esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### Weikendorf Bridge  
The framework presented in the previous chapters is implemented in a case study of the Weikendorf railway bridge, which belongs to the Austrian Federal Railway (OBB). The bridge is a single-span plate girder bridge made of reinforced concrete, and was built in 1967 on the route Ganserndorf-Marchegg in the state of Lower Austria. In 2014, the bridge was repaired, as can be seen in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Condition State (CS)** & **Condition and Restrictions** & **Action** \\ \hline Class 1 & CS1 & Very good condition, no restrictions & No actions \\ Class 2 & CS2 & Good state of preservation, no restrictions & No actions \\ Class 3 & CS3 & Poor state of preservation, no restrictions & Subjected to repair \\ Class 4 & CS4 & Very poor state of preservation, no restrictions & Subjected to repair—sometimes \\ Class 5 & CS5 & Very poor state of preservation, immediate restrictions & Subjected to renewal \\ \hline \hline \end{tabular}
\end{table}
Table 10: Condition rating system used by the OBB, with assigned condition, restriction, and action for each class [5].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & Reinforced concrete & Stone and brick & Steel \\ \hline
**Percentage of Bridges** & 70\% & 17\% & 13\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Bridge distribution by material in the OBB bridge stock in 2016 [46].  
Figure 2: Case study—the Weikendorf Bridge, side view after the repair in 2014.  
From screening design documents, it can be seen that the bridge was erected using concrete classes B160, B225, and B400, according to the norm ONORM B 3302 [57]. The cross-section of the bridge is depicted in Figure 3, where a concrete class is associated with each element, as given in design documents.  
In Table 11, for the concrete classes envisaged in the design documents, the prescribed w/c ranges are presented according to [58]. Additionally, currently used concrete classes are assigned according the standard ONORM B 4710-1 [59].  
From the elements of the bridge presented in Table 11, only the wings are exposed to rain, and hence can be considered in the class XC4. Due to the fact that durations of propagation periods \(t_{prop}\), presented in Table 5, were given only for exposure class XC4, only the wings of the bridge could be analyzed.
## 2 Related Works
### UAV Applications in Smart Farming  
Drones are rapidly evolving in the field of agriculture and can perform numerous tasks, such as weed mapping [14], soil and crop status monitoring [15], pesticide spraying [16], diagnosis of insect pests [17], and artificial pollination [18].  
In [19; 20], an autonomous drone was developed for spraying pesticide and fertilizer on farms. Utilizing such a technique can enhance spraying efficiency and reduce pesticide usage and the risk of worker poisoning. The work in [21; 22] used a drone for mapping. The maps can provide useful information such as in the monitoring of farm areas, soil conditions, and crop status. The authors in [23; 24] developed methods for monitoring crop conditions by analyzing high-resolution crop data.  
In addition, the utilization of drones has been addressed in the literature [25; 26; 27; 28] as an enabler for providing a reliable and cost-effective wireless communications solution for smart farming. By exploiting features such as autonomy, mobility, and adjustable altitude, drones can enhance a wireless network's capacity, reliability, and energy efficiency [29].  
The work in [30] investigated the feasibility of utilizing small commercial drones for indoor livestock monitoring. As a GPS signal is unavailable in indoor scenarios, the performance of visual simultaneous localization and mapping (VSLAM) algorithms was examined. The results showed that by equipping drones with the aerial VSLAM algorithm, indoor livestock monitoring can be feasible. The authors in [31] designed a system for sheep monitoring and tracking based on image processing techniques. The study demonstrated the hardware and software configuration for developing a drone and the measurement results showed that the system can reliably (with an accuracy of 89-97%) detect sheep on a farm. However, the drawback of the proposed system was the high power consumption of the utilized onboard companion computer to run the image processing algorithms.  
For smart farming, the type of drone can be chosen based on factors such as the kind of environment, kind of application, required quality of service (QoS), flying altitude, movement speed, and maneuverability. For example, low-altitude drones are known as an appropriate and cost-effective approach for data collection from sensors in remote areas [32]. Compared to fixed-wing drones, rotary-wings drones weigh less, have more maneuverability, and can stay stationary over a given area. In contrast, fixed-wings drones fly faster, are able to carry more payload, and are more energy-efficient than the rotary-wing drones but need to move forward to remain airborne [5].
# Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics
## 3 Materials and Methods
### Attacker Model  
To develop a jamming attacker model, certain assumptions regarding the network configuration of the robots had to be established beforehand. We assumed that the three Clearpath Husky robots were equipped with wireless radios, allowing them to become mobile nodes. Each node would be able to transmit and receive data packets, forming a generic autonomous vehicular ad-hoc network, similar to what is described in an IEEE 802.11 bd network. In addition, each node would be able to leave and rejoin the network without causing the ad-hoc network to fail. Due to the proximity of the robots, we simplified the overall model by assuming that no signal loss or degradation occurs from separation distances between the robots.  
The jamming attacker model developed for this effort allows for a choice between simulating a constant jammer and a random jammer. The constant jammer type was chosen due to it being the worst-case jamming attack [12], while the random jammer type allows us to test a greater range of unique jamming scenarios in which follower vehicles end up being partially in the jamming zone. The other types of jammers described are focused on disguising jamming attacks. Since detecting disguised jamming attacks was not a focus of this effort, we focused on constant and random attacker models.  
In developing the attacker model, a few key assumptions were made regarding its configuration and capabilities. Firstly, the jammer would be a single node, acting as a single point of jamming in the environment. The jammer was assumed to not be restricted by its power source, meaning it would run continuously for indefinite periods of time without any issue. In addition, it would be frequency unbounded, meaning that the jammer would successfully jam on all frequencies the network communicated on. This maximized the impact of the jamming attacks, making it so we considered packets to be dropped completely when going from the sender to receiver. Implementing jamming in this fashion allowed us to simulate an upper bound on successful denial-of-service attacks [7]. To  
Figure 5: Convoy of Clearpath Husky robots in the Gazebo robotics simulator with each robot’s sensor visualization in ROS tools.  
simplify the jamming model, we assumed that all areas of the jamming zone were equally strong, with no wireless communication being possible anywhere within the jammed area. The jammer developed for this effort is configurable with the following inputs:  
* lat, long--latitudinal and longitudinal coordinates for the center of the jammer;
* \(\tau_{\text{J}}\)--radius of the jamming area, centered at lat, long;
* type of jammer;
* \(\tau_{\text{J}}\)--jamming time for random jammer;
* ts--sleep time for random jammer.  
The latitudinal and longitudinal coordinates for the jammers center and the jamming radius create a cylindrical jamming zone. Figure 6 illustrates the jamming attack model targeted at a robotic convoy.
## 3 Experimental Setup and Results
### Experimental Setup  
In this study, the experiment equipment mainly includes char making, steam generation, char-steam gasification, and a gas analyzer. First, coal is dried in a burner at 101 \({}^{\circ}\)C for 12 h after being crushed into powder under 2 mm. Then it is placed in a vacuum tube for char and coke oven gas (COG) production. The infrared-heated reaction tube has a maximum heating rate of 50 \({}^{\circ}\)C/s and can heat the coal to 1000 \({}^{\circ}\)C in 20 s. COG gas is collected by bags after processing water washing, cooling, and filter. The prepared char will be placed in a fixed bed reactor to react with steam that is generated in a steam generator. Gas from char-steam reactor is detected through an online gas analyzer (GA) and collected by bags. Agilent 490 pro chromatogram is used to analyze the final components of all collected gases. The experiment setup is shown in Figure 2.
## 2 Materials and Methods
### Electrical Characterization  
To investigate the influence of xGNPs on the electrical properties of the composite samples, which consisted of 30 \(\times\) 30 \(\times\) 3 mm\({}^{3}\) square plates, their electrical resistance was measured using the DC series 2840 resistance meter supplied by B&K Precision (22820 Savi Ranch Pkwy, Yorba Linda, CA, USA) with a 4-point probe configuration Kelvin test lead. To avoid any unwanted effects due to the presence of porosity, the measurements were carried out while the samples underwent compressive loading to promote the closing of the pores. With this aim, the tests were performed using the MTS Insight 5 Universal Electromechanical System supplied by MTS S.r.l. (Strada Pianezza 289, Torino, Italy) equipped with a load cell of 5 kN, as schematized in Figure 2. To achieve a proper identification of the effect of the applied load on the electrical resistance, 10 different loads, ranging from 0.1 kN to 1 kN, were investigated.  
The resulting electrical conductivity (\(\sigma\)) of the samples was then calculated as the inverse of the resistivity (\(\rho\)), according to Equation (3):  
\[\sigma=\frac{1}{\rho}=\frac{l}{RA}, \tag{3}\]  
where \(l\) is the thickness of the sample, \(A\) is its cross-section, and \(R\) is the measured resistance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature/Parameter** & **Type/Value** \\ \hline Laser source [-] & Diode \\ Laser wavelength [nm] & 808 \\ Laser spot diameter [\(\upmu\)m] & 400 \\ Laser nominal power [W] & 5 \\ Laser scanning speed [mm/s] & 80 \\ Maximum volume [mm\({}^{3}\)] & \(110\times 160\times 230\) \\ Layer thickness [\(\upmu\)m] & 125 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main features of the laser sintering machine and standard process parameters for bulk PA12 sample fabrication.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 3 Proposed Protocol
### Cluster Formation Phase
#### 3.2.4 Node Leaving a Cluster  
The OPRP offers a planned method for member nodes to leave a cluster. This results from a member node changing its path or accelerating or decelerating. The nodes most likely to leave a cluster are edge nodes, which are found close to their boundaries. Even when the node is not an edge node, it transforms into one shortly before leaving. Every member node in OPRP maintains a threshold, \(\epsilon\), to keep itself connected to a specific cluster. When a node wants to leave its cluster, this threshold is evaluated concerning an immediate neighbor node. Once this threshold is reached, the member node communicates a leaving message to its CH, indicating that it is willing to leave the cluster. The CH announces its departure and deletes any entries related to that node from its list. OPRP suggests updating \(\epsilon\) as follows for a node that wants to leave its cluster:  
\[\epsilon=\mu\times\Delta, \tag{6}\]  
where \(\Delta\) represents a fading component and \(\mu\) is the node's transmission range that contributes to the definition of \(\epsilon\) for a node in terms of \(\mu\). As soon as a leaving node reaches the fading area, also known as \(\mu-\epsilon\), which is located around the boundaries of a given node's communication range, the leaving node begins to leave the associated cluster. Depending on the circumstances, the departing node may join another cluster or form its own cluster.  
Algorithm 4 offers instructions for removing a node from a specific cluster, utilizing our improved k-medoids algorithm. If the departing node is a CH, it specifies the CH re-election criteria. In that algorithm, Nr stands for the randomly chosen node, which is chosen as a temporary CH upon an existing CH's departure from its cluster. This method takes the inputs K and N, and its output is a notification of Ni's departure. The algorithm's complexity is on average \(\Theta(\text{n})\). The algorithm's output is the Ni node leaving a notification each time.  
```
Input:\(K,N\) Output: Learning notification of \(N_{i}\)
1. Begin:
2. repeat:
3. if\(K\
eq\varphi\)then
4. if\(\eta(N_{i},N_{i+1})>\varepsilon\) or \(\eta(N_{i},N_{i-1})>\varepsilon\)then
5. if\(N_{i}\subseteq CH_{i}\)then
6. CH\({}_{i}\gets N_{i}\)
7. perform CH selection
8. else
```  
**Algorithm 4**Node Leaving.
# The POSEIDON Ocean Observing System: Technological Development and Challenges
## 4 System Efficiency
### E1m3a  
The subsurface CT sensors attached in the buoy hull with brackets in 1 m depth suffer from failures and biofouling indicated by the higher percentage of NON-OPERATION and QC-rejected data caused by the high energetic and productive surface layer (Figure 13). The sensor is powered and operated by the buoy control unit, in contrast with the sensors in the remaining depths that operate autonomously, so in case of failure of the buoy, there is no internal power unit and data logger to back up the sensor operation. The fouling that grows not only on the sensor body, but also in the submerged buoy hull and equipment,  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Station Name** & **Date of the Incident** & **Incident** & **Impact** \\ \hline HCB & Unknown & Unidentified ship collides with the station. & The station meteo mast and sensors damaged \\ ATHOS & 29 September 2018 & Fishery activities close to the station & Mooring line failure, emergency recovery \\ E1M3A & 14 May 2019 & Removal of surface equipment from the station & Recovered by the coast guard, the equipment included an active GPS tracker \\ Saronikos & 13 December 2019 & Fishery trawling activities close to the station & Total loss of the station \\  & & Failure of the mooring line caused by fishing equipment (hooks and fishing lines) attached to the mooring line & Mooring line failure, emergency recovery \\ \hline \hline \end{tabular}
\end{table}
Table 5: Incidents of vandalism in the POSEDION buoys.  
affects more drastically the salinity measurements because it alternates the dimension of the conductivity cell, the sensing element of CT sensor that is used to produce salinity data. The temperature measurements are less affected by fouling. The deeper CT sensors have less QC-approved data compared to the first 100 m because of mooring line/inductive cable failures in the deeper layers. The failures caused the loss of the RT transmission initially and eventually the total loss of instrumentation and data stored locally in the sensor loggers.
## 9 Conclusions and Future Work
Furthermore, while low-complexity AMiDS models can benefit from imbalanced learning, the sampling of imbalanced data using oversampling methods can result in slow model training compared to that produced with hybrid- and ensemble-based methods due to the large amount of data generated to increase the distribution of the minority class. However, the models did not incur a high training time in this study. MioT and CioT devices generally have low energy and storage requirements. Moreover, they operate in hostile or semi-hostile environments where anomalous events are usually more frequent than in regular IoT environments. Therefore, it is essential to consider imbalanced learning methods that are not memory or computationally intense. Further, characterizing the MioT and CioT as environments and systems operating under hostile events dictates a critical need to train low-complexity AMiDS models using passive and active adversarial learning. In the future, we plan to expand the pool of generative methods to apply methods such as the Wasserstein GAN (WGAN) and examine their impacts on the performance of the AMiDS models. Furthermore, we will explore the use of online or stream ML learning versus batch or static ML learning to improve the AMiDS models' predictability in the case of new or unseen anomalous examples. In doing so, we aim to select the most suitable imbalanced learning method to build highly predictive AMiDS models to address the rare prediction case. Then, we will compare the resulting models with state-of-the-art AMiDS models. In addition, we will consider the mapping of MioT and CioT data, which, unlike image data, are non-continuous, containing both categorical and numeric data for adversarial learning. Finally, this study considered the data imbalance problem in light of the rare event case to examine the impact of imbalanced learning on the performance of low-complexity AMiDS models to detect intrusions in CioT systems and MioT environments. Conceptualization, G.A. and D.B.R. ; methodology, G.A. and D.B.R. ; investigation, G.A., D.B.R. and A.R. ; resources, D.B.R. and A.R. ; writing--original draft preparation, G.A. ; writing--review and editing, G.A., D.B.R. and A.R. ; supervision, D.B.R. ; project administration, D.B.R. and A.R. ; funding acquisition, D.B.R. All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the DoD Center of Excellence in AI and Machine Learning (CoE-AIML) at Howard University under Contract Number W911 NF-20-2-0277 with the U.S. Army Research Laboratory, the Microsoft Research and the U.S. National Science Foundation under grant number 1828811 and CNS/SaTC 2039583 and Microsoft Research Gift Funds. Any opinions, findings, conclusions, or recommendations expressed in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the funding agencies.
## 4 Blockchain-Based Fine Dust Management System Model for Construction Sites
### System Architecture  
Figure 2 is the proposed system, a blockchain network that uses the \"Hyperledger Fabric platform\". It is possible to create and manage each blockchain by allocating one construction site to one blockchain of the channel through the multi-channel function. In addition, by utilizing the MSP (membership service provider) and PKI (public key infrastructure), network participants can use application functions.  
Figure 1: Workflow of the proposed fine dust management system.  
The JSON format value generated using the IoT installed at the construction site is transmitted through wireless transmission protocols, such as Wi-Fi and Bluetooth, and it is stored in a distributed ledger. Because the photos uploaded by the contractor are large-capacity information, converting the original photos into digital data and using them on the blockchain is challenging. The HMac (hash-based message authentication code algorithm) [36] mechanism was applied to solve this problem. The basic operation principle is similar to that of the HMac algorithm; however, the information sent and received is a picture. Hash values, which are low-capacity data corresponding to photos, are extracted using the SHA-256 (Mac function) and uploaded to the blockchain. The recipient can check the photo's integrity by regenerating the original photo's hash value and comparing it with the hash value of the photo uploaded to the blockchain in order to determine the similarity between the two hash values. As a contractor may upload pre-taken or inappropriate photos, the photos are taken and uploaded simultaneously in connection with the camera API.  
Decentralized applications (DApps) running on the blockchain are divided into contractors, inspection agencies, and civic groups in terms of their roles. A contractor receives a notification of corrective action and uploads a photo using a DApp. Inspection agencies and civic groups can view fine dust management at construction sites through the DApp. In addition, inspection agencies have the authority to manage the setting of fine dust concentration regulations for construction sites.
# The Ability to Control VOC Emissions from Multilayer Building Materials
###### Abstract  
The work aimed to investigate which parameters of the electrically powered radiant floor heating system are connected with the intensity of VOC total emissions and emissions from individual layers, which can be effectively changed and controlled to obtain energy savings in the ventilation process. For this purpose, experimental studies of VOC emissions from specially designed LRFHS samples (Laboratory Radiant Floor Heating System) were carried out, along with simulations of real thermal conditions of samples of layered systems containing separate heaters and various materials layers. The TD-GC-MS chromatography was used to assess the trends of VOCs concentration changes in 480 h in a test chamber (simulating real conditions) for several LRFHS systems of multilayer construction products with built-in individual heating systems, in two stabilised temperatures, 23 \({}^{\circ}\)C and 33 \({}^{\circ}\)C, two stabilised relative humidities, 50% and 80% and three air exchanges per hour ACH on levels 0.5, 1.0 and 1.5. The obtained results indicate that the models used to determine emissions from single-layer products correspond to the description of emissions from multilayer systems only to a limited extent; some inner layers of floor systems are giving diffusion resistance or intensification of diffusion. A new emission model is proposed. The time-emission concentration curves for dry and wet environments differ significantly; reducing the VOC concentration in the air for the number of exchanges above 1.0 ACH is relatively inefficient. Authors also mapped out new research directions; for example, the experiment showed that not all of the VOC contaminants are ventilated just as easily and perhaps, considering their concentration of resistant impurities, chemical structure and diffusion resistance through the layers, there is a need to determine their weights.  
floor heating system; construction product; indoor air; chamber test emission; VOC; TVOC; indoor environment quality +
Footnote †: journal: Applied Sciences