esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of IEEE 802.15.4 Standard
### Brief Overview of ESS  
This section discusses the efficient superframe structure introduced in [23] as shown in Figure 2. In ESS, CFP precedes soon after the beacon frame and then followed by CAP. In case, there is no GTS request received by the PAN coordinator then, there is no CFP and CAP will commence right after the Beacon frame. CFP duration in ESS is similar in size as offered by the standard, however, each CFP slot duration is halved to increase the slot capacity to 14 instead of 7. The salient feature of ESS is to avoid the unnecessary CAP delay faced by a GTS requesting node in the next BI. This significantly reduces the network delay faced by the GTS requesting nodes. Besides, 14 GTS can accommodate more GTS requesting nodes to transmit their data in a BI, that improves the network throughput. The reduced delay and improved throughput in ESS is achieved at the cost of some modifications in the existing parameters of the standard.  
The Superframe structure of ESS contains minimum 9 CAP slots and maximum 14 CFP slots excluding the beacon frame. The Superframe Duration of ESS (\(SD_{ESS}\)) and Beacon Interval of ESS (\(BI_{ESS}\)) are calculated as: depends upon the value of Superframe order (\(SO\)), aNumSuperframeSlot (NSS) and aBaseSlotDuration (BSD) as:  
\[SD_{ESS}=\delta+(960\times 2^{SO}) \tag{6}\]  
\[BI_{ESS}=\delta+(960\times 2^{BO}) \tag{7}\]  
here, \(0\leq SO\leq BO\). Default value of _aUnitBackoffPeriod_ is 20 Symbols. The Beacon Duration in symbols, \(\delta\), in Equation (8) is computed as:  
\[\delta=(m+3*n)\times 2(Symbols) \tag{8}\]  
A large number of applications offer adaptive data traffic with an adaptive duty cycle to meet QoS. Both IEEE 802.15.4 standard and ESS do not entertain the adaptive data traffic due to their fixed \(SO\) and \(BO\) values in a superframe. This results in either not entertaining nodes optimally or poor GTS utilization in a superframe duration. When nodes have data requests which are more than the available limit, then PAN coordinator will not be able to accommodate maximum data requesting nodes. However, when data requests by each node is less than the available slot capacity, then GTS  
Figure 3: GTS request frame format of IEEE 802.15.4.  
utilization is compromised. In this work, an active period of the superframe structure is fine tuned to optimally adjust the data requesting nodes in CFP.
# A Method for Adapting Stereo Matching Algorithms to Real Environments
## 2 Related Work
### Pattern-Based Calibration  
[PERSON] presented a highly influential paper on stereo camera calibration [16]. His work has been implemented in a well-known library called OpenCV [6]. The method proposed by [PERSON] is based on taking a series of images of a 2D pattern that has black squares. These images are made using a stereo camera, utilizing different angles and points of view with respect to the location of a chessboard pattern. The purpose of preparing an image set is to determine the coefficients that characterize the analyzed stereo camera. In the OpenCV version of [PERSON]'s work, these coefficients are applied to Equation (1), which describes the transformation of a point \(\mathbf{P_{w}}\) from the 3D coordinates of a real space to point \(\mathbf{p}\), which is located in a 2D plane.  
\[\mathbf{sp}=\mathbf{A}[\mathbf{R}|\mathbf{t}]\mathbf{P_{w}} \tag{1}\]  
where \(\mathbf{R}\) and \(\mathbf{t}\) are rotation and translation matrices, \(\mathbf{A}\) is a camera intrinsic matrix, and \(\mathbf{s}\) is a scaling factor.  
Matrices \(\mathbf{R}\) and \(\mathbf{t}\) are determined by coefficients. In this paper, the same model as the one used in the fourth version of the OpenCV library is used, in which sixteen coefficients are taken into account, including six radial coefficients, four tangential distortion coefficients, four thin-prism-distortion coefficients, and two angular parameters for rotation [18].  
Another calibrating method was described by [PERSON] et al., who participated in the preparation of KITTI Vision Benchmark Suite [19]. They proposed calibrating cameras using sets of chessboards placed in different locations in a 3D space. [PERSON] et al. proposed applying calibrating patterns generated from partly random noise [20]. The pattern that they proposed contained more features of varying scales than a chessboard pattern. It was designed for calibrating sets of multiple cameras. [PERSON] et al. used a pattern containing dots of different sizes. Recently, deep learning methods have also been adapted to the field of calibrating cameras; [PERSON] et al. described a calibrating method intended for use with large-scale scenes [21].
## 5 Results
### Computer Calculation Settings  
In this section, we evaluate the statistics of coverage of the sum bit rate in (5) for both DS and US transmissions. Bit rates are calculated using (1) and the corresponding equations for FEXT for both DS direction and US direction. To evaluate the averages of FEXT in (15) and (17), a Monte Carlo based approach has been adopted. In each trial, we generate the number of active wired subscribers at each distributor in accordance with a binomial statistic with parameter \(p\) (the user activity) over \(M_{m}-K_{m}\) pairs with \(M_{m}\) the number of pairs at the \(m\)-th distributor; the \(K_{m}\) is the number of (always) active pairs assigned to the \(m\)-th wireless 5G node. In the same trial we generate several realizations of frequency selective FEXT used to generate the coefficients of the DS and US channel matrices \(\mathbf{H}_{k}\) for each sub-carrier. The FEXT selective terms have been assumed to be statistically independent and identically distributed in accordance with a complex zero mean Normal random variable with unit variance. Averages of the residual FEXT terms with respect to the FEXT frequency selective terms have been taken first to evaluate the FEXT powers in (15) for DS and in (17) for US. These are then used to evaluate the SINR per sub-carrier and then to calculate the bit rate per single copper line. The sum bit rate in (5) is obtained by adding the bit rates for the \(K_{m}\) copper pairs assigned to the \(m\)-th 5G node. We have repeated calculations for each Cabinet and for each distributor connected to this Cabinet as indicated in the database. The parameters of the considered VDSL2 technology as well as the statistics of the FEXT fluctuations are now detailed. The VDSL2 profile 35b transmission technology is considered i.e., the maximum VDSL2 frequency has been set to \(f_{M}=35.32\) MHz and the overall transmission power is set to \(14.5\) dBm. The VDSL2 gap \(\Gamma\) in (3) is \(12\) dB. The flat transmitter power spectrum is assumed (i.e., no bit loading algorithm has been considered for VDSL2); the minimum and the maximum number of bits per sub-carrier are \(1\) and \(15\), respectively. The considered FEXT coupling coefficient is \(\chi_{F}\cong 3.6\times 10^{-20}\)[25]. Finally the mean \(\mu_{dB}\) and the standard deviation \(\sigma_{dB}\) of FEXT fluctuations are \(11.65\) dB and \(5\) dB, respectively [22]. A copper cable from the Cabinet including \(200\) pairs and \(4\) binders has been considered and the coupling coefficients among binders are those indicated in [23].  
Figure 4: Percentage of distributors connected by copper to the new added Cabinets as a function of \(d_{OE}\) and for the three groups of cities.
###### Abstract  
The aim of the present study was to explore the correlation between the land-use/land cover change and the flash-flood potential changes in Zabala catchment (Romania) between 1989 and 2019. In this regard, the efficiency of GIS, remote sensing and machine learning techniques in detecting spatial patterns of the relationship between the two variables was tested. The paper elaborated upon an answer to the increase in flash flooding frequency across the study area and across the earth due to the occurred land-use/land-cover changes, as well as due to the present climate change, which determined the multiplication of extreme meteorological phenomena. In order to reach the above-mentioned purpose, two land-uses/land-covers (for 1989 and 2019) were obtained using Landsat image processing and were included in a relative evolution indicator (total relative difference-synthetic dynamic land-use index), aggregated at a grid-cell level of 1 km\({}^{2}\). The assessment of runoff potential was made with a multilayer perceptron (MLP) neural network, which was trained for 1989 and 2019 with the help of 10 flash-flood predictors, 127 flash-flood locations, and 127 non-flash-flood locations. For the year 1989, the high and very high surface runoff potential covered around 34% of the study area, while for 2019, the same values accounted for approximately 46%. The MLP models performed very well, the area under curve (AUC) values being higher than 0.837. Finally, the land-use/land-cover change indicator, as well as the relative evolution of the flash flood potential index, was included in a geographically weighted regression (GWR). The results of the GWR highlights that high values of the Pearson coefficient (r) occupied around 17.4% of the study area. Therefore, in these areas of the Zabala river catchment, the land-use/land-cover changes were highly correlated with the changes that occurred in flash-flood potential.  
Keywords:Zabala; Landsat images; multilayer perceptron; total relative difference-synthetic dynamic land-use index; flash-flood potential index; geographically weighted regression +
Footnote †: journal: Journal of Applied Mechanics and Physics
# RCM: A Remote Cache Management Framework for Spark
## 1 Introduction
From practical experience, the cached data should be placed on the server with the highest processing efficiency. However, it is difficult to build a dynamic matching algorithm for real-time scheduling due to the constantly changed workload. In response to the above challenges, we propose RCM, a remote cache management framework, mainly including a cache weight generation module (CWG), cache replacement module (CREP), and cache placement module (CPL). Firstly, CWG takes the main  
Figure 1: Adding a cache layer to Spark cluster. influencing factors, such as response time, the number of query, data size and time loss, into consideration. It uses the ratio method to establish a cache weight model for quantifying the importance of each cache. Based on the weight model, CREP maximizes the sum of cache data weight in limited cache resources to improve the cache resource utilization. Next, CPL builds a dynamic matching algorithm for real-time scheduling cache data. To prove the effectiveness of RCM, RCM is deployed on Redis servers. Through Spark Web Api [10], RCM manages the cache data from the Spark cluster. The experiments confirm that RCM has better performance than advanced cache management strategies. The main contributions are as follows:  
1. The CWG module and CREP module are proposed to solve the problem of cache replacement. CWG design a new weight by consideration of the response time of the query database, the number of queries, the data size and time loss. Based on CWG, CREP replaces low weight data in a greedy ideology. 2. The CPL module is proposed to solve the problem of matching the best servers. First of all, we give the definition of cooperation efficiency. Then, the cache data are placed on the most efficient server in a KM-matching [11] algorithm. 3. A remote cache management framework for Spark, called RCM, is proposed, including CWG, CREP and CPL. In order to verify the proposed framework, we test the CREP module and CPL module individually. In the end, RCM is tested and gives 42.1% improvement at most. The remaining sections of this paper are organized as follows. Section 2 describes the related work. Section 3 proposes models and optimization goals. Section 4 gives related algorithms. The experiments and analysis are in Section 5. Finally, Section 6 is a conclusion.
# Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
## 3 Proposed Routing Protocol
### Dynamic Decision Based on Node Relationships
\[C(a,b)=\frac{CT_{a}^{b}}{allCT_{a}}+\frac{\overline{ET_{a}^{b}}}{allET_{a}}+ \frac{\overline{MS_{a}^{b}}}{allMS_{a}} \tag{11}\]  
Relationship between node \(a\) and node \(b\) is defined by (12):  
\[rel(a,b)= \begin{cases}friend,&FTh\leq C(a,b)\\ colleague,&CTh\leq C(a,b)<FTh\\ stranger,&0\leq C(a,b)<CTh\end{cases}  \tag{12}\]  
In (12), \(FTh\) and \(CTh\) represent the friend threshold and colleague threshold, and after multiple simulation verifications, the network performs best when \(FTh\) and \(CTh\) are set to 0.15 and 0.09, respectively. Based on node relationships, integrating message remaining time and message forwarding count as one of the decision factors allows for more accurate and efficient forwarding decisions. When nodes \(a\) and \(b\) establish a connection, if nodes \(a\) and \(b\) have a friend relationship with the message destination node, then compare their relationship values and forward the message to the node with a greater relationship value. If nodes \(a\) and \(b\) have a colleague relationship with the message destination node, not only consider the relationship value but also take into account the message forwarding count, remaining message time, and historical encounter interval. The encounter interval between node \(a\) and node \(b\) is \(NT_{a}^{b}\), and their average encounter interval within the period \(T^{\prime}\) (\(T^{\prime}<T\)) is calculated by (13), and \(n\) is the number of intermittent encounters between them within the period \(T^{\prime}\). \[\overline{NT_{a}^{b}}=\frac{\sum_{t=1}^{n}NT_{a}^{b}}{n} \tag{13}\]  
If two nodes have not encountered each other for more than one period \(T^{\prime}\), then the historical encounter interval is updated to:  
\[\overline{NT_{a}^{b}}=(\overline{NT_{a}^{b}})_{T}e^{-N}+T^{\prime}(1-e^{-N}) \tag{14}\]  
\((\overline{NT_{a}^{b}})_{T}\) represents the average encounter interval within the period \(T\). \(N\) represents the number of periods during which the nodes have not encountered. \((\overline{NT_{a}^{b}})_{T}\) is equal to 0 when two nodes have not encountered each other for more than one period \(T\). Compare the \(TTl\) of \(m_{d}^{i}\) with the historical average encounter interval. If the hop count of \(m_{d}^{i}\) is less than 3 and the remaining time is greater than the \(\overline{NT_{a}^{d}}\) and \(\overline{NT_{b}^{d}}\), then perform a relationship value comparison; otherwise, forward the message to node \(b\). If nodes \(a\) and \(b\) have a stranger relationship with the message destination node \(d\), meaning that the probability of nodes \(a\) and \(b\) transmitting the message to the destination node is low, then the message is not forwarded and remains at node \(a\), awaiting the next connection.
## 3 Results
### Microbial Detection Using FreshCheck  
Results obtained with FreshCheck before and after the application of different biocides against _L. monocytogenes_ and _S. enterica_ are shown in Table 5.  
As shown in Table 5, in all cases, FreshCheck changed to green when it was tested before the application of each biocide studied, showing the microbial contamination of _L. monocytogenes_ and _S. enterica_, respectively. In contrast, in all cases the FreshCheck spray remained purple after the application and rinse of all biocides, revealing the absence of microbial charge and confirming that all tested biocides are able to completely inactivate the microbial contamination and that no signal is obtained after successful disinfection. A sample image of the stained coupon can be seen in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Biocide** & **Before Disinfection (Both Microorganisms)** & \
\begin{tabular}{c} **After Disinfection** \\ _(L. monocytogenes)_ \\ \end{tabular} &
\begin{tabular}{c} **After Disinfection** \\ _(S. enterica)_ \\ \end{tabular} \\ \hline
1 & Green & Purple & Purple \\
2 & Green & Purple & Purple \\
3 & Green & Purple & Purple \\
4 & Green & Purple & Purple \\
5 & Green & Purple & Purple \\ \hline \hline \end{tabular}
\end{table}
Table 5: Color observed after application of FreshCheck on surfaces inoculated with _L. monocytogenes_ or _S. enterica_, before and after disinfection.  
Figure 7: TBF 300 applied on stainless steel coupons inoculated with the test microorganisms without biofilm formation (**upper**) and non-inoculated (**lower**). After 15 min contact time and washing, no residual staining is observed.  
FreshCheck was applied on stainless steel coupons containing different levels of _L. monocytogenes_ contamination, after inoculation with serial dilutions of a suspension containing \(10^{7}\) CFU/mL. Slide contact plates were also applied on a parallel experiment to compare the sensitivity of both methods for detection of microbial contamination. Table 6 shows the results of the comparative study between FreshCheck and contact slides, detecting different concentrations of _L. monocytogenes_ contamination.  
Results of Table 6 show that FreshCheck has a very high sensitivity detecting microbial contamination on surfaces. Contact slides were only to recover bacteria able to form visible colonies from surfaces inoculated with a suspension of at least \(10^{3}\) CFU/mL. In contrast, FreshCheck spray shows a positive reaction at all levels of contamination tested, even at values as low as 10 CFU/mL.
# Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network
###### Abstract  
This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for'resnet18', 'alexnet', and 'vgg16', respectively meanwhile, the 'googlenet' (AP = 0.84) and'mobilenetv2' (AP = 0.87) also demonstrate comparable _AP_ values. In terms of computing speed, the 'alexnet' takes the least computational time, the'squeeze' and'resnet18' are ranked second and third respectively; therefore, the'resnet18' is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role.  
crack detection; YOLO network; feature extractor; feature extraction layer; computational cost; detection precision
# Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm
## 2 System Description  
### Buck Converter  
Switched-mode power converters are highly efficient systems prominently used in a wide range of applications like mobile and computer power supplies, electric cars, television, etc. The Buck or step-down converter is a switched mode regulator used to convert a DC voltage of higher magnitude to a lower magnitude DC voltage. A high-power switching device like MOSFET or IGBT is switched on or off at a high frequency to facilitate this conversion. The output voltage can be varied by controlling the on time of the power device by pulse width modulation [37]. The basic buck converter has a DC source, switching device, diode, filter inductor, and capacitor and load, as shown in Figure 0(a).  
When the control signal is given such that the switch is closed, the diode D is reverse biased and when the switch is turned off, the diode conducts so that the current through the inductor is uninterrupted. From the inductor volt-balance equations, it can be derived that the output voltage \(V_{o}\) is equal to \(DV_{g}\), where the switch duty cycle is given by \(D=T_{on}/T\), \(V_{g}\) is input voltage, and T is the switching period. The waveforms for the continuous conduction mode (CCM), where the inductor current does not become zero, are as shown in Figure 0(b). The inductor current increases and decreases during respectively during the on-time (\(T_{on}\)) and off-time (\(T_{off}\)) of the pulses. The design parameters of the buck converter can be formulated based on [38; 39]. The minimum value of inductor and capacitor for continuous mode conduction can be selected as:  
\[L_{min}=\frac{(V_{g}-V_{o})D}{\Delta i_{L}f} \tag{1}\]  
\[C_{min}=\frac{(1-D)V_{o}}{8 Lf^{2}\Delta V_{o}} \tag{2}\]  
Here \(f\) is the switching frequency, and \(V_{g}\) and \(V_{o}\) are the input and output DC voltages. \(\Delta i_{L}\) is the inductor current ripple, which is assumed to be 20-30% of output current. Similarly \(\Delta V_{o}\) is the output voltage ripple, normally assumed to be 1-5% of the output voltage, depending on the design criteria. The value of filter inductance \(L\) should be chosen to be greater than \(L_{min}\) for continuous conduction, and capacitance \(C\) has to be greater than \(C_{min}\).
## 3 Passive Systems
### Field-passive Fluids in Energy Dissipating Systems
#### 3.2.3 Passive Body Armor  
Among all the applications involving shear thickening fluids, body armoring may be the one with the most prolific emergence in the last decade. In the list of body armoring applications, one can find bulletproof vests (Figure 6) [120; 184; 185; 186; 187; 188; 189], stab resistance fabrics [190; 191], anti-pucture gloves [192], helmet liners [193; 194], athletic gear [195] or back protectors (_SuperSpine_) [105]. It is also important to highlight here that in body armor applications the use of viscoelastic liquids has not been explored yet.  
In most of these inventions referenced above, there is a common feature, the shear thickening fluid is incorporated to the system by impregnating a piece of fabric. [PERSON] et al. [196] recently reviewed the state of the art regarding the enhanced impact resistance performance of textiles by the applications of STFs, where they stated that the energy absorption mechanism of STF treated textile structure is yet to be well understood and further investigation and quantitative verification is still necessary. Nevertheless, they highlighted that mechanisms improving the energy absorption are related with a combination of three factors, i.e., the energy dissipation due to shear thickening behavior, the increased yarn to yarn friction (yarm pull out energy) and a better coupling and load transfer between fiber to fiber and yarn to yarn.  
Beyond applications in personal protective equipments, the reinforcement of fabrics with STFs have inspired other applications, like in an expandable spacecraft [197; 198]. These types of habitable structures have multiple flexible layers for different purposes, like retaining an atmosphere. Among them, there is also a protective micro meteor orbital debris (MMOD) layer, which is responsible for the protection against the impact of hypervelocity particles. The use of an impregnated layer material in MMOD provides a greater protection than the one afforded by a layer material without the shear thickening fluid.