esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 E-barter Case Study
### System Analysis and Design with SEA_ML
#### 3.1.1 System Analysis with MAS and Organization Viewpoint  
An agent-based E-barter system consists of agents that exchange goods or services of owners according to their preferences without using any currency. The system analysis phase is performed by considering the MAS viewpoint of SEA_ML language. In fact, this viewpoint provides an overview of the system which is shown in Figure 2. When considering the structure of the system, the EbarterSystem constitutes of two semantic web organizations called Customers organization which include Customer agents, and Management organization where the Matchmaker agent and ServiceAgent agents reside. It is worth indicating that entities given in this overview can be considered as stereotypes and in the real system implementation, there can be many instances of these entities. For example, there can be many agents of type ServiceAgent working in this system.  
In this MAS, a Matchmaker agent, which is defined as a SWA, handles the interaction between Customer agents and ServiceAgents. This agent is responsible for registering SWS provided by each ServiceAgent in the system and matching proper services with customers. To infer about semantic closeness between offered and purchased items based on some defined ontologies, Matchmaker may use SWS. Conforming to its matchmaking definition, Matchmaker needs to discover the proper SWS, interact with the candidate service and realize the execution of SWS after an agreement.  
Customer agents represent the end users in the E-barter system. This agent receives the user's offer and purchase items and interacts with Matchmaker and other Customers to realize bartering. At the first stage, this agent interacts with Matchmaker to find out if there is a proper service containing candidate customers. In case of success, it receives the service addresses and interacts with those services to get the list of suitable customers. These services contain ontologically close customers with our customer needs. In case of failure, the Matchmaker simply registers the customer into the proper service. A Customer agent, having the list of candidate customers for bartering in hand, starts to negotiate with them one by one to make an agreement and realize bartering.  
The ServiceAgent agents represent the E-bartering SWSs in the system. They interact with the Matchmaker agent to register, update, and un-register the SWSs used in the system.
# Hybrid Adaptive Controller Design with Hysteresis Compensator for a Piezo-Actuated Stage
## 3 HAC Design
### Stability Analysis of System  
We define the Lyapunov function as follows:  
\[V_{k}=\frac{1}{2}\theta_{k}\textsuperscript{T}\theta_{k} \tag{25}\]  
where \(\theta_{k}=(\mathbf{\Phi}_{k}-\mathbf{\Phi}^{*})\). The derivation of (25) is as follows:  
\[\begin{split}\Delta V_{k}&=V_{k+1}\textsuperscript{T}V _{k}\\ &=\frac{1}{2}\theta_{k+1}^{T}\theta_{k+1}-\frac{1}{2}\theta_{k}^{T }\theta_{k}\\ &=\left[\frac{1}{2}\theta_{k+1}+\frac{1}{2}\theta_{k}\right] \textsuperscript{T}[\theta_{k+1}\textsuperscript{T}\theta_{k}]\\ &=\left[\frac{1}{2}\theta_{k+1}\textsuperscript{T}\theta_{k}+ \frac{1}{2}\theta_{k}+\theta_{k}\right]\textsuperscript{T}[\theta_{k+1} \textsuperscript{T}\theta_{k}]\end{split} \tag{26}\]  
We define \(\Delta\theta_{k}=\theta_{k+1}-\theta_{k}\). Using (22), \(\Delta\theta_{k}\) can be rewritten as:  
\[\begin{split}\Delta\theta_{k}&=\theta_{k+1}-\theta _{k}\\ &=(\mathbf{\Phi}_{k+1}-\mathbf{\Phi}_{k})\\ &=-\frac{1}{T_{k}}\int_{l_{k}}^{l_{k+1}}\frac{\sigma(t)\varepsilon (t)\psi(t)}{1+\psi^{T}(t)\psi(t)}dt\end{split} \tag{27}\]  
By substituting (21) and (28) into (26), we obtain:  
\[\begin{split}\Delta V_{k}=&-\frac{1}{2}\Bigg{[}- \frac{1}{T_{k}}\int_{l_{k}}^{l_{k+1}}\frac{\sigma[\theta_{k}^{T}\psi(t)+k_{k}^ {*}\eta(t)/m(t)]\psi(t)}{1+\psi^{T}(t)\psi(t)}dt+2\theta_{k}^{T}\Bigg{]} \cdot\\ &\Bigg{[}\frac{1}{T_{k}}\int_{l_{k}}^{l_{k+1}}\frac{\sigma[ \psi(t)+k_{k}^{*}\eta(t)/m(t)]\psi(t)}{1+\psi^{T}(t)\psi(t)}dt\Bigg{]}\end{split} \tag{28}\]  
We define \(\mathbf{\eta}^{*}(t)=\eta(t)\theta_{k}^{T-1}\). \(\Delta V_{k}\) can be rewritten as:  
\[\begin{split}\Delta V_{k}&=-\frac{1}{2}\bigg{[}- \frac{1}{T_{k}}\int_{l_{k}}^{l_{k+1}}\frac{\sigma[\psi(t)+k_{k}^{*}\eta^{*}(t) /m(t)]\psi(t)}{1+\psi^{T}(t)\psi(t)}dt+2\theta_{k}^{T}\bigg{]}\cdot\\ &\bigg{[}\frac{1}{T_{k}}\int_{l_{k}}^{l_{k+1}}\frac{\sigma[\psi( t)+k_{k}^{*}\eta^{*}(t)/m(t)]\psi(t)}{1+\psi^{T}(t)\psi(t)}dt\bigg{]}\\ &=-\frac{1}{2}\theta_{k}^{T}[2\mathbf{I}-\mathbf{P}_{k}]\mathbf{P }_{k}\theta_{k}\end{split} \tag{29}\]  
where  
\[\mathbf{P}_{k}=\frac{1}{T_{k}}\int_{l_{k}}^{l_{k+1}}\frac{\sigma(t)[\psi(t)+k _{k}^{*}\mathbf{(t)}^{*}/m(t)]\psi(t)}{1+\psi^{T}(t)\psi(t)}dt \tag{30}\]  
According to (24), there will be \(\frac{k_{k}^{*}\|\mathbf{\eta}^{*}(t)\|}{m(t)}\leq\|\psi(t)\|\) and \(0\leq\mathbf{P}_{k}<2\mathbf{I}\). Hence, from (29), we obtain \(\Delta V_{k}\leq 0\). Thus, the stability of the closed-loop system under the hybrid adaptive controller based on hysteresis compensator is demonstrated.
# Defacement Detection with Passive Adversaries
###### Abstract  
A novel approach to defacement detection is proposed in this paper, addressing explicitly the possible presence of a passive adversary. Defacement detection is an important security measure for Web Sites and Applications, aimed at avoiding unwanted modifications that would result in significant reputational damage. As in many other anomaly detection contexts, the algorithm used to identify possible defacements is obtained via an Adversarial Machine Learning process. We consider an exploratory setting, where the adversary can observe the detector's alarm-generating behaviour, with the purpose of devising and injecting defacements that will pass undetected. It is then necessary to make to learning process unpredictable, so that the adversary will be unable to replicate it and predict the classifier's behaviour. We achieve this goal by introducing a secret key--a key that our adversary does not know. The key will influence the learning process in a number of different ways, that are precisely defined in this paper. This includes the subset of examples and features that are actually used, the time of learning and testing, as well as the learning algorithm's hyper-parameters. This learning methodology is successfully applied in this context, by using the system with both real and artificially modified Web sites. A year-long experimentation is also described, referred to the monitoring of the new Web Site of a major manufacturing company.  
adversarial learning; anomaly detection; defacement response; Security Incident and Event Management; Security Operations Center
# Optical Interference Suppression Based on LCD-Filtering
## 2 Fundamentals and Experimental Set-Up
### Geometric Considerations  
Let us first discuss the geometric requirements. Based on the two-dimensional sketch in Figure 1, we can derive the distance between LCD and PD as  
\[f=\frac{l}{2\cdot\tan\left(\text{FOV}/2\right)}, \tag{1}\]  
with  
\[l\gg a, \tag{2}\]  
where \(a\) is the edge length of the PD and \(l\) the size of the LCD filter. For simplicity, we restrict to a PD with square detector area, resulting in an aperture similar to a pyramidal horn antenna.  
Next, we consider a point light source at distance \(d\) from the PD. A light beam emanating from this source and targeting to the PD is passing a maximum number of  
\[N=\left\lceil\frac{a}{p}\left(1-\frac{f}{\cos\vartheta\cdot d}\right)\right\rceil+1 \tag{3}\]  
LCD pixels per dimension. If we furthermore require \(d\gg f\), we can interpret the incident light as a bundle of parallel rays with a width similar to the size of the PD. Correspondingly, the above result simplifies as  
\[N\approx\left\lceil\frac{a}{p}\right\rceil+1. \tag{4}\]  
When switching all \(N\) pixels to the transmissive mode, the angular width of the resulting beam pattern as a function of the angle of incident \(\vartheta\) is given by  
\[\Theta\left(\vartheta\right)=\arctan\left(\frac{N\cdot p}{2\cdot f}+\tan \vartheta\right)+\arctan\left(\frac{N\cdot p}{2\cdot f}-\tan\vartheta\right) \tag{5}\]  
Figure 1: Geometry of detector with the LCD-based pre-filter.  
and can be later used for the calculation of the remaining interference passing the LCD at its opened section.
## 4 Discussion  
### Combination of MODIS and Landsat to Study Vegetation Phenology and Production  
Results showed that grassland phenology and production were jointly controlled by climate and management practices. The management practices such as burning, grazing, and baling examined in this study can be considered anthropogenic disturbances on vegetation. The climate- and human-induced disturbances on vegetation have different spatial and temporal extents with various severity. For example, long-term and short-term flash droughts can exert different influences on vegetation phenology and production [44; 56]. Additionally, the baling-affected area and grazing intensity can be different among fields [37; 57; 58]. To balance the consideration of spatial and temporal resolutions in studying vegetation phenology and production, the combination of coarser spatial resolution but higher temporal resolution datasets (i.e., MODIS) and finer spatial resolution but lower temporal resolution datasets (i.e., Landsat) becomes necessary and straightforward. These types of combinations can be used to better characterize regional and localized disturbances on vegetation.  
Utilizing the higher temporal resolution of MODIS and higher spatial resolution of Landsat enables the characterization of inter- and intra-annual variations of vegetation phenology and production. More importantly, it can help ascribe the causes of variations in vegetation phenology and production with the auxiliary climate data and management records, which provides more ecological understanding of those disturbances [59; 60] and more possibility to extrapolate to a larger scale. The ever-increasing remote sensing data volumes and computational methods have enabled the wide application of different satellite datasets to study vegetation dynamics. The Google Earth Engine, which contains many global satellite datasets including MODIS and Landsat, has led to rapid advances in land cover and land use change, disturbances detection, and regional modeling [61; 62; 63]. Using this free and powerful platform, one can study both the global and localized vegetation phenology and production through combining different satellite datasets, such as MODIS and Landsat.
## 2 State of the Art  
When half-joints started to be used, most studies were aimed at understanding their behavior and the most appropriate calculation methods. In recent times, due to emerging degradation issues, the research became focused on strengthening systems.  
One of the first research studies on dapped end beams was performed by [PERSON] and [PERSON] [15] in 1979, to evaluate the resistance of dapped end beams grounding on the procedure proposed by [PERSON] in 1976 [16]. It is based on the application of the static theorem of plasticity theory.  
In 1983, [PERSON] carried out a new experimental campaign based on the approach proposed by [PERSON] and [PERSON] [15] but using inclined reinforcement in the discontinuity zone [17]. Over the years, the calculation of discontinuity zones was increasingly performed with Strut and Tie Models (STM). The STM is based on the static theorem (lower limit) of plasticity theory. To date, it is the only method recommended in design standards to calculate discontinuity zones. The first application of STM to the calculation of half-joints was performed by [PERSON] in 1991 [18]. Today the STM proposed by [PERSON] in 2018 [19], which can also take into account damage conditions, and the simplified model proposed by [PERSON] in 2019 [20] are very popular.  
In addition to calculation methods, important studies have been conducted on the parameters influencing the strength of dapped end beams, in particular the arrangement of reinforcement in the discontinuity zone [21; 22; 23].  
In recent years, research on this structural typology has focused mainly on strengthening systems and numerical simulation using finite elements.  
The most used strengthening systems can be grouped into two types:  
* Fiber Reinforced Polymers (FRP) or Carbon Fiber Reinforced Polymers (CFRP) strengthening systems [12; 24; 25; 26; 27; 28];
* Structural strengthening systems based on external prestressing [14; 29].  
Strengthening performed with FRP or CFRP shows very high variability in effectiveness. The increase in load-bearing capacity can vary between 10% and 80% and depends on fiber orientation, the geometry of the discontinuity zone, and the arrangement of original  
Figure 2: Schematic representation of the proposed procedure. (**a**) Original structure. (**b**) Application of steel jacketing. (**c**) Lifting of the deck and removal of old bearings. (**d**) Positioning of new bearings connected to the steel strengthening system. (**e**) Re-positioning of the supported beam. (**f**) Final result.  
rebar reinforcement in the discontinuity zone. Reinforcements performed with external prestressing are generally more efficient.  
Most of the studies focus on strengthening techniques based on the application of composite materials. The main advantage of these methods is that they do not require additional damage to the joint, such as drilling holes during repair phases. On the contrary, external prestressing systems show overall better performance but are more complicated and difficult to install.
## 4 Effect of Non-Idealities Analysis
 \tag{16}\]  
where \(\text{P}=\text{C}_{1}^{\prime}\text{C}_{2}^{\prime}\text{G}_{\text{3p}}+\text{C}_ {1}^{\prime}\text{C}_{\text{3p}}\text{G}_{\text{2p}}+\text{C}_{2}^{\prime}\text{ C}_{\text{3p}}\text{G}_{\text{1p}}\text{Q}=\text{C}_{1}^{\prime}\text{G}_{\text{2p}} \text{G}_{\text{3p}}+\text{B}_{1}\text{C}_{1}^{\prime}\text{G}_{\text{2p}}\text{ g}_{\text{m}1}+\text{C}_{2}^{\prime}\text{G}_{\text{1p}}\text{G}_{\text{3p}}+ \beta_{1}\text{C}_{2}^{\prime}\text{G}_{\text{1p}}\text{G}_{\text{2p}}+\text{s} \text{C}_{1}^{\prime}\text{C}_{2}^{\prime}\text{C}_{\text{3p}}\text{R}=\text{G}_{ \text{1p}}\text{G}_{\text{2p}}\text{G}_{\text{3p}}+\beta_{1}\text{G}_{\text{1p}} \text{G}_{\text{2p}}\text{g}_{\text{m}1}+\alpha_{2}\alpha_{3}\text{g}_{\text{m}2} \text{g}_{\text{m}3}\text{G}_{\text{1p}}\text{G}_{\text{1}}^{\prime}\text{C}_{1}^{ \prime}=\text{C}_{1}+\text{C}_{\text{1p}}\text{C}_{2}^{\prime}\text{C}_{2}^{ \prime}=\text{C}_{2}+\text{C}_{2p}\text{C}_{1p}=\text{C}_{\text{o}1}+\text{C}_{ \text{P}}\text{C}_{2}^{\prime}\text{C}_{\text{2p}}=\text{C}_{\text{o}2}+\text{C}_{ \text{o}4}+\text{C}_{\text{P}}\text{G}_{\text{3p}}\text{C}_{\text{3p}}=\text{C}_{ \text{qj}}+\text{C}_{\text{o}3}+\text{C}_{\text{n}1}+\text{C}_{\text{n}2}\text{ G}_{\text{1p}}=\text{I}_{\text{R}_{\text{P}}}^{\prime}\text{G}_{\text{2p}}=\text{I}_{\text{R}_{ \text{2p}}}\text{G}_{\text{3p}}=\frac{\text{I}}{\text{R}_{\text{P}}}\text{R}_{ \text{3p}}\text{,R}_{\text{1p}}=\text{R}_{\text{0j}}//\text{R}_{\text{P}}\text{,R}_{ \text{2p}}=\text{R}_{\text{0j}}//\text{R}_{\text{0j}}//\text{R}_{\text{9}}\text{,}\]According to Equation (16), non-ideal parasitic elements and non-ideal current transfer error parameters influence \(\omega_{\rm o}\) and Q. Neglecting the third-order effects, the biquad can operate as an ideal case by satisfying the following conditions:  
\[\frac{1}{sC_{1}^{\prime}}<<R_{\rm IP} \tag{17}\]  
\[\frac{1}{sC_{2}^{\prime}}<<R_{\rm 2P} \tag{18}\]  
\[sC_{\rm 3p}+G_{\rm 3p}<<g_{\rm m1} \tag{19}\]  
Hence, D\({}_{1}\)(s) of Figure 5 can be approximated as:  
\[D_{2}(s)=\beta_{1}s^{2}C_{1}^{\prime}C_{2}^{\prime}g_{\rm m1}+\alpha_{2} \alpha_{3}sC_{1}^{\prime}g_{\rm m2}g_{\rm m3}+\alpha_{1}\alpha_{2}\alpha_{3}g _{\rm m1}g_{\rm m2}g_{\rm m3} \tag{20}\]  
In this case, \(\omega_{\rm o}\) and Q were modified as:  
\[\omega_{\rm o}=\sqrt{\frac{\alpha_{1}\alpha_{2}\alpha_{3}g_{\rm m2}g_{\rm m3} }{\beta_{1}C_{1}^{\prime}C_{2}^{\prime}}} \tag{21}\]  
\[Q=g_{\rm m1}\sqrt{\frac{\alpha_{1}C_{2}^{\prime}}{\beta_{1}\alpha_{2}\alpha_{ 3}C_{1}^{\prime}g_{\rm m2}g_{\rm m3}}} \tag{22}\]  
The sensitivities of the parameters \(\omega_{\rm o}\) and Q in Equations (21) and (22) were not larger than unity in magnitude. Therefore, the proposed biquad can be classified as an insensitive circuit.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 3 Architecture Design
### Event-Driven Architecture for Edge Computing  
Event-driven architecture (EDA) [20] centers around event production, detection, consumption, and reaction, where \"event\" signifies a significant state change. EDA excels in systems requiring real-time operations, asynchronous communication, and high scalability [20; 21]. While traditional EDA often centralizes event handling [22; 23], EDA for edge computing, like smart transportation, requires optimization for low-latency and local data processing to minimize network overhead and response time. In other words, edge computing tailors EDA to address its inherent challenges: real-time data processing, resource-constrained environments, and distributed computational nodes.  
Prominent EDA systems include Kafka Streams [24], Azure Event Grid [25], and RabbitMQ [26]. Kafka Streams excels in scalability and fault tolerance but might not fit resource-constrained environments due to its complex setup. Azure Event Grid shines within the Azure ecosystem, providing managed autoscaling. RabbitMQ, known for its adaptability, supports varied protocols but can require nuanced configuration.
# Analysis of the Interaction Effects of Shield Structure Oblique Passing under an Existing Tunnel
## 3 Three-Dimensional Numerical Model
### Finite Element Calculation Parameters  
The mechanical properties of the fill and dolomite were both simulated in this paper using the Mohr-Coulomb constitutive model (considering the rock tensile strength). The physical and mechanical properties of the surrounding rock and lining materials were selected based on the results of indoor tests and in situ tests, with reference to the national standard \"Engineering Rock Classification Standard\" (GB50218-94) and the experience of Guiyang metro construction. Table 1 shows the determined parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stratum** & **Depth (m)** &
\begin{tabular}{c} **\(\gamma\)** \\ **(kN/m3)** \\ \end{tabular} & **E (GPa)** & \(\mu\) & **c (kPa)** & \(\varphi\) (\({}^{\circ}\)) \\ \hline Fill & 1.3 & 18.5 & 0.005 & 0.33 & 8 & 17 \\ Medium weathering & 22.0 & 27.4 & 1.8 & 0.20 & 520 & 18 \\ dolomite & & & & & & \\ Medium weathering & & & & & & \\ argilliferous dolomite & & & & & & \\ Medium weathering & & & & & & \\ dolomite & & & & & & \\ \hline \hline \end{tabular} Note: \(\gamma\) = stratum unit weight; E = elastic modulus; \(\mu\) = Poisson’s ratio; c = cohesion; \(\varphi\) = angle of internal friction.  
\end{table}
Table 1: Calculation parameters of the soil layer.  
The tunnel shield machine selection method used for this section of the project utilized a composite EPB shield (as illustrated in Figure 4), where the cutter opening rate was 38%, the outer diameter of the lining was 6.20 m, the inner diameter was 5.50 m, the thickness of the lining was 0.35 m, and the ring width was 1.50 m. The lining was divided into 6 blocks, where there was 1 block of capping block (F) + 2 blocks of neighboring blocks (L\({}_{1}\), L\({}_{2}\)) + 3 standard blocks (B\({}_{1}\), B\({}_{2}\), B\({}_{3}\)). We used a universal wedge ring and staggered joint assembly, and the rings were connected by bolts. The major construction parameters of the shield machine are listed in Table 2, and the lining ring division and reinforcement are shown in Figure 5. Both the lining and the EPB shield were isotropic elastic materials. Both the lining and the EPB shield shell were isotropic elastic materials and had elastic moduli of 40 GPa and 207 GPa, respectively. The moduli of elasticity of the lining and the EPB shield shell were 40 GPa and 207 GPa, and their corresponding Poisson's ratios were 0.20 and 0.29, respectively. Table 3 shows the material parameters of the existing tunnel and shield construction.  
Figure 4: Front view of the EPB shield.  
Figure 3: Schematic diagram of finite element model: (**a**) three-dimensional numerical model; (**b**) spatial location model of old and new tunnels.
## 4 The Proposed Scheme: LDAKM-EIoT
### Dynamic Node Addition Phase
#### 4.3.1 Dynamic IoT Device Addition  
The upcoming procedure is needed to add a new IoT device, say \(D_{ni}\), in the required part of the IoT environment.  
**DD1.** The _TA_ picks a new unique identity \(ID_{D_{ni}}\) for the new IoT device \(D_{ni}\) and computes its corresponding pseudo identity as \(RID_{D_{ni}}=h(n\mid||ID_{D_{ni}})\), temporal credentials \(TC_{D_{ni}}=h(RID_{D_{ni}}\mid||ID_{EN}\mid||ID_{TA}\mid\mid\mid\mid RTS_{D_{ni}})\), \(AD_{ni}=h(K_{EN-D_{ni}}\mid\mid ID_{D_{ni}}\mid\mid ID_{EN}\mid\mid RTS_{D_{ni}})\) where \(RTS_{D_{ni}}\) is registration  
Figure 3: Abridging of the authentication and key agreement phase.  
timestamp generated for \(D_{ni}\) and \(K_{EN-D_{ni}}\) is the shared secret key between IoT device \(D_{ni}\) and the edge node \(EN\) generated by the \(TA\). \(TA\) also generates a unique temporary identity \(TID_{D_{ni}}\) for \(D_{ni}\) that is different from the previously deployed IoT devices.  
**DD2.** The \(TA\) stores the credentials \([RID_{D_{ni}},TID_{D_{ni}},TC_{D_{ni}},AD_{ni},h(\cdot)]\) in the memory of \(D_{ni}\) before its placement in the deployment area. Furthermore, the \(TA\) securely sends the information \(\{RID_{D_{ni}},TID_{D_{ni}},TC_{D_{ni}},AD_{ni}\}\) related to \(D_{ni}\) to \(EN\) so that the \(EN\) can update these information in its database corresponding to the newly added IoT device \(D_{ni}\). Note that for secure communication between \(TA\) and \(EN\), a pre-shared symmetric key among them is essential, and it is generated by the \(TA\) prior to the placement of the \(EN\).