esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review and Experimental Evaluation on Split Learning
## 2 Taxonomy and Review of SL Paradigms
### Intermediate Data Aggregation-Based Approaches  
Unlike model weight aggregation, some SL paradigms aggregate intermediate data, such as gradients and smashed data, from different clients to exchange local information while minimizing communication costs.  
In SGLR [58], as shown in Figure 5a, the server aggregates the local gradients of a subset of clients rather than the model parameters. This approach reduces the amount of information shared among clients and eliminates the client decoupling problem. Inspired by existing work on learning rate acceleration [105; 106], the learning rate in SGLR is divided into two parts to improve the overall training performance and address the server-side large effective batch problem [58].  
In LocFedMix-SL [98], as shown in Figure 5b, the server mixes the smashed data from different clients to create new smashed data for forward propagation. This smashed data mixing increases the effective batch size at the model's top layers to match its higher effective learning rate. Each client has an auxiliary network designed to maximize mutual information between the smashed data and the input data. This makes the relationship between the smashed data and model input tighter, improving model performance and training efficiency.  
Figure 5: Illustration of intermediate data aggregation-based SL paradigms: (**a**) SLGR; (**b**) LocFedMix-SL; and (**c**) CutMixSL.  
In CutMixSL [66], SL and ViT are combined and a new type of data called CutSmashed is proposed, as shown in Figure 5c. In this method, each client creates their own CutSmashed data by randomly masking a portion of their original smashed data. The clients then send these partial data to a mixer, which combines them and sends them to the server for training. This method reduces communication costs as clients only need to send a portion of their data, and the server can use multi-casting to send back the gradient. Furthermore, CutSmashed data provide better privacy protection as reconstructing the untransmitted regions is difficult.  
To address the scalability issue in PSL paradigms such as SplitFed [46], Mix2 SFL [60] introduces SmashMix and GradMix on the basis of SplitFed. The SmashMix involves pairwise mixing of smash data as input for the server-side model, while the GradMix refers to averaging the obtained gradients and returning them to the client. This approach can also accelerate the convergence speed, thereby reducing communication costs.  
In the context of energy theft detection [107], the Three-Tier Split Learning (TTSL) [99] was proposed to reduce communication costs and protect privacy. In TTSL, multiple aggregators are employed to average and aggregate the smashed data of the clients before sending them to the server. Through this approach, it is more difficult for honest but curious servers to infer private information from aggregated smashed data, and the communication cost is also reduced.
# Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental Data
## 1 Introduction
Therefore, long-term data, measured under these different climatic conditions, are needed to accurately evaluate the performance of calibration. Second, a very limited range of data were used in the previous studies. [PERSON] et al. [20] used air temperature and hourly global solar irradiation, and [PERSON] et al. [24] used only air temperature. It is well known that low-cost air temperature sensors are influenced by the  
Figure 1: Appearance of the air temperature sensor of the Automated Meteorological Data Acquisition System (AMeDAS). The sensor is covered by a radiation shield made of stainless steel. There is 5-ms\({}^{-1}\) air flow in the radiation shield generated by a forced aspiration system. range of environmental factors noted above. Including these in the input data should greatly improve the accuracy of low-cost air temperature sensors. In this study, we developed a calibration method using long-term air temperature measurement by low-cost sensors and taking account of multiple environmental factors. The data used covered a total of 305 days between January and December, with October data excluded. Data recorded in different parts of Japan were used to investigate the location specificity of our calibration method. To take account of the effect of multiple environmental factors, machine learning was used. Finally, the accuracy of our calibration method was compared with the calibrated air temperature data measured by the high-accuracy sensors of AMeDAS.
## 3 Results
### Mechanical Properties of Plasma-Treated Hemp Fiber Composites  
The tensile properties of the epoxy, HFRE, and HF\({}_{\text{tr}}\)RE composites are shown in Figure 8a and Table 3. The HFRE composite that was used as a reference had a tensile strength of 40.39 \(\pm\) 0.52 MPa and an elongation at break equal to an average value of \(6.27\pm 0.41\%\).  
Figure 6: Surface morphology of hepn fiber was untreated and treated with Ar, Ar+N\({}_{2}\), and Ar+O\({}_{2}\) plasma, Note: Main figures before expansion (1000\(\times\)) and insets show the same images after expansion (15,000\(\times\)).  
Figure 7: SEM image of **(a,b)** cracking patterns of composite samples, **(c)** a model pattern of fracture area observed in side view, **(d,e)** a fracture surface overview of composite samples, **(f,g)** a fracture surface overview of composite samples after expansion (600\(\times\)), and **(h)** a model pattern of fracture area observed in top view.  
The tensile strength of the HF\({}_{\rm tr}\)RE composites was also examined, with all plasma-treated samples performing better than control, with the (Ar+O\({}_{2}\))HF\({}_{\rm tr}\)RE composite having an optimal tensile property for a microscopic increase in the maximum value as 46.52 \(\pm\) 0.54 MPa, representing a 15.2% increase. The elongation to break values showed no significant difference between the HF\({}_{\rm tr}\)RE composites and control.  
The energy absorption of the composites was calculated by finding areas under stress-strain curves (Figure 8b) as the maximum (130.64 \(\pm\) 5.40 MPa\(\cdot\)mm\(/\)mm), which were observed in the case of (Ar+O\({}_{2}\))HF\({}_{\rm tr}\)RE composites. This significantly improved energy absorption leads to the tensile strength and elongation at break of the composites being increased directly.  
These improvements in the mechanical properties can be due to a better interfacial bonding between the plasma-treated hemp fibers and epoxy matrix. This increased surface roughness and exposure of microfibrils on the fiber surface enhances mechanical interlocking and adhesion, leading to efficient stress transfer and delayed failure [28; 29; 30].
# Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification
## 3 Probabilistic Similarity Measure
In order to estimate the mean and variance of Gaussian pdf \(p(\delta)\), we compose the set of intra-class difference vectors \(\Omega\) using support samples, which can be defined as:  
\[\Omega=\big{\{}\delta_{ij}\big{|}\ \delta_{ij}=\mathbf{x}_{i}-\mathbf{x}_{j},\ \mathbf{x}_{i},\ \mathbf{x}_{j}\in\mathbf{c}_{k},\ k=1,\ \dots,\ N\big{\}}. \tag{4}\]  
Then, the mean vector \(\mathbf{\mu}_{\Omega}\) and the covariance matrix \(\sum_{\Omega}\) can be estimated from the set \(\Omega\), and the density function \(p(\delta)\) that we want to know can be written as:  
\[p(\delta)\propto\exp\biggl{(}-\frac{1}{2}(\delta-\mathbf{\mu}_{\Omega})^{T}{\sum _{\Omega}}^{-1}(\delta-\mathbf{\mu}_{\Omega})\biggr{)}. \tag{5}\]  
Noting that the higher value of \(p(\delta)\) implies a higher likelihood that the two data making up \(\delta\) belong to the same class, the similarity measure \(S_{G}(\mathbf{x},\ \mathbf{x}\prime)\) for two samples \(\mathbf{x}\) and \(\mathbf{x}^{\prime}\) is defined as the value proportional to \(p(\delta)\), such as:  
\[S_{G}(\mathbf{x},\ \mathbf{x}\prime)=-\big{(}\mathbf{x}-\mathbf{x}^{\prime}-\mathbf{\mu}_{\Omega} \big{)}^{T}{\sum_{\Omega}}^{-1}\big{(}\mathbf{x}-\mathbf{x}^{\prime}-\mathbf{\mu}_{\Omega} \big{)}. \tag{6}\]  
The efficiency of the obtained similarity value has been confirmed in various application problems [26; 27; 32]. In the prototypical network, its classifier uses Euclidean distance, which is the special case of the probabilistic similarity with \(\mathbf{\mu}_{\Omega}=0\) and unit covariance matrix. In this paper, we apply the general covariance matrix in the learning of embedding function as well as classification. It should also be remarked that this similarity is different from the conventional Mahalanobis distance [25] that uses covariance of original samples \(\mathbf{x}\). By using intra-class difference vectors, the larger number of samples can be used to estimate the covariance matrix \(\sum_{\Omega}\), and can obtain more accurate estimation.
## 4 Spatial Audio in Virtual and Augmented Reality for Soundscape Design
[27] proposed an augmented reality headset in which they use open-back headphones with pairs of external and internal microphones to achieve sound playback that is very similar to natural listening scenario. These devices and upcoming innovations would be useful for projects that involve altering the soundscapes of existing locations. They could be used in soundscape studies to test different hypothesis in an immersive environment, which allows both virtual reproduced and real ambient sounds to be heard at the same time. AR devices have the advantage of including different elements of the perceptual construct of the soundscape, including meteorological conditions and other sensory factors [7]. Moreover, if accurate spatial sound is used with these AR devices, it would enable the soundscape researchers to fuse the virtual sound sources seamlessly with the real sound, thus enabling highly accurate interpretation of auditory sensation by the user. The viability of an augmented reality audio-visual system has been demonstrated with a consumer AR headgear and open-backed headphones with positional translation- and head-tracked spatial audio, as shown in the left half of Figure 8.  
In essence, VR systems with high fidelity spatial audio are well suited for an immersive acoustic environment with a good degree of control under laboratory conditions; whereas AR systems with high fidelity spatial audio are more immersive but are subject to a high degree of variability in the in situ environment. Hence, soundscape practitioners should exercise care in the selection of audio-visual mediums and should consider the complementary nature of VR and AR techniques mentioned.
###### Abstract  
In recent years, several studies focused on the synthesis of lignin-based nanoparticle in aqueous solution and its potential applications of the drug carrier were investigated. In this study, soda lignin (SL) nanoparticles (i.d. 128-560 nm) were synthesized by the nanoprecipitation process at three different concentrations (1, 2, and 4 mg/mL THF) with various molecular sizes of soda lignin (NP-F1, NP-F2, and NP-F3) obtained from sequential solvent extraction. The average molecular weights of SL, F1, F2, F3, F4, and F5 were 3130, 1190, 2550, 3680, 5310, and 14,650, respectively. The average size of the spherical lignin nanoparticle was a minimum of 128 nm for NP-C1 and the size increased up to 560 nm with increasing concentration. Particle surface charge increased with increasing concentration from \(-\)26 mV for NP-C1 to \(-\)38 mV for NP-C4. Contrary to expected general trends in polymeric nanoparticles, there was no remarkable change or trend with increasing lignin molecular weight since chemical structures of each lignin fraction are also remarkably different. Further studies to learn correlation between properties of lignin nanoparticle and its additional details regarding the chemical structures is needed.  
l igmin; nanoparticle; sequential solvent extraction; nanoprecipitation; colloid +
Footnote †: journal: Applied Sciences
## 2 Bayesian Optimization
### Kernel Functions  
Kernel functions are essential in BO since they define the shape of the prior and posterior distributions. For a function to be a valid kernel, it needs to satisfy [PERSON]'s theorem [60].  
A symmetric function \(k(\mathbf{x},\mathbf{y})\) defined on \(\mathcal{X}\times\mathcal{X}\), where \(\mathcal{X}\) is some input space, is a valid kernel function if and only if it satisfies the following conditions:  
1. Symmetry : \(k(\mathbf{x},\mathbf{y})=k(\mathbf{y},\mathbf{x})\) for all \(\mathbf{x},\mathbf{y}\in\mathcal{X}\).
2. Positive semidefiniteness: For any finite set of points \(\mathbf{x}_{1},\mathbf{x}_{2},\dots,\mathbf{x}_{n}\in\mathcal{X}\), where \(i,j\in\{1,...,n\}\), the corresponding kernel matrix \(\mathbf{K}(\mathbf{x}_{i},\mathbf{x}_{j})\) is positive semidefinite.  
If there exists a mapping function \(\phi(\mathbf{x})\) that maps \(\mathbf{x}\) to a higher-dimensional space such that the inner product in that space is equivalent to the kernel function \(k(\mathbf{x},\mathbf{y})=<\phi(\mathbf{x}),\phi(\mathbf{y})>\), then according to [PERSON]'s theorem, \(k(\mathbf{x},\mathbf{y})\) is a valid kernel function. Kernel functions have important properties commonly used to modify and non-linearly combine existing kernels to obtain new ones better suited for handling specific problems, such as the following:  
1. Linearity: If \(k_{1}(\mathbf{x},\mathbf{y})\) and \(k_{2}(\mathbf{x},\mathbf{y})\) are valid kernel functions, then for any constants \(a,b\geq 0\), the linear combination \(a\cdot k_{1}(\mathbf{x},\mathbf{y})+b\cdot k_{2}(\mathbf{x},\mathbf{y})\) is also a valid kernel function.
2. Product: If \(k_{1}(\mathbf{x},\mathbf{y})\) and \(k_{2}(\mathbf{x},\mathbf{y})\) are valid kernel functions, then their product \(k(\mathbf{x},\mathbf{y})=k_{1}(\mathbf{x},\mathbf{y})\cdot k_{2}(\mathbf{x}, \mathbf{y})\) is also a valid kernel function.
3. Exponential: If \(k(\mathbf{x},\mathbf{y})\) is a valid kernel function, then \(\exp(k(\mathbf{x},\mathbf{y}))\) is also a valid kernel function.
4. Function: If \(k(\mathbf{x},\mathbf{y})\) is a valid kernel and \(f:\mathcal{X}\rightarrow\mathcal{R}\), then \(g(\mathbf{x},\mathbf{y})=f(\mathbf{x})k(\mathbf{x},\mathbf{y})f(\mathbf{y})\) is also a valid kernel.
# Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data
## 1 Introduction
### Research Questions  
Based on the above arguments, the present work deals with three research questions:  
* RQ1: How to incorporate various heterogeneous data sources about marine traffic, vessel information and sea conditions towards modeling and understanding potential hazardous situations.
* RQ2: How to exploit historical data towards creating a simulation tool for inferring about future situations under conditions of uncertainty.
* RQ3: How to create useful visualization and insights for the domain, in order to help experts formulate policies, regulations, reform emergency plans, etc.
## 2 Literature Review
[39] identified an interesting impact: digitalization has a greater impact on energy in low-income countries and underdeveloped regions and a lesser impact on energy in high-income countries and developed regions. Recently, [PERSON] et al. [40] conducted a study with the primary aim of evaluating the potential of Industry 4.0 technologies in optimizing renewable energy production. Following analyses and research, the authors concluded that Industry 4.0 could make intermittent renewable energy more accessible in several industries, including solar, wind, hydropower, and biomass energy generation. Specifically, they argue that virtual power plants and microgrids can facilitate energy distribution, ultimately increasing the number of renewable energy users. Based on existing studies, it can be inferred that digitalization has a significant and positive impact on the performance of the renewable energy sector, contributing to increased efficiency, cost reduction, and the stimulation of technological innovation in this field. The studies demonstrate that the adoption of digital technologies by renewable energy enterprises not only improves operational performance but also facilitates cooperation among various entities in the sector, leading to a broader and concerted adoption of these technologies. As observed in the literature, most studies focus solely on the impact of digital transformation on greenhouse gas emissions or the use of renewable energy, without considering the complex role that digitalization can play in both dimensions of environmental performance. This limited approach neglects the way in which digitalization can influence and mediate the interaction between these two aspects, highlighting the need for a more integrated analysis that fully explores the potential of digital transformation in promoting ecological sustainability. Additionally, there is a significant gap in research on the impact of digital transformation on environmental performance in developing countries in Europe. Most existing studies focus on China or more heterogeneous groups, such as the entire European Union, leaving the specific situation of emerging European economies largely unexplored. The contribution of this study lies in addressing this gap by providing a detailed analysis of how digital transformation influences environmental performance in developing European countries, such as Romania, Bulgaria, Poland, and Hungary. This research not only extends the theoretical understanding of the interactions between digitalization and environmental performance but also offers empirical results that support practical recommendations for policymakers in these regions, thereby contributing to a more balanced and sustainable development. This underscores the importance of implementing digital policies in emerging European economies, highlighting the ways in which digital technologies can support the transition towards more sustainable economic practices.
## 6 Conclusions  
In this paper, we have proposed and implemented a CMOS sensor for spatial optical communication of a long range of 100 m and moderate data rate of 1 Mbps. Using the CMOS sensor with 640 \(\times\)240 pixel array and 640 \(\times\)240 LPR cell array, a LED light of 1 MHz carrier frequency is successfully received. A proposed model of the transient response of the LPR cell agrees with the result of the device simulations and measurements. Spatial optical communication between a non-moving object (LED light source) and the non-moving receiver (ISC chip) has been demonstrated. The demonstration of optical communication for a moving target light source is left as a near future subject.
## 7 Results  
### Performance of the Communication System  
Six trials were conducted for communication testing purposes. Lettuces were used as a model of perishable food to be monitored in the trials, which lasted 10-12 days approximately. Trials 1 to 4 used two slave nodes for the three stages and one gateway node to provide internet access during the transportation and distribution stage. Trials 5 and 6 used three slave nodes during the three stages connected to several Wi-Fi networks previously configured in the setup file of the nodes. The devices were deployed at different places inside the commodities boxes. To check the communication performance in the different tests, the device records in the SD card packets, which were successfully sent at their corresponding sampling time (received packet). When the communication is unsuccessful, the device records the frame to send it later, and in that case, the attempt is marked as lost packet in order to evaluate the success rate in the communication tests of the devices in the different trials. Table 2 shows the total amount of data monitored and sent successfully to the server.  
The ratio between received and lost data packets shows the communication performance. Although lost data were recovered as soon as the nodes restored Internet access, the valid data ratio demonstrates that Wi-Fi can be used as a communication carrier in these processes.  
Wireless transmission performance at 2.4 GHz frequencies, such as those used in Wi-Fi, is influenced by temperature and humidity conditions [58, 59].  
These disturbances were reflected in the tests by some inter-sensor sending rates. However, 14 of the 18 tests that were carried out in temperature conditions between 10 and 2 \({}^{\circ}\)C had performance rates above 90%, which supports the use of this wireless transmission technique for the specific conditions of transport and storage of perishable products. However, it should be noted that non-communicated data are stored internally by the nodes and sent later when conditions are suitable.  
Figure 11: Stages of the postharvest supply chain used for real test of the system.
###### Abstract  
An efficient synthesis route was developed for the preparation of multiwalled carbon nanotube (MWCNT) nanohybrids using azide-terminated poly(methyl methacrylate) (PMMA) via a combination of reversible addition fragmentation chain transfer (RAFT) and the click reaction. A novel azido-functionalized chain transfer agent (DMP-N\({}_{3}\)) was prepared and subsequently employed to mediate the RAFT polymerizations of methyl methacrylate (MMA). The RAFT polymerizations exhibited first-order kinetics and a linear molecular weight dependence with the conversion. The kinetic results show that the grafting percentage of PMMA on the MWCNTs surface grows along with the increase of the reaction time. Even at 50 \({}^{\circ}\)C, the grafting rate of azide-terminated PMMA is comparatively fast in the course of the click reaction, with the alkyne groups adhered to MWCNTs in less than 24 h. The successful functionalization of PMMA onto MWCNT was proved by FTIR, while TGA was employed to calculate the grafting degree of PMMA chains (the highest GP = 21.9%). Compared with the pristine MWCNTs, a thicker diameter of the MWCNTs-g-PMMA was observed by TEM, which confirmed the grafted PMMA chain to the surface of nanotubes. Therefore, the MWCNTs-g-PMMA could be dispersed and stably suspended in water.  
multi-wall carbon nanotube (MWCNT); azide-alkyne click chemistry; RAFT polymerization; PMMA +
Footnote †: journal: Applied Physics Letters