esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### ROC Curves for TUG  
ROC curve analysis was conducted to assess the ability of the TUG test to predict falls during the 12-month follow-up period. When analyzing the overall sample without age stratification, the TUG test did not demonstrate significant discriminatory ability (\(p\) = 0.136). However, when applying the ROC curve to the three previously defined age groups, distinct results were observed. For the 50-60 years age group, a cut-off point of 6.9 s was identified, yielding a sensitivity of 86.7%, specificity of 66.7%, and an AUC of 78.3%, indicating a good ability to distinguish between fallers and non-fallers (\(p\) = 0.001). In contrast, for the 60-70 years and 70 years or older age groups, the TUG test did not show significant discriminatory ability (\(p\) = 0.300 and \(p\) = 0.137, respectively).
## 1 Introduction
### Input Data
#### 2.2.4 Cropland Data Layer  
The USDA Cropland Data Layer (CDL) is a crop-specific land cover raster created annually for the continental United States by the NASS using moderate resolution satellite imagery and extensive agricultural ground observations [33]. It is distributed in AEA projection and North American 1983 datum (NAD83). The CDL was first produced in 1997 for North Dakota but has covered the contiguous US yearly only since 2008. The product has approximately 130 classes and a spatial resolution of 30 m at best. We regrouped the CDL layers into ten classes (Table S1) and then used this reclassified data to generate sample datasets for input to the RFCs. The reclassified CDL layer also provided a reference against which to evaluate the predicted land cover maps. To work with HLS data, the CDL data were reprojected into UTM zone 14N. Due to differences in the original projections and data, the reclassified CDL, ARD, and HLS pixels are not perfectly co-aligned. While offsets between the CDL and ARD pixels are only about 3 m in both latitude and longitude directions, offsets between the CDL and HLS pixels are 15 m (half pixel) in each direction. We did not resample these data into a common grid, as this step would introduce another source of uncertainty into the analysis.
## 5 Qualitative Evaluation
### General Requirements  
Table 1 lists all general requirements from the IETF AAA recommendation. The first both issues, scalability and fail-over, are covered by our DHT-based approach, due to the nature of the employed DHT protocol [21]. A mutual authentication is done between the client and the mapping system based on the used asymmetric cryptography principle. Transmission level security is generally supported if it is required by one party, however, it has to be noted that our system resides on layers below the transmission layer. Therefore, it is transparent to an eventual Transmission Layer Security. The data object confidentiality is only partially covered because we secure the transmission of data, but the mapping system is open to everyone. This is because any authenticated client is able to initiate lookup requests. As we use signatures to sign mapping responses within our architecture, integrity of the data objects can be regarded as fully covered. The transport of certificates is not required because the system is not relying on certificates at all. We, therefore, regard this aspect as out of scope. As all security-related data is transmitted in a trustworthy manner--encrypted and signed--the system fulfills  
all AAA reliability requirements. IPv4 and IPv6 is completely transparent for our architecture, in fact, our approach is not limited to IP routing respectively addressing. We therefore support both IP versions as well as possible future network layer protocols. Correspondingly, also proxies are higher layer issues and are therefore fully supported. The requests and replies from and to the mapping system are not supposed to be altered on their way. This is ensured by the integrity checks. We, therefore, consider the audability aspect to be out of scope. Our system does not require a shared secret in terms of a common token--note the difference between shared credentials and [PERSON]'s shared secret. We solely rely on asymmetric cryptography. Similarly to the support of future protocols (network layer and higher), service specific attributes can seamlessly be included into the communication pattern as this does not affect our proposed architecture.
## 2 Power Supply and Energy Storage Solutions for Off-Grid Base Stations  
Following the emerging concept of green telecommunication networks, the realization of powering BS sites using sustainable solutions has started to receive significant attention. Therefore, various studies and developments have been done to help telecom operators shift away from using diesel generators as their primary power supply solution for BSs. It is being realized that by moving away from diesel generators, the unreliability factors and the high O&M costs usually associated with this solution can be avoided. This section summarises the renewable energy solutions in the telecommunication sector and highlights the various power supply and energy storage solutions for off-grid BSs that have been proposed. Figure 1 provides a summary of related works that have investigated green wireless network optimisation strategies within smart grid environments. In addition, [PERSON] et al. [9] have studied resource allocation algorithm design for energy-efficient communication in an orthogonal frequency-division multiple access (OFDMA) downlink network with hybrid energy harvesting BS. Reference [10] has discussed a point-to-point communication link where the transmitter has a hybrid supply of energy. Specifically, the hybrid energy is supplied by a constant energy source and an energy harvester, which harvests energy from its surrounding environment and stores it in a battery which suffers from energy leakage.  
In addition, in a study conducted by the GSMA, which is a mobile trade organization, 320,100 renewable-based off-grid BS sites have already been rolled out in 2014. This number is expected to increase further in 2020 with about 389,800 sites [20].
## 2 Materials and Methods
### Statistical Analysis  
Before further statistical analysis, all data were checked by the normality test (Kolmogorov-Smirnova test) and the outliers were removed, based on the corresponding stem-leafplot. The homogeneity tests of variances were also applied to ensure that the significances were higher than 0.05. The mean concentrations and storages of the C and N fraction were calculated in the four wetlands and one-way analysis of variance (ANOVA) was conducted to indicate the OM differences in the four wetlands. A two-way ANOVA was conducted to determine the effects of two wetland types (constructed/natural wetlands) and two wetland habitats (invaded/normal wetland habitats) on the OM concentrations and storages for each sediment depth. Meanwhile, the contents of C and N fractions and LFOC LFON ratio of all sampling sites were also independently analyzed for the effects of two sediment depths. The above-mentioned steps were conducted in SPSS (version 21.0).  
To evaluate microbial alpha diversity, we calculated the abundance-based coverage estimator (ACE) index, using an ACE calculator (The Department of Microbiology & Immunology at The University of Michigan, Detroit, MI, USA. 2009. [[https://www.mothur.org/wiki/Ace](https://www.mothur.org/wiki/Ace)]([https://www.mothur.org/wiki/Ace](https://www.mothur.org/wiki/Ace))) [32]. Observed OTUs were counted in Mothur, and OTU-level ranked abundance curves were generated to compare the richness and evenness of OTUs among groups (Figure S1). Principal Component Analysis (PCA) of bacterial phyla was performed in Canoco (version 4.5, Wageningen University and Research Centre, Vakhn, Netherlands, 2002) [33]. All bacterial taxa at the phylum level and the ACE index were checked to remove the outliers, and all data used obeyed normal distribution. For the potential effects of alligator weed on bacterial composition, we conducted the mean value analysis of the bacterial phyla in the four wetlands, which differentiated between the invaded and normal habitats. A two-way ANOVA was conducted to determine the significant differences of bacterial taxa and ACE index between constructed/natural wetlands and between invaded/normal habitats. The Benjamini-Hochberg algorithm was processed to control false discovery rate at the significance level of 0.05 for the two-way ANOVA of bacterial phyla in R (version 3.2.2) [34]. Cluster analysis by the unweighted pair-group method with arithmetic means was conducted to the bacterial phyla among the four wetlands that differentiated between invaded and normal habitats in R (version 3.2.2). Pearson correlation analysis was also conducted to assess the potential associations between the bacterial taxa and OM fractions, using the SPSS software (version 21.0). Concretely, the \(p\)-values for the Pearson correlation analysis meant the significances between the two variables in this study indicated significant correlation, and extremely significant correlation when the \(p\)-values were less than 0.05 and 0.01, respectively.
## 5 Sensors
### LiDARs  
LiDAR (Light Detection and Ranging) is a cutting-edge technology that uses laser pulses to measure distances, creating highly accurate 3D maps of the environment. In the context of unmanned ground vehicles LiDARs are essential for enabling autonomous navigation, obstacle detection, and terrain mapping. By providing detailed spatial data, LiDAR systems allow UGVs to navigate complex environments, such as agricultural fields, with precision and efficiency. The basic working principle of LiDAR involves a Laser Emitter, which fires short pulses of laser light towards the environment, a Receiver, which captures the reflected light from objects, measuring the time it takes to return, and a Scanning System, which aims the laser beam to cover a large area. The most widely used scanning solution is mechanical spinning, offering a high signal to noise ratio. However, its bulky design and long scanning time make it sensitive to vibrations. Other common scanning systems include microelectromechanical systems (MEMS), flash, and optical phased arrays. MEMS are a more compact version of mechanical spinning and can be programmed to adjust the scanning trajectory for better system integration. Flash systems, which use optical diffusion, are an alternative to rotating scanning systems, but their performance is limited by the power of the laser source, especially for long-range measurements, as a single diffused laser must cover the entire area of interest. Optical phased arrays modulate the phase of light, allowing for control over the wave-front shape and eliminating the need for time-consuming laser scanning. This method is particularly suitable for high-vibration, heavy-duty applications [33]. A Processing Unit calculates the distance based on the time delay, combining these data with the angle of the emitted laser to generate a 3D point cloud. In agriculture, UGVs equipped with LiDAR use these point clouds to sense obstacles, map the terrain, and monitor crop growth. LiDAR is highly effective in outdoor environments due to its ability to operate under various lighting conditions and over large distances [34]. While 3D LiDARs provide a complete understanding of the environment, 2D LiDARs offer simpler and cost-effective solutions for tasks that do not require vertical scanning, such as basic navigation, collision avoidance, and mapping in flat or controlled environments. They are often found in robots or UGVs that operate on level surfaces like warehouses or in controlled environments. An overview of the main LiDARs is outlined in Table 2. Different models for outdoor use are compared according to their different characteristics, such as angular resolution, scan rate, max measuring range, field of view, accuracy, and interface.
## 5 IoRT Systems Based on Space-Borne NTN Networks
### Enhancement of Edge Computing Capabilities
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **NTN Architecture** & **Description** \\ \hline \multirow{3}{*}{[116]} & \multirow{3}{*}{Generic} & Comparative performance evaluation of CoAP and MQTT protocols for rapid data delivery on satellite random access channels compliant with the DVB-RCS2 standard. \\ \cline{1-2}  & & Development of a QoE-aware satellite constellation design scheme to enhance user experience in satellite IoT networks. \\ \hline \multirow{3}{*}{[118]} & \multirow{3}{*}{Generic} & Development of a cooperative resource allocation mechanism that utilizes link condition feedback and adaptive coding to dynamically allocate resources and ensure consistent and reliable video streams. \\ \cline{1-2}  & & Development of a joint optimization approach for successfully managing both data queuing delay and power consumption at the downlink of a NOMA-based satellite IoT system. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._To enhance the performance of IoT applications in LEO satellite networks, the authors of [124] propose a hybrid offloading architecture that integrates satellite mobile edge computing, which enables IoT devices to directly access satellite-based computational resources, eliminating the need for distant cloud servers and minimizing transmission and computation energy consumption. This approach alleviates bandwidth constraints and minimizes transmission delays. Additionally, the authors present a multi-agent actor-critic RL algorithm to optimize outsourcing policy decisions while taking into account the limitations of satellite resources. Table 9 summarizes the research efforts that have explored the use of space-borne NTN networks to enhance edge computing capabilities for IoT applications. These efforts have demonstrated the potential of space-based NTNs to provide low-latency, high-bandwidth connectivity to remote IoRT devices, enabling the offloading of computation tasks to satellites for processing. This can significantly improve the performance and scalability of IoRT applications, particularly in resource-constrained environments.
## 3 Algorithms for Validation and Ranking  
Based on the challenges described in Section 1, the following solutions are proposed:  
* Slope-based signature calculation as additional curve similarity metric to enhance a distance-based, semi-automatic measurement validation process;
* Feature-based and prioritize ranking metric to sort the sensors according to their performance towards a given analyte.  
Before getting started, the formal conventions are introduced. In this work a vector is denoted with \(\mathbf{x}\in\mathbb{R}^{|\mathbf{x}|}\) where \(|\mathbf{x}|\) is defined as the amount of the vector's elements. The vector element at index \(i\) is referenced by \(\mathbf{x}[i]\) with \(1\leqslant i\leqslant|\mathbf{x}|\). A window with size \(w\in\mathbb{N}\) around an index \(i\) can be interpreted as a vector itself containing only values from the original vector \(\mathbf{x}\) with the limits \(l\leqslant i\leqslant r\).  
\[\mathbf{x}\langle i,w\rangle\quad\text{with}\quad l=\begin{cases}i-w&\text{if }i-w>1,\\ 1&\text{else}\end{cases}\quad\text{and}\quad r=\begin{cases}i+w&\text{if }i+w<| \mathbf{x}|,\\ |\mathbf{x}|&\text{else}\end{cases} \tag{1}\]  
Let \(\mathbf{v}=(1\quad 2\quad 3\quad 4\quad 5)\) be a vector with five elements. An exemplary window could then be \(\mathbf{v}\langle 3,1\rangle=(2\quad 3\quad 4)\). Furthermore, the last \(n\) elements of the vector could also be addressed via the window \(\mathbf{v}\langle|\mathbf{v}|,n-1\rangle\). For \(n=3\), this gives \(\mathbf{v}\langle 5,2\rangle=(3\quad 4\quad 5)\).  
A measurement is a vector \(\mathbf{m}\in M\) with \(M\) being the set of all resistance measurements of one or multiple sensor screenings, as introduced in Section 2.1. The elements of this vector with their corresponding indices represent the sampled values and the time base of the measurement. Following this notation, the different segments of a measurement are defined as \(\mathbf{m}_{\text{a}}\) for the analyte exposure, \(\mathbf{m}_{\text{b}}\) for the baseline and \(\mathbf{m}_{\text{c}}\) for the clearing phase.  
The residual standard deviation used in the work is defined as [14]:  
\[s(\mathbf{x},\mathbf{r})=\sqrt{\frac{\sum_{i=1}^{|\mathbf{x}|}(\mathbf{x}[i]- \mathbf{r}[i])^{2}}{|\mathbf{x}|-2}},\ |\mathbf{x}|>|\mathbf{r}| \tag{2}\]  
All notation conventions, including those introduced above, are summarized in Table 2 as a compact overview.
## 3 Results and Discussion
### Results from Matlab  
Table 1 shows the results obtained from this research for the children's screams, a car passing by and the sound of the grinder.  
In the case of the children's screams, the listening results showed that this short signal becomes annoying at the mean amplitude parameter value of 0.51. Furthermore, since the amplitude potentiometer was set from 0 to 1 (where 0 indicates 0 dB and 1 denotes 70 dB), it can be concluded that 0.51 corresponds to the absolute sound pressure level of 35.70 dB above the level of the stream acoustic environment, which corresponded to 85.70 dBA or 15.60 sones including both stream sound environment and short signal.  
Figure 6: Time window envelope settings used for processing the short signals according to the selected parameters of the amplitude, duration and the amplitude growth rate.  
Following the same analogy for the passing of a car, a mean amplitude parameter value of 0.42 was obtained which corresponded to the absolute sound pressure level of 79.40 dBA or 14.75 sones.  
Finally, for the sound of the grinder, it was concluded that the sound becomes irritable at a mean value of the amplitude parameter 0.47, which is equivalent to the absolute sound pressure level of 82.90 dBA or 14.95 sones. The loudness values were calculated using the [PERSON]'s loudness model [32; 33; 34; 35; 36] for each short signal including the background stream acoustic environment, using the 100 milliseconds time intervals.  
When analyzing the duration of the short signals, the mean values were obtained in the following order: for the screaming children, 1.10, for the car passing by, 0.98, and for the grinder, 0.92. The duration potentiometer has a range of 0-2, where 0 means zero seconds and 2 means two seconds. It can now be concluded that all three short signals for the majority of listeners become annoying after a minimal duration of one second.  
Furthermore, the mean values of the amplitude growth rate were obtained: for the children's screams, 0.55, for the cars passing by, 0.63, and for the grinder, 0.54. In addition, the signal's amplitude growth level is expressed in sones per second and the mean values are the following: 17.30, 16.67 and 16.21 sones per second.  
If we observe the obtained mean results for the three distracting short signals (shown in Table 1) it can be concluded that the \"most important\" parameters, regarding the human annoyance with a particular sound, are the amplitude and the duration. The test has shown that the sound will become annoying to the listener if its sound pressure level is at least 34.30 dB above the level of the stream acoustic environment and if the sound is no shorter than one second.
# A Review of Optical Neural Networks
## 3 Development Processes and Categories
### Fiber-Based Optical Neural Networks
#### 3.3.1 Microfibers  
Throughout the years, the field of neuromorphic engineering has been dedicated to the development of practical artificial neurocomputing devices that mimic the functions of the biological brain. To date, neuromorphic systems have been demonstrated with software running on conventional computers or complex electronic circuit configurations [43,71,72]. However, the efficiency of such systems is low compared with that of human neuromorphic systems. Accordingly, the search for alternative materials and structures to achieve efficient neuromorphic components is an area of intense research.  
Optical fibers have been implemented in functional materials such as semiconductor compounds [73, 74, 75]. The physical properties of sulfur-based alloys are of interest because they are altered by light and exhibit brain-like functions in terms of plasticity and their form of transmission [76, 77, 78]. The signals of this type of microfiber display characteristics of biological signals transmitted in the brain via light transmission as seen in Figure 3[79, 80, 81]. In addition, microfhers can monitor neural activity on the cortical surface and in deep brain regions [82, 83]. Accordingly, microfhers appear to be a competitive category of artificial neuromorphic components and will likely be widely used in neuroscience research and medical applications in the future.  
Figure 2: Implementing D\({}^{2}\)NNs. (**a**) Several transmission or reflection layers are 3D printed, and each point on the layer is an artificial neuron with a given weight connected to other neurons in the next layer via optical diffraction [52]. (**b**) Diffractive process units (DPUs) are designed as a large-scale perceptron class optoelectronic computing building block that can be programmed to build different deep neural networks implemented with programmable optoelectronic devices, such as digital micromirror devices (DMDs), phased spatial light modulators (SLMs), and CMOS sensors [61]. (**c**) An optical convolutional layer with an optimizable phase mask is conceived to implement a convolutional neural network using the intrinsic convolution performed using a linear spatial non-turning image system [60].
## 3 Mission Overview
### Data Retrieval and Use
It would also provide a framework for enhanced information extraction and the interpretation of dynamic features of the atmosphere occurring within the UTLS. This is particularly relevant for creating climatologies of the tropopause structure, including the identification of stratosphere-troposphere exchange events and tropopause folds, and to resolve structures and finer-scale fluxes in the UTLS and lower stratosphere, such as the polar vortex or water vapour fluxes above the top of deep convective clouds. Such assimilation systems are likely to operate on finer resolutions than are currently the state-of-the-art, which will make corresponding high resolution measurements ever more necessary.