esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Damage Detection Framework
### Autoencoder Description  
The framework is based upon the autoencoder, an auto-associative neural network that is designed to reconstruct its input as the output. The autoencoder is trained using only healthy data and'memorizes' them to capture and retain the effects of the environmental and operating conditions (EOCs) in its weights and biases. Once a signal under a damaged structure is acquired and fed into the autoencoder, the error between the reconstruction and the input is higher than those from healthy signals and hence the anomalous case is differentiated from the nominal signal.  
The autoencoder is composed of five sequential parts: the input, the encoder, the bottleneck, the decoder, and the output (Figure 8). The layers of the encoder and decoder are mirrored via the bottleneck, hence resembling an hourglass. The data are passed through the encoder, which has a descending number of neurons, and reach the bottleneck layers, where the data are compressed in a low-dimensional latent space. Subsequently, the decoder uses the data in the latent space to reconstruct the input of the model as the output. The number of layers in the encoder and decoder is selected to be two based on our previous study [15].  
Neural networks, including autoencoders, rely on activation and loss functions. Activation functions modify neuron outputs using nonlinear methods to capture complex data relationships. Training an autoencoder minimizes the loss function, which measures the difference between the actual output and the input. Common loss functions are the mean absolute error and mean squared error, with lower values indicating better training  
Figure 8: Schematic of the autoencoder structure.  
effectiveness. The number of neurons, the activation function, and the learning rate are optimized for this study using the Bayesian optimizer and are presented in detail in Table 3. The autoencoder uses the Adam training algorithm and the loss function is the mean absolute error (MAE).
## 2 Policy Goals of Urban Transition in the Oresund Region
### Local Policy Goals and Challenges of Urban Transition
#### 2.2.2 Goals of Urban Transition in Copenhagen City  
The city council of Copenhagen passed an ambitious \"Climate plan\" in 2009 setting a target for a 20% CO\({}_{2}\)-reduction by 2015, a goal achieved in 2011. A new climate plan KBH 2025 reached out for CO\({}_{2}\) neutrality in 2025 [3].  
The Copenhagen climate plan KBH 2025 (ibid. p. 10) [3] defined six main areas of intervention for goal achievement, including:  
* Conversion from fossil fuels to renewable energy (75% of CO\({}_{2}\) reduction);
* Climate friendly transport system (10% of CO\({}_{2}\) reduction);
* Construction and retrofit of the building stock to reduce energy consumption and increase indoor climate (10% of CO\({}_{2}\) reduction);
* Adaptation of behavior of citizens and companies (4% of CO\({}_{2}\) reduction);- Development of urban structures that underpin climate adaptation (1% of CO\({}_{2}\) reduction).  
Moreover, as a large business entity Copenhagen municipality itself represents 5% of total heating and electricity consumption, so refurbishing of municipal floor space counts both through direct savings and by setting a good example of applying higher targets.  
Beyond the overarching goal of CO\({}_{2}\) neutrality, the climate plan targets other subgoals and envisions spin-off effects in terms of better urban quality, public health and of quality of life (ibid., p. 9) [3]. CO\({}_{2}\) reductions (75%) are to be achieved through converting energy production to renewable energy, by lowering energy consumption and through technical solutions such as e.g., heating pumps. The expected contribution to the climate plan of energy savings from construction and retrofit of the building stock is relatively small, 10% (ibid., p. 15) [3].  
Considering that the largest share of CO\({}_{2}\) emission in Copenhagen stems from electricity, Table 3, it can be seen that the goals of urban transition have a wider scope than merely reducing energy consumption for heating. Irrespectively, heating is often at the center of attention in building renovation projects, in part since energy cost savings serve as a motivational factor.  
According to the Copenhagen climate strategy investments in a transition to a CO\({}_{2}\) neutrality are assumed to be a healthy business strategy with expected returns on investments, e.g., on the presumption that investments will pay back through preventing damages and through market spin-offs associated with knowhow that can be exported [3] (pp. 4-5), although it is unclear from the strategy if or how benefits and costs are aligned among market participants, or what is the aggregation level of economic analysis in this respect. A problem of principal-agent arises, if societal goals are to be achieved at private costs.  
It is concluded that justification of large investments in refurbishing of the building stock is sought in accordance with the urban transition agenda both in Sweden and Denmark, although the relationship between CO\({}_{2}\) and energy consumption is much stronger in Denmark than in Sweden.
## 1 Introduction
[27] used non-linear dynamic inversion to design the outer-loop control law and L1 adaptive control method to design the inner-loop control law to achieve the control of thrust vectoring takeoff and landing aircraft. [PERSON] [28] used the L1 adaptive control method to study the position control problem during the hovering phase of a tail-mounted vertical takeoff and landing aircraft. [PERSON] [29] proposed a longitudinal control law design method for unmanned aerial vehicle landing and takeoff based on L1 adaptive control. It can be seen that current research is mainly focused on the control allocation, control methods, and control strategies for conventional vertical takeoff and landing aircraft, while the control research on the special takeoff and landing mode of distributed-power unmanned aerial vehicles, such as jumping and descending, is not sufficient. Distributed-power takeoff and landing/vertical takeoff and landing unmanned aerial vehicles have multiple flight stages, including takeoff and landing, vertical takeoff and landing, transition flight, and cruise forward flight. The flight speed, attitude angle, aircraft dynamics characteristics, and control effects of control components undergo significant changes in each flight stage, which poses great challenges to the control scheme and controller design of distributed-power short takeoff and landing/vertical takeoff and landing unmanned aerial vehicles. Therefore, in response to the problems of strong coupling between distributed-power and wing surfaces, significant aerodynamic effects of propeller slipstreams, and control allocation under redundant distributed-power control mechanisms in distributed-power fixed-wing unmanned aerial vehicles, this paper conducted research on unmanned aerial vehicle dynamics modeling methods, flight dynamics characteristic analysis, and special takeoff and landing control strategy design. Firstly, in response to the problem of poor accuracy of aerodynamic models under the influence of dynamic slip flow, a full aircraft dynamic model considering the influence of distributed-propeller slip flow was established through theoretical derivation combined with flight experiment correction. Secondly, in response to the special jumping and descending flight process of unmanned aerial vehicles, the jumping and descending flight schemes were determined through performance analysis during the climbing phase and performance research during the descending phase. Finally, based on the simulation analysis of the longitudinal and lateral heading control efficiency of the drone, the longitudinal, lateral, and heading control strategies for the drone were determined, and corresponding takeoff and landing control laws were designed. Through full digital simulation and actual flight tests, the unmanned aerial vehicle dynamics model,characteristics, and takeoff and landing controller were verified to achieve attitude stability and flight safety during the takeoff and landing process of distributed-power aircraft.
## 3 Results
### Canonical Discriminant Analysis  
Table 5 shows the canonical loadings of the 10 sensors on the first two canonical variates (Can1 and Can2), the relative percentage of variance, and the canonical correlations between the original variables and the canonical variates for each time.  
The first canonical variate relative to T1 and T7, respectively, accounted for 85% and 72% of the variance, whereas for the other times, the magnitude of this value is greater than 95%. At each time, the canonical correlations were very high and significant (\(p<0.0001\)), being greater than 0.95 for the first canonical variate and with a range from 0.72 to 0.97 for the second. At T1 and T8, the first canonical functions were dominated by large positive loadings from sensors for aromatic compounds (2-W55, 3-W3C and 1-W1C) and by negative loadings from 9-W2W sensors reactive to nitrogen oxides and ozone. On the contrary, this latter sensor showed positive loadings at T6 and T7. Large positive loadings were found at T2, T5, and T6 for sensor reactive to sulfur compounds and terpen (10-w3S) and at T3 and T4 for sensor selective to methane (6_W1S).  
Figure 2 shows the scatter plots of the canonical scores of the samples for the two canonical variates at each time.  
At T1, the honey samples grouped on the basis of the storage temperature but were not clearly separated as in subsequent times. From T2 to T7, the group stored at ST35 was separated from the other two based on the first canonical variable, and at the end of the storage period, it was hardly distinguishable from the group at ST25. The second canonical variable is instead the discriminating factor for the groups at ST15 and ST25 that appear clearly separated starting from T4.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Time** & **ST15** & **ST25** & **ST35** \\ \hline T0 & 12.1 & 12.1 & 12.1 \\ T1 & 11.5 & 11.8 & 8.4 \\ T2 & 10.1 & 9.9 & 6.6 \\ T4 & 13.2 & 11.3 & 5.1 \\ T8 & 11.8 & 6 & 3.7 \\ \hline \end{tabular}
\end{table}
Table 4: Diastase activity (DN) of the pooled honey samples at four times for each storage temperature (ST).
###### Abstract
It plays a critical role in determining the cloud cover, cloud layers [44], cloud classification [4; 19; 45], and cloud thermodynamic phase determination [46]. The broadband radiative impact of clouds is largely determined by their height [39]. In addition, as a critical parameter, CTH products are assimilated into numerical weather prediction (NWP) models [47; 48]. First, AGRI and MODIS instruments and their respective level-2 CM and CTH retrieval algorithms are presented.
## 7 Sorbents Used in the TFME Technique
### Carbon Nanomaterials  
Carbon nanomaterials (CNMs) span a group of various allotropic carbon structures, which include, for example, fullerenes, carbon tubes, and graphene. CNMs are characterized by unique physical properties. They have a very high specific surface area, very high tensile, and high-temperature mechanical strength, as well as unique electrical and thermal conductivity. They also exhibit high affinity to many organic compounds. The properties enable extensive applications of carbon nanostructures in many fields of science and technology, including as adsorption materials [77].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sorption Phase** & **Preparation** & **Analytic** & **Matrix** & **Detection** & **Reference** \\ \hline DUT-52/PVDF & bar coating & PCPs & cosmetics & \begin{tabular}{c} UHPLC- \\ UV/VIS \\ \end{tabular} & [70] \\ \hline Co-MOF-74/polyfam & electrospinning coating & \
\begin{tabular}{c} anti-cancer \\ drugs \\ \end{tabular} &
\begin{tabular}{c} water \\ sewage, \\ plasma \\ \end{tabular} & HPLC-UV & [71] \\ \hline MIL-53(Al)/PVDF & & & & & \\ MIL-53(Fe)/PVDF & & & & \\ MIL-100(Fe)/PVDF & bar coating & estrogens & urine & HPLC-FLD & [72] \\ MIL-101(Cr)/PVDF & & & & & \\ UiO-66(Zr)/PVDF & & & & & \\ \hline ZIF-8/LDH/GO/PVDF & bar coating & caffeine & urine & HPLC-UV & [73] \\ \hline amino-Zr-MOF/PAN & electrospinning coating & pesticides & water & CD-IMS & [74] \\ \hline MIL-100(Fe)/PS/cellulose & dip coating & PCPs & pool water, cosmetics & HPLC-PDA & [75] \\ \hline MOF-199/PS & electrospinning coating & aldehydes & urine & HPLC-VWD & [76] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Examples of the use of MOFs in TF-SPME.
# How to Make Augmented Reality a Tool for Railway Maintenance Operations: Operator 4.0 Perspective
## 2 Theoretical Background
### AR Capabilities  
Nowadays, the operator knows the current state of a component by consulting a paper or digital work description containing the right configuration and information. Instead of forcing the worker to waste time consulting paperwork and interpreting the information provided into the work instructions, the proposed technology projects directly on the component giving accurate maintenance instructions and relevant information.  
AR is considered key for improving the transfer of information from the digital to the physical world of the smart operator [28]. Moreover, AR has incorporating capabilities to new human-machine interfaces to maintain IT applications and assets. It displays real-time feedback about the smart maintenance processes and assets to the smart operator to improve decision making. This technology supports the smart operator in real-time during manual maintenance procedures by becoming a digital assistance system [27]. Hereby, the operator reduces the time used for reading printed work instructions and documents, looking into computer screens or tablets, and following strict procedures. Operators no longer need to run the risk of using old or outdated paper documents and instructions. Digital information systems provide operators with the latest updated information. Other advantages the tool provides for the operator are its ability to offer intuitive information and combining operator intelligence and flexibility with error-proofing systems to increase the efficiency of manual steps [29]. AR offers a powerful tool for supplying the operator with contextualized and customized information [30]. Altogether, AR can improve the quality, reliability, maintenance time, and reduce the failure rate.  
Operator 4.0 will have access to the data coming from the train components and sensors in the maintenance facility. Besides this information source, knowledge can be gathered from other operators in the maintenance facility or even outside the facility from professional (social) networks. All information needs to be delivered to the operator and adapted, contextualized, and transformed to make it understandable such that decisions can be made resolutely and thoroughly.
# Image Quality Assessment of Augmented Reality Glasses as Medical Display Devices (HoloLens 2)
## 4 Discussion
### Evaluation of Color Uniformity  
The color uniformity evaluation revealed that HoloLens 2 inconsistently displayed colors across different display areas. In 25 out of 30 assessments, HoloLens 2 surpassed the maximum permissible deviation for primary display devices, demonstrating its current unsuitability for medical applications dependent on color-coded visualization.  
[PERSON] et al. [27] evaluated the color uniformity of HoloLens 2 using nine distinct colors and found that color homogeneity was generally maintained under optimal conditions. Nevertheless, color shifts occurred at varying viewing angles. Aligning with our results, [PERSON] et al. [27] noted that blue remained the most stable color.  
Similarly, [PERSON] et al. [28] and [PERSON] et al. [25] detected luminance inhomogeneities across multiple display points. [PERSON] et al. [28] applied a software-based flat field correction technique, which enhanced homogeneity but diminished the overall screen luminance.
# Virtual Reality Collision Detection Based on Improved Ant Colony Algorithm
## 4 Flow of the Algorithm  
In the preliminary detection stage, the spherical and OBB bounding boxes are selected, the binary tree is selected as the tree structure and the Bounding Volume Hierarchy is constructed in a top-down manner. The depth-first traversal method is used to traverse the tree's structure, and the triangular primitive pairs without a collision are quickly removed. In the accurate detection stage, the bounding box with a collision after preliminary detection is processed by random sampling to form a search space composed of feature pairs. The optimized ant colony algorithm is introduced to solve the optimization problem of the search space, as shown in Figure 2. The specific steps are as follows:  
1. Establish a hierarchical bounding box for the target part to be detected and traverse the hierarchical bounding box to see whether the leaf nodes intersect.
2. If there is a collision between the leaf nodes, feature sampling is performed on the primitive patches between the collision nodes to form a two-dimensional discrete search space.
3. Initialize the ant population \(M\), the pheromone intensity, visibility, population location and number of iterations of the algorithm.
4. Calculate the optimal solution of each ant and the current global optimal solution and select the optimal solution after a comparison.
## 4 Case Study
### Results and Discussion
Moreover, by comparing different combinations in (j), it can also be deduced that with the increase in the number of combinations, the growth rate of the effectiveness index of the best SB combination decreases. It is worth noting that when the combination includes preventive SBs Y4, Y6, and Y7, as well as protective SBs F1 and F4, \(\textit{Eff}_{\text{SB}}\) increases significantly. If and only if these SBs are included, the \(\textit{Eff}_{\text{SB}}\) reaches 34.3%, accounting for 91% of the highest \(\textit{Eff}_{\text{SB}}\). This indicates that these five SBs play an important role in the safe operation of the storage tank. These findings provide a reference for decision-makers. For the sake of safety, when all SBs cannot be realized in actual production, at least these five SBs should be included in the system, as they are essential. Figure 10: Effectiveness indexes of different safety barriers. Figure 11: The effectiveness index of an arbitrary combination of the eleven safety barriers of the natural gas storage tank. The combination mode is denoted as \(C_{11}^{n}\), where \(n\) represents the number of SBs included. (j) Box-plot of the effectiveness index for nine combination modes. Through the above analysis, it is revealed that the storage tank failure risk can be greatly controlled and mitigated in the two stages of system maintenance detection and operation. Therefore, regular inspection and maintenance plans can be formulated to ensure the normal operation and effectiveness of the storage tank system. On the one hand, in the routine maintenance of the storage tank, the monitoring of potential risks and accidents should be strengthened, which needs to focus on checking and maintaining the deformation of the storage tank, safety electrical equipment, and the integrity of the alarm system equipment. On the other hand, it is necessary to formulate a perfect fire emergency mechanism, conduct regular simulation exercises, and strengthen the emergency response ability of fire alarms and fire suppression systems. Combining the analysis results with the design, maintenance, and operational strategies of the system can effectively improve the safety of tank systems, reduce potential accident risks, and protect people and the environment.
## 3 Biofilms  
### Formation Steps of Biofilms  
The process of biofilm formation can be summarized in four steps: initial attachment, microcolony formation, maturation, and dispersion (Figure 3). The first step is the process where planktonic bacteria get close to the material surface. This is caused by a combination of physical forces (e.g., attractive van der Waals and repulsive electrostatic forces) and chemical forces (e.g., the cohesion between microbial cells) [6]. The appendages (e.g., fimbiae, pili, and flagella) strengthen the attachment between the bacterial cell and the surface once the bacteria get in touch with the material surface [11]. Moreover, environmental factors, such as temperature and pH, influence the bacterial adhesion. The hydrophobicity of the material surface also reduces certain repulsion forces between the bacteria and material [6]. Di Ciccio et al. alluded that bacteria more likely attach to hydrophobic surfaces (polystyrene) than hydrophilic surfaces (stainless steel) [18]. It is notable that the bacterial attachment is still reversible, which makes it relevant for the prevention of biofilm growth.  
Figure 2: This graph shows the relation between time and the growth of bacteria in a laboratory environment [17].  
The process of multiplication and division of the bacteria starts following the attachment stage. This leads to the EPS originating as a layer that protects the bacteria and this stage is also called irreversible attachment [19; 20]. The complex structure of multiple different microcolonies that formed on the surface renders them more resistant to antibiotics and disinfectants. The amount of antibiotic that is needed can range between 100-1000 times more than free-swimming planktonic bacteria [13]. The reciprocal actions between the microcolonies affect the metabolic products [21].  
In this stage, certain molecules, the so-called autoinducers, are used as signaling molecules to communicate from cell to cell [22]. The autoinducers make use of a technique called quorum sensing. This communication mechanism between the microbial cells makes the EPS matrix able to adapt to multiple situations. The protection of the complex EPS architecture provides the bacteria the freedom to grow into a mushroom-like structure [23]. Channels are formed between the different forming colonies within the three-dimensional structure of EPS. The channels function as a circulatory water system that allows for nutrients to venture deep into the biofilm and prevent the bacterial growth termination [24]. In the last stage, an oversaturation of microbial cells is observed. Bacteria will disperse from the biofilm into the liquid phase once the EPS temporarily removes the protective layer at the top of the mushroom-like structure. The sessile cells are converted into motile forms, resulting in a dispersion of the microbial cells.
# IoT-Portrait: Automatically Identifying IoT Devices via Transformer with Incremental Learning
## 3 Analysis of IoT Traffic
### Specific Ports  
In addition to IP and MAC addresses, communication ports can also be used as another criterion. IoT devices usually use specific ports to communicate with cloud servers. In contrast, non-IoT devices may use a much more comprehensive range of services with thousands of infrequent ports [22], so the port number can also be used to identify IoT devices accordingly. For commonly used ports, we can also infer the services they might use. For instance, SmartThings Hub primarily connects to the server's TCP port 433, which is used by default for HTTPS service. The target port of NetGear Station is mainly UDP port 10001, which takes more than 98% of all packets. The rest of the packets go to TCP port 80, indicating the HTTP service. Over 95% of the packets of WoShida Cam go to the server's TCP port 44730.