esa-sceva/satcom-chunk-collection huggingface dataset:
# Methane Production Potential from Apple Pomace, Cabbage Leaves, Pumpkin Residue and Walnut Husks
## 3 Results  
### Properties of Analysed FVR and Inoculum  
The analysed substrates differed significantly (\(p<0.05\)) from each other in terms of both physical and chemical properties (Table 1). As expected, the FVR tested in this study were characterised by a high moisture content (80.59-93.87%). The high moisture content of tested substrates resulted in relatively low TS values, which in the case of apple pomace and walnut husks did not exceed 20%, and in the case of pumpkin residue and cabbage leaves was lower than 10% of fresh weight.  
The VS content ranged between 85 and 98%. Similar values were observed for walnut husks and cabbage leaves, while significantly lower VS was found for pumpkin residue. The highest VS was in apple pomace. The TOC content in the analysed substrates was similar and amounted to ca. 430 g kg\({}_{\mathrm{BDM}}\)\({}^{-1}\) (Table 1) which is close to the average TOC content (456 g kg\({}_{\mathrm{BDM}}\)\({}^{-1}\)) in food waste reported in the literature [56]. The pumpkin had the highest TKN, TP and K content, while the lowest content of TKN and TP was found in walnut husks. In turn, the lowest K content was found in apple pomace.  
An important parameter determining the conditions in which biogas production takes place is the relationship between the nutrients that affect the living conditions of the bacteria. The lowest C:N ratios were found in pumpkin residue and cabbage leaves (Table 1). The C:N ratios obtained for apple pomace and walnut husks were much higher than the maximum value (24:1) reported for food waste [56]. Mutual relations between the contents of N and P were more even and ranged from 5:1 to 13:1 (Table 1) which is much higher than the optimal ratio in anaerobic digestion (3:1) [57].  
The inoculum used in the study was characterised with TKN content similar to the pumpkin residue and cabbage leaves, but with higher TP and K content compared to most of the residues.
## 2 Related Work  
### DDoS in IoT Environments  
A lot of researchers have recently focused on the domain of DDoS attacks executed against or through IoT devices. For example, a novice attack that reflects the applicability of denial of service attacks in IoT is presented in [14]. Specifically, a DDoS attack method for IoT system is discussed and implemented, while its effects are evaluated by monitoring various metrics such as CPU and memory utilization.  
Multiple simulation scenarios are presented in [15], to evaluate the effects of using wireless sensor networks for committing amplified reflection DDoS attacks. The results show that conducting a successful real world DDoS attack by using wireless sensor networks, requires employing a significantly large number of infected installations.  
In [16] the capability of consumer IoT devices to participate in reflective DDoS attacks is discussed. Authors evaluate specific household IoT devices available on the market in terms of reflective capability, amplification factor, duration, and intensity rate for DDoS attacks.  
There are also some recent research efforts regarding detection and prevention of DDoS attacks against and/or through IoT installations. In [17] an agent based system is proposed, in order to detect DDoS attacks in IoT devices. Their short technical analysis uses a blacklisting/white-listing approach, to prevent the network from being taken down. Their experimental analysis shows that they have only run minimal simulations with less than 100 nodes, which is not representative of real world networks.  
An innovative approach called AntibIoTic, which is a worm-like software that infects devices in the same way that real bot-net software does, is presented in [18]. After infecting the device, AntibIoTic protects it or notifies the owner about anomalies regarding its functionality. While the idea is interesting, the paper lacks any technical justification.  
In [19] a lightweight defense mechanism that is based on utilizing devices themselves is discussed. The presented approach is interesting, but it requires devices that are able to protect themselves or the network from attacks. In practice this limits the applicability of such a solution. In [20] authors point out that IoT devices' exponential growth will enforce the use of IPv6 protocol and they discuss methods to detect attacks against the most important vulnerabilities of the specific protocol. The authors present an approach that can detect specific attacks such as wormhole and flooding attack in IPv6 sensor network environment.  
The use of leaning automata, in order to set up a method for detecting DDoS attack is proposed in [21]. Their approach is based on a Service-oriented architecture (SOA)design, which is a software design that moves the detection functionality away from the devices. The learning automata are used in order to decide the optimal sampling rate in which the system should calculate packet rates for traffic flows present in the system.
# Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization
## 3 Control Methodology
### Description of the Control Blocks
#### 3.2.3 Active Tower Damping Control (ATDC)  
The main way to deal with the mitigation of fore-aft movement tower in region IV is operating on pitch control through the ATDC. Tower motion can be measured instantly using an accelerometer mounted in the nacelle [64]. ATDC allows to reduce tower loads considerably without adversely affecting the quality of power or speed regulation. The tower dynamics can be modelled as a second-order system exhibiting damped simple harmonic motion [65]:  
\[\mathrm{M\ddot{x}_{t}+D\dot{x}_{t}+Kx_{t}=F+\partial F} \tag{22}\]  
where \(x_{t}\) is the tower f-a displacement; F is the applied force, which in this case is predominantly the rotor thrust; \(\partial F\) is the extra thrust added by the pitch angle action; M and K are related to the tower modal mass and the modal stiffness, respectively; and D is the damping term, which is usually small. Nevertheless, the effective damping of equation (22) can be increased with a particular additional damping \(D_{p}\) if \(\partial F\) is made proportional to \(-\dot{x}_{t}\) by means of an extra pitch angle action \(\partial\beta\). Since measuring acceleration is less complicated than measuring velocity, the tower acceleration is measured and then integrated to provide an estimate of \(\dot{x}_{t}\). Then, according to the equations in (23), the extra pitch action \(\partial\beta\) can be obtained from the partial derivative of the thrust with respect to the pitch (\(\partial F/\partial\beta\)), the estimate of \(\dot{x}_{t}\), and the additional damping \(D_{p}\)[65]. The fraction with \(D_{p}\) and \(\partial F/\partial\beta\) terms is included into a gain \(K_{f-a}\), which is the design parameter to be tuned in the ATDC.  
The increment of pitch action \(\partial\beta\) is added to the pitch control signal of the generator power (or speed) controller, which provides the control action FA(k) of Equation (20) (or \(\beta_{f-a}\) signal in the proposed control scheme in Figure 3).  
\[\begin{split}\partial F=\frac{\partial F}{\partial\beta} \partial\beta=-D_{p}\dot{x},\\ \beta_{f-a}=\partial\beta=\frac{-D_{p}}{\partial F/\partial\beta }\dot{x}=-K_{f-a}\dot{x}\end{split} \tag{23}\]
## 7 Scalability of the Proposed Solution  
In order to evaluate the scalability of the proposed solution, it was implemented in a boost converter, the circuit topology of which differs from that of the buck converter. Therefore, it was first necessary to adapt the approach presented in [59] to allow the simulation of the boost converter in Python, which was later validated using LTspice.  
Then, the same methodology that was applied to the buck converter was executed. A new dataset was generated, consisting of 625 distinct operating conditions (Figure 30)  
Figure 28: Scenario 2 (Table 1): Input SNR, input signal ripple (\(\Delta\)i\({}_{\text{in}}\), \(\Delta\)i\({}_{\text{out}}\), and \(\Delta\)v\({}_{\text{out}}\)) under clean and noisy conditions, and noise component.  
Figure 29: Model (17) performance under noisy input signals (Figure 27).  
and eight distinct severity levels (Figure 6), resulting in a total of 625 \(\times\) 8 = 5000 samples (Figure 31).  
After the feature selection described in Section 5.1, the data were normalized using Equation (4). Next, in order to evaluate the performance of the proposed solution, model (8) was trained and tested on the full dataset (Figure 31) in 100 independent runs. In each run, 20% of the original samples were randomly selected for training, while the remaining 80%, never observed by the models, were used for testing. The training and testing sets differed in each evaluation. The results of the 100 models are shown in Figure 32.  
Figure 31: Boost converter dataset, derived from 5000 simulations, containing all primary features, ESR, C, and target variable (class or capacitor Status).  
Figure 30: Range of randomly defined operating conditions, regarding the boost converter dataset consisting of 625 distinct operating conditions, where R denotes the load resistance and D the duty cycle. The values of \(\text{v}_{\text{in}}\) (5 V) and L (44 uH) are kept constant throughout all simulations.  
Figure 32: Evaluation results for 100 distinct LR models, each trained on 20% of the dataset (Figure 31) and tested on the remaining 80%.  
The average value of the performance metrics across the 100 models exceeded 99.5%, which demonstrated that the proposed solution is scalable to converters with different topologies.  
The buck converter generates a DC output voltage lower than the DC input voltage and produces high input current ripple, while the boost converter provides a DC output voltage higher than the DC input voltage with reduced input current ripple. Despite these fundamental topological and operational differences, the proposed solution maintained robust performance, with all four-performance metrics reaching excellent values in all scenarios that were analyzed, regardless of the converter topology and the presence of noise.
## 5 Opportunities and Challenges
### Telecommunications Industry  
In the future, society will experience an intelligent era of universal connectivity. The next-generation mobile communication network will bridge the gap between humanity and the digital world. The evolution of the core network is crucial in the process of integrated networking. The core network acts as a convergence point for network businesses and applications, driving future development. Additionally, the core network is the center of the entire network topology, connecting various terminals and access networks, with a domino effect on the entire network. To adapt to the integration of communication and computation, the following areas need improvement in the process of core network evolution:  
* Firstly, the increasing number of network functions leads to more service interactions between network functions, weaker security mechanisms for service interactions, and complex operational steps for traffic switching configurations when switching between new and old versions of services.
* Secondly, the observability of network function services is poor, the visualization of topology logic is weak, the storage of service operation logs is not standardized, and performance indicators are not standardized, which results in a large amount of manual operations in maintaining network function services and locating and troubleshooting issues.
* Finally, operators already have a pool of virtualized resources in the network function virtualization (NFV) stage, which are mostly built and distributed in the form of virtual machine resources. Therefore, it is necessary to consider reusing and migrating virtualized resources into a cloud-native telecommunications infrastructure, running the 5G network in the form of containerized resources, and further improving the flexibility and resource utilization of the network.
## 3 System Model and Problem Formulation  
### System Model  
We consider a two layer downlink heterogeneous network scenario, which includes an MBS and \(K\) SBSs. The SBSs have low transmit power and cover a small area. The index set of the base station (BS) is \(K=\{0,1,\ \ldots\ k\ \ldots\ K\}\), wherein the index of \(m=0\) denotes MBS. We assume that MBS and SBS work in different frequency bands, so the cross-layer interference between them can be avoided. Since each SBS has a small coverage area and low transmit power, they can use spectrum resources with acceptable interference levels. Therefore, the downlink signal-to-noise ratio models of the SBS and the MBS are different in the following scenario. Each BS has a bandwidth resource of \(W_{k}\) and each SBS has an equal amount of bandwidth resources. The BS is distributed with slices of \(N\) types of services, the index of the slice is \(n\) and the bandwidth resources of the BS are shared by the slices. The users are randomly distributed in the coverage of the MBS. The set is \(I=\{0,1,\ldots i\ \ldots 1\}\). The number of users in each service is \(U_{n}\). Users distributed in the coverage of the SBS can obtain a data stream from the SBS and can also obtain a data stream from the MBS at the same time. As shown in Figure 1, we used Visio to draw our proposed research scenario. Table 1 summarizes the main notations of this study. In this heterogeneous network system, in order to prevent MBS from overloading, we propose an association mechanism based on the dual connectivity. Users within the coverage area of the SBS will first connect to the SBS which they belong, and use the SBS as the primary BS. During the resource allocation process, the second connection will be used when the resources of the SBS are insufficient in a time slot. Therefore, the user who opens the second link will receive resources from the SBS and the MBS in the next time slot. Figure 1: An illustration of the system model. In addition, users who are only within the coverage of the MBS can only be connected to the MBS. When the user is connected to the MBS, the signal-to-noise ratio (SNR) of the user connected to the MBS is  
\[SNR_{m}=\frac{P_{m}G_{i,m}}{\sigma^{2}} \tag{1}\]  
and when a user is connected to SBS, the downlink signal-to-interference-noise ratio (SINR) can be denoted as  
\[SINR_{k}=\frac{P_{k}G_{i,k}}{\sum\limits_{j=\{1,2,\ldots,n\},j\
eq k}P_{j}G_{ i,j}+\sigma^{2}},\ k=\{1,2\ldots,K\} \tag{2}\]  
where \(P_{m}\) and \(P_{k}\) are the transmit power of the MBS and the SBS, respectively. \(G_{i,m}\) and \(G_{i,k}\) are the channel gains from the MBS and the SBS to the user \(i\), respectively.\(\sigma^{2}\) is the average background noise power. The interference between SBSs is included in the Equation (2). According to [PERSON] theory, the downlink transmission rate connected to the MBS and the SBS can be respectively defined as
# Inference of Drawing Elements and Space Usage on Architectural Drawings Using Semantic Segmentation
## 4 Experiment
### Data Preprocessing and Labeling  
The image preprocessing process for learning is shown in Figure 5 below. The images were cut in a way that a single image carried a single drawing. Each drawing was placed at the center so that any elements other than the drawing were not included in each image. Then, to prevent the colored drawing from hindering the learning, the entire images were converted into black and white ones.  
Figure 4: Conversion Process.  
Figure 3: Korean government data portal.  
In deep learning, insufficient data are likely to cause overfitting, in which case a model generated based on training performs the image segmentation smoothly with the existing data but not with new data. To avoid overfitting, the data augmentation [32] was adopted in this experiment. The data augmentation helps improve the performance of a model by adding fake data when the dataset to be used for training the model is insufficient. A few operations including flipping, brightness variation, coordinate translation, rotation and zooming may be used to augment the data. In the present experiment, given the formulaic characteristics of drawings, the pixel shifting was applied to the original and target data, with ten pixels moved up and down and to the left and right.  
When the histogram of an image is concentrated too much on a certain domain, it is far from a good image due to the low contrast. The contrast evenly distributed across all domains makes a good image. Therefore, as the image processing technique ensuring a good image, the histogram equalization was applied. In the process, to avoid extreme darkness or brightness, the CLAHE (contrast limited adaptive histogram equalization, which controls the contrast by imposing limits) was applied to the original images. Thus, 3430 pieces of data, which were ten times more than the original data, were obtained, with 70% (2400) and 30% (1030) used as the training and test data, respectively.  
The experiment largely comprised two parts. The first part concerned the recognition of such elements as walls, doors, windows on drawings, while the second part concerned the recognition of such space as rooms, living rooms and bathrooms. The elements on architectural drawings were classified into five elements, i.e., walls, windows, hinged doors, sliding doors and evacuation doors. The space usage was classified into seven types, i.e., rooms, entrances, balconies, dress rooms, bathrooms, living rooms, evacuation space, and pantries.  
As shown in the Figure 6 below, each of the 343 images of drawings to be used for the training and test underwent the labeling (Figure 7) based on the foregoing indexing.  
Figure 5: Before and after image preprocessing.  
Figure 6: Example of data labeling process: pre-processed image (**left**), labeling of drawing elements (**center**), labeling of space usage (**right**) (The transparency was set to 55% to ensure visibility).
# Evaluation of Resilient Modulus and Rutting for Warm Asphalt Mixtures: A Local Study in Iraq
## 3 Results and Discussions
### Resilient Modulus Test (M\({}_{r}\)) Results  
Figure 6 displays the findings of the resilient modulus values for the 12 prepared cylindrical specimens, which are tested at four test temperatures (20, 25, 30, and 40) \({}^{\circ}\)C subjected to a haversine waveform of 1 Hz frequency which has 0.1 s loading duration and 0.9 s rest period. As seen in the figure, a temperature rise has a considerable impact on the resilient modulus. The findings show that the WMA mixtures of two percentages of zeolite have the lowest value of resilient modulus, whereas the HMA mixture has a greater value of the resilient modulus which means that the WMA mixture is affected greatly by the temperature increase like the HMA mixture. The WMA mixture that has 0.3% zeolite shows a higher value of the resilient modulus than the 0.5% zeolite specimen. Despite this result, the WMA shows a good resilient modulus that can compete with the HMA. The aggregate skeleton of the compacted specimen may be a reason for this result. The HMA specimen compacted at a higher compaction temperature than the WMA specimens. Given that loads can be transferred from one aggregate to another more successfully with a more durable aggregate skeleton or aggregate-aggregate connection, the foaming effect of zeolite addition results in a low increase in the asphalt binder volume, which enables sufficient coating of the surface of aggregate during the mixing process at a lower temperature and may raise the modulus of the asphalt mixture. The higher resilient modulus leads to thinner asphalt layers in the first phase of the design, which produces a more economical pavement design. This result complies with [PERSON] [40].  
Figure 5: Marshall Flow results.  
Figure 6: Resilient modulus result values.  
There is no statistical difference, as seen in Figure 6, between HMA and 0.3% of zeolite WMA at low temperatures (20 and 25) \({}^{\circ}\)C whereas the 0.5% zeolite WMA mixture has a significant difference from HMA and 0.3% zeolite WMA mixture. At higher temperatures (30 and 40) \({}^{\circ}\)C there is a significant difference between the HMA mixture and the WMA mixtures and no significant difference between the two percentages of zeolite.
# Unsupervised Learning Approach for Anomaly Detection in Industrial Control Systems
## 3 Proposed Method
### Data Preparation
#### 3.3.1 Implementing Cluster Segmentation  
The K-means algorithm is an unsupervised learning algorithm that groups data into K clusters. It works by minimizing the variance of the distance difference between the given data and each cluster. The sum of squared errors (\(SSE\)) within each cluster, which is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Timestamp** & **P1\_B2004** & **P1\_B2016** & **P1\_B3004** & **P1\_B3005** & \(\ldots\) & **P4\_ST\_TT01** & **Attack** \\ \hline
11 July 2021 & 0.08771 & 0.88504 & 476.76703 & 1014.79321 & \(\ldots\) & 27170 & 0 \\
10:00:00 & 0.08771 & 0.88619 & 476.76703 & 1014.79321 & \(\ldots\) & 27171 & 0 \\
11 July 2021 & 0.08771 & 0.88836 & 476.76703 & 1014.79321 & \(\ldots\) & 27170 & 0 \\
11 July 2021 & 0.08771 & 0.89214 & 476.76703 & 1014.79321 & \(\ldots\) & 27171 & 0 \\
10:00:03 & 0.08771 & 0.89214 & 476.76703 & 1014.79321 & \(\ldots\) & 27171 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Train1.csv, version 22.04 of the HAI dataset.  
Figure 3: Hardware-in-the-loop (HIL) process flow diagram.  
the sum of the squares of the Euclidean distances between each data point and its cluster center, is expressed by the following formula.  
\[SSE=\sum_{i=1}^{\mathbf{k}}\ \ \sum_{x\in C_{i}}\ \parallel x-\mu_{i}\parallel^{2}\]  
where \(\mathbf{k}\) is the number of clusters. \(C_{i}\) is the set of data points in the ith cluster. \(x\) is a data point in cluster \(C_{i}\). \(\mu_{i}\) is the centroid or mean of all the data points in cluster \(C_{i}\). \(\|x-\mu_{i}\|^{2}\) is the square of the Euclidean distance between the data point \(x\) and the centroid \(\mu_{i}\). Figure 4 shows the number of tags between clusters. To cluster the data, we selected tags that showed similar patterns of behavior. The optimal number of clusters was set at the point where the \(SSE\) value of K-means dropped sharply, and our study set it at six groups obtained by the optimal value.  
Table 3 shows the number of tags classified by clustering. Clustering is a technique for grouping data with similar characteristics, which is useful for characterizing and analyzing data. The number of all tags was 86 and was reduced to 3 dimensions using PCA. Data scaling is a common preprocessing step in data analysis that involves adjusting the values in your data to a particular range or scale. This can make variables with different units comparable or prevent variables with larger values from becoming more important during model training.  
Cluster 4 is shown in Figure 5. The data were visually grouped based on similar characteristics, with each time series reflecting a distinct pattern or behavior. This allows you to monitor the health and performance of the system.  
Figure 4: Number of tag clusters.  
\begin{table}
\begin{tabular}{c c} \hline
**Cluster\_Count** & **Cluster\_Size** \\ \hline
0 & 41 \\
1 & 26 \\
2 & 9 \\
3 & 6 \\
4 & 3 \\
5 & 1 \\ \hline \end{tabular}
\end{table}
Table 3: Size of each cluster.