esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.2.1 Pruning of Observations with Missing Values  
For each of the stations belonging to the study, all the observations in which a missing value is found for any of the four pollutants were eliminated, i.e., if a value was missing for a given measurement of a pollutant on a given day, the record for that measurement of the four pollutants at that station was eliminated. This results in a data set that consists of 100% of real, observed values and has no empty observations. Table 3 shows the resulting number of observations for each station after pruning, and Figure 2 clarifies the procedure followed in a schematic way.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Station**}} & \multirow{2}{*}{**Code**} & \multirow{2}{*}{**Site**} & **Latitude** & **Longitude** & \multirow{2}{*}{**Elevation (m)**} \\  & & & **(Degree)** & **(Degree)** & **(Degree)** \\ \hline ST 1 & PL0184A & Czesetchowa & 50.836389 & 19.130111 & 265 \\ ST 9 & PL0242A & Zabrze & 50.316500 & 18.772375 & 255 \\ ST 11 & PL0529A & Sosnowiec & 50.285956 & 19.184399 & 250 \\ ST 12 & PL0008A & Katowice & 50.264611 & 18.975028 & 273 \\ ST 18 & PL0239A & Rybnik & 50.111181 & 18.516139 & 245 \\ ST 19 & PL0240A & Tychy & 50.099903 & 18.990236 & 252 \\ ST 20 & PL0241A & Wodzislaw & \multirow{2}{*}{50.007629} & \multirow{2}{*}{18.455548} & \multirow{2}{*}{271} \\  & & Slaski & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stations.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **ST 1** & **ST 9** & **ST 11** & **ST 12** & **ST 18** & **ST 19** & **ST 20** \\ \hline Total observations & & 43,494 & 43,784 & 43,370 & 43,641 & 43,681 & 43,582 & 43,768 \\  & NO\({}_{2}\) & 357 & 343 & 116 & 311 & 392 & 62 & 222 \\ Missing values & NO\({}_{X}\) & 358 & 344 & 115 & 312 & 396 & 62 & 235 \\  & PM\({}_{10}\) & 174 & 605 & 173 & 867 & 238 & 106 & 168 \\  & SO\({}_{2}\) & 419 & 310 & 255 & 281 & 203 & 9
## 3 Flexible Demand Resources (FDRs) and Demands Response Program (DRPs)
### Time-Ahead Dynamic Pricing (TADP) Demand Response Program  
The cost of generation and the corresponding cost of electricity are highly affected by the shortages and surplus of power generated in the power system. Short periods of mismatch in load demand and generation might necessitate an over-sizing or additional capacity in the ESS that might not be necessary or efficiently used during normal operating times. A remedy to this challenge is to offer motivating electricity prices to influence a time shift in FDRs by the end user. A longer pricing horizon ahead of time can guarantee end-user participation in the DRP. Thus, in TADP DRP, time-ahead electricity pricing profile formulated as a function of the mismatch in the forecasted demand and generated power is relayed to the end user an hour (one period) in advance.
## 5 Discussion
### Sensitivity Analysis  
The sensitivity coefficient was the rate of change of the ratio between NPP and meteorological factors, such as precipitation, temperature, and sunshine hours.  
\[\mathrm{S_{V_{i}}}=\lim_{\mathrm{V_{i}\to i}}\left(\frac{\mathrm{\Delta{\rm{ imu}}/{\rm{NPP}}}}{\mathrm{\Delta{V_{i}}/{V_{i}}}}\right)=\frac{\mathrm{\partial{\rm{NPP}}}}{ \mathrm{\partial{V_{i}}}}\times\frac{\mathrm{V_{i}}}{{\rm{NPP}}} \tag{34}\]  
\(\mathrm{S_{V_{i}}}\), which is non-dimensional, is the sensitivity coefficient of \(\mathrm{V_{i}}\). NPP and \(\mathrm{\Delta{\rm{NPP}}}\) are the NPP and the rate of change, respectively. \(\mathrm{V_{i}}\) and \(\mathrm{\Delta{V_{i}}}\) are meteorological factors and the rate of change, respectively. A greater sensitivity coefficient corresponds to greater influence of the meteorological factors on NPP. The annual variation of sensitivity coefficient (Figure 12) also has unique distribution characteristics. Judging from the characteristics of multi-year changes, the sensitivity coefficient of the temperature and precipitation is dominated by stable fluctuation. The annual changes are not obvious, and the sensitivity coefficient of the temperature fluctuates around 0.63. The sensitivity coefficient of precipitation decreased from 2007 (the sensitivity coefficient of 2007 is 0.48). The sensitivity coefficient of sunshine hours showed an upward trend. The fluctuation is small from 2003 to 2005, and the sensitivity coefficient is approximately 0.4. This finding indicates that the sensitivity of the sunshine increased over the 11 years.  
Figure 11: Monthly changes of estimated NPP and precipitation (A), temperature (B) (from 2001 to 2011).  
Figure 12: Sensitivity coefficient of precipitation, temperature, and sunshine hours (from 2001 to 2011).
## 2 Background
### Overview of DoS and DDoS Attacks on CC
#### 2.2.2 Impact of HTTP-GET Flooding DDoS Attacks on CC  
CC security is crucial for maintaining service availability to end users. Typically, CCCs obtain CC services from CSP via the HTTP protocol, which is susceptible to misconfigurations and vulnerable to attacks if not properly secured. Clients acquiring online services from CSP via HTTP are often subjected to HTTP-GET flooding DDoS attacks. The HTTP-GET flood attack is similar to DDoS attacks, except instead of using data packets, it overloads the server with a large volume of HTTP-GET requests [27]. During an adversary-initiated HTTP flood attack, the attacker may use rented or own servers to bombard HTTP GET/POST requests to the targeted victim's VM, leading to a significant increase in resource consumption and losses for the victim. Finally, the targeted host is overburdened and swamped by HTTP flood attacks, compromising the entire CC. HTTP flood attack is challenging to detect because it uses valid HTTP requests to the web server [28] to overrun VM resources. Furthermore, HTTP-GET flood attack traffic is difficult to differentiate from real traffic because they use regular uniform resource locator (URL) queries at a normal rate. Because the traffic volume in HTTP-GET flood attacks is often below the thresholds of most rate-based detection approaches, the attack goes undetected [29]. Additionally, attackers use valid HTTP packets without any anomaly in the packet's structure or flags, whereas the IDS is mostly designed to detect a malformed or anomalous combination of flags. The consequence of this attack is an EDoS attack because the CC's auto-scaling feature exacerbates the damage by allowing the CSP to provide an excessive pool of resources to CCCs to fulfill the service-level agreements (SLA). The limit set on the CCC's pay-per-use invoicing system could prevent excessive user charges; however, it could also shut down many CC services with a single attack. Detecting HTTP-GET flood DDoS attacks in the CC environment requires a thorough understanding of the attacks and users' behaviors.
## 5 Conclusions  
A solar radiation model for the prediction of hourly solar irradiance has been developed. The model is simple in that it utilises a blend of basic theoretical and empirical results and concepts and requires routine meteorological data as input. Preliminary model evaluation has shown that the model works well, especially for long sunshine duration and/or high daily solar radiation flux. The application of the model within an urban air quality model has shown that it is simple to implement and suitable for situations where a high degree of accuracy is not required, and practicality is essential.  
In conclusion, it was shown that adopting relatively simple 'textbook' material, i.e., basic theory/information, leads to reasonably good solar radiation modelling, with outcomes that can be used for applications where accuracy is not a major requirement. It may be possible that the paradigm adopted here for solar radiation modelling can be considered for the modelling of other processes, notably those that pertain to the environment, where complexity is a stumbling block, and abstraction is warranted.  
This research received no external funding.  
Not applicable.  
All the raw and processed data, as well as the computer code and associated executable version of the program for the model discussed in this paper, are available to anyone for research purposes only. All those interested can contact the author directly via email.  
The author wishes to thank Loughborough University and the University of Nottingham, both in the United Kingdom, for providing the meteorological data for the Loughborough and Sutton Bonington sites, respectively. The author is grateful and appreciative of the constructive comments made by the reviewers and the editors.  
The author declares no conflict of interest.
## 3 The Semantic Social Network of Things Middleware (S2 NeTM)
### S2 NeTM Ontology
By using these properties, the S2 NeTM ontology can provide a more detailed representation of social relationships between entities within an IoT environment. The ontology diagram in Figure 5 illustrates the various object properties that can be used to represent social relationships between entities. Figure 4: Representation of the user within the S2 NeTM ontology. Figure 3: S2 NeTM ontology. The _trustBy_ object property, as illustrated in Figure 5, can be used to represent the trust relationship between two entities in a smart environment. For example, consider a scenario wherein a smart parking system is deployed in a city. The system consists of multiple components, including sensors, cameras, and a backend server. The sensors are responsible for detecting the availability of parking spots, while the cameras are used to monitor the parking lot. The backend server processes the data from the sensors and cameras and provides real-time parking information to drivers. In this scenario, we can define the \"_trustBy_\" object property to represent the trust relationship between the components of the system. For instance, the sensors can trust the cameras to provide accurate parking data, and the backend server can trust the sensors and cameras to provide reliable information. We can represent this trust relationship using the \"_trustBy_\" object property, where the domain is the entity that trusts, and the range is the entity that is trusted. For example, we can define \"Sensor1 _trustBy_ Camera1\" to indicate that Sensor1 trusts Camera1 to provide accurate parking data. Similarly, we can define \"BackendServer _trustBy_ Sensor1\" to indicate that the BackendServer trusts Sensor1 to provide reliable information. By representing trust relationships between entities, we can ensure that IoT systems are secure and privacy-preserving. The S2 NeTM ontology provides a comprehensive model for the integration of semantic information derived from heterogeneous entities and objects within an IoT ecosystem. The ontology's main purpose is to represent entities in IoT environments, including devices, open data entities, and crowdsourcing entities, along with their social relationships. By using the semantic description of IoT data, open data, and crowdsourcing data, S2 NeTM enables the creation of more personalized and context-aware IoT applications. Additionally, the S2 NeTM ontology is designed to be interoperable, which means that it reuses other interoperable ontologies such as FOAF, SSN, and IoT-lite. This interoperability ensures that S2 NeTM can be used in different domains, such as smart cities, smart homes, and smart health. Ultimately, the S2 NeTM ontology plays a critical role in the advancement of IoT-based solutions by providing a comprehensive model for integrating and representing heterogeneous entities and objects, thereby enabling better communication and collaboration, and achieving common goals.
# A Review of Video Object Detection: Datasets, Metrics and Methods
## 2 Datasets and Evaluation Metrics  
### Datasets  
Many datasets have been provided for specific applications [91; 92; 93]. For video object detection, the most commonly used dataset is the ImageNet VID dataset [5], which is a prevalent benchmark for video object detection. The dataset is split into a training set and a validation set, containing 3862 video snippets and 555 video snippets, respectively. The video streams are annotated on each frame at the frame rate of 25 or 30 fps. In addition, this dataset contains 30 object categories, which are a subset of the categories in the ImageNet DET dataset [93].  
In the ImageNet VID dataset, the number of objects in each frame is small compared with the datasets used for static image object detection such as COCO [92]. Though the ImageNet VID dataset is widely used, it has limitations in fully reflecting the effect of various video object detection methods. In [94], a large-scale dataset named YouTube-BoundingBoxes (YT-BB) was provided, which is human-annotated at one frame per s on video snippets from YouTube with high accuracy classification labels and tight bounding boxes. YT-BB contains approximately 380,000 video segments with 5.6 million bounding boxes of 23 object categories, which is a subset of the COCO label set. However, the dataset contains only 23 object categories and the image quality is relatively low due to its collection by hand-held mobile phones.  
In 2018, a dataset named EPIC KITCHENS was provided in [95], which consists of 32 different kitchens in 4 cities with 11,500,000 frames containing 454,158 bounding boxes spanning 290 classes. However, its kitchen scenario poses limitations on performing generic video object detection. Moreover,there exist the following other datasets that reflect specific applications: the DAVIS dataset [96] for object segmentation, CDnet2014 [97] for moving object detection, VOT [98] and MOT [99] for object tracking, Sports-1M data set [100] with segment-level annotations, HMDB-51 data set [101] with segment-level annotations for various human action categories, TRECVID [102] for video retrieval and indexing, the Caltech Pedestrian Detection data set [103] for pedestrian detection, and the PASCAL VOC dataset [104; 105] for object detection. In addition, some works based on semi-supervised or unsupervised methods have been considered in [106; 107; 108; 109].  
For video object detection with classification labels and tight bounding boxes annotation, currently there exists no public domain dataset offering dense annotations for various complex scenes. To enable the advancement of video object detection, more effort is thus needed to establish comprehensive datasets.
## 2 Materials and Methods
### Exergy Efficiency of the Heat Exchanger  
As can be seen from the Figure 2, the exergetic efficiency of the heat exchanger (\(\eta_{ex,h}\)) can be expressed as:  
\[\eta_{ex,h}=\frac{\dot{Ex}_{a}}{\dot{Ex}_{fg,in}-\dot{Ex}_{fg,out}} \tag{4}\]  
where the \(\dot{Ex_{fg,in}}\) and \(\dot{Ex_{fg,out}}\) are the specific exergy inlet and outlet of the heat exchanger; \(\dot{Ex_{a,in}}\) is the specific exergy outlet of the heat exchanger, which can be calculated followed by Equation (5) [25; 26; 27]:  
\[\dot{Ex}_{a}=\dot{m_{a}}\left\{\begin{array}{l}(C_{a}+\omega_{a}C_{v})(T_{a} -T_{0})-T_{0}\Big{[}(C_{a}+\omega_{a}C_{v})\ln\Big{(}\frac{T_{a}}{T_{0}}\Big{)} -(R_{a}+\omega_{a}R_{v})\ln\Big{(}\frac{P_{a}}{P_{0}}\Big{)}\Big{]}\\ +T_{0}\Big{[}(R_{a}+\omega_{a}R_{v})\ln\Big{(}\frac{1+1.6078\omega_{0}}{1+1.6 078\omega_{a}}\Big{)}+1.6078\omega_{a}R_{a}\ln\Big{(}\frac{\omega_{a}}{\omega_ {0}}\Big{)}\Big{]}\end{array}\right\} \tag{5}\]  
where the mass flow rate of the dry air (\(\dot{m_{a}}\)) can be calculated followed by Equation (6) [28]:  
\[\dot{m_{a}}=\rho_{a}V_{a}A_{dc} \tag{6}\]  
where \(\rho_{a}\) is the density of the dry air, which can be determined using Equation (7) [29]:  
\[\rho_{a}=101.325/0.287T \tag{7}\]  
The humidity ratio of the air (\(\omega_{a}\)) in Equation (5) can be determined by using the following Equation (8) [30]:  
\[\omega_{a}=0.622\frac{\phi P_{vs,a}}{P_{a}-\phi P_{vs,a}} \tag{8}\]  
Considering the flue gas is the mixture of many chemical compositions, the specific heat, and exergy depending on the chemical composition of fuels, excess air ratio, and gas temperature. The exergy calculation model developed by [PERSON] et al. was adopted to calculate the exergy of the flue gas [31]:  
\[\dot{Ex}_{fg}=c_{p,fg}m_{fg}\Big{[}(T_{fg}-T_{0})-T_{0}\Big{(}\ln\frac{T_{fg }}{T_{0}}\Big{)}\Big{]} \tag{9}\]  
\[c_{p,fg}=\frac{c_{p,CO_{2}}}{a_{C}+b_{N}+c_{H}+d_{S}}\cdot\frac{m_{tot,steo}}{ m_{fg}}+f_{A} \tag{10}\]  
\[c_{p,CO_{2}}=0.1874\times 1.000061^{T_{fg}}\times T_{fg}^{0.2665} \tag{11}\]
## 3 Overview of HO Management in 5G UDSC Network  
5G has almost same working condition as other cellular network has is composed of cell and sectors. Data can be transfer with the help of radio waves. These cells are connected either through wire or wireless with network at the back hand side. The encoding technique used by 5G technology is orthogonal frequency division multiplexing (OFDM) [28; 29]. Mobile connectivity and mobile subscribers are continuously expanding to address service requirements, high data rates, changes in customer behavior and diverse use cases ranging from wireless nodes to robotics, chatbots and self directed transportation. Wireless industry and mobile operators need evolvement and unceasing growth to cope these challenges. Henceforth, by having a creative network design techniques and presence of advance modules, wireless industry can fulfill the exponential growth of data traffic which will reach to 77.5 exabytes per month by 2022 [30].  
Likewise, GSM association estimated that total number of IoT devices will be around 25.1 billion by 2025 [31]. The statistics prove that existing mobile networks and operators are insufficient to complete the coverage and capacity demands of connected things. Mobile operating companies therefore must alter existing models, designs and operators to combat future challenges. Different research papers proposed the concept of MIMO to instigate the spectrum efficiency of 5G networks [32]. Similarly, mmWave communication was then introduced to improve the transmission bandwidth of 5G signals. But throughput and energy consumption were major challenges at that time.
# Optical Channel Selection Avoiding DIPP in DSB-RFoF Fronthaul Interface
###### Abstract  
The paper presents a method of selecting an optical channel for transporting the double-sideband radio-frequency-over-fiber (DSB-RFoF) radio signal over the optical fronthaul path, avoiding the dispersion-induced power penalty (DIPP) phenomenon. The presented method complements the possibilities of a short-range optical network working in the flexible dense wavelength division multiplexing (DWDM) format, where chromatic dispersion compensation is not applied. As part of the study, calculations were made that indicate the limitations of the proposed method and allow for the development of an algorithm for effective optical channel selection in the presence of the DIPP phenomenon experienced in the optical link working in the intensity modulation-direct detection (IM-DD) technique. Calculations were made for three types of single-mode optical fibers and for selected microwave radio carriers that are used in current systems or will be used in next-generation wireless communication systems. In order to verify the calculations and theoretical considerations, a computer simulation was performed for two types of optical fibers and for two selected radio carriers. In the modulated radio signal, the cyclic-prefix orthogonal frequency division multiplexing (CP-OFDM) format and the 5G numerology were used.  
fronthaul; Xhaul; DSB-RFoF; A-RoF; B5G; 6G; DIPP; optical channel selection +
Footnote †: journal: Applied Optics and Photonics  
22 September 2021  
Accepted: 20 November 2021  
Published: 22 November 2021  
MDTI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Encryption of 3D or Higher-Dimensional Images: A Review
## 2 Plenoptic Images
### Three-Dimensional Point Clouds  
Another modality of plenoptic images is referred to as a 3D point cloud. While light field images adopt a regular representation model, where pixels are arranged in a fixed grid with uniform spacing, point clouds employ an irregular representation model, where points are distributed in a space, typically three-dimensional, defined by a specific coordinate system. Strictly speaking, a point cloud can be represented as a list of points identified by three coordinates; each point may or may not be associated with one or more attributes [21]. For instance, Figure 5 displays two point cloud models obtained from the point cloud database at the University of Sao Paulo [25]. Each point in these clouds contains an attribute related to color intensity at that specific location and is defined within a three-dimensional space, capable of assuming any position within a continuous range along the spatial axes \(x\), \(y\), and \(z\). The point cloud depicted in Figure 5 contains 8,128,921 points, whereas the cloud in Figure 5 comprises 150,388 points.  
This type of data is typically acquired using 3D scanners or LIDAR (light detection and ranging) sensors. In the case of LIDAR sensors, the equipment emits laser pulses, and the distance to a specific point is calculated by measuring the time difference between the emission and the detection of the reflected pulse from an object. Additionally, the frequency of the reflected wave allows for the extraction of color information for the point. A notable application that leverages LIDAR sensors for the construction of point clouds involves autonomous vehicles (e.g., [26]), which are equipped with such sensors on their rooftops to identify objects in their vicinity. Moreover, other applications of point clouds include the creation of augmented/virtual reality environments for training and/or entertainment [27], acquisition of medical imaging for diagnostics [28], reconstruction of historical monuments for preservation [29; 30], and urban area reconstruction [31], among others.