esa-sceva/satcom-chunk-collection huggingface dataset:
# Dispersive FSO Performance Estimation with Gaussian Pulses and Laplace Modeled Time Jitter
## 2 System Model
### Chromatic Dispersion Influence Estimation  
As the pulsewidth of the Gaussian pulse, \(2T_{pw}\), of a propagating pulse along a dispersive channel spreads, its amplitude, \(B\), is correspondingly reduced (and vice versa) in order for the energy under the pulse envelope to be maintained at any point of the path [18; 24; 35; 36]. If \(T\) symbolizes the time instant within a moving frame of a propagating pulse, \(T=0\) always corresponds to the pulse frame center. Equation (3) provides the instantaneous, normalized irradiance, \(I_{t}\), affected by chromatic dispersion and the TJ at the receiver [18; 37; 38]:  
\[I_{t}(z,T)=B(z)I_{T}(z,T)=B(z)\exp\Bigl{\{}-\bigl{[}T/T_{pw}(z)\bigr{]}^{2} \Bigr{\}}, \tag{3}\]  
where \(B(z)=\Bigl{[}1+2T_{0}^{-2}C\beta_{2}z+T_{0}^{-4}\bigl{(}C^{2}+1\bigr{)}\beta_{2 }^{2}z^{2}\Bigr{]}^{-1/2}\) and \(T_{pw}(z)=\Bigl{[}T_{0}^{2}+2C\beta_{2}z+T_{0}^{-2}\)  
\(\bigl{(}C^{2}+1\bigr{)}\beta_{2}^{2}z^{2}\Bigr{]}^{1/2}\), with \(T_{0}\) being the original (\(z=0\)) half-pulsewidth, \(C\) representing the chirp parameter, \(\beta_{2}\bigl{[}ps^{2}/km\bigr{]}=1.75n(\omega)P_{h}\bigl{(}\pi c^{2}\lambda T _{h}\bigr{)}^{-1}\times 10^{15}\) representing the chromatic dispersion parameter, \(c\) denoting the vacuum light speed, \(\omega[rad/s]=2\pi\times 10^{6}\upsilon\lambda^{-1}\) being the angular frequency of a light beam operating at wavelength \(\lambda[\upmu\mathrm{m}]\) and travelling at speed \(\upsilon[\mathrm{m/s}]\) through the medium, with refractive index \(n(\lambda)=1+77.6\times 10^{-6}\Bigl{(}1+7.52\times 10^{-3}\lambda^{-2} \Bigr{)}P_{h}/T_{h}\), which depends on the prevailing pressure \(P_{h}[mbar]=2.23\times 10^{-6}\bigl{(}44.41-h\times 10^{-3}\bigr{)}^{5.256}\) and temperature \(T_{h}[K]=288.19-6.49\times 10^{-3}h\) conditions at the altitude \(h[\upmu\mathrm{m}]\) above the ground surface [18; 37; 38]. The TJ factor, \(I_{T}\), is indeed related to the TJ effect since its value deviates from unity exclusively when \(T\
eq 0\), indicating a time-jittered pulse detection.  
The case of \(C=0\) corresponds to an unchirped pulse, \(C>0\) indicates an up-chirped pulse, while \(C<0\) describes a down-chirped Gaussian pulse, which initially sharpens and further flattens. This changeover is placed at the critical distance, \(z_{c1}\), [11; 37]:  
\[z_{c1}=\frac{|C|T_{0}^{2}}{\beta_{2}(C^{2}+1)}, \tag{4}\]  
while the restoration of the original pulse dimensions (\(I_{t,z=0,T=0}=I_{t,z_{c2},0}\)) is placed at a second critical distance that is twice as much as the first one, \(z_{c2}=2z_{c1}\), [37].
## 4 Empirical Results and Analyses
### Results for Exits following BBTS Viewed by the Conventional Design  
By employing daily data of these two cryptocurrency spot prices over the data period 2016-2022, the CHR, number of trades, and AHR for trading these two spots are measured based on diverse BBTSs. Then, CHR (%), number of trades, AHR (%), average duration day, and maximum duration day for trading Bitcoin spot (ETH spot) are presented in Panel A (Panel B) of Table 2 utilizing 6 BBTSs (i.e., six combinations with two different SDs and three different MA periods).  
Table 2 shows that when trading the Bitcoin spot and ETH spot using the BBTS with default parameters, the AHRs (i.e., 19.98% and 50.52%) are impressive for investors. Furthermore, when the BBTS is adjusted to the 60-day MA rather than the 20-day MA, the AHRs are even more impressive for investors (i.e., 52.12% and 77.03%) for trading Bitcoin spot using BBTS (60, 2) and ETH spot using BBTS (60, 1). Even when the 10-day MA is used instead of the 20-day MA for the BBTS, the AHRs remain satisfied for investors (i.e., 13.51% using BBTS (10, 2) for the Bitcoin spot and 33.07% using BBTS (10, 2) for the ETH spot).  
In Panel A of Table 2, the AHRs for various cases are compared, indicating that AHR may increase as the SD increases from one SD to two SD for the Bitcoin spot using these BBTSs. However, in Panel B of Table, this study finds that the above findings might not be disclosed in BBTS (60, 2) for trading ETH spots. We, therefore, might not conclude that increasing SD would enhance AHR for trading these spots. Additionally, in Equation (1), the holding return (HR) on trading these cryptocurrency spots (i.e., Bitcoin and ETH spots) is calculated as \((\text{Bi/Si})-1\).  
Figure 1: The trend of the Bitcoin price from 2016 to 2022.  
Figure 2: The trend of the ETH price from 2016 to 2022.  
While the research design described above (referred to as the conventional design in this study) produces acceptable outcomes, more favorable results can be obtained by integrating the insights of a heatmap matrix (referred to as the new design in this study), as illustrated below.
## 3 System Description  
The recommended ACAS standards towards flight transponder interrogations [36; 41] suggest using a directional antenna mounted on the top of the aircraft and a monopole antenna mounted on the bottom for monitoring aircraft equipped with Mode A/C interrogators. The utilization of directional antennas on the bottom side of the aircraft is optional. Furthermore, a uniform planar array consisting of four or five monopole antenna elements, with inter-element spacing of a quarter wave, is employed for DF purposes [15]. Similar to the mentioned recommendations, our system assumes the aircraft is equipped with two cuboid M\({}^{3}\)PAs. One is mounted on the top of the UAV or manned aircraft, the other on the bottom. In contrast to using either monopole or directional antennas, the cuboid M\({}^{3}\)PA used in our system is able to provide a monopole radiation pattern and two directional radiation patterns simultaneously, while being mutually orthogonal. Hence, it can be utilized in a collision avoidance system for both transponder interrogations and DF, consequently eliminating the need for a separate planar array.
# Innovative Sensor Approach to Follow _Campylobacter jejuni_ Development
## 1 Introduction
Several optical [12], acoustic [13] and electrochemical [14] techniques have been proposed for traducing the hybridization with the specific target nucleic acid to the pathogen detection [15]. As an example, quartz crystal microbalance (QCM) immunosensors, using monoclonal and polyclonal antibody systems coupled with the use of gold nanoparticles (AuNPs) to increase the sensitivity, were used [16]. In this way, a limit of detection (LOD) of \(10^{4}\) CFU/mL was reached; however, this kind of sensor is limited to a single use and consequently does not have a low usage cost. The same LOD was reached using a colorimetric aptasensor, that can be used for on-line applications and gives its response in 30 min [17]. As an example, to overcome time consumption and single-use limitations, approaches based on nanowire gas sensor technology could be employed. Nanowire gas sensors base their action principle on the analysis and recognition of the volatile fingerprint of a determinate sample. This kind of approach has already been applied successfully in many different fields as human microbiota monitoring [18], and environmental monitoring [19; 20]. In particular, regarding food microbial contamination, nanowire tech was able to recognize the presence of a determinate microorganism throughout the set of volatile organic compounds (VOCs) emitted when growing in a determinate matrix [21; 22; 23]. In comparison with the aforementioned sensor technologies, nanowire gas sensors exhibit advantages of nanostructured materials such as long-term stability for sustained operations, high rate surface/area, drastically reduced time of response and the possibility to be reused as well. In this study, an array of these sensors has been used inside a portable device called Small Sensor System (S3), described in detail in Section 2.3. The aim of this work was to find and identify the VOCs set that characterizes _C. jejuni_ through Gas-Chromotagraphy Mass-Spectrometry (GC-MS) and to assess the capability of S3 to distinguish between samples inoculated with this microorganism and control specimens to follow _C. jejuni_ temporal development. The success of this study can lay the foundation to deepen the research in this field in order to use S3 as a tool for prevention of illness and food quality control in the future.
## 3 System Model  
As shown in Figure 1, we consider a T2T communication over a multi-hop wireless ad hoc network. In this scenario, since the coverage of one hop is very limited, the train needs the assistance of wayside relays for multi-hop transmission. There are multiple relays within the communication range of each train and wayside node; hence, they need to select the most suitable next-hop wayside relays among these candidate nodes. For example, \(R_{2}\) may communicate with \(R_{3}\), \(R_{4}\), and \(R_{5}\), but \(R_{3}\) is chosen as the next hop node by considering factors such as channel quality and delay.  
Therefore, for \(N\) trains running on the rail, denoted as \(T=\{T_{1},T_{2},\ldots,T_{n},\ldots T_{N}\}\), there are \(M\) wayside relays distributed beside the rail, denoted as \(R=\{R_{1},R_{2},\ldots,R_{m},\ldots R_{M}\}\). The train has high mobility; in order to ensure the quality of the T2W transmission, we assume that there are two orthogonal frequency bands available: band 1 for train-to-wayside (T2W) transmission [28] and band 2 for wireless wayside-to-wayside (W2W) transmission. Since the transmission is on two orthogonal channels, there is no interference between T2W and W2W transmission, while multiple W2W transmissions at the same time will cause interference.  
In multi-hop transmission, all relays follow the decode-and-forward (DF) principle. Furthermore, we assume that the whole system is stationary in the time slot \(t\), and that the transmit power of the nodes does not change. All channels follow quasi-static Rayleigh fading, such that the channel gain between node \(a\) and node \(b\) can be represented as follows:  
\[h_{a,b}=X_{a,b}\;d_{a,b}^{-\beta/2}, \tag{1}\]  
where \(h_{a,b}\) is the instantaneous channel gain of link \(a\to b\), \(X_{a,b}\) is the fading coefficient, and \(d_{a,b}\) and \(\beta\) indicate the distance between two nodes and the path-loss exponent [29].
## 3 Results
### Hazard Curves and Uniform Hazard Spectra (UHIS)  
Hazard curves, i.e., PGA values computed for various probabilities of exceedance for 50 years, were constructed for Athens and the other main towns of each group (Figure 7a). It is important to highlight that soil classification was taken into account for each site in the computational scheme, implying that PGA values increased in the specific PSHA output. The three sites with the lowest seismic hazard levels were Athens, Spetses, and Hydra, whereas the top two were Kythira and Antikythira. The curves of the remaining sites were in close proximity, indicating that similar peak ground motions were expected for Salamina, Aegina, Methana, and Poros. The capital of Salamina Island, however, was ranked third among the sites with greater seismic hazard levels, as it is geographically close to the western coast of Attica, where high PGA levels were determined. It is important to  
Figure 6: The spatial distribution of PGV values for a return period of 950 years for each of the five sub-areas. (**a–e**) explanations as in Figure 3.  
note that ground motions in the order of 1000 cm/s\({}^{2}\) did not occur for any of the depicted probabilities of exceedance, indicating that such extreme values are not likely to occur for the range of [0.010, 0.999] probabilities of exceedance for a 50-year time span. In addition to PGA hazard curves, the uniform hazard spectrum (UHS) in terms of spectral acceleration was also constructed for the aforementioned sites for the period range of [0.1, 2.0] s, as the GMPE utilized by [PERSON] and [PERSON] [78] proposed. Given that there is no S\({}_{\text{a}}\)-GMPE for the slab tectonic environment, all UHS were created by taking into account the branches of the logic tree referring to crustal seismicity only. Similar to the PGA hazard curves, Antikythira, Kythira, and Salamina possessed the highest S\({}_{\text{a}}\)-spectra, whereas the lowest S\({}_{\text{a}}\) values were observed at Athens, Spetses, and Hydra.
## 9 Discussion and Findings
Consumers need to be made aware that their privacy is assured when they avail themselves of services in public locations, such as when sharing the consumer's charging location history and charging and discharging profile. The public charge scheduling setup presents the issue of DGs sending private information or erroneous data to affect grid operation and load dispatch scheduling. Even though there is research on DGs, communication strategies concerning privacy issues, their effect on DG DSM scheduling in coordination with secure communication protocols, and procedures to mitigate them have not been explored in detail. * Meta-heuristic optimization techniques have been studied in a few research formulations, and their efficiency in forecasting the load and charge schedule of DGs in DSM operation can be exploited to a greater extent with the discovery of newer and more efficient meta-heuristic techniques. This would ensure better computation with less complexity in arriving at a proper solution. * Consumer comfort needs to be given a higher priority in DSM operations regarding their drive cycle usage and charge/discharge patterns. * The maximum penetration of EVs in the grid system can facilitate the better usage of RES generation, and the high capacity of EV BESS can provide ample reserves for power relaying, which are necessary in cases of intermittent generation sources. The DSM operation, in the case of DGs, ensures the maximum utilization of the BESS capacity in conjunction with RES generation. * The centralized control architecture of DGs is necessary for setting up standards of DSM operation and charge scheduling. * The higher penetration of DGs into the distribution grid and the DSM operation associated with them can cause problems during peak usage periods, when other factors such as voltage drops and thermal overloading of transformer equipment and cables might occur. * Robust control and device monitoring and remote upgrade capabilities in DG DSM architecture are important, as they may facilitate further upgradation and provide better and more reliable operation and communication. * Most DGs can be connected to the Internet through the global system for mobiles (GSM), Wi-Fi, ZigBee, and other communication networks, which aggregators can exploit and coordinate the operation thereof among constituent EVs as dispatchable loads to the distribution grid [185]. * In the DSM environment, DGs lack methodologies to maximize revenues and grid utilization. The primary reason can be attributed to the lack of policies for participating entities in wholesale electricity markets, and low priority being given to commercializing DSM due to environmental, economic, and social barriers [186,187].
# Robust Cross-Validation of Predictive Models Used in Credit Default Risk
## 6 Experimental Framework
### Validation Algorithms
#### 6.3.4 Cross-Validation  
This is the largest validation group (CV) this study focused on, mainly due to the usual superior performance of this methodology compared to the previous ones, as will be confirmed by the results of the next section, and is aligned with previous studies in more generic contexts.  
There are 48 different variants implemented, divided into several subgroups according to the methodology used in the partitioning. In all cases, 50 repetitions [2; 27] were conducted to make sure that a stable enough estimate was reached by all the cross-validation techniques.  
The original k-fold cross-validation and stratified cross-validation techniques are identified by \"CV:fold/k/r\" and \"CV:SCV/k/r\", respectively, where the \"k\" suffix indicates the value of the k-fold parameter and \"r\" is the number of repetitions made (\(r=50\)). Exceptions are the LOOCV and the RSCV with \(k=2\) \"CV:RSCV/2\", because no repetitions are possible.  
Other CV methods have been named in the same way. The representative splitting cross-validation is identified as \"CV:RSCV/k/r\"; distribution optimally balanced stratified cross-validation as \"CV:DOB-SCV/k/r\"; the proposed enhancement to this method as \"CV:DOB-WOE/k/r\"; the distribution balanced cross-validation with algorithm versions 1 and 2 as \"CV:DB-SCV1/k/r\" and \"CV:DB-SCV2/k/r\"; and the corresponding enhancement to the latter as \"CV:DB-WOE2/k/r\".  
Similarly, the acronym chosen for maximally shifted cross-validation was \"CV:MS-SCV/k/r\". This partitioning methodology is more heterogeneous by definition and should lead to more volatile results in contrast with the robust methods that are going to be challenged. The MS-SCV has been included in this study to check if its performance is the opposite of the most homogenous partitioning methods in the same situation.  
Stochastic cross-validation using a discrete uniform distribution over the integers between 2 and 20 to choose the k-fold value for each repetition has been labelled as \"CV:RndCV/0/r\".  
Finally, the proposed fuzzy cross-validation methods have been labelled as \"CV:FUkfold/0/r\" for the uniform distribution version where the integer k-fold parameter was chosen randomly from 2 to 20, while the same method using a normal distribution N(10, 5) capped to minimum and maximum values of 2 and 20, respectively, has been identified as \"CV:FNkfold/0/r\".  
Except for the stochastic and fuzzy methods, where the \(k\) parameter is not specified deterministically, the values tested for the k-fold parameter were \(k=2\), 5, 10, 15, and 20. The unusual higher values of \(k=15\) and 20 have been added to the more common values \(k=2,5\), and 10 of other researches [2; 6; 12; 25; 26] to check if the robust partitioning algorithms decrease the variability of smaller test sets enough to leverage the larger size of the training sets used for calibrating the models.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 7 Algorithms and Protocols
### Formation Flying  
The formation flying algorithm is designed using a leader-follower scheme [14; 49; 50]. Figure 11 presents the protocol for the formation flying coordination.  
Specifically for this protocol, the leader drone receives the mission specification and mission control commands (service calls) from the GCS. The mission specification consists of a list of waypoints for both the leader drone and follower drones. These waypoints are pre-generated according to the mission i.e., by the Cloud Services. The leader drone coordinates hereafter the mission progress with the follower drone by controlling the distribution of the mission element, i.e., waypoints. Then, the waypoint is executed by each drone using its onboard position controller. The leader drone monitors the execution progress of the follower drone by subscribing to its position and velocity messages. Finally, the leader drone releases the next mission element when itself and the follower drone achieves the current mission elements, i.e., reaches the target positions defined by the waypoints. This iteration continues until the last waypoint of the mission specification is reached.  
Formation flying requires communication between drones. In our protocol validation work, the communication system is established based on a WiFi network, where the ground control station and the drones are connected. For larger swarms, this process can be cascaded hierarchically where a follower drone would be able to also assume the role of a leader for other followers.
## 1 Introduction
### Flexibility of Distribution Network with DG  
With the mass connection of DG and the increasing demand for load, the flexibility of urban distribution networks mainly relates to two aspects. On the one hand, the distribution network can quickly adjust and control flexible resources, such as interruptible loads, to flexibly adapt to the connection of DG and loads and ensure secure and economical operation. On the other hand, the distribution network can respond to various possible disturbances to guarantee secure and reliable operation. For example, when WF, PV, and other distributed resources fluctuate, the distribution network should have the ability to guarantee that the renewable resources are fully absorbed. When the distribution network is in normal operation, it should have the ability to effectively deal with various uncertain disturbances and maintain optimal operation status at all times [32]. The flexibility of distribution networks, including DG, is defined as the ability to dispatch flexible resources and respond to various predictable or unpredictable disturbances to the distribution networks, aiming to deal with the uncertainty and fluctuation caused by the connection of DG into the distribution network [12].  
Multi-Objective Optimization Model for Accommodation Capability of DG, Considering Distribution Network Flexibility  
In order to evaluate the maximum accommodation capacity of DG through optimal programming, this paper proposes a novel multi-objective optimization model for the accommodation capability of DG considering distribution network flexibility. Figure 1 shows the methodology diagrams flows of the multi-objective optimization model. The overall methodology flow includes four steps: information input, modeling, solving, and results output. First, the basic information of the distribution network with DG is input into the model, assuming that the installed DG capacity at each node is determined. Second, the output power of the flexible resources, including PV, WF, and superior grids, is regarded as a real decision variable that impacts the objective functions of the optimal operation model with consideration of the secure operation constraints. Mathematically, the above planning model is a large-scale, nonlinear programming problem, which can be solved with the commercial optimization solver CPLEX (a high-performance mathematical programming solver for linear programming, mixed-integer programming and quadratic programming) after linearization. Finally, the three optimal objective values are output to reflect the economy, security, and flexibility of the distribution network comprehensively.