esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling and Parameter Estimation in CSADT
### ADT with Random Effects  
In engineering applications, there are three major sources of variability that do cause uncertainties in the degradation curves: unit-to-unit variability, temporal variability, and variability caused by operational and environmental conditions [24]. Among these, due to some examples, such as the nature of the material, the geometry of the components, and variability within units, unit-to-unit variability is often modeled as a random effect of the degradation parameters.  
However, there are some difficulties to depict the accelerated degradation process if both drift parameter \(\lambda\) and diffusion parameter \(\sigma_{B}\) are considered as stochastic parameters. To simplify this problem, some scholars assume that different samples under a certain stress have different drift parameters, while all diffusion parameters remain constant with the same values in some literature. Therefore, random effects are introduced in the drift parameter, assuming that \(\eta\sim N\left(\mu_{\eta},\sigma_{\eta}^{2}\right)\), where \(\mu_{\eta}\) and \(\sigma_{\eta}^{2}\) are the mean and variance of the parameters \(\eta\), respectively.  
Given the random effects and the relationship of parameters in ADT, the PDF and CDF of FHT under stress \(S_{k}\) from Equations (4) and (5) is deduced as follows.  
\[f_{S_{k}}(t)=\frac{D}{\sqrt{2\pi\left(\kappa\varsigma_{k}+\sigma_{\eta}^{2} \varsigma_{k}^{2}\Lambda(t)\right)\left(\Lambda(t)\right)^{3}}}\exp(-\frac{ \left(D-\mu_{\eta}\varsigma_{k}\Lambda(t)\right)^{2}}{2\left(\kappa\varsigma_ {k}+\sigma_{\eta}^{2}\varsigma_{k}^{2}\Lambda(t)\right)\Lambda(t)})\frac{d \Lambda(t)}{dt} \tag{14}\]  
\[F_{S_{k}}(t)=\Phi(\frac{\mu_{\eta}\varsigma_{k}\Lambda(t)-D}{\sqrt{\left( \kappa\varsigma_{k}+\sigma_{\eta}^{2}\varsigma_{k}^{2}\Lambda(t)\right) \Lambda(t)}})+\exp(\frac{2\mu_{\eta}D}{\kappa}+\frac{2\sigma_{\eta}^{2}D^{2} }{\kappa^{2}})\Phi(-\frac{2\sigma_{\eta}^{2}\varsigma_{k}^{2}\Lambda(t)D+ \kappa\left(\mu_{\eta}\varsigma_{k}\Lambda(t)+D\right)}{\kappa\sqrt{\left( \kappa\varsigma_{k}+\sigma_{\eta}^{2}\varsigma_{k}^{2}\Lambda(t)\right)\Lambda (t)}}) \tag{15}\]  
From Equation (15), the MTTF of the product under the working stress (\(S_{0}\)) can be approximated as follows in Equation (16).  
\[\texttt{T}_{MTTF}=E(T)=E\left(\frac{D}{\Lambda}\right)=\frac{D}{\sigma_{\eta} ^{2}}\exp\left(-\frac{\mu_{\eta}^{2}}{2\sigma_{\eta}^{2}}\int_{0}^{\pi}\exp \left(\frac{\mu^{2}}{2\sigma_{\eta}^{2}}\right)dx\right)=\frac{\sqrt{2D}}{ \sigma_{\eta}}D\left(-\frac{\mu_{\eta}}{\sqrt{2\sigma_{\eta}}}\right) \tag{16}\]  
where \(D(\cdot)\) is Dawson integral.
# A Velocity-Aware Handover Trigger in Two-Tier Heterogeneous Networks
## Overall Handover Procedures
### System Architecture  
As a rule, the objective of handover is to pick the best service for clients, but preposterous parameters will influence the nature of the call. In this manner, we consolidated two systems, IEEE 802.21 MIH and PMIPv6, in order to perform a powerful handover. PMIPv6 is a network-based localized mobility management protocol that empowers IP mobility for the UE without involving its contribution to any mobility-related signaling. The components of the PMIPv6 standard are the Mobility Access Gateway (MAG) and the Local Mobility Anchor (LMA). The MAG performs mobility-related signaling, tracks the movement of the UE, and confirms the UE by trading restricting messages with the LMA. The LMA is a topological anchor point for the UEs that stores all the routing data and the UE's Home Network Prefix (HNP) to guarantee the UE's reachability state in the corresponding local domain [21]. Note that since the UE moves between various MAGs in PMIPv6, handover latency cannot be avoided. Along these lines, keeping in mind the end goal to diminish the handover latency, the UE needs to execute the MIH to empower the connection between cross-layers.  
The MIH system is institutionalized by the IEEE MIH working group to maintain consistent mobility handover across heterogeneous wireless technologies. The MIH and PMIPv6 are used next to each other, keeping in mind the end goal of reducing handover latency and packet loss. This encourages the MIH to establish Media-Independent Handover Functions (MIHFs) to upgrade the handover execution to the clients with versatility crosswise over heterogeneous remote systems. The MIHF utilizes the gathered data from both UE and network infrastructure to give service abstraction to the upper layers utilizing an adequate interface type for the candidate network so it can enhance the experience of the UE by supporting handover in diverse technologies. This component helps the upper layer (for example, PMIPv6 and the MIH clients) to keep the service running and to allow service adjustments for various levels of QoS.  
The nature of handover will be influenced by velocity, particularly when the velocity is high, which can cause a diverse range of handover-related failures (for example, too-late handover, too-early handover, and PP handover). However, the conventional technique does not track velocity. In these unique circumstances and for better decision-making, we will mull over the velocity and maintain the stability of handover. The entrance point occasionally sends guides from which we can remove RSS. Taking into account that the estimation of RSS is affected by the separation from the UE to AP, it is implicitly additionally impacted by velocity. This drives us to examine the variability of the RSS utilizing AVAR.
# High-Precision Pseudo-Noise Ranging Based on BOC Signal: Zero-Bias Mitigation Methods
## 1 Introduction
The statistical characteristics of the zero-bias for aperiodic spreading code sequences are analyzed, while the maximum and standard deviation are used to evaluate their performances. Using the equivalent reference waveform method [23; 24; 25], the analytical expressions of the maximum and standard deviation are derived, and the quantitative relationship between the statistical properties of the zero-bias, the sampling frequency, and correlation interval are obtained. Two receiver parameter design methods, whose flexibility allows their effectiveness under a variety of constraints, based on this quantitative relationship are proposed, which can efficiently suppress the zero-bias. In addition, a series of simulations analyze the performance of this approach while the effects of radio frequency (RF) front-end bandwidth, Doppler-frequency deviation, and thermal noise are considered. The article is organized according to the following structure. The sampling distortion of the BOC signal is modeled in Section 2. The statistical characteristics of the BOC signal zero-bias are analyzed in Section 3. Two parameter design methods for zero-bias mitigation are described in Section 4. The performance of the algorithm is simulated in Section 5, and a summary is given at the end of the article.
# Improving School Transition Spaces Microclimate to Make Them Liveable in Warm Climates
## 3 Results and Discussion
### Analysis of Previous Thermal Behaviour before Strategies Implementation  
The thermal tempering performance of the courtyard in case 1 was not particularly relevant (see Figure 12). The microclimatic analysis carried out shows a thermal delta (TD; Equation (2)), up to 4-6 \({}^{\circ}\)C for an outdoor temperature range between 25 and 30 \({}^{\circ}\)C.  
\[\text{TD}=\text{Outdoor Temperature}-\text{Courtyard Temperature}, \tag{2}\]  
Data loggers placed inside the courtyard show an average TD up to 2 \({}^{\circ}\)C. The limited thermal behavior improvement of the courtyard is due to two fundamental aspects: on the one hand, the courtyard geometry, since it has a low AR, which does not allow great stratification and compromises the thermal behavior. On the other hand, the black pavement finishing material, capable of absorbing a great amount of solar radiation, and turning it into overheating. However, the same low AR allows the courtyard to be properly ventilated at night, greatly reducing daytime overheating.  
The courtyard thermal behavior in case 2 was better than in the previous case study, tempering better the outdoor temperatures, and achieving a TD up to 4 \({}^{\circ}\)C at the time of the maximum daily temperature (Figure 13). For a courtyard with an AR lower than 1, this is an adequate TD [3]. In this case, to be able to carry out the subsequent comparison, the results show the values measured on the south-west and south-east facing facades, with hardly any difference between the two. The low depth of the courtyard implies that there is hardly any thermal stratification inside, as in case study 1. In this case, the pavement was white gravel (albedo range close to 0.7), a less absorbent material than in case study 1.  
Figure 11: The installation process of the vegetal facade in case study 2.  
Figure 12: Previous thermal behavior (12–19 October) of case study 1.
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 6 Infrastructure Control Policy
### Global Policy  
The IM global policy implements the Analyze and Plan steps of the centralized MAPE loop. It coordinates the adaptation actions proposed by the decentralized RMs and can enforce strategies aimed to balance the number of computing resources acquired across different regions of the infrastructure. We design a simple coordination policy that accepts the reconfiguration requests proposed by the different RMs. However, more sophisticated solutions can be devised to better control the way the infrastructure is adapted. For example, the IM could exploit the information coming from the RMs to improve resource utilization by interacting with the AM; to this end, the RM could expose aggregated information on the utilization or price of resources, or on resources that can be consolidated by migrating some of the application operators.  
Conceptually, the global policy periodically runs the following steps: (1) reconfiguration request prioritization; (2) acceptance of requests; and (3) post-reconfiguration actions.  
In the _reconfiguration request prioritization_ step, the global policy prioritizes the reconfiguration requests proposed by the RMs and determines which are the most worthy to be applied. The prioritization criterion depends on the specific global policy; it can favor resources belonging to a specific region (e.g., for political or economical reasons) or resources with specific quality attributes (e.g., price, availability, and processing capacity).  
In the _acceptance of requests_ step, the global policy decides which reconfiguration requests should be granted. The choice ranges from simply accepting all the proposed requests to limit somehow their number. This step allows the global policy to deal with a limited budget for the acquisition of computing resources. We consider this step to be performed by exploiting the sorted list of most worthy reconfiguration requests. In this paper, to design and evaluate decentralized RM policies, our simple global policy grants all the proposed reconfiguration requests.  
In the last step, _post-reconfiguration actions_, the global policy can interact with the AM to propose either migration or consolidation operations. This step can be used by the IM to improve resource efficiency and load balancing, and/or limit resource wastage. For example, if the IM detects that computing resources within a region are under-utilized, it can propose the AM to consolidate its applications on a fewer number of resources, to free and dismiss some of the computing nodes. The IM can also determine that using resources belonging to a specific regions is not convenient anymore, therefore it may undertake actions to free all the used region resources. Our global policy does not currently perform any post-reconfiguration action and we let the exploitation of policies for the interplay between the IM and the AM to future work.
## 4 Experimental Assessment of Damping Coefficients
### Results of the Experimental Campaigns
#### 4.3.2 Tests under Railway Traffic
It can be seen that for lower acceleration values, there is more dispersion in the damping coefficient values, with variations between 1% and 11%. For higher acceleration values, the dispersion of the damping coefficient values tends to decrease, with variations between 4% and 6%. Higher acceleration values in the train acceleration records in free vibration are a good reference for a better estimation and a smaller dispersion in the damping coefficient values, using the Prony method for the estimates. In the same figure, an example is highlighted for each of the bridges for which, in Figure 17, the acceleration records are represented, where the different levels of signal amplitude in the various bridges are visible. Figure 16: Damping coefficient estimates based on the Prony method for the four filler-beam bridges, as a function of the maximum acceleration for the eigenfrequencies. Figure 18 presents the damping coefficient values extracted from the Prony method for the four filler-beam bridges, as a function of each bridge span length. In the Figure is presented as well the results reported from the ERRI D214 committee [1] for filler-beam bridges and the respective lower limit for the damping coefficients from the EN1991-2 [2]. It can be observed that, for span lengths between 4 m and 8 m, the results obtained with the Prony method agree well with the results from the ERRI D214 database. However, for span lengths between 10 m and 12 m, the results of the Prony method are more dispersed, due to the low acceleration levels in which the damping coefficients were calculated, as demonstrated in Figure 16. This shows that this methodology should be applied for higher acceleration records, in order to obtain more precise damping coefficients. The results obtained with the Prony method may allow an update to the ERRI D214 database and further revisions of the EN1991-2 regarding the limits for the damping coefficient values. Figure 17: Acceleration records for those in which the damping coefficient values were calculated in Figure 16: (**a**) Canelas, (**b**) Cascalheira, (**c**) Vale da Negra, (**d**) Braç do Cortico.
# FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
## 3 Proposed Method
### Online Template Updating  
Due to factors such as the shooting angle, fast motion, and occlusion, the target appearance tends to change drastically during tracking, making it unreliable to solely rely on the initial template. To deal with this problem, numerous trackers adopt online template-updating strategies to capture the appearance changes. However, most trackers set a static threshold and update the template only when the confidence score exceeds the set threshold or at a predetermined interval. Obviously, the deficiency of such trackers is that when the threshold is set unreasonably or the trackers cannot keep up with the rapid changes in the target, they are prone to tracking drift or loss of the target due to error accumulation from low-quality templates. To overcome this limitation, we design a dynamic threshold-based online template-updating strategy, which facilitates the robustness of our FETrack.  
Regarding online template updating, the following two principles are usually followed. (1) The quality of the updated templates should be as high as possible, with excellent discrimination and accuracy, which means that the threshold should not be set too small. (2) The updated template should be as close as possible to the target of the current frame, which means that the updated template has the highest similarity to the target of the current frame. Based on the above criteria, we design the following dynamic threshold:  
\[\sigma_{i}=\sigma_{i-1}\frac{\sigma_{i-1}}{\sigma_{i-1}+s_{i}}+s_{i}\frac{s_{i }}{\sigma_{i-1}+s_{i}} \tag{10}\]  
where \(\sigma_{i}\) denotes the dynamic threshold, \(i\) refers to the current frame (starting from 2), and \(s_{i}\) denotes the confidence score of the current frame. Essentially, we add a dynamic weight to update the threshold adaptively. If the confidence score \(s_{i}\) of the current frame is relatively low, it means that the target may have undergone significant changes and the previous template is no longer applicable to the target of the current frame. As a result, we assign the previous template a low weight to update a new dynamic template as soon as possible.
## 5 GNSS Positioning Performance of Xiaomi Mi 8  
This section presents the positioning performance of Xiaomi Mi 8 by SPP and the aforementioned time differenced filter algorithm with the static and dynamic data. In addition, we also discuss the benefits of multi-GNSS and dual-frequency for Xiaomi Mi 8 positioning in terms of positioning accuracy and availability.
# Wireless Sensor Networks for Enabling Smart Production Lines in Industry 4.0
## 2 Methodology
### Initial Physical Layer Network Planning
#### 2.4.2 Delay and Latency  
Apart from coverage and minimum throughput, maximum delay and latency are important QoS parameters for IIoT. Delay is mainly caused by the processing time in the network equipment, and by higher-layer protocols. In the literature, MAC layer enhancements are proposed to decrease end-to-end latency in multi-hop networks [38]. Examples are an airtime reservation system [39], slotted MAC [40], and minimization of duty cycling delays [41].  
[PERSON] et al. proposed to minimize latency via a cluster-based WSN design [42]. In this work, we use the hybrid concept that is proposed by [PERSON] et al. [43], where a hybrid network consisting of WiFi and ZigBee devices is used to enable a WSN in a train. The network is arranged in clusters, and ZigBee technology is used within clusters. As a multi-hop ZigBee network has high end-to-end delays and traffic congestion, the intra-cluster links use WiFi technology. However, the train environment in [43] differs significantly from the industrial environment we consider in this paper. We assume that WiFi APs, which are typically connected close to the ceiling, are connected to the network controller via Ethernet. As this might not always be the case for BLE and ZigBee APs, they can connect to a gateway that relays information via WiFi. MAC-layer time-slicing is used to obtain low-latency communication links.  
If the latency exceeds a given threshold, it is assumed that too many clients are connected to a single AP, and it is verified whether clients can connect to a nearby AP, by performing a throughput calculation of the network with the constrained AP removed from the floor plan.
# Transforming Monochromatic Images into 3D Holographic Stereograms Through Depth-Map Extraction
## 1 Introduction
Specifically, we first restored and enhanced the source image, applied a colorization algorithm, and then employed a state-of-the-art depth-estimation model to generate a depth map. The depth information was then used to synthesize a series of perspective images, which were subsequently encoded into a digital stereoscopic hologram. By integrating these techniques, accurate and emotionally resonant 3D holograms can be generated using legacy photographs. Our approach accurately preserves the visual characteristics of the original black-and-white images in the final digital hologram portrait. This study has significant implications for cultural heritage preservation, personal archiving, and other applications that requires the creation of realistic holographic representations from sparse data. The remainder of this paper is organized as follows. Section 2 reviews prior work on depth-map generation and holographic display techniques in detail. Section 3 explains the proposed method for generating digital stereoscopic holograms from a single black-and-white image, which is based on image processing pipelining, depth-map estimation, 3D reconstruction, and holographic encoding with a holographic stereogram printer. Section 4 reports the results and discusses the implications and limitations of the proposed approach. Section 5 concludes the study.
# Sensitivity Estimation for Differentially Private Query Processing
## 4 Sensitivity Estimation for Join Queries
### Sketch-Based Sensitivity Estimation
#### 4.3.1 Sketch-Based Multi-Join Size Estimation  
The basic idea of AGMS is mapping the values \(v_{1}\), \(v_{2}\),..., \(v_{|dom(A)|}\) of a join attribute \(A\) in a relation \(R\) into four-wise random variables \(\xi(v_{1})\), \(\xi(v_{2})\),..., \(\xi(v_{|dom(A)|})\), where each \(\xi(v_{i})\in\{-1,+1\}\) and \(\Pr[\xi(v_{i})=+1]=\Pr[\xi(v_{i})=-1]=1\). The AGMS sketch for a relation \(R\) is  
\[sk(R)=\sum_{i\in dom(A)}f(i)\xi(v_{i}), \tag{20}\]where \(f(i)\) is the frequency of \(v_{i}\). The \(sk(R)\) can be computed using one-pass scanning of \(R\). The product of two sketches \(sk(R_{1})\) and \(sk(R_{2})\) for two relations \(R_{1}\) and \(R_{2}\) is an unbiased estimate of the join size of \(R_{1}\Join R_{2}\): \(\mathbb{E}[sk(R_{1})\cdot sk(R_{2})]=|R_{1}\Join R_{2}|\).  
Each relation in a multi-join query may contain multiple join attributes. The AGMS can be used to estimate multi-join size by defining a distinct random family \(\xi_{1}\), \(\xi_{2},\ldots,\xi_{n}\) for each equi-join attribute pair. The sketch for each relation \(R\) can be written as  
\[sk(R)=\sum_{t\in R}\prod_{i\in JA}\xi_{i}(t[i]), \tag{21}\]  
where \(JA\) is the set of all the join-pair attributes, and \(t[i]\) is the value of attribute \(i\) of tuple \(t\). We use the following example to show how to compute the sketches for the relations in Figure 2. Consider the example in Figure 2; we define two families of four-wise independent random variables for the join attributes \(B\) and \(C\) as \(\xi_{1}\) and \(\xi_{2}\). Three separate sketches are constructed for \(R_{1}(A,B)\), \(R_{2}(B,C)\), and \(R_{3}(C,D)\) as  
\(sk(R_{1})=\sum_{t\in R_{1}}\xi_{1}(t[B])\), \(sk(R_{2})=\sum_{t\in R_{2}}\xi_{1}(t[B])\cdot\xi_{2}(t[C])\), \(sk(R_{3})=\sum_{t\in R_{3}}\xi_{2}(t[C])\).  
The value of \(X=sk(R_{1})\cdot sk(R_{2})\cdot sk(R_{3})\) gives an unbiased estimate of for \(R_{1}\Join R_{2}\Join R3\). Although one estimate is not sufficiently accurate, a boosting technique can further improve the accuracy by conducting averaging and median-selection on several independent estimates. The final boosted estimate is the median of \(s_{2}\) variables \(Y_{1}\),..., \(Y_{s2}\), where each \(Y_{i}\) is the average of \(s_{1}\) independent estimates \(X_{1}\),..., \(X_{s1}\). To simplify the expression, we only use the mean of \(s1\) independent estimates to denote the join size estimation based on AGMS sketch in the following parts.
# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
## 3 Results
### Modeling Accuracy Evaluation  
The validation accuracy for Multiple Linear Regression, Stepwise Multiple Regression and Principal Component Regression was 0.199, 0.398 and 0.422, respectively, all of which were less than 0.6, and the p-values were relatively high, indicating less than ideal yield estimation results. On the other hand, the validation accuracy for the Neural Network and Random Forest methods was 0.729 and 0.946, respectively, with corresponding p-values of 0.2143 and 0.1405. As shown in Table 6, the model constructed using the Random Forest method exhibited the highest accuracy, yielding the most favorable experimental results.
## 3 Experiments
### Raspberry Pi and 5G Module Power Consumption  
Even though the ESP32 microcontroller has low power consumption characteristics, making it ideal for battery-powered devices, the gateway, which is a Raspberry Pi 4, and the 5G cellular module have different power profiles. Within this subsection we explore the gateway's needed energy demands under different operating conditions, such as with \"active\", \"idle\", and \"full-load\" scenarios. By taking real-time power consumption measurements under different bandwidth consumption levels, we aim to highlight the power costs for remote or off-grid deployments where energy consumption is a critical factor.