esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT Privacy and Security Challenges for Smart Home Environments
## 2 Key Issues in Cyber Security and Privacy  
The Internet has grown from a useful research tool for universities into a fundamental utility, as important as electricity, water and gas. Wherever there is a valuable resource, there is also crime which seeks to gain value from the illicit use of that technology, or to deny the use of that resource to others. The interconnected nature of the Internet means that Internet resources can be attacked from any location in the world, and this makes cybersecurity a key issue. Cybersecurity revolves around three main themes.  
_Confidentiality_ is about keeping data private, so that only authorized users (both humans and machines) can access that data. Cryptography is a key technology for achieving confidentiality.  
_Authentication_ is about verifying that data has not been tampered with, and that the data can be verified to have been sent by the claimed author. Non-repudiation (i.e., avoiding denial by a sender that they actually sent a message) is sometimes considered separately, but we include it here as a subset of authentication.  
_Access_ refers to only allowing suitably authorized users to access data, communications infrastructure and computing resources, and ensuring that those authorized users are not prevented from such access.  
The Information Security Breaches Survey is an annual cyber threat report commissioned by the UK Department for Business, Innovation and Skills and conducted by PriceWaterhouseCoopers. The latest survey in 2015 [7] shows that security breaches are on the rise; 90% of large organizations experienced cyber breaches in 2015 compared to 81% in 2014, and 74% of small businesses also suffered security breaches, indicating a double-digit year-on-year growth rate of 14%.  
Now that the Internet has become a mission-critical component of modern business, cybersecurity has become an indispensable component of information systems. However, as cybersecurity is enhanced, cybercrime is evolving to be more extensive, more destructive and more sophisticated. In Smart Homes, the ability of householders to manage their systems securely requires trusted and intuitive automated systems to assist in network management. Without such systems, the security and privacy threats of the Smart Home are likely to outweigh the advantages.
## 2 Sample and Experiments  
In order to examine the degradation of OLEDs, samples having blue fluorescent emissive layers (EMLs) were prepared on a glass substrate and encapsulated to get rid of any effect of water vapor and oxygen on the device degradation. The emission wavelength and the size of the sample were -450 nm and 2 \(\times\) 2 mm\({}^{2}\), respectively. See Figure 1 for a schematic representation of the device. Indium tin oxide (ITO) and Al are used as an anode and a cathode, respectively, while \(\alpha\)-NPD and Alq\({}_{3}\) layers are inserted as a hole transport layer (HTL) and an electron transport layer (ETL). The LiF layer is intended as an electron injection layer (EIL).  
In order to investigate the device degradation, constant current-density stresses were applied for 168 h. The driving conditions were set to be 2.5 \(\times\)\(I_{\text{D}}\) and 15 \(\times\)\(I_{\text{D}}\), respectively, where \(I_{\text{D}}\) was the normal device operation current density. As the devices were operated, luminance-current-voltage (\(L\)-\(I\)-\(V\)) characteristics were measured. The data were collected in shorter steps during the first 24 h of stress to investigate the initial variation in detail. After that, the data were collected in steps of 24 h. A Keithhey 2602B sourcemeter was used for characterizing the electrical properties and applying the constant current density stresses. The luminance of the samples was detected by a Si photodiode (Hamamatsu S2201).
# Research Progress of Zero-Busbar Technology Based on Heterojunction Photovoltaic Modules
## 3 Results and Analysis
### Reliability Analysis  
Unlike other electrical products, the lifetime of PV modules is much longer, with a general lifespan of 25-30 years. However, the complex outdoor environment poses a threat to their long-term stability. Therefore, it is necessary to conduct strict indoor reliability tests on PV modules [28].
# Human Cognitive and Motor Abilities in the Aging Workforce: An Information-Based Model
## 3 A Review of Experimental Tests of Motor-Cognitive Abilities from Information Theory Perspective
### Tests on Cognitive Abilities
#### 3.1.4 The Experience of Deary and Der  
In the study of Deary and Der [54], an experimental research with a large number of participants tested the correlation of the reaction time (RT) of male and female populations differently aged. The size of the samples (900 subjects aged in 16-63 years) gives high reliability to the results obtained. The RT was measured using a portable device with a display and a five response keys; the keys were labelled 1, 2, 0, 3, and 4 from left to right. For the evaluation of \"single-choice\" RT the subjects were asked to place a finger of their preferred hand over the central \"0\" key and they were instructed to press it as quickly as possible after the number \"0\" appears on the display. For the \"four-choices\" RT, the subjects were asked to place the second and the third finger of each hand over the keys labelled 1, 2 (left hand) and 3, 4 (right hand) and they were instructed to press the corresponding key when one of the four digits appears on the display. In the former case (single choice RT), the subject processes one bit of information in each test run; in the latter case (four choices RT) the subject processes 2 bits of information. Starting from the Hick's law [55] a linear dependency between the reaction time RT and the information volume to be processed is adopted:  
\[\text{RT}=\text{SRT}+\text{T}_{\text{p},\text{c}}\times\text{I}_{\text{c}} \left[\text{s}\right] \tag{5}\]  
where, SRT (Simple Response Time) is the sum of all-time delays not associated with decision-making, T\({}_{\text{p},\text{c}}\) (s/bit) is the time to process one bit of information, and I\({}_{\text{c}}\) is the information volume (in bit) processed in the cognitive task, as per Equation (1).
# Impact of Disruption on Ship Emissions in Port: Case of Pandemic in Long Beach
## 3 Model and Methodology
### Ship Emission Estimation Model
#### 3.3.5 Emission Factors for Different Engine Types  
Many external factors can influence emission factors, including engine type, fuel type, and engine condition. When computing each ship's emissions data, estimating the emission factor for each type of emission based on the circumstances is required.  
IMO has introduced the MARPOL Convention, in which Annex VI outlines various ECA zones [46]. Numerous nations have established sulphur-restricted zones to control sulphur emissions in the region. Californian waters lie inside the MARPOL Annex VI-designated North American ECA area. Under a policy adopted in 2014 in California, USA, the state additionally enforces its low-sulphur fuel requirements from the California Air Resources Board Ocean Going Vessel fuel regulations, which demand a sulphur level of less than 0.1% [47]. Due to regulatory requirements, MGO 0.1% sulphur is added to all ships by default during calculating air pollution from the ship in the POLB. This study estimates the actual emissions of each component separately, as the emission factors of the various compounds produced by ship engine types vary. To calculate precisely, we compiled the data from previous studies, which are displayed in Table 4[48].
# Carbon Nanodots as Peroxidase Nanozymes for Biosensing
## 5 Doped GQDs/C-Dots as POD Nanozymes  
Given the remarkable effect of element doping on PL properties of GQDs, [PERSON] and co-workers [49] synthesized nitrogen-doped GQDs (N-GQDs) through strong acid oxidation of three dimensional N-doped graphene aerogel (3D NGA), using dopamine or 3,4-dihydroxyphenethylamine (DA; an important neurotransmitter in mammalian's brain and body, and a well-known precursor in the synthesis of polydopamine [50]) as a nitrogen source. The as-produced N-GQDs presented intrinsic POD-like activity and could catalyze the TMB + H\({}_{2}\)O\({}_{2}\) system. The time-dependent absorbance changes at \(\lambda\) 652 nm indicated that with increasing concentration of N-GQDs, the reaction rate and absorbance changes also increase significantly. This may be attributed to the effect of N-doping that can effectively influence the spin density and the charge distribution of carbon atoms, and thus enhances the density of the catalytically active centers on the graphene surface with low stereohindrance for binding redox species in catalytic reactions [51]. The POD-like activity of N-GQDs was also found to be dependent on pH, temperature and H\({}_{2}\)O\({}_{2}\) concentration, similar to that of HRP. The kinetic studies revealed that the \(K_{\mathrm{m}}\) value of N-GQDs for TMB substrate was 22.9-fold less than that of HRP, indicating that N-GQDs presents a higher affinity to TMB. Using the POD-like catalytic activity of N-GQDs, a selective and quantitative colorimetric assay was developed for detection of H\({}_{2}\)O\({}_{2}\) and glucose with detection limits of 5.3 uM and 16 uM, respectively. The practical utility of this assay was further demonstrated in glucose detection in diluted serum as well as three commercial fruit juice samples. As a novel natural enzyme mimic, N-GQDs are expected to facilitate the application of the present protocol in medical diagnostics.  
Very recently, [PERSON], [PERSON] and co-workers [52] have synthesized N-doped C-dots (with trace amounts of iron(III)) by hydrothermal treatment of branched polyethylenimine and hemin, an iron-protoporphyrin and natural mimetic enzyme. The POD-like activity of N-doped C-dots has been exploited to develop the both colorimetric and fluorometric assays for DA detection with respective detection limits of 0.4 uM and 20 nM. The assay offers high sensitivity, selectivity and good feasibility in the analysis of DA in human serum even in the presence of multiple interferences except a high concentration of strong reducing agents.
# Quality of Service and Congestion Control in Software-Defined Networking Using Policy-Based Routing
###### Abstract  
Managing queuing delays is crucial for maintaining Quality of Service (QoS) in real-time media communications. Customizing traditional routing protocols to meet specific QoS requirements--particularly in terms of minimizing delay and jitter for real-time media--can be both complex and time-intensive. Furthermore, these protocols often encounter challenges when adapted for vendor-specific hardware implementations. To address these issues, this paper leverages the programmable features of Software-Defined Networking (SDN) to simplify the process of achieving user-defined QoS, bypassing the limitations of traditional routing protocols. In this work, we propose a policy-based routing module that integrates with traditional routing protocols to ensure QoS for real-time media flows. QoS is achieved by rerouting the flow along a new low-latency path calculated by the proposed module when the queuing delay exceeds a certain threshold. The experimental results demonstrate that the proposed solution significantly enhances the performance of traditional routing protocols within an SDN framework, effectively reducing the average end-to-end delay by 80% and total packet loss by 73%, while also improving jitter and alleviating network congestion.  
QoS; congestion; SDN; policy-based routing; programmable networks +
Footnote †: journal: Applied  
_Applied_  
_sciences_
# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 2 Related Work
### Store-Carry-Forward Mechanism  
The disconnected network or network partition problem has been extensively investigated in Delay Tolerant Networks (DTNs) to exploit opportunities to complement intermittent connections among mobile devices [20; 21]. However, the data dissemination in VANETs has its own characteristics. The connection of vehicles is easily interrupted due to the high mobility of the vehicles themselves, the building obstacles, the interference and the short range communication [22; 23]. The goal of the data dissemination is to efficiently inform all vehicles in a ROI about event-driven messages such as emergency messages. Therefore, the Store-Carry-Forward (SCF) mechanism not only needs to organize packets to be exchanged by an end-to-end paradigm in the one-hop range, but also to discover opportunities to start a new dissemination process in a further vehicular network.  
Most of literature about the SCF mechanism investigated the selection of _SCF-agent_ vehicles that can store and carry the data until they encounter new opportunities to forward it. DV-CAST can recognize two types of vehicles to be selected as the SCF-agent in highway scenarios. One is the last vehicle of a cluster driving in the same direction as the source vehicle, the other is the first vehicle of a cluster moving against the source vehicle [8]. UV-CAST used a gift-wrapping algorithm to select all boundary vehicles to be the SCF-agent. It is to be noted that this algorithm is a distributed version, and the set of SCF-agent vehicles is always a superset of all boundary vehicles. Therefore, UV-CAST would trigger more redundant transmissions. DRIVE did not resort to beaconing, but it only used the vehicles outside the ROI as information ferries to handle the network partition problem. It is obvious that most transmission opportunities would be missed. AMD did not suffer this problem, and it used beaconing to determine whether a vehicle is a _tail_ or not. If a vehicle makes a transition from _tail_ to _non-tail_, a new broadcast will be triggered. However, the directional sector classification of AMD cannot accurately recognize the irregular road topology. Consequently, it fails to detect some transitions so it misses some opportunities to forward data messages.
## 4 Discussion
### Future Work  
Several promising directions follow from this research. First, multi-cycle simulations should be conducted to evaluate how traceability and overall system performance evolve across successive closed-loop iterations. Such simulations could reveal whether accumulated data adversely impacts query efficiency or manageability, thus informing optimal data retention strategies that maintain provenance integrity without compromising operational performance. Second, piloting WEave in a real-world supply chain--such as a sustainable fashion brand implementing garment take-back initiatives--would offer practical validation. Deploying WEave across selected suppliers and recyclers on cloud infrastructure would provide essential feedback on system performance and usability, facilitating real-world refinement and enhancing user adoption. Third, extending WEave into digital twin environments represents an exciting opportunity. Integrating sensor data (e.g., shipment tracking, recycling processes) to trigger on-chain events could enable real-time automation, thereby tightening traceability integration with adaptive supply chain modeling.  
A critical area for future development involves advanced smart contract mechanisms tailored for complex circular economy scenarios, which current blockchain frameworks inadequately address. Specifically, sophisticated on-chain algorithms are needed to model quality degradation across multiple reuse cycles. For example, algorithms tracking fiber strength deterioration in textiles or contamination accumulation in plastics could provide essential insights for optimizing reuse pathways. Additionally, addressing complexities inherent in mixed-material recycling, where existing smart contracts fall short due to simplistic assumptions about material transformations, necessitates incorporating probabilistic quality models and machine learning algorithms within smart contract logic.  
Evaluating the economic implications of adopting Weave, including infrastructure investments, operational efficiencies, and value generation through enhanced transparency, would further support industry acceptance. Smart contracts designed to automate incentives and rewards for sustainable practices could align participant behaviors closely with circular economy objectives. Furthermore, a systematic analysis of system resilience under failure conditions is essential. Investigating participant dropout scenarios, particularly involving recyclers and waste processors, could identify risks of orphaned return records and incomplete material flows. Network partitioning events that isolate regional facilities from the blockchain network, along with consensus mechanism failures during peak processing periods, could lead to significant operational disruptions. Future research should develop simulation frameworks to rigorously test system behavior under these adverse conditions and create robust recovery protocols ensuring continuity of circular economy operations despite (blockchain) infrastructure disruptions.
## 4 Algorithm
### Double Deep Q-Network with Prioritized Experience Replay
Lines 1-5 correspond to the initialization phase; lines 9-11 represent the interaction process between the agent and the environment; lines 12-22 cover the model training and parameter updating phase, which mainly includes the Prioritized Experience Replay mechanism, loss function calculation, and Q-value computation. ```
0:\(\theta\), \(\theta^{-}\), \(\gamma\)
0:Updated Q-Network parameters \(\theta\)
1:Initialize \(\theta\) and \(\theta^{-}\) with random parameters;
2:Initialize Replay Memory \(D\) to store experience tuples;
3:Initialize environment;
4:Initialize the priority \(p_{i}\) for each experience in \(D\) with small values (e.g., \(\epsilon\));
5:Set \(max\_epochs=5000\);
6:for episode from 1 to max_epochs do
7: Initialize state \(s_{t}\) from environment;
8:while episode is not done do
9: Select action \(a_{t}\) based on epsilon-greedy policy;
10: Execute \(a_{t}\), observe \(r_{t}\) and \(s_{t+1}\);
11: Store experience \((s_{t},a_{t},r_{t},s_{t+1})\) in \(D\);
12: Sample a mini-batch of experiences from \(D\) based on priority \(p_{i}\);
13: For each experience in the batch, compute importance sampling weights \(\omega_{i}\);
14: For each experience in the batch, compute the target \(y_{t}\);
15: Update parameters \(\theta\) by minimizing the weighted loss function \(L(\theta)\);
16: Perform gradient descent on \(\theta\) to minimize \(L(\theta)\);
17: Update priority for each experience based on TD error \(p_{i}\);
18: Update Target Q-Network parameters: \(\theta^{-}\leftarrow\theta\);
19:endwhile
20:endfor
```  
**Algorithm 3** DDQN-PER-Based In Situ Data Management Method
# Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs
## 1 Introduction
The AMSR-E SST retrieval algorithms developed by remote sensing systems (RSS), provide SST on a \(10\,\mathrm{km}\) grid for their L2 products and on a \(25\,\mathrm{km}\) for their L3 product, but the region contributing to the recorded SST at each of these grid locations is, as noted above, thought to be \(\mathcal{O}\left(45\times 75\,\mathrm{km}\right)\). This means that each point in the ocean is seen by \(\mathcal{O}\left(35\right)\) pixels. It should therefore be possible to deconvolve the AMSR-E SST field yielding a true \(10\,\mathrm{km}\) product. The AMSR-E footprint. In both of the cases outlined above, an accurate estimate of the shape and size of the region contributing to the AMSR-E SST retrievals will benefit the analyses. For example, the use of a footprint (the weighting function mapping the temperature distribution at the ocean surface to a retrieved SST value) that is not representative of the AMSR-E SST product will result in artifacts in the differences between AMSR-E SST fields and MODIS SST, increasing the variance of the difference, as will be shown. Microwave SST data deconvolution also requires incorporation of the footprint to meet the demand for optimal reconstruction; selecting an appropriate footprint is a key preliminary step. For instance, the procedure described in [9] is based on the knowledge of the footprint measurement model. To the best of our knowledge there are no published studies detailing the shape of the AMSR-E SST footprint, although the footprints size is commonly referred to as ranging from \(50\,\mathrm{km}\)[10] to \(58\,\mathrm{km}\), with the most common reference to \(56\,\mathrm{km}\)[2], close to the average of the axes of the \(6.93\,\mathrm{GHz}\) footprint. The objective of the work presented herein is to determine the shape of the AMSR-E SST footprint and any dependencies that it might have on cell position, latitude, and time. From the perspective of this study, it is important to recognize the distinction between the footprint of a given channel of an instrument and the footprint of the retrieved SST values, which may depend on more than one spectral channel (discussed in Section 2.1), each with its own footprint, and/or on additional processing, such as averaging.