esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Challenges for Users of Extensible Smart Home Hubs: A Systematic Literature Review
## 3 Results
### Synthesis of Key Security Challenges
#### 3.2.7 Insecure Extension Runtime  
The unique feature of the extensible smart home hub is that it provides an extension runtime. This feature makes the hub infinitely versatile but also significantly impacts hub security requirements. A clear trust boundary between the hub core and its extensions is needed to ensure hub security. Several included papers [21; 29; 32] highlight the extension runtime as one of the main attack vectors in extensible smart home hubs through either malicious or vulnerable extensions. [PERSON] et al. [21] note that this is especially true when extensions are made by third parties, often with less quality control and maintenance. [PERSON] and [PERSON] [1] specifically found that Home Assistant, the most popular open-source hub, has no firm extension isolation, compromising the confidentiality and integrity of other extensions running on the hub. Furthermore, extensions are free to perform actions without being authenticated, reducing the ability to audit logs and locate malicious extensions.  
Some solutions to this challenge already exist in select systems. [PERSON] et al. [29] include Apple Homekit as an example of a smart home system using sandboxing technology to isolate extension runtimes, an approach also suggested by [PERSON] and [PERSON] [1]. Other suggested improvements to extension runtime security include those by [PERSON] and [PERSON] [4], proposing to use a modified version of IETF's MUD standard to label extensions with intended use cases; [PERSON] et al. [21], demonstrating a user expectation-based extension manager; [PERSON] et al. [32], suggesting a behavior-based approach; and [PERSON] et al. [29], proposing third-party verification of extensions like in smartphone app stores. Finally, multiple papers bring up the need for better access control to ensure that extensions only have required access [1; 21; 23; 25; 27].
# Innovative Approaches for Minimizing Disinfection
## 4 DBP Minimization Approaches
### Advanced Water Treatment Technologies
#### 4.5.2 Activated Carbon  
Adsorption using carbon-based adsorbents has been employed since the 18 th century and continues to be one of the most preferred and promising techniques for water treatment, especially for eliminating DOC and harmful contaminants at low concentrations. This method is favored for its ease of operation, minimal maintenance needs, high efficiency, and straightforward design. However, the cost-effectiveness of adsorption methods relies heavily on the accessibility and cost of carbon-rich precursor materials like wood, petroleum, coal, and coconut shells [10].  
Industrial activated carbon, used for odor elimination, the filtration of hazardous materials, and water treatment, has historically consumed thousands of tons of wood, coal, and petroleum annually. Considering economic and environmental constraints, new technologies have emerged to manufacture activated carbon from cost-effective, carbon-rich sources such as agricultural waste, sewage sludge, used coffee grounds, and industrial solid waste [10].  
Activated carbon is considered an effective technology for removing DBP precursors due to its extensive surface area. In a two-stage biofiltration process consisting of a sand biofilter combined with a biologically active GAC filter, it was found that this setup accounted for 60.63% \(\pm\) 16.64% of the total DBP precursor removal. Using powdered activated carbon (PAC) significantly enhanced the removal of THM and N-DBP precursors. Research has shown that PAC treatment alone may not effectively prevent the formation of N-DBPs, but when combined with pre-oxidation using permanganate, it can significantly reduce the formation of both. This process also demonstrated a sufficient removal of N-DBP precursors, effectively removing low molecular weight, positively charged, and hydrophilic organic substances. The choice of activated carbon used can influence the efficiency of NOM removal. PAC has a larger surface area than GAC and can be utilized at different stages of treatment. A newly developed coconut shell-based GAC has demonstrated higher DOM removal, releasing fewer microbes, metabolites, and smaller microbial complexes than conventional GAC [14].
## 3 Pathway Metabolic Engineering Strategies to Improve Glycerol-Based Bioprocesses
### Lipids
#### 3.1.1 TAG Biosynthetic Pathway
With the single deletions they studied the impact of the loss of a single acyltransferase gene for TAG and/or SE accumulation, whereas with the double and triple deletions, the functions of ARE1 (acyl-CoA:sterol acyltransferase) and DGA2. Finally, the quadruple mutant was employed to study whether other genes possibly contribute to lipid synthesis. The JMY3580 strain, with a reconstructed TAG synthesis pathway, accumulated over 40% of lipids in biomass, while in wild type strain did not exceed 20% on a glycerol-based medium (C/N 90). In fed-batch grown cells, when glycerol was added during cultivation, lipid accumulation above 50% was achieved. Fatty acid composition was also altered when DGA2 was the only diacylglycerol acyl-transferase, with an especially low percentage of linoleic acid present [52]. Engineering the TAG biosynthetic pathway can also be performed to tailor-make the lipid pool, owing to the different FA composition required by each application. [PERSON] et al., enhanced the lipid synthesis from glycerol (both pure and crude) by overexpressing the _GUT1_, _SCT1_, and _DGA1_ genes (Figure 1). They found that _SCT1_ overexpression resulted in a 10-fold increase in linoleic acid (18:2) synthesis and improvement of the polyunsaturated FA pool, while co-expression of _GUT1_ and _SCT1_ yielded high production of mono- and polyunsaturated FA (MUFA and PUFA) from crude glycerol. Low pH (3.0) was the most suitable for TAG biosynthesis using crude glycerol. When _SCT1_ was overexpressed, a high content of PUFA was accumulated, mainly linoleic acid (C18:2), with more than 20% in the total FA pool in the shake-flask study. Overexpression of the _DGA1_ gene redirected the synthesis of FA to stearic acid (C18:0) and resulted in high production both at pH 6.0 and 3.0. Simultaneous overexpression of _GUT1_ and _SCT1_ yielded a three-fold improvement in C18:2 synthesis from crude glycerol and at low pH [36].
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 6 Real Shipping Movement Model  
The Automatic Identification System (AIS) is a global maritime safety system by which ships over 300 gross tonnage on international voyages, over 500 gross tonnage in any case, and all passenger vessels are required to broadcast their identification, position, heading, and speed every 10 s if moving or every three minutes if at anchor. This is an international legal requirement in effect since 31 st December 2004 (International Maritime Organisation, nd, [190]). The data transmitted by AIS is a collation of static information from the ship's register and dynamic data obtained from GPS. This is transmitted on the marine Very High Frequency (VHF) band in the range of 161.975-162.025 MHz and is usually received, processed, and displayed by other ships' navigation systems. More technical information on AIS can be found in (International Maritime Organisation, 2003, [191]). AIS signals can be picked up by a standard marine VHF radio receiver, decoded via a commercially available interface, such as the NASA AIS Radar Receiver (NASA Marine Instruments, n.d., [192]), and displayed on a computer using ShipPlotter software (COAA, nd, [193]). A website called ShipAIS ([PERSON], nd, [194]) collates information gathered from a number of receivers around the Irish Sea and from other parts of the UK and produces a real-time display of ship movements. The data is also logged so that historical records are available. The website owner has kindly supplied the data used in this research. Similar facilities are available from other sites, such as www.marinetraffic.com (MarineTraffic, nd, [195]).  
Figure 10: Normalized Routing Load for Battlefield Model.  
Trace data for shipping in the Irish Sea captured by the AIS and sourced by the website owner for ShipAIS ([PERSON], nd, [194]) is used to construct a simulation (in ns-2) that exactly matches the real-world movement of these ships. The communications modelled are designed to establish whether connectivity can be achieved in such a situation. Details of the geographical area, MANET nodes, and communications used are discussed in the subsequent sections.
# Resource Allocation for GBR Services in D2D-Enabled Communication
## 3 Resource Allocation for GBR Services
### 3-D Assignment of CU, DU, and Channel
```
1:Initialize \(u~{}=~{}[0,\ldots,0]\), \(ub~{}=~{}\infty\), \(lb~{}=~{}-\infty\), \(t~{}=~{}0\), \(T~{}=~{}100\), \(\epsilon~{}=~{}0.01\), \(\sigma~{}=~{}1\)
2:while\(t<T\)
3:\(t~{}=~{}t+1\)
4:Compute \(\phi(u)\) & \(lb\), i.e., solve RP1
5:\(lb~{}=~{}\max[lb,\phi(u)]\)
6:Find solution \(\eta^{*}\) of AP2 using the solution \(\xi^{*}\) of RP1
7:\(ub~{}=~{}\min\{ub,\psi(\eta)\}\)
8:if\(lb\geq(1-\epsilon)ub\) break
9:end if
10:Compute sub-gradient \(v\)
11:\(v_{l}~{}=~{}\frac{\partial\phi(u)}{\partial u_{l}}\), for all \(l\in\mathcal{L}\)
12:If \(v~{}=~{}[0,\ldots,0]\) break
13:end if
14:Compute step size \(\sigma\)
15:Compute new Lagrange multipliers \(u~{}=~{}max\{0,u+\sigma v\}\)
16:end while
```  
**Algorithm 1** Sub-optimal algorithm based on sub-gradient method. Since only a single user is assigned to a channel and a channel is allowed to be assigned to a single CU-DU pair, the above matching algorithm works well with a constraint of \(N=M=L\). When the number of CUs is different from that of DUs, a channel is assigned to either a CU or a DU without CU-DU pairing. This is known as an unbalanced assignment problem and can be solved based on the modified Hungarian method [29]. By introducing dummy users or channels, the constraint of \(N=M=L\) can be preserved, i.e., the unbalanced assignment problem can be transformed into a balanced assignment.
# Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation
## 2 Materials and Methods
### Construction of a Driving Intention Model Based on Cross-Modal Knowledge Distillation  
A driving intent model leveraging machine vision is constructed using the CMKD framework [33] in Figure 6.  
Figure 5: FBCSP feature extraction flowchart.  
In predicting \(y_{i}\) given \(x_{i}\), \(x_{i}^{\prime}\) provides supplementary information for the pair (\(x_{i}\),\(y_{i}\)). Specifically, \(x_{i}\) represents a video segment of driving stimuli, while \(x_{i}^{\prime}\) corresponds to the EEG signals recorded as subjects viewed the segment. \(y_{i}\) denotes the associated driving intent (e.g., left turn, right turn, avoidance, or normal driving). During training, the teacher model \(f_{t}\) is first trained exclusively on EEG feature data \(x_{i}^{\prime}\). The trained \(f_{t}\) then generates predictions using a temperature parameter \(T\) to improve the model's capacity to distinguish commonalities and differences between categories. Equation (10) gives the mathematical representation:  
\[s_{i}=\sigma(f_{t}(x_{i}^{\prime})/T), \tag{10}\]  
where \(\sigma\) represents the softmax function. Subsequently, the student model \(f_{s}\) is trained exclusively on the video data \(x_{i}\).  
The loss function during training is defined through Equation (11):  
\[Loss=\frac{1}{n}\sum_{i=1}^{n}\big{[}(1-\lambda)l_{CE}(y_{i},\sigma(f_{s}(x_{i })))+\lambda l_{CE}(s_{i},\sigma(f_{s}(x_{i})))\big{]}, \tag{11}\]  
where the imitation factor \(\lambda\) regulates the balance between the predicted soft labels \(s_{i}\) and the true labels \(y_{i}\), while \(l_{CE}\) denotes the cross-entropy loss function.  
During training, the EEG signal model incorporates the subject consensus processing method from the reference [34]. Specifically, the teacher model predicts soft label values using EEG data from all subjects corresponding to each stimulus video segment. For a given subject, the prediction result obtained by the teacher model is [0.23, 0.66, 0.11, 0.10], and the output results of other subjects follow a similar format. Then, the prediction results of all subjects are averaged category-wise to obtain the mean output of the teacher model for all subjects under the video stimulus. For instance, the mean of all subjects' outputs for this video segment is [0.33, 0.65, 0.21, 0.36]. Based on this, the mean result is first scaled using a temperature parameter \(T\), which adjusts the smoothness of the soft label distribution. Finally, the temperature-scaled result is normalized through a softmax layer to obtain the final soft labels. These soft labels serve as the target for the student model, guiding it to progressively approximate the teacher model's prediction distribution during the training process.
## 3 Results and Discussion
### Adsorbent Characterization
#### 3.2.5 BET Analysis  
The surface areas (SAs) of ADAV and TTAV300 were measured, as shown in Figure 7. The obtained data showed that Langmuir SAs were 3.940 and 7.076 m\({}^{2}\)/g for ADAV and TTAV300, respectively. Besides, the single point total pore volumes were 0.011197 and 0.020461 cm\({}^{3}\)/g for ADAV and TTAV300 samples, respectively. Moreover, the single point average pore radii were found to be 98.6\({}^{\circ}\)A for ADAV and 68.4\({}^{\circ}\)A in the case of TTAV300. The obtained data indicated that the thermal treatment process of the AV samples did not have a significant impact on the surface area and the pore size of the prepared samples and, as a result, the adsorption efficiency of TTAV samples was not affected by thermal treatment [45; 46]. On the other hand, using the International Union of Pure and Applied Chemistry (IUPAC) definition for mesoporous and macroporous materials, the pore diameter patterns of both ADAV and TTAV300 showed two types of pores: mesoporous (less than 50 nm in diameter) and macrospores (greater than 50 nm in diameter). These types of pore systems serve to increase the surface area of the adsorbent and hence its adsorption capacity [16].  
Figure 6: Raman spectra of ADAV and TTAV300.
###### Abstract
The key innovation of the approach is that the RT model inversion scheme underpinning it has been designed explicitly for parameter retrieval from global observations of albedo, in a suitable form for ingestion into terrestrial vegetation models. As a result, the assumptions underlying the retrieval scheme are consistent from the perspective of both observations and the resulting parameters. Below, we present a brief discussion of definitions of LAI and fAPAR, including some caveats as to how they are interpreted in RT retrieval schemes. Following this, we outline the retrieval process (described in full elsewhere) and then provide details of the new GlobAlbedo-derived products. We then present a comparison between the resulting products and the MODIS LAI and fAPAR products at various scales, including at flux tower sites, across individual MODIS tiles (1200 \(\times\) 1200 km) and across the entire northern and southern hemisphere land surfaces. We stress at the outset that these comparisons are not intended to try and establish which set of products is \"right\"; the impossibility of actually measuring LAI and fAPAR directly at anything other than trivially small scales rules this out. Our aim is in fact the opposite: we suggest that these comparisons illustrate where consistency between retrieval schemes can be expected and where not; they also highlight how different assumptions made in the retrieval process can be mistakenly interpreted as differences in land surface properties. We emphasize the need for clarity regarding model assumptions and explicit calculation of the resulting uncertainty.
# Computational Fluid Dynamics Applied to
## 5 Results
### Categorization of Relevant Papers Exploiting the Overlapping Grids Approach  
The literature search has revealed 11 relevant articles using the OG approach. These are reported in Table 5. It is possible to notice that this approach has been exploited primarily in the study of the gears (10 papers out of 11) and no applications to bearings were found. This result was predictable because the OG approach allows simulating the topological variation of the mesh (as in engaging gears). However, in the bearings, the topology of the mesh does not change because there are always axis-symmetric elements that interact. This approach allows simulating a level of complexity of 5 for the modelling of a planetary gearbox but, in this case, no experimental validation has been performed [40].  
The OG approach have been exploited especially for simulating transmission gears in the aeroengine [110; 111; 112] and in automotive transmissions [38; 39; 113]. Moreover, a splash-lubricated gearbox was studied in [34] and jet-lubricated gearboxes in [36; 114]. A gerotor pump was modelled in [115].  
It is interesting to notice that OG is implemented by commercial software only. In particular, the commercial software STAR-CCM+ was used in [36; 39; 40; 114] and FLOW 3D was exploited  
Figure 4: Number of studies exploiting the rigid mesh motion and sliding meshes approaches shown based on the accuracy achieved and the complexity of the modelling.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Paper** & **Year** & **Software** & **System** & **Target** & **Validation** & **Accuracy** & **Complexity** \\ \hline
[110] & 2009 & Comm. & Gears & Both & Yes & 80\%–90\% & 4 \\
[113] & 2014 & Comm. & Gears & Both & Yes & 80\%–90\% & 4 \\
[111] & 2014 & Comm. & Gears & Both & Yes & 90\%–95\% & 4 \\
[39] & 2015 & Comm. & Gears & Both & No & N.A. & 3 \\
[112] & 2017 & Comm. & Gears & Both & Yes & 80\%–90\% & 4 \\
[36] & 2017 & Comm. & Gears & Fluxes & No & N.A. & 4 \\
[114] & 2018 & Comm. & Gears & Fluxes & Yes & Qualitat. & 4 \\
[40] & 2019 & Comm. & Gears & Both & No & N.A. & 5 \\
[37] & 2019 & Comm. & Gears & Both & Yes & \textless{}80\% & 4 \\
[38] & 2019 & Comm. & Gears & Both & Yes & 80\%–90\% & 4 \\
[115] & 2019 & Comm. & Pump & Both & Yes & 90\%–95\% & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification of the pertinent papers exploiting the overlapping grids approach.  
in [37; 111; 112]. In Figure 5, it is possible to see that most of the studies simulate a level of complexity of 4 achieving an accuracy of 80%-90%. In the studies emerged, an accuracy \(>\)95% is not observed.
## 5 Results and Discussion
### Long Term Analysis
Namely, as the PWV and the IWV depends on the height of the tropospheric column (Equation [7]), the values of PWV are inversely correlated with altitude (Figures 4 and 5) and emerge larger in summertime, coherently with the climate in the target region. Figure 5 shows the dependence of PWV on height above sea level for the analyzed stations. Based on the similarity of the ZTD on both GPS and DInSAR, [PERSON] et al. [44] used DInSAR data, with high spatial and temporal resolution, to model the local seasonal variations of ZTD in a region of high topographic relief, the Kilauea volcano (Hawaii). They successfully retrieved an altitude-dependent ZTD correction to be applied to the local monitoring GPS network time-series, where meteorological data were not available. For a better visualization of the variability of PWV over the year, a so-called violin plot is used (Figure 6). This representation, which consists of a combination of a classic box plot with a rotated KDE (kerneldensity estimation) on each side, is better suited to dealing with multimodal data and can therefore be used as a simple outlier-detection technique. The PWV mean annual cycle for all the NeVoCGPS stations is shown in Figure 6b. For the PWV mean values, December-February represent the lowest values for all stations and are generally less variable (Figure 6a,b). The highest values are reached during July and August. The increasing trend in PWV mean values from winter to summer is steeper than the decreasing ones from summer to winter. This type of analysis also shows no significant differences between the different stations in the network, with the exceptions of the topographical effect and altitude dependence (Figure 6c). This would suggest almost homogeneous climatic conditions in the analysed sectors of the GPS network. Figure 5: Regression of the average PWV for each station versus its altitude above sea level. Figure 6: Violin plots for PWV values: (**a**) daily values for PACA site used as an example, (**b**,**c**) monthly values for several NeVoCGPS sites. Each PWV value is plotted with a kernel density estimate (the violin shaped envelope), where the individual daily/monthly mean values (circles) are also plotted within each shape. The annual cycle (**a**,**b**) and the variability of PWV with height (**c**) are clearly visible; the white circle indicates the median, the thicker grey bars in the middle of the violin indicate the interquartile range (IQR), the lower/upper adjacent values (the grey lines stretched from the bar)—defined as first quartile \(-1.5\) IQR and third quartile \(+1.5\) IQR, respectively. Plots have been produced with the Matlab® code by [PERSON] and [PERSON] [45].
# Towards Automatic License Plate Detection
## 4 Results and Discussion
### Qualitative Analysis  
Although Figures 2 and 3 show some examples of license plate detection by the proposed approach on the PKU dataset, the points mentioned below highlight some important observations during our simulations:  
* The license plates were located accurately in the images with: (**a**) a normal environment having a single license plate and (**b**) slight sunshine at different day times, as is the case in the PKU-G1-G2 categories. A few of such images are shown in Figure 2. However, it also extracts irrelevant candidate regions (false positives), which are indicated by blue rectangles in the fifth row in Figure 2.
* Images in the PKU-G3 category are captured mostly at night, where the light conditions are very poor with low illuminations. The third row in Figure 2 reveals that the proposed technique handles these conditions effectively. Similarly, in the PKU-G4 category, approximately half of the images are captured at night, and in most images,only the bonnets and bumpers of the vehicles are visible. The fourth row in Figure 2 shows that the proposed LPD algorithm accurately works under the aforementioned conditions.
* The PKU-G5 category is the most challenging category in the dataset. Most of the images have huge variations in the illuminations with multiple vehicles and complex backgrounds, as shown in Figure 3. In this category, we observe that our proposed method generates additional candidate regions mostly around the red text in the PKU database and near the region of high beam intensity. However, Figure 3 also reveals that our proposed algorithm is able to accurately locate the exact positions of the license plates. For complex images/video frames, a suitable complexity reduction method might be needed before license plate localization [9; 10].  
Figure 5: (**a**) Variation in parameters and (**b**) Computational complexity comparison.