esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analytical Model of DACBB
### Assumptions  
In actual situation, misalignment angle of inner ring may be non-constant because of time-varying external load [36]. If the non-constant angular misalignment is considered, the study will be very difficult. Hence, in the analysis of current work, angular misalignment is considered as a constant value. Furthermore, in general, both inner and outer raceways of bearing may move and deflect under external load [7, 42]. However, in order to simplify the analysis, it is sufficient to consider the relative displacement between the inner and outer rings. As a result, the bearing can be conveniently analyzed by assuming that the outer ring is fixed in the space and the inner ring displaces under the external load. And the problem then is to establish the equilibrium of inner ring. Meanwhile, this simplified  
Figure 1: Three configurations of DACBB: (**a**) back-to-back (DB) or ‘O’ arrangement; (**b**) face-to-face (DF) or ‘X’ arrangement; (**c**) tandem (DT) or series arrangement.  
Figure 2: Angular misalignment of DACBB.  
approach is also adopted by lots of researchers [7; 42; 53]. Moreover, to establish an analytical model of DACBB, the following assumptions should also be made:  
1. The contact between rolling elements and raceways satisfies the Hertzian point contact.
2. The rolling elements are evenly distributed.
3. Each row of DACBB has the same structural parameter and operating condition.
4. The inner and outer groove centers and ball center are collinear even though the inner ring of DACBB tilts a small angle relative to the outer ring.
5. The centrifugal effect of rolling element is ignored in the analysis.
# A Framework for Synthetic Agetech Attack Data Generation
## 5 Proposed Data Generation Methods
### Data Validation  
The data obtained using Method 1 and Method 2 were further analyzed. Each dataset consists of 2500 samples with approximately 14% of the dataset being attack records. The data were split into training and test datasets in a stratified manner, then used to train and test different machine learning models. The machine learning models implemented include Random Forest, K-Nearest Neighbor (KNN), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) classifiers. We explored many machine learning methods but these are the ones that achieved the best performance. The models were evaluated by computing two performance metrics, namely, accuracy and F-beta score. Accuracy is a commonly used metric for classification problems, but considering that the datasets are imbalanced, F-beta score is a better measure of performance and also it particularly penalizes more a misclassification error where an attack record is marked to be benign, minimizing false negatives. Table 2 shows the performance metrics for the dataset obtained using Method 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy** & **F-Beta Score** \\ \hline Random Forest & 0.9860 & 0.7593 \\ \hline KNN & 0.9820 & 0.9402 \\ \hline XGBoost & 0.9340 & 0.7593 \\ \hline LightGBM & 0.9840 & 0.8824 \\ \hline CatBoost & 0.9520 & 0.8286 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Agetech_temp_elements_var_attack dataset (Method 1) metrics.  
Figure 16: Agetech normal data—first 10,000 records.  
Figure 17: Agetech synthetic records generated by PAR—10,000 records.  
From Table 2, it can be observed that all models have a high accuracy score, which means there were few misclassified records. K-Nearest Neighbor has the highest F-beta score and is thus a better classifier for benign and malicious temperature sensor records for this dataset compared to the other models. Table 3 shows the performance metrics for the dataset generated using Method 2.  
From Table 3, it can be noted that, for this dataset, the CatBoost classifier achieved the highest F-beta score. It has an F-beta score of 0.9855, which indicates that it was able to classify most of the records properly. Moreover, all the accuracy scores are high, indicating that there are a few misclassification errors but the models performed well.
## 4 Proposed Approach
### Initialization Phase  
The BS broadcasts a periodic beacon signal at the initial stage. Based on the received signal, nodes can estimate the transmission quality to the BS (the RSSI value). After receiving this beacon, each node broadcasts a periodic \(Tx\_QUALITY\) message in its transmitting range. After receiving a \(Tx\_QUALITY\) message, a node updates its local state and replies by sending an \(LSV\) beacon. When the new node \(i\) receives \(LSV\) beacons from its neighborhood, it updates the \(CRL_{i}\) list and computes its weight \(W_{i}\) using Equation (5), then rebroadcasts a clustering message that contains the updated information. The \(LSV\) beacon is broadcasted every time that a local information changes, so that network nodes can maintain consistent and updated information about their local environment. The initialization process is described in Algorithm 1. Figure 4 summarizes the initialization phase.  
Figure 4: Sequence diagram of the initialization phase.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(1D_{i}\) & Identity of device \(i\) \\ \(W_{i}\) & Weight of \(i\) (computed using Equation (5)) \\ \(Mychi_{i}\) & Relative Cluster Head of \(i\) \\ \(CH\_weight_{i}\) & Weight of \(Mychi_{i}\) \\ \(Dist(i,Mychi_{i})\) & Distance between \(i\) and \(Mychi_{i}\) (in term of hops) \\ \(P_{i}\) & Parent of \(i\) in the aggregation path toward the relative CH \\ \(Tr_{i}\) & Transmitting range of \(i\) \\ \(Deg(i)\) & Degree of \(i\) (Number of node in the neighborhood of \(i\)) \\ \(LSV_{i}\) & Local State Variable of \(i\) \\ \(CRL_{i}\) & Clustering Record List (Neighbors clustering information received) \\ \(TCR_{i}\) & Two-hop connectivity ration of \(i\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Notation used in the proposed approach.  
```
Code for each node \(i\) Variables: \(ID_{i}\): identifier \(CRL_{i}=Null\) \(N_{i}=\emptyset\) \(Deg_{i}=0\) \(W_{i}=0\) \(RSSI_{i}=-100\) dBm Upon receiving BS signal:  Update \(RSSI\) value  Broadcast \(Tx\_QUALITY_{i}\) message Foreach received \(LSV\) Beacon do: \(|N_{i}|=|N_{i}|+1\)  Update \(TCR_{i}\) value using Equation (2) \(Deg(i)=|N(i)|\)  Update \(CRL_{i}\) list Endfor  Update \(W_{i}\) value using Equation (5)  Broadcast \(LSV_{i}\) Beacon Upon receiving \(Tx\_QUALITY_{j}\) message:  Send \(LSV_{i}\) beacon to \(j\)
```  
**Algorithm 1** Initialization phase.
# Delay-Sensitive Multi-Sensor Routing Scheduling Method for Underground IoT in Mines
## 2 Related Work  
Currently, there is a substantial amount of research on wireless sensor routing and scheduling, encompassing various applications in different scenarios. For example, WSNs are deployed in underwater environments [7], mobile scenarios [8], and acoustic sensor networks [9]. These scenarios present unique challenges, requiring tailored approaches for optimizing routing and scheduling. Broadly, the routing and scheduling algorithms for WSNs can be classified into the following categories.
## 5 Experiments
### Computational Cost Analysis
#### 5.5.2 Memory Footprint  
In Table 5, we record the maximum memory footprint of our methods. For QFeP, the additional memory is consistently required for the preserved KV cache. However, this memory overhead is much smaller than that used in the fine-grained quantization (AQ1), as QFeM utilizes only three tokens for the cache. Contrary to QFeP, QFeM shows inconsistent memory utilization. For example, the 7B model with QFeM exhibits memory usage similar to AQ2, while the 70B model with QFeM incurs additional consumption for a sequence length of 1K. This is attributed to the use of W8A16 for the unquantization modules in QFeM. To tailor the memory usage or inference speed, an alternative strategy can be utilized for QFeM, such as applying fine-grained activation quantization to the unquantization modules instead of using W8A16.
## 4 Simulation and Discussion
### Composite Scattering of Canyon/Valley Composite Rough Surface with Different Surface Roughness  
In this subsection, the bistatic composite scattering characteristics of the canyon and valley environment with different roughness are investigated. The slope angle \(\alpha_{1}=30^{\circ}\), \(\alpha_{3}=30^{\circ}\), the RMS height \(h_{\text{rms}}\) and the wind speed \(v_{19.5}\) are as shown in Figure 16. The incident angle is \((\theta_{i},\varphi_{i})=(-30^{\circ},90^{\circ})\), and the observation angles are \((\theta_{s},\varphi_{s})=(-90^{\circ}\sim 90^{\circ},90^{\circ})\). The illustration of simulation geometry is the same as Section 4.3, which is shown in Figure 12. Numerical results are shown in Figure 16; the specific peak values are listed in Table 3 in the order of black, red, blue, green and purple curves. As shown in Figure 16 and Table 3, the roughness of the canyon/valley environment mainly affects the peak values. Take Figure 16a as an example. Comparing the black curve and red curve, when the roughness of the bottom increases from \(0.2\lambda\) to \(0.5\lambda\), while the roughness of the slopes remains unchanged, the peak values at \(\theta_{s}=30^{\circ}\) decrease from \(81.5529\) dBsm to \(76.6757\) dBsm, while the peak values at \(\theta_{s}=-30^{\circ}\) remain nearly constant. The reason could be that with the increase in bottom surface roughness, the specular reflection of the bottom gets weaker. Referring to the analysis in Section 4.2 and 4.3, the specular reflection characteristic of the bottom decides the peak values at \(\theta_{s}=30^{\circ}\). As a result, the peak values at \(\theta_{s}=30^{\circ}\) decrease. We can draw the same conclusion by comparing the green and purple curves.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
###### Abstract  
Modern software application development imposes standards regarding high performance, scalability, and minimal system latency. Multi-threading asynchronous programming is one of the standard solutions proposed by the industry for achieving such objectives. However, the recent introduction of the reactive programming interface in Java presents a potential alternative approach for addressing such challenges, promising performance improvements while minimizing resource utilization. The research examines the migration process from the asynchronous paradigm to the reactive paradigm, highlighting the implications, benefits, and challenges resulting from this transition. To this end, the architecture, technologies, and design of a support application are presented, outlining the practical aspects of this experimental process while closely monitoring the phased migration. The results are examined in terms of functional equivalence, testing, and comparative analysis of response times, resource utilization, and throughput, as well as the cases where the reactive paradigm proves to be a solution worth considering. Across multiple scenarios, the reactive paradigm demonstrated advantages such as up to 12% reduction in memory usage, 56% faster 90 th percentile response times, and a 33% increase in throughput under high-concurrency conditions. However, the results also reveal cases, such as data-intensive scenarios, where asynchronous programming outperforms reactive approaches. Additionally, possible directions for further research and development are presented. This paper not only investigates the design and implementation process but also sets a foundation for future research and innovation in dependable systems, collaborative technologies, sustainable solutions, and distributed system architecture.  
Java; asynchronous programming; multi-threading; reactive programming; migration from asynchronous to reactive
# An Assessment Scheme for Road Network Capacity under Demand Uncertainty
## 4 Numerical Experiments
### Critical Link Identification with Uncertain Demand  
Volume-to-capacity (V/C) ratio measures the link's congestion. The link with the largest saturation rate is the bottleneck that affects the capacity of the whole network. However, the location of the bottleneck links is also different for different varieties of demand pattern. We must consider the impact of uncertain demand patterns on critical link identification results. The highly saturated number is counted at each link whenever its V/C ratio is greater than 0.9 [23]. As a result, link saturation ratios are calculated as [total saturated number/number of samples].  
This subsection only verifies the 500 random samples with normal distribution \(N(\overline{q}_{ij},0.25\overline{q}_{ij}^{2})\) and the interval \([q_{ij}^{L},q_{ij}^{\text{L}}],\forall i,j\). The experiments calculated the V/C of each link under the nominal travel demand in Figure 9(a). Then, the high-saturation rate was solved for 500 travel demand samples; the highly saturated numbers are shown in Figure 9(b). The numbers in Figure 9(c) are the same as in Figure 6. Only the V/C of link 10-16 and link 16-10 was greater than 0.9 under the nominal demand. However, there are six links of the road network where V/C is likely to be greater than 0.9 under demand uncertainty. Figure 9(c) shows that links 10-16 and 16-10 have high saturation rates between 85% and 98%, and link 16-17, link 17-16, link 17-19, and link 19-17 have high saturation rates between 20% and 35%.  
Figure 10: **(a)** The V/C rate of each link under nominal demand; **(b)** the link saturation under uncertain travel demand.  
Figure 9: The efficiency of the Sioux-Falls network.  
Traffic managers can apply traffic management methods for important links to raise the capacity of links and optimize traffic network design in order to improve the effectiveness of network operation and boost network capacity. In this section, based on the critical link results with uncertain demand obtained from the above solution, the capacity of the six high-saturation links derived in the previous subsection is increased by 10%. The before-and-after comparison of the capacity with nominal demand and uncertain demand is shown in Figure 11. The reliability of the network capacity value of 68,505 increased from 0.8 to 1. Table 2 shows that the 80% reliability of network capacity under normal distribution with uncertain demand increased by 23%, and the improvement under the nominal demand case increased the road network capacity by only 15%, which was due to the traffic managers failing to fully understand the information about the critical links. Thus, critical link analysis under uncertain demand using the high-saturation-link analysis method can aid traffic managers in obtaining more comprehensive information and serve as a basis for improving network performance.
# Performance Assessment of Waste Cooking Oil-Modified Asphal Mixtures
## 3 Laboratory Experimental Program
### Specim Preparation, Conditioning, and Testing  
The unmodified mixtures were prepared by mixing the raw materials (i.e., virgin binder and aggregates) using a planetary mixer at the specified laboratory mixing temperature of 165 \({}^{\circ}\)C. Loose samples were scooped from the prepared mixtures to measure the theoretical maximum specific gravity (G\({}_{\text{mm}}\)) following AASHTO T 209 [81]. The remaining materials were short-term aged following AASHTO R30 [82], then compacted using a SuperPave gyratory compactor. The fabricated test specimens were 150 mm in diameter by 62 mm in height for both tests. At least two specimens have been fabricated for each test. After that, the bulk specific gravity (G\({}_{\text{mb}}\)) was measured following AASHTO T312 [83]. Later, specimens were checked to achieve the target air void content (i.e., 7 \(\pm\) 1.5%). Then, it was conditioned at the desired testing temperature (i.e., 25 \({}^{\circ}\)C and 54.5 \({}^{\circ}\)C for IDEAL and High-IDT, respectively) for 2 h \(\pm\) 10 min before testing. Finally, the specimens were tested at a 50 mm/min loading rate using Marshall testing equipment.  
A similar approach has been followed for the modified WCO asphalt mixtures, except for replacing the virgin binder with the modified WCO asphalt binder. The literature review showed that researchers prepared the modified binder by mixing the required dosage of WCO oil with the virgin binder using a high-shear mixer. However, it was conducted at varied speed (200-4000 revolutions per minute (rpm)), time (15-120 min), and temperature (120-190 \({}^{\circ}\)C) [22; 26].
## 3 Results and Discussion
### In vivo Antifungal Activity of Propolis Extracts Against Stemphylium vesicularium
Different letters denote significant differences among treatments efficacy (\(p<0.05\)). Figure 3: Decrease of the disease incidence relative to control after 14 days of incubation at 20 \({}^{\circ}\)C in the curative (left) and prophylactic (right) assays. Results are presented as means \(\pm\) standard deviation of two replicates Different letters denote significant differences among the decrease of disease incidence relative to control (\(p<0.05\)). propolis extracts against phytopathogenic fungi in different fruits and plants. [PERSON] et al. [22] studied the in vivo antifungal effect of propolis extracts against _P. expansum_ and concluded that propolis was effective in controlling the growth of this pathogen in \"Rocha\" pear. In that study, after 4 days of incubation, a reduction of the lesion diameter and of disease incidence of about 40% was observed in fruits treated with propolis extract [22]. Propolis ethanolic extracts also demonstrated in vivo efficacy in controlling the disease caused by _P. digitatum_ in oranges, reducing its incidence by up to ~78% [47]. When applied to coffee plant seedling leaves, ethanolic propolis extracts were able to reduce the incidence and severity of _Cercospora coffeicola_ infection, in a dose-dependent manner [48]. Propolis extract, combined with cinnamon oil incorporated with gum arabic as the base coating, proves to be effective for postharvest anthracose (_Colletotrichum capsaicin_) control in chilli [49]. Other in vivo assays with two varieties of apples, namely Dorestt Golden and Red delicious, revealed that propolis extract inhibited the fungal growth on apple fruits compared with control, being effective in inhibiting _Alternaria alternata_, _Aspergillus niger_, _Fusarium_ sp., and _P. expansum_[50]. On the other hand, plant extracts [51], synthetic antimicrobial peptides [52], or the use of antagonists [3; 53] are described as promising strategies to control _S. vesicularium_. [PERSON] et al. [51] showed the effectiveness of plant extracts, particularly of _Azadirachta indica_ and _Datura stramonium_, to control _S. vesicularium_ blight disease of onion (_Allium cepa_ L.) under greenhouse conditions [51]. These extracts proved to be very effective at controlling the disease, with a disease reduction percentage that reached up to ~90% before and after _S. vesicularium_ inoculation. Similarly, [PERSON] et al. [52] showed the efficacy of different synthetic antimicrobial peptides against _S. vesicularium_ in detached \"Conference\" pear leaves. The authors concluded that prophylactic application of these antimicrobial peptides did not reduce the infection, but curative application did. In agreement with these results, propolis seems to be more efficient in reducing the disease incidence when applied after pathogen inoculation (curative assay) than when applied before pathogen inoculation (prophylactic assay). This result is particularly interesting as scarce fungicides with post-infection action are available for use on pears [52].
## 7 Conclusions  
We have set up a tower-based network for spectral measurements that has generated a first year of measurements in narrow bands of red, NIR, and two green bands, and broadband measurements of PAR. The measurements were used to derive reflectances in corresponding bands, NDVI, and fractions of PAR. The network currently is co-located with eddy covariance towers, and covers two coniferous forests, one deciduous forest and two peatland sites. We plan to extend this spectral network to cover more ecosystems in Fennoscandia. The measurements add to the knowledge of ecosystem processes for more accurate determination of phenological transitions and primary productivity. The data also provide information for validating satellite products and calibrating new algorithms for such products. The results in this paper show that there is great potential for characterizing subtle variations in land surface phenology at high temporal resolution using a combination of PAR and multi-spectral measurements. Our computations showed that NDVI sensors mounted in off-nadir directions away from the principal plane were only moderately affected by variations in anisotropic reflectance.  
We decomposed the ecosystem-reflected PAR fluxes into contributions from forest canopy and ground (understorey layers, including vegetation, litter fall, bare soil, and snow), and partitioned the total absorbed PAR into canopy-absorbed and ground-absorbed. This enabled detailed analysis of PAR reflectance changes and PAR absorption of the different ecosystem components throughout the year, thus providing data for better calibration of GPP models. We showed that the \"true\" canopy transmittance and interceptance of PAR are different from those computed from direct measurement. This discrepancy can be large for bright backgrounds.  
The tower-based NDVI measurements agreed very well with MODIS data during the growing season, and were much smoother. The frequency of good tower measurements is much higher than that of MODIS, allowing for a more detailed analysis of phenological events. The data will thus enable thorough evaluation of the methods used in current remote sensing practice for satellite data, e.g., methods for temporal compositing and data smoothing.