esa-sceva/satcom-chunk-collection huggingface dataset:
# Combining Contrastive Learning with Auto-Encoder for Out-of-Distribution Detection
## 3 Methodology
### Feature Reconstruction Based on Auto-Encoder  
The auto-encoder is aimed at learning a manifold of the distribution of training datasets by a manner of unsupervised learning. It condenses input features with high dimensionality into a compact embedding space to effectively reconstruct the original input. The auto-encoder is structured by an encoder \(f\) and a decoder \(g\). Suppose the input feature is \(\mathbf{x}\), and then the loss of the auto-encoder is the square of the Euclidean distance, square \(L_{2}\) norm, i.e.,  
\[\mathcal{L}_{ae}=||\mathbf{x}-g(f(\mathbf{x}))||_{2}^{2}. \tag{3}\]  
Figure 1: The architecture of the proposed method.  
By minimizing the loss, the decoder fits a manifold that represents the unique distributions of training datasets. It is worth noticing that the input features of the auto-encoder are the output features of the backbone. The feature representations from the backbone based on the contrastive learning supervised learning style contain discriminative semantic information, and the auto-encoder structure can build a unique distribution space for each in-distribution sample. In this way, the network consisting of the backbone and the auto-encoder can effectively identify the out-of-distribution samples.
# Numerical Analysis of Bicycle Helmet under Blunt Behavior
## 1 Introduction
In addition, the Department of Transport of London claims that there is limited evidence to suggest that some standards are safer than others [14]. The standard assessed in this research is the EN 1078 [15], which, among others, considers the capacity of absorbing energy, the construction and shape of the helmet, and the properties of the retention system. The EN 1078 [15] tests helmet energy absorbing capacities with curbstone and flat anvils at 4.57 and 5.42 m/s, respectively. To assure compliance, the helmeted head must have an acceleration lower than 250 g in both anvil tests. Most studies on helmets are experimental, although numerical research has taken place [16; 17; 18; 19; 20; 21; 22]. Previous numerical models include flat and curbstone anvils, some under standardized conditions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Europe** & **United States** & **Australia New Zealand** & **China** \\ \hline Flat Anvil & Velocity: 5.42 m/s & Velocity: 6.2 m/s & Height: 1.5 m & Velocity: 6.2 m/s \\  & Max acc: 250 g & Max acc: 300 g & Max acc: 250 g & Max acc: 300 g \\  & Velocity: 4.57 m/s & Velocity: 4.8 m/s & Not & Velocity: 4.8 m/s \\  & Max acc: 250 g & Max acc: 300 g & Tested & Max acc: 300 g \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Helmet testing thresholds imposed by different testing standards. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Flat Anvil J/(100 kgm/s\({}^{2}\))** & **Curbstone Anvil J/(100 kgm/s\({}^{2}\))** \\ \hline Europe & 5.88 & 4.17 \\ America & 6.40 & 3.84 \\ Australia/New Zealand & 5.88 & Not Tested \\ China & 6.40 & 3.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Minimum energy absorbed for same head form and helmet weight. The main conclusions of these studies are drawn from accelerations and stresses on the head. Some of these methods include the Peak Linear Acceleration [23; 24], Head Impact Power (HIP) criterion [25], Gadd Severity index [26; 27], Generalized Acceleration Model for Brain Injury Threshold (GAMBIT) Criterion [28], Head Injury Criterion (HIC) Criterion [29], and Rotational Injury Criterion (RIC) criterion [30]. To perform innovative research, this paper introduces its approach to the numerical method in Section 2 with the development and validation of the model against experimental impacts. In Section 3, multiple head injury criteria and head injury modes are assessed including the energy absorbed by the helmet, and a parametric study is carried out for the development of an injury risk curve. Finally, the discussion and conclusions are presented in Sections 4 and 5, respectively.
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 7 Conclusions
### Practical Implications  
The results of our study are eminently practical. Our design takes the feature of ZigBee to communicate with WiFi-embedded devices directly without any gateways. Therefore, it can be easily deployed in the IoT environment. At the same time, most of the computing is processed on the WiFi device. This means that there is not too much extra cost on the energy consumption of ZigBee nodes. Our design does not sacrifice the existing ZigBee communication to support CTC because it is transparent to the ongoing ZigBee communication. As mentioned above, our study has great prospects in heterogeneous wireless communication in IoT.
# An Analytical Model for Interference Alignment in Broadcast Assisted VANETs
## 3 Model of Broadcast Assisted Transmission Based IA Application in VANETs  
In this section, we construct IA application model in VANETs. At first, V2V and V2I broadcast model is constructed as basis for IA. Then IA constraints are formulated. In order to support multi-hop, a data-driven assisted transmission model is constructed according to characteristics of safety related information.
## 4 Conclusions  
In this study, four typical sprayers were used for pesticide application in the wheat field. The total deposition on the plants and losses to the ground, the uniformity of the deposition, droplets penetrability, characteristics of the deposition, control efficacy on wheat aphids and working efficiency were compared in this research. The conclusions are shown as follows:  
1. The total deposition of the UAV sprayer was not significantly different from the other three sprayers, but the losses to the ground were the lowest.
2. The UAV sprayer had a poor deposition uniformity (CV = 87.2%) and droplets penetrability (0.26 \(\upmu\)g/cm\({}^{2}\) on the third top leaf), which need to further improve in the future. By comparison, the SPB sprayer has the best deposition uniformity (CV = 32.1%) and droplets penetrability (0.52 \(\upmu\)g/cm\({}^{2}\) on the third top leaf).
3. The area of coverage, number of spray deposits and droplet size varied with spray volume and the nozzle type of the different sprayers. Compared with other sprayers, the deposition characterizations of the UAV sprayer were a lower area of coverage (2.2%), a flower spray deposits (28.2 points/cm\({}^{2}\)), finer droplet size (VMD = 124.0 \(\upmu\)m), higher concentration and lower spray volume.
4. Although the spray deposition characterizations of the UAV sprayer were different from the three other sprayers, the control efficacy of applying 70% Imidacloprid-Regent WDG on wheat aphids was comparable with other sprayers. The control efficacy of UAV sprayer on Day 7 after the application was 70.9%.
5. The working efficiency of the UAV sprayer was 4.11 ha/h, which was 1.7, 2.6, and 20.0 times those of SPB, KMB and EAP sprayer, respectively. This is the greatest advantage of the UAV sprayer.  
The experiment demonstrated the feasibility and high efficiency of the UAV sprayer. The deposition uniformity and the droplets penetrability of the UAV also need to be improved. Due to the lower coverage and poor deposition uniformity, the effective measures, such as optimizing the spraying system or adding adjuvant in the tank to improve deposition uniformity and penetrability would be needed in the future.  
[PERSON], [PERSON], H.Y., H.Q., P.C., [PERSON] and [PERSON] conceived the idea of the experiment. [PERSON] performed the experiments and analyzed the data. [PERSON] and [PERSON] wrote and revised the paper.  
The study was funded by The National Key Research and Development Plan: High Efficient Ground and Aerial Spraying Technology and Intelligent Equipment (2016 YFD0200700), Science and Technology Planning Project of Guangdong Province (2017B010117010), The leading talents of Guangdong province program (2016 LY06G689), and Key science and technology plan of Guangdong Province (2017B010116003).  
The authors declare no conflict of interest.
# Secure Data Sharing in Federated Learning through Blockchain-Based Aggregation
###### Abstract  
In this paper, we explore the realm of federated learning (FL), a distributed machine learning (ML) paradigm, and propose a novel approach that leverages the robustness of blockchain technology, FL, a concept introduced by Google in 2016, allows multiple entities to collaboratively train an ML model without the need to expose their raw data. However, it faces several challenges, such as privacy concerns and malicious attacks (e.g., data poisoning attacks). Our paper examines the existing EIFFeL framework, a protocol for decentralized real-time messaging in continuous integration and delivery pipelines, and introduces an enhanced scheme that leverages the trustworthy nature of blockchain technology. Our scheme eliminates the need for a central server and any other third party, such as a public bulletin board, thereby mitigating the risks associated with the compromise of such third parties.  
federated learning; privacy-preserving; blockchain +
Footnote †: journal:  
0000-0001-6500-78  
about privacy and robustness attacks against FL and provide some direction for mitigating them. The German BSI published a detailed report on specific privacy risks, such as membership inference attacks and model inversion attacks [8]. Simultaneously, in terms of model quality, integrity challenges stem from participants acting maliciously by injecting poisoned or malformed inputs during the FL process, where even a single malicious input can have a significant impact [9; 10]. Apart from these risks, there are also other related concerns for FL in practice, and a comprehensive survey on them is provided in [11]. To mitigate these risks, various solutions have been proposed. For example, with secure aggregation, instead of sharing the plaintext local update, only masked updates are transmitted, so that the server can aggregate the global model correctly without the knowledge of individual updates [12; 13]. In addition, differential privacy is also widely used, adding noise to the exchanged values to provide additional robustness [14; 15]. In the literature, one notable contribution to addressing these challenges is the EIFFeL framework [16]. By leveraging a public bulletin board, EIFFeL aims to preserve the privacy of input data from different clients as well as guarantee the integrity of the inputs from these clients (e.g., the value of inputs should be in proper ranges).
# Shifting of Meteorological to Hydrological Drought Risk at Regional Scale
## 2 Materials and Methods  
### Study Area  
The Soan River, initiating from the Murree highlands, is a major tributary of the Indus River and a key hydrological entity of the Pothar region of Pakistan. The Soan River flows through Chirah and Dhoke Pathan hydrological gauging stations (Figure 1) before being a part of the Indus River. The total drainage area of the Soan River basin is 6842 km\({}^{2}\) with an elevation ranging from 265 to 2274 m. The Soan basin is characterized by gentle to the steep slope and monsoon fed streams which generate almost all of the basin flow with mean annual precipitation of 1465 mm, and the mean annual temperature ranges from 8 to 22 \({}^{\circ}\)C. The Simly Dam, which spans the Soan River, provides drinking water to Islamabad, Pakistan's capital, as well as water for irrigation activities in the Pothohar region [32]. Furthermore, for a better understanding and evaluation of the spatial variation of drought, the study area was divided into two sub-basins: Chirah (hereafter sub-basin 1) and Dhoke Pathan (hereafter sub-basin 2). In the current study, monthly precipitation data of five meteorological stations within the basin (Murree, Zero Point, airport, Dhamial, and SAWCRI) from 1983 to 2015 was collected from Pakistan Meteorological Department (PMD). The average value of four stations lying in sub-basin 2 was used to estimate the meteorological drought in sub-basin 2, whereas the data of the remaining one station, i.e., Murree was used for meteorological drought analysis at sub-basin 1. Similarly, the monthly streamflow data of two hydrological gauging stations, i.e., Chirah and Dhoke Pathan was collected from Water and Power Development Authority (WAPDA) for the same period.
## 3 Experimental Study
### Experimental Results and Discussion
#### 3.2.2 Evaluation of Change Point Detection Methods  
Change point detection is crucial in engine-specific target labeling for RUL prediction. To demonstrate the effectiveness of our proposed pwlf-based change point detection method, we conducted evaluations using the widely recognized PELT algorithm [70] for the change point detection and various parameter settings of pwlf. The relevant evaluations are presented in Table 8 and best performances are presented in bold.  
In the evaluation of the change point detection, we applied the proposed 1D-CNN-LSTM network with the difference feature within the context of this research. Ten repetitions were performed for each method, and the results are presented with their respective average and standard deviation values. Notably, the outcomes obtained with the complete set of parameters for the pwlf method exhibited superior performance compared with the PELT method. Furthermore, when we examined the pwlf method while adjusting the line-segment parameter, an increase in the parameter values resulted in decreased prediction errors and improved prediction performance. The suggested approach for change point detection, which encompassed a 5-line segment configuration, yielded a substantial improvement in motor-specific target labeling for RUL prediction.
# Endodontic Management of Endo-Period Lesions
## 2 Materials and Methods
### Clinical Case 3  
A healthy 30-year-old Caucasian woman presented at the clinic with a chief complaint of discomfort in the first right upper molar. On intra-oral clinical examination, the tooth was asymptomatic. The tooth was restored with a composite resin restoration, and the periodontal pocket depth was 6-8 mm. **Radiographic examination** Preoperative CBCT imaging revealed previous root canal obturation, with a radiolucent area in the periapical region of all the roots, and mucosal thickening of the maxillary sinus membrane (Figure 3a,b). **Endodontic Diagnosis** Pulpal diagnosis--previously treated Periapical diagnosis--symptomatic apical periodontitis Primary endodontic--secondary periodontal lesion **Periodontal Diagnosis** Localized periodontitis Stage IIITreatment  
After a discussion with the patient about the treatment options, a decision was made in favor of endodontic treatment followed by periodontal treatment.  
Treatment was performed under magnification using an operating microscope. After local anesthesia (2% lidocaine with 1:100,000 epinephrine), the tooth was isolated using a rubber dam, and an endodontic access cavity was refined. The root canals were prepared and disinfected with the Remover 30/07 instrument (MicroMega, Coltene, Roeko, Germany). During the instrumentation process, the canals were irrigated with 5% sodium hypochlorite solution with an endoactivator and 17% EDTA solution. A total of seven root canals were identified (Figure 3d,e). The canals were instrumented according to the HyFlex EDM protocol.  
In the buccal canals, obturation was performed with GuttaFlow Bioseal (Coltene, Roeko, Germany) (Figure 3f). Due to significant resorption of the apical region, the apical third of the palatal canal was filled with BioDentin (Dentsply, Switzerland, Ballaigues), followed by GuttaFlow Bioseal. A temporary filling with glass-ionomer cement was applied. Periodontal therapy was applied after completion of the endodontic treatment.  
**Follow-up**  
At a clinical evaluation (after 11 months), the tooth was restored with a monolithic zirconia crown. At this time, the tooth was asymptomatic, and the periodontal pocket depth did not exceed 3 mm.  
CBCT radiography at the same time revealed healing of the periapical lesion (Figure 3g-l). No pathological changes were observed in the maxillary sinus area.
## 3 Methodology
### Landslides Inventory Validation  
Two Landsat level 1 image scenes (geometrically corrected and radiometrically calibrated), acquired on 20 May 1998 and 22 October 1999 by the Landsat TM and Landsat ETM+ sensors (Figure 5), were processed as follows.  
1. A dark object subtraction algorithm was used to correct atmospheric effects on the multispectral bands.
2. Inversion of principal component bands, produced by PCA, was used to reduce noise.
3. The ETM+ panchromatic band (15 m) was used to enhance the spatial resolution of the ETM+ multispectral bands.
4. As landslides occurred on terrain contain clays (mud rocks) [22], band ratio 7/5 was applied to distinguish argillic from nonargillic materials [23].
5. A map of change was created, from both band ratios, using image differencing change detection.
6. The resultant change maps were classified using the IsoData algorithm, which is more efficient than other unsupervised classification algorithms (k-means and Expectation Maximization) for automatic extraction of objects from multispectral data [24].  
Finally, we masked areas that were not of interest in the analysis (water bodies, urban areas, and agriculture areas) using land cover/land use mapping of the study area.  
Figure 5: (**a**) 20 May 1998 by Landsat TM sensors (**b**) 22 October 1999 by Landsat ETM+ Sensors.
# The Effect of Insertion Angles and Depths of Dental Implant on the Initial Stability
## 4 Discussion
[43] inserted 60 implants (40 vertical implants and 20 tilted implants) into 10 patients with partially edentulous mandibles, and concluded that no significant difference was evident between the ISQ and PTV of the tilted and axial implants. The main reason for this may be the inconsistent size of the dental implant fixtures. Regarding the effects of insertion depth on the primary stability of dental implant fixtures, this study discovered that supracrestally placed implants exhibited lower stability than subcrestally placed implants, a result similar to those in other studies. [PERSON] et al. [50] and [PERSON] et al. [51] also mentioned that inserting the implants at 1 mm-2 mm toward the subcrestal position yielded a more stable implant ISQ. However, some studies contended that the insertion depth of the implant does not affect primary stability. [PERSON] et al. [52] noted in a clinical study that the PTVs of crestallyand subrestally placed implants were \(-1.77\pm 3.26\) and \(1.77\pm 3.57\), respectively, and neither exhibited any significant difference. As for the effect of fixture-abutment connection type on the initial stability of the dental implant, [PERSON] et al. [53] indicated that the ISQ did not reveal statistically significant differences between the dental implant with external and internal hexagon connection. In this study, the external hexagon implants were selected due to this type is the most common used for all-on-4 treatment. However, the effect of insertion angles and depths of the internal hexagon dental implants on the initial stability should be further investigated. This study has several limitations. First, because fresh human jawbone with consistent bone quality is difficult to obtain, artificial bones were selected for the experiment in this study. Only one degree of artificial bone quality was simulated in this study, and the heterogeneous features of real human bones were thus not simulated. Second, the heating effect during drilling or inserting the implant was ignored in this study, due to the use of artificial bone in this study. Third, only one size of fixture was simulated in this study, and the experiment was performed using only three levels of insertion depth and angle degrees. Fourth, only two primary stability indicators of the fixture were measured in this study, and the stress and strain distribution on the marginal bone were not considered.
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 6 Implementation, Results, and Analysis
### Result Categories
#### 6.3.3 Impact of Operator Requirements on Energy-Saving Feature Activation and Savings
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline
**S.No** & **Parameter** & **Unit** & **S1** & **S2** & **S3** \\ \hline
1 & Total simulation instances & & 1000 & 1000 & 1000 \\ \hline
2 & Total simulation instances when carrier adaptation was invoked & \% & 26.5 & 26.8 & 26.9 \\ \hline
3 & Total simulation instances when carrier adaptation was invoked and had a & \% & 82.6 & 85.0 & 76.2 \\  & positive contribution towards energy savings & & & & \\ \hline
4 & Total simulation instances when carrier adaptation was invoked and simulation & \% & 57.6 & 94.3 & 69.8 \\  & outcome category was \(MNO_{1}\) & & & & \\ \hline
5 & Probability of turning off & & & & \\  & \(=>1\) band & \% & 84.91 & 83.52 & 83.98 \\  & \(=>2\) bands & \% & 13.48 & 14.86 & 15.10 \\  & \(=>3\) bands & \% & 1.35 & 1.45 & 0.92 \\  & \(=>4\) bands & \% & 0 & 0.16 & 0 \\  & \(=>5\) bands & \% & 0.27 & 0 & 0 \\ \hline
6 & Average energy savings by turning off & & & & \\  & \(=>1\) band & \% & 3.9 & 4.9 & 3.9 \\  & \(=>2\) bands & \% & 8.5 & 5.5 & 5.4 \\  & \(=>3\) bands & \% & 10.0 & 9.9 & 3.7 \\  & \(=>4\) bands & \% & 0 & 12.0 & 0 \\  & \(=>5\) bands & \% & 3.1 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Probability of turning off a different number of bands across all simulation cases (operator’s perspective).
## 3 Materials and Methods
### Dynamic Graph Generation
#### 3.3.2 Spatio-Temporal Correlation Matrix Generation  
In the previous section, we ignore the influence of long-term dependency in the temporal dimension on spatial correlations. However, these effects are common in real-life. For instance, the frequent usage of water over several time steps can directly affect the future demand for water. Therefore, we analyze the impact of information transmission in the time dimension on spatial correlation at each time step. Finally, we generate a spatio-temporal correlation matrix to quantify this impact.  
Firstly, we use long short-term memory (LSTM) to handle the time series, with the aim of capturing long-term dependencies and conducting positional encoding. Then, we apply a self-attention mechanism to calculate the temporal attention weights for the output of the LSTM. Subsequently, the obtained attention weights are used to weight the LSTM output \(\chi_{E}\), resulting in the output \(\chi_{Tem}\). Finally, we transform \(\chi_{Tem}\) into the embedding vectors of the corresponding nodes and then calculate spatial attention to obtain the spatio-temporal correlation matrix \(W_{TS}\). The formula is as follows:  
\[\chi_{E}=LSTM\Big{(}squeeze(permute(\chi)W_{s})^{T}\Big{)}\in\mathbb{R}^{B \times H\times N_{h}} \tag{8}\]  
\[\chi_{E}=permute(unsqueeze(FC(\chi_{E}))W_{u})\ \in\ \mathbb{R}^{B\times N\times H \times F_{1}} \tag{9}\]  
\[\chi_{Tem}=softmax\Bigg{(}\frac{\big{(}\chi_{E}W_{Q}\big{)}\big{(}\chi_{E}W_{ K}\big{)}^{T}}{\sqrt{d_{k}}}\Bigg{)}\cdot\big{(}\chi_{E}W_{V}\big{)}\in\mathbb{R}^{B \times N\times H\times F_{2}} \tag{10}\]  
\[\chi_{Tem}=permute(FC(\chi_{Tem}))\in\mathbb{R}^{B\times N\times F_{1}\times H} \tag{11}\]  
\[W_{TS}=softmax\Big{(}\sigma\Big{(}\big{(}\chi_{Tem}W_{1})W_{2}(W_{3}\chi_{ Tem})^{T}+bais\Big{)}\Big{)}\ \in\ \mathbb{R}^{B\times N\times N} \tag{12}\]  
where \(W_{s}\in\mathbb{R}^{F\times 1}\), \(W_{u}\in\mathbb{R}^{1\times F_{1}}\), \(W_{Q}\in\mathbb{R}^{F_{1}\times F_{2}}\), \(W_{K}\in\mathbb{R}^{F_{1}\times F_{2}}\), \(W_{V}\in\mathbb{R}^{F_{1}\times F_{2}}\), \(W_{1}\in\mathbb{R}^{H}\), \(W_{2}\in\mathbb{R}^{F_{1}\times H}\) and \(W_{3}\in\mathbb{R}^{F_{1}}\)