esa-sceva/satcom-chunk-collection huggingface dataset:
# Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques
## 6 Conclusions  
We develop a robust throughput and QoS optimization scheme for a smart building environment that allocates channels to mobile users, IoT devices and network monitors based on their required QoS. The results highlighted that the proposed scheme shows 39.68% and 12% to 33.8% better performance in terms of reliability (low bit error rate) and channel idle time, respectively. Considering the overall throughput of the network, the proposed scheme provides 16.25% and 75% higher throughput as compared to the greedy and random sharing schemes, respectively. Simulation results also demonstrate that the proposed scheme shows a significantly lower blocking rate for both low and high traffic modes compared to the existing schemes. Therefore, the proposed scheme can support a higher density of users with a competitive performance gain. In the future, we will extend the proposed framework for energy efficiency and incorporate the concept of RF energy-harvesting for IoT devices to achieve energy-balancing and support the green communication paradigm.
# Deep Clustering of Student Code Strategies Using Multi-View Code Representation (CMVAE)
## 4 Experiment and Discussion  
### Experiment Setting  
#### 4.1.1 Datasets  
In this study, the proposed method was evaluated on datasets from two programming languages (Python and C#) to verify its effectiveness for the code strategy clustering task. The datasets are detailed below:  
* CodeNetPython800 ([[https://developer.ibm.com/exchanges/data/all/project-codenet/](https://developer.ibm.com/exchanges/data/all/project-codenet/)]([https://developer.ibm.com/exchanges/data/all/project-codenet/](https://developer.ibm.com/exchanges/data/all/project-codenet/)), accessed on 21 July 2024) (Unlabeled): A subset of the extensive CodeNet programming problem dataset introduced by IBM. It contains 800 Python programming problems, with 300 student code submissions per problem, totaling 240,000 Python programs.
* Sorting-Python Dataset ([[https://github.com/big-tangyuan/sorting_algo_py](https://github.com/big-tangyuan/sorting_algo_py)]([https://github.com/big-tangyuan/sorting_algo_py](https://github.com/big-tangyuan/sorting_algo_py)), accessed on 21 July 2024) (Labeled): This dataset consists of 151 Python sorting algorithm programs collected from GitHub, covering six classical sorting strategies: Selection Sort, Bubble Sort, Merge Sort, Heap Sort, Quick Sort, and Insertion Sort.
* PaCon Dataset ([[https://sites.google.com/view/paconproj/](https://sites.google.com/view/paconproj/)]([https://sites.google.com/view/paconproj/](https://sites.google.com/view/paconproj/)), accessed on 21 July 2024) (Labeled): This dataset, constructed in the PaCon paper [23], is publicly available and is based on the Code-Hunt dataset. After a series of filtering processes, it includes only correct and C#-compatible code submissions, with nine programming problems and a total of 519 student submissions. The dataset was manually labeled according to different problem-solving strategies.
* Sorting-C# Dataset (Labeled): In the PaCon paper [23], the authors collected 66 C# sorting algorithm programs, covering five classical sorting strategies: Bubble Sort, Heap Sort, Insertion Sort, Quick Sort, and Merge Sort.
# Towards a Heat-Resistant Tethered Micro-Aerial Vehicle for Structure Fire Sensing
## 4 Thermal Analysis  
### Preliminary Thermal Analysis  
The thermal-electrical analogy is used [35] to estimate the thermal performance of the UAV. Every structural element that separates the inside volume of the quadcopter from the free outside air is expressed in Table 2 in terms of its thermal conductivity, thickness, and equivalent thermal resistance.  
Any usable combination of these materials must include at least 0.4 mm of CFRP for structural integrity. Furthermore, to protect this structural material from degradation, exposure of this material to the external environment should be avoided. Based on these requirements, some alternatives are proposed in Table 3. From these alternatives, the honeycomb-carbon sandwich + foam option is the most suitable one, offering a good compromise between weight and equivalent thermal resistance.  
A section diagram of the selected configuration can be seen in Figure 3. A combination of polycarbonate and carbon fiber felt is selected for the ducts due to the reduced available space. The polycarbonate material is selected because it can be 3D printed to a complex shape, while the carbon fiber felt offers high-temperature resistance and low thermal conductivity. Similarly, a lateral band of carbon fiber felt is included to cover the lateral space between the frame's parallel panels. Both configurations can be seen in Figure 4.  
Various configurations were iterated for each component through a stationary heat conduction simplified model to validate all material choices before proceeding with the transient thermal analysis.  
\begin{table}
\begin{tabular}{l l l l} \hline \multirow{2}{*}{**Material**} & \multicolumn{2}{c}{**Thermal**} & \multirow{2}{*}{**Tableness (mm)**} & \multirow{2}{*}{**Equivalent**} \\  & **Conductivity (W/mK)** & & \\ \hline CFRP Plain Weave & 0.600 & 0.4 & 0.038 \\ \hline Carbon Fiber Felt & 0.120 & 2.0 & 0.955 \\ \hline Honeycomb Aramid & 0.020 & 1.5 & 4.296 \\ \hline Honeycomb Air & 0.029 & 1.5 & 2.974 \\ \hline Polycarbonate & 0.200 & 0.6 & 0.386 \\ \hline \end{tabular}
\end{table}
Table 2: Material thermal properties.  
\begin{table}
\begin{tabular}{l l l} \hline \multirow{2}{*}{**Panel Option**} & \multirow{2}{*}{**Weight (g)**} & \multicolumn{2}{c}{**Equivalent Thermal**} \\  & & **Resistance (1/K)** \\ \hline Honeycomb–carbon & & 62.66 & 2.24 \\ sandwich + foam & & \\ \hline Carbon–foam–carbon sandwich & 58.57 & 1.03 \\ \hline Foam–carbon 0.8 mm & 55.99 & 1.03 \\ \hline Carbon–honeycomb–carbon sandwich & 51.36 & 1.83 \\ \hline \end{tabular}
\end{table}
Table 3: Frame panel alternatives.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 5 Proposed Algorithm
### Proposed DL LSTM Algorithm
#### 5.2.1 Handover Logic
The \"1\" decision denotes a handover to be performed, while a \"0\" decision implies no handover is required, and the UE remains connected with the current BS. The \"1\" decision refers to an exception handover that should only occur if the CQI of the potential BS is better than that of the current BS to avoid the risk of radio link failure. If the resulting decision is to handover, the UE proceeds to the handover procedure phase, where the current BS initiates the handover to the desired potential BS.
## 3 Dynamic Window Approach
### Evaluation Function  
After sampling the velocity space, a kinematic model based on the robot predicts multiple possible motion trajectories. Subsequently, these trajectories are evaluated, and the best motion trajectory is selected, and the robot moves forward according to the velocity of the best trajectory. In order to enable the robot to reach the goal point successfully, an adaptive heading factor c is introduced, enabling the robot to be directed more precisely towards the target while minimizing the risk of collisions. The evaluation function associated with the DWA algorithm is as follows:  
\[G(v,w)=\sigma(a*c*heading(v,w)+\beta*dist(v,w)+\gamma*velocity(v,w)) \tag{12}\]  
where \(heading(v,w)\) represents the function that evaluates the azimuth, \(dist(v,w)\) signifies the distance to the nearest obstacle on the map at the endpoint of the predicted path, and \(velocity(v,w)\) stands for the robot's current linear velocity. The terms \(\alpha\), \(\beta\), \(\gamma\), and \(\sigma\) are the weights for each corresponding factor. As in Equation (13), \(\lambda\) is the adjustment coefficient, \(\theta_{cur}\) denotes the robot's current heading angle, and \(\theta_{des}\) is the target orientation angle. \(y_{goalt}\) is the vertical coordinate of the robot's temporary target point in the next time frame, \(x_{goalt}\) is the horizontal position of the robot's provisional target at the next moment, and \(v_{x}\) and \(v_{y}\) represent the robot's sideways displacement.  
\[c\ =1+\lambda\cdot|\theta_{des}-\theta_{cur}| \tag{13}\]  
\[\theta_{cur}\ =\arctan(v_{y}\,,v_{x}\ ) \tag{14}\]  
\[\theta_{des}\ =\arctan(y_{goalt}\ -y_{t}\,,x_{goalt}\ -x_{t}\ ) \tag{15}\]
## 2 Materials and Methods
### Survey Instrument  
Researchers have established various soundscape indicators as assessment standards. A large proportion of soundscape assessment research is primarily based on semantic, sociological, and psychological methods [45; 46]. In the ISO standards of collection methods, questionnaire-based soundscape assessment (ISO 12913-2, Method A) is currently the recommended tool. Different descriptors have been widely considered and employed in soundscape evaluations [35; 38].  
The soundscape evaluation method used in this work was inspired by the ISO protocol. The questionnaire was designed according to pioneer studies of soundscape evaluations in public open spaces and ISO 12913-2 [15; 33; 45]. It covered the interviewee's social/demographic background, noticed sounds, and evaluations of annoyance and acoustic comfort. The questions about the interviewee's social/demographic background included age, gender, education, occupation, residency status (whether a resident of the OCT), and visit frequency. The noticed sounds were selected from a list of various sound sources by answering the question \"which sounds have you noticed here?\". The list of sound sources, created by the researchers through in situ observation, included natural sounds (wind, water, rain, insects, frogs, and birds), human sounds (talking, peddling, children playing, singing, walking, stepping, running, music, and dancing), and mechanical sounds (road traffic, air conditioners, construction, radio broadcasts, loud-speakers, and helicopters). Subjective evaluations of annoyance and acoustic comfort used a 5-point ordinal category scale that agreed with ISO 12913-2: 2018. The response alternatives for annoyance and acoustic comfort were \"strongly agree\" (1), \"agree\" (2), \"neither agree nor disagree\" (3), \"disagree\" (4), and \"strongly disagree\" (5).
# Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks
## 2 System Model
### UWA Cooperative Communication System Model
\(\gamma_{SD}\) is the SNR of signals received by node \(D\) sent from node \(S\) in \(t\)th round of data transmission, which can be expressed as  
\[\gamma_{SD}{}^{(t)}=\frac{P_{S}G_{SD}{}^{(t)}}{\sigma^{2}} \tag{2}\]  
where \(P_{S}\) is the transmission power of the source node, and \(G_{SD}{}^{(t)}\) represents the channel gain of \(S\)-\(D\) communication link. \(\gamma_{r_{i}D}\) is the SNR of signals received by \(D\) sent from node relay node \(r_{i}\) in \(t\)th round, which can be represented as  
\[\gamma_{r_{i}D}{}^{(t)}=\frac{P_{r}G_{r_{i}D}{}^{(t)}}{\sigma^{2}} \tag{3}\]  
where \(P_{r}\) is the transmission power of the relay node and \(G_{r_{i}D}{}^{(t)}\) represents the channel gain of \(r_{i}\)-\(D\) communication link.
# Kalman-Filter-Based Tension Control Design for Industrial Roll-to-Roll System
## 3 Design of a Signal Filter  
Signal processing of the sensor using a filter is essential to achieve precise control. Filters have a trade-off between noise reduction and phase response, so the more noise is reduced, the higher phase delay. The phase delay has an adverse effect on the phase margin and stability of the system. In this paper, it was possible not only to eliminate the noise contained in the observed data but also to minimize the phase delay by using the KF. The KF is an optimal estimation method that finds the true value of variables from a set of noisy measurements. In this section, we introduce how to design the KF and signal processing results. The design procedure of the KF is as follows. Firstly, the state space model of the linear system should be constructed. Subsequently, the state variables of the system are estimated by the KF algorithm using the state space model and measured values of the target system.  
Figure 2: Experimental results of system identification: (**a**) Empty film; (**b**) Half film; (**c**) Full film.  
The KF algorithm has two steps: prediction process and estimation process. Figure 3 illustrates the relationship of the KF with the actual system [15].
## 3 IFC-Based Bridge Information Modeling with Document Metadata  
### IFC Schema for Bridge Model and Document Information  
The most recent official IFC released by buildingSMART International (bSI) is version IFC4.0.2.1 [25]. IFC4.0.2.1 has opened up the possibility of future extensions for civil infrastructures (_IfcCivilElement_), although they will be removed in a future version. The unofficial version currently being developed (IFC4.3 RC4) includes alignments (_IfcAlignment_), roads (_IfcRoad_), railways (_IfcRailway_), ports (_IfcMarineFacility_), and bridges (_IfcBridge_). Regarding the bridge structure, IFC4.3 RC3 includes the _IfcBridge_ entity in order to represent the spatial information of the bridge as a subtype of _IfcFacility_. _IfcBridge_ has an enumeration type to embody a spatial function for the subspace of the bridge [26]. More specific spatial components constituting the bridge structure can be managed using the _IfcBridgePartTypeEnum_ type of _IfcFacilityPart_. The representation of the physical element of the bridge can be used in the subtypes of _IfcBuiltElement_.  
We used the schema developed by [PERSON] et al. [24] instead of IFC4.0.2.1 or IFC4.3 RC4 for the following reasons:  
* IFC4.3 RC4 is a schema under development that has not yet been officially released. Therefore, current BIM authoring tools, such as Autodesk Revit, cannot handle the information generated by IFC4.3 RC4;
* Information on the bridge structure components covered by IFC4.3 RC4 is limited; it does not define bridge-specific and bridge-related attributes that should be treated as entity and attribute level.  
_IfcBridgeAddMeshfree_, proposed by [PERSON] et al. [24], extends additional entities focusing on the bridge structure and bridge components (see Figure 6). The detailed elements of bridges regarded as enumeration types in IFC4.3 RC4 are also defined as entities.  
In the _IfcBridgeAddMeshfree_ schema, _IfcBridge_ is used to manage the spatial information of the bridge structure itself, _IfcBridgeSpan_ is used to segregate the spatial information based on the bridge's along the road, and _IfcBridgeSpacePart_ is used for the transverse direction of the bridge. In order to represent the physical object of the bridge, it was categorized into girder part, slab part, abutment and pier part, and detailed member component part. Each of these contain enumeration types for detailed types or functions.  
Figure 5: Performance evaluations based on precision, recall, GAH, PAH, GAA, and PAA.  
External document-related information can be managed through the _IfcDocumentInformation_ entity in the IFC schema. The _IfcDocumentInformation_ can capture metadata, such as the document name, document purpose, and/or revision information of an external document, but not the document content. We linked each content fragment of a document corresponding to a model component rather than linking an entire document for an entire 3D model because _IfcDocumentInformation_ can be associated with all IFC objects through the _IfcRelAssociatesDocument_ entity. The implementation of _IfcDocumentInformation_-related information will be described later.
## 4 Results
### Dynamic Setup
#### 4.2.3 Dilution of Precision  
In this instance, Figure 11 illustrates the average DOP obtained by observations carried out while driving.  
Figure 11: Average dilution of precision (DOP) results for the different positions of the car on a route including highway, city, and forest following the path of Figure 4. For DOP, the lower the value, the better.  
Figure 10: Results of the average of satellites used in different positions of the car on a route including highway, city, and forest following the path in Figure 4.  
In only one of the 11 studied scenarios, the device with circular polarization performed better. The device using the linearly polarized antenna booster functioned better in the remaining scenarios (10/11). Although the accuracy was higher in the linear polarization scenarios in this instance, the outcomes were relatively comparable.
## 3 Control Scheme Analysis without TDE  
The dynamics of an n-DOF CKCM manipulator can be represented in joint-space as follows [25]:  
\[\mathbf{M}(\mathbf{q})\ddot{\mathbf{q}}+\mathbf{C}\big{(}\mathbf{q},\dot{ \mathbf{q}}\big{)}\dot{\mathbf{q}}+\mathbf{G}(\mathbf{q})+\mathbf{F}\big{(} \mathbf{q},\dot{\mathbf{q}}\big{)}+\boldsymbol{\tau}_{d}=\boldsymbol{\tau} \tag{5}\]  
where \(\mathbf{M}(\mathbf{q})\in\mathbb{R}^{n\times n}\) stands for the generalized inertia matrix, \(\mathbf{C}\big{(}\mathbf{q},\dot{\mathbf{q}}\big{)}\in\mathbb{R}^{n\times n}\) is the Coriolis/centripetal matrix, \(\mathbf{G}(\mathbf{q})\in\mathbb{R}^{n}\) is the gravitational vector, and \(\mathbf{F}\big{(}\mathbf{q},\dot{\mathbf{q}}\big{)}\in\mathbb{R}^{n}\) is the friction forces.  
Suppose system (5) can be decomposed into n decoupled systems and is presented by the following:  
\[\mathbf{\overline{M}}\ddot{\mathbf{q}}+\mathbf{H}(\mathbf{q},\dot{\mathbf{q}} \ddot{\mathbf{q}})=\boldsymbol{\tau} \tag{6}\]  
where \(\mathbf{\overline{M}}\) is a constant diagonal matrix, and \(\mathbf{H}(\mathbf{q},\dot{\mathbf{q}},\ddot{\mathbf{q}})\) represents the necessary CKCM dynamics and the disturbance torques. Then, from (5) and (6), we obtain the following.  
\[\mathbf{H}(\mathbf{q},\dot{\mathbf{q}}\ddot{\mathbf{q}})\mathbf{=}\big{[} \mathbf{M}(\mathbf{q})-\mathbf{\overline{M}}\big{]}\ddot{\mathbf{q}}+\mathbf{ C}(\mathbf{q},\dot{\mathbf{q}})\dot{\mathbf{q}}+\mathbf{G}(\mathbf{q})+ \mathbf{F}(\mathbf{q},\dot{\mathbf{q}})+\boldsymbol{\tau}_{d} \tag{7}\]  
Now we apply an input \(\boldsymbol{\tau}\) such that the following is the case.  
\[\boldsymbol{\tau}=\mathbf{\overline{M}}\mathbf{u}+\mathbf{H}(\mathbf{q},\dot{ \mathbf{q}}\ddot{\mathbf{q}}) \tag{8}\]  
In order to implement (8), the control system must evaluate \(\mathbf{H}(\mathbf{q},\dot{\mathbf{q}},\ddot{\mathbf{q}})\) which in light of (7) requires heavy computation and thereby making the proposed control scheme impractical and not suitable for real-time control applications. Consequently, some computationally efficient estimation of \(\mathbf{H}(\mathbf{q},\dot{\mathbf{q}},\ddot{\mathbf{q}})\) is needed for the control scheme, which the TDE subsystem could provide. Finally, the Synchronization Subsystem will enhance the overall performance of the control system by using the synchronization errors instead of the conventional joint errors. Detailed development of the above subsystems will be presented below.
## 1 Introduction
Before pressing, the press head must be heated to a specific temperature to enable oil extraction. The temperature to which the press is heated depends on the press design and the raw material being processed and is often provided as a recommendation by the pressmanufacturer. The efficiency of the pressing primarily depends on the type of raw material, particularly the oil content in the feedstock, as well as on the preparation of the feedstock, its particle size distribution, hull content, and the presence of remaining impurities, etc. The design and power of the press, as well as pressing conditions (pressure, temperature, speed, process duration, moisture content of the feedstock, etc. ), also affect the efficiency of the pressing process [28; 29; 30]. Key challenges include variability in seed composition, which directly affects oil yield and pressing efficiency. Specifically, high hull and impurity content can increase mechanical wear and reduce throughput, while low hull content may hinder effective oil drainage. [PERSON] et al. [8] established a strong negative correlation (R\({}^{2}=-0.8153\)) between the hull content in sunflower seeds and oil yield from cold pressing in 20 newly developed sunflower hybrids, with hull content ranging from 24.81 \(\pm\) 0.48% to 46.74 \(\pm\) 0.99%. Impurities can also introduce oxidative compounds, impacting the shelf-life of the final product [31]. As a by-product of cold-pressed oil production, press cake remains [8; 32]. Sunflower press cake is primarily used as animal feed due to its poor functional properties, dark color, high fiber content, and low digestibility. However, by reducing the proportion of hulls and impurities in the raw seeds, sunflower proteins from press cake could potentially be utilized in human nutrition [33]. Considering that the preparation of sunflower seeds for pressing involves drying, dehulling, and removing impurities, the aim of this study is to examine the impact of hull and impurity content on the composition of the obtained cake (moisture and oil content) and the pressing oil yield of cold-pressed oil produced from high-oleic sunflower seeds using a screw press. For the purpose of identifying the high-oleic sunflower oil sample, the fatty acid composition, iodine value, and refractive index were analyzed. Additionally, multiple linear regression models were developed to predict the moisture and oil content in the cake and pressing oil yield as a function of hull and impurity content in the seed. Hierarchical cluster analysis was also applied to determine the similarity between samples in terms of capacity and yield during pressing.
Among them, vision can provide pose estimation in a long-term slow motion state, and IMU can compensate for the positioning deviation during fast motion with its high-frequency measurement advantage. The current fusion of vision and IMU is mainly divided into the following two categories: Kalman filter [20; 21; 22; 23; 24; 25] fusion and nonlinear optimization [26; 27; 28; 29; 11] fusion. [PERSON] et al. [30] used the Kalman filter algorithm to fuse RGB-D images and IMU data to obtain the real-time pose of a mobile robot, and they bounded a single-line lidar to the mobile robot for indoor point cloud reconstruction. However, the Kalman filter can only be optimized based on the data of the previous frame, it cannot obtain the global optimal solution, and the lidar mounted on the wheeled robot platform is not suitable for the scanning of complex terrain in forest areas. To solve the above problems, in this paper, we propose a real-time reconstruction method of a 3D point cloud based on visual-IMU fusion with a single-line lidar, and we construct a low-cost handheld forest information collection device. The method adopts multi-sensor tightly coupled fusion technology to provide accurate pose estimation for a single-line lidar. Then, we obtain an accurate 3D point cloud map using point cloud projection, densification, and loop closure correction based on pose estimation. Finally, we verify the mapping accuracy and real-time performance through experiments, and the method proposed in this paper can also be seamlessly extended to other devices, such as a multi-beam lidar and a solid-state lidar.