esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology and Experimentation
### Simulation Environment  
Network simulator 2.35 is used to compare and evaluate the performance of AODV, AOMDV, and DSDV routing protocols in WBAN different mobility models. The position for the sink node is fixed in this scenario. The number of nodes varies from 25 to 75. Table 2 below shows the rest parameters used for simulation. All sensor nodes and both sinks use the same fixed communication radius in data transmission. This scenario considers static consumption of power and ideal conditions for different data traffic; the \(P_{\text{ci}}\) (average power consumption) for a node is denoted as:  
\[P_{\text{ci}}=\text{ N}_{\text{sec}}\text{N}_{\text{ant}}\big{(}\text{A}_{ \text{i}}P_{\text{tx}}+\text{B}_{\text{j}}+P_{\text{BHi}}\big{)} \tag{1}\]  
N\({}_{\text{sec}}\) is taken to keep count of the number of sectors, and N\({}_{\text{ant}}\) will be providing the antennas per sector with respect to every node in place. P\({}_{\text{tx}}\) is the average of the total power of all mobile nodes, and P\({}_{\text{BHi}}\) is the communicated power for each mobile node. The constant A\({}_{\text{i}}\) signifies that share of the P\({}_{\text{ci}}\), which is straight proportionate to the communicated power from a moving node, while B\({}_{\text{j}}\) signifies that share of power that is spent without any dependence on the average communicated power from a node in WBAN. Let T\({}_{\text{het}}\) be the period for overall data transfer of a heterogeneous network, and EE\({}_{\text{het}}\) is the overall consumed energy; then, time efficiency can be calculated as:  
\[\text{Te}=\frac{\text{EE}_{\text{het}}}{\text{T}_{\text{het}}} \tag{2}\]  
Various quality-of-service constraints, including PDR, average throughput, average end-to-end delay, and consumed energy, are used to calculate the overall efficiency of WBAN. Three different scenarios with varying number of nodes (25, 50, and 75) with static sink node are implemented using the direction-finding procedure for evaluation in network simulator.
## 3 SAT-Hadoop-Processor
### Satellite Measurement and RS Data Acquisition  
In this study, we gathered data from the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) via the Mediterranean Dialogue Earth Observatory (MDEO) ground station installed at the Abdelmalek Essaadi University of Tangier in Morocco [26] and the Earth Observation Portal (EOP). Besides, we obtained RS data from the Earth Observation System Data and Information System (EOSDIS) of NASA, the Infusing Satellite Data into Environmental Applications (NESDIS) of the NOAA, and the Copernicus Open Access Hub (COAH) platform operated by the ESA. The RS data collected came from many polar satellites flying in Sun-Synchronous Orbit (SSO)--MetOp, NOAA, and Sentinel series, AQUA, TERRA, AURA, etc.--and Geostationary Earth Orbit (GEO) satellites, notably the Geostationary Operational Environmental Satellite (GOES-NP and RU) series and the Meteosat Second Generation (MSG), etc.  
In our study, we acquired data from several passive and active satellite sensors [27]: Spinning Enhanced Visible and InfraRed Imager (SEVIRI), Infrared Atmospheric Sounding Interferometer (IASI), Visible Infrared Imaging Radiometer Suite (VIIRS), Atmospheric Infrared Sounder (AIRS), Moderate Resolution Imaging Spectroradiometer (MODIS), Microwave Limb Sounder (MLS), Advanced Baseline Imager (IMG), Sea and Land Surface Temperature Radiometer (SLSTR), etc. The used satellite instruments have different spatial resolutions ranging from 375 m to 5 km. and a spectral resolution diverting from  
Figure 1: The general architecture of the SAT-Hadoop-Processor software.  
microwave (MW), shortwave (SW), near-infrared (NIR), infrared (IR), visible (V), and ultraviolet (UV). Besides, the temporal resolution varies from a few minutes to a few days. Wget [28], dhusget [29], and Sentinelsat [30] Linux libraries have been used to download RSBD in NRT from the links detailed in Table 3. The use of the commands (CMD) lines is noted as follows:  
* **CMD 1:** wget-r-nc--accept=_Extension_--no-parent--dns-timeout=500-P _Output path_--user=_Username_--password=_Password Link &
* **CMD 2:** bash dhusget-sh-d _Link_-u _Username_-p _Password_-m \"_Sentinel_-5 _P\"_-i _TRPOMI-t _TimeAgo (second)_-T \"si\"-o product-O _Output path_ &
* **CMD 3:** sentinelsat-u _s5 pquest_-p _s5 pquest_-s _StartDate_-e _EndDate_-d--sentinel 5--producttype _L2_O3_--location _CityName_--url \"_[https://s5 phub.copernicus.eu/dltus/_](https://s5 phub.copernicus.eu/dltus/_)\"
## 6 Radio Frequency Environments  
Many telecommunications base stations (TBSs) and high-power digital television (DTV) transmitters in urban and suburban areas contribute to ambient RF energy. Suppose mobile devices, Wi-Fi access points, and home appliances such as microwave ovens are included. In that case, the density of these sources and the resulting ambient power density can vary significantly from one location to the next [72].  
Due to the high power density in urban areas of DTV, GSM900, GSM1800, and 3G, these bands have been identified as potential harvesting bands. There is a greater chance to capture considerable electromagnetic energies in urban areas than in semiurban areas. The urban areas have more RF transmitters on top of buildings than semiurban areas [73]. Many empirical models have been developed to better understand the factors and phenomena that influence the power levels of electromagnetic signals during their propagation in urban areas [74]. The wide disparity in harvestable RF power between urban and semiurban areas and rural areas shows that the environment influences the amount of RF power that can be harvested. Rural areas have lower densities of base transceiver stations, user transmitting elements, and different morphologies, contributing to this discrepancy [75]. To filter the channels with the highest measurements in each band, a banded input RF power density threshold was chosen [26]. RF measurements in urban, and semiurban areas are summarized in Table 1.
# Time Aware F-Score for Cybersecurity Early Detection Evaluation
## 3 Materials and Methods
### Metrics  
For the early detection problem evaluation it is important to obtain models that not only make correct predictions but also that those are taken as soon in time as possible. In order to do so, a metric that is able to consider the time used for making the prediction is needed. Several metrics had been presented to fulfill that task, among which: Early Risk Detection Error \(ERDE\)[1], \(F\)-_latency_[5] and Time aware Precision \(TaP\)[3].  
In this paper, we define the metric _Time aware F-score_ or \(TaF\) for short, to overcome some limitations of the previous metrics and to ease the interpretation of the results. This metric presents a behaviour more similar to \(F\)-_latency_, than previous ones, but improving the configuration parameters. Reflecting the same idea as \(TaP\), a penalization point and the degree of penalization is defined as problem-based instead of based on the particular values of the dataset. In this sense, it is more natural to do so that to set it based on the mean of those values. This last point is also crucial because in a real world streaming situation, it will not be possible to get the complete image or range of values before one specific item is processed.  
The metric has been defined as follows:  
\[TaF_{o,\lambda}(e_{i},k)=\left\{\begin{array}{ll}1&\text{if }TP\wedge k\leq o \\ 1-pf_{o,\lambda}(k)&\text{if }TP\wedge k>o\\ 0&\text{if }\text{delay}\end{array}\right.\]  
\[TaF(E,k)=\frac{\sum_{i\in E}TaF(e_{i},k)}{|E_{TP}|+\frac{1}{2}(|E_{FP}|+|E_{ FN}|)}\]  
The cases defined in \(TaF_{o,\lambda}\) use the same principle as in the previous metrics (\(ERDE\) and \(TaP\)) but just for the correctly detected positive cases (true positives, \(TP\)). The maximum value is obtained if the item is identified before the point of measure \(o\), otherwise a penalization function named \(pf(k)_{o,\lambda}\) is applied.  
For the final \(TaF(E,k)\) value an aggregation of intermediate results is performed in order to use the final value as a penalized count of true positive cases. That is to use it instead of \(|E_{TP}|\), applying, then, the F-score function. The number of real true positive cases \((TP)\) is depicted as \(|E_{TP}|\), whereas negative cases are shown as \(|E_{FP}|\) and \(|E_{FN}|\) for false positives and false negatives respectively.  
For this metric, the penalty function is defined as follows, to give values in the range \([1,0]\) which generates an output in the metric in the same range. This maintains the relation with the output values of F-score.  
\[pf(k)_{o,\lambda}=-1+\frac{2}{1+e^{-\lambda(k-o)}}\]The values of penalization shown in Figure 2 for different values of the parameter \(\lambda\) display how the results for individual entities \(e_{i}\) affects to the final value of \(TaF\). In every instance a proper prediction is emitted although, the amount of \(items\) required to achieve that prediction varies as presented on X-axis. It must be observed that the output range of the function for \(TaF\) metric is \([0,1]\).
## 4 Performance Analysis  
### Computational Environment Settings  
To test the system's performance, we used a city map. OpenStreetMap [28] was used to create this map. The tool OS-M2 WKT [29] is used to transform OpenStreetMap XML files to WKT format. These files, which are in text format, are used to store map-related information. The motion of cars is based on the shortest path map, and the average arrival rate of traffic flow on every route section is determined depending on the vehicle motion historical documents. The simulation lasts 168 h, and the vehicle's wireless communication range is 40 m. The message size ranges from 200 to 500 megabytes, and the message Time-To-Live (TTL) is 30 min. A vehicle's velocity is between 20 and 60 km per hour, and communication costs 50.007 per megabyte. To get the average score of every performance metric, we run each simulation scenario 100 times. The subsequent four performance indicators are looked as the following subsections.
## 3 Results and Discussion
### Influence of the Proteolytic Malt Modification Level on De Novo Formation
The shift and ratio indicate that _de novo_ formation of aging aldehydes can occur directly starting from freshly bottled beer. The concentration of bound aldehydes increased with higher amino acid concentration, indicating imine formation. The malt modification level had a strong influence on the entire aging potential. During all stages of natural and forced aging, the sum of free aldehydes, bound-state aldehydes, and precursors was increased by a higher malt modification level. The difference between B1P1 and B1P2 was low because both samples had the lowest difference between the soluble nitrogen content of the used malts [1]. However, it could be stated that a higher proteolytic malt modification level increases the _de novo_ formation of free aging aldehydes by an elevated pool of precursors in fresh beer. Regarding the result of the preceding article [1] and this study, it could be concluded that the release of the bound-state and _de novo_ formation of aging aldehydes occurred simultaneously. However, both studies aimed to comprehensively elucidate whether one of both pathways was dominant at a certain aging period. Therefore, a calculation was performed to evaluate the importance of the release of aging aldehydes. The sum of the concentrations of free and bound (4-VP releasable) aging aldehydes [1] in fresh beer was compared with the concentration of free aldehydes of the following sampling points during natural aging for each barley variety and malt modification level. The aging period, where the calculated sum in fresh beer exceeded the concentration of free aldehydes in the aged beer, was defined as the release-dominated stage. Neglecting the simultaneously occurring _de novo_ formation, within these months of aging, the free aldehydes were mainly formed by their observed bound-state concentrations in fresh beer. Table 2 summarizes the release-dominated aging period of each variation. Longer ranges indicate that _de novo_ formation of aging aldehydes is less important. A higher malt modification level at B2 tended to favor earlier importance of _de novo_ formation pathways, whereas no influence was observed for B1 variations. Generally, it could be shown that the 4-VP releasable bound-state aldehyde was predominant toward aging aldehyde formation up to 4 months of natural aging. The result confirmed the findings of the study by [PERSON] et al., in which Strecker degradation by 3-DG spiking was observable after prolonged storage (>6 months) [15]. The sample set showed varied release-dominated aging periods. Therefore, _de novo_ formation became important starting from 1 to 4 months of natural aging in this study.
## 3 VRDeepSafety Simulation Environment
### Dataset Generation
#### 3.2.2 Communication Integration  
Effective communication between vehicles and infrastructure is essential for fully autonomous vehicles (AVs). VRDeepSafety enhances the vehicle's perception and response with an advanced V2X communication framework. This integration enables continuous data exchange with the infrastructure and other vehicles to improve situational awareness and support proactive navigation.  
Key features of V2X communication in VRDeepSafety include the real-time transmission of spatial data, such as the height, velocity, and trajectory of nearby vehicles and obstacles. This dynamic exchange gives a complete and up-to-date view of the driving environment so AVs can make more informed and proactive decisions and reduce collision risk.  
Beyond safety, VRDeepSafety's V2X capabilities allow AVs to process data from multiple sources, including traffic lights, road signs, speed advisories, and weather updates. This multi-source input enables the vehicle to make context-aware decisions, especially in high-traffic or complex scenarios where quick adaptation is critical.  
In addition, the V2X framework enhances situational awareness by predicting the actions of nearby road users and potential hazards. Continuous communication with other vehicles and infrastructure helps to predict risks in real time and improves hazard prediction and risk management. This reduces response time, prevents early collision, and strengthens vehicle and pedestrian safety.  
In summary, the V2X communication system is a cornerstone of VRDeepSafety and enhances the perceptual and interactivity of autonomous vehicles. By merging data from various sources, it provides a comprehensive understanding of the driving environment and anticipates potential hazards. This contributes to a safer and more efficient transportation system, enhanced vehicle operation, and improved overall road safety.
## 4 Basic Concepts about Applied Cryptography Mechanisms
### Fernet Algorithm  
Fernet is a cryptography technique that offers a straightforward mechanism for authenticating and encrypting data employing symmetric AES-128 in CBC (Cipher Block Chaining) mode with public-key cryptography Standards (PKCS7) padding to allow various lengths of 128 bits (16 bytes) [27]. The Fernet algorithm ensures that the data are encrypted, making it impossible for them to be modified or decoded without the key. Fernet is a recent symmetric encryption and decryption system that ensures message authenticity. This allows recipients to detect any alterations from the original transmission. To avoid common mistakes made by inexperienced developers, Fernet provides a secure key generation technique, utilizing efficient encryption (AES), and enhancing security through random \"salt\" value generation with CBS mode and PKCS7 padding. Fernet supports both symmetric and private keys, where a single key is used for encryption and decryption. However, it may have limitations with large files, requiring a single memory load of the entire buffer [28].  
The Fernet keys make sure that an encrypted template file cannot be revealed or read without the secret key, making it challenging for an attacker (\(\mathcal{A}\)) to circumvent or access the database server [29]. A symmetric key approach is used to ensure that the encrypted transmission cannot be changed, brute-forced, or decoded without the key. To enhance security, every character in the key undergoes base64 URL-safe encoding, which includes substituting reserved, illegible, or non-ASCII characters. It makes sure that the keys are handled correctly and that no mistakes happen that an \(\mathcal{A}\) would try to take advantage of. PKCS7 padding and Fernet both utilize the 128-bit cipher block chaining (CBC) mode of the advanced encryption standard (AES). PKCS7 padding is employed to fill in the vacant bits, ensuring that the cipher remains in multiples of 128 bits. For password key usage, HMAC (hash-based message authentication code) is employed, serving two functions: confirming the authenticity and integrity of a message. To enhance security, HMAC is combined with a straightforward 256-bit hashing algorithm (SHA256) [30]. The encryption procedure performs SubBytes(), RowsShift(), AddRoundKey() and MixColumns() operations, while the decryption procedure performs operations in reverse to obtain the plaintext. Higher levels of trust, security, authenticity, privacy, user authorization, non-repudiation, information integrity, and secrecy are provided by Fernet. This algorithm guards against attacks including impersonation, shoulder surfing, pharming, identity theft, and dictionary threats [10].
## 3 Improved PSO-RBFNN Model
### Improved PSO Algorithm  
Selection of RBFNNs parameters is critical to the network performance and deserves careful considerations. An RBFNN with inappropriate parameters will weaken the accuracy and effectiveness of this network. To overcome this drawback, gradient-based strategies, such as back-propagation (BP) algorithm [21], were applied to adjust the RBFNN parameters. However, such approaches may have limited global search capability. PSO [23; 24], as a simple and efficient global optimization algorithm, was used to determine the optimal parameters of RBFNNs. Considering parameters adjustment and update mechanism simultaneously, two modifications on the basic PSO are proposed for better performance.
# Measurement and Modeling of Narrowband Channels for Ultrasonic Underwater Communications
## 4 Ultrasonic Fading Modeling for UAC  
Among the statistical models considered for the fading shown in Figure 1, the \(\kappa\)-\(\mu\) shadowed distribution outperforms others in our measurements of ultrasonic UAC. In the next subsections, details of the model and a verification of the fit to the collected data are presented.
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
## 4 Numerical Results  
We obtained numerical results for 1Z, 2Z and 3Z systems and compared their performances. We assumed the following:  
* The number of cells in a zone, \(n\), is 4.
* The probability of returning to the last zone, \(\theta\), is 0.4.
* Registration cost of one registration, \(U\), is 4 and paging cost for one cell, \(V\), is 1 (\(U=4\), \(V=1\)).
* Incoming and outgoing call rates are both 1 call per hour (\(\lambda_{i}=\lambda_{o}=1\)).
* UE's sojourn time in a zone, \(T_{m}\), follows an exponential distribution with mean 1/4 (\(\mu=4\)).  
The probability of returning to the last zone, \(\theta\), is chosen arbitrarily so that it is larger than 0.25 (symmetric random walk) referring to other research results [1; 9; 10; 11; 12].
## 1 Introduction  
InGaAsSb/AlGaAsSb quaternary quantum well systems have recently become increasingly prominent due to their potential application as semiconductor diode lasers emitting at wavelengths of around 2 \(\upmu\)m, which are in high demand for a variety of applications, including fire detection, infrared imaging sensors, molecular spectroscopy and low loss optical fiber communications [1; 2; 3; 4]. However, the high threshold current density, strongly temperature sensitive 2 \(\upmu\)m InGaAsSb/AlGaAsSb LDs caused by Auger recombination rates at the MQW active region and large carrier leakage losses in cladding layers remain a great challenge [5; 6]. In order to solve this problem, more attention is now focused on the improvement of molecular beam epitaxy(MBE) fabrication technology of antimonide LDs, but the reports about optimization for the structure of 2 \(\upmu\)m InGaAsSb/AlGaAsSb LDs are rare. It is reported that a proper increase of the quantum well number in the active region is an effective solution to solve the above problems, but a high number can degrade the output characteristics of LDs [7; 8].  
Based on band engineering and carrier transportation mechanisms [9; 10], 2 \(\upmu\)m InGaAsSb/AlGaAsSb muti-quantum well laser diode with carrier blocking layer(CBL) is proposed, with the aim of reducing the threshold current density and improving injection efficiency.
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
## 4 Measurement Results
### Data Processing  
As described in Section 2, channel estimation in IEEE 802.11 physical layer was performed in frequency domain. Thus, measured samples in our experiment were also obtained in frequency domain, which is represented as follows:  
\[H\left(f,l,n\right), \tag{4}\]  
where \(f,l\), and \(n\) denote frequency (equivalently, subcarrier index), location index, and sample index, respectively. Location index indicates the different locations in the same measurement point, described in Figure 6, to obtain spatial averaging results.  
We obtained 100 samples per each measurement and average them to reduce the effect of white noise (this could be possible if we obtained samples within channel coherence time; since our experiment was performed under controlled environment, it was applicable in our situation), given by  
\[\bar{H}\left(f,l\right)=\sum_{n=1}^{100}H\left(f,l,n\right). \tag{5}\]  
Figure 7: Measurement example.  
The channel impulse response, \(h\left(\tau,l\right)\), can be obtained by applying IFFT on \(\bar{H}\left(f,l\right)\) in Equation (5). Accordingly, the instantaneous power delay profile (PDP) and the averaged PDP (APDP) are calculated, respectively, as follows [15]:  
\[\begin{split} PDP\left(\tau,l\right)&=\left|h\left(f, l\right)\right|^{2},\\ APDP\left(\tau\right)&=\frac{1}{\left|\mathcal{L }\right|}\sum_{l\in\mathcal{L}}PDP\left(\tau,l\right),\end{split} \tag{6}\]  
where \(\mathcal{L}\) denotes a set of different locations in a measurement point in Figure 6. Additional useful metrics such as mean excess delay (\(\tau\)) and root mean square (RMS) delay spread (\(\sigma_{\tau}\)) are calculated based on APDP and defined as follows [16]:  
\[\begin{split}\tau&=\frac{\int_{-\infty}^{\infty}APDP (\tau)\tau d\tau}{\int_{-\infty}^{\infty}APDP(\tau)d\tau},\\ \sigma_{\tau}&=\sqrt{\frac{\int_{-\infty}^{\infty}APDP (\tau)\tau^{2}d\tau}{\int_{-\infty}^{\infty}APDP(\tau)d\tau}-\tau^{2}}.\end{split} \tag{7}\]