esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 2 Background
### Smart LSs and EdgeML  
Within this paper, the EdgeML term does not refer either to the EdgeML library under development at Microsoft, nor to the EML framework mentioned in Section 2.6. Hereinafter, EdgeML simply denotes that ML models are thought to be run as close as possible to the edge-constrained IoT devices of an AlIoT architecture that implements a smart LS. The adoption of the EdgeML computing paradigm is relevant in the smart LS domain, as briefly explained in the following. Both indoor and outdoor smart LSs can enhance considerably the well-being and safety of citizens of advanced smart cities. But there are at least three stringent requirements these systems must meet: cut down the latency, limit the network overhead, and protect personal data:  
* The latency constraint is particularly stringent for smart street LSs where light controllers are responsible for dynamically adapting the light intensity to traffic conditions to ensure the safety of drivers.
* The number of luminaires worldwide is becoming huge (about 360 million street lights are foreseen on the globe by 2029 (source: [[https://smart-cities-marketplace.ec.europa.eu/sites/default/files/2021-06/Smart%20](https://smart-cities-marketplace.ec.europa.eu/sites/default/files/2021-06/Smart%20) Lighting%20 Factsheet_0.pdf]([https://smart-cities-marketplace.ec.europa.eu/sites/default/files/2021-06/Smart%20](https://smart-cities-marketplace.ec.europa.eu/sites/default/files/2021-06/Smart%20) Lighting%20 Factsheet_0.pdf) (accessed on 10 November 2024)), consequently the amount of the data collected by them is huge as well. Processing the data collected by the luminaires at the edge of the IoT network is a best practice to limit network overhead.
* It has been remarked that because future indoor and outdoor LSs become smarter, they need to collect more data about people and their daily activities [58]. This situation poses a twofold ethical concern. Firstly, the collected data must be securely archived to be protected against violation of privacy; secondly, the use of personal data must comply with the personal-data protection laws. Limiting the transit of personal data on the network is a precondition to protect it.
# Planar Microwave Resonant Sensors: A Review and Recent Developments
## 4 Prototype Examples
### Differential-Mode Sensors  
Differential sensors based on a pair of uncoupled lines each one loaded with a resonant sensing element have been reported. The considered resonant elements are OCSRRs [62], SRRs [63], OSRRs [67; 69], CSRRs [66], and DB-DGS resonators [64; 65]. We report in this paper three prototype examples, including a sensor for complex permittivity measurement of liquids (based on DB-DGSs) [64],  
Figure 23: Measured responses of the sensor for different combinations of dielectric loads in the channels. From [58], reprinted with permission.  
Figure 24: Measured frequency response of the sensor for various water–ethanol solutions (**a**) and representation of the resonance frequency splitting and notch magnitude difference, as a function of the ethanol concentration (**b**). From [58], reprinted with permission.  
Figure 25: Real (**a**) and imaginary (**b**) part of the complex dielectric constant of water–ethanol solutions as a function of the ethanol content, inferred from the data of Figure 24 and the mathematical model developed in [58]. From [58], reprinted with permission.  
a sensor for the determination of volume fraction of solute in liquid solutions (based on OSRRs) [69], and a sensor devoted to the measurement of extremely small concentrations of electrolytes in DI water [66].
## 5 Discussion
### Limitations in Satisfying Security Requirements for Offline Payment Function
#### 5.1.2 Trusted Third Party (TTP) Involvement  
In the research conducted by [PERSON] et al. [46] and [PERSON] et al. [66], the method of the trusted third party (TTP) involvement can be considered in a transaction. TTP observes transactions and monitors the users of the transaction system to prevent fraudulent activities. In addition, there have been methods devised to prevent fraudulent activities by the direct intervention of TTP during the transaction process, not only for monitoring. TTP can intervene in the protocol for withdrawal and deposit. Therefore, the currency circulating in the offline wallet of the paper or payee can be disclosed using TTP. This method of using TTP is being considered a security enhancement measure in many CBDC studies [106; 107]. However, TTP can intervene only under the circumstance when connected to an online network. Thus, when transacting via the protocol for offline payment, it is not possible to observe the status of the offline payment protocol directly, since TTP is not available offline. For this reason, it is not easy to introduce the method of trusted third party (TTP) involvement in an offline payment function.  
1.3 Pre-Certification for Members Participating in the Transaction or Post-Certification of Transaction Confirmation  
[PERSON] et al. [43] proposed a method to prevent double-spending transactions by receiving tokens with the same serial number from a third party. However, this method cannot proceed during the transaction unless both parties are pre-approved. Thus, this method is difficult for applying CBDC to various offline payment functions, since it is difficult to use CBDC in general.  
[PERSON] et al. [61] proposed suggestions on the methods to certify a transaction. Numerous online CBDC systems also use a method of authorizing users by verifying them in advance [108]. However, it is difficult to confirm that the two entities have received the certificates offline. Additionally, malicious users are capable of easily deleting the certificates issued. For this reason, it is critical to carefully review the prevention of repudiation in an offline payment using a certificate.
# Creating a Parallel Corpus for the Kazakh Sign Language and Learning
###### Abstract  
Kazakh Sign Language (KSL) is a crucial communication tool for individuals with hearing and speech impairments. Deep learning, particularly Transformer models, offers a promising approach to improving accessibility in education and communication. This study analyzes the syntactic structure of KSL, identifying its unique grammatical features and deviations from spoken Kazakh. A custom parser was developed to convert Kazakh text into KSL glosses, enabling the creation of a large-scale parallel corpus. Using this resource, a Transformer-based machine translation model was trained, achieving high translation accuracy and demonstrating the feasibility of this approach for enhancing communication accessibility. The research highlights key challenges in sign language processing, such as the limited availability of annotated data. Future work directions include the integration of video data and the adoption of more comprehensive evaluation metrics. This paper presents a methodology for constructing a parallel corpus through gloss annotations, contributing to advancements in sign language translation technology.  
Keywords:Kazakh sign language; parallel corpus; sign language; machine translation; deep learning; sequence to sequence model +
Footnote †: journal: Applied sciences
# A Simplified CityGML-Based 3D Indoor Space Model for Indoor Applications
## 3 Simplified 3D Indoor Space Model (3 DSISM) Based on CityGML
### LOD of the 3 DSSISM
#### 3.1.2 Definition of the LOD for Indoor Spaces  
The LODs concept of CityGML 3.0 allow to describe a detailed indoor space structure with a coarse exterior building shape or even a user-defined geometric and semantic LOD [26; 51]. Therefore, we try to describe the detailed indoor space while ignoring the exterior building information. The geometric model in Section 3.1.1 and the corresponding semantic classes are used to describe indoor spaces and building components in detail, while exterior building shape is not represented in the LOD defined in this research.  
In this work, indoor building spaces are decomposed into subspaces, which can be semantically represented as rooms, corridors, stairs, elevators, etc., according to the function of indoor spaces. There are no gaps between subspaces nor can they penetrate each other. The UML diagram of the 3D indoor space model is shown in Figure 6.  
Figure 5: UML diagram of adapted 3D indoor spatial geometry model based on CityGML.  
Each subspace is represented by geometric primitive _Solid_, which is enclosed by its boundary surfaces. The semantic class _BoundarySurface_ defined by CityGML2.0 is adopted for boundary surfaces, such as _InteriorWallSurface_, _FloorSurface_ and _CeilingSurface_. Since the 3D indoor model proposed in this paper describes only indoor space information, the semantic information that describes the building exterior, such as _RoofSurface_, _WallSurface_, and _GroundSurface_, has been removed from this model, and only the semantics that describe indoor spaces are retained.  
For geometrically unenclosed spaces, a virtual surface, _ClosureSurface_[22], can be used to virtually enclose the spaces to form a closed solid. For example, an open balcony is not a closed space and _ClosureSurface_ can be used to enclose it to construct a closed object. A corridor or hall is an open space that connects various rooms and stairs. Corridors and staircases are two different functional spaces and are usually connected without doors. Therefore, a _ClosureSurface_ is employed as the common boundary surface to connect a corridor and stairs.  
The adjacent spaces are connected through a common boundary surface using XLinks to describe the topological relationship of indoor spaces. As each 3D spatial entity in indoor spaces occupies its own independent space, these entities will not intersect each other. Hence, the topological relationships of indoor spaces are only the following: Contains, touch, and disjoint.
## 3 Results
### Applications
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Area** & **Author** & **Goal** & **Algorithm** \\ \hline Alzheimer’s diseases & [83] & Clustering & K-means Clustering \\  & [89; 90] & Clustering & DBSCAN \\  & [92; 93; 94] & Clustering & SOMS \\  & [119; 124] & Classification & KNN \\  & [132] & Classification & Naive Bayes \\  & [138; 139; 140] & Classification & Bayesian Networks \\  & [155] & Classification & Neural Networks + SVM \\  & [157] & Classification & Neural Networks + KNN \\  & [167] & Classification & SHIMR \\  & [178] & Classification & Naive Bayes + SVM + RF \\  & [179] & Classification & RF \\ \hline Heart and vascular diseases & [180] & Classification & RF \\  & [96; 97] & Classification & SVM \\  & [110; 114] & Classification & ID3 \\  & [115] & Classification & KNN \\  & [126; 127; 128] & Classification & Naive Bayes \\  & [142] & Classification & Bayesian Networks \\  & [148] & Regression & Linear regression \\  & [181; 182] & Classification & DL \\  & [183] & Regression & Gradient boosting \\  & [184] & Classification & KNN + RF + DT \\ \hline Hepatic diseases & [99] & Classification & SVM \\  & [113] & Classification & ID3 \\  & [185] & Regression & Linear regression \\  & [115] & Classification & KNN \\  & [129; 185] & Classification & Naive Bayes \\  & [186] & Classification & Ensemble Feature Selection \\  & [170] & Classification & Cross-sectional models \\ \hline Infectious diseases & [78; 82] & Clustering & K-means Clustering \\  & [85] & Clustering & DBSCAN \\  & [72; 98; 101; 102; 103; 104; 105] & Classification & SVM \\  & [107; 111] & Classification & ID3 \\  & [72; 121; 123] & Classification & KNN \\  & [133] & Classification & Naive Bayes \\  & [71; 147; 148; 149; 150; 151; 153; 154] & Regression & Linear regression \\  & [151; 154] & Regression & Logistic regression \\  & [71; 156] & Classification & Neural Networks + SVM \\  & [165] & Classification & Random Forest \\ \hline Renal diseases & [112] & Classification & ID3 \\  & [115] & Classification & KNN \\  & [129] & Classification & Naive Bayes \\  & [144, 148] & Regression & Linear regression \\  & [144] & Regression & Logistic regression \\  & [162] & Classification & Neural Networks + Naive Bayes \\  & [162; 163] & Classification & Neural Networks + SVM \\  & [187] & Classification & SVM \\ \hline Other diseases & & & \\ Vision & [164] & Classification & Neural Networks \\ Digestive & [166] & Regression & RF \\ Cutaneous & [168] & Regression & GloVe \\ Respiratory & [169] & Classification & SVM + RF \\ Rare & [188; 189] & Classification & KNN + RF + NB + DL \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
### Paper Contributions  
With the above-mentioned motivational and problem statements, this paper queries the state-of-the-art approaches, including FL framework and DRL agent, with promising beneficial factors for self-managing collaborative AI model deployment in future intelligent IoT applications. Several existing surveys have presented DRL for IoT, FL in edge networks, CI/EI architectures, and ZSM perspective for 5G networks; however, the correlations between DRL and FL for long-term and closed-loop orchestration are not fully considered in a massive IoT environment. This article aims to contribute an in-depth review of using DRL for optimizing (edge) FL model communications, which subsequently supports a major section of preeminent objectives in ZSM and CI/EI. Table 1 presents the important acronyms with descriptions used in the paper. The summary of our contributions is given as follows:  
* We discuss the key elements and execution flows of FL implementation in IoT scenarios. Edge FL is also discussed to illustrate EC-assisted model updates for local IoT devices. Furthermore, existing solutions to tackle data privacy, training, inference, compression, edge aggregation, IoT participant selection, and resource optimization are outlined to specify the competence of (edge) FL-based approaches. Federated optimization is described in this paper for maintaining massive IoT taxonomies, data heterogeneity, and the complexity of practical aggregation.
* We review the DRL components for IoT networks and the system architecture for driving the DRL agent applicable in network virtualization. The observability, configurability, and computability of the network architecture for DRL implementation are presented, which specify the set of states, actions, and immediate/long-term rewards used by researchers to construct autonomous policy management between the agent and IoT environment.
* We discuss DRL as an enabler approach for assisting FL algorithms in massive IoT use cases. DRL-based FL policy optimization is coined by considering various aspects such as resource optimization, model offloading decisions, model update scheduling, and aggregation policies.
* We provide various scenarios of IoT application deployments using DRL and FL to engage in each domain's feature with different state observations, action adjustments, and reward formulations. The heterogeneity of DRL- and FL-enabled IoT services, including IIoT, smart automation, medical services, IoV, and environmental context detection, is reviewed as a guideline for applying in real-world use cases.
* We point out the challenging issues and future research directions for enhancing the applicability, adaptability, privacy, autonomy, and optimality of DRL-based FL in massive IoT network architecture.
## 4 Uncoded BER of APD OOK Receivers
As a simple explanation for this behavior, the amplification of the TIA can be chosen as being higher to offer a better SNR, but at the same time, the bandwidth is reduced through \(1/R_{TI}\). Figure 10: BER from Ppb for a sqrt(\(B\))-dependent TIA noise density, according to the data rate, plus the adaptation of the reception filter bandwidth. Sensitivity curves with \(l_{dm}\) are shown for comparison (for 1 Gbps, no BGL). Figure 9: BER from <\(N\)>, with a constant TIA noise density and adaption of receiver filter only. Same parametrization as Figure 8.  
So, in another setup, one adopts the bandwidth of the TIA (and thus its \(R_{TI}\)) with the sqrt(B) characteristic of the current noise density, \(i_{n}\), as shown in Figure 11 and in Table 11 for a 500 MHz bandwidth. For data rates of 100 Mbps/1 Gbps/10 Gbps, the noise densities are 0.66 pA Hz\({}^{-0.5}\), 2.1 pA\(\cdot\)Hz\({}^{-0.5}\), and 6.6 pA Hz\({}^{-0.5}\), respectively. This results in TIA-adopted sensitivity for the TNL and partly in the APD, leading to all sensitivity runs being independent of \(r\) since, in the calculation of Q, both noise bandwidth-adoption methods will then cancel, with an increase in the received signal power (solid lines in Figure 10). When adding a fixed \(I_{dm}=2.5\) nA, however (dashed lines in Figure 10), its changing ratio vs. the received power will again lead to \(r\) dependency, and we see a strong performance degradation specifically with SNL due to the false detection of electrons from \(I_{dm}\). For \(TNL\), however, the plot against \(I_{dm}\) is too close to the original curve to be distinguishable (red line(s)). In practical systems we might see both RFE characteristics; when both \(B\) and the \(R_{TI}\) can be optimized to one fixed \(r\), the sensitivity improves and so does the constant for Ppb (Figure 10). When, however, in a variable-rate system, only the bandwidth of the Rx filter can be adopted to \(r\), the overall sensitivity will reduce with a decreasing \(B\) when thermal noise is present, as shown in Figure 9.
# Structural Behaviour of Aluminium-Timber Composite Beams with Partial Shear Connections
## 2 Materials and Methods
### The Results of the Numerical Analyses  
The comparison of the plastic resistance of the aluminium-timber composite beams to sagging bending obtained in the theoretical, experimental, and numerical simulations is presented in Table 6.  
Figure 15 shows the load versus deflection responses, while Figure 16 shows the load versus slip responses from the bending tests and the numerical simulations. The damage of the LVL was taken into account in the numerical simulations thanks to the use of the Hashin damage model. Figure 17 presents the damage initiation variables (HSNFTCRT is the maximum value of the fibre tensile initiation criterion, HSNFCCRT is the maximum value of the fibre compressive initiation criterion [73]). The failure mode of the composite beams obtained in the numerical analyses was the same as the one from the laboratory tests. One can observe the damage of veneers in the sections of the LVL slab subjected to the highest tensile stress and the highest compressive stress near the loading plates. In the composite beam with full shear connection (analysis 1), the section of the LVL slab subjected to tension was smaller than in the composite beams with partial shear connections.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Analysis**} & \(\eta\) & \(M_{pl,simulation}\) & \(M_{pl,theory}\) & \(M_{pl,test}\) \\  & [**–**] & [**kN\(\cdot\)m**] & [**kN\(\cdot\)m**] & [**kN\(\cdot\)m**] \\ \hline
1 & 1.00 & 68.7 & 78.2 & – & – \\
2 (B120) & 0.90 & 64.4 & 72.5 & 68.4 & 0.94 \\
3 (B160) & 0.70 & 63.3 & 61.2 & 66.9 & 0.95 \\
4 (B200) & 0.60 & 63.9 & 55.5 & 65.7 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The plastic resistance to sagging bending of the aluminium–timber composite beams.  
Figure 14: The relationship between \(M_{pl}\) and \(\eta\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Beam**} & \(\eta\) & \(M_{pl,q}\) **from** & \(M_{pl,q,test}\) **from** & \\  & [**–**] & **Equation (2) [kN\(\cdot\)m**]** & **the Tests [kN\(\cdot\)m]** & \(M_{pl,q}/M_{pl,q,test}\)** \\ \hline B120 & 0.90 & 72.5 & 68.4 & 1.06 \\ B160 & 0.70 & 61.2 & 66.9 & 0.91 \\ B200 & 0.60 & 55.5 & 65.7 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The plastic resistance to sagging bending of the aluminium–timber composite beams with partial shear connections.  
Figure 15: The load versus deflection responses from the test and the numerical analyses: All curves (**a**), B120 (**b**), B160 (**c**), B200 (**d**).  
Figure 16: The load versus slip responses from the test and numerical analyses: All curves (**a**), B120 (**b**), B160 (**c**), B200 (**d**).
# Carbon Balance in Salt Marsh and Mangrove Ecosystems: A Global Synthesis
## 2 Allocation of Carbon Stocks  
Salt marshes and mangrove forests both store large quantities of organic carbon (CORG) in soils and, to a lesser extent, in plant biomass (Figure 1). On average, soil CORG to a depth of 1 m comprises 77% and 95% of the total CORG stocks in mangrove forests and salt marshes, respectively.  
Carbon stocks in mangroves and salt marshes; above-ground biomass CORG accounts for 15% and 1% of total CORG in mangroves and salt marshes, respectively. Mangrove below-ground biomass CORG accounts for twice (8%) the total carbon stocks of salt marshes (4%). Total CORG stock in mangrove forests is twice as great as in salt marshes on a per hectare basis (Table 1). Globally, mangrove CORG stocks (6.17 Pg) are, on average, three times greater than salt marshes (1.84 Pg) due to greater stocks on both a per area basis and the fact that there are 1.5 times more mangrove forests than salt marshes worldwide (Table 1). Of course, carbon stocks vary greatly within both ecosystems as a function of ecosystem age, intertidal position, and species composition, as well as in terms of geographic, climatic and environmental factors.  
Figure 1: Median organic carbon stocks vested in above- and belowground plant biomass and in soils to a depth of 1 m in salt marshes and mangrove forests on a per hectare basis. Mean (\(\pm 1\) standard error) values and references are listed in Table 1.  
Median \(\mathrm{C_{ORG}}\) content in salt marsh (5.8%) soils is greater than in mangrove soils (2.6%), but there is a wide spread of values in both wetland types, varying from 0.1% to 30% [50; 51]. The wide range of soil \(\mathrm{C_{ORG}}\) content undoubtedly reflects different geomorphological, climatic and environmental factors, as well as ecosystem age, species composition and hydrology. Similarly, C/N ratios are highly variable for the same reasons, varying within both marshes and mangroves, by 5/1 to 60/1 [50; 51], respectively.