esa-sceva/satcom-chunk-collection huggingface dataset:
# Fingerprinting Indoor Positioning Based on Improved Sequential Deep Learning
## 3 System Overview
### Position Determination
#### 3.2.2 Wknn  
When the RP distribution is relatively uniform within a region, the DNN training performance will be excellent. Conversely, if the RP distribution is uneven or sparse, the DNN's training effectiveness will decline. To address this, during the model training phase, we set a decision threshold: if the final loss function value exceeds this threshold, the WKNN algorithm is invoked.  
1. Input: \(M_{j}\) training data \(\{S_{i}\}_{i\in\mathcal{T}\cap\mathcal{F}_{j}}\), a test fingerprint \(S_{0}\), and the number of neighbours \(k\).
2. Identify \(\{S_{0}^{1},S_{0}^{2},S_{0}^{3}\}\) as the first, second and third strongest APs in \(S_{0}\).
3. For each \(S_{i}\) in \(\mathcal{T}_{j}=\mathcal{T}\cap\mathcal{F}_{j}\), do the following:  
1. Identify \(\{S_{i}^{1},S_{i}^{2},S_{i}^{3}\}\) as the first, second and third strongest APs in \(S_{i}\).
2. If \(\{S_{i}^{1},S_{i}^{2},S_{i}^{3}\}\) is equal to \(\{S_{0}^{1},S_{0}^{2},S_{0}^{3}\}\):  
1. Add \(S_{i}\) to \(\mathcal{T}_{j}^{\prime}\).
3. If \(\mathcal{T}_{j}^{\prime}\) is empty:  
1. Set \(\mathcal{T}_{j}^{\prime}\) equal to \(\mathcal{T}_{j}\).
2. For each \(S_{i}\) in \(\mathcal{T}_{j}^{\prime}\):  
1. Let \(B(i)\) be the number of APs observed in both \(S_{i}\) and \(S_{0}\).
2. Calculate \(C(i)\) using the formula: \[C(i)=\frac{1}{R}\sum_{r=1}^{R}|S_{ir}-S_{0r}|-2B(i)\]  
1. Select the \(k\) RPs with the smallest \(C(i)\) values, denoted as \(\mathcal{T}_{j}^{\prime\prime}\).  
Step 6. For each \(S_{i}\) in \(\mathcal{T}_{j}\):  
Step 6.1. If \(S_{i}\in\mathcal{T}_{j}^{\prime\prime}\):  
i.  Calculate \(d_{i}=\text{EuclideanDistance}(S_{0},S_{i})\).  
ii.  Set \(w_{i}=\frac{1}{(d_{i})^{2}}\).  
Step 6.2. Else:  
i.  Set \(w_{i}=0\).  
Step 7. For each \(S_{i}\) in \(\mathcal{T}_{j}\):  
Step 7.1. Normalize the weights as follows:  
\[W_{i}=\frac{w_{i}}{\sum_{i=1}^{M_{i}}w_{i}}\]  
Step 8. Output: Return \(W\).  
Steps 1-8 in Section 3.2.2 illustrates the process of enabling WKNN to determine the weight \(W\in\mathbb{R}^{M_{j}\times 1}\) when DNN is not applicable. First, the trained classification network is used to predict the region where the test point resides, and all training data within that region are extracted. Next, the sequence numbers of the three strongest APs are identified for both the test point and the training data, with the matching training data recorded. Then, similarity values are calculated for these training data, and the \(k\) most similar training points are selected. Finally, the distance function, typically the Euclidean distance, is used to calculate the RSSI distance and weight.
## 3 Materials and Methods
### YOLOv8 Model Structure
In the Head, the two functions are respectively applied to multi-scale feature maps (such as cv20 and cv31); the Sigmoid function is used to process the target probability; and the Softmax function is used to process the classification probability to achieve accurate positioning and multi-class discrimination of the detection results. In terms of loss function setting, YOLOv8 uses CIoU (Complete Intersection over Union) and DFL (Distribution Focal Loss) for bounding box regression loss calculations and uses binary cross-entropy to process classification loss to improve the performance of small target detection and the overall detection effect. In the feature fusion stage, YOLOv8 uses path aggregation structures (such as a combination of FPN and PAN) to achieve efficient integration of feature maps from different depths. This step enables the model to have stable performance when detecting small targets (such as cracks on the surface of gray bricks or moss point distribution) and large targets (such as the entire brick missing or a large area of pollution). The detection head uses the anchor-free method to output position regression and category classification results. Each layer of prediction branches can handle targets of a specific scale and achieve unified detection capabilities across the full range of sizes. The model's final output shows the location of the object, the likelihood of its category, and a confidence score, while the non-maximum suppression (NMS) algorithm helps eliminate duplicate predictions to make sure the detection results are accurate and unique. To better adapt to the needs of building damage detection, we strictly followed the native configuration specifications of YOLOv8, set the image input size to 512 \(\times\) 512, and used the \"x\" version model to obtain stronger feature extraction capabilities. At the same time, we used its default three-layer detection head structure (Head0, Head1, and Head2), corresponding to the three-layer feature maps of P3, P4, and P5, covering small, medium, and large spatial scales. In the detection process, the model can find and identify each gray brick in the image and assess its damage, which helps in counting the gray bricks and analyzing the damage on their surfaces. In addition to enhancing the model's visualization, expression, and engineering practicality, this paper integrates the feature fusion heatmap in the simplified path, which clearly shows the response area focused on by the model in feature maps of different scales. The final output result (Output) has a size of 512 \(\times\) 512 \(\times\) 3, where different color channels represent different prediction categories (such as missing, cracked, contaminated, etc.) to intuitively show the positioning and classification effect of YOLOv8 in gray brick damage detection.
# The Viable but Non-Culturable (VBNC) State, a Poorly Explored Aspect of Beneficial Bacteria
## 3 The VBNC State in Beneficial Bacteria
### Betaproteobacteria and the VBNC State  
In the group of Betaproteobacteria, the only beneficial bacterium where the VBNC state has been documented is _Cupiravidus metallidurans_. This bacterium is metallophilic, found in environments containing high concentrations of heavy metals and industrial wastes rich in toxic heavy metals, often mixed with recalcitrant organic compounds and hydrocarbons [67]. It is an ideal bacterium for bioaugmentation purposes in environmental applications due to its strong resistance to environmental stress factors and its adaptation capacity [68]. Another specific application is its ability to convert gold chloride into 24-carat gold in one week [69]. These observations suggest that bacteria actively contribute to the formation of gold grains in surface environments [70]. With this bacterium, the conditions that can induce its loss of cultivability have been studied, as well as the conditions that can return it to a cultivable state. The addition of water and gluconate is sufficient for _C. metallidurans_ to be culturable in 24 h [49]. Due to its interesting applications and ability to persist in toxic environments, it is an excellent model to study the mechanisms of coping with heavy metal stress. This unique ability to metabolize toxic substances and enter the VBNC state without water and carbon sources could help us to understand how the origin of life occurred.
# Analytical Prediction of Balling, Lack-of-Fusion and Keyholing Thresholds in Powder Bed Fusion
## 1 Introduction
[17] summarized several thermal models for the prediction of temperature profiles in laser processing techniques. [PERSON] et al. [18] proposed a closed-form temperature prediction model for AM processes, which could consider the boundary conditions. [PERSON] et al. [19] proposed a physics-based analytical modeling approach to calculate the grain size in the products fabricated by additive manufacturing. [PERSON] et al. [20] employed an analytical thermal model to predict the molten pool size in metal additive manufacturing and then correlated the molten pool size with the part porosity evolution by regression analysis. [PERSON] et al. [5] developed an analytical strategy to calculate the lack-of-fusion porosity in powder bed fusion processes, considering the influence of un-melted powders on the final porosity of the products. [PERSON] et al. [8] improved [PERSON]'s thermal equation by using a heat source with Gaussian distribution and employed the improved thermal model for the prediction of molten pool size. The calculated molten pool geometries were then used as inputs for the prediction of defect generation in additive manufacturing. However, the boundary conditions were not considered in that paper. Moreover, the properties of powder materials were not incorporated. Analytical methods to consider boundary heat loss, effects of powder bed packing onpowder material properties are not available yet in the prediction of thresholds for defects formation in LPBF. In this paper, analytical modeling methods were proposed for the prediction of defects generation in laser powder bed fusion additive manufacturing. The molten pool dimensions were first calculated by an analytical thermal model, with the consideration of heat input from laser power, heat loss at part boundaries and effect of powder bed porosity (void fraction in the powder bed) on material properties. The predicted molten pool geometries were then employed in the calculation of the thresholds for the lack-of-fusion, balling and keyholing defects to occur. One criterion for the generation of lack-of-fusion defect, one criterion for balling and one criterion for keyholing were employed in this study. The presented analytical methods did not incorporate any iteration-based calculations, which ensure their high computational efficiency. The predicted results were compared to experimental observations of Ti6 Al4V, Inconel 718 and 316L stainless steel for validation of the presented analytical models.
###### Abstract
Combining imaging with computational fluid dynamics assessment of FFR is known to increase the specificity of diagnosing lesion-specific ischemia [12]. It is facilitated by ready availability of open source advanced scientific platforms [13; 14; 15; 16], including those developed in house (Virtual Cardiac Physiology Laboratory) [17; 18]. Typically, computation of FFR combines an imaging-generated 3D coronary geometry coupled to models of coronary hemodynamic physiology. Others have used the approach to study a spectrum of processes involving FFR estimation refinement [19], interplay among multiple stenosis complexes [20], and perioperative treatment assessment [21], among several other applications. Computer modelling can be performed at a simple lumped parameter or detailed 3D spatial scales. As a specialized high performance computing application, 3D modelling cannot be performed onsite by the clinician. Due to the large variety of data collection resulting in the need to explore parameter spaces [22; 23], large scale computations remain unwarranted in a clinical environment. Recent studies demonstrate the deployable nature of lumped parameter (0D) modelling in a clinical environment. Our recent study, where the role of peripheral arterial disease in hypertension was addressed, illustrates a 0D model deployable nature [24]. We also used 0D hemodynamic modelling of the whole human model to test the effects of treatments such as hypothermia and exercise on systemic circulation [17]. The debilitating effects of atrial fibrillation on cerebral circulation were illuminated recently by [PERSON] et al. [25]. However, the availability of computationally efficient coronary blood flow models remains limited [26]. It was therefore relevant to develop an open source and extensible coronary model. _Study aims:_ In this work, an existing lumped parameter (0D) model of the coronary vasculature [27] was further developed and used to demonstrate important factors that regulate FFR. Specifically, the dependence of FFR on the nature of stenosis (focal or diffuse) and on micro-vascular status was investigated. Further, a partial rank correlation coefficient (PRCC)-based sensitivity analysis [1; 28; 29] was performed to determine the impact of model parameters on FFR. For this purpose, a 0D modelling approach was found to be suitable as the study's goal was to understand coronary flow in the presence of pathological conditions. It can be appreciated that model identification (personalization), although highly desirable, was not essential in this theoretical study. As such, the presented model is theoretical in nature, in which a better understanding of pathophysiological processes was prioritized over model personalization.
## 3 Results
### RQ1: Which Electrophysiological Signals May Index Emotional Processing?
#### 3.1.5 Databases of Electrophysiological Data  
To analyze the relation between emotions and electrophysiological signals, most researchers collected data from an in-home sample. However, only one work [26] considered the use of SEED [68] and DEAP [69] databases for its study.  
For emotion pattern recognition based on electrophysiological activity, two databases were found: SEED and DEAP. The former is a free EEG dataset developed by Shangai Jiao Tong University in 2015 [68]. It contains the electrocortical activity of 62 channels from 15 Chinese subjects (seven males, eight females) while they were watching film clips. These stimuli were 15 Chinese film clips and were categorized in positive, neutral, and negative emotions. The signals were recorded with an ESI NeuroScan System at 1000 Hz. On the other hand, DEAP includes electrophysiological data of 32 channels of EEG activity and 13 channels of peripheral physiological signals [69]. They were recorded from 32 participants (16 females) using a BioSemi ActiveTwo system at a sampling rate of 512 Hz. A total of 40 music videos were presented in 40 trials. At the end of each trial, participants rated each video in terms of arousal, valence, like/dislike, and dominance using the SAM [70].
# An Enhanced Message Distribution Mechanism for
## 5 Simulation and Evaluation
### The Experiment of Protocol-Irrelevant Mode  
The protocol-independent acceleration strategy in the EMD module needs the support of protocol-independent controllers and data plane devices. However, most of the existing simulation tools only support OpenFlow devices. In terms of controllers, POPOX [29] and PNPL [30] are quite immature, and the projects have stopped maintenance and updates; in terms of the data plane, the software-based POF switch prototype [31] has ceased to support, and commercial hardware switches (e.g., Huawei NE40E and NE5000E) are difficult for completing experimental verification under a certain scale. As a result, we use an alternative plan and still use the virtual network constructed by mininet as the tested network, in which the node is OpenvSwitch, which supports OpenFlow 1.3. During the experiment, OpenFlow is used to complete the handshake phase and the topology discovery. When running normally and getting the southbound message event, the parsing module is not called, but the feature matching is directly completed by the EMD module.  
In addition, in order to ignore the influence of apps' routing decisions, the experiment in this section uses a chain network and an end-to-end delay to measure the effectiveness of protocol-independent strategies, in which independent variable is the path length.  
In Figure 10, \(+i\) means the controller uses the protocol-irrelevant mode and the others are the same meanings as before. Because of a chain network, the path-finding algorithm consumes basically the same time, so the delay difference can be considered to be the communication cost between the switch and the controller. The left picture in Figure 10 is the overall picture of the end-to-end delay under various path lengths, it is not so intuitive enough that we use the simple_switch app's delay as a benchmark to redraw the growth rate under the three modes into the right picture. It can be seen that, when the path length is small, the protocol-independent mode is not significantly effective. However, when the path is long, this mode can reduce up to 220.192 us from the Openflow one, a decrease rate of 5.9% (from 4233.782 us to 4013.59 us when the path length is 16 hops).  
In addition to the reduction of delay, scalability and flexibility are the main advantages of protocol-irrelevance. Unfortunately, there is no suitable experiment to visually demonstrate. However, we believe that protocol-irrelevant methods with certain performance guarantees will be more favored by developers.
# A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET
## 4 Evaluation
### Comparison of PDR  
In addition to average CBR, we also evaluated other metrics, such as the total number of packets sent, received, and lost during the simulation time. Figures 7 and 8 compare the total number of packets sent by each approach for the low and high vehicle density scenarios, respectively. The PDR represents the percentage of sent packets that were successfully received by a vehicle and is shown in Figures 9 and 10. The proposed QBACC approach has the highest PDR value, at 98.7%, for high vehicle densities, and achieves the second-best results for low vehicle densities, with a PDR of 98.2%, just slightly lower than the PDR of 98.8% obtained using 5 Hz.  
The number of lost packets, representing the packets that were not received by any vehicles during the simulation, is shown in Figures 11 and 12. The BER, which calculates the percentage of sent packets that were lost, is shown in Figures 13 and 14. QBACC consistently had low BER values, with the lowest value for 500 vehicles and the second-lowest value for 300 vehicles. The MDPRP BER values were slightly higher than QBACC for both cases; moreover, 5 Hz had slightly lower BER for 300 vehicles, but significantly higher BER for 500 vehicles. A comparison table of total sent packets, total lost packets, BER, and PDR is listed in Table 4.  
Figure 8: Total sent packets with 500 vehicles.  
Figure 7: Total sent packets with 300 vehicles.  
Figure 9: Packet delivery rate with 300 vehicles.  
Figure 11: Total lost packets with 300 vehicles.  
Figure 12: Total lost packets with 500 vehicles.  
Figure 10: Packet delivery rate with 500 vehicles.
###### Abstract  
Ordinary Portland cement (OPC) manufacture determines about 8% of the global anthropogenic CO\({}_{2}\) emissions. This has led to both the cement producers and the scientific community to develop new cementitious materials with a reduced carbon footprint. Calcium sulfoaluminate (CSA) cements are special hydraulic binders from non-Portland clinkers; they represent an important alternative to OPC due to their peculiar composition and significantly lower impact on the environment. CSA cements contain less limestone and require lower synthesis temperatures, which means a reduced kiln thermal energy demand and lower CO\({}_{2}\) emissions. CSA cements can also be mixed with supplementary cementitious materials (SCMs) which further reduce the carbon footprint. This article was aimed at evaluating the possibility of using different amounts (20 and 35% by mass) of water potabilization sludges (WPSs) as SCM in CSA-blended cements. WPSs were treated thermally (TT) at 700\({}^{\circ}\) in order to obtain an industrial pozzolanic material. The hydration properties and the technical behavior of two different CSA-blended cements were investigated using differential thermal-thermogavimetric and X-ray diffraction analyses, mercury intrusion porosimetry, shrinkage/expansion and compressive strength measurements. The results showed that CSA binders containing 20% by mass of TTWPSs exhibited technological properties similar to those relating to plain CSA cement and were characterized by more pronounced eco-friendly features.  
low-CO\({}_{2}\) binders; calcium sulfoaluminate cements; supplementary cementitious materials; hydration; expansion; mechanical strength +
Footnote †: journal: Applied Sciences
# Advancements in Research on Duck Tembusu Virus Infections
## 2 Genomic Characteristics  
Since the early 21 st century, there have been successive reports of TMUV circulation in East Asia and Southeast Asia. However, during the continuous circulation, these virus isolates have undergone cluster separation and diverged phylogenetically from the TMUV cluster. Based on the reported full-length genome sequences of TMUV and the ML trees of the E, NS1, and NS5 gene sequences, they are divided into four major phylogenetic clusters: TMUV, Cluster 1, Cluster 2, and Cluster 3. The TMUV cluster contains the first TMUV virus isolated in 1955 (TMUV MM1775), strains discovered in Sitiaawan in 2000 (STWV), and strains found in Taiwan, China in 2019 (TMUV-TP1906). The virus strains isolated in Thailand in 2007 and two strains isolated in Malaysia in 2012 form a monophyletic lineage, named Cluster 1. Over the past decade, most strains isolated in China and Thailand have formed a branch called Cluster 2, which is further subdivided into two subgroups: Cluster 2.a and Cluster 2.b. Since 2010, most strains isolated in Thailand and a small number from China belong to Cluster 2.a, while most isolates from China are assigned to Cluster 2.b. In recent years, two different research groups have identified a new cluster called Cluster 3 within the TMUV group, which collects virus isolates from China and Thailand [15; 30; 31; 32; 33] (Figure 2).  
The genome of DTMUV consists of a non-segmented, single-stranded positive-sense RNA, approximately 11 kb in length [34]. Mature DTMUV particles are spherical, with a diameter of 50 to 75 nm, exhibiting a typical enveloped flavivirus structure. The RNA core appears dark and dense, with the capsid and lipid bilayer clearly visible. The capsid forms regular hexagons, and the outer structural domain of the E glycoprotein appears striped rather than showing visible spikes [35]. The genome comprises a single open reading frame (ORF), flanked by 5\({}^{\prime}\) and 3\({}^{\prime}\) untranslated regions (UTRs). The 5\({}^{\prime}\) end possesses an m7 GppAmp structure (cap 1), while the 3\({}^{\prime}\) end lacks a poly-A tail [36]. This ORF encodes a polyprotein precursor containing 3425 amino acids. Subsequently, the polyprotein undergoes cleavage by the host signal peptidase, furin protease [37], and the virus's serine protease NS2B/NS3. This results in the formation of three structural proteins (capsid protein, prM membrane protein, and envelope protein E) involved in the assembly of infectious viral particles and the virus's cellular entry and release processes. Additionally, seven non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5) play roles in viral transcription and replication, concurrently participating in the modulation of the host's antiviral innate immune response [38; 39] (Figure 3).