esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Case Study Application
### Value Engineering Study
#### 8.3.5 Alternatives Generation Using VE-BIM Integration in the Creativity Phase  
In this phase, BIM integration serves as a comprehensive repository for all essential information regarding the alternatives. This integration facilitates the development of cost-effective alternatives that fulfill essential functions. The VE team uses the VE-BIM integration add-in to access and search the VEIDEA data bank for relevant VE ideas. \"Element\", \"Work Results\", and \"Space by Function\" are selected, with their corresponding descriptions assigned as \"Tile Flooring\", \"Flooring\", and \"Office Spaces\", respectively. Figure 28 illustrates the parameters and their corresponding descriptions used to search for flooring material ideas. The action for the displayed ideas was assigned as \"Pending Evaluation\".  
For each proposed alternative, a distinct BIM model was generated. These alternatives were evaluated in the subsequent phase. In this case, alternatives for changing the flooring material are as follows:  
* Porcelain tile (original design).
* Ceramic tile.
* Terrazzo tile.  
The action status of ideas reviewed during the Value Analysis/Value Engineering phase was recorded and displayed. If the designer had already made a decision regarding an idea, the decision was stored and appeared in the action field adjacent to the idea.  
A key advantage of BIM integration is the ability to perform accurate quantity take-off. The BIM model facilitates the extraction of precise cost data for each alternative, as illustrated in Figure 29.  
Figure 28: Search results for flooring material ideas.
# Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
###### Abstract
Despite considerable efforts in gait recognition, the aforementioned methods still encounter the following challenges: (1) there is a need for muti-scale feature extraction to capture more robust spatial-temporal representations, thereby enhancing the accuracy of gait recognition especially in case of appearance camouflage; (2) few methods have taken view angle into consideration explicitly and the detection or estimation of viewpoint has been somewhat overlooked, which can exert an essential impact to improve the recognition ability of existing approaches. With such considerations, we propose an advanced gait recognition network, namely two-path spatial-temporal feature fusion module and view embedding module. The two-path spatial-temporal feature fusion module consists of multi-scale feature extraction (MSFE), frame-level spatial feature extraction (FLSFE) and multi-scale temporal feature extraction (MSTFE). Firstly, MSFE is deployed to facilitate the effective extraction of shallow features, which can extend the receptive field and enable the extraction of multiple internal features within different regions. Subsequently, we introduce a two-path parallel structure containing FLSFE and MSTFE, which aims to effectively extract the multi-scale spatial-temporal information across various granularities. In FLSFE, we develop an innovative residual convolutional block (R-conv) to capture both global and local gait features by the special design of residual operation. Meanwhile, in MSTFE, we design independent branches of temporal feature extraction with varying scales and integrate the temporal features in an attention-based way. For reasonable refinement of extracted features, a view embedding module is constructed to reduce the negative impact of viewpoint differences, which uses view prediction learning to calculate the best view and embeds the view information into the multi-scale spatial-temporal characteristics to obtain the final features. Extensive experiments conducted on the two public datasets CASIA-B and OU-MVLP have demonstrated that our method has outperformed other state-of-the-art methods, showing its superior performance in gait recognition.
# Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures
## 2 Materials and Methods
### Bootstrap Method  
The Bootstrap method developed by Efron [33] may be used for smaller samples and is quite flexible concerning the assumptions made. The Bootstrap method applies the actual distribution of the measurement errors, which are then propagated using an appropriate Monte Carlo scheme. That is, the Bootstrap method can be used to estimate the statistical (parameter) uncertainty.  
Fatigue tests take very long time as it can take millions of cycles before the failure of one specimen, and changing the frequency of load application could lead to erroneous results. The Bootstrap method can be used to generate more synthetic data, which can then be used to estimate the parameter uncertainties as an alternative to the use of MLM described above.  
Residuals are estimated by subtracting the calculated number of cycles to failure from the observed number of cycles in logarithmic scale. These residuals are plotted in Figure 1(a), considering the case when run-outs are not included. This histogram indicates that an assumption of residuals as white noise is satisfactory and it is uniformly distributed with a mean value equal to zero. In this case, the Bootstrap method can be used, but in applications where run-outs are part of the data, the Bootstrap method cannot be used directly, as seen in Figure 1(b).  
If we plot the residuals along with their index (data number), they are random without considering run-outs, which is a basic requirement for using the Bootstrap method, as seen in Figure 2(a). Random in this context means that residuals should not follow a pattern [34]. Whereas in Figure 2(b) with run-outs, residuals are following a pattern, so this requirement to apply the Bootstrap method is not fulfilled here.  
Therefore, it can be concluded that Bootstrapping can be used for estimating parameter uncertainty only in the case of no run-outs.
## 4 State of Art about Data Availability in L2 Rollups
### On-Chain (Rollup Mode)
#### 4.1.2 Blob-Carrying Transaction  
Initially, the calldata was the only available option for rollups to ensure on-chain data availability. However, using calldata for transaction data storage led to higher Gas fees and limited scalability. For instance, storing a single non-zero byte costs 16 units of Gas, and a single zero byte is 4 units of Gas [36]. Furthermore, Ethereum currently has a restriction of 30 M Gas per block, allowing a maximum size of 1.8 MB per block. To overcome these challenges, on 13 March 2024, Ethereum implemented EIP-4844 (Ethereum Improvement Proposal), also known as Proto-Danksharding [37]. This improvement is part of the Ethereum scaling roadmap, addressing both the cost and performance limitation of the previous calldata method. The EIP-4844 proposal introduced a new type of transaction, called carrying transaction in short blob [38]. The blob persists in the beacon node for a short time (almost two weeks), significantly enhancing the scalability of the Ethereum network and data availability in L2 [39]. The beacon nodes are special nodes in the network mainly responsible for coordinating validators and maintaining consensus in Ethereum's Proof of Stake (PoS) beacon chain. Blobs do not allow direct access to Ethereum Virtual Machine (EVM) or smart contracts because their design enables efficient data storage and retrieval without requiring direct interaction with the EVM. Ethereum nodes store blobs separately from main transaction data for efficient processing and validation of transactions. Each blob holds 4096 field elements, and each element size is 32 bytes; therefore, a single blob can hold \(4096\times 32=128\) KB of data. Furthermore, each block can hold up to 16 blobs or 2 MB of data, significantly improving storage and cost compared to calldata. A zk-rollup called Linea [23] is the first to implement blob for data availability fully, while other zk-rollups, such as Starknet [20], Optimism [27], and Scroll [22], provide both blob and calldata options.
## 5 Case Study: Applying CVMan to Analyze Clones in Ubuntu
### Answering PQ4  
When a new vulnerability is disclosed, the interface forwards it to the vulnerability management component. Then, the vulnerability management component extracts the vulnerable lines of code of the patch and annotates the clone landscape of the existing programs. Finally, CVMan outputs the vulnerable clones and the TCG with the annotation. Defenders can check the annotated code fragments and their clones and can verify whether they are affected by the vulnerability. Then, defenders can take appropriate defensive measures, such as fixing the vulnerability or performing input validation.  
For example, the vulnerability CVE-2020-8648 was reported in the NVD. This is a use-after-free vulnerability in Linux, which is located in procedure _n_tty_receive_buf_common_ in  
Figure 6: The clone-incurred vulnerability surface and clone-incurred vulnerability lifetime in Linux. (**a**) The clone-incurred vulnerability surface for Linux. (**b**) The CDF for the clone-incurred vulnerability lifetime in Linux.  
the file _drivers/try/n_tty.c_. CVMan annotated the code snippet in the existing clone landscape and found that Linux in Ubuntu 18.04-20.04 was affected by this vulnerability.  
Table 3 presents examples of clone-incurred vulnerabilities. For example, CVE-2019-10638 affects procedure _ipro6_select_ident_, and CVMan identified three vulnerable procedures in three Linux versions based on Linux TCG. As another example, corresponding to vulnerability CVE-2019-15214, CVMan identified 10 clones in multiple Linux versions based on Linux TCG and found clones in Alsa-driver, which is based on Ubuntu SCG.
# Deep Neural Network Equalization for Optical Short Reach Communication
## 4 Conclusions  
The algorithmic deficits in kernel identification for Volterra nonlinear equalizers and their general large architectural complexity have motivated investigations in deep neural network nonlinear equalizer alternatives. In coherent 88 Gbaud DP-16 QAM 600 Gb/s measurements, deep neural network nonlinear equalizers proved to reflect systematic nonlinearities more accurately than 5 th order memory polynomial and full kernel Volterra nonlinear equalizers. They outperform by 0.5 dB and 0.8 dB, respectively. Based on back-to-back measurements, where optical and electrical components nonlinearities predominate. The disadvantages of deep neural network nonlinear equalizers include their vast parameter space, which is hard to manage and their long training process. While this article highlights performance gains of DNN NLEs over classical Volterra schemes, their implementation complexity has yet to be compared in greater detail in future work, in order to demonstrate its overall superiority.  
Conceptualization, M.S., C.B., M.K. and S.C.; Data curation, C.B. and F.P.; Formal analysis, M.S.; Methodology, M.S.; Resources, F.P.; Software, M.S.; Supervision, M.K.; Visualization, M.S. and C.B.; Writing--original draft, M.S. and C.B.; Writing--review & editing, M.K., C.B., M.K., S.C. and S.P.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Modeling and Control
#### 2.2.5 Controller System  
The controller system in shown in Figure 12. For this design, the input comes from a handheld remote controller (RC). The RC transmits the signal to the receiver side, which is connected to an onboard Arduino microcontroller. The microcontroller then sends a command signal to the servo motors where the links keep their positions or increase or decrease incrementally, based on the independent joint control approach.  
As a result, the center of mass is shifted. The main objective of the control system is to control the slider position correctly. Our intention is to balance the system to ensure the center of mass of the entire system is preserved. When the manipulator moves, a countering moment is added by moving the battery. The block diagram of the closed loop system is shown in Figure 13.  
Figure 12: Mechatronics system for the aerial manipulator.  
Figure 13: Control system block diagram.  
The desired input position, \(x_{d}\), of the battery to achieve the balance in the system is calculated based on an equilibrium of moments given in Equation (3). \(x_{d}\) is then fed into the control system, where the linear actuator sends a signal to the counterbalance displacement system (motor driver and DC motor), which provides the required motion for battery displacement. The actual output position of the battery, \(x\), is measured using the encoder that is then fed back into the system through the feedback loop. The PID controller adjusts the actuator to accommodate for the difference in the desired output (\(x_{d}\)) and the actual output. The loop remains active to allow for continuous adjustments to any changes in the input or changes due to the surroundings.  
The motion controller used in the battery slider mechanism is a proportional integral derivative (PID) controller. The goal is to achieve a fast response from the PID tuning, while minimizing the transient dynamics as much as possible, ensuring a fast response time, a minimization of overshoot/oscillations, and having a small steady state error.  
The PID controller calculates the error, \(e\), as the difference between the actual position of the slider, \(x\), and the desired position, \(x_{d}\). The controller then attempts to minimize this error by sending an adjusted output, \(u\), to the motor that allows the system to approach stability. The control law for the PID controller is  
\[u(t)=K_{p}e(t)+K_{i}\int e(t)dt+K_{d}\frac{de(t)}{dt} \tag{11}\]  
where \(K_{p}\), \(K_{i}\), and \(K_{d}\) are the gains of the controllers.
# Novel Microscopic Approach to Particle Size Evaluation in Colloidal Systems
## 2 Experimental Section
### Confocal Laser Scanning Microscopy (CM)  
Confocal laser scanning microscopy (CM) operates differently from a regular optical microscope as it focuses light on and images the sample one point at a time through a pinhole [29; 30]. A light beam passes through this pinhole, falling on the objective lens via a beam splitter. This illuminating light beam is focused by the objective to a diffraction-limited spot at the focal plane. If the objective lens or the sample specimen is adjusted or shifted in order to focus the light spot on the sample surface, then the shift of the objective lens or the sample specimen determines the information gathered on the vertical dimension of the surface topography. When the sample surface is stationary, such as in the case of this study, the objective is moved by a piezo-electrical transducer so as to focus the light beam laser spot on the sample surface. As soon as the laser spot is focused on the surface, the light reflected back passes through the pinhole and is detected by the detector. If the sample surface is not in focus, the size of the laser spot on the detector plane is larger and there is less light intensity transmitted. The coinciding nature of the focal planes of illumination and imaging systems is how the term confocal microscopy was coined [31].  
The specimens on microscope slides were observed under a Leica inverted TCS SP8 confocal scanning laser microscope equipped with 20\(\times\) and 40\(\times\) objective lenses. An argon laser excitation wavelength at 488 nm and an emission window of 509 nm were used.  
The fluorescence exited at 555 nm and the fluorescence emissions was assessed at 569 nm 40 \(\times\) 0.3 NA HC PL fluorotar lens.  
Preparation of Glass Slides for Optical (OM) and Confocal Microscopy  
* A glass slide was placed on an electric heating plate which was set to between 150 and 200 \({}^{\circ}\)C.
* Using a spatula, a small scoop of solid bitumen was taken and smeared on the hot glass slide.
* After a few seconds, a glass cover slip was placed on the part of the glass slide covering the smeared bitumen.
* The glass slide was removed from the heating plate and the cover slip was gently moved in a circular motion to continuously smear the bitumen sample until it became so thin in that light could pass through it; see Figure 1.  
The glass slide was allowed to cool, and then microscopic analysis was carried out.  
Figure 1 shows how the bitumen samples placed between the 2 slides appears transparent and can be said to be penetrable by light rays.  
Figure 1: The appearance of the final slide to be analyzed viewed from different angles of observation.
## 5 Models for the Effective SNR
### Average Model for the Effective SNR  
The ideal model for the effective SNR proposed in Section 5.1 assumes an optimistic, best-case scenario and, as such, the expressions provided in Propositions 1 and 2 represent upper bounds to the actual performance of CA. A model that can provide a closer approximation to the actual performance would be desirable. However, as discussed in Section 5, an exact mathematical model for the effective SNR in such a case is unlikely to be feasible. Thus, this section proposes a simple approximation, where the effective SNR is obtained as the average of the SNR in each CC, \(\gamma_{\rm CA}^{\rm avg}=\frac{1}{N}\sum_{n=1}^{N}\gamma_{n}\). The average is commonly employed as an approximation due to its ability to provide a representative value that reflects the overall trend or central tendency of a set of values. This approach allows us to summarise the set of SNR values in each CC in a concise and meaningful way, making it possible to obtain results that can be expected to be closer to the actual effective SNR than the ideal model of Section 5.1. While this approach may not capture accurately the impact of all the MAC/PHY processes that determine the performance of CA, the average is in general a widely accepted measure that offers a reliable estimation in many practical scenarios. Moreover, it provides a simple yet powerful model that can be conveniently employed in subsequent analytical manipulations.  
**Proposition 3**.: _Average SNR model for CA with homogeneous SNR_  
_The PDF and CDF of the instantaneous effective SNR under an average CA scenario with homogeneous SNR are given, respectively, by_  
\[f_{\rm TCA}(x) =\frac{N^{N}}{(N-1)!}\frac{x^{N-1}}{\overline{\gamma}^{N}}\exp \biggl{(}-N\frac{x}{\overline{\gamma}}\biggr{)} \tag{8}\] \[F_{\rm TCA}(x) =\frac{\gamma(N,Nx/\overline{\gamma})}{\Gamma(N)}. \tag{9}\]  
Proof.: Note that \(\gamma_{\rm CA}^{\rm avg}=\gamma_{\rm CA}^{\rm ideal}/N\). If \(X\) and \(Y\) are random variables related as \(Y=X/N\), then their PDFs are related as \(f_{Y}(z)=Nf_{X}(Nz)\), which yields (8) from (3). The CDF in (9) follows from the direct integration of the PDF in (8), following the same steps as in the proof of Proposition 1.  
**Proposition 4**.: _Average SNR model for CA with heterogeneous SNR_  
_The PDF and CDF of the instantaneous effective SNR under an average CA scenario with heterogeneous SNR are given, respectively, by_  
\[f_{\rm TCA}(x) =N\sum_{n=1}^{N}\frac{\Omega_{n}}{\overline{\gamma}_{n}}\exp \biggl{(}-N\frac{x}{\overline{\gamma}_{n}}\biggr{)} \tag{10}\] \[F_{\rm TCA}(x) =1-\sum_{n=1}^{N}\Omega_{n}\exp\biggl{(}-N\frac{x}{\overline{ \gamma}_{n}}\biggr{)} \tag{11}\]  
_with \(\Omega_{n}\) defined in (7)._  
Proof.: The same as for Proposition 3, applied to (5) and (6).  
**Remark 1**.: _Note that if \(N=1\), then (3)-(4), (5)-(6), (8)-(9), and (10)-(11) reduce to (1)-(2), respectively._  
Proof.: By substitution, noting where appropriate that \(\Omega_{1}=1\) and \(\gamma(1,x)=1-\exp(-x)\) (equation (8.350.1) in [34]).
# Feasibility of Non-Remanufactured Waste Bottle Glass as Supplementary Cementitious Material
###### Abstract  
Theoretically, glass can be recycled entirely, but there are several requirements for remanufacturing. For the first time, this work studied industrial bottle waste glass (WG), which cannot be used to remanufacture new glass as a cement replacement for concrete applications. Wet and dry milling treatments were performed to reduce the particle size of WG and remove fibre and plastic contaminants. The different waste glasses treated were characterised by chemical, physical, and morphological analysis. Afterwards, mortar-level studies followed, using raw WG and wet-milled WG (AGWG) as a 10% Portland cement replacement. Mechanical and several durability indicators were assessed. WG and AGWG incorporation improved mortar performance against water capillary absorption, chloride ingress, and alkali-silica reaction. The unfavourable effect, namely, mechanical strength loss on glass-modified mortars, was mainly due to fibre contaminants observed by SEM on WG and AGWG samples. Even though wet milling reduced the amount and length of the fibre contaminants, it still did not guarantee adequate mechanical strength for the mortar. Thus, additional or alternative treatments, such as thermal treatments, must be explored.  
industrial waste glass; waste recycling; waste treatment; supplementary cementitious materials; cement; concrete +
Footnote †: journal: Applied Sciences
## 3 Description of the ALCOAi Model
The general architecture of a transformer consists of an encoder and a decoder, as depicted in Figure 2. The encoder takes as input a sequence of data and generates a representation of that sequence. The decoder then uses this representation to generate an output sequence. In time-series forecasting with external data, the input sequence is typically a window of historical time-series data along with the corresponding values of external variables. The task of detecting when an ALCOAi principle is going to be violated is posed, again, as a regression problem: given a set of input data the model creates a set of ALCOAi principle predictions for the next production batch. At each subsequent iteration, the output of the model at the previous iteration is concatenated to the input to produce predictions further in time (Figure 3). Similar to the regression task, we employ the MSE loss, but this time the individual losses are also summed in the time axis as in the following formula:  
\[L_{MSE}=\frac{1}{NT}\sum_{0}^{T}\sum_{i}^{n}(p_{it}-y_{it})^{2} \tag{2}\]  
Figure 1: The pipeline of the ALCOAi model for the regression of ALCOAi values based on non-homogeneous data processing. where \(T\) is the number of future predictions that we need, \(p_{it}\) is the predicted value of the \(i_{th}\) ALCOA+ principle at time \(t\) (in batches), and \(y_{it}\) is the actual value of the \(i_{th}\) ALCOA+ principle at time \(t\). By setting manual thresholds (according to individual internal business constraints) for each one of the ALCOA+ principles, the ALCOAi model is capable of detecting when and whether the next principle violation will occur, in terms of batch productions, if all conditions are kept the same as they are at the time of analysis. In this way, the model offers the functionality of ALCOAi principle violation probability on a per-future-batch basis (e.g., the probability to violate an ALCOAi principle at the next batch, the one after that, etc.). In this way, and depending on the results of the analysis, an ALCOAi trend curve can be constructed (Figure 4). Figure 3: The ALCOAi forecasting process based on the transformer [28]. The per-batch ALCOAi values are sorted in a chronologically descending manner and fed to the transformer batch by batch. When the last (i.e., most recent) set of values is forwarded in the network, the output of the transformer reflects the predicted ALCOAi values of the next batch. This output is then fed to the input of the transformer model to obtain the prediction of the next batch. This process can be repeated indefinitely. Figure 2: The model for the forecasting of future ALCOAi values based on previous evaluations.