esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System
### Step Length Estimation  
Step length estimation is calculated based on how much larger or smaller the stride length is compared to the reference stride length. When estimating the step length, it is necessary to determine the magnitude of the acceleration compared to that of normal walking. Let \(A_{z}\) be the acceleration in the direction of gravitational acceleration and let \(A_{x}\) and \(A_{y}\) be the acceleration in the horizontal direction. These accelerations are a rotated value of the acceleration sensor value \((a_{x},a_{y},a_{z})\) based on the result of attitude estimation. We define the maximum value of \(A_{z}\) during step counting as \(A_{z,max}\) and the minimum value as \(A_{z,min}\). In addition, we define the norm of the maximum absolute value \(A_{x,max}\),of \(A_{x}\), \(A_{y}\) as \(A_{xy,max}\). The magnitude of acceleration \(A_{ave}\) is obtained using the average, defined as follows:  
\[A_{ave}=\frac{(A_{z,max}-A_{z,min})+A_{xy,max}}{2}. \tag{38}\]  
In addition, because we need to compare the acceleration to that of normal walking, we redefine it so that the magnitude of the acceleration during normal walking is 0:  
\[A_{adjust}=A_{ave}-C_{normal} \tag{39}\]  
where \(C_{normal}\) is a constant that must be obtained by calculating the magnitude of acceleration during normal walking by experiment.  
Let \(l\) be the estimated step length and \(l_{s}\) the reference step length; then, \(l\) can be calculated by the following Equation (40):  
\[l=l_{s}+K_{l}\cdot g(A_{adjust}). \tag{40}\]  
The reference step length \(l_{s}\) is calculated by the following Equation (41) [4] when the height is \(h\) [cm]:  
\[l_{s}=h-100 \tag{41}\]  
where \(K_{l}\) is the maximum value of the step length adjustment, e.g., \(K_{l}=30\) cm, and the function \(g(x)\) changes the value from \(-30\) cm to \(+30\) cm, then is added to the standard step length \(l_{s}\) to compute \(l\). The function \(g(x)\) is defined as follows.  
\[g(x)=\left\{\begin{array}{ll}1&(x\geq 1)\\ x^{3}&(-1<x<1)\\ -1&(x\leq-1)\end{array}\right. \tag{42}\]  
By cubing, even if there is some error in \(A_{adjust}\), it will be small if the error is not large.  
From Equations (40)-(42), \(l\) can be calculated as in Equation (43):  
\[l=(h-1)+K_{l}\cdot g\bigg{(}\frac{(A_{z,max}-A_{z,min})+A_{xy,max}}{2}-C_{ normal}\bigg{)}. \tag{43}\]  
Therefore, it is possible to estimate the step length by experimentally obtaining the constants \(K_{l}\) and \(C_{normal}\) for the cases of hand-held walking and hand-swinging walking, respectively.
# Mobile-AI-Based Docent System: Navigation and Localization for Visually Impaired Gallery Visitors
###### Abstract  
Smart guidance systems in museums and galleries are now essential for delivering quality user experiences. Visually impaired visitors face significant barriers when navigating galleries due to existing smart guidance systems' dependence on visual cues like QR codes, manual numbering, or static beacon positioning. These traditional methods often fail to provide adaptive navigation and meaningful content delivery tailored to their needs. In this paper, we propose a novel Mobile-AI-based Smart Docent System that seamlessly integrates real-time navigation and depth of guide services to enrich gallery experiences for visually impaired users. Our system leverages camera-based on-device processing and adaptive BLE-based localization to ensure accurate path guidance and real-time obstacle avoidance. An on-device object detection model reduces delays from large visual data processing, while BLE beacons, fixed across the gallery, dynamically update location IDs for better accuracy. The system further refines positioning by analyzing movement history and direction to minimize navigation errors. By intelligently modulating audio content based on user movement--whether passing by, approaching for more details, or leaving mid-description--the system offers personalized, context-sensitive interpretations while eliminating unnecessary audio clutter. Experimental validation conducted in an authentic gallery environment yielded empirical evidence of user satisfaction, affirming the efficacy of our methodological approach in facilitating enhanced navigational experiences for visually impaired individuals. These findings substantiate the system's capacity to enable more autonomous, secure, and enriched cultural engagement for visually impaired individuals within complex indoor environments.  
AI-aided system; indoor navigation; gallery experience; smart docent system 2020
## 2 Problem Description and Model Formulation
### Model Formulation  
#### 2.2.1 Calculation Method of Travel Time for the Cross Time Section  
A driving time calculation method was designed based on time division. The working time of the distribution center is divided into multiple time periods, and the vehicle driving speed is different in different time periods. Let \(F\) be the length of the period; \(H=\{H_{0},H_{1},\cdots,H_{L}\}\) is a set of all time, \([H_{h-1},H_{h}]\) is the \(h-th\) period. The driving speed of vehicles in different time periods is shown in Figure 1. \(d_{ijk}^{h}\), \(t_{ijk}^{h}\) and \(g_{ijk}^{h}\) respectively represent the distance, time, and speed of vehicle \(k\) on the road section \((i,j)\) within the time period \(h\); \(D_{ij}\) is the distance of the road section \((i,j)\); \(D_{ij}^{h}\) is the distance of vehicle \(k\) finishing \((i,j)\) remaining distance after time \(h\); \(L_{ik}\) is the point in time when vehicle \(k\) leaves customer \(i\); \(h_{k}\) is the remaining drivable time of vehicle \(k\) within the time period \(h\), then the calculation steps of vehicle \(k\)'s driving time \(t_{ijk}\) on the road section \((i,j)\) are as follows:  
1. Calculate the travel time in the initial period. \(d_{ijk}^{h}=g_{ijk}^{h}h_{k}\). If \(d_{ijk}^{h}\geq D_{ij}\), then \(t_{ijk}^{h}=D_{ij}/g_{ijk}^{h}\), \(t_{ijk}=t_{ijk}^{h}\), end of calculation; If \(d_{ijk}^{h}<D_{ij}\), \(D_{ij}^{h}=D_{ij}-d_{ijk}^{h}\), \(t_{ijk}^{h}=h_{k}\), turn to step 2.
2. \(\zeta=1\); \(d_{ijk}^{h+\zeta}=g_{ijk}^{h+\zeta}F\), if \(d_{ijk}^{h+\zeta}<D_{ij}^{h+\zeta-1}\), then \(t_{ijk}^{h+\zeta}=F\), \(D_{ij}^{h+\zeta}=D_{ij}^{h+\zeta-1}-d_{ijk}^{h+\zeta}\), repeat step 2; otherwise, \(t_{ijk}^{h+\zeta}=D_{ij}^{h+\zeta-1}/g_{ijk}^{h+\zeta}\), \(t_{ijk}=\sum\limits_{h\in H}t_{ijk}^{h}\), the calculation of the driving time of section \((i,j)\) is completed.
# An In-Depth Survey of Bypassing Buffer Overflow Mitigation Techniques
## 5 Compiler Based Mitigation Techniques  
In the previous sections, we have discussed some proposed techniques supported on Hardware and Operating system level to mitigate buffer overflow attacks. We have seen that after so many years of development, those mitigation's can still be bypassed. In this section, we are going to discuss the compiler-based countermeasures that have been proposed to perform automatic detection and prevention against buffer overflow attacks. Table 6 shows the summary of these protection mechanisms. [PERSON]. [51] presented the ProPolice compiler, also called Stack Smashing Protector (SSP) approach based on an improvement in StackGuard. SSP rearranges the memory layout to place pointer or function arguments below local buffers/ arrays in stack memory that protect against code-injection and code-reuse attacks. StackShield is a GNU GCC extension [52] that adds instruction in the program during compilation to maintain a duplicate stack in a different segment where return addresses are copied. It protects against stack smashing attacks as it would not be possible for an attacker to overwrite both addresses through a single vulnerability. Ref. [53] proposed Return Address Defender (RAD), a patch to the GCC compiler that adds safety code to create a protected area for copying return address. It helps the administrator to  
Figure 27: printf in libc using readelf.  
Figure 26: Python Script Part 2.  
detect the attack and catch the intruder in real-time by sending a real-time message and email. The tool named Address Sanitizer has been implemented in GCC by Google to detect errors in memory [54]. It is used to detect Out-of-bounds, Use-after-free, and memory leaks. Ref. [55] presented in a compiler approach named StackGuard or Stack Canary.
## 7 Results
### Objective Skin Parameter Analysis  
At six months post-treatment, both More Young and 7D groups showed sustained improvements, though some differences were observed in the magnitude and persistence of effects.  
As shown in Table 4, the More Young treatment exhibited superior long-term improvements in skin parameters compared to the 7D system, with enhanced retention of skin elasticity and pore size reduction at six months post-treatment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Skin** & **Baseline** & **Post-Treatment** & **6-Month** & **\% Change** & **\% Change** \\
**Parameter** & **(Mean \(\pm\) SD)** & **(2 Months)** & **Follow-Up** & **(More Young)** & **(7D)** \\ \hline Wrinkles & \(75.2\pm 5.3\) & \(52.4\pm 4.8\) & \(55.7\pm 5.1\) & \(-25.9\%\) & \(-18.7\%\) \\ \hline Texture & \(62.1\pm 6.1\) & \(45.8\pm 5.4\) & \(48.2\pm 5.7\) & \(-22.4\%\) & \(-16.9\%\) \\ \hline Elasticity & \(58.7\pm 4.9\) & \(70.5\pm 5.2\) & \(68.9\pm 5.5\) & \(+17.4\%\) & \(+12.8\%\) \\ \hline Spots & \(48.9\pm 5.7\) & \(36.2\pm 4.9\) & \(38.6\pm 5.1\) & \(-21.1\%\) & \(-14.5\%\) \\ \hline UV Spots & \(71.4\pm 6.2\) & \(54.9\pm 5.6\) & \(57.3\pm 5.9\) & \(-19.7\%\) & \(-13.2\%\) \\ \hline Brown Spots & \(53.8\pm 5.3\) & \(40.1\pm 4.7\) & \(42.5\pm 5.0\) & \(-20.1\%\) & \(-14.0\%\) \\ \hline Pores & \(68.2\pm 5.8\) & \(50.7\pm 5.2\) & \(53.1\pm 5.4\) & \(-22.1\%\) & \(-15.8\%\) \\ \hline Red Areas & \(60.5\pm 5.5\) & \(43.6\pm 4.9\) & \(45.9\pm 5.2\) & \(-24.1\%\) & \(-17.3\%\) \\ \hline Porphyrins & \(55.3\pm 5.6\) & \(38.9\pm 4.8\) & \(41.2\pm 5.0\) & \(-25.5\%\) & \(-18.2\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Long-term skin parameter outcomes at baseline, 2 months post-treatment, and 6-month follow-up for More Young and 7D devices.  
Figure 17: Clustered boxplot of degree of improvement (DOI) for More Young vs. 7D.  
The More Young treatment exhibited greater long-term improvements across all skin parameters compared to the 7D system. Wrinkle reduction, skin elasticity, and pore size improvements remained stable at six months in both groups, with More Young consistently maintaining a higher percentage of improvement. A slight regression (3-6%) in parameters such as wrinkles, texture, and spots was observed in both groups, indicating a gradual decline over time. Notably, the More Young group demonstrated significantly better retention of skin elasticity, with a 68.9% improvement from baseline compared to 63.4% in the 7D group.
# Sustainable and Reliable Information and Communication Technology for Resilient Smart Cities
## 5 ICT Reference Architectures and Open Urban Platform for Smart Cities  
Reference architectures as discussed in Section 2.3 are seen as propositions for blueprints of the ICT infrastructure in smart cities. ICT reference architectures have been developed along the concept of Open Urban Platforms (OUP), which have initially been worked out on a European level [3, 4] and subsequently standardized [2] by standardization bodies such as the DIN in Germany. OUP is a special type of blueprint that postulates a very high level of openness for urban ICT by prescribing the utilization of open interfaces and open standards for the data and information exchange between infrastructure components, systems and processes in a smart city. Furthermore, OUP advocates the usage of open data and open source as key data sharing and code transparency principles. However, these two principles are mere recommendations, as opposed to the open interfaces/standards requirement that is the key prerequisite for establishing an ICT infrastructure in conformance to OUP.  
Further tasks of an urban ICT reference architecture include the provisioning of a unified view on the ICT strategy of the city in question, as well as by the elucidation of the interfaces between the various technological layers and components within a city. Another important aspect is the need to be able to accommodate existing systems under the overall umbrella of an ICT reference architecture. Especially within the scope of open urban platforms, one of the main goals is the intention to enable the replication of smart city solutions across cities, thereby creating an eco-system and a new market in order to boost smart city approaches in terms of functionalities, deployments and applications.  
Figure 4 provides a high-level view on the standardized Open Urban Platform concept. The overall structure is constituted by a set of layers describing the information processing paths within a city, roughly starting from the data sources (e.g., sensors), continuing with  
Figure 3: The structure of the dependability concept according to [45].  
the communication network that enables the overall city interconnectivity, the databases and cloud infrastructures in the backend and the services which utilize the urban data in order to provide added value to various stakeholders such as the city/community, companies and the citizens. The overall layered structure is accompanied by cross-layer aspects relating to privacy and security as well to the management of the overall infrastructure and its common services.  
The detailed OUP definition in [4] provides various so-called capabilities for each layer/pillar that basically capture the functionality and the aspects which are to be implemented in this layer/pillar. Such capabilities are for example (Complex) Event Processing and Metadata Management for the \"3. Data Management and Analytics Capabilities\" layer or by Sensoring and Measuring and Time and Position Keeping for the \"0. Field Equipment/Device Capabilities\" layer.
# Field Tests on Eco-Friendly Railway Precast Concrete Slab
## 3 Field Test Set-Up  
### Installation of the B2S Panels  
A total of eight concrete slabs were installed in a tunnel of the operating Seoul subway lines (i.e., Line #1): four ecofriendly B2S panels and four conventional B2S panels. The total length of the tested B2S panels was 10 m. This line passed through a residential area. In this field tests, the performance of the eco-friendly B2S panels such as structural behaviors and a noise level were measured and compared with the conventional panels. For the selection of the field-testing site, some conditions were carefully considered: (1) the same rail profile without any irregularity; (2) a section of the lines without acceleration and deacceleration for trains; (3) same geometry including the cant and radius of the line; and (4) the same train speed. The first 5 m section consisted of four conventional B2S panels and the next 5 m section consisted of four eco-friendly B2S panels. The field installation process followed four steps: (1) removal of the ballast, (2) panel installation, (3) pouring the fillers between panels, and (4) completion of the installation. Figure 3 shows the installation process of 8-B2S panels in the operational line. The installation of the panels took place after the regular operation hours in order to minimize the obstruction of the operation of the subway line.  
Strain gauges, accelerometers, and linear voltage differential transducers (LVDTs) were mounted on the rails, tunnel walls, and the B2S panels. Those sensors were installed to measure vertical and lateral wheel loads, rail bottom stress, vertical and lateral displacements, vertical acceleration of panels, and lateral acceleration of tunnel walls. Figure 4 shows the plan view and sectional view of the schematics of the sensor locations of the field-testing site.  
Figure 2: Fabrication process of the B2S panels: (**a**) Rebar assembly, (**b**) Pouring concrete, (**c**) Curing, and (**d**) Prototype of the B2S panels.  
Figure 5 shows a picture of the installed sensors to measure wheel loads, displacements, stress, and accelerations.  
Figure 4: Sensor implementation plan.  
Figure 3: Installation process of the B2S panels: (**a**) Before removing the ballast, (**b**) Installation of panels, (**c**) Pouring fillers, and (**d**) Completion of the installation.  
Four strain gauges (full bridge) mounted on the surface of the rail to measure vertical and lateral loads (see Figure 4 Sensor implementation plan). The static vertical and lateral wheel load calibrations were conducted. Figure 6 shows the vertical wheel load calibration test. A hydraulic jack was used to apply a known vertical load with the increment of 10 kN. The calibration test was repeated three times. A linear regression analysis was carried out to obtain the calibrated relation between the vertical load and the strain change. This calibrated relation was used to measure the dynamic wheel load (Figure 6b).
## 3 Fault Diagnosis Transfer Model  
To enable bearing fault diagnosis under different loading conditions, this paper proposes a novel transfer learning method by combining the domain-adversarial neural network with the channel reconstruction unit module (CRU). Initially, the signal undergoes pre-processing, converting the original signal into a one-dimensional tensor, which is then fed into the feature extractor. Subsequently, domain-invariable features are extracted through adversarial learning to facilitate modal recognition for unlabeled target domain samples. The model structure is depicted in Figure 1, and additional details are described below.
## 3 Determination of Evaluation Index Weights of CBM Development
### Determination of Subjective Weight Based on BWM  
The calculation process follows the steps of BWM in reference [55]. The calculation steps are as follows:  
1. Select the best criterion (\(\text{a}_{\text{B}}\)) and the worst criterion (\(\text{a}_{\text{W}}\)) among the eight indices, respectively permeability and reservoir temperature;
2. Determine the preference of the best criterion over all the other criteria using a number 1\(\sim\)9. The resulting Best-to-Others vector would be: \(\text{A}_{\text{B}}=(\text{A}_{\text{B1}},\text{ A}_{\text{B2}},\text{ }\ldots,\text{ A}_{\text{Bn}})\). The results are shown in Table 2.
3. Determine the preference of all the criteria over the worst criterion using a number 1\(\sim\)9. The resulting Others-to-Worst vector would be: \(\text{A}_{\text{w}}=(\text{A}_{\text{1w}},\text{ A}_{\text{2w}},\text{ }\ldots,\text{ A}_{\text{nw}})^{\text{T}}\). The results are shown in Table 3.
4. The optimal weight for the criteria is the one where for each pair of \(\
icefrac{{w_{\text{B}}}}{{w_{\text{j}}}}\) and \(\
icefrac{{w_{\text{j}}}}{{w_{\text{w}}}}\), \(\
icefrac{{w_{\text{B}}}}{{w_{\text{j}}}}=\text{ a}_{\text{Bj}}\) and \(\
icefrac{{w_{\text{j}}}}{{w_{\text{w}}}}=\text{ a}_{\text{jw}}\). For all j, we should find a solution where the maximum absolute differences  
Figure 2: The average value and gray standard deviation histogram of evaluation parameters of 5 target blocks.  
\(\left|\frac{w_{\text{B}}}{w_{\text{j}}}-A_{\text{Bj}}\right|\) and \(\left|\frac{w_{\text{j}}}{w_{\text{w}}}-A_{\text{jw}}\right|\) for all j is minimized. Considering the non-negativity and sum condition for the weights, the following problem has resulted:  
\[\begin{array}{c}\text{min }\xi,\\ \text{s.t.,}\\ \left|\frac{w_{\text{B}}}{w_{\text{j}}}-A_{\text{Bj}}\right|\ \leq\ \xi,\text{ for all }j,\\ \left|\frac{w_{\text{j}}}{w_{\text{w}}}-A_{\text{jw}}\right|\ \leq\ \xi,\text{ for all }j,\\ \sum\limits_{\text{j}}w_{\text{j}}\ =\ 1\\ \text{w}_{\text{j}}\ \geq\ 0,\text{ for all }j,\end{array} \tag{1}\]  
where \(a_{\text{Bj}}\) indicates the preference of the best criterion B over criterion j, \(a_{\text{jW}}\) indicates the preference of the criterion j over the worst criterion W, and \(\xi\) is a constant.  
Solving this model, we have:  
\(w_{1}^{*}\ =\ 0.0675,\ w_{2}^{*}\ =\ 0.1349,\ w_{3}^{*}\ =\ 0.0810,\ w_{4}^{*}\ =\ 0. 2025,\ w_{5}^{*}\ =\ 0.0506,\ w_{6}^{*}\ =\ 0.3671,\ w_{7}^{*}\ =\ 0.0450,\ w_{8}^{*}\ =\ 0.0514,\ \xi^{*}\ =\ 0.84\). The consistency ratio is \(a_{\text{BW}}\ =\ \text{a}_{67}\ =\ 9\), the consistency index for this problem is 5.23, and the consistency ratio is 0.84/5.23 = 0.16, which implies a very good consistency.  
The weight values of reservoir depth, gas content, reservoir thickness, reservoir pressure gradient, critical reservoir ratio, permeability, reservoir temperature, and Young's modulus determined by the BWM were  
\[W_{\text{aj}}=\ (0.0675,\ 0.1349,\ 0.0810,\ 0.2025,\ 0.0506,\ 0.3671,\ 0.0450,\ 0.0514).\]
# Hierarchical Gated Recurrent Unit with Semantic Attention for Event Prediction
## 3 Our Proposal  
Most of current methods are based on RNN for event prediction, which can only extract the sequence information of events. Here, we propose a hierarchical event prediction model based on time series and prior knowledge. The model extracts the time-based event information in event descriptions by GRU and provides the prior knowledge of current events link by ALN. The semantic selective attention mechanism is used to fuse time-based event information and prior knowledge and guide the event prediction. Among them, ALN extracts the semantic background of current eventlink by associated relation among words in event links, which is used as the prior knowledge of event prediction.  
Our model takes event links as input for prediction. An event link consists of a sequence of related events that occur over a short period. The current event link which is being used for prediction provides time-based event information. All historical event links provide prior knowledge. At first, we define the event and the event link included in the model. We consider that an event \(E=\{w_{1},\cdots,w_{N}\}\) is a sequence of words, where \(N\) is the length of event \(E\). An event link \(L=\{E_{1},\cdots,E_{M}\}\) is a sequence of \(M\) events composed in temporal order. Note that a special token \(\langle\text{end}\rangle\) is added at the end of each event.  
Gated Recurrent Unit (GRU) [10] is used to encode time-based event information. GRU is a variant of Recurrent Neural Network (RNN) [22], which is a type of neural network for processing sequence data. The original RNN cannot capture long sequence information because of long-term dependencies [23]. GRU solves the problem of long-term dependencies with a gate function and is now widely used in real-world applications.  
The event we defined is represented in the form of a sequence of words. There is naturally a time series between words in the expression of an event. The description of predicted events generated by our model also has the word order relationship. Therefore, we construct a hierarchical architecture model for event prediction based on GRU. It is the basic unit of calculation in the HS-GRU.
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 2 Background and Related Work
### Satellite Security
By leveraging the controller's global view to dynamically install flow rules and employing content naming with in-network caching of popular content, the frameworkcan rapidly redirect or cache malicious requests under DDoS attacks, thereby distributing load and alleviating stress on core links. The main characteristics and drawbacks of these representative DDoS mitigation techniques are compared in Table 1.  
Prior schemes improve path unpredictability but introduce excessive path stretch and ignore the inherent risk of each route. Deep learning and blockchain approaches, however, incur high deployment overhead and cannot block stealthy DDoS attacks on board satellites in real time. Our work delivers a practical routing strategy for satellite networks that steers traffic away from high-risk bottleneck links while ensuring that path lengths remain bounded.