esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Their work involved trajectory tracking and balance stabilization of a prototype motorcycle, although the design and debugging process for the fuzzy logic controller proved to be complex. [PERSON] et al. [32] utilized a point mass model to formulate model predictive control (MPC) for trajectory tracking, deriving the steering dynamics through a step response matching procedure. [PERSON] [33] designed an effective motorcycle path tracking simulation using optimal linear preview control theory, a technique commonly applied to vehicle path tracking. [PERSON] et al. [34] developed a genetic fuzzy control system for a driverless bicycle, allowing control parameters to adapt to variations in bicycle speed. They formulated the equations of motion under the constraint of rolling no-slip contact between the wheels and the ground. [PERSON] et al. [35] investigated small electric motorbikes, modeling them with a multibody approach that divided the bike into four rigid bodies, each exhibiting distinct characteristics. This method resulted in lower-order models that are easier to simulate and suitable for applying classical or modern control strategies. However, the model's limited operating range and significant deviation from real-world motorcycle dynamics are notable drawbacks. Most previous studies have focused primarily on the dynamic modeling and simulation of motorcycles, often neglecting the simultaneous consideration of self-stabilizing control and trajectory tracking control. Additionally, there has been limited attention given to slippery road conditions and pre-braking systems. To enhance the safety of motorcycle operation on low-adhesion roads, this study proposes a control method that combines pre-emptive brake control with front-wheel steering self-balancing. The pre-emptive brake control aims to reduce speed before entering low-adhesion conditions, while the front-wheel steering helps maintain motorcycle balance. The front wheel steering self-balancing algorithm consists of two layers: the outer loop manages trajectory tracking, and the inner loop stabilization controller ensures body balance. Using the multi-body dynamics software BikeSim in conjunction with Matlab/Simulink for joint simulation, the study employs a fifth-degree polynomial as the obstacle avoidance trajectory. Verification of trajectory tracking and pre-emptive brake control is conducted under four different working conditions. The structure of the remainder of the paper is organized as follows: Section 2 focuses on the control objectives, analyzing two prevalent scenarios and discussing countermeasures, and explains the integration of two control methodologies, which include preview following and front-wheel steering for self-balancing. Section 3 is dedicated to the simulation and analysis of the control algorithms across four typical scenarios, presenting the findings in a comparative manner. Finally, Section 4 concludes the study by summarizing the key points and offering a forward-looking perspective. The graphical abstract is presented in Figure 1.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameters** & **Group A** & **Group B** & **\(p\)** & **95\% CI** \\  & **(n = 38)** & **(n = 39)** & **\(p\)** & **[Lower/Upper]** \\ \hline
**VAS** (score) & 6.39 \(\pm\) 2.331 & 6.87 \(\pm\) 1.838 & 0.321 & \(-\)1.429/0.475 \\ \hline
**ODI** & & & & \\ (score) & 20.58 \(\pm\) 10.859 & 20.82 \(\pm\) 10.123 & 0.920 & \(-\)5.006/4.523 \\ \hline
**FTF** & & & & \\ (cm) & 23.76 \(\pm\) 18.807 & 27.46 \(\pm\) 17.111 & 0.369 & \(-\)11.857/4.460 \\ \hline
**TRLF** & & & & \\ (cm) & 42.58 \(\pm\) 2.678 & 41.05 \(\pm\) 4.883 & 0.094 & \(-\)0.267/3.322 \\ \hline
**TLLF** & & & & \\ (cm) & 42.18 \(\pm\) 4.152 & 41.23 \(\pm\) 5.446 & 0.391 & \(-\)1.249/3.156 \\ \hline
**HF** & & & & \\ (degree) & 82.89 \(\pm\) 19.920 & 70.51 \(\pm\) 27.381 & 0.026 & 1.488/23.276 \\ \hline
**MSTFF** & & & & \\ (score) & 3.63 \(\pm\) 0.819 & 3.56 \(\pm\) 0.754 & 0.641 & \(-\)0.290/0.425 \\ \hline
**MSTE** & & & & \\ (score) & 3.37 \(\pm\) 0.970 & 3.44 \(\pm\) 0.821 & 0.836 & \(-\)0.475/0.340 \\ \hline
**MSTRLF** (score) & 3.45 \(\pm\) 0.978 & 3.49 \(\pm\) 0.854 & 0.878 & \(-\)0.456/0.377 \\ \hline
**MSTLLF** (score) & 3.42 \(\pm\) 1.030 & 3.49 \(\pm\) 0.790 & 0.886 & \(-\)0.482/350 \\ \hline
**HRQL** & & & & \\ (score) & 27.68 \(\pm\) 13.449 & 27.00 \(\pm\) 13.578 & 0.825 & \(-\)5.452/6.821 \\ \hline \hline \end{tabular} VAS = Visual Analog Scale; ODI = Oswestry disability index; FTF = finger-to-floor distance; TRLF = trunk right lateral flexion; TLLF = trunk left lateral flexion; HF = hip flexion; MSTFF = muscle strength trunk forward flexion; MSTE = muscle strength trunk extension; MSTRLF = muscle strength trunk right lateral flexion; MSTLLF = muscle strength trunk left lateral flexion; HRQL = health-related quality of life; \(p\) < 0.05.  
\end{table} 
Table 2: Descriptive statistics (mean \(\pm\) SD) and comparison of the initial parameters (confidence interval 95%) between the two groups.
# Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data
## 2 Background and Related Works
### Open Knowledge Graphs  
The knowledge graph is excellent at expressing the relationship between large-scale heterogeneous data in multiple domains [21; 22; 23]. The knowledge graph is expressed as a triple comprising a subject, a predicate, and an object. The subject and the object are represented by nodes as entities. The predicate represents the relationship between entities and is represented by edges. The knowledge graph, first proposed by Google in 2012, aims to improve search results by using semantic search information accumulated from various sources [21]. It is currently used in semantic search engines, recommendation systems, voice search, question-and-answer systems, and AI systems in various domains.  
Among the various previously proposed open knowledge graphs, representative studies of the cross-domain are Wikidata [24], DBpedia [25], and YAGO [26]. Freebase was excluded in this study because it was shut down in 2017, and data have been transferred to Wikidata [27]. Wikidata is a collaborative large-scale knowledge-graph-based global community [24] that aims to provide information commonly used in Wikimedia projects, such as Wikipedia. Each item has a unique ID preceded by the item name with a Q (e.g., Q8068 (\"Flood\")). Therefore, it can provide information in any language without needing translation. Consequently, Wikidata supports reusability and extensibility. DBpedia is an open knowledge graph built by extracting information from various Wikimedia projects [25]. DBpedia is linked to other knowledge graphs, such as Wikidata, Freebase, and OpenCyc, and acts as a hub for linked data. DBpedia describes 4.58 million entities, including persons, places, creative works, organizations, species, and disease domains. DBpedia has the advantage of automatic modification as Wikipedia changes and supports multiple languages and various domains. Meanwhile, YAGO was automatically built from other sources, such as Wikipedia, GeoNames, and WordNet. YAGO3 provides more than 10 million entities and contains more than 120 million facts about these entities as of 2019. One of the advantages of YAGO is that it has a manually evaluated accuracy of 95% [26].
# Electrospun Nanofibrous Membranes for Tissue Engineering and Cell Growth
## 2 Electrospinning  
Generally, electrospinningning is performed by introducing a polymer solution or melt into a strong electric field, either by pressing it through a needle, or by coating a wire or a rotating cylinder etc. with the fluid polymer. The latter, also called needleless technology, usually has a higher production performance and can often be scaled up from laboratory to industrial scale [24; 25; 26].  
Using a static collector plate, the produced nanofiber mats usually contain incidentally oriented nanofibers [27; 28]. These kinds of nanofiber mats are often used in biotechnology and biomedicine. As previously mentioned, aligned nanofiber mats can be advantageous for cell growth. They can be prepared by using a rapidly rotating collector along with blades [17; 18; 29]. For special cases, as with magnetic nanofibers, a magnetic field can be used to support fiber alignment [19; 30], while introducing conductive, and ideally also grounded, areas in the substrate can also be used to prepare oriented nanofiber mats [31]. Another method for modifying the electric field during electrospinningning is given by adding dielectric coatings on parts of the substrate. [PERSON] et al. report on a cylindrical collector rotating at high speed that is shielded by two dielectric films [32]. This diverts the airflow due to the cylinder rotation and also amplifies the electronic field in a thin gap between them, in this way providing alignment and position control of the fibers. Moreover, nanofiber layers partially grown on conductive structures and partly consolidated can lead to an interesting mixture of aligned and chaotic layers, with different mechanical properties [33].  
Electrospinning of nanofiber mats can be carried out from different polymers and polymer blends [34; 35; 36]. Some polymers can be electrospun from an aqueous solution, which makes the process very simple and environmentally friendly. However, a crosslinking step is then required if the nanofiber mats are to be used in humid environments [37; 38; 39; 40]. Most of the water-resistant polymers need to be spun from toxic or corrosive solvents, while only some of them can also be electrospun from low-toxic solvents such as dimethyl sulfoxide (DMSO) [41]. The spinnability from DMSO is one of the reasons that polyacrylonitrile (PAN) is often used in electrospinning [16; 17; 18; 42; 43; 44]. The possibility of using PAN as a precursor for carbon nanofibers is another reason why this material is often electrospun [45; 46; 47]. The broad range of spinnable and co-spinnable polymers and other materials makes nanofiber mats attractive for applications in many fields, not only in biotechnology and biomedicine [13; 15; 16; 48; 49; 50]. Here, however, we concentrate on materials applicable for tissue engineering and other cell growth applications.
## 2 Materials and Methods
### Evaluation Method
#### 2.3.1 Experimental Setup  
We set up a simple experimental network implemented with several virtual machines in a single physical computer. Figure 7 represents the network topology. For the simplicity of the experiment, there are only four virtual machines. One is a Ryu controller that runs the proposed scheme as an application. The other one is an Open vSwitch (OVS, Open vSwitch v2.17.0). An attacker and a normal host are connected to the OVS.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Variable Definitions**} \\ \hline M & Memory size of a bloom filter \\ r & The number of rows (filter length for source address) \\ c & The number of columns (filter length for destination address) \\ e & the size of an element (\# of bits for each counter) \\ \(k_{s}\) & \# of hash functions for source address \\ \(k_{d}\) & \# of hash functions for destination address \\ p & Total false positive ratio \\ \(p_{s}\) & False positive ratio of a row for source \\ \(p_{d}\) & False positive ratio of a column for destination \\ S & \# of items for source addresses (e.g., \# of source IP/port pairs) \\ D & \# of items for destination addresses (e.g., \# of destination IP/port pairs) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variable definitions.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Parameters**} & **Value** \\ \hline Number of counters (r \(\times\) c) & 1248 \(\times\) 9248 \\ Counter size (e) & 8-bit \\ Number of ash functions (\(k_{s}\)) & 3 \\ Number of hash functions (\(k_{d}\)) & 3 \\ Probability of false positive (\(p_{s}\) and \(p_{d}\)) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of bloom filter.  
We experimented in four different experimental environments with this experiment topology. Table 3 shows the four environments. Environment 1 runs the proposed scheme which calculates the hard and idle timeouts based on the historical statistics. In Environment 2 and 3, the timeouts are set statically. Environment 2 uses the default timeout policy of Ryu controller, which sets no hard timeout and 10 s idle timeout. The only difference between Env 2 and Env 3 is that Env 3 assigns the idle timeout and hard timeout on a single flow rule at the same time. Env 3 shows that using both timeouts simultaneously is more effective than the existing timeout methods in terms of flow table management. Finally, Environment 4 runs FTGuard [8] which protects flow tables from attacks.
## 4 Results and Discussions
### Thermal Performance Results and Discussions
This can be due to the increase in the heat transfer coefficient, as shown in Figure 7A, and the increase in the heat losses. Therefore, the values of the Nusselt number augmentations and the heat transfer coefficient enhancements are higher than the thermal efficiency enhancements, as shown in Figure 8, which means that using nanofluids results in better heat transfer conditions between the tube and the VP1 thermal oil. However, the thermal losses, especially at low temperatures, are extremely low. Thus, the thermal efficiency enhancement margin is restricted [43]. Figure 11 presents the exergy efficiency for different temperatures and concentrations, where the results of the base fluid can be justified as in [53]. According to the obtained results, the highest value is observed at high concentrations and temperatures, and reached 37.828%; this is due to the fact that the amount of heat for useful exergy grows. Figure 8: Enhancement (**A**) heat transfer coefficient and (**B**) dimensionless Nusselt number versus inlet temperatures. Figure 9: Pressure drop for the base fluid and hybrid nanofluids at different temperatures and concentrations.
## 2 Materials and Methods
### Design of the Device
#### 2.1.5 Power Consumption and Techniques Used for Its Reduction  
FruiTemp runs on a 1200 mAh Li-po battery which can be recharged by means of a micro-USB connector while in operation. Power consumption reduction was achieved  
Figure 3: **(a)** The data logger interior consists of an Adafruit 32u4 proto, an Adafruit featherwing logger, an ADS1115 ADC with a gain amplifier and an LM4040 Voltage reference breakout. **(b)** The case of the system is an IP 54 flanged box. **(c)** The MC65 precise medical sensors. **(d)** The JS8746A environment temperature probe with its enclosure.  
using software and hardware techniques. The microcontroller periodically goes to sleep, and the watchdog timer [35] awakes it for temperature sampling. The microcontroller is clocked at a low frequency of 8 Mhz. When the device is measuring and recording, which means that it is awake from the sleep function, it takes 6 s to measure and log the data and has an averaged power consumption of approximately 9.735 mAh. When the device enters sleep mode the power consumption drops to an average 1.944 mAh approximately. The average consumption of the device including both modes is 3.148 mAh, for a logging interval of 1 sample per minute.
## 3 Background and Preliminaries
### Wireless Sensor Networks Application Areas  
Sensor networks may be composed of different types of sensors, such as thermal, visual, infrared, and magnetic among others, based on the type of the application they are designed for. This enables WSNs to monitor a variety of conditions that include but are not limited to: temperature, humidity, light, pressure, noise, speed direction of an object, size of an object, and noise levels [33]. The unique features of WSNs ensure that they can be used for a wide range of applications [4]. The authors of [3] categorized WSN applications into two categories: monitoring and tracking. Each category is further categorized into many subcategories. A broad number of monitoring and tracking applications are already implemented and currently in service for public use or industry. However, describing such applications is out of the scope of this survey. Figure 2 shows the classification of WSN application areas with some examples.
# Parameter Identification Methods for Hyperelastic and Hyper-Viscoelastic Models
## 2 Parameter Identification Method for the Hyperelastic Model  
The constitutive models representing the hyperelastic properties of rubbers mainly include the statistical models, the strain-invariant-based models, and the principal-stretch-based models, among which the Ogden model is able to fit the experiment data when rubbers undergo large deformations (\(\lambda<7\)). In the following sections, the parameter identification method for the hyperelastic model and the numerical verification of these identified parameters are presented in detail.
## 3 Plant Raw Materials and Compounds with Proven Immune-Enhancing Effects
### Visci Herba--Viscum album L.  
The characteristic compounds of _Visci herba_ are lectins belonging to the group of glycoproteins and polysaccharides.  
Pharmacological Properties  
Studies have shown that lectins are the main compounds in mistletoe herb that are responsible for the immunostimulatory effect, primarily by stimulating T-lymphocytes. Under in vivo conditions, they also stimulate phagocytosis, causing the biosynthesis of various cells as an immune response. Aqueous lectin-standardized extracts in pharmacological studies show dose-dependent stimulation of the immune system. Acidic polysaccharides present in the herb play an important role in supporting this effect [71].  
Preparations of aqueous extracts of mistletoe herb are used in medicine. So far, about fifty clinical studies have been conducted. All of them concerned the effect of aqueous extracts of mistletoe herb, administered parenterally, in the treatment of various types of cancer [72; 73].  
The lectins present in the plant material show strong cytotoxicity and mutagenicity. Lectin ML I is an outstanding immunositulator and an inhibitor of metastasis formation in some cancers. Aqueous extracts of mistletoe herb are components of preparations used in complementary and alternative medicine as immunomodulators in the treatment of certain cancers. The immune effect is closely correlated with the activity of lectins present in the raw material and depends on the dose. Therefore, it is necessary and very important to standardize preparations for the lectins they contain [74].  
Numerous preclinical and clinical studies have proven that mistletoe herb extracts exhibit immunomodulatory effects. A key role in the cellular response against tumor cells is played by natural cytotoxic NK cells. According to the latest immunological research, from a scientific point of view, mistletoe herb is classified as a raw material that stimulates the immune system to control and inhibit the growth of tumors. It is not surprising that the smaller the tumor, the better the effects of the therapy. It should also be noted that the use of mistletoe requires a great deal of experience on the part of the doctor, particularly with regard to the source of the raw material, the size of the dose used, and the frequency and appropriate timing of its application. Failure of therapy is often the result of negligence of any of the factors mentioned above [75].
## 2 Data and Methods Used
### Ambient Noise Data  
For the single station ambient noise measurements two CityShark-II datalogers (24-bits resolution) coupled with Lenartz-3D 5 sec seismometers were used. In the framework of the present work, 80 single-site measurements were performed in the city of Durres (Figure 9, red dots) during the period 18-21 February 2020. Most of the measurements were performed with a seismometer-to-ground coupling on soil conditions to avoid any high frequency contamination from an artificial surface layer (e.g., cement, asphalt, etc.). A typical ambient noise measurement installation is presented in Figure 9. All measurements were taken during the daytime, avoiding the impact of near source anthropogenic noise as much as possible. The duration of each measurement was 30 min per site.  
Figure 8: East-west cross section and lithology (**upper**); \(\rm V_{s}\) profile of the DURR station for the upper 30 m (**lower**).  
The mesh of investigated sites was denser in the western part of the city. This choice was based on the fact that observed structural damage from the Nov. 26 mainshock, was greater at the central-to-western part of the city as well as on the steeper basement morphology of the western part, as it is evident in Figure 6. The minimum and maximum distances between the measurement points were 175 m and 750 m, respectively.  
Urbanization of the Durres city has been evolved on a soft sediment basin that is elongated in a north-south direction. Soft soil deposits of the Durres basin deepen from its edges to the center, reaching a depth of \(\sim\)130 m (Figure 6), indirectly implying low fundamental frequencies (f\({}_{0}\) < 1 Hz) for most of the investigated sites.
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 2 Proposed Method
### Network
#### 2.3.1 ASDnet  
ASDNet is a lightweight neural network specifically designed for resource-constrained environments. It processes spectral features to extract deep latent representations. The  
Figure 2: Architectural designs of the proposed classification networks for ASD. (**a**) SpecMFN: Combines SpecNet for further spectral feature processing and MobileFaceNet for classification, optimized for high-sensitivity detection. (**b**) ASDNet: A lightweight network tailored for resource-constrained environments, featuring eight layers of 1D convolution and ReLU activation to efficiently extract latent features while maintaining a balance between performance and efficiency.  
network architecture, as detailed in Table 1, combines one-dimensional convolutions with ReLU activation functions to perform feature extraction and compression. To enhance computational efficiency, ASDNet utilizes larger convolution strides, thereby significantly reducing the number of parameters and computational overhead, while maintaining robust detection performance.  
The ASDNet architecture consists of eight processing steps, each comprising a one-dimensional convolution operation followed by a ReLU activation function. The computation process for each step is described as follows:  
\[f_{i,k}=\sigma\left(\sum_{j\in\mathcal{R}_{i}}w_{j,k}\cdot x_{j}+b_{k}\right), \tag{5}\]  
where \(f_{i,k}\) represents the output of the \(k\)-th convolution kernel in the \(i\)-th layer, \(\mathcal{R}_{i}\) denotes the receptive field of the neuron, \(w_{j,k}\) is the weight coefficient of the convolution kernel, \(b_{k}\) is the bias term, and \(\sigma(\cdot)\) is the ReLU activation function.  
The extracted latent feature \(z\) can be computed using the classification network \(f_{\text{ASDNet}}(\cdot)\), as follows:  
\[z=f_{\text{ASDNet}}(x;\theta), \tag{6}\]  
where \(\theta\) represents the parameters of ASDNet.  
During the training phase, both ASDNet and SpecMFN employ ArcFace Loss to optimize classification performance. ArcFace Loss introduces an angular margin, which enhances intra-class compactness and inter-class separability, thereby improving the model's discriminative ability. The loss function is defined as follows:  
\[L=-\frac{1}{N}\sum_{i=1}^{N}\log\frac{e^{s\cdot(\cos(\theta_{y_{i}}+m))}}{e^{s \cdot(\cos(\theta_{y_{i}}+m))}+\sum_{j\
eq y_{i}}e^{s\cdot(\cos(\theta_{j}))}}, \tag{7}\]  
where \(\theta_{y_{i}}\) denotes the angle between the input feature and the corresponding class center, \(m\) is the angular margin, \(s\) is the scale parameter, and \(N\) is the batch size.