esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 OpenFlow Flow Table Memory Management
### Flow Rule Aggregation
However, the weakness of Agg-Extable relies in its inherent architecture, as this solution required large number of entries in the front-end table, which may significantly pressurize the ExTable and can lead to overflow problem. In addition, it may be challenging to adopt such solutions in large scale networks, especially with the variability exhibited by flows. [PERSON] et al. in [68] compact TCAM entries aimed to reduce the size of the flow entries in the flow table by inserting short tags in the packet header to identify the original number of bits used to store entries for OpenFlow switches. This is achieved by leveraging the dynamic programming capability of SDN to route the packets using those short tags. The approaches have optimized the TCAM storage space with a significant reduction in TCAM power consumption for a given number of flows. However, the solution required a change in the packet header and method to populate the flow table. Moreover, adding an identifier to each incoming packet in ASIC-based storage is hard, because it is not the standard operation, thus, causing the packet to be processed slowly with the performance penalty [56]. [PERSON] et al. in [56] proposed Minnie, which composed of two modules; compression and routing module. The flow table is configured with 1000 rules as the size when the number of rules exceeds the 1000 limit, Minnie triggers the compression module, this way rules are compressed by source, destination, and default, an optimal rule is chosen as the best rule. Thereafter, the shortest path is used to route the flow without overloading the flowtable on the topology where the number of nodes increases. Minnie focuses on achieving better compression ratio and computation time; however, with the variability exhibit by flows, some flow tends to live for a short or long period. Minnie overlooks incorporating flexible timeout and eviction mechanism to remove useless entries to free space for a new flow. Besides, the different compression methods employed may introduce another overhead with system instability.
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Task**}} & \multicolumn{1}{c}{\multirow{2}{*}{**Section**}} & \multicolumn{2}{c}{**Processed Data Type**} \\ \hline \multicolumn{1}{c}{\multirow{2}{*}{3D Model Reconstruction}} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{1-1}  & \multicolumn{1}{c}{} & [9; 12; 17; 18; 20; 21; 23; 26; 27; 28; 29; 30; 35; 36; 42; 45; 46; 47; 48; 52; 53; 54; 55; 57; 60; 61; 63; 64; 74; 76] & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline CBIR & 5.1.2 & - & [97; 98; 114] & - & - \\ \hline Environment Desc. & \multirow{2}{*}{5.1.3} & \multirow{2}{*}{-} & [90; 95; 96; 99; 103; 107; 115; 119] & - & - \\ Creation & & & & & \\ \hline Floor Plan & \multirow{2}{*}{5.1.4} & \multirow{2}{*}{-} & [104; 117; 120; 122] & - & - \\ Vectorization & & & & & \\ \hline Floor Plan & \multirow{2}{*}{5.1.5} & [8; 25; 31; 33; 37; 44; 53; 58; 62; 68; 71; 78] & [89; 151; 153; 170] & [79; 83] & [77; 172; 176; 177; 183; 185; 189; 204] \\ Predict./Gen. & & & [13; 14; 19; 67] & [138; 139] & [80; 82] & [180; 190; 191; 194] \\ \hline \multicolumn{1}{c}{\multirow{2}{*}{Room Classification}} & \multirow{2}{*}{5.1.7} & [15, 73] & [143; 149; 150; 152; 155; 157; 158; 169] & [81; 84; 85] & [173; 175; 178; 179; 186; 188; 201; 203; 205] \\ \hline \multicolumn{1}{c}{Change Detection} & \multirow{2}{*}{5.1.8} & [43] & - & - & - \\ \hline \multicolumn{1}{c}{Map Segmentation} & \multirow{2}{*}{[50]} & [1, 82; 127; 128; 135; 136; 140; 142; 144; 145; 146; 147] & - & [192; 193; 197; 198; 206] \\ \cline{3-3} Plan Segmentation & & & [86; 88; 91; 100; 101; 102; 105; 106; 108; 109; 112, 113; 118, 121; 123; 124; 125; 126] & - & - \\ \cline{3-3} Point Cloud & & [16; 32; 34; 38; 39; 41; 59; 65; 66; 69; 207; 208] & - & - & - \\ \hline \multicolumn{1}{c}{VR/AR} & \multirow{2}{*}{[40]} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\ \cline{3-3} Robot & & & & & \\ Expl./Localization & & & & & \\ \cline{3-3} Path Planning & & [70] & [133] & - & - \\ \cline{3-3} Localization & & [22; 56] & [93] & - & [24; 181; 196; 199; 200; 209] \\ \hline \multicolumn{1}{c}{Map Alignment/-} & \multirow{2}{*}{5.1.11} & \multirow{2}{*}{-} & [187] & - & [130; 148] \\ \cline{3-3} Plan Alignment/- & & & & & \\ \cline{3-3} Matching & & & & & \\ \cline{3-3} Plan Alignment/- & & & & & & \\ \cline{3-3} Matching & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Referenced papers grouped by their application and processed data type. Figure 4: Diagram presenting third constructed taxonomy: accomplished task.
# Remote Technologies as Common Practice in Industrial Maintenance: What Do Experts Say?
## 4 Results
### Answering the Research Questions
#### 4.2.2 Technology  
* **Data security:** Production systems generate, store and send security-relevant information. This information flow can be influenced by implementing a remote maintenance strategy. It is necessary to check which information reaches external information processing systems (e.g., cloud-based applications) through the remote process via interfaces and whether these meet the company's security requirements.
* **Adequacy of technology:** Intelligent manufacturing systems vary widely in structure and functionality. This results in different requirements for the remote maintenance technology needed. For the development of a suitable remote maintenance strategy, the technical characteristics of the manufacturing system must therefore be identified and used as the basis for deciding on the appropriate remote technology. In addition, the current remote technology is not yet able to transmit all signals of the manufacturing system (noise, odors, vibrations). It is necessary to check whether information about such signals is needed for the maintenance process. Furthermore, it is a challenge to select the appropriate remote technology. Knowledge of the current developments in remote technology variants is usually not available in sufficient detail in the companies to be able to make an informed purchasing decision. Further aspects to be considered from a practical point of view are stability of the connection, bandwidth or loss of information during transmission.
## 1 Introduction
Morphological effects due to dam construction are commonly reported based on hydrological series of discharges and sediment loads, and historical bathymetries at limited gauging stations [7; 23; 24; 28; 30; 31; 32]. Recent advances in geographic information systems (GIS) have resulted in a wider use of GIS tools for estimating morphological changes in rivers due to damming [4; 11; 33]. However, these applications have also been focused on specific areas without considering the analysis of geological records. In this paper, GIS tools are used to analyze post-dam trends and to quantify the evolution of the channel width and sinuosity over time by correlating the magnitude of those changes with the hydrological and geological settings of each river reach. The river width is adopted in this research as the reference parameter to establish the magnitude of the morphological changes, considering its key relevance in channel geometry and the river character [34; 35; 36; 37]. In addition to the channel width, sinuosity is also included in this work to consider possible planform changes. The channel width and sinuosity are obtained using all the information available for the study sites, including their planform from remote sensing (satellite images, aerial photographs), hydrology (gauging stations), and geology. The geology was analyzed from a regional to a local scale, considering confinement, composition (formations), and erodibility of the banks to understand the magnitude of the morphological changes that each river reach experiences after dam construction. Compensatory mitigation is intended to look for a way to repay the negative effects of development projects [38]. The definition of biodiversity offsets focuses on restoring the altered functions of the intervened ecosystem [39]. This analysis is important to establish the extent of the alteration in the ecosystem, defining a clear application of this work. Considering that our interest is to identify the elements that are relevant to delimiting the expected morphological changes due to dam construction in a simplified manner, other important factors, such as land use changes, are not included. This paper aims at identifying and discussing the changes in the channel width and sinuosity upstream and downstream of four large dams located in Colombia (SouthAmerica), considering this region as an appropriate representative of tropical systems. The work also explores the integration of hydrological and geological information in the analysis to establish a way to identify the areas that are more susceptible to those changes. Our results can help in setting the extent of the affectations and the biotic compensation areas due to new projects to be considered in the definition of biodiversity offsets.
## 4 Results
### Road Disaster Situation  
The inundation extent of the storm determined by the threshold segmentation method above was converted into vector surface layers, and the pre-processed road network data of the study area was overlaid, and the extracted inundated road network was shown in Figure 9 using the GIS spatial analysis method as shown in Equation (6). With the help of spatial analysis tools, the road network conditions within the inundated area during the 15 th to 20 th were partitioned for quantitative statistics to obtain specific road damage, as shown in Table 2.  
\[C=(A\cup B)-(A\cap B) \tag{6}\]  
where \(A\) is the set of road elements within the water body before the storm, \(B\) is the set of road elements within the water body after the storm, and \(C\) is the set of road elements within the affected water body.  
Figure 8: Variation of the water body area in each district of Zhengzhou.  
Figure 9: Flooded Road network in Zhengzhou.  
The monitoring results in Table 2 show that the roads in the main urban area of Zhengzhou are seriously affected, with a total length of 1324.63 km. The most seriously flooded road among the five main urban areas is in Zhongyuan District, with a total inundation length of 349.17 km. The reason for such a phenomenon is that this jurisdiction is one of the earliest built-up areas in Zhengzhou. Due to the lack of scientific planning and imperfect drainage facilities in the early stages, the high-intensity rainfall exceeded its drainage capacity in a short period, resulting in many small-scale rainstorms waterlogging within a certain range. In addition, the long-term urban construction makes the residential buildings and various public facilities in this area have a high density, and the road network is relatively dense. The natural surfaces and grass are covered with asphalt or concrete, and the area of impervious water has increased dramatically. A large amount of rainwater floods the dense urban roads due to the lack of urban drainage capacity under heavy rainfall in a short period, which in turn causes numerous safety hazards.
# IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices
## 4 IoTDS: A Host-Based Botnet Detection System for IoT
### Process Details
#### 4.4.1 Model Induction Phase  
The Model Induction phase, illustrated in Figure 2, consists of gathering host's data and building a model. First, the IoTDS Agent collects the memory and CPU utilisation, the number of running tasks and the CPU temperature at every second for a training interval of \(y\) seconds. Then, the data instances are extracted from these observations according to an \(s\)-second time-window to induce the behaviour model. The value \(x_{i}[j]\) of a data instance \(i\) for a particular feature \(j\) will be the arithmetic mean of the observations collected for \(j\) during the \(i\)-th \(s\)-second time-window. All the extracted data instances for the \(y\)-second training interval are included in a CSV file, which is sent to the IoTDS Management Console in an HTTP-POST request through an HTTPS-based connection.  
Then, in the Management Console, all features for each instance are scaled according to their minimum and maximum values between a range from 0 to 1, defined by the following equation:  
\[x_{i}[j]=\frac{x_{i}[j]-min(x[j])}{max(x[j])-min(x[j])},\forall i\in I,\forall j\in J \tag{10}\]  
where \(i\) corresponds to a given instance, \(I\) to all instances collected during that period, \(j\) to a feature in the \(J\) feature space, and \(x_{i}[j]\) to the value present in the feature \(j\) for the instance \(i\).  
Once the instances are normalised, the Management Console induces the behaviour model, which is stored in a file and sent in response to the IoTDS Agent's previous request for the model.  
Figure 2: Description of the Model Induction phase.
## 2 Materials and Methods
### Multi-Tool Analysis  
The following section describes the results of the multi-tool analysis (MTA). Refer to Figure 3 for details related to the de-icing tools used, as they are referred to by a range of numbers in the figures of this section. Using the four icing conditions identified in Section 2.5, the plates were subjected to manual MTA de-icing cycles with a gradation in tool hardness. The results are presented in Table 3.  
The results are presented in terms of the score obtained as per the procedure explained in Section 4. To recap, the scores can range from 20, which is considered the most effective, to 0, which is considered the least effective, as seen in red in Table 3. For simplicity, the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **1: Reference** & **2: Nusil** & **3: Swix** & **4: Surfellent** & **5: Wearlon** \\ \hline \multirow{3}{*}{High-intensity freezing rain} & Score: 0 & Score: 6 & Score: 10 & Score: 1 & Score: 0 \\  & Hard, ridged & Hard & Hard & Hard, ridged & Hard, ridged \\  & polymer & polymer & polymer & polymer & polymer \\  & 5 passes & 5 passes & 1 pass & 5 passes & 5 passes \\  & Ice remaining & & & & 1 ee remaining \\ \hline \multirow{3}{*}{Low-intensity freezing rain} & Score: 0 & Score: 6 & & Score: 9 & Score: 9 \\  & Hard poly- & Medium & Medium & Medium & Medium \\  & mer 5 passes & polymer & polymer & polymer & polymer \\  & Ice remaining & 5 passes & & 2 passes & 2 passes \\ \hline \multirow{3}{*}{Dry snow} & Score: 20 & Score: 14 & Score: 20 & Score: 20 & Score: 15 \\  & Brush & Soft polymer & Brush & Brush & Soft polymer \\  & 1 pass & 2 passes & 1 pass & 1 pass & 1 pass \\ \hline \multirow{3}{*}{Adhering snow} & Score: 5 & Score: 5 & Score: 10 & Score: 10 & Score: 8 \\  & Hard & Hard & Medium & Medium & Medium \\  & polymer & polymer & polymer & polymer & polymer \\  & 1 pass & 1 pass & 1 pass & 1 pass & 3 passes \\ \hline \hline \end{tabular}
\end{table}
Table 3: MTA results.  
tool name and the number of passes were added. Each type of precipitation is treated individually and is discussed below.
# Thermoforming Process Effect on Performances of Thermoplastic/Recycled Carbon Fiber Composites
## 3 Results and Discussion
### Thermal Characterization
further heating, the rCF/MAPP60 at 10 \({}^{\circ}\)C/min exhibits a single peak corresponding to the crystallization of the \(\beta\)-phase at 160 \({}^{\circ}\)C.  
The geometric density was calculated using Equation (2):  
\[\rho=\text{m/V} \tag{2}\]  
where \(\rho\) is the density (g/cm\({}^{3}\)), m is the sample's weight (g), and V is the measured volume (cm\({}^{3}\)). The obtained density values are reported in Table 2. The surface density of samples was measured by averaging the weight of coupons to their surface. Densities of 530 \(\pm\) 10 gsm and 560 \(\pm\) 30 gsm were measured for rCF/MAPP and rCF/PA6 carpets, respectively. By the calculated density, it is possible to obtain the average porosity of each sample. The average porosity, P, was obtained by applying Equation (3):  
\[\text{P}=1-\rho/\rho_{\text{th}} \tag{3}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Description** & \begin{tabular}{c} **T\({}_{\text{g, DSC}}\)** \\ **[\({}^{\circ}\)C]** \\ \end{tabular} & \begin{tabular}{c} **T\({}_{\text{m, DSC}}\)** \\ **[\({}^{\circ}\)C]** \\ \end{tabular} & \
\begin{tabular}{c} **Density** \\ **[g/cm\({}^{3}\)]** \\ \end{tabular} &
\begin{tabular}{c} **Porosity** \\ **[\%]** \\ \end{tabular} \\ \hline PA6/60 1L & 50.2 \(\pm\) 0.3 & 216.0 \(\pm\) 0.5 & 1.2 \(\pm\) 0.08 & 6.5 \(\pm\) 0.02 \\ PA6/60 2 LM1 & 54.4 \(\pm\) 0.3 & 220.4 \(\pm\) 0.6 & 1.1 \(\pm\) 0.15 & 13.2 \(\pm\) 0.07 \\ PA6/60 2 LM2 & 54.3 \(\pm\) 0.4 & 220.4 \(\pm\) 0.5 & 1.2 \(\pm\) 0.02 & 9.9 \(\pm\) 0.02 \\ PA6/60 3 LM1 & 50.5 \(\pm\) 0.5 & 221.5 \(\pm\) 0.6 & 1.1 \(\pm\) 0.11 & 14.0 \(\pm\) 0.05 \\ PA6/60 3 LM2 & 48.8 \(\pm\) 0.4 & 220.5 \(\pm\) 0.7 & 1.1 \(\pm\) 0.01 & 16.7 \(\pm\) 0.01 \\ MAPP/60 1L & 46.4 \(\pm\) 0.3 & 163.9 \(\pm\) 0.4 & 0.9 \(\pm\) 0.03 & 7.3 \(\pm\) 0.01 \\ MAPP/60 2 LM1 & 51.4 \(\pm\) 0.6 & 165.8 \(\pm\) 0.3 & 0.9 \(\pm\) 0.2 & 8.0 \(\pm\) 0.03 \\ MAPP/602 LM2 & 53.1 \(\pm\) 0.4 & 163.9 \(\pm\) 0.5 & 1.0 \(\pm\) 0.02 & 4.1 \(\pm\) 0.02 \\ MAPP/60 3 LM1 & 46.5 \(\pm\) 0.6 & 164.4 \(\pm\) 0.7 & 0.9 \(\pm\) 0.02 & 4.8 \(\pm\) 0.02 \\ MAPP/60 3 LM2 & 45.3 \(\pm\) 0.5 & 164.9 \(\pm\) 0.4 & 1.0 \(\pm\) 0.01 & 5.0 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of DSC, density and porosity analysis. Figure 6: DSC curves of the sample **(a)** I and II scan of PA6/60 and **(b)** I and II scan of MAPP/60. where \(\rho_{\rm th}\) is the theoretical density obtained by considering the rule of mixture [35]. The values of \(\rho_{\rm th}\) for PA6/60 and MAPP/60, considering fiber volumes (v\({}_{\rm fS}\)) of 29.4% and 25.1%, equal 1.33 g/cm\({}^{3}\) and 1.13 g/cm\({}^{3}\), respectively. Table 2 summarizes the results obtained from differential scanning calorimetry (DSC), density and porosity analyses.
## 4 Experiments and Analysis of Results
### Analysis of Results
#### 4.4.7 Head Number Results for Multi-Head Self-Attention Mechanisms  
To determine the impact of the number of heads in multi-head self-attention on model performance, this study conducted experiments on the pesticide dataset using different numbers of self-attentive heads, and the results are shown in Figure 8. We can see that the model achieved the highest accuracy, recall, and F1 value at Head_Num = 3. Compared to Head_Num = 1 (when only the self-attention mechanism was used, the three metrics increased by 0.9, 2.4 and 1.7 percentage points, indicating that more attention heads can capture more information and help the model to learn more complex features). Conversely, the lowest model effect was found at Head_Num = 4. This phenomenon was attributed to the likelihood of the model overfitting on training data when too many attentional heads were employed, diminishing its ability to generalize on unseen data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Models** & **Training Time (s)** & **Inference Time (s)** & **F1** \\ \hline \multirow{3}{*}{PRD} & GraphRel & 178 & 67 & 0.733 \\  & TPLinker & 162 & 57 & 0.943 \\  & BiRTE & 157 & 54 & 0.951 \\  & CasRel & 133 & 41 & 0.932 \\  & MD-SERel & 116 & 34 & 0.966 \\ \hline \multirow{3}{*}{DuIE} & GraphRel & 1006 & 395 & 0.512 \\  & TPLinker & 801 & 272 & 0.672 \\  & BiRTE & 743 & 254 & 0.675 \\  & CasRel & 589 & 140 & 0.628 \\  & MD-SERel & 552 & 91 & 0.689 \\ \hline \hline \end{tabular}
\end{table}
Table 7: A comparison of the computational efficiency of the models.  
Figure 8: The results of different self-attention head numbers.
## 4 Evaluation over Use Cases  
This section demonstrates the application of the proposed security evaluation methodology to two specific use cases: an ICT Gateway (ICT GW) within a smart grid environment and an AI Investments platform. Considering the maturity levels defined in Table 8, the methodology has been applied in both scenarios at the \"managed\" level. We have a partial automation of this process due to the usage of specific tools, and basic metrics have been selected to provide a proof of concept.  
Figure 5 provides an overview of the tools and concepts selected to support the instantiation of the methodology. In particular, the context establishment phase makes use of our predefined set of claims and TP. The risk identification phase is supported by the ResilBlockly tool, which automatizes the risk analysis and the generation of an initial MUD file. As testing tools, we use GraphWalker for MBT and a fuzzing tool developed within the BIECO project. Risks are aggregated and evaluated using the SecurityScore tool, which also produces a security label that is visualized in the BIECO GUI. The instantiation considers additional tools for the generation of the extended MUD (MUD updater tool) and for the detection of misbehaviors based on the MUD (auditing tool). The following dedicated subsections detail how the tools are used in each use case, supporting the automatization of the methodology.
# Micromechanical Characterization of Diatom Frustules of Multiple Origin
## 2 State-of-the-Art  
Diatoms are eukaryotic, photosynthetic, and unicellular. Its growth depends on physical and chemical surrounding conditions, particularly turbidity, temperature, pH, nutrients, and salinity. These algae develop a siliceous skeleton denominated \"frustule\" [1; 2; 3; 9; 25].  
Their growth depends on the concentration of Vitamin B12, amount of light, dissolved silica, nitrogen and phosphorus, and they are considered indicators of water quality [26; 27].  
After the death and organic decomposition of the algae, the frustules are deposited on the bottom of oceans or lakes, forming Diatomaceous Soils [4; 5; 6]. These soils have been found in different parts of the world, such as the United States, China, Peru, Japan, Denmark, Mexico, Colombia, Argentina, polar and equatorial belts, and the South American subduction zone. Frustules can be found whole or as remains of opalized algal cellsincorporated within clay, organic, siltstone or quartz matrices [7]. The deposits result from the accumulation of amorphous silica from the frustules of diatoms [28].  
Diatoms extract silica from their natural aqueous environment, forming the frustules. Silica can come from solutions in streams, by the reaction of adjacent rocks, or by the influx of volcanic ash. Phosphorus is the primary nutrient, mainly from the weathering of nearby rocks [29]. Diatoms play a significant global role in the biochemical cycling of carbon and silica in the water column [30]. Frustules are notable for having multiple pores or areoles, elongated, spherical or disk shapes, and variable sizes between 10 and 200 \(\upmu\)m [8].  
Diatom cells have two overlapping elements (thecas): the external (epitheca) and the internal (hypotheca). Their composition is about 95% polymerized silicic acid, similar to quartz. Two types of diatoms are recognized: pennate and centric. The first presents bilateral symmetry, which is usually found in freshwater. The second report is on radial symmetry, which is related to marine environments [13; 31; 32; 33].  
The fossil record indicates that they evolved from the Cretaceous; fossil diatom deposits from the end of this period are known as diatomaceous earth [26]. The \"omamentations\" or characteristic descriptions of the frustules are caused by the differential arrangement or attachment of silica in the cell wall [26] and are helpful for visual classification [33].