esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Studied Settings
### Comparison Criteria  
In this study, the evaluation of forecasting models considers both point prediction accuracy and uncertainty quantification. For MCDO, each forecast is obtained by performing 20,000 stochastic forward passes with dropout enabled at inference. This yields a distribution of predictions at each time step, from which the predictive mean and standard deviation are calculated. The resulting statistics are then used to compute forecast intervals and assess model reliability. To evaluate the forecasting models, four key criteria are considered:  
* Root Mean Square Error (RMSE): RMSE measures the average magnitude of the errors between predicted and actual values [34]. It is defined as \[RMSE=\sqrt{\sum_{t=1}^{M}(y_{t}-\hat{y}_{t})^{2}/M},\] (16) where \(\hat{y}_{t}\) represents the forecasted value at time \(t\), \(y_{t}\) is the actual observed value, and \(M\) is the total number of forecasts. A lower RMSE indicates higher prediction accuracy.
* Mean Absolute Percentage Error (MAPE): MAPE measures the average absolute percentage difference between the predicted and actual values, expressed as a percentage. It is defined as \[MAPE=\sum_{t=1}^{M}|(y_{t}-\hat{y}_{t})/y_{t}|/M\times 100.\] (17) This metric provides a scale-independent measure of forecast accuracy [35]. Lower MAPE values reflect better model performance.
* Coverage of Interval Forecasts (CIF): CIF refers to the proportion of times the actual values fall within the model's predicted confidence intervals. For instance, a 95% interval should ideally contain the true values 95% of the time. This metric assesses how well the model quantifies uncertainty [36].
* Width of Forecast Intervals (WIF): WIF quantifies the average width of the prediction intervals. Narrower intervals suggest more precise forecasts, while wider intervals may indicate greater uncertainty [37]. When interpreted alongside CIF, WIF helps evaluate the sharpness and reliability of the interval predictions.  
All performance metrics are computed on the test set, which consists of the final segment of the time series excluded from training. This setup ensures that forecast quality reflects generalization to unseen data. Due to the temporal dependency in time series, standard cross-validation is not applicable. Therefore, a fixed train-test split is adopted to maintain the chronological order of observations [38; 39].  
In the simulation study, the time series is sampled every six days, producing 300 data points across a five-year span. The last 12 months, corresponding to approximately 60 observations, are held out as a test set to evaluate model performance.
# A Trajectory Generation Method for Multi-Rotor UAV Based on Adaptive Adjustment Strategy
## 3 Method
### B-Spline Curve and Security Guarantee Method
#### 3.1.2 Convex Hull Property of B-Splines and Guarantee of Dynamic Feasibility  
To assure that the generated trajectory meets the multi-rotor UAV dynamic limits, the velocity and acceleration obtained from trajectory derivation should not exceed the maximum value of multi-rotor UAV velocity and acceleration. This paper uses the convex hull property of the B-spline curve to make the trajectory meet this requirement.  
The B-spline curve is a combination of several segmented Bezier curves. According to the convex hull property of the Bezier curve, each Bezier curve is included in the convex hull composed of its control points. Figure 1 shows the property of the convex hull of the B-spline curve. The red-dotted line is a track of the B-spline curve. The red-dotted quadrilateral formed by \(\mathbf{Q}_{2}\), \(\mathbf{Q}_{3}\), \(\mathbf{Q}_{4}\), \(\mathbf{Q}_{5}\) is the control polygon of the trajectory. As shown in Figure 1, the local track of the B-spline is surrounded by its corresponding control polygon.  
As described in Section 3.1.1, the velocity curve is derived from the position curve. The acceleration curve is derived from the velocity curve. Both are B-spline curves that satisfy the convex hull property. Therefore, to ensure the trajectory dynamic feasibility, that is, the trajectory speed and acceleration do not exceed the dynamic limits of the multi-rotor UAV, it is only necessary to ensure that the absolute values of the trajectory speed and  
Figure 1: The B-spline convex hull property. The solid green line is a B-spline curve. The red-dotted line is a segment of the B-spline curve. The red-dotted quadrilateral formed by \(\mathbf{Q}_{2}\), \(\mathbf{Q}_{3}\), \(\mathbf{Q}_{4}\), \(\mathbf{Q}_{5}\) is the control polygon.  
acceleration control points do not exceed the maximum speed and acceleration. Therefore, B-spline control points must meet the following conditions:  
\[\begin{array}{l}|\mathbf{V}_{i}|=\left|\frac{p_{i}(\mathbf{Q}_{i+1}-\mathbf{Q}_{i})}{i_{i+ p}+1-i_{i+1}}\right|\leq v_{max},\\ |\mathbf{A}_{i}|=\left|\frac{(p_{\rm b}-1)(\mathbf{V}_{i+1}-\mathbf{V}_{i})}{i_{i+p}+1-i_{ i+2}}\right|\leq a_{max}\end{array} \tag{8}\]  
By adjusting the position and knot vector of control points, the speed and acceleration control points can meet the dynamic constraints of Formula (8) so that the whole trajectory can meet the dynamic constraints of the multi-rotor UAV.
# Genomic and Metabolic Characteristics of the Pathogenicity in Pseudomonas aeruginosa
## 4 Metabolic Pathways Associated with Pathogenicity
### Virulence Factors
#### 4.1.3 Secondary Metabolites  
_P. aeruginosa_ uses peptides and proteins, especially a series of secondary metabolites that give them an advantage in different environments, as virulence factors. These metabolites negatively affect prokaryotic and eukaryotic cells, through growth inhibition or cell death [106]. Within the secondary metabolites, the most important classes for virulence are phenazine, alkyl quinolones, homoserine lactones, and rhamnids [107]. Phenazines, such as pycyoxanin, are one of the most important secondary metabolites of _P. aeruginosa_. They are redox-active, colored heterocyclic compounds and are responsible for the green fluorescence of the _P. aeruginosa_[108]. Basically, pycyoxanin can cause harmful oxidative stress to host cells by generating reactive oxygen species, superoxide, and hydrogen peroxide [109,110]. The genes required to produce pyocyanin are two single genes, _pltzM_ and _pltzS_ (responsible for the final conversion into pycyanin) and two seven-gene homologous operons, phzA1B1C1D1E1F1G1 and phzA2B2C2D2E2F2G2 [111]. The operons encode the synthesis of phenazine-1-carboxylic acid from its precursor, chorismic acid. Phenazine-1-carboxylic acid is then converted to pyocyanin by a methyltransferase encoded by the _pltzM_ gene and a flavin-containing monooxygenase encoded by the _pltzS_ gene [112].  
_P. aeruginosa_ is one of the few organisms capable of synthesizing hydrogen cyanide (HCN) [113]. This secondary metabolite is maximally produced under high cell densities  
Figure 4: Schematic representation of a flagellum in _P. aeruginosa_. _P. aeruginosa_ expresses a single polar flagellum. It is composed of a basal body: a L-ring connected with the LPS layer, a P-ring associated with the peptidoglycan layer, the MS-ring is located on the plasma membrane. The basal body is associated to a motor/switch, responsible for generating the rotation of the filament. The outer part of the flagellum is composed of a hook (protein FlgE), and a filament (protein FliC). The filament ends with capping (protein FliD).  
and at low oxygen tension during the transition from exponential to stationary growth phase. The HCN synthase is encoded in an operon, _hcnABC_[114]. The CN- ions are non-competitive inhibitors, as they can bind to Fe\({}^{3+}\) in heme, which in turn binds to cytochrome c oxidase, consequently preventing oxygen from binding. Thus, the host cell is unable to perform aerobic respiration [115]. Among bacteria, HCN has been shown to kill competitors by increasing their susceptibility to antibiotics, through inhibition of efflux pumps that depend on cytochrome oxidase activity [115,116].
# Experimenting with Routing Protocols in the Data Center: An ns-3 Simulation Approach
## 6 Discussion and Conclusions  
As a general result, we can conclude that the FRR port to ns-3 DCE is functionally correct and promisingly scalable. In fact, running control plane-only experiments (i.e., without the zebra daemon) on a single server, we were able to achieve competitive results as Sibyl running on a cluster of computing nodes. In terms of execution times, the results were very satisfactory. ns-3 DCE shows a performance almost close to the Sibyl-emulated environment for scenarios up to 320 nodes, which were run on infrastructures with similar resources. On the other hand, for scenarios with more than 320 nodes, ns-3 DCE shows an average time 2.4 times higher than Sibyl, but with a cumulative vRAM ratio of 5.2 times lower. The port validation included a theoretical analysis of the behavior of BGP for the datacenter on multi-plane fat-tree topologies. This analysis exposes how to find a formal expression that describes the growth of control packets injected into the network after a failure scenario. Although this analysis validated the results obtained, it is worth noting that the implementations of the routing protocols may be subject to race conditions or limited by the available resources that slightly vary the behavior based on optimizations of the implementations. In this paper, we focused on BGP in the datacenter, and briefly commented about the execution of other routing daemons of the FRR suite. In this regard, another straightforward line of future work is to undertake a thoroughly testing of other routing daemons, in principle in the MSDC scope. To this end, Openfabric (IS-IS with flooding reduction) is already implemented and ready to run. Overall, to the best of our knowledge, in this work we provide a functionally correct and scalable FRR port to ns-3 DCE, ready to use by researchers and practitioners alike. For the time being, we only focused on the control plane of Fat-Tree network routing protocols, reaching competitive results with less resource consumption. A foreseeable line of research shall include the forwarding plane, enabling research on traffic behavior in MSDC and/or other topologies. Conceptualization, L.A., E.G., M.R., S.A. and F.V. ; methodology, L.A., E.G., M.R. ; software, S.A. and F.V. ; validation, S.A., F.V. and L.A.; formal analysis, L.A., E.G. and M.R. ; investigation, L.A., E.G. and M.R. ; writing--original draft preparation, L.A., E.G., M.R., S.A. and F.V. ; writing--review and editing, L.A., E.G. and M.R. ; visualization, L.A., S.A. and F.V; supervision, E.G. and M.R. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the Uruguayan National Research and Innovation Agency (ANII) under Grant No. POS_NAC_M_2020\(1\)163847. Not applicable. We would like to thank our colleagues from the Computer Networks research group at Universita Roma Tre, Italy, for the facilitation of execution times reported by the Sibyl framework in their environment. The authors declare no conflict of interest.
# Self-Organizing and Scalable Routing Protocol (SOSRP) for Underwater Acoustic Sensor Networks
## 3 Proposed Protocol
### Packet Transmission  
In the last phase, the packet is transmitted from source node to the sink using multi-hop communication, where intermediate nodes are selected based on smallest hop count and shortest distance between source and base station.  
It is important for a source to select an efficient forwarding node to conserve the energy and minimize the delay. Therefore, on acquiring the data, the source node checks the local routing table for the selection of next hop. The routing table holds neighbor ID, hop count and distance from transmitting node to destination. The forwarding node is selected using path selection criteria where hop count and distance are compared among all the entries stored in the table for the purpose of packet transmission. Upon, selection of forwarding node, source node adds a header containing sink address, selected forwarding node ID, source address/ID, timestamp, hop count and distance. The forwarding node ID is defined for a neighbor to recognize that the packet is meant for it and to avoid other neighboring nodes to transmit it to the sink. As a result, it diminishes the possibility of receiving multiple copies of the same packet. After attaching the header to the data packet, the data is transmitted. Upon reception of packet, the receiving node (selected node) sends an acknowledgement packet to source. This process repeats at each node until the packet reaches the sink node. However, the scope of acknowledgement is limited to hop to hop. If a route failure is detected (i.e., no acknowledgement is received by sending node) the algorithm selects an alternate path to transmit the data based on the routing table information stored in it, repeating the process from the path selection phase.
# Improving Monarch Butterfly Optimization Algorithm with Self-Adaptive Population
## 5 Simulation Results
### PSO vs. SPMBO
#### 5.2.2 \(D=60\)
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Best** & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Worst**} & \multicolumn{2}{c}{**SD**} \\ \hline  & **MBO** & **SPMBO** & **MBO** & **SPMBO** & **MBO** & **SPMBO** & **MBO** & **SPMBO** \\ \hline F01 & \(\textbf{0.03}\) & 0.12 & 59.21 & **39.72** & 153.90 & **133.30** & 61.82 & **43.95** \\ F02 & \(\textbf{0.05}\) & 0.38 & \(5.50\times 10^{14}\) & \(\textbf{1.14}\times\textbf{10}^{9}\) & \(1.68\times 10^{16}\) & \(\textbf{1.78}\times\textbf{10}^{10}\) & \(3.05\times 10^{15}\) & \(\textbf{4.34}\times\textbf{10}^{9}\) \\ F03 & \(1.34\times 10^{4}\) & \(\textbf{16.56}\) & \(2.50\times 10^{9}\) & \(\textbf{1.96}\times\textbf{10}^{9}\) & \(6.33\times 10^{9}\) & \(\textbf{6.03}\times\textbf{10}^{9}\) & \(2.12\times 10^{9}\) & \(\textbf{2.03}\times\textbf{10}^{9}\) \\ F04 & \(5.20\times 10^{6}\) & \(\textbf{3.18}\times\textbf{10}^{6}\) & \(7.19\times 10^{6}\) & \(\textbf{5.21}\times\textbf{10}^{6}\) & \(9.68\times 10^{6}\) & \(\textbf{7.14}\times\textbf{10}^{6}\) & \(1.33\times 10^{6}\) & \(\textbf{9.78}\times\textbf{10}^{5}\) \\ F05 & \(\textbf{24.82}\) & 117.20 & \(1.26\times 10^{6}\) & \(\textbf{1.11}\times\textbf{10}^{6}\) & \(3.02\times 10^{6}\) & \(\textbf{2.76}\times\textbf{10}^{6}\) & \(\textbf{1.07}\times\textbf{10}^{6}\) & \(1.17\times\textbf{10}^{6}\) \\ F06 & \(\textbf{0.02}\) & 0.21 & 196.60 & \(\textbf{149.60}\) & \(\textbf{625.20}\) & \(635.90\) & \(228.00\) & \(160.10\) \\ F07 & \(\textbf{8.62}\times\textbf{10}^{-4}\) & 0.08 & \(3.44\times 10^{8}\) & \(\textbf{2.84}\times\textbf{10}^{8}\) & \(1.54\times\textbf{10}^{9}\) & \(\textbf{1.50}\times\textbf{10}^{9}\) & \(From Table 5, it can be observed that, for best and mean function values, SPMBO has the absolute advantage over the PSO algorithm on all thirteen benchmarks. For worst values, SPMBO and PSO have similar performance. Unfortunately, for SD values, SPMBO is defeated by PSO. From the results in Tables 4 and 5, we can draw a brief conclusion, SPMBO performs better than PSO on most cases, though the SD of the SPMBO algorithm must be further improved.
# Effects of Microbiota on the Soiling Process of PV Modules in Arid Zones of the Atacama Desert
## 2 Materials and Methods  
### Study Locations  
This study was conducted in two different locations in the Atacama Desert (Figure 1A). The first study site was Antofagasta, specifically at the Universidad de Antofagasta (UA), Coloso campus (23.70\({}^{\circ}\) S, 70.42\({}^{\circ}\) W), as shown in Figure 1A. This site features small-scale photovoltaic plants of the mc-Si and CdTe types, which are used for lighting and electricity supply to its facilities as part of a carbon neutrality policy (Figure 1B). According to the Koppen classification, this location has a hot desert climate (BWH) and is situated in an urban residential area with a strong influence from the Pacific Ocean.  
Figure 1: Location of soiling sampling sites in the Atacama Desert. (**A**) Chile, climate classification according to Köppen [14], and an image of the Atacama Desert (satellite image obtained from the NASA Worldview application, part of the NASA Earth Observing System Data and Information System (EOSDIS)), ([[https://worldview.earthdata.nasa.gov](https://worldview.earthdata.nasa.gov)]([https://worldview.earthdata.nasa.gov](https://worldview.earthdata.nasa.gov)), accessed on 30 March 2024). (**B**) Sampled PV modules installed at the UA. (**C**) Sampled PV modules at the PSDA.  
The second study site was the Plataforma Solar del Desierto de Atacama (PSDA, for its acronym in Spanish). The PSDA belongs to the UA and is operated by the ATAMOSTEC consortium. It is located at an altitude of 1000 m in the interior of the Atacama Desert (24.09\({}^{\circ}\) S, 69.93\({}^{\circ}\) W) in a strategic area for the installation and development of photovoltaic projects in northern Chile. This site features pilot-scale photovoltaic plants of the mc-Si type, with a 20\({}^{\circ}\) slope from the soil and a northern orientation. According to the Koppen climate classification, this location is characterized by a BWk climate, which corresponds to a cold desert arid zone (Figure 1A). The PSDA (Figure 1C) is a natural laboratory for the study and testing of different concentrating solar power (CSP) and PV technologies.  
In both locations, the atmospheric conditions were measured using meteorological stations. The ambient temperature (T\({}_{\rm amb}\)) and relative humidity (RH) were measured with a thermometer and hygrometer, (models CS215-L11 and HMP60-L11, Campbell Scientific, Edmonton, AL, Canada), respectively. The wind speed was measured with a 05103-5 anemometer. The resolution of each measurement was 1 s and recorded as an average value per minute. The results presented in this document correspond to one year from January to December 2022.
## 3 Design of CP-ABE
### Security Model  
This section discusses the security model for the proposed PHLC scheme. This scheme was constructed based on [PERSON]'s scheme [23] by re-simulating their works. Hence, this scheme's security model is based on a security game between adversary \(\mathcal{A}\) and challenger \(\mathcal{B}\) as presented in [23]. The following is a full description of the game:  
Setup. To obtain the public parameters \(PK\) and the master key \(MSK\), Challenger \(\mathcal{B}\) runs the setup algorithm. The Challenger \(\mathcal{B}\) holds the master key \(MSK\) and sends adversary \(\mathcal{A}\) the public parameters \(PK\).  
Phase 1: Adversary \(\mathcal{A}\) adaptively issues a secret key query to the key generation module. For each query on an attribute set \(S_{i}\), challenger \(\mathcal{B}\) returns a Secret Key \(SK_{\text{si}}\) to Adversary \(\mathcal{A}\).  
Challenge. Adversary \(\mathcal{A}\) generates two messages \(M_{0}^{*}\), \(M_{1}^{*}\), \((\,|\,M_{0}^{*}\,|\,=\,|\,M_{1}^{*}\,|\,)\) and an access structure \(((A^{*},\,\rho^{*}),\,\,\mathcal{T}_{0}^{*})\), \(((A^{*},\,\rho^{*}),\,\,\mathcal{T}_{1}^{*})\) in Phase 1 with the restriction that none of them can fulfill any of the queried attributes set \(S_{i}\). In response, challenger \(\mathcal{B}\) selects a bit \(b\)\(\leftarrow\) {0,1}, choose \(Q_{0}\), \(Q_{x}\in\mathbb{G}p4\) at random. The challenge ciphertext \(CT^{*}\) of the message \(M_{b}^{*}\) was then computed under the access structure \(((A^{*},\,\rho^{*}),\,\,\mathcal{T}^{*})\), and the challenge ciphertext \(CT^{*}\) was sent to Adversary \(\mathcal{A}\).  
Phase 2: Repeat Phase 1. The adversary \(\mathcal{A}\) request a private key, however none of the attribute \(S_{\text{i}}\) met the access structure.  
Guess: If \(b=b\prime\), the adversary \(\mathcal{A}\) produces a guess bit \(b^{\prime}\in\{0,1\}\) and wins the game.  
In this game, the adversary \(\mathcal{A}\)'s advantage is defined as \(\left|\Pr[b=b^{\prime}]-\frac{1}{2}\right|\), where the probability is divided by the number of random bits used by the adversary \(\mathcal{A}\) and the challenger \(\mathcal{B}\).  
**Definition 2**.: _A PHLC scheme with hidden access policy is fully secure if all polynomial-time adversaries have at most a negligible advantage in the security game._  
Figure 2: System Model.
## 2 Materials and Methods
### Thin Film Characteristics  
Oxide phase purity and crystallography of the films were investigated via high-resolution X-ray diffraction (HRXRD) using a four-circle diffractometer. Measurements were performed in the in-plane and out-of-plane configurations. The thickness of the films was characterized by X-ray reflectivity (XRR) measurements.
## 2 Miniaturized X-ray/Gamma-ray Imaging Payload
### Focal Plane X-ray/Gamma-ray Imaging Detector  
The focal plane X-ray and Gamma-ray imager is provided by the semiconductor pixel detector Timepix [13]. Timepix operates in room temperature and in vacuum and provides photon-counting sensitivity which is valuable for long exposures of weak intensity objects such as stellar X-ray sources in space. The pixel detector consists of the state-of-the-art Timepix ASIC chip which is bump-bonded to a 2 mm thick CdTe sensor (Figure 4). The high-Z material for the sensor and thickness provide enhanced detection of hard X rays and gamma rays up to 500 keV. The detector provides a high-granularity matrix of 256 \(\times\) 256 = 65 k pixels of size 55 \(\upmu\)m and independent per-pixel signal electronics.  
The detector is implemented in the miniaturized MiniPix-TPX architecture [26] which consists of highly integrated readout electronics. The connector interface for the detector is USB 2.0 for integrated control, power, and readout of the pixel detector. In this implementation architecture Timepix can be readout a with data rate of up to 45 frames per second (fps). The semiconductor sensor receives bias voltage (\(-\)150 V) by separate voltage source (Figure 4). Overall power consumption is below 2 W. About two thi  
Figure 4: The hybrid semiconductor pixel detector Timepix (**a**) consisting of the Timepix ASIC chip bump-bonded to the radiation sensitive semiconductor sensor (CdTe 2000 \(\upmu\)m thick). The detector radiation sensitive area is 14 mm \(\times\) 14 mm = 2 cm\({}^{2}\), shown in (**b**).  
Figure 3: The optical-collimator element of the payload consisting of four segments of distinct response, see text. Size of the entire element 26 mm \(\times\) 24 mm \(\times\) 11.4 mm (height) (see Figure 1).  
the form of heat by the detector ASIC chip which is necessary to cool by passive (thermal contact) cooling coupling to the payload casing enclosure.