esa-sceva/satcom-chunk-collection huggingface dataset:
# Querying on Federated Sensor Networks
## 6 Conclusion  
For querying on the Federated Sensor Network (FSN), we developed the Layered Federated Sensor Network (L-FSN) Protocol. L-FSN allows layered management of islands by inter-island and intra-island querying protocols according to the path selection policies. We evaluated the applicability of L-FSN with a comparison of various querying protocols running on the flat FSN model. For comparison, we selected a querying protocol from geometrical, hierarchical cluster-based, hash-based, and tree-based categories and ran these protocols on the entire FSN topology in order to evaluate the flat management. We found that the layered management of FSN by L-FSN increases the querying performance with respect to energy-efficiency, query resolving distance, and querying latency compared to flat management. Then, we ran these protocols, which were evaluated for flat federation, as intra-island querying protocols of L-FSN. We found that L-FSN's performance regarding energy-efficiency and query resolving latency can be improved with a selection of intra-island querying protocols according to the size of each island.  
In this paper, we evaluated L-FSN protocol on the UB topology. In order to observe protocol behavior on different scenarios, various simulation environments can be developed for evaluation of L-FSN protocol. L-FSN requires authentication of each sensor node to a gateway. This property of L-FSN supports some degree of mobility of island nodes. For improving the performance of L-FSN, investigation of node mobility, heterogeneity, availability and reliability effects on intra-island routing protocol selection were left for future work. In this paper, we evaluated shortest path selection policy for inter-island communication. For future work, various diverse path selection policies can be developed and evaluated.
# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
## 4 Results and Discussions
### Accuracy of Noise Prediction  
The accuracy for prediction of noise levels is tested in dB levels. The noise prediction model used the routing algorithm for incorporating terrain parameters during prediction. Accurate terrain parameters are expected to improve accuracies of noise prediction. The accuracy of noise prediction is tested in two ways. _One_: comparing the effect of using new LiDAR algorithm generated routes on noise prediction in relation with theoretically generated routes (which is having no error) on noise prediction. _Two_: Determination of error in prediction of instantaneous noise levels for RGIPT campus in relation with measured noise levels at different locations of the campus.  
2.1 Deviations in New Algorithm Generated Routes, Which Are Extracted Using LiDAR Data Are Compared with Routes (Having No Error) Extracted Theoretically. (Deviations Are Related in Terms of Predicted Noise Levels)  
A simulated ground with a pair of noise source and receiver was planned. A building was also placed between the source and receiver. The routes and terrain parameters were estimated for this scene theoretically with principles of physics (i.e., zero error in determination of principal routes). Noise levels were predicted at the destination for  
Figure 15: (**a**) “\(1\)” is the first intersection point moving from source to destination around the sides of the building. Similarly, “\(2\)” is the second intersection point. The blue route is the desired route from the algorithm and the rest yellow routes are the deviated routes, when the first intersected point has shifted value upward and downward in the Z direction. (**b**) Graph shows the accuracy of the route around the sides.  
source-generated noise level. The routes and terrain parameters were also estimated separately in the above realistic scenario. Error and deviations were simulated in the above noise propagation scenario from the source to the destination. Errors in terrain data measurement with LiDAR, deviations in extracted routes with new algorithm and error in measurement in noise levels were estimated to determine the deviations in predicted noise level at the destination in comparison with noise level predicted theoretically at the destination (Figure 16. Estimated error in LiDAR data, terrain parameter extraction and noise data measurement leading the uncertainty in prediction are detailed in Supplementary Section S3.1.
## 1 Introduction
A decline on growth and productivity is associated with stunting, chlorosis, necrotic spots, white flocking, purpling, and leaf tip burn, that progresses from older to younger leaves [18; 19]. Similarly to Al, Mn toxicity can alter the uptake of other essential nutrients, since it generally competes for shared transporter proteins [20; 21; 22]. For instance, Mn toxicity symptoms can many times be confused with light Fe deficiency [23]. To counteract soil acidity and derived ion toxicities in agricultural soils, pulverised calcitic lime (Ca carbonates and oxides) or dolomitic lime (Ca and Mg carbonates) are commonly used. However these soil correctives have shown significant drawbacks, namely, by greatly increasing farming costs; by being seldomly used in the required amounts; and, depending on soil characteristics, by being countered by the soil's strong buffering capacity [24]. Besides Mg and Ca (used for soil correction as dolomitic lime), Si is also known to counter Mn and Al toxicities and, depending on plant species and/or variety, can influence mechanisms of detoxification and compartmentation, either by preventing internal metal excess or by influencing internal metal distribution [23; 25; 26; 27]. In the acidic Cambic soils of south-eastern Portugal, agricultural productivity is heavily affected by Mn toxicity and low fertility [28]. Defining strategies to diagnose Mn toxicity and improve wheat production has been an on-going research effort [29; 30; 31]. In wheat growing in an acidic Cambic soils, the occurrence of Mn toxicity symptoms depends heavily on maintaining shoot Mg/Mn ratios above 20 [32; 33; 34; 35]. In fact, wheat can mobilise various root and shoot nutrients to counter Mn toxicity. Magnesium, Ca, Mn, and P can be redistributed intracellularly in wheat roots and shoots as a response to toxic levels of bioavailable Mn [28]. While Ca accumulates in the root apoplast, Mn, along with P and Mg, are redirected to the shoot vacuole as a detoxification strategy. The present study characterises the contents of Mn, Al, Fe (commonly promoted in acidic soils), and Si (that has shown beneficial effects under Mn toxicity) in the soil solution and wheat roots, shoots, and their respective subcellular fractions (fractions enriched in cell wall debris, organelles, or vacuole contents). This work assessed the effect of rising levels of soil bioavailable Mn on the uptake and subcellular redistribution of these elements in wheat tissues. Screening the mechanisms involved in wheat detoxification allows for the establishment of more precise agricultural practices that enhance natural detoxification processes.
## 3 Results
### Absolute Configuration Assignment  
The availability of pure enantiomers of **BT2-T6** on a multimilligram scale is mandatory to produce homochiral oligomeric enantioselective sensors to apply in electrochemical enantioreognition studies [3; 12; 13; 14; 15; 16; 17]. In this type of chiral conducting oligomers, the conjugated system is responsible both for the electrochemical properties and molecular chirality, hence the name inherently chiral. The structural design of the oligomers requires the stereochemical characterization of the enantiopure monomer precursors.  
The absolute configuration of enantiomers of **BT2-T6** was empirically established according to their ECD properties. As shown in Figure 6, the first eluted enantiomer, at a concentration of 0.3 mg mL\({}^{-1}\), showed a strong positive Cotton effect in dichloromethane solution, with positive and negative ECD bands centered at 433 and 383 nm, respectively. A very similar ECD profile was recorded for the (\(P\))-enantiomer of the chiral analog **BT2-T4**, which differs in the number of thiophenes as wings (i.e., four instead of six) and for which the stereochemical characteristics were determined in a preceding work [3]. Thus, the strict correlation between ECD spectra allowed assigning the (\(P\)) configuration to the first eluted enantiomer of **BT2-T6** on the Chiralpak IB CSP and (\(M\)) configuration to the more retained enantiomer. Notably, the atropisomeric compounds **BT2-T4** and **BT2-T6** exhibited the same order of enantiomer elution on the Chiralpak IB CSP with the (\(M\))-enantiomer eluted first and the (\(P\))-enantiomer eluted second. In addition, it is worth highlighting that the univocal correlation between the CD sign exhibited by the enantiomeric peak (Figure 2) at the wavelength of 380 nm and absolute configuration (i.e., the negative CD sign corresponds to (\(P\)) absolute configuration and vice versa). This means that the online CD detection during HPLC enantioseparation provides a direct readout of the stereochemistry of **BT2-T4** and **BT2-T6**.  
As for **BT2-T4** case, the (\(P\))-enantiomer was dextrorotatory and the value of the specific rotation at a wavelength of 589 nm was remarkable, at +1821 (c = 0.4, dichloromethane).  
Figure 5: Optimized semipreparative HPLC resolution of 5.2 mg of **BT2–T6**. Chromatographic conditions: column, Chiralpak IB (250 mm \(\times\) 10 mm, 5 \(\upmu\)m); mobile phase, \(n\)-hexane–methanol–dichloromethane 90:5:5 (\(v/v/v\)); column temperature, 15 \({}^{\circ}\)C; flow rate, 5.5 mL min\({}^{-1}\); detection, UV at 360 nm.  
Figure 6: ECD spectra of the enantiomers of **BT2–T6**.
# Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities
## Abbreviations  
The following abbreviations are used in this manuscript:  
RF: Radio frequency  
UWB: ultra-wideband  
TDoA: Time difference of arrival  
TWR: Two-way ranging  
RTLS: Real-time locating systems  
PHY: Physical layer  
ToF: Time of flight  
BLE: Bluetooth low energy  
CSMA/CA: Carrier-sense multiple access with collision avoidance  
CDMA: Code division multiple access  
RSSI: Received signal strength indicator  
FCC: Federal Communication Commission  
e.i.r.p: Effective isotropic radiated power  
LDC: Low duty cycle  
DAA: Detect and avoid  
PRF: Pulse repetition frequency  
SDS-TWR: Symmetrical double sided two-way ranging  
MAC: Medium access control  
SHR: Synchronization header  
PHY: Physical layer header  
SFD: Start of frame delimiter  
CAP: Contention access period  
CFP: Contention free period  
TDMA: Time division multiple access  
PL: Path loss  
FSPL: Free space path loss
## 4 Roundness Error Evaluation  
### Mathematical Model  
The most commonly used methods for roundness error evaluation are the least-squares method (LSM), the minimum zone circle (MZC) method, the maximum inscribed circle (MIC) method and the minimum circumscribed circle (MCC) method [34]. In this paper,LSM and MZC are used to calculate the roundness error at the same time. The mathematical models of the two methods are described in Equations (7) and (8).  
Let \(Xi\) (\(x_{i}\), \(y_{i}\)), (\(I\) = 1,2,3,..., \(n\), \(n\) > 3) be the coordinates of the measuring points in the actual circumference of the circle under test. The mathematical model of the least-squares method is:  
\[f(a_{L},b_{L},R_{L})=min\sum_{i=1}^{n}\biggl{(}\sqrt{\left(x_{i}-a_{L}\right)^ {2}+\left(y_{i}-b_{L}\right)^{2}}-R_{L}\biggr{)}^{2} \tag{7}\]  
where (\(a_{L},b_{L}\)) and \(R_{L}\) are the center and radius of the least-squares circle, respectively.  
The mathematical model of the minimum zone method is:  
\[f(a_{M},b_{M})=min\biggl{(}max\sqrt{\left(x_{i}-a_{M}\right)^{2}+\left(y_{i}-b _{M}\right)^{2}}-min\sqrt{\left(x_{i}-a_{M}\right)^{2}+\left(y_{i}-b_{M}\right) ^{2}}\biggr{)} \tag{8}\]  
where (\(a_{M},b_{M}\)) is the center of the minimum zone circle.
# Potential of Homomorphic Encryption for Cloud Computing Use Cases in Manufacturing
## 3 Encryption for Cloud Computing in Manufacturing
### Potentials and Challenges of Homomorphic Encryption for Cloud Computing
#### 3.2.2 Current Challenges of Homomorphic Encryption  
Despite the potential that homomorphic encryption offers for cloud computing, there are still several challenges regarding the implementation of homomorphic encryption in several branches in practice, as well as in manufacturing companies.  
Homomorphic encryption, especially fully homomorphic encryption, currently has high energy requirements due to high computational efforts. These computational efforts result in longer computing times, thus lowering efficiency, which is why homomorphic encryption is often not yet suitable for practical use [35]. In addition to the efficiency problem, the possible uses of homomorphic encryption are also limited by the characteristics of the application in question. Highly complex algorithms or the processing of disproportionately large datasets is currently not feasible with homomorphic encryption methods [19]. Furthermore, no \"one-fit-all\" solution for homomorphic encryption exists yet. There are several domain-specific homomorphic encryption algorithms existing (e.g., for medical data); however, finding a suitable homomorphic encryption technology requires time and expertise and very much depends on the use case [33].  
Nevertheless, these challenges are qualitative and must always be considered regarding the use cases and the use case requirements. Therefore, in Section 3.3, an experimental comparison in conducted to more deeply analyze the challenges and potential of homomorphic encryption for manufacturing.
# CYK Parsing over Distributed Representations
## 3 Preliminaries
### CYK Algorithm  
The CYK algorithm is a classical algorithm for recognition/parsing based on context-free grammars (CFGs), using dynamic programming. We provide here a brief description of the algorithm in order to introduce the notation used in later sections; we closely follow the presentation in [4], and we assume the reader is familiar with the formalism of CFG ([1], Chapter 5). The algorithm requires CFGs in Chomsky normal form, where each rule has the form \(A\to BC\) or \(A\to a\), where \(A\), \(B\), and \(C\) are nonterminal symbols and \(a\) is a terminal symbol. We write \(R\) to denote the set of all rules of the grammar and \(NT\) to denote the set of all its nonterminals.  
Given an input string \(w=a_{1}a_{2}\cdots a_{n}\), \(n\geq 1\), where each \(a_{i}\) is an alphabet symbol, the algorithm uses a two-dimensional table \(P\) of size \((n+1)\times(n+1)\), where each entry stores a set of nonterminals representing partial parses of the input string. More precisely, for \(0\leq i<j\leq n\), a nonterminal \(A\) belongs to set \(P[i,j]\) if and only if there exists a parse tree with root \(A\) generating the substring \(a_{i+1}\cdots a_{j}\) of \(w\). Thus, \(w\) can be parsed if the initial nonterminal of the grammar \(S\) is added to \(P[0,n]\). Algorithm 1 shows how table \(P\) is populated. \(P\) is first initialized using unary rules, at Line 3. Then, each entry \(P[i,j]\) is filled at Line 11 by looking at pairs \(P[i,k]\) and \(P[k,j]\) and by using binary rules.  
```
1:for\(i\gets 1\) to \(n\)do
2:for each \(A\to a_{i}\) in \(R\)do
3: add \(A\) to \(P[i-1,i]\)
4:endfor
5:endfor
6:for\(j\gets 2\) to \(n\)do
7:for\(i\gets j-2\) to \(0\)do
8:for\(k\gets i+1\) to \(j-1\)do
9:for each \(A\to BC\) in \(R\)do
10:if\(B\in P[i,k]\) and \(C\in P[k,j]\)then
11: add \(A\) to \(P[i,j]\)
12:endif
13:endfor
14:endfor
15:endfor
16:endfor
17:endfor
18:endfor
```  
**Algorithm 1** CYK(string \(w=a_{1}a_{2}\cdots a_{n}\), rule set \(R\)) **return** table \(P\).  
A running example is presented in Figure 1, showing a set \(R\) of grammar rules along with the table \(P\) produced by the algorithm when processing the input string \(w=aab\) (the right part of this figure should be ignored by now). For instance, \(S\) is added to \(P[1,3]\), since \(D\in P[1,2]\), \(E\in P[2,3]\), and \((S\to DE)\in R\). Since \(S\in P[0,3]\), we conclude that \(w\) can be generated by the grammar.
## 5 Experimental Evaluation
### Experimental Sets  
Experiment 1: The recommendation performance of APGCN-CF is systematically evaluated against several baseline methods across two distinct datasets. A consistent set of evaluation metrics is employed to generate and assess personalized recommendation lists.  
Experiment 2: To investigate the effectiveness of the graph convolutional network (GCN) component in APGCN-CF, which is utilized for capturing high-order node representations prior to collaborative filtering, we conduct an ablation study. Specifically, we construct a baseline variant, denoted as APGCN-CF-B, by removing the GCN component while maintaining all other architectural elements intact. The recommendation performance of APGCN-CF-B is then comprehensively compared against the complete APGCN-CF model through systematic evaluation of their generated recommendation lists.  
Experiment 3: To evaluate the importance of spatio-temporal characteristics in minor agricultural product recommendation, we conduct an ablation study examining how temporal and geographical features influence recommendation quality. We design three variants of our model: APGCN-CF-C (retaining geographical information while removing temporal features), APGCN-CF-D (preserving temporal information while excluding geographical features), and APGCN-CF-E (removing both temporal and geographical components). By systematically comparing the performance of these variants against the complete APGCN-CF model across both datasets, we can quantitatively assess the individual and combined contributions of spatio-temporal features to recommendation effectiveness.
## 3 Method
### Loss  
The loss function that we used includes two types of losses:  
\[L=L_{score}+\alpha\cdot L_{diff} \tag{8}\]  
where \(L\) is the total loss of one iteration; \(L_{score}\) is the score loss between the predicted score and annotated score; \(L_{diff}\) is the rank loss between the clearest image and distorted images. \(\alpha\) is a Boolean value to control \(L\).  
The score loss makes the predicted score closer to the annotated score, which is defined as:  
\[L_{score}=\frac{1}{M}\sum_{i=1}^{M}smooth_{L_{1}}(\delta_{i}-s_{i}) \tag{9}\]  
\[smooth_{L_{1}}=\begin{cases}0.5x^{2}&if\ x<1\\ |x|-0.5&otherwise\end{cases} \tag{10}\]  
where M is the batch size used in the training process; \(smooth_{L_{1}}\) is the smooth \(L_{1}\) loss; \(\delta\) is the predicted score, and s is the annotated score.  
The Rank-IQA [21] method has proven that the ranking information between distorted images is useful to make a CNN model more consistent with HVS. We decided to use an improved rank loss [28] and it is defined as:  
\[L_{diff}=\frac{1}{M-1}\sum_{d=1}^{M-1}smooth_{L_{1}}((\delta_{c}-\delta_{d})- (s_{c}-s_{d})) \tag{11}\]  
where \(\delta_{c}\) and \(s_{c}\) represent the predicted and annotated score of the clearest image in one group of microscopic images; \(\delta_{d}\) and \(s_{d}\) represent the predicted and annotated score of the distorted image in the same group of microscopic images. The improved rank loss contains the information of score loss. When the rank order is correct in one iteration (\(\delta_{c}\geq\forall\delta_{d}\)), score loss is calculated twice, which is not conducive to model training. Therefore, we used a Boolean value \(\alpha\) to control the total loss \(L\), and it is defined as:  
\[\alpha=\left\{\begin{array}{cc}0&\left(\sum\limits_{d=1}^{M-1}\max(\delta_{d }-\delta_{c},0)\right)=0\\ 1&else\end{array}\right. \tag{12}\]
# Introducing _Time and Space_
###### Abstract  
Time is an essential element in today's world, spreading over multiple applications that range from societal activities up to those requiring the highest precision for scientific purposes. The Einsteinian relativistic theories proved that time and space cannot be decoupled. Four-dimension space-time reference systems are described to represent phenomena on Earth and beyond. Their definitions in the frame of general relativity and the transformations between them have been established by the International Astronomical Union [1, 2].  
Time and frequency metrology enable activities supported by precise timing and synchronization related to the critical infrastructure on Earth and in space research activities. Optical frequency standards (OFSs), based on different species and transitions developed in many metrology institutes, have surpassed the accuracy achievable by the realization of the current definition of the second by a factor of up to 100 [3], compelling the international metrology community to select one or several of these transitions [4] for a new definition of the SI second. This redefinition can be expected by 2034, provided that a set of conditions are fulfilled; an essential requirement to confirm their performance is comparing their frequencies over all distances and demonstrating their fractional frequency agreement at the \(10^{-18}\) level. This requirement is fulfilled by optical fibre links, over baselines up to about 2000 km [5].  
OFSs achieve relative frequency uncertainties of order \(10^{-18}\) and are promising to some emerging applications and innovative fields of research requiring frequency uncertainty between \(10^{-15}\) and \(10^{-20}\). These include the studies of the structure and composition of the universe, tests of fundamental physics, improving the knowledge of the Earth's potential gravity field, etc.  
The space exploitation and exploration make use of the latest technological and scientific achievements. New devices, such as satellites and satellite constellations, strongly impact the fields of communication and Earth observation. Encouraged by the performance of these systems, the time and space communities are challenged by a new era in the Moon's exploration with the establishment of cis-lunar references to enable operations and communications with, around, and on the Moon [6].  
_Time and Space_ (ISSN 2813-9526) [7] aims to encourage scientists to publish both theoretical and experimental research, including papers on scientific research and the technological applications of atomic clocks and timekeeping devices and their use in fundamental physics experiments on the ground and in space, relativity, relativistic metrology, and geodesy.  
_Time and Space_ is an international, peer-reviewed, open access journal covering the various aspects of time and space in physics published quarterly online by MDPI.  
The author declares no conflict of interest.
# Towards a Catalogue of Self-Sovereign Identity Design Patterns
## 2 Background and Related Work  
### Self-Sovereign Identity  
Self-sovereign identity (SSI) is a decentralised, user-centric approach to digital identity management that enables the actions of identification, authentication, and authorisation [3], without the need for external intermediaries [4] responsible for identity provisioning and management in the current centralised and federated approaches, where the power of control resides at the service or identity providers, as presented in Figure 1, where the blue colour represents an entity with control in a particular interaction. On the other hand, within the SSI, this control is shifted to each involved entity, while interactions can be based purely on peer-to-peer connections between the involved entities, who are the only ones accountable for generating, storing, and managing identifiers and correlated cryptographic keys, as well as acquiring, storing, and sharing their credentials that contain identity attributes that other entities attest to. The main advantage of the approach is that it enables entities (individuals/users and organisations) to self-create, control, and manage their identifiers and associated attributes, i.e., identity data [5]. At the same time, it allows controlling the flow of these data during digital interactions, increasing security and privacy by using cryptographic mechanisms.