esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectrum Values in Suburban/Urban Environments above 1.5 GHz
## 2 Analysis of Spectrum Values
### Urban Environment
Back to Equation (5), _SINR_ can be calculated from Equation (2) and thus is used to calculate \(P_{R}\) and \(I\). Rather than Equation (3), the equation defined in technical report 36.942 for 3 GPP (GPP (2016) is used here:  
\[P_{R}=\left\{ \begin{array}{l}P_{T}-\max(L+SF-G_{T}-G_{R},MCL)\\,\ \text{outdoor user}\\ P_{T}-\max(L+SF-G_{T}-G_{R}+L_{O2I},MCL)\\,\ \text{indoor user}\end{array} \right. \tag{8}\]  
where \(P_{T}\) is the transmitter power of the BS antenna, \(G_{T}\) is the transmitter antenna gain and \(G_{R}\) is the receiver antenna gain. The path loss \(L\) and outdoor to indoor penetration loss \(L_{O2I}\) are calculated from the 3D path loss model and the low-loss out to the indoor penetration model in technical report 38.901 for 3 GPP (GPP (2016), respectively. Please note that the indoor user is standing by the wall or window, so we assume no inside loss after signal penetration through the wall or window. Additionally, \(SF\) is shadow fading (GPP (2016) and MCL is minimum coupling loss (GPP (2016)). In Figure 1, the interference \(I\) in (2) is the sum of the power of three received interference signals transmitted by the adjacent three BSs:  
\[I=\left\{ \begin{array}{l}\frac{3}{1}\big{\{}P_{T}-\max\big{(}L_{i}^{\prime} +SF-G_{T}-G_{R},MCL\big{)}\big{\}}\\,\ \text{outdoor user}\\,\ \frac{3}{1}\big{\{}P_{T}-\max\big{(}L_{i}^{\prime}+SF-G_{T}-G_{R}+L_{O2I},MCL \big{)}\big{\}}\\,\ \text{indoor user}\end{array} \right. \tag{9}\]  
where \(L_{i}^{\prime}\) is the path loss of the interference signal transmitted by interference BS \(i\) and is also calculated with the 3D path loss model. When combining Equations (2) and (5) with different distances between the user and the BS, the maximum achievable capacity for all eight user types can be estimated. Since the 3D propagation model in (GPP (2016) supports frequency bands ranging from 500 MHz to 100 GHz in urban environments, the spectrum from 500 MHz to 40 GHz is investigated for both UMa and UMi-street canyon scenarios. The parameters used in the analysis for these scenarios are summarized in Table 3. The path loss models for different scenarios and frequency ranges are also summarized in Table 4.
## 1 Introduction
### Related Work  
Since the concept of PRE was introduced by [PERSON] et al. [1], there have been many papers that have proposed PRE schemes with different properties to meet various application demands, and most of them focus on unidirectional PRE. The first CCA security unidirectional PRE scheme without random oracle was proposed in 2008 by [PERSON] and [PERSON] [7]. [PERSON] et al. [8] proposed a traceable unidirectional PRE scheme to prevent the proxy from abusing its re-encryption keys. [PERSON] and [PERSON] [5] proposed the first identity-based PRE (IBPRE) scheme, in which the ciphertext can be re-encrypted from one identity to another identity. Later, many solutions [9; 10] were proposed to meet this requirement. [PERSON] et al. [11] introduced IBBE into IBE to construct an IBBE-based PRE scheme. There also exist many other extensions of the PRE scheme, such as time-based PRE [12], attribute-based PRE [13; 14], function-based PRE [14], conditional PRE [15; 16], etc.  
Ciphertext only can be transformed in the same cryptosystem by deploying the above schemes. In fact, there are many different cryptosystems that have been deployed. A cross-domain re-encryption scheme is also a requirement. [PERSON] et al. [17] linked the identity-based encryption and the identity-based broadcast encryption by allowing the transformation of a ciphertext of the IBE system into a ciphertext of the IBBE system. [PERSON] et al. [18] constructed a concrete CDSS protocol which allowed the ciphertext to transform from the PKE scheme into an IBE scheme. Obviously, CDSS satisfies the cross-domain property. [PERSON] et al. [19] proposed a UPRE scheme. It is a general cross-domain proxy re-encryption scheme from an existing PKE scheme to another existing PKE scheme, which does not care about what the PKE scheme is.  
However, these PRE schemes mainly provide ciphertext transformations, and the user cannot fully control the delegation path in these multi-hop PRE schemes. To address this problem, [PERSON] et al. [6] constructed an autonomous path PRE scheme to enable a user to designate a path of his preferred data users. However, the scheme needs certificates for the user in the system (for more details, see [6]). All of the data users in the delegation path are selected by the user, and no one can branch off the designated delegation path with meaningful decryption. This paper aims to address autonomous path transformation in an identity-based setting.
# Urban Open Platform for Borderless Smart Cities
## 3 Case Study of Tallinn and Helsinki
### Data Exchange Platform for the Cross-Border Region
Currently, the two cities still operate as digital islands but the federation of data exchange platforms could effectively lead to joint digital services based on real-time data requests from urban and governmental databases, hence also benefiting the commuters and macro-regions. Continuing with the example of population registry, a person moving from Tallinn to Finland could automatically mean erasing residence status in Tallinn and transferring it to Helsinki. Again, the implementation of federated data exchange layers is not only a technological challenge, but the main assumption is that this is politically desirable (multiple parties agree on how data can be exchanged and are motivated to do so, that is, create novel institutions). Figure 3: The X-Road platform. Source: Estonian Information System Authority. The next step for smart cities could be the integration of various sensor data by implementing an open and interoperable platform for connected sensors or a range of entities. That is, in addition to \"citizen-based\" databases, there could be interconnected registries, both public and private, for entities such as unity meters (gas, electricity, water), vehicles (cars, buses, trains, etc. ), home appliances, heating, lighting and waste management systems, weather forecast data, etc. In Estonia, there is a first step towards this, namely the Estfeed platform, which connects close to 600,000 electricity users, with most end-users able to trace their energy consumption via connected meters over the Internet [25]. This platform, running on X-Road, links data sources and applications and provides a user interface for customers to see and manage their energy consumption data and rights. For perfectly federated smart cities, such a federation could be the next step, after having integrated the public registries.
## 1 Introduction
Given that privacy norms differ significantly across cultures, applying the TAM allows for a systematic comparison of how cultural factors shape attitudes and behavioral intentions toward ARSGs [9; 14; 15; 16]. By grounding a study in the TAM, researchers can isolate and measure the influence of cultural context on technology adoption while maintaining a validated theoretical structure. This model is especially suitable for comparing the U.S. and S. Korea, where differing social values and privacy expectations may yield distinct technology acceptance patterns. This cross-cultural study explored the specific relationships between privacy concerns and the key TAM components of perceived usefulness, perceived ease of use, attitude toward using ARSGs, and behavioral intention to use ARSGs. The remainder of this paper is organized as follows. Section 2 reviews the literature on ARSGs, privacy concerns, and cultural differences in technology acceptance, and presents the study's hypotheses and research questions. Section 3 describes the research methods, including the sample, measurements, and analysis procedures. Section 4 presents the results of the data analysis. Section 5 discusses the findings and their implications. Finally, Section 6 concludes the paper with theoretical contributions, practical implications, and suggestions for future research.
## 1 Introduction  
Plant parasitic nematodes (PPNs) cause severe damage to a wide range of crops, including agriculture crops, vegetables and even medicinal plants [1, 2, 3]. The root-knot nematode (_Meloidogyne_ spp.) is one of the most important obligate plant parasitic nematode species; infections by this species induce root galling, prevent plant growth, and cause complex diseases in synergy with other soil-borne pathogens [4, 5]. PPNs cause losses of more than USD100-150 billion worldwide every year, more than half of which are due to _Meloidogyne_ spp. [6, 7, 3, 8]. In the past, the primary method of controlling plant nematodes was to use chemical nematicides [9]. However, these chemicals are toxic; have negative side effects on the environment, animals, and humans; and have been restricted in many countries [10, 11, 12]. In addition, the crop rotation and selection of resistant varieties can also control PPNs, but the effectiveness of these methods is limited [13]. Therefore, new, effective and environmentally friendly biological control options are urgently needed [11].  
As can be seen from previous studies, the use of biological control agents to control PPNs are gaining popularity [12; 14]. Soil microorganisms are important biological control resources, and metabolites produced by these microorganisms are effective against several pathogenic bacteria, insects and nematodes [15]. As for microbial metabolites, volatile organic compounds (VOCs) have attracted research attention in recent years due to their strong nematicidal activity [16; 17]. The greatest advantages of VOCs are their good dispersibility and penetration into the soil. Additionally, the application of VOCs in agricultural practice could be both economically affordable and less toxic to humans than conventional nematicides [18]. Microbial VOCs have effects on various nematodes [19]. For example, the six VOCs produced by _Bacillus altitudes_ AMCC1040, namely 2,3-butanedione, acetic acid, 2-isopropylcnythamine, 3-methylbutyric acid, 2-methylbutyric acid, and octanoic acid, showed significant nematicidal activity against _Meloidogyne incognita_[20]. _Pseudomonas putida_ isolated from the Antarctic soil produced seven VOCs, namely 1-(ethenyloxy)-octadecane, 2-undecanone, 2-nonanone, (Z)-hexen-1-ol acetate, dimethyldisulfide, 1-undecene, and 2-octanone, which were effective against _M. incognita_ and _Caenorhabditis elegans_[21].  
Previously, we obtained _Bacillus velezensis_ GJ-7 isolates from the rhizosphere soil of _Panax notoginseng_, which displayed resistance against _Meloidogyne hapla_[22], but little is known about whether volatile organic compounds (VOCs) were produced by the strain. In the present study, we further evaluated the nematicidal activity of the volatile gas of GJ-7 in vitro and identified the VOCs using solid-phase micro-extraction-gas chromatography-mass spectrometry (SPME-GC-MS) analysis. In addition, we also evaluated their nematicidal, fumigation, and chemotaxis activities against _Meloidogyne hapla_.
## 2 Materials and Methods  
### Flux Tower Measurements  
#### 2.1.1 Experimental Site Description  
The experiments were conducted at Yucheng Comprehensive Ecological Station (CN-YuC; 36.95\({}^{\circ}\)N, 116.63\({}^{\circ}\)E), which is located in the southwest of Yucheng County, Shandong Province, with a mean altitude of 28 m. This area is the hinterland of Huang-Huai-Hai Plain, and the landform type is the alluvial plain of the Yellow River. The soil is an alluvial deposit of the Yellow River and mainly consists of eutric cambisols and solonchaks. The surface soil is rich in sandy loam. CN-YuC is an agricultural experimental station for maize-wheat rotation. The growing period of the wheat begins in mid-November and continues until the beginning of June in the following year, and maize is sowed immediately after harvesting the winter wheat and reaped in the middle of October. The average annual rainfall in this area is 590 mm, about 70% of which falls between June and August, the growing period of summer maize.
## 7 Evolutionary Methods
### Applications in Smart and Digitalized Food Systems  
The ongoing digital transformation of the food industry has significantly broadened the applications of evolutionary methods. As smart technologies and the Internet of Things (IoT) become integral to food production and supply chains, evolutionary methods are being adapted to handle real-time decision-making requirements. For example, PSO has been integrated with IoT sensors to monitor and optimize storage conditions for perishable goods. By enabling real-time adjustments to environmental variables, such as temperature and humidity, this approach has reduced spoilage rates by 12% [72].  
In another example, GAs combined with predictive analytics have been employed to enhance production scheduling. This integration allows food manufacturers to dynamically forecast demand and adjust production plans accordingly, resulting in a 10% improvement in operational efficiency [73]. These applications highlight the growing importance of evolutionary methods in managing the complexity and variability of modern food systems, particularly as these systems become increasingly reliant on real-time data.  
Hybrid optimization approaches are also gaining traction in smart food systems. Studies such as \"Multiobjective scheduling of energy-efficient stochastic hybrid open shop with brain storm optimization and simulation evaluation\" demonstrate the effectiveness of combining evolutionary algorithms with advanced simulation techniques [74]. These hybrid methods are particularly well-suited for addressing stochastic and uncertain conditions, ensuring that optimized solutions remain effective across a wide range of scenarios. This capability is essential in smart food systems, where operational decisions must often account for variability in supply chains, consumer demand, and environmental conditions.
## 6 Procedure of Data Delivery to a Mobile Sink  
In this section, we outline the different phases involved in reporting observed data to the mobile sink. Without loss of generality, we consider the reference scenario shown in Figure 2, where a mobile sink comes in contact with some field sensors that come across in its communication range. The time a node is in communication range of a mobile sink is considered as contact time and the contact area is the area in which the sink is reachable by the node within its communication range as shown by the dashed arc in Figure 2. A node can only deliver sensory data to sink if either it detects the presence of sink in its radio communication range (single-hop communication) or it knows someone who can possibly come in contact with the sink (multi-hop communication). Therefore, for data delivery to the sink, a node must go through three phases, namely sink discovery, route determination, and finally the actual data transfer [14]. These three phases are briefly discussed in the following paragraphs:  
Sink DiscoveryDue to the dynamic network topology caused by sink mobility, sink discovery is the first step towards reporting the sensed data to a sink. Since the sink keeps on changing its position, nodes need to continuously keep track of the new location of the sink if no sink-assistants are operating in the network. Sink discovery is greatly affected by nodes' duty-cycles and the speed of the sink [14]. Any node that detects the presence of sink in its communication range will report to the rest of the network about this development and will immediately start transferring the buffered data (if any) to the sink. However, the temporary contact time should be long enough so that the sink's neighbors can complete the data transfer to the sink, failure to which results in huge data delivery latency. For prompt sink discovery, some approaches [20; 21; 22] employ multiple radios: a long-range one for data communication and another short-range one for awaking nodes. The short-range radio listens to the channel continuously and activates the long-range radio upon detecting a tone from the sink. However, most of commercially available nodes do not have such dual-radio hardware support [14].  
_Route Planning_--Upon discovery of the sink, the sensor nodes will determine an optimal route to reach the sink. In this regard, they can adopt multi-hop communication or can exploit cluster-based communication. The goal in routing is to reach to the sink with minimum cost and depending on the application, the cost could be energy consumption associated with the number of hops in a particular route, the data delivery latency following that route, or the reliability (in terms of packet loss ratio).  
_Data Transfer_--Finally, data is delivered to the sink with the goal of ensuring maximum utilization of the contact time with the sink, thereby maximizing the throughput. The data transfer is not only affected by channel conditions, but also by the distance between source nodes and sink which keeps on changing subject to the sink's speed [14].
## 2 Materials and Methods  
Preparation of Decorated In\({}_{2}\)O\({}_{3}\) Particles (Au/In\({}_{2}\)O\({}_{3}\), Pd/In\({}_{2}\)O\({}_{3}\), Pt/In\({}_{2}\)O\({}_{3}\))  
All the chemical reagents for the synthesis of sensing materials were of analytical grade and used without further purification: indium (III) nitrate hydrate (In (NO\({}_{3}\))\({}_{3}\)\(\cdot\)\(\mathrm{Ar}_{2}\)O) ([PERSON], 99.9%), HAuCl\({}_{4}\) ([PERSON], 99.99%), and hexamethylenetetramine ([PERSON], 99%), NaBH\({}_{4}\) ([PERSON], 98%) were used. The experiments were conducted according to the procedure presented in Figure 1. Catalyst-decorated In\({}_{2}\)O\({}_{3}\) nanoparticles were synthesized by the thermal decomposition technique using reagents such as indium (III) nitrate hydrate InN\({}_{3}\)O\({}_{3}\)\(\cdot\)6H\({}_{2}\)O, hexamethylenetetramine C\({}_{6}\)H\({}_{2}\)N\({}_{4}\), sodium borohydride H\({}_{4}\)BNa, gold (III) chloride trihydrate HAuCl\({}_{4}\)\(\cdot\)3H\({}_{2}\)O, chloroplatinic acid solution H\({}_{2}\)Cl\({}_{6}\)Pt, and palladium (II) chloride solution Cl\({}_{2}\)Pd, which were purchased from Sigma-Aldrich. A detailed experimental process of preparing catalyst-decorated indium oxide nanoparticles is shown in Figure 1. First, 1 mmol of indium (III) nitrate hydrate and 1 mmol of hexamethylenetetramine were added to 400 mL of distilled water and mixed. This mixed aqueous solution was heated to 100 \({}^{\circ}\)C and then maintained for 2 h with the solution refluxing. After the reaction was completed, the solution was cooled down to room temperature and then was centrifuged at 3500 rpm for 20 min, and finally dried at 100 \({}^{\circ}\)C for 24 h, which led to an intermediate phase, indium hydroxide powders In (OH)\({}_{3}\). For dispersing the dried powder, 40 mL of distilled water was added to 0.2 g of the synthesized indium hydroxide, after 2 mL of 0.01 M HAuCl\({}_{4}\)\(\cdot\)3H\({}_{2}\)O (Cl\({}_{2}\)Pd, H\({}_{2}\)Cl\({}_{6}\)Pt) was added to the dispersed solution, and 1 mL of 100 mm H\({}_{4}\)BNa was rapidly added as well. At the same time as the H\({}_{4}\)BNa was added, the solution turned reddish brown and was refrigerated for 2 h. Distilled water and ethanol were diluted and centrifuged 5 times. The sample with the catalyst was dried at 80 \({}^{\circ}\)C for 24 h. The dried samples were maintained at 300 \({}^{\circ}\)C (5/min) for 2 h and cooled to room temperature.  
Figure 2 shows a schematic diagram of an In\({}_{2}\)O\({}_{3}\) nanoparticles-based CO gas sensor and an Interdigital electrode (Interdigital Electrode) pattern, which was fabricated as follows. First, the sensing ink was prepared for application of the sensing material to the IDE pattern by gently sonicating it, and immersing 0.02 g In\({}_{2}\)O\({}_{3}\)-based sensing material in 4 mL D.I water for 2 h. Second, it was applied to the IDE substrate through an injection syringe and the drop position was accurately observed with an optical microscope. The In\({}_{2}\)O\({}_{3}\) nanoparticle detection material was formed on the IDE substrate by injecting 0.1 L of ink drop per cycle and repeating drying.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 6 Challenges, Opportunities, and Research Directions
### Channel Estimation Challenges  
Channel estimation plays a crucial role in ensuring reliable communication in FL over wireless networks. Accurate estimation of the wireless channel state is essential for effective resource allocation, beamforming, and interference management, all of which directly influence FL performance [144]. In architectures like IRS-assisted and UAV-supported networks, channel estimation faces additional challenges due to multihop channels and dynamic environments. For example, IRS-assisted systems require the estimation of both direct and indirect channels, making the process computationally demanding [145]. Similarly, UAV mobility leads to rapid changes in channel conditions, causing outdated channel state information. This, in turn, degrades the QoS, slows FL convergence, and significantly increases energy consumption [146].  
While numerous studies have explored enhancing channel estimation in wireless systems using various techniques, FL-enabled wireless systems require robust and adaptive estimation methods that can operate under low-latency constraints. Additionally, these methods must strike a balance between estimation accuracy and computational efficiency, especially for energy-constrained devices. Therefore, a comprehensive evaluation of channel models is necessary to understand the impact of various factors across different wireless system architectures. Furthermore, developing channel estimation algorithms that can withstand external disturbances will be crucial for the continued success of FL in wireless environments.
## 4 From Exhaustive Search to Non-Linear Analysis
### Non-Dimensionalization  
To make the analysis easier we reduced the number of parameters using non-dimensionalization. Thus, the rescalings:  
\[\bar{T}=\frac{T}{h_{T}},\ \ \bar{C_{a}}=\frac{C_{a}}{C_{naive}}\ \ \bar{F_{\beta}}=\frac{F_{\beta}}{e_{T,\beta}},\ \ \bar{D}=\frac{D}{\theta_{D}},\ \ \bar{t}=\frac{t}{r_{e}}\]  
and the introduction of parameters:  
\[r_{1}=\frac{r_{T}}{r_{e}},\ \ r_{2}=\frac{h_{T}}{K},\ \ r_{3}= \frac{a_{T}C_{naive}}{r_{e}},\] \[r_{4}=\frac{r_{a}}{r_{e}},\ \ r_{5}=\frac{\mu_{Ca}}{r_{e}},\ \ r_{6}= \frac{r_{T\beta}h_{T}}{r_{e}e_{T,\beta}},\] \[r_{7}=\frac{\mu_{\beta}}{r_{e}},\ \ r_{8}=\frac{\mu_{D}}{r_{e}}\]  
give us the non-dimensionalization of the system (1)-(4). We omit the tilde notation in the rescaling equations. Therefore, the system becomes  
\[\frac{dT}{dt} =r_{1}T(1-r_{2}T)-r_{3}C_{a}\bigg{(}\frac{T}{T+1}\bigg{)}\left( \frac{a_{T,\beta}F_{\beta}+1}{F_{\beta}+1}\right) \tag{5}\] \[\frac{dC_{a}}{dt} =C_{a}\bigg{(}\frac{D}{D+1}\bigg{)}+r_{4}D_{on}-r_{5}C_{a}\] (6) \[\frac{dF_{\beta}}{dt} =r_{6}T-r_{7}F_{\beta}\] (7) \[\frac{dD}{dt} =-r_{8}D. \tag{8}\]