esa-sceva/satcom-chunk-collection huggingface dataset:
# Nodal Accuracy Improvement Technique for Linear Elements with Application to Adaptivity
###### Abstract  
In the finite element method, the conventional linear elements have long been precluded, due to their low accuracy of nodal displacements, from the analysis of super-convergence and adaptivity via the element energy projection (EEP) technique. To overcome this problem, in this paper, a nodal accuracy improvement technique is proposed for linear elements in 1D to 3D problems. In this method, a residual nodal load vector is derived with the conventional EEP solution, and a simple back-substitution process can generate the improved nodal displacements without changing the global stiffness matrix. Subsequently, an improved EEP scheme for linear elements is proposed based on the improved nodal displacements. Finally, by using the improved EEP solution as an error estimator, a two-phased adaptive algorithm is presented. Numerical examples show that the accuracy of nodal displacements is improved from the second-order convergence to the fourth-order convergence by using the nodal accuracy improvement technique, and the EEP solutions for element interior displacements are improved from the second-order convergence to the third-order convergence by using the improved EEP scheme. Therefore, the improved EEP scheme can be effectively used as an error estimator in adaptivity analysis for linear elements, which turns out to be efficient in general and even outperforms cubic elements for singularity problems.  
finite element method (FEM); element energy projection (EEP); linear elements; nodal accuracy improvement; super-convergence; adaptive analysis +
Footnote †: journal: Applied  
doi:10.3390/app1305284
# Slow Pyrolysis as a Method of Treating Household Biowaste for Biochar Production
## 3 Results and Discussion
### Properties of Biochar  
The characteristics of the biochars produced from the slow pyrolysis of various biowastes are summarized in Table 2.  
Among all the analyzed samples, the biochar obtained from kitchen biowaste at a pyrolysis temperature of 400 \({}^{\circ}\)C showed the most favorable fuel-related properties. This biochar contained the highest levels of volatile matter (20.49%), carbon (58.02%), hydrogen (4.00%), and recorded the highest higher heating value (HHV) at 22.68 MJ/kg. It also had the lowest ash content among the samples, at 18.57%. These attributes suggest good potential for energy recovery, indicating that this biochar could be suitable for use as a fuel. However, this biochar also presented some drawbacks. Its relatively high nitrogen content (3.64%) could lead to increased nitrogen oxide (NO\({}_{\text{x}}\)) emissions during combustion, raising environmental concerns. Additionally, the high content of combustible compounds (81.43%) may pose risks during storage due to the possibility of spontaneous ignition. The yield of this kitchen biochar was 36.64%, which is not only the highest observed for kitchen-derived biochar in this study, but also exceeds the typical yield for biochar produced via slow pyrolysis, which is around 35% [12]. Biochar with such properties could be used for carbon sequestration or soil improvement through enhanced water and nutrient retention. In contrast, the biochar obtained from spring and autumn garden biowaste demonstrated similar properties to each other across corresponding pyrolysis temperatures, but differed significantly from kitchen biochar. The yields of both garden biochars were nearly double that of kitchen biochar. However, they also contained more than three times the ash content of kitchen biochar, contributing to significantly reduced values of volatile matter, carbon, and hydrogen content. As a result, their HHVs were less than half those observed for kitchen biochar. Furthermore, the garden-derived biochar exhibited a lower carbon content and HHV compared to their respective feedstocks before pyrolysis. Considering that effective biochar should be rich in carbon and have a calorific value in the range of
# An Architecture for a Tri-Programming Model-Based Parallel Hybrid Testing Tool
## 2 Background
### An Overview of Common Run-Time Errors
#### 2.3.3 Race Conditions  
Race conditions occur when multiple tasks try to access and modify a shared resource simultaneously. This can result in unexpected behaviours or errors [27]. An example of a race condition is when OpenMP threads change data while an MPI is moving data, thereby creating inconsistencies at the receiver's end.
# Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model
## 1 Introduction
In this paper, the data points related to downtimes are removed to reserve only fault-free and fault data provided by the SCADA system for the purpose of fault and no-fault predictions. Thus, a non-stationary Time2 Vec activation function is demanded to deal with the yielded asynchronous data. * A novel deep-learning neural network model, T2V-LSTM, with an optimal non-stationary activation function, is modelled to improve the model performance of LSTM, successfully detecting over 84.97% of faults in advance. The comparative studies between T2V-LSTM, LSTM, and other ML classifiers are investigated for overall and individual fault predictions based on performance metrics, including accuracy, recall scores, precision scores, and F-scores [16]. The paper is organised as follows. Section 2 provides the SCADA operational and status data, and the modelling process is introduced with data pre-processing, feature engineering, and fault prognosis. The methodology studies of T2V-LSTM and the processes of model optimisation are presented in Section 3. Section 4 investigates the comparative predictive results from T2V-LSTM, LSTM, and other classifiers, and Section 5 presents a discussion of this investigation. The key results and contributions are summarised in Section 6.
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 4 Methodological Approaches in Crash Research
### Machine Learning and Data Mining Approaches  
As computational capabilities expanded, the field embraced machine learning and artificial intelligence techniques. [PERSON] and [PERSON] [41] compared statistical and machine learning methodological approaches for crash severity prediction, evaluating various classification algorithms, including Nearest-Neighbor Classification, finding that NNC had the best predictive performance, while K-means clustering improved model performance. [PERSON] and [PERSON] [64] explored neural network methodological frameworks for predicting driver injury severity, demonstrating early applications of artificial intelligence techniques to traffic safety analysis.  
[PERSON] et al. [65] developed genetic mining rule methodological frameworks utilizing stepwise rule-mining algorithms for crash severity analysis, integrating data mining techniques with traditional statistical modeling and demonstrating the effectiveness of a two-stage mining framework in capturing the joint effects of risk factors. [PERSON] et al. [46] developed count data modeling approaches using traffic conflict techniques as surrogate safety measures, providing alternative methods for assessing road safety when crash data are limited. [PERSON] et al. [47] presented methodological frameworks for identifying crash risk through the coupling of in-vehicle data with kinetic parameters, advancing the integration of real-time vehicle dynamics in safety assessment.  
[PERSON] et al. [66] employed gradient boosting decision trees (GBDTs) to address key challenges in crash analysis, particularly the multicollinearity inherent in real-world traffic data. Their approach demonstrates strong predictive accuracy across four crash indicators while ranking 27 influential factors--revealing crucial insights into variable importance that traditional \"black-box\" machine learning methods obscure. This methodological contribution advances the field by combining robust statistical performance with interpretability, enabling researchers to identify and understand complex relationships within crash datasets.
# Misconfiguration in Firewalls and Network Access Controls: Literature Review
## 3 Misconfigurations
### Redundancy  
Redundancy is the least severe of the misconfigurations. Redundancy is as simple as it sounds: it is when two different rules cover the same potential packets and take the same action [4]. Similar to shadowing, this can take place as two identical rules or as a general rule followed by a more specific one. Say, for instance, a firewall has two rules that block ports 20-30; these rules are redundant because both rules perform the same actions on the same packets. It is also a case of redundancy if a rule blocks that same range of 20 to 30 but a second rule only blocks port 22. Since port 22 is in the range of 20 to 30, there is no need to have another rule blocking port 22 since it is already blocked by the first rule.  
Redundancy is not as straightforward when it comes to inter-firewall or inter-component misconfigurations. Reference [11] identifies an inter-firewall redundancy anomaly when a downstream firewall blocks traffic that an upstream firewall is already blocking. A rule-blocking specific traffic from outside the network is not needed if an upstream firewall already does so. If there is concern for insider threats, such traffic could occur if the attacker is on the internal network already making the need for some redundant rules between multiple firewalls necessary. This will still cause that rule to be evaluated twice on outside traffic but could nonetheless protect from traffic from an attacker within the same network. Whether or not inter-firewall redundancy is a misconfiguration depends entirely upon the threat level from within the user's own network.  
Redundancy is one of the few misconfigurations that has no impact on the overall security of a network. Having multiple copies of the same rule, even across multiple devices does not affect policy enforcement. The only effect redundancy has on a network is to performance [6]. The issue with redundant rules is that it slows down enforcement. A firewall will try to match a packet to one of its existing rules and if it does not match any, it takes a default action. The problem with redundancy is that whenever a packet comes in, it gets checked by more rules than it should have to. Every time a packet is checked against a redundant rule, time and performance of the device is wasted. Redundant rules can be safely removed from a firewall as their removal will have no impact on security and will result in better performance from the firewall.
# V2X Communication over Cellular Networks: Capabilities and Challenges
## 4 Delay Analysis for V2X Communications
### 5G-NR Delay
#### 4.2.2 User Plane (Uu Interface)
Moreover, using a local breakout scheme with a standalone Local Gateway (L-GW), the overall core network delay for LTE may be reduced from 20 to 12 ms [22]. In the case of the 5G core network, the packet needs to traverse from gNB to UPF through the N3 interface and subsequently from UPF to the V2X AS through the N6 interface (i.e., for unicast mode) [23], always assuming that the UE is in the RRC CONNECTED state and the Protocol Data Unit (PDU) session is established. In order to provide a complete picture of the E2E delay, we assume a 3 ms delay for each interface, as in [22]. The assumptions for 5G-NR evaluation are presented in the UC Delay Evaluation Section.
## 3 Results and Discussion
### LD TOF MS Analysis of Hemp Seed Oil  
Considering that the high nutritional quality of hempseed oil is essentially determined by its fatty acid composition, the application of MS-based chemical component profiling offers significant opportunities to obtain detailed information that can be directly correlated to oil quality [36].  
A typical distribution of the major fatty acids in industrial hemp seed oil is as follows [37]: palmitic acid (C16:0); 6.66-6.98%), stearic acid (C18:0); 2.08-2.82%), oleic acid (C18:1; 9.38-13.00%), linoleic acid (C18:2; 55.56-56.58 %), \(\alpha\)-linolenic acid (C18:3; 14.69-17.27%) \(\gamma\)-linolenic acid (C18:3; 2.56-4.49%). The fatty acid composition of the hemp seed oils, determined in accordance with [38; 39] obtained by Soxhlet and supercritical CO\({}_{2}\) extraction with and without ultrasound pretreatment is reported in Table 3.  
No significant differences were found when the hemp seed oil was extracted by supercritical fluid extraction using CO\({}_{2}\) as a fluid or by Soxhlet. The main components were oleic acid (O) (18:1), linolenic acid (L) (18:2) and linolenic (Ln) (18:3) (Table 3). The determined polyunsaturated fatty acids (PUFA) were 81% of total fatty acids, while the monounsaturated and saturated fatty acids amounted to 11.4-11.7% and 7-7.5%, respectively. The ratio of polyunsaturated to saturated components was 11-11.5%. The determined omega 6/omega 3 ratio for all samples (2.7-2.9%) is slightly different from the commonly accepted value (3-3.3%) because this approach does not allow for distinguishing \(\alpha\)-linolenic acid from \(\gamma\)-linolenic acid. This composition agrees with results reported in Canada [40; 41] and for hemp cultivated in Europe [10].
# A Literature Survey on Electrical-Current-Assisted Friction Stir Welding
## 2 Principle of EA-FSW Method  
The EA-FSW is a developed process that is a combination of FSW and resistance welding. The EA-FSW, as a hybrid welding method, benefits from frictional heat and electrical current, and frictional heat plus resistance heat is used as the welding heat source for joining the workpieces. In EA-FSW, the workpieces are firmly placed to the joining fixture, in order to avoid their separation and any movements during the joining process. A rotational tool with a relatively thin pin is rotated and moved downward to insert it into the interface of the workpiece. Generally, the FSW tool is made of a hard material, and after penetration of the workpiece interface, the FSW tool moves forward along the joint line. Simultaneously, electrical current is provided, which flows between the FSW tool and the workpiece interface. In this stage, electrical resistance heat generates in the joint line and adds to the frictional heat produced by the rotational tool. The electrical resistance heats the joint line and is combined with frictional heat to soften and plasticize the workpieces.  
The additional electrical current makes the workpieces softer and decreases the welding force. This phenomenon increased the traverse speeds of the welding force to build a sound solid-state welding joint. This method can be implemented for the joining of ferrous and non-ferrous metals. Moreover, the hard materials can be joined much easier with this method. The FSW tool's working life increases by providing an additional heat source during the joining process. Higher welding quality and mechanical properties of final joints can be achieved at a shorter welding time. Due to the decreasing welding force, the joint line can be produced faster in EA-FSW. With extra electrical heating, the internal defects formation, such as wormholes and uncomplete mixing, is minimized. On the other hand, the residual stress of welded plates after EA-FSW is much lower than in the regular FSW process. This is due to the processing of material at elevated temperatures. The additional equipment needed to add the FSW process is simple, relatively cheap, and easy to use [24]. The electrical current is applied from a DC or AC power supply for the EA-FSW. The type of power supplier may be different in similar or dissimilar joints. The leads are fixed on the workpiece or welding tool for the passing electrical current, while insulating components, such as a ceramic plate, are fixed on the fixture or tool. The amount of current depends on the raw materials that are supposed to be welded. The electrical current would be greater for hard materials than for soft materials. Because a high current leads to overheating or melting of the raw materials, the used current range should be calculated according to electrical resistance properties. After that, the range of used electrical current should be calculated and considered for tests.
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 4 Applications of Deep Learning in Agriculture
### Identification of Weeds  
Identifying all the weeds on a farm is difficult and typically unnecessary. However, accurately recognizing enormous weeds can be a critical first step in effective control. At certain phases of growth, different weed species may appear remarkably similar. Still, they differ considerably in their life cycle, mode of reproduction, effects on plants, and responsiveness to control tactics. In [48], an RGB + NIR camera created a CNN-based classification system to detect and distinguish sugar beet plants from weeds over natural fields using an RGB + NIR camera. The system gave good accuracy in seeing sugar beet plants among the weeds.  
The work [49] proposes a classification vision system to classify individual plants from multi-plant images captured in natural corn field environments. Therefore, a dataset of 15,240 images that contains nine plant species, grouped into the classes Crop, NLW, and BLW, was generated. Images were captured under these natural cornfield environments, and plants were of different growth stages. A classical approach to CNN carried out the classification of the plants in the dataset.
# Audio-Visual Action Recognition Using Transformer Fusion Network
## 2 Related Work  
Audio-visual action recognition has attracted considerable attention in recent years as researchers strive to leverage the synergistic relationship between audio and visual information, thereby improving the performance of action recognition systems. One of the earliest approaches [19] to audio-visual action recognition focused on fusion techniques, including early fusion, late fusion, and intermediate fusion. These methods aimed to combine audio and visual features at different stages of the training process, whether at the input level, decision-making stage, or intermediate points throughout the training process.  
With the advent of the deep learning method, significant advancements have been made in audio-visual action recognition. Researchers have employed convolutional neural networks (CNNs) to learn spatial features from video frames and recurrent neural networks (RNNs) and long short-term memory (LSTM) to capture temporal patterns [20; 21] in audio and visual data. Various strategies have also been proposed to fuse the features, including concatenation, element-wise summation, and attention mechanisms [22; 23].  
More recently, transformer-based models have gained popularity in audio-visual action recognition because of their ability to model long-range dependencies and capture complex interactions between audio and visual modalities. The Multimodal Bottleneck Transformer (MBT) [24] introduces a transformer architecture that constructs multi-modal bottleneck tokens to efficiently fuse video and audio features from image and audio transformers, surpassing traditional late-fusion methods.  
In this paper, we deviate from using the Video Swin transformer [13] and opt for a single Swin Transformer [25] designed for still images. We apply this single transformer to all modalities--image, video, and audio--enabling greater resource efficiency without compromising competitive performance.
## 5 Conclusions  
In response to the obvious blind zones in current dual-perspective cheating behavior detection systems, we designed a three-camera monitoring installation plan based on the overhead, face, and horizontal perspectives. To expand the scope of examination monitoring while ensuring real-time system performance, a three-perspective adaptive cheating behavior detection system driven by candidate gaze direction was proposed. Nine gaze directions were categorized as either normal, horizontal perspective, or overhead perspective. Based on the gaze direction recognition results, the cheating tool detection model and cheating behavior determination model under the best perspective were automatically selected. To quickly and accurately identify cheating tools, a lightweight object detection network model, Lightweight-YOLOv5-CA, was proposed. Through online testing results, it was verified that the three-perspective capture and evidence collection method can clearly describe cheating behavior and effectively expand the monitoring area. The adaptive cheating behavior detection model driven by gaze direction recognition effectively extends the monitoring area while ensuring real-time performance. Among them, the accuracy of the gaze direction recognition model from the face perspective reached 92.4%, and the mAP50 of overhead and horizontal object detection reached 97.4% and 98.8%, respectively. In the final cheating behavior determination model, the accuracy reached 95.4%. The system has been put into use in a certain school, and the application effect is good after verification, which realizes intelligent monitoring, greatly reduces students' cheating, and saves invigilators' resources. In the future, the system will be further promoted in other schools, exploring potential problems in practical application, and enriching the resources of the three-perspective dataset to improve the universality of the system.  
Conceptualization, H.W.; Methodology, Z.H.; Software, Y.J.; Validation, Z.H.; Formal analysis, Z.H. and Y.J.; Investigation, Y.J.; Resources, Z.H.; Data curation, Y.J.; Writing--original draft, Y.J.; Writing--review & editing, Z.H., G.W. and H.W.; Supervision, Z.H., G.W. and H.W.; Project administration, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Postgraduate Education Reform Project of Jiangsu Province grant number JKCT23_C075.  
Ethical review and approval were waived for this study, due to we have recruited volunteers and they have signed informed consent statement for portrait usage.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Cellular and Virtualization Technologies for UAVs:
## 3 Testbed Components Description
### Cellular-Assisted UAV Communications
This VPN allows the communication between both elements thanks to the encapsulation, and subsequent routing, of the information that has to be sent. Furthermore, the VPN service provides additional advantages that are beneficial for the testbed infrastructure. For instance, since any UAV is able to change the BS (as a standard ground user equipment) due to the inherent UAV mobility, its IP address might change when this movement is performed. However, by using the address space provided by the VPN service, these handovers should be transparent for the UEs as long as the UAV has connectivity to the Internet, because the VPN will deal with the routing since the device will not modify its VPN IP address, even though its IP address (the one providing Internet to the RPi) might change. Moreover, using a VPN provides an additional layer of security, as all the communications between its elements will be encrypted to allow its transmission through the VPN tunnel, preventing man-in-the-middle attacks or the  
Figure 3: Details of UAV payload, and UAVs in flight. unauthorized attachment of malicious nodes to the network. Details on including data encryption, such as VPN service, can be found in our previous work [43].