esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Discussion
#### 4.2.3 Effect of Gasket Fillet  
The O-ring is only in contact with the gasket in the deflation state, so the change in the gasket fillet only affects the performance of the O-ring in the deflation state. Figure 17a shows the maximum von Mises stress curves of the O-ring with various gasket fillets. As the gasket fillet increases from 0 to 0.4 mm at 15 \({}^{\circ}\)C and 70 MPa, the maximum von Mises stress in the deflation state consistently surpasses that in the inflation state. The results show a higher likelihood of O-ring damage in the deflation state. Additionally, the increase in von Mises stress in the deflation state is almost negligible, indicating that the influence of the gasket fillet on the von Mises stress in the deflation state can be disregarded.  
Figure 17b shows the maximum contact pressure curves of the O-ring with various gasket fillets. The maximum contact pressures of both contact surfaces I and II are greater than 70 MPa, indicating effective sealing performance across a wide range of gasket fillets. As the gasket fillet increases from 0 to 0.4 mm at 15 \({}^{\circ}\)C and 70 MPa, the maximum contact pressure in the deflation state of contact surface I consistently remains lower than the other three situations (inflation contact surface I, inflation contact surface II, and deflation contact surface II), indicating relatively poorer sealing performance in the deflation state of contact surface I. Furthermore, the maximum contact pressure of contact surface II in the deflation state remains almost unchanged, while the maximum contact pressure of contact surface II fluctuates by approximately 3% in the deflation state. The result shows that the influence of the gasket fillet on the sealing performance in the deflation state can be ignored. In summary, the influence of the gasket fillet on the performance of the combined sealing structure is found to be negligible.
# Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill
###### Abstract  
The field of prognostic maintenance aims at predicting the remaining time for a system or component to continue being used under the desired performance. This time is usually named as Remaining Useful Life (RUL). The current study proposes a novel approach for the RUL estimation of coating segments placed on a hot rolling mill machine. A prediction method was developed, providing real-time updates of the RUL prediction during the rolling milling process. The proposed approach performs energy analysis on measurements of segment surface temperatures and hydraulic forces. It uses nonparametric statistical processes to update the predictions, within a prediction horizon/window, indicating the number of remaining products to be processed. To assess the probability of failure within the defined prediction window, Maximum Likelihood Estimation is used. The proposed methodology was implemented in a software prototype in the MATLAB environment and tested in an industrial use case coming from a steel parts manufacturer, facilitating testing and validation of the suggested approach. Real-world data were acquired from the operational machine, while the validation results support that the proposed methodology demonstrates reasonable performance and robustness against product type variations.  
Keywords:cyber-physical systems; data analysis; energy analysis; hot rolling mill; predictive maintenance; Remaining Useful Life; tool-wear monitoring +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Model Evaluation Metrics  
The performance of the anomaly detection model is assessed using the following metrics: accuracy, recall, precision, and F1-score.  
_Accuracy_ represents the percentage of correct predictions out of all predictions and is given by  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\cdot 100\% \tag{2}\]  
where  
\(TP\)**--**true positive, correctly classified \"Faulty\" cases,  
\(TN\)**--**true negative, correctly classified \"Healthy\" cases,  
\(FP\)**--**false positive, incorrectly classified \"Faulty\" cases,  
\(FN\)**--**false negative, incorrectly classified \"Healthy\" cases,  
High accuracy means that the model predicts both positive and negative classes well.  
_Recall_ measures the percentage of correctly predicted positive cases relative to all actual positive cases:  
\[Recall=\frac{TP}{TP+FN}\cdot 100\% \tag{3}\]  
High recall means that the model detects all positive cases well, but may have more false alarms.  
_Precision_ expresses the percentage of correctly predicted positive cases relative to all predicted positive cases:  
\[Precision=\frac{TP}{TP+FP}\text{-}100\% \tag{4}\]  
High precision means that the model rarely generates false alarms, but may miss some positive cases.  
\(F1-score\) is the harmonic mean of precision and recall:  
\[F1-score=2\cdot\frac{Recall\cdot Precision}{Recall+Precision}\cdot 100\% \tag{5}\]  
\(F1-score\) is useful when a balance between precision and sensitivity is needed. A high value indicates that the model effectively balances precision and recall, ensuring accurate fault detection with minimal false alarms.
## 5 Multi-Path Routing Protocol
### Complete Disjointed and Brained Path  
There are two popular routing protocols, Dynamic Source Routing (DSR) [118] and Ad-hoc On-demand Distance Vector (AODV) [117] in WSNs. DSR keeps multiple paths in the routing table for increasing reliability. This gives DSR good fault tolerance. AODV uses multi-round discovery, exploring alternative paths to establish a route. However, both DSR and AODV are on-demand routing protocols, which may lead to significant latency and overhead at every path-finding stage. Multi-path routing eliminates the routing discovery process unless there is no path available.  
In general, there are two kinds of multi-path routing, as illustrated in Figure 8: (1) complete disjointed path: all paths do not share common nodes except the source and destination nodes. There is much research on this topic. The algorithm to find \(k-disjoint\) paths earlier can be found in [119; 120; 121; 122]. The second is (2) trained paths: the paths are not completely disjoined; there are many partially-disjoint alternate paths. There is a tradeoff between resilience and maintaining overhead [47]. For comparable resilience to patterned failures, the brained multi-path expends only 33% the energy of complete disjoint paths and has 50% higher resilience to isolated failures [8].  
Figure 8: Disjoint paths and brained paths. (1, 2, 6) and (1, 3, 5, 6) are disjoint-paths. (1, 3, 4, 6) and (1, 3, 5, 6) are brained paths.  
Formally, there are two ways to evaluate the relation between two paths: disjointness and stretch [122]:  
\[disjointness=1-\frac{|P_{primary}\cap P_{backup}|}{|P_{primary}|} \tag{4}\]  
\[stretch=|\frac{P_{backup}}{P_{primary}}| \tag{5}\]  
\(P_{primary}\) and \(P_{backup}\) are defined as the set of links in the primary and the backup path. The resilient network needs \(disjointness\) close to one and a stretch as small as possible. Paths must be chosen to achieve the highest reliability possible, however. The work in [123] proves that in order to improve the reliability of multi-path routing, if the primary path is the shortest path, then the second path should be as short as possible; while if the primary path is not the shortest path, then the second path should differ from the first as much as possible.
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 3 Link Budget of Chipless RFID Arrays  
The link budget analysis is a crucial parameter used to evaluate the effectiveness of a chipless RFID system. It determines the total gains and losses in the communication framework, which helps establish the maximum distance possible for reliable communication. Chipless RFID systems do not have an integrated circuit (IC), which makes the link budget analysis different from conventional RFID setups. In chipless RFID, wireless communication between a reader and a tag occurs through the backscatter of electromagnetic waves. However, this scenario introduces complex design challenges due to the intricate interplay among the reader, tag, and the radio channel.  
The interactions between the reader, tag, and radio channel are complex and can affect the power received by the tag (\(P_{in}\)) and the power backscattered into the reader (\(P_{bs}\)). These interactions can limit the received or backscattered power (\(P_{r}\)) to levels below a critical threshold, which equals the sensitivity of the reader (\(P_{s}\)). If the power falls below this threshold, the reader may not detect the backscattered signal, and communication will become unreliable. Therefore, accurately predicting received powers is essential for the effective design and operation of modulated backscatter systems in chipless RFID. The link budget equation for chipless RFID can be expressed as  
\[\rm Link\,Budget=P_{t}+G_{t}-FSPL_{1,2}+\sigma_{\rm tag}-G_{r}-P_{s} \tag{4}\]  
where \(P_{t}\) is the transmitted power by the reader, \(G_{t}\) is the gain of the RFID transmitting antenna, \(FSPL_{1,2}\) is the free-space path loss in the forward and backward link, \(\sigma_{\rm tag}\) is the RCS of the chipless tag, \(G_{r}\) is the gain of the RFID receiving antenna, and \(P_{s}\) is the sensitivity of the reader.  
A positive link budget indicates that the received power at the RFID reader is equal to or greater than the receiver's sensitivity (\(P_{s}\)), ensuring successful detection and reliable communication with the chipless RFID tag. In contrast, a negative link budget means the received power is below the required sensitivity, leading to unreliable detection or complete communication failure.
# Natural Creation of Large Rock Cavern: Can We Construct Them? Jenolan Caves as a Case Study
## 4 Discussion of the Findings Based on Two Parts of Stability Simulations
### Findings on Rock Reinforcement as Stabilization Measure for Rock Opening  
The stability of a natural cave is predominantly controlled by the rock structure and rock quality around the cave. A massive or thickly bedded rock structure above the cave is considered to be part of the roof-support element to maintain a stable opening. For a man-made rock opening when applying rock reinforcement as a stabilization measure, the reinforced rock arch/beam can be formed at the periphery of the opening through the application of confining pressure via the pattern rockbolts to improve the overall rock mass strength and deformation modulus within the bolted zone. This reinforced rock arch performs similar functions as and has similar characteristics to a structural fixed beam/arch, as described in Section 3 above.  
According to structural fixed beam and arch models [87; 91], the size and deformation modulus of the beam/arch element influence the support span. In terms of the rock-reinforcement technique, the size of the \"zone of compression\" within the pattern bolted zone is determined dependent upon the bolt length and spacing to be introduced for the excavation [81; 82; 37; 83; 84]. Importantly, the rock-reinforcement technique cannot be effectively developed if the compression zone is formed across a highly compressible feature, such as a fault or a clay seam, or a highly fractured rock mass with a Q-value [47] < 0.1 or Geological Strength Index (GSI) < 30 [93].  
The reinforced rock beam can be in straight form, but also can be in arched sharp that have been designed in most of the rock openings such as transport tunnels, mine-drifts, and underground powerhouse rock caverns. Structural beam models reveal that arched beam is much stronger than straight beam if the beam is in same size and use of same material. Also, different arch shapes (i.e., in terms of a ratio of arch rise to span) can carry different loadings if the beam is in same size and same span of the structure.  
In this study, Model M7 (Figure 8b), Models MM-3 (Figure 10c), and Models MM-4 (Figure 10d) show that the opening with rock reinforcement as stabilization measure gives a significant improvement by controlling deformation to the opening correspondence to Models M1 (Figure 6a), MM-1 (Figure 10a), and MM-2 (Figure 10b), respectively. The assessed deformation in Model M7, Model MM-3, and Model MM-4 is 40 mm, 32 mm, and 20 mm, respectively; and the deformation in Models M1, MM-1, and MM-2 is 67 mm, 46 mm, and 25 mm, respectively. This improvement of deformation is due to better rock mass strength and deformation modulus being improved within the bolted zone of rock mass formed around the opening periphery. Recap the results of Models M1 (UCS 30 MPa of rock) and M2 (UCS 50 MPa of rock), it has demonstrated the difference of engineering responses and deformations between low strength rock and high strength rock of opening.
# A Review on the Performance of Linear and Mixed Integer Two-Stage Stochastic Programming Software
## 5 Scenario Decomposition Methods
### Dual Decomposition (DD) Method
#### 5.1.2 Branch-and-Bound Method  
The DD algorithm proposed by [PERSON] and [PERSON] [17] uses the bound \(Z_{LD}\) to discard nodes from the first-stage search domain. Algorithm 3 summarizes the branch-and-bound procedure. The set \(\mathcal{P}\) denotes the group of active problems and \(TC^{i}\), the lower bound associated with program \(\mathcal{P}_{i}\in\mathcal{P}\). Commonly, the Lagrangian dual problem yields first-stage solutions that differ in value from one scenario to another. For those instances, a candidate \(\hat{x}\) is estimated by applying a rounding heuristic on the average solution \(\sum_{\omega\in\Omega}\tau_{\omega}x_{\omega}^{i}\). Note that Algorithm 3 can be applied not only to problems with mixed-binary variables, but to problems with general mixed-integer variables as well. The branching steps assume that the integer variables can be nonbinary. More, recently [PERSON] and [PERSON] [51] proposed a new branching strategy based on Dantzig-Wolfe decomposition that allows reduction in the number of nodes and decreases the overall solution time.  
```
Input: Branching tolerance of continuous variables \(\delta>0\)
1 Set \(NAC\longleftarrow\) false, \(z_{UB},\longleftarrow\infty\), \(\mathcal{P}=\{PNAC\}\) while\(|\mathcal{P}|>0\)do /* Lower bounding procedure */ Select problem \(\mathcal{P}_{i}\) from \(\mathcal{P}\) and SOLVE (18) to get the lower bound \(Z_{LD}^{i}\) and \(x_{\omega}^{i}\) Eliminte problem \(\mathcal{P}_{i}\) from \(\mathcal{P}\) if\(Z_{LD}^{i}=-\infty\) (_infeasibility of a subproblem_) or \(Z_{LD}^{i}\geq z_{UB}\)then go to line 2 elseif\(\sum_{\omega\in\Omega}H_{\omega}x_{\omega}^{i}=0\)then \(\hat{x}^{i}\longleftarrow x_{j}^{i}\) for any \(j\in\Omega\) \(NAC\longleftarrow\) true
1 else \(NAC\longleftarrow\) false
2 perform rounding heuristic to obtain \(\hat{x}^{i}\) /* Upper bounding procedure */ Compute \(TC_{p}^{i}\) from \(\hat{x}^{i}\) using Equation (10) if\(TC_{p}^{i}<z_{UB}\)then \(z_{UB}\longleftarrow TC_{p}^{i}\) \(x^{*}\longleftarrow\hat{x}^{i}\)  eliminate from \(\mathcal{P}\) all the problems \(\mathcal{P}_{j}\) with \(Z_{LD}^{j}\geq z_{UB}\) else go to line 2 /* Branching procedure */
3 ifnot\(NAC\)then  Select a component \(x_{(k)}\) of \(x\) and add two new problems to \(\mathcal{P}\) by adding constraints: \(x_{\omega,(k)}\leq\hat{x}_{(k)}^{i}-\delta\) and \(x_{\omega,(k)}\geq\hat{x}_{(k)}^{i}+\delta\) for all \(\omega\) in \(\Omega\) (if \(x_{(k)}\) is continuous) \(x_{\omega,(k)}\leq\lfloor\hat{x}_{(k)}^{i}\rfloor\) and \(x_{\omega,(k)}\geq\lceil\hat{x}_{(k)}^{i}\rceil\) for all \(\omega\) in \(\Omega\) (if \(x_{(k)}\) is integer) returnOptimal solution \(x^{*}\) and \(z_{UB}\)
```  
**Algorithm 3**DD Branch and Bound method
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 6 Discussion of Approach Planning Results under Severe Weather Conditions
### Results of Controllers' Validations
Not all controllers were able to answer all the questions because their training and work experience did not give them sufficient knowledge of all the work positions where severe weather support could conceivably be provided (by their own appraisal). Particularly important for our controllers' support system evaluation were the comments, suggestions, and criticismsof the participants. For this reason, a generous comment field was added to each question, which was also used by most participants. These comments were intended to further develop the system so that it could also be operationally used in the future. In addition to a general level of approval, this kind of open user feedback is also important to ensure that further developments do not neglect the needs and requirements of air traffic controllers. Evaluation of the responses showed the overall comprehensive satisfaction of the involved controllers with the extended system and its newly developed functionalities (Table 2). For the numbers in parentheses, the value 1 represents the answer \"strongly disagree\", the value 4 represents \"neutral\", and the value 7 represents \"strongly agree\", with the other values in between assigned accordingly. The ATCOs exhibited high agreement with the notion that an AMAN can be operationally improved by integrating adverse weather information (6.4/7) and adverse weather guidance support (6/7). They also showed a high level of agreement with the concept that an extended AMAN would lead to improved decision making (6/7). Furthermore, they reported their agreement with the statement suggesting that they would feel comfortable using the visual aids on the radar display (6/7). All in all, they found the proposed concept for extended Arrival Management very useful (6/7). In summary, the evaluation of the validation with controllers showed that they would very much like to use a system that supports them in approach planning and flying around severe weather areas, both from a planning and visual point of view, as it would make their daily work easier and thus also safer. It is noteworthy that they placed more emphasis on weather information than on planning support.
# Analysis of Tizen Security Model and Ways of Bypassing It on Smart TV Platform
## 5 Example of Attack Scenario
### Kernel Level Exploitation
#### 5.2.2 Second Stage Shellcode  
The first thing that our second stage shellcode is doing after being loaded is loading all necessary libraries and resolving symbols. Usually, this job is done by the ld.so, however, our code is not a part of a regular executable, so we have to take care of it ourselves. This is performed in init_ctx function (Listing 8).  
```
staticvoidSC_SUB_DECLinit_ctx(sc_ctx_tsctx) { structstatst;  intfd;  unsignedintlbl_base;  fd=sc_open(ctx->c_lbld_Q_RQNIX);  sc_stat(ctx->c_lbld_Q_RQNIX);  void *ld_memsc_mmmp(0,st.st_size,PROT_READ,MAP_PRIVATE,fd,0);  if(ld_mem); {  libdl_base=find_libdl_base(ctx);  ctx->dlopen=(void*)((find_sym(ld_mem,ctx->c_dlopen)&0 xFF)|libdl_base);  ctx->dlsym=(void*)((find_sym(ld_mem,ctx->c_dlsym)&0 xFF)|libdl_base);  sc_mummap(ld_mem,st.st_size); } sc_close(fd);  ctx->m=ctx->dlopen(ctx->c_libc,MY_RTID_LAZY);  ctx->fopen=ctx->dlsym(ctx->m,ctx->c_fopen);  ctx->fprintf=ctx->dlsym(ctx->m,ctx->c_fprintf);  ctx->fflush=ctx->dlsym(ctx->m,ctx->c_fflush); }
```  
Listing 8: Definition of init_ctx function.  
This function simply loads libdl.so and finds addresses for two important functions: dlopen and dlsym. After that, it uses ASCII function names to find addresses of other APIs needed by shellcode. Armed with all the API we need, we can start the exploitation process. In the beginning, we have to make sure our process (and all its children) is not closed when the browser crashes or exits. Therefore, we prepare a piece of code that deals with that (Listing 9).  
```
ctx->pid=ctx->fork(); if(ctx->pid) exit_parent(ctx); //firstchild ctx->pid=ctx->setsid(); ctx->signal(SIGHTUP,SIG_IGN); ctx->pid=ctx->fork(); if(ctx->pid) exit_parent(ctx); //secondchild //Setthecurrentworkingdirectorytotherootdirectory. asmvolatile(\"ADRLR9,ROOT_DIR;\"); ctx->pid=ctx->chdir(str); //Settheuserflicecreationmasktozero. ctx->umask(0); ctx->pid=ctx->sysconf(_SC_OPEN_MAX); for(ctx->i=0;ctx->i<ctx->pid;ctx->i++) sc_close(ctx->i); asmvolatile(\"ADRLR9,DEV_NULL;\"); sc_open(str,_ORDONLY); sc_open(str,_WORDNY); sc_open(str,_OKWRN); sc_open(str,_OKWRN); asmvolatile(\"ADRLR9,CHILD_READY;\"); ctx->fprintf(ctx->fp,str,ctx->getpid()); ctx->ffflush(ctx->fp);
```  
**Listing 9**Spawning child process and closing all \"links\" with parent.  
Its job is to simply fork, send signal, fork second time, change directory to /, set user file creation mask to zero, close all files opened by parent (and inherited by us), and open /dev/null for stdin,stdout, and stderr since applications requires them and we will not be needing them anyways.
## 2 Related Work
### Efficient Backbone STDCNet  
For real-time semantic segmentation models, an efficient backbone network plays a decisive role in improving segmentation performance. These architectures commonly employ strategies to optimize both the network's depth and width, simultaneously addressing the limitations related to computational power and memory usage. By deeply learning and extracting more precise and richer image feature representations, the model's accuracy and ability to preserve details in semantic segmentation tasks are significantly enhanced, allowing the model to gain a deeper understanding of the semantic information within the image.  
STDC [16] employs a Short-Term Dense Concatenate Module (STDC module), which hierarchically fuses multi-scale features through dense concatenation and dilated convolutions, as detailed in [16]. As shown in Figure 2, the STDC module architecture cleverly adopts a design with a stride of 2, where M represents the input channel dimension and N represents the output channel dimension. Notably, with the exception of the first block, where the kernel size is explicitly set to 1, the kernel size for all subsequent blocks is uniformly configured to 3. This approach seeks to optimize the network's performance by systematically reducing the dimensionality of the feature maps while simultaneously integrating feature aggregation representations. In doing so, it efficiently removes redundant components from the structure, while markedly increasing the network's effective receptive field, thereby augmenting its capacity to process intricate feature information.
## 3 Materials and Methods
### Respondent Demographics  
As shown in Table 1, respondent answers were gathered from varying company sizes, regions and industrial fields, along with participant responses to questions related to 3D printing usage and 3D model outsourcing affinities.  
The majority of participants are employed in micro-companies with fewer than ten employees, followed by very large corporations with more than 1000 employees, large companies with 251 to 1000 employees, medium-sized companies (51 to 250 employees), and small-sized companies (10 to 50 employees). The distribution of primary industrial activities of organizations was wide, ranging from industrial manufacture to consultancies, aerospace and medical products. A total of 60% of participants' companies do not apply 3D printing within their supply chains, while 40% of 3D printed components are distributed internally and externally (international and national AM service providers). Although 60% of participants do not use 3D printing within their supply chains, they do use 3D printing in one form or another. Companies that apply 3D printing in their supply chains use this set of technologies to manufacture and/or procure industrial end-use parts (Rapid Manufacturing and Rapid Tooling), which are determined as integral elements of an assembly or single components. Rapid prototyping is excluded from this definition since parts for demonstration or design iterations do not belong to operative supply chains. The acceptance of companies to send their models to an external AM service provider is at the level of 60%.