esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 5 Implementation
### Generation and Synchronization of Frequency Hopping Sequences
Once the initial synchronization is achieved, both parties use the shared true random sequence for frequency hopping. To maintain synchronization over time, the system incorporates periodic resynchronization, dynamic key updating, and robust error handling mechanisms, ensuring continuous alignment and enhanced security. By integrating public-key cryptography for initial key agreement and symmetric key encryption for ongoing secure communication, our system effectively combines the strengths of both cryptographic approaches. Public-key cryptography facilitates secure key exchange without prior shared secrets, while symmetric key encryption offers efficiency for real-time data protection. It is important to note that while the use of public-key cryptography to establish a symmetric key is a standard practice, the unique aspect of our system lies in the secure distribution and synchronization of true random frequency hopping sequences. Unlike PRNG-based systems where the receiver can independently generate the same sequence using a shared seed, our approach requires careful management of the true random sequences. The encrypted transmission of these sequences ensures that they remain confidential and that only authorized receivers who possess the correct symmetric key can participate in the communication. This adds a layer of security, making it significantly more challenging for adversaries to predict or intercept the frequency hopping pattern.
# Efficient I/O Performance-Focused Scheduling in High-Performance Computing
## 3 Design
### Scheduling
#### 3.3.1 Status Lists  
Jobs do not arrive at regular intervals, and their arrival times often do not align with the check time. To ensure efficient scheduling, incoming jobs must be held until the next check time, when each job can be allocated the necessary resources for processing. This approach allows us to process jobs in batches, thereby minimizing the overhead associated with constantly checking for new arrivals. After a job begins processing, it is essential to monitor its status continuously in order to determine whether it has successfully completed or is still running. This ensures that resources are not wasted on jobs that have failed or stalled and that they can be reallocated to other pending tasks. Furthermore, jobs that are not suitable for the current HPC system due to insufficient resources or lower IOPS need to be promptly identified and excluded. By doing this, such jobs can be redirected to alternative environments that offer the required higher IOPS, preventing resource bottlenecks and improving overall system performance. To achieve this level of efficient scheduling, we categorize jobs into four statuses:  
* **Waiting List:** Jobs that are waiting for the necessary resources to become available so they can begin execution.
* **Working List:** Jobs that are currently being executed using the assigned resources.
* **Excluded List:** Jobs that cannot be processed at the moment due to insufficient resources or other constraints.
* **Ended List:** Jobs that have successfully completed their execution.  
When a job arrives, if its IOPS requirement does not exceed the maximum IOPS of the system, it is placed in the waiting list. If the IOPS of job requirement exceeds the maximum IOPS, then it is moved to the excluded list. Jobs that are currently allocated resources and are in the process of execution are placed in the Working list. After a job completes its execution, it is moved to the ended list. Based on the status of the jobs in the working and ended lists, we calculate the remaining IOPS that the system can offer. When a job starts execution and is added to the working list, we reduce the available IOPS by the amount required by that job, as those resources are now allocated; conversely, when a job completes and moves to the ended list, the IOPS initially assigned to it are released and added back to the pool of available IOPS, increasing the remaining IOPS for other jobs. Managing jobs based on these statuses can optimize resource allocation, reduce processing delays, and maintain high overall system efficiency.
## 3 Results
### Data Robustness (Completeness)  
As a background for examining emotion intensities, we briefly explored data robustness (completeness) on the \"stabilized\" (test\({}_{\text{stable}}\)) data series (\(n=53\)), together with the 7 non-stabilized data series from test\({}_{3}\). Data robustness or completeness in the present context means the ratio of successfully extracted emotion data within the total data series, as a specific (simple) measure of data quality. As explained above, at any data point, the FR8 system's failure to assigning emotion intensity values may be either due to the failure to find the face in a video frame (FIND_FAILED logged for all emotions), or due to the failure to fit a face model onto the face found (FIT_FAILED logged for all emotions). Thus, data robustness, as a proportion of numerical emotion intensity data logged within all data points (all frames), can be assessed equivalently from any single emotion dimension.  
The mean data robustness ratio for all subjects was 0.861 (SD: 0.134; min: 0.436; max: 0.998). The average frequency of face finding failure was 0.0566 (SD: 0.065; min: 0.0001; max: 0.240); the average frequency of face model fitting failure was 0.082 (SD: 0.084; min: 0.002; max: 0.417). This level of data completeness is fairly good, as compared with other recent AEFEA studies [12]. As noted in Section 3.1.1, FIT_FAILED and FIND_FAILED data points remained remarkably stable across tests and comparisons. Importantly for further analyses below, data robustness did not differ between the groups (two-tailed independent samples t-test with different variations; ns).
## 1 Introduction
### Contributions  
In this article, we consider an UAV-assisted secure FSO relay communication network, where a BS transmits information to multiple remote sensors with the help of an UAV relay. More specifically, the UAV can effectively decode the received information using SIC, and then forwards it to multiple remote sensors. All the sensors adopt SIC for information decoding. We derive the closed-form expression for decoding outage probability at all sensors. We employ a DRL approach to arrive at a near-optimal scheme, for transmit power allocation, to minimize the maximum decoding outage probability for all sensors. Accordingly, approximate closed-form expressions for optimal transmit power levels can be obtained. The main contributions of our paper are summarized as below:  
* We analyzed the decoding outage probability for all sensors, in terms of an UAV-assisted FSO relay communication network, where all closed-form expressions were derived.
* We used a DRL-based approach to tail the optimal scheme, for transmit power allocation, to minimize the maximum decoding outage probability of all sensors. Note that the relationship between input and output can be modeled using a well-trained policy network, approximate analytical expressions for optimal transmit power levels can be obtained. To the best of our knowledge, this is the first work to investigate an optimal transmission scheme for UAV-assisted FSO communication network in the IoT from the perspective of fairness.
* We illustrate some design trade-offs and compare various design approaches through selected numerical examples. These results can greatly facilitate the fairness-aware design of UAV-assisted FSO communication networks in the IoT.  
The rest of this paper is organized as follows. Section 2 presents the system and FSO channel model under consideration. The closed-form outage probabilities for all authenticated sensors are derived in Section 3. Section 4 addresses the issue of parameter optimization in terms of fairness-aware outage probability minimization. After that, we present some selected numerical examples and discuss some design trade-offs in Section 5. Finally, some concluding remarks are presented in Section 6.
## 2 Materials and Methods
### Study Locations and Experimental Procedures
#### 2.1.1 Research Location and Measurement Routes
For route A (from the base station on Ondo Road, Akure, to Oda Town), 70% falls within the suburban area, including from the base station to the Arakale roundabout (0-7 km) and from the State Secretariat roundabout to Oda Town (10-15 km). The urban area comprising 30% is between the Arakale roundabout and the State Secretariat roundabout. Similarly, for route B (from the base station to Ita-Ogbolu Town), 30% falls within the urban area (from Oyemekun Grammar School to the Oloko-Chicken Republic junction; 4.5-6 km), while the remaining routes from the base station to Oyemekun (0-4.5 km) and from the Oloko junction to Ita-Ogbolu Town (6 to 16 km) falls within the suburban area, covering about 11 km out of the total 16 km. For this reason, the Akure routes were treated as suburban areas.
# Scale-Free Random SAT Instances
## 3 Scale-Free Random Formulas  
In this section, we describe the _scale-free random SAT formulas_ model.  
We consider \(k\)-SAT formulas over \(n\) variables, denoted by \(x_{1},\ldots,x_{n}\). A formula is a conjunction of \(m\) possibly repeated clauses, represented as a multiset. Clauses are disjunctions of \(k\) literals, noted as \(l_{1}\vee\cdots\lor l_{k}\), where every literal may be a variable \(x_{i}\) or its negation \(\
eg x_{i}\). We identify \(\
eg\
eg x\) with \(x\). We restrict clauses to not contain repeated occurrences of variables. This avoids simplifiable formulas such as \(x\lor x\lor y\) and tautologies like \(x\vee\
eg x\lor y\). In general, we represent every variable by its index, and negation as a minus, writing \(i\) instead of \(x_{i}\), and \(-i\) instead of \(\
eg x_{i}\). In other words, a variable \(x\) is a number in \(\{1,\ldots,n\}\), and a literal a number in \(\{-n,\ldots,n\}\) distinct from zero. We use the notation \(\pm x\) to denote either \(x\) or \(\
eg x\). The number of occurrences of literal \(l\) in a formula is denoted by \(k_{l}\), and \(K_{x}=k_{x}+k_{\
eg x}\) denotes the number of occurrences of variable \(x\). The size of a formula \(F\) is \(|F|=mk\).  
In the following, we will use the notation \(P(x)\sim f(x)\) to indicate that random variable \(x\) follows the probability distribution \(f(x)\). The notation \(f(n)\approx g(n)\) indicates that \(\lim_{n\to\infty}\frac{f(x)}{g(x)}=1\) (Notice that \(f(n)\approx g(n)\) is equivalent to \(f(n)=g(n)(1+o(1))\). When \(g(n)=\Theta(1)\), then \(f(n)=g(n)(1+o(1))\) is equivalent to \(f(n)=g(n)+o(1)\).).  
Definition 1: In the scale-free model, given \(n\), \(m\) and \(\beta\), to construct a random formula, we generate \(m\) clauses independently at random from the set of \(2^{k}\binom{n}{k}\) clauses, sampling every valid clause with probability  
\[P(l_{1}\vee\cdots\lor l_{k})\sim\prod_{i=1}^{k}P(l_{i})\]  
where every literal \(l_{i}\) is sampled with probability  
\[P(x)=P(\
eg x)\sim x^{-\beta}\]  
In practice, we generate a variable \(x\) with probability \(P(x)=x^{-\beta}/\sum_{i=1}^{n}i^{-\beta}\), negate it with probability \(1/2\), repeat the process \(k\) times, and reject clauses containing repeated variables.  
Therefore, the probability of a clause satisfies the inequality  
\[P(l_{1}\vee\cdots\lor l_{k})\geq\frac{k!\prod_{j=1}^{k}|l_{i}|^{-\beta}}{(2\sum_{i =1}^{n}i^{-\beta})^{k}} \tag{2}\]
## 2 Materials and Methods
### Data Preprocessing and Analysing  
In this study, the VMS data for all vessels were downloaded from the BeiDou civilian branch service provider (Figure 2) and mainly included positioning time, latitude, longitude, speed, and heading information. The temporal and spatial resolutions were 3 min and 10 m, respectively. Three sample vessels equipped with scientific fishery observers recorded fishing logs and information such as the net deployment; the casting and hauling time; and the corresponding latitude, longitude, and water depth. Current data for Haizhou Bay from June to July were obtained from the Ocean Science Data Center of the Institute of Oceanography, Chinese Academy of Sciences ([[http://msdc.qdio.ac.cn/](http://msdc.qdio.ac.cn/)]([http://msdc.qdio.ac.cn/](http://msdc.qdio.ac.cn/)), accessed on 15 January 2022). The data processing workflow is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Stages** & **Status** & **Detailed Information** \\ \hline Departure from port & Sailing & The captain sails at high speed to gain more fishing time. \\  & Casting & The _A. chinensis_ stow nets are connected with anchors and are cast \\  & Casting & in sequence. \\ Fishing operation & Waiting & After casting, the vessel is fixed with an iron anchor. _A. chinensis_ are carried \\  & Waiting & by currents into the nets used for fishing. \\  & Hauling & The catch is transported back to the port by speed boat, and the vessel \\  & Hauling & continues to operate at the fishing ground. \\  & Suspending & Vessels generally stop operating at night. That is similar to the state of \\  & & waiting for the catch. \\  & Exploring & Sailing & Sailing occurs at high speed to achieve more fishing time. \\  & Returning to port & Sailing & In case of bad weather or the end of fishing activities, the vessels sail at \\  & & high speed to return to port. \\ \hline \end{tabular}
\end{table}
Table 1: Description of the five stages within the voyage of the vessels fishing for _Acetes chinensis_.  
Figure 1: Schematic diagram of _A. chinensis_ stow nets.  
Figure 3: Data processing workflow of this study.  
Figure 2: Spatial distribution of the vessel monitoring system (VMS) data of three sample vessels.
## 3 Results and Discussions
### Rainfall Simulations by WRF-Only and the Fully Coupled WRF/WRF-Hydro
#### 3.1.3 Indices for the Spatial Rainfall Distribution  
WRF-only and the fully coupled WRF/WRF-Hydro spatial indices of the six storm events were as provided in Figure 5 and the relative heat map of the indices errors was in Figure 6. Judging from the color levels in Figures 5 and 6, for both the fully coupled WRF/WRF-Hydro and WRF-only, the indices errors were smaller in space than in time. Similar to the results of the temporal indices, the event simulation values of Type 1 and Type 2 were closer to observations than those of Type 3 and the coupled WRF-Hydro enhanced the simulations of homogeneous spatial storms. In fact, the spatial inhomogeneity might not affect the coupled WRF/WRF-Hydro in improving WRF-only. What distinguishes it from the temporal indices was that the fully coupled WRF/WRF-Hydro could achieve better spatial indices than WRF-only for six events and only the POD and FAR errors of Event 3 and the CSI, FAR and RMSE errors of Event 6 were slightly larger than those of WRF-only. For Event 5, the fully coupled system was worse than WRF-only in the temporal indices, but the result of spatial indices showed an improvement, especially the MBE error from 0.831 to 0.635, which might indicate the potential of WRF-Hydro for the spatial distribution of a heavy rain.  
Figure 5: Evaluation values of the indices for the spatial rainfall distribution of the six storm events: (a) WRF-only; (b) the fully coupled WRF/WRF-Hydro.  
Figure 6: Spatial heat map of the indices errors: (a) WRF-only; (b) the fully coupled WRF/WRF-Hydro.
## 2 Basic Theory
### Subdomain Adaptation  
The purpose of global domain adaptation and subdomain adaptation is to reduce distribution discrepancies. Subdomain adaptation focuses on reducing the distribution discrepancies of data within the same category between different domains. The difference between global domain adaptation and subdomain adaptation is shown in Figure 1. The left side of Figure 1 describes the distribution of the original data between the target domain and the source domain, the upper right part describes the global domain adaptation and the lower right part describes the subdomain adaptation. Although the global domain adaptation alignment aligns the distribution of data in two domains, there are significant discrepancies in the distribution of the same type of data between different domains, leading to type aliasing. Subdomain adaptation aligns the data distribution of two domains for each type, which can avoid type aliasing.  
MMD is used to measure the global data distribution discrepancies between two domains, while LMMD is usually used to measure the distribution discrepancies between two subdomains. The mathematical expression of LMMD is shown in Equation (7).  
\[LD_{H}(X_{s},X_{t})=\frac{1}{C}\sum_{m=1}^{C}\left\|\frac{1}{n_{s}^{m}}\sum_{i =1}^{n_{s}^{m}}\phi(x_{i}^{m})-\frac{1}{n_{t}^{m}}\sum_{j=1}^{n_{s}^{m}}\phi( x_{j}^{m})\right\|_{H}^{2} \tag{7}\]  
Figure 1: Global domain adaptation and subdomain adaptation.  
where \(n_{s}^{m}\) and \(n_{t}^{m}\) represent the number of samples for category \(m\) in the source domain and target domain, respectively, and \(C\) represents the number of labels.
## Appendix A Condensation Technology
The efficiency of condensation technology, which allows for better utilization of the fuel's primary energy compared to other heating systems, is due not only to the additional use of the heat from water vapor condensation in flue gases but also to reduced losses through cooler flue gases. When discussing condensation technology, three key terms are regularly mentioned: efficiency, average efficiency, and nominal heat load of the boiler. These key terms are explained in the following sub-sections.
# Analyzing the Performance of the S3 Object Storage API for HPC Workloads
## 1 Introduction  
With the increased prevalence of cloud computing and the increased use of the Infrastructure as a Service (IaaS), various APIs are provided to access storage. The Amazon Simple Storage Service (S3) API [1] is the most widely adopted object storage in the cloud. Many Cloud storage providers, like IBM, Google, and Wasabi, offer S3 compatible storage, and a large number of Scale-Out-File Systems like Ceph [2], OpenStack Swift [3] and Minio [4] offer a REST gateway, largely compatible with the S3 interface. HPC applications often use a higher-level I/O library such as NetCDF [5] or ADIOS [6] or still the low-level POSIX API. Under the hood, for the interaction with the storage system, MPI-IO and POSIX are still widely used, while object storage APIs such as DAOS [7] are emerging. If the performance characteristics of S3 are promising, it could be used as an alternative backend for HPC applications. This interoperability would foster convergence between HPC and Cloud [8, 9] and eventually lead to consistent data access and exchange between HPC applications across data centers and the cloud.  
In this research, a methodology and a set of tools to analyze the performance of the S3 API are provided. The contributions of this article are: (**a**) The modification of existing HPC benchmarks to quantify the performance of the S3 API by displaying relevantperformance characteristics and providing a deep analysis of the latency of individual operations; (**b**) the creation of a high-performance I/O library called S3 Embedded [10], which can be used as a drop-in replacement of the commonly used libs3 [11], and which is also compatible and competitive with the HPC I/O protocols, and optimized for use in distributed environments.  
The structure of this paper is as follows: Section 1.1 presents related work. Section 2 describes the test scenarios and defines the relevant metrics that will be addressed using our benchmarks. We extend the scope of two existing HPC MPI-parallel benchmarks, namely the IO500 [12] and MDWorkbench [13], to assess the performance of the S3 API. Section 3 describes the experimental procedure, the used systems, and the methodology of the evaluation conducted in this work. The tests are performed on the Mistral [14] Supercomputer. Section 3.2.3 analyzes the obtained latency results. Section 4 introduces the S3 Embedded library and its possible use. The last section summarizes our findings.
## 5 Material and Methods  
The main goal of our project was to assist in the conservation of the site. A comprehensive and accurate digital model of the Benzu rock shelter and cave was generated, which involved adapting the methodology to the characteristics of both spaces.  
To maintain a complete record of all the steps taken during data collection, documentation sheets were used to gather all relevant information, including the site, date, operator, number of photographs, camera settings, etc. To carry out the three-dimensional survey using photogrammetry and 3D scanning, state-of-the-art equipment was employed, providing optimal and precise results in terms of the morphometric fidelity of the cavity, its dimensions, topography, etc., including the following:  
- A Nikon D3100 with 14 MP, equipped with an 18-55 mm lens (Manufactured by Nikon Corporation, Tokyo, Japan).  
- A high-speed Leica RTC360 laser scanner (Manufactured by Leica Geosystem, Heerbrugg, Switzerland) with an integrated HDR spherical imaging system and visual inertial system (VIS) for real-time registration. It features automatic object removal with its double-scan function. The scanner has a 360\({}^{\circ}\) horizontal and 300\({}^{\circ}\) vertical field-of-view, with a range of up to 130 metres and the ability to record up to 2,000,000 points per second. Remote control of the functions is available via a mobile device using Leica Cyclone Field 360 software (v. 2025.0.0), which allows real-time 2D and 3D data visualisation and automatic scan alignment.  
- An iPad Pro (Manufactured by Apple, Cupertino, CA, USA).  
- A Leica GS18T GNSS RTK (Manufactured by Leica Geosystem, Switzerland), which combines GNSS and IMU (tilt compensation), connected to multiple constellations to receive real-time corrections.  
- Additionally, different software applications were used for the processing phase, each tailored to its respective method before combining the data. For photogrammetric processing, Agisoft Metashape 1.8 was used, while Leica Register 360 and Leica 3 DR (v. 2021.0) were employed for 3D scanning. These software applications were installed on a high-performance computing system comprising an Intel i7 8700 4.6 GHz 8-core processor, 64 GB of RAM, and an 8 GB AMD Radeon GPU.  
The workflow sequence was as follows (Figure 3):  
Figure 3: Workflow for 3D data acquisition and processing.
## 2 Materials and Methods
### Data Analysis  
Multivariate analysis was performed using The Unscrambler X software (v11, CAMO ASA, Oslo, Norway). The NIR spectra were smoothened and pre-processed using the Savitzky-Golay second derivative (second order polynomial and a smoothing window size of 10 points) [47]. Principal component analysis (PCA) was performed to visualise the data structure and identify patterns, trends, outliers in the spectra, and other dominant features in the samples according to feed and instars [48; 49]. Partial least squares regression discrimination models (PLS-DA) between the NIR spectra and reference data (dummy values for each feed) were developed using full cross-validation (leave one out) [50]. The optimal number of factors for the calibration model was selected based on the minimal value of the predicted residual sum of squares (PRESS), and the highest correlation coefficient (R\({}^{2}\)) between actual and predicted values was used for the selection of the optimal number of factors for the calibration model being developed. The PLS models were evaluated using the standard error of cross-validation (SECV) and the coefficient of determination in cross-validation [50; 51]. Linear discriminant analysis (LDA) was also used to classify the 5 th and 6 th instar samples based on the feed source used to rear the larvae.