esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Layer Methods for Ad Hoc Networks--Review and Classification
## 4 Discussion  
A total of 69 publications were reviewed between the years 2002 and 2023. The publication distribution by year is represented as a histogram (Figure 6). In the reviewed publications, cross-layer methods use different combinations of OSI model layers. Using the algorithm for finding associative rules FPG (frequent pattern growth) [113], the frequency of occurrence of different combinations of layers in the considered publications (Table 14) has been calculated. Cross-layer methods most commonly use the channel, network, and physical layers (Table 14). Without the first three layers, ad hoc networks cannot exist; the first three layers have the greatest influence on ad hoc networks. Applying FPG to the goals of the methods in the reviewed publications, we obtained the following results (Table 15). The optimization of line parameters is the most widespread compared to the optimization of route parameters. It happens mainly because, to optimize route parameters, one needs to obtain a more holistic view of the network, which is harder and incurs more service data overhead compared to obtaining neighborhood knowledge. The two most popular parameters are line throughput and energy consumption, and the sum of their frequencies is more than half (0.57). It happens because all other line parameters influence and define throughput. The line throughput optimization implicitly optimizes other parameters. Route parameters, in turn, are dependent on the line parameters and on the energy consumption. If the node battery is depleted, the route with that node is disrupted. \begin{table}
\begin{tabular}{r r} \hline \hline
**Frequency** & **Layer Combination** \\ \hline  
5) \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Layer usage frequency in reviewed publications. Figure 6: Cross-layer methods for ad hoc networks publication activity. We can see in the reviewed works that cross-layer methods fall into two broad categories: first, methods concentrated on one layer using other layers, and second, methods concentrated on joint optimization of multiple layers. Inside these two broad categories, ten goals for using a cross-layer approach are visible:  
1. MIMO, beamforming. 2. Resource reservation. 3. Random channel access. 4. Cooperative transmission. 5. Routing metrics. 6. Routing protocols. 7. TCP adaptation to ad hoc networks. 8. Overlay network. 9. Application data flow adaptation to network state. 10. Optimization of multiple layers working simultaneously. In previous works [9; 28; 36; 37; 38; 39; 40; 41], cross-layer methods were classified by used layer combinations. Such classification is exhaustive and poorly reflects the essence of cross-layer methods. Based on the goal of using the cross-layer approach, we deduced the new
# Ultimate Compressive Strength of H-Section Stub Columns Subject to Random Fitting Corrosion Damage
## 3 Results and Analyses
### Influence of Width-to-Thickness Ratio of Plate
#### 3.2.1 Influence of Flange Thickness  
The influence of the width-to-thickness ratio of the flange on the ultimate strength of H-section columns was studied, considering two different web thicknesses of 7.5 and 9.5 mm. The flange thickness varied as 11.5, 9.5 and 7.5 mm. Figure 11 illustrates the impact of flange thickness on the ultimate strength of the columns. The findings indicate that the change in the width-to-thickness ratio of the flange resulting from variation in flange thickness minimally affected the ultimate strength under different levels of pitting intensities. For the thinner web (\(t_{w}\) = 7.5 mm), at a lower level of pitting intensity (\(DOV\) 4.8%), the reductions in ultimate strength for the three flange thicknesses (11.5, 9.5 and 7.5 mm) were 12.6%, 15.5% and 13.9%, respectively, with the maximal difference in strength reduction of about 2.9%, as shown in Figure 11a. Similarly, at a higher level of pitting intensity (\(DOV\) 13.8%), the reductions in ultimate strength for the three flange thicknesses were 24.2%, 26.6% and 23.7%, respectively, also with a difference of about 2.9%. It was reported that coefficient of variation (COV) was up to 12.6% for the unstiffened plate under the same \(DOV\)[25]. In view of such inherent strength variation resulting from random pitting corrosion, the impact of the width-to-thickness ratio of the flange on ultimate strength of H-section columns can be considered negligible.  
A similar influence of the width-to-thickness ratio was observed for the thicker web (\(t_{w}\) = 9.5 mm), as depicted in Figure 11b. The ultimate strength reductions were 13.3%, 13.2% and 14.0% for the three flange thicknesses, respectively, at the \(DOV\) 4.8%. Additionally, at the \(DOV\) 13.8%, the strength reductions were 23.5%, 25.9% and 25.1%, respectively. It is worth noting that there was a minor discrepancy in the strength reduction resulting from the variation in flange thickness under the same \(DOV\).  
Figure 11: Influence of flange width-to-thickness ratio on ultimate strength: (a) \(t_{w}\) = 7.5 mm; (b) \(t_{w}\) = 9.5 mm.
## 1 Introduction
### Collecting Passenger Flow Data  
To study the relation between dwell time delays and passenger flows, it is necessary to collect data on passenger volumes, which can be collected in different ways. A commonly used method is the use of manual observations, where observers record the number of boarding and alighting passengers and the dwell time duration [13; 17; 18; 19]. Technological advancements in recent years have opened up other, more automated, data collection methods. An example of such a technological advancement is an automatic passenger count system. Automatic passenger count systems are onboard systems that record the number of boarding and alighting passengers. In addition to registering the number of passengers, such systems record actual dwell times on a more detailed level compared to signalling data. An example of the use of automatic passenger count to study dwell times for trains can be found in [20], which made use of several years of automatic passenger count data from Stockholm to study the influence of passengers on dwell times. Another example is in [21], which divided dwell times into different elements in an attempt to develop a dwell time scheduling model. Their study showed how the speed of boarding and alighting passengers changes during the dwelling process and the importance of the boarding and alighting process on the length of dwell times.
## 4 Experimental Results and Discussion  
In this section, we verify the effectiveness of the proposed method on a benchmark dataset. We also analyze the experimental results of satellite image pairs and multi-date satellite images, respectively. We use Python programming in Linux to implement our method, and the main dependency of our code is the Geospatial Data Abstraction Library (GDAL).
## 3 Results  
### Optimization of mRNA Vaccine Delivery by Electropation  
To optimize EP conditions for mRNA vaccine delivery, we injected 10 \(\upmu\)g of firefly luciferase (Fluc) mRNA into the biceps femoris muscle of the mice and then performed IM-EP at varying voltages (20, 40, 60, and 80 V), while keeping the duration (10 ms) and pulse number (12) constant, repeated twice (Figure 1A). Bioluminescence imaging revealed that EP significantly increased Fluc expression in a voltage- and time-dependent manner. As shown in Figure 1B (left panel), we noted the highest expression at 80 V, peaking at 48 h post-EP. Fluc activity was predominant on day 2 and persisted until day 37 at all voltages.  
The analysis performed on day 2 after IM-EP revealed a voltage-dependent enhancement of Fluc mRNA expression. Compared with the controls, we noted significantly higher expression levels at 40, 60, and 80 V (Figure 1B, right panel). However, as the expression levels did not differ significantly between 60 and 80 V, we considered 60 V as the optimal voltage, balancing efficacy and safety concerns. Therefore, in all subsequent experiments, we performed IM-EP at 60 V.  
We next evaluated whether the SARS-CoV-2 mRNA vaccine could be efficiently delivered to the muscle and translated into protein via IM-EP (60 V). After injecting the mRNA vaccine at 5 and 25 \(\upmu\)g doses, we performed EP to enhance delivery efficiency. As shown in Figure 1C, ELISA of muscle tissue extracts revealed a dose-dependent increase in spike protein expression, with the 25 \(\upmu\)g group showing significantly higher expression than the 5 \(\upmu\)g group. These results suggest that IM-EP effectively promotes the expression of mRNA vaccines, further supporting its potential as an efficient delivery method for mRNA-based immunization.
## 4 Proposed Approach
```
1:\(padd_{i}\) is the probability for using the normal distribution \(N(a,\sigma)\) with mean value \(a\) and standard deviation \(\sigma\) by the \(i\)-th population
2:\(|A_{ci}|\) is the current archive size of the \(i\)-th population
3:\(algbest_{i}\) is the currently best-found position by the \(i\)-th population
4: Generate random number \(rand\) from the interval \([0,1]\)
5:if\(rand\leq padd_{i}\) and \(|A_{ci}|>0\)then
6: Generate random integer \(r\) from \([1,|A_{ci}|]\)
7:\(a=0.5\cdot(A_{ci}+algbest_{i})\)
8:\(\sigma=|A_{ci}\mathit{r}algbest_{i}|\)
9: Generate new individual \(ind_{new}=N(a,\sigma)\)
10:else
11: Generate new individual \(ind_{new}\) around the \(algbest_{i}\)
12:endif
```  
**Algorithm 2** Generating of the new individuals  
As was already noted, all populations communicate with each other by exchanging individuals. However, in this modification of the fuzzy-controlled COBRA, part of the worst individuals of each population is replaced by the new individuals generated by a scheme similar to the one described above (using normal distribution), but instead of \(algbest_{i}\) the current best-found position by all populations is used and the external archive is also randomly chosen. Thus, the proposed success history-based position adaptation method of the potential solutions depends on the probability \(p_{a}\) (there are three values for this probability, or more specifically, one value for each component algorithm that uses its archive during the execution), the maximum archive size \(|A_{i}|\) and probabilities \(padd_{i}\) (one for each component algorithm).
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 4 Assisting Drivers around Stop Sign Intersection
### Driving Assistance Protocol at Stop Sign Intersections
#### 4.2.2 Recommending Smooth Stop  
We assume that vehicles are equipped with the tools that allow them to accurately determine their speed and exact locations on the road network. The distance where the vehicles have to start reducing their speed and the deceleration rate is important for vehiclesto stop smoothly. The longer the braking distance and the smaller the deceleration rate, the smoother the stop process at the targeted intersection. Drivers need to start reducing their speed earlier when they travel at a higher speed. Bringing the vehicle directly to a hard stop may lead to losing control.  
The traveling speed, road slope, weather conditions, and road conditions affect the safe breaking distance and deceleration rate. Equation (1) computes the safe breaking distance (\(d\)) to stop in meters [21]. This is based on the vehicle's traveling speed (\(V\)) and deceleration rate (\(a\)). The measuring unit of vehicles' speed is (km/h), and the deceleration unit is (m/s\({}^{2}\)).  
\[d=0.039\times V^{2}/a \tag{1}\]  
Each vehicle that arrives at a safe distance from the intersection is recommended to decelerate its speed smoothly. This is considering the average speed of vehicles on that traffic flow and the road's conditions.
# An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine
## 2 Feature Engineering Based on Circle Model
### The ELM Theory  
Proposed in 2006, the ELM is a learning method based on a feedforward neural network with a single hidden layer [19; 20; 21; 22]. The theory of the ELM is briefly introduced below:  
Let \((x_{i},t_{i})\) be \(N\) different random samples, where \(x_{i}=[x_{i1},x_{i2},\cdots,x_{\min}]^{T}\in R^{n}\) and \(t_{i}=[t_{i1},t_{i2},\cdots,t_{im}]^{T}\in R^{m}\), \(N\) are the number of nodes in the hidden layer, and \(g(x)\) is the activation function. Then, a neural network with a single hidden layer can be described as:  
\[\sum_{i=1}^{\text{N}}\beta_{i}g_{i}(x_{j})=\sum_{i=1}^{\text{N}}\beta_{i}g_{i} (\text{w}_{i}\cdot x_{j}+b_{i})=o_{j},j=1,2,\ldots,N \tag{19}\]  
where, \(w_{i}=[w_{i1},w_{i2},\ldots,w_{in},]^{T}\) and \(\beta_{i}=[\beta_{i1},\beta_{i2},\ldots,\beta_{imr}]^{T}\) are the weight vector and weight between the i-th hidden layer node and each input layer node, respectively; \(b_{i}\) is the threshold of the i-th hidden layer node. The output of the ELM can be expressed as:  
\[f(x)=\sum_{i=1}^{L}\beta_{i}g(a_{i},b_{i},x)=\beta\cdot h(x) \tag{20}\]  
Figure 6: Feature normalization based on the circle model.  
According Huang's Theory [16], the decision function of ELM can be expressed as:  
\[f(x)=sign(\sum_{i=1}^{L}\beta_{i}G(a_{i},b_{i},x))=sign(\beta\cdot h(x)) \tag{21}\]  
In the ELM algorithm, the input layer parameters are randomly set up while the weight coefficients of the output layer are obtained by solving the Moore-Penrose generalized inverse of the hidden layer output matrix. Therefore, the algorithm runs much faster than the other neural network with a single hidden layer and the support vector machine (SVM). The ELM classification algorithm can improve the fault diagnosis of linear circuit, in both training speed and test speed.
## 4 Implementation
### Data Structure  
As mentioned above, the TinyCoAP components are organized following the conceptual layering of CoAP. The message layer is build on top of the 6 LoWPAN implementation distributed in TinyOS named Blip [44]. CoapBlip also uses this 6 LoWPAN stack. Should Blip receive a UDP packet, it checks the presence of the CoAP header. If this header is present, the interface provided by CoapPDU saves it in a CoAP PDU. This PDU is stored in the memory previously allocated through PoolC. The use of PoolC allows TinyCoAP to establish at compile time the maximum size a packet can have and the maximum number of packets it can handle. The maximum length of options and the maximum number of packets that can be queued by a node can also be specified. These features make TinyCoAP robust against possible memory leaks and always provide it with room in the memory for the incoming packets. Furthermore, TinyCoAP is easily adaptable to the requirements of different applications. The TinyCoAP PDU data structure is designed it to be used with PoolC. It avoids the use of pointers for accessing to the different parts of the PDU. Table 1 shows the CoAP PDU defined in CoapBlip and TinyCoAP.  
In TinyCoAP, the received CoAP message is initially stored in the UDP buffer as a void element. This element is then converted into a coap_pdu_t structure and stored in the memory pool. Once the PDU structure has been created, the UDP buffer is ready to receive a new incoming packet. TinyCoAP enables the maximum payload allowed for requests and responses to be defined at compile time. Thus, the memory usage can be adjusted to the application requirements and to the particular use of the sensor.  
CoapBlip uses pointers for accessing the different parts of the PDU. Should a CoAP packet be received, CoapBlip stores it in a buffer allocated through malloc and initializes the pointers defined in coap_pdu_t. This buffer is placed at UDP level and its size is always equal to the maximum packet size allowed by CoapBlip. Thus, although CoapBlip uses malloc, the memory is always allocated with the same size.
###### Abstract  
With the emergence of small cell networks and fifth-generation (5G) wireless networks, the backhaul becomes increasingly complex. This study addresses the problem of how a central SDN orchestrator can flexibly share the total backhaul capacity of the various wireless operators among their gateways and radio nodes (e.g., LTE enhanced Node Bs or Wi-Fi access points). In order to address this backhaul resource allocation problem, we introduce a novel backhaul optimization methodology in the context of the recently proposed LayBack SDN backhaul architecture. In particular, we explore the decomposition of the central optimization problem into a layered dual decomposition model that matches the architectural layers of the LayBack backhaul architecture. In order to promote scalability and responsiveness, we employ different timescales, i.e., fast timescales at the radio nodes and slower timescales in the higher LayBack layers that are closer to the central SDN orchestrator. We numerically evaluate the scalable layered optimization for a specific case of the LayBack backhaul architecture with four layers, namely a radio node (eNB) layer, a gateway layer, an operator layer, and central coordination in an SDN orchestrator layer. The coordinated sharing of the total backhaul capacity among multiple operators lowers the queue lengths compared to the conventional backhaul without sharing among operators.  
Keywords:backhaul; multiple timescales; resource sharing; Software-Defined Networking (SDN) +
Footnote †: _Accepted: 22 August 2019_; Published: 26 August 2019
## 3 Results
### Fluoride Immobilization Mechanism  
Combining the results of leaching and microanalysis, CaF\({}_{2}\) is the main way of immobilizing fluorine. It is hypothesized that precipitation fixation may be the main mode of fluorine fixation in the early stage of hydration, as shown in Figure 12. Gypsum was the most dominant provider of Ca\({}^{2+}\). The PCPB samples with lower cement content had better fluorine immobilization efficiency in the early stage of hydration. The generation of the CaF\({}_{2}\) precipitates may attach to the surface of anhydate linker in large quantities in the early stages, reducing the efficiency of the linker-water combination, which causes a slowdown of the overall hydration rate. More fluorine inhibits the reaction of C\({}_{2}\)S and C\({}_{3}\)S from the XRD results, and their products are again the main contributors to the PCPB strength development. This further explains the lower overall strength of the PCPB samples with lower cement content at 7 days of curing. As the hydration process proceeds, the reaction of CaF\({}_{2}\) reaches a certain equilibrium. The immobilization of fluorine is mainly caused by cement hydration. As shown in the middle and late stages of hydration in Figure 12, fluorine immobilization is mainly achieved by physical encapsulation caused by the formation of a dense hydration product structure, as well as by ion exchange in the hydration product and, thus, the formation of complexes. Additionally, the mechanism of fluorine immobilization by cement hydration is shown more specifically in Figure 13. Physical encapsulation occurs mainly in the first and middle stages of hydration [63; 64]. Before the generation of CaF\({}_{2}\) precipitates reaches equilibrium, the CaF\({}_{2}\) precipitates covering the anhydate linker may be covered by new hydration products, resulting in immobilization in the form of physical encapsulation [64]. For hydration products, there may also be complex forms formed by ion exchange embedded in gypsum crystals or adsorbent materials exhibited in the form of adsorbent materials.  
Figure 12: Fluoride immobilization mechanism in different hydration stage.
## 3 Results and Discussion  
### The 2015-2016 El-Nino  
The El Nino-Southern Oscillation (ENSO) is the main source of global interannual variability [16]. The different signs of precipitation and temperature anomalies observed, in addition to the heating area of the Pacific Ocean, reflect the extent of its interference in atmospheric conditions. In South America, such anomalies are mainly associated with factors such as the displacement of the Walker Cell, the strengthening of the subtropical jet stream and a wave train from the Pacific to southern South America [17].  
The El Nino episode that occurred between 2015 and 2016 (EN 15-16), since its prediction, had its intensity compared to the strongest events of the 20 th century [1]. Considering the precipitation values for the September-November quarter, the negative anomalies (below average rainfall) over the northern portion of South America (Amazon and Northeast and Midwest regions of Brazil) were more intense than the El Nino episodes of the years 1982 and 1997 [4]. Also, according to the same authors, in this quarter, the positive anomalies (above-average rainfall) in the southeast region of South America were not as spatially expressive as in the previous episodes mentioned.  
Despite the differences between EN 15-16 and other episodes, in the former there was a higher frequency of cyclone formation in subtropical latitudes of the Atlantic Ocean, leading to moisture convergence over southeastern South America [4]. Cyclogenesis also contributed to the canalization of the low-level jet stream east of the Andes, increasing moisture transport from the tropical to the subtropical region. Finally, [4] further mention that in EN 15-16 there was a positive atmosphere circulation anomaly of geopotential height in the Pacific Ocean in the proximity of South America, considered important for the positive precipitation anomalies in southeastern South America.  
Observing the data of the Southern Oscillation Index [10], one can notice the evolution of EN 15-16, whose component of atmospheric circulation, associated with the differential heating of the waters of the equatorial Pacific Ocean, evolved from positive to negative values, since May 2013, reaching lower values, especially after March 2015 (Figure 3). The period between September 2015 and February 2016 presented the greatest ENSO intensity, in its negative phase of the SOI, a period that coincided with abundant rainfall in the northern region of Parana.