esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Hardware and Software Components  
The Bitcraze Crazyflie 2.1 drone served as the experimental platform, selected for its compact size, modular design, and expandability. Several expansion decks and components were integrated to enhance its sensing and flight capabilities:  
* **Crazyflie 2.1 Drone:** Figure 2a illustrates the main aerial platform for this research. It is a lightweight, open-source nano quadcopter developed by Bitcraze, equipped  
Figure 1: System architecture diagram illustrating the integrated components of the multifunctional drone control framework.  
with an STM32F405 microcontroller, built-in IMU, and expansion deck support via an easy-to-use pin header system [23]. Its modular design allows seamless integration of multiple decks for advanced sensing and control functionalities.
* **Crazyradio PA:** Figure 2b is a 2.4 GHz radio dangle used to establish wireless communication between the host computer and the Crazyflie 2.1 drone [24]. The PA (Power Amplifier) version enhances signal strength and communication reliability, particularly in environments with interference or extended range requirements.
* **Flow Deck V2:** Figure 2c provides optical flow-based position estimation and altitude measurement using a downward-facing time-of-flight (ToF) sensor and an optical flow camera [25]. This enables the drone to maintain stable flight without external positioning systems.
* **Multi-Ranger Deck:** Figure 2d utilizes infrared ToF distance sensors to detect obstacles in five directions--front, back, left, and right [26]. This facilitates obstacle avoidance by continuously monitoring the drone's surroundings.
* **AI-Deck 1.1:** Figure 2e features a camera module for video streaming, allowing users to navigate the drone beyond their direct line of sight [27]. The AI-Deck includes a GAP8 RISC-V multi-core processor, enabling onboard image processing and low-latency streaming to a ground station. A Linux system is required for setting up and flashing the AI-Deck, as the software tools provided by Bitcraze (Malmo, Sweden), such as the GAP8 SDK, are optimized for Linux-based environments.  
Gesture-based control was implemented using Google's MediaPipe Hands, a computer vision framework capable of tracking 21 key landmarks on a user's hand. The gesture recognition software was implemented in Python (Version 3.11.0), processing webcam input to identify predefined gestures and mapping them to corresponding flight control commands for the drone. The system was executed in Visual Studio Code (Version 1.101) on a laptop running Windows 11 with an Intel Core i9 processor, 64 GB of RAM, and an integrated webcam, which provided sufficient performance for processing and drone communication. The system leveraged Bitcraze's Crazyflie Python library for wireless communication, ensuring seamless execution of gesture-based commands.
# Capturing Discriminative Information Using a Deep Architecture in Acoustic Scene Classification
## 3 Proposed Framework  
### Adopting the LCNN  
The LCNN is a deep learning architecture that was initially designed for face recognition when the data contain noisy labels [15]. Its primary feature is a novel operation referred to as the max feature map (MFM), which replaces the non-linear activation function in the DNN. The MFM operation extends the concept of maxout activation [22] and adopts a competitive scheme between the filters of a given feature map.  
The implementation of an MFM operation can be denoted as follows. Let \(a\) be a feature map derived using a convolution layer, \(a\in\mathbb{R}^{K\times T\times F}\), where \(K\), \(T\), and \(F\) refer to the number of output channels, time-domain frames, and frequency bins, respectively. We split \(a\) into two feature maps, \(a_{1}\) and \(a_{2}\), where \(a_{1}\), \(a_{2}\in\mathbb{R}^{\frac{K}{2}\times T\times F}\). The MFM applied feature map is obtained using \(Max(a_{1},a_{2})\), element-wise. Figure 2b illustrates this MFM operation.  
Specifically, our design of the LCNN is similar to that of [16], with some modifications. The architecture of [16] is a modified version of the original LCNN [15] that applies additional batch normalization after a max-pooling layer. Table 1 provides details about the proposed system architecture. Each block contains conv_a, MFM_a, BatchNorm, Conv, MFM, and CBAM. In total, four blocks are implemented. The number of blocks is determined based on the comparative experiments.  
Figure 2: Comparison of ReLU activation function (**a**) and MFM (**b**). Orange, green, and white indicate negative, positive, and zero values, respectively. ReLU removes all negative values, while MFM considers the element-wise maximum.
# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 6 Comparative Analysis of Proposed Framework with the Existing Frameworks
artificial neural network models--in general, they can be adapted to any kind of dataset. However, the major reason behind the success of current deep learning applications is the huge of amount of labeled data that the machine learning community has been able to provide. Although there has been much advancement in unsupervised and semi-supervised deep learning methods, the majority of the state-of-the-art results have been for supervised problems. So, how can we decide whether selecting the deep learning framework is suitable, for example, in the self-optimizing, self-healing and self-protection modules of SON in future networks? An important consideration for the proposed framework is its comparison in terms of computational complexity. We used the data on computational complexity of nearest neighbor approaches from the paper [59]. Table 4 shows the computational complexity of different approaches. In Table 4, \(N_{tr}\) is the number of training instances, \(N_{te}\) is the number of testing instances, \(d\) is the dimensionality of the data, \(I\) is the number of iterations, and \(L\) is the number of layers for neural networks. We compared the approaches in terms of training and testing computational complexity, the requirement of optimization of an objective, and the regularization ability. A central challenge in determining the computational complexity of any machine learning algorithm is that many methods are based on iterative algorithms, and the number of iterations depends on the number of data samples. In the case of a neural network model, there will be N*d computations whenever the algorithm completes one pass over the N data samples. However, the algorithm may converge after being exposed only to a few or a constant number of data samples. Although most machine learning algorithms are almost linear in terms of the number of data samples and number of features, there could be also other cases. There are strong reasons to adopt the proposed framework from the recent development in deep learning. First, the massive amount of data that is already available and which is projected to grow in future means that deep learning is the solution since the majority of other machine learning methods harness data up to a certain level and then stop learning any further. The development of 5G would allow the amount of data that can be fully harnessed with deep learning to be produced. Secondly, the computational power, especially HPC and cloud computing, would further provide the platform for the rapid training of deep learning models. Finally, the algorithm research or development that is happening in the deep learning domain has now become easily marketable, which means that more resources in terms of capital and manpower will be invested in it. The proposed framework provides the basic setup which can be extended to deep learning, while other existing frameworks consist of techniques that can only provide solutions to current problems.
## 4 Experiments and Results
### Robotic Gripper Teleoperation Execution
As shown in Figure 9 and Table 1, the robotic gripper can grasp from a tiny spoon to a big toy ball, a fragile egg to a metal bottle, and a light balloon to a heavy bottle of drinking water. It can handle even bigger objects due to the modular and reconfigurable fingers, which can adapt to different sizes of the grasped object. As shown in Figure 9 and Table 2, the RRI value of the five fingers are well matched with the finger state (open or closed) of the gripper during the teleoperation experiment.
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
## 3 Quantum Immune Security Solutions
### Efficiency of NewHope and Frodo Key Exchange Algorithms  
The energy efficiency of cryptography depends mainly on the computational complexity of algorithms. Particularly, the energy costs depend on the use of different processors (CPUs, GPUs or crypto accelerators). However, also the use of memory (RAM, disk) as well as communication methods (transmissions and receiving through radio, fixed lines, or internal busses) affect the costs.  
Figure 3 illustrates and compares computations executed by NewHope and Frodo. Other RingLWE algorithms, such as HILA5, are similar and follow the same procedures as NewHope, but may have some variations on the parameter selection. The main phases of the algorithms are:  
1. Random number generation--The procedure itself is not costly from the energy efficiency perspective. However, good entropy sources may not be available for IoT devices.
2. Error distribution sampling--NewHope samples errors from the binomial distribution while Frodo presents four optimal discrete distributions. For NewHope, sampling is done once for both sides. For Frodo, the sampling is done once for [PERSON] and twice for [PERSON]. The sampling costs for optimized NewHope is around 5% of the total computation for both [PERSON] and [PERSON] [5].
3. Generation of public and private matrices with errors--[PERSON] and [PERSON] use these matrices to agree on and generate a shared key. These phases include polynomial multiplications that are simple to implement also for memory and processor restricted embedded devices. However, algorithms require a large amount of computations that consumes energy. This is computationally the most expensive part as e.g., 15% of NewHope processing for [PERSON] and [PERSON] goes here [5]. Hence, NewHope implementations apply Number Theoretic Transform (NTT) techniques like fast Fourier transformation to improve the performance [5; 42]. Frodo does not have the benefits that the ring structure brings but tries to minimize the costs of generating public parameters.
4. Encoding and transmitting/decoding and receiving of messages between [PERSON] and [PERSON]--In NewHope, the first pass contains 1824 bytes and the second pass 2048 bytes [5]. In Frodo, which uses ideal lattices that require longer parameters, the first pass contains 11,377 bytes and the second 11,296 bytes [6].
5. Error reconciliation--determining the exact key from the agreed matrices with errors. This phase also takes considerable processing power. For instance, around 11% of [PERSON]'s and 6% of [PERSON]'s computing time in NewHope is spent on error reconciliation [5].
## 5 Performance Testing
### Fingerprint Collection Time Test  
The effect of shorter fingerprint collection time on positioning accuracy and time saved was evaluated. Similar to the fingerprint spacing test, 12 iBeacons were deployed on the ceiling and the fingerprints were collected at 1 m spacing in Room ZB214. iBeacon signals were collected at every point for 10 s and the data were trimmed progressively to shorter collection times, at 1 s intervals, until 1 s. The positioning result, the relationship between fingerprint collection time and the positioning accuracy were analysed.  
Testing of fingerprint collection time was conducted with the same configuration, using 12 iBeacons installed on ceiling. The CDF comparison of the collection time by RMSE, under different collection times, is shown in Figure 12. As the collection time increased, the RMSE of positioning accuracy decreased. The RMSE decreased substantially from a collection time of 1 s to 3 s, suggesting that very short collection times cause large uncertainties in positioning. There was a significant reduction in variance at a collection time of 4 s, and similar positioning errors were found in collection time of 4 s to 10 s. Although improvements were observed in the error rate with every additional second of collection time, improvements in RMSE from 6 s to 10 s were extremely marginal, with a drop from 1.53 m to 1.49 m.  
Figure 11: CDF comparison of collecting fingerprint at different space intervals.  
The fingerprint collection time is also a parameter that can be altered to either save time or increase accuracy, depending on the limitations of the deployment. Our results demonstrated that a fingerprint collection time of at least 6 s was required for satisfactory accuracy, for use in indoor deployments. Whereas improvement in accuracy was observed beyond 6 s, it was almost imperceptible compared to significant leaps in accuracy when moving from 1 s collection time to 2 s, or from 2 s to 3 s. When deploying iBeacon positioning systems, our recommendation is a collection time of at least 6 s at each grid point, as no discernible positive effect was seen for collection times over 10 s.
# An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications
## 5 Conclusions  
Nonlinearity inherited in the LED severely degrades the performance of OFDM VLC systems due to the high PAPR in OFDM signals. In this paper, we proposed the use of an ANN predistorter to pre-emphasize the DCO-OFDM VLC signal. The nonlinearity effects of the LED could be adequately compensated for with the capability of universal function approximation of the ANN. As observed from both computer simulations and experimental verification, the ANN predistorter achieved considerable performance improvement over the existing NLMS and Chebyshev polynomial predistortion methods in terms of the BER and EVM of the received signal constellation.  
Conceptualization, J.-K.L.; methodology, J.-K.L.; software, [PERSON]-K.L. and [PERSON]-[PERSON]; validation, J.-K.L. and [PERSON]-[PERSON]; formal analysis, [PERSON]-K.L. and [PERSON]-H.C.; investigation, [PERSON]-K.L. and [PERSON]-H.C.; resources, J.-K.L.; data curation, [PERSON]-K.L. and [PERSON]-[PERSON]; writing--original draft preparation, J.-K.L.; writing--review and editing, J.-K.L.; visualization, [PERSON]-K.L. and [PERSON]-[PERSON]; supervision, J.-K.L.; project administration, J.-K.L.; funding acquisition, J.-K.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The data presented in this study are all available in this article.  
The authors declare no conflict of interest.
## 3 Proposed System
### Reduce Packet Loss with TTL  
A few packets are dropped due to loops in the networks. Sometimes packets have to travel a greater number of routers than usual. This may result in packets being not delivered to the destination within a reasonable length of time. There are some mechanisms to avoid congestion in the network and to redirect the packets to nearby sub routers. When a packet travels a greater number of routers unexpectedly, its TTL value goes to zero before reaching the packet to the destination. This motivates the proposal of an adaptive TTL so that even when the packet travels more routers than usual, it should not be discarded by any router. This avoids unnecessary retransmissions in the network and it leads to reduced packet losses. An adaptive TTL can be calculated as follows.  
\[\left.\begin{array}{l}\text{TTL}_{\text{new}}=\text{Max}(\text{TTL}_{\text {min}},\ \text{Min}(\text{TTL}_{\text{rt}},\ \text{TTL}_{\text{max}}))\\ \text{TTL}_{\text{rt}}=\alpha*\text{TTL}_{\text{est(i)}}+(1-\alpha)\text{TTL}_ {\text{est(i-1)}}+\left(\text{TTL}_{\text{est(i)}}-\text{TTL}_{\text{est(i-1 )}}\right)\end{array}\right\}\ 0.5\leq\alpha<1\ (11)\]  
Variables:  
\(\text{TTL}_{\text{min}}=\text{static}\) specified minimum value; \(\text{TTL}_{\text{max}}=\text{static}\) specified maximum value  
\(\text{TTL}_{\text{rt}}=\text{Recent}\) reflect changes;  
\(\text{TTL}_{\text{est(i)}}\), \(\text{TTL}_{\text{est(i-1)}}\) are the two most recent observations.  
Sometimes increasing the TTL value may lead to security problems in the network because with increased TTL packet can traverse a longer route. There might be chances that with the increased TTL, the packet can enter into an unauthorized private network. Security mechanisms have to be provided to avoid security problems for an increased TTL packet. For this, popular asymmetric cryptographic algorithm, RSA with Carmichael function is applied to each increased TTL packet or any packet that needs security over the network. The advantage of this encryption scheme is that it produces small private key value compared with usual Euler totient function in RSA. The original recipient can decrypt the cipher text with less time than usual. This helps to prevent security threats for any increased TTL packet.
## 2 Methodology
### Governing Equations for the Flow Field and the Improved MLBFS  
#### 2.2.1 Governing Equations  
For effective simulation of incompressible multiphase flows, the governing equations reproduced by the multiphase lattice Boltzmann flux solver [42] can be written as:  
\[\frac{\partial\mathbf{W}}{\partial t}+\
abla\cdot\mathbf{F}=\mathbf{S}, \tag{18}\]  
where \(\mathbf{W}=\left(p,\rho uc_{s}^{2},\rho vc_{s}^{2}\right)^{T}\); \(\rho\) is the fluid density; \(c_{s}\) is the speed of sound given by the lattice velocity model; \(u\) and \(v\) are, respectively, the fluid velocity components in the \(x\) and \(y\) directions; \(p\) represents the pressure; \(\mathbf{F}\) represents the flux; and \(\mathbf{S}\) represents source terms:  
\[\mathbf{F}=\left(\sum_{\alpha=0}^{N}\mathbf{e}_{\alpha}f_{\alpha}^{eq},\sum_{\alpha=0} ^{N}\mathbf{e}_{\alpha}\mathbf{e}_{\alpha}f_{\alpha}^{\wedge}\right)^{T}, \tag{19}\]  
\[\mathbf{S}=\left(-\mathbf{u}\cdot\
abla\left(p-pc_{s}^{2}\right),c_{s}^{2}\mathbf{F}_{s} \right)^{T}, \tag{20}\]  
\[f_{\alpha}^{eq}(\mathbf{r},t)=w_{\alpha}\left[p+pc_{s}^{2}\left(\frac{\mathbf{e}_{ \alpha}\cdot\mathbf{u}}{c_{s}^{2}}+\frac{(\mathbf{e}_{\alpha}\cdot\mathbf{u})^{2}-(c_{s}| \mathbf{u}|)^{2}}{2c_{s}^{4}}\right)\right], \tag{21}\]  
Here, \(\mathbf{e}_{\alpha}\), \(N\), \(w_{\alpha}\), and \(c_{s}\) are given by the lattice velocity model; \(\mathbf{e}_{\alpha}\) is the particle velocity in the \(\alpha\) direction; \(N\) is the number of discrete particle velocities; \(w_{\alpha}\) are the coefficients; \(c_{s}\) is the speed of sound; \(\mathbf{r}\) represents a physical location; \(f_{\alpha}^{eq}\) is the original equilibrium particle distribution function; \(f_{\alpha}^{\wedge}\) is the function of \(f_{\alpha}^{eq}\), applied in the flux reconstruction; and \(\mathbf{F}_{s}\) is the surface tension force. In this article, the lattice velocity model is the D2Q9 model shown in Figure 2: each vector represents the particle velocity in the corresponding direction. The speed of sound \(c_{s}=c/\sqrt{3}\), \(c=\delta_{x}/\delta_{t}\), \(\delta_{x}\) is the lattice spacing, \(\delta_{t}\) is the streaming time step, \(w_{0}=4/9\), \(w_{1}=w_{2}=w_{3}=w_{4}=1/9\), and \(w_{5}=w_{6}=w_{7}=w_{8}=1/36\).  
Figure 1: Illustration of the structure for two-phase flows.  
The surface tension force \(\mathbf{F}_{s}\) has many different forms. The potential form from [41] is adopted in the present study, and it is defined as:  
\[\mathbf{F}_{s}=\mu_{C}\
abla\mathbb{C}, \tag{22}\]
## 3 Historical Review to State-of-the-Art
### Ceres
#### 3.3.3. CERES-Terra/Aqua
The accuracy of shortwave irradiance estimated by CERES-Terra/Aqua ADMs was evaluated by [PERSON] et al. [46] using direct integration and irradiance consistency tests. Direct integration tests, which involve directly integrating the observed radiances over a given region and comparing them to the ADM-estimated irradiance, show that the overall bias in regional monthly mean top-of-atmosphere shortwave irradiance is less than 0.2 W m\({}^{-2}\) and the root-mean-square-error (RMSE) is less than 1.1 W m\({}^{-2}\). The bias and RMSE were found to be very similar between Terra and Aqua. Irradiance consistency tests were used to assess the accuracy of instantaneous irradiance, which involve comparing irradiance estimated from nadir and oblique viewing angles using CERES along-track observations and temporally and spatially matched MODIS observations, and using irradiance converted from multi-angle MISR observations. The averaged top-of-atmosphere instantaneous shortwave irradiance uncertainties from these two tests were found to be about 2% for cloud-free scenes, and around 2.7% to 3.7% for all-sky scenes. In general, the top-of-atmosphere shortwave irradiance uncertainties were larger for thin broken clouds than for moderate and thick overcast clouds.
## 2 Methods  
This study reviews the existing literature reporting the phenolic compounds, pigments, and vitamins in halophyte biomass and provides information about the potential of these plants in multi-product biorefinery applications. Several scientific databases were used to retrieve articles, including those of Elsevier, Taylor & Francis, Springer, and MDPI, but other peer-reviewed articles and some book chapters were also considered. Only articles written in English were considered, and the review focused on studies published in and after the year 2010. However, a few papers from 2008 and 2009 were also included. The review was performed to investigate the amount of the following compounds reported for botanical extracts from the biomass of edible halophytes:  
* Proanthocyanidins (condensed tannins);
* Phenolic compounds (total phenols);
* Chlorophyll;
* Carotenoids;
* Vitamins.  
The main keywords used in the search were: halophytes, phytochemicals, bioactive compounds, proanthocyanidin, condensed tannin, pigments, chlorophyll, carotenoid, and vitamins. Some of the reviewed studies compared the composition of different biomass batches of same species, or extraction methods. From these studies, the data from the  
Figure 1: Simplified process chart of halophyte-based biorefinery.  
sample with the highest concentration of the compound of interest was presented in the results. This review also briefly summarises the extraction methods and analytical procedures used to obtain, quantify, and identify the bioactive compounds. The strength of the solvent was also noted whenever it was specified in the original study.