy using the Envisat-ASAR PSInSAR is high. The accuracy of PSInSAR results is further validated using observed GNSS data by two sample \(t\)-test and discussed in Section 5.1.
## 4 Mission S-Net  
To demonstrate the above proposed network concept, the nanosatellite based mission S-band network of distributed nanosatellites (S-NET) was designed and implemented. The following sections describe the mission architecture.
## 3 Data Results and Analysis  
### PS-InSAR Deformation Monitoring Results of South-North Water Diversion Project High Fill Canals  
Sentinel-1A radar images from October 2016 to June 2017 were used to monitor the deformation of the high-fill canal in Wenzhuang Village, Ye County, Middle Route of the South-North Water Diversion Project using PS-InSAR technology. The subsidence results are shown in Figure 6, and the number of extracted PS monitoring points is much larger than the density of benchmarks. There is an obvious surface deformation phenomenon in about 1 km area on the east side of the canal, and the magnitude of the deformation reached 36 mm. It can be found the deformation on the east side of the canal is more serious than that on the west side of the canal. Since the data are ascending orbit data, the satellite orbits around the polar orbit, its observation direction is the lower right side of the vertical flight direction, and the projection on the ground is nearly from west to east. Therefore, when detecting features with strong reflectivity and obvious deformation, the angle of the east side of the canal forms a nearly vertical reflection with the satellite, which is more reflective. On the contrary, the reflectivity of the west side of the canal is weaker, which has an impact on the real monitoring results.  
Figure 6: PS-InSAR deformation monitoring results of the high fill canal in Wenzhuang Village, Ye County during December 2016–June 2017 (**a**) point location map comparing PS-InSAR data with second order leveling datas; (**b**) section of canal focus area; (**c**) point location map of longitudinal section and focus deformation area.  
Refined analysis of the canal in the section from pile number 210 + 130 to pile number 211 + 750 in Wenzhuang Village, Ye County, Middle Route of the South-North Water Diversion Project. There is obvious regional subsidence at F1 and F2 in Figure 6, and the causes of subsidence are divided into the following cases. The F2 monitoring point is at the exit of the ferry due to the fast flow rate of the canal, causing a small piece of serious deformation of the canal. At the location of point F1, it is located at the bend in the canal, above the bridge of the provincial highway; there are often large trucks passing through, and the speed is faster, which also causes the structure of the canal below to change easily.