esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Abnormal Data Detection  
### Abnormal Data and Evaluation Indexes  
A problem inevitably brought about by the massive amount of BIM data is that errors occur in the modeling process during data generation, even though abnormal data only account for a small percentage compared to normal data. The detection of abnormal data is very difficult but necessary.  
If only the index of accuracy was used, it would be very misleading. Assuming that the proportion of abnormal data is 1%, in this case, the output of the setting model is  
Figure 13: Confusion matrix about Data4.  
normal; that is, no matter what the input data is, the final output is normal. In this case, the accuracy of abnormal data detection can be as high as 99%, since only 1% of the data is misclassified. This result is obviously unacceptable, because we have not found all the abnormal data, even though the accuracy rate is 99%. This shows that in the special context of anomaly detection, merely using the index of accuracy, is not enough, and a new index is required for objectively evaluating the final experimental results, ideally a combination of Accuracy, Precision, Recall and F1-score.  
Next, we define normal and outliers. The so-called normal data refers to data points whose attributes match their labels, and the abnormal data refers to data points whose attributes do not match their labels. The latter may be divided into two categories. For example, BEAM is considered as normal data here, while one is an abnormal attribute value (wrong attribute value appearing, which may be one or more). The other category refers to an abnormal value of the label; that is, the name of the data is not BEAM, but their label has wrongly been added to BEAM, as seen in Table 13.  
Taking the BEAM type data as an example, we now need an approach for identifying abnormal data. The solution is: add the label 0 to normal data, and 1 to abnormal data. Of course, the above operation is used for the two types of abnormal data, as seen above. Next, we need to investigate the main sources of abnormal data. Here, we see that there are two main categories again. The first is the data whose label is not BEAM, generally considered to be the data with an abnormal label. For the other one, there is an artificial change in the properties of the normal BEAM that render it abnormal. For example, in the Number of Points in the column attribute, 3 and 16 frequently appear as the normal value of BEAM. Here, we set 6 or 50, and so on as an abnormal value. A similar idea is applied to change the last second attributes of AREA. The normal attributes are denoted as zero, the abnormal ones can be changed by non-zero data, such as 1, 2, 3, and so on.
# A Review on the Lifecycle Strategies Enhancing Remanufacturing
## 5 Research Gaps and Future Work  
### Research Gaps  
Despite the fact that the domain of remanufacturing is increasingly gaining more popularity and attracting more companies nowadays, there are still various uncertainties encountered with few or no concrete solutions proposed. Some of the gaps are listed below:  
1. There have been improvements in design for remanufacturing concepts and implementations in the supply chain. However, there are still limitations since there are no standards put in place so that manufacturers of the same product worldwide can apply the same DfRem principles. This causes a lot of discrepancies.
2. Online monitoring is being used mostly for high value products and it is limited for lower value products. Even for the high value products with online monitoring systems in place, using the data is still problematic as there is no mechanism to share information across stakeholders.
3. For EoL collection methods, there are methods of collection that have been used for a very long time now, e.g., from dumping sites. With emerging technologies such as smart lifecycle data and 14.0, there needs to be an upgrade in collection methods that matches the high level of technology. A more improved method for waste collection would greatly reduce the challenges faced in collecting and sorting EoL products.
4. Another gap concerns the product lifecycle data collected. Data collection is done for certain products and it is very useful for I4.0.There are two issues here: (a) there are no tools for the information sharing for remanufacturing, and (b) even if the tools are available, the stakeholders are reluctant to share the information. This gap causes a lot of difficulties in remanufacturing since product information determines the production process which the product will follow during its remanufacturing stage.
5. With new technologies being used in remanufacturing activities, there are quite a few case studies that show how these new technologies are being used in real time on products. Most of the work is still in the theoretical stage and for the practical stages there is much work to be done to improve the situation. The more researchers there are using case studies to explain their discoveries, the greater the chances of them being realized are.
6. The RUL of a product is a very important element that can help the remanufacturing timing. There are still research gaps with regard to how exactly the RUL of a product can be calculated at any particular stage in its lifecycle. Various calculations have been made for certain products but they have not really been implemented because they are not all coherent.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 2 Literature Review
### Range of MANET Applications
#### 2.3.2 Vehicular Networks  
Vehicular ad hoc networks (VANET) are a subset of MANET. In general, this applies to road-based vehicles, but most of the same principles also apply to ships at sea. In VANET, the movement of the nodes is not completely free within the operational area, but is largely constrained to roads, shipping lanes, or some other limit areas. Entry to and exit from the MANET may be by powering up/down (starting/switching off the car) or by physically coming into the network. For example, it would be possible to establish a MANET covering a section of the motorway network, where a node joins in as it travels down an entry ramp and leaves as it goes up an exit ramp. It would be possible to deploy a MANET for the following scenarios: within a city centre or any other useful geographical area or within a subset of the vehicles in an area, such as buses or taxis. These vehicular application areas involve the use of mobile nodes within ad hoc, self-configuring networks to enable communications without relying on pre-installed infrastructure. In summary, VANET's facilitate vehicle-to-vehicle (V2V) communications (using V2V communication protocols) or vehicles and fixed roadside access points (via Vehicle to Infrastructure Interaction (V2I) communication protocols) ([PERSON] et al., 2013, ([PERSON] et al., 2013)). Dedicated short range communications (DSRC) and IEEE 802.11p wireless access for vehicular environment (WAVE) has been approved as the standard for the Physical (PHY) and Media Access Control (MAC) layers for vehicular networks ([PERSON] et al., 2013, ([PERSON] et al., 2013)).
## 2 Material and Methods
### Membrane Stabilization Ability
#### 2.12.2 Hemolysis by Heat Induction  
The 2 mL reaction mixture was prepared by taking 1 mL of _P. emplica_ or _A. indica_ extract of differential concentrations (100-600 ug/mL) or aspirin and 1 mL human RBC suspension (10% \(v/v\)). In the control solution, normal saline replaced extract or aspirin. Aspirin (200 ug/mL) was taken as a reference drug. The tubes were gently inverted to mix the contents and were incubated at 56 \({}^{\circ}\)C for half an hour. After cooling, the reaction mixtures were centrifuged at 2500 rpm for 5 min at room temperature. The supernatants were collected, and its absorbance was recorded at 560 nm. Blank had only phosphate buffer [28]. The percentage of membrane stabilization activity or percentage protection from heat-induced denaturation of RBC membrane was calculated by the formula mentioned below.  
\[\text{Percentage protection}=[(\text{Bc}-\text{Bs})/\text{Bc}]\times 100\]  
where Bc denotes the absorbance of the control, and Bs denotes the absorbance of sample in the presence of extract/aspirin.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 3 Methodology
### Iterative Training Algorithm  
Within the MMCL framework, the core aim of the loss optimization strategy is to foster coherent predictions among models, yet preserve each model's autonomy in its individual optimization processes. To accomplish this, we introduce an iterative training algorithm that blends consistency evaluation with dynamic loss tuning, thereby enabling inter-model knowledge exchange and collective enhancement. In this section, we elaborate on the loss function's optimization strategy from three perspectives: consistency assessment, dynamic loss adjustment, and algorithmic implementation.
## 3 Proposed Method
### Adaptive Bin Estimator
#### 3.3.2 Bin-Initialization MLPs (INIT)  
The input of INIT is the first embedding feature denoted by \(\mathrm{e}_{0}\), which is obtained from the bottleneck of the decoder. INIT generates an initial bin-division vector, which is treated as coarse bin division to be the starting point of refining the bin division. This type of MLP generates an initial bin-division vector \(\mathbf{b}_{0}\in\mathbb{R}^{q}\), where \(q\) is the initial number of bins. \(\mathbf{b}_{0}\) is viewed as the most coarse bin-division vector, and \(q\) is set as 4 through our extensive trials. In the subsequent bin-estimating layers, each bin will be consecutively split into two new bins layer by layer. For fitting the range of training datasets, the bins of the initial bin-division vector \(\mathbf{b}_{0}\) need to be re-scaled via normalization. The rescaling can be realized by renewing the centers of bins in \(\mathbf{b}_{0}\) by  
\[c\left(b_{0}^{i}\right)=d_{\min}^{0}+\left(d_{\max}^{0}-d_{\min}^{0}\right) \cdot\left(\vec{b}_{0}^{i}/2+\sum_{j=1}^{i-1}\vec{b}_{0}^{j}\right),\quad i=0,1,\ldots(q-1) \tag{4}\]  
where \(c\left(b_{0}^{i}\right)\) is the center depth of \(b_{0}^{i}\) and \(\vec{b}_{0}^{j}=b_{0}^{j}/|\mathbf{b}_{0}|=norm\left(b_{0}^{i}\right)\) is the normalized \(j\)th bin in \(\mathbf{b}_{0}\); \(d_{\min}^{0}\) and \(d_{\max}^{0}\) denote the predicted minimal depth and maximal depth of bias prediction MLP, \(\mathrm{B}_{0}\), respectively. Table 2 shows the detailed architecture of INIT MLPs.
# Neural-Network-Assisted Polar Code Decoding Schemes
## 3 Neural Network-Assisted Polar Code Decoding Scheme  
Considering that the traditional fast SC decoding algorithm can effectively reduce the decoding time step, in this paper, we will reduce the BER of fast SC decoding structure. [PERSON] studied the decoding performance of the polar code neural network decoder based on CNNs, RNNs, long short-term memory, and MLP under different network designs and parameter settings [34]. In comparison, it can be seen that the CNN-based decoder can achieve a better performance with fewer parameters. Therefore, we consider using the CNN to improve the decoding performance and call the scheme neural network assisted (NNA) decoding scheme.
# Secure Cryptographic Key Encapsulation and Recovery Scheme in Noisy Network Conditions
## 5 Key Encapsulation and Recovery Method Using CRP
### Example
Although there is a scattering of points on this line indicating cases of unsuccessful enhancement, the overall distribution indicates a clear trend of improvement. Notably, the effect of the enhancement does not appear to be uniform across all response lengths (as indicated by the varying density of points below the trend line in each subplot), yet it consistently indicates a tendency towards error reduction. This pattern demonstrates the general efficacy of the enhancement process. This reduction is substantial, considering that the number of potential keys to be generated is contingent upon the sizes of the generated lists, as shown in Equation (2).
## 4 Conclusions  
LPS is an essential hallmark of the OM in the majority of Gram-negative bacteria species. The reason why this complex glycolipid is essential for these bacteria is object of a long-standing debate, as certain genera of Gram-negatives do not require LPS to assemble a functional OM and to survive [107, 108]. What is clear is that the asymmetry of the OM, ensured by the presence of LPS in its outer leaflet, is fundamental for the barrier proprieties of the OM that contribute to the generalized resistance of Gram-negative bacteria to several antibiotics and allow Enterobacteria to colonize the gut, where high concentration of detergent-like molecules as bile salts need to be tolerated [12]. LPS is a potent activator of the innate immune response and an important virulence factor, making its maturation and assembly at the OM a problem of high biological relevance.  
The work of the past 10 years has allowed the discovery of the transenvelope protein machinery that transports the amphipathic LPS molecules across the periplasm at the expense of ATP hydrolysis. Remarkably, the assembly of this machine is finely regulated to ensure correct and co-ordinated OM synthesis. However, several questions remain to be solved. For instance, it is not clear whether the transenvelope structure is always present in the cell or if it is only transiently built and, in this case, whether the presence of LPS in the outer leaflet of the IM is the signal that triggers bridges formation. Moreover, while the interaction between the OM translocon and LptA has been characterized in detail, the assembly of the IM LptBFGC sub-complex and its interaction with LptA is not fully understood. Additional unsolved problem are the number and arrangements of LptA molecules in the transenvelope bridge, as well as the mechanism of LPS insertion in the OM by LptDE. Finally, the studies on LPS export have been performed using _E. coli_ K12 strains, that produce a rough-type LPS [43, 44], but it is conceivable that in naturally occurring strains the addition of the _O_-antigen moiety to the core-lipid A would generate a considerable steric hindrance. Is some other unknown protein/protein complex involved to the transport of such bulky molecules within the periplasm? Interestingly, it has been suggested that under specific growth conditions the macrolide transporter MacAB-ToIC [109] might be involved in LPS transport to the cell surface, as MacA has been shown to contain two LPS binding sites that are essential for the functionality of the transporter in drug resistance _in vivo_ and for ATPase activity stimulation _in vitro_[110]. It would be interesting to investigate whether this or other apparently non-related transporters might in turn become essential for growth in strains producing a smooth-type LPS.  
It is clear that we are still far from drawing the complete picture of the molecular mechanisms of LPS transport within bacterial envelope and further studies exploiting innovative strategies are, thus, required.
## 1 Introduction
Consequently, based on these outcomes, the authors investigate the connectivity between autonomous vehicles-to-everything (AV2X) and its characteristics, and the major contributions of this article are summarized as follows:  
* The proposed adaptive approach develops an efficient method to enhance the connectivity between AV2X by applying a method assisted by V2V communications. * An optimization problem to ascertain that the communication between AV2X is reliable, and to achieve the required system QoS was formulated. * Based on the required QoS, the AV was used to decide whether to reach the destination through the direct link or through cooperative communication by finding the optimum position of the relay-vehicle. This enables determination of the required distance between AV2X to fulfil the required QoS. * The proposed adaptive model was evaluated in terms of throughput, packet loss rate, packet delivery ratio and average delivery latency for the vehicular communication under different conditions, such as transmission power, and channel condition (path loss exponent), interference, vehicle mobility, vehicle density and vehicle speed. These findings can optimize the system performance for the whole network in a vibrant environment. This can be achieved by finding the optimum inter-vehicle position to communicate with the autonomous vehicle (AV) or to relay information to everything. The proposed paper is organized in five sections. Following the introduction, Section 2 discusses the relevance of this research to other work. The system model, the optimization problem formulation and the autonomous vehicles communication scenario of the proposed model are presented in Section 3. Simulation results and discussions are provided in Section 4. Finally, the paper is concluded in Section 5.
# Human Activity Recognition for Assisted Living Based on Scene Understanding
## 6 Design and Implementation
### Using HAR Results in the SmartCare System  
Depending on the monitored patient, the SmartCare system can integrate a wide range of sensors for different aspects, such as  
* Vital parameters: heart rate (Fitbit Versa Smartwatch), blood pressure (Omron), blood glucose meter (Contour Plus), etc.
* Home automation: smart dimmer (AD146), smart switch (Fibaro Double Switch 2), valve actuator (Popp Flow Stop 2), smart lock (Danalock V3), waterleak detector (Abus Z-wave SHMW), panic button (Orvibo HS1 EB), smart light bulb (Osram Smart A60), ambient temperature, gas sensor, etc.
* Physical activity: steps, fall detection, consumed calories (Fitbit Versa), the video monitoring system. SmartCare has a predefined set of sensors depending on the health status of the assisted person:
* Alzheimer's disease: mandatory (smart lock, water tap, flood sensor, panic button), recommendation (switch, ambient temperature), nice to have (smart bulb, vibration sensor, humidity, HAR).
* Diabetic: mandatory (blood pressure, blood sugar), recommendation (oxygen saturation), nice to have (panic button, HAR, ambient temperature, humidity).
* Hypertensive: mandatory (blood pressure, heart rate), recommendation (oxygen saturation), nice to have (panic button, HAR, ambient temperature, humidity).
* Obese: mandatory (blood pressure, blood sugar), recommendation (heart rate), nice to have (HAR, ambient temperature).
* etc.  
Due to the possibility of a life-threatening event, a rule engine implemented on the Gateway uses information from all connected devices and systems and defines the rules according to the particularities of the monitored person. The WHO and the EU have standardized the use of such rules for the assisted living applications. Accordingly, we define three alert classes: notification (patient), alert (patient, caregiver) and emergency (caregiver, initiate help procedure). Some examples of rules are:  
1. If the light intensity is below 300 lux (which is the recommended value for an adult's bedroom), the light is turned on (the dimmer is at a value greater than 0) and the patient is detected (HAR), then the light intensity value is increased step by step up to 300 lux.  
Figure 20: MQTT message sent to the SmartCare Gateway.  
2. If the patient is using the sink (HAR) and a leak is detected, then the electricity is turned off (smart switch, smart plug), the tap is closed and the caregiver is alerted.
3. If the patient serves the meal (HAR) and the blood sugar is above the upper limit for diabetes, then the patient and the caregiver are alerted. The implementation of the HAR system is available at [[https://github.com/[PERSON]/VideoMonitoring](https://github.com/[PERSON]/VideoMonitoring)]([https://github.com/[PERSON]/VideoMonitoring](https://github.com/[PERSON]/VideoMonitoring)), accessed on 20 October 2022.
# Infrared Nanosecond Laser Texturing of Cu-Doped Bioresorbable Calcium Phosphate Glasses
## 3 Results and Discussion
### Absorption Characteristics of Calcium Phosphate Glass  
Pure calcium phosphate glass does not appreciably absorb in the near-infrared (NIR) region. Doping the glass matrix with copper metal ions essentially reduces the absorption bandgap. This increased absorption in the IR could be attributed to the presence of Cu\({}^{2+}\) ions in octahedral coordination facilitating intra-configuration (d-d) transitions [30,31]. The measured UV-Visible absorption spectra for the fabricated glasses reported in Figure 2 are coherent with the aforementioned effect of copper-ion doping into the glass matrix. Indeed, an apparent enhancement in optical absorption was observed at NIR wavelengths. It was also observed that the absorption enhancement at deep UV, i.e., \(\sim\)300 nm, was much greater than that at NIR wavelengths. However, this study envisages the possibility of using an industrial-grade IR ns-laser (\(\lambda\) = 1064 nm) instead of more expensive UV ns or pico-second/femto-second (ps/fs) lasers.
# RCA-PixelCNN: Residual Causal Attention PixelCNN for Pulsar Candidate Image Lossless Compression
## 4 Experiments
### Results and Analysis
#### 4.4.2 Practical Coding Algorithm  
In the actual data compression process, we combine the proposed RCA-PixelCNN model with arithmetic coding to achieve efficient data compression. The specific process is as follows:  
Model Training: Firstly, we train the RCA-PixelCNN model using the HTRU1 dataset to obtain a model that accurately models the data distribution.  
Density estimation: Using the trained RCA-PixelCNN model, we input the data to be compressed into the model. The model predicts the probability distribution of each pixel based on its density distribution model.  
Encoding: We use the arithmetic coding algorithm for actual compression encoding. This algorithm maps the pixel sequence to a compact binary encoding based on the predicted probability distribution from the model. High-probability pixels are assigned fewer bits, while low-probability pixels are assigned more bits.  
Decoding: We predict the probability distribution of each pixel using the trained model and previous decoded pixels. Then, we use the arithmetic coding algorithm to reverse the encoding process, recovering the original pixel sequence from the binary encoding.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **NLL (bpp)** \\ \hline GMM & 3.54 \\ STM & 4.11 \\ PixelCNN Base Model & 3.11 \\ \hline RCA-PixelCNN & 2.82 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of RCA-PixelCNN with other models in terms of negative log likelihood (NLL).  
Figure 5: Training speed and stability comparison between PixelCNN Base Model and RCA-PixelCNN.  
We provide detailed pseudocode descriptions of the arithmetic encoding and decoding processes based on the RCA-PixelCNN in Algorithm 1.  
```
0: use our model to compute: \(a_{i}=p(x_{i}|x_{1},\cdots,x_{i-1}),i\in\{1,2,\cdots,D\}\) for each \(x_{i}\), \(i=1,2,\cdots,D\)do [AC] encode symbol \(x_{i}\) with probability \(a_{i}\) endfor  Decoder: for each \(x_{i}\), \(i=1,2,\cdots,D\)do  with decoded pixels \(x_{1},\cdots,x_{i-1}\), use our model to compute: \(a_{i}=p(x_{i}|x_{1},\cdots,x_{i-1})\) [AC] decode symbol \(x_{i}\) with probability \(a_{i}\) endfor
```  
**Algorithm 1** Arithmetic coding with RCA-PixelCNN. [AC] stands for arithmetic coding