esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Background and Significance of the Research  
The control of articulated mechanical systems relies on a high level of system understanding [1], expressed mainly through multibody dynamics models [2; 3; 4]. However, this approach is restrictive for systems with a high degree of complexity or uncertainty [5; 6]. In particular, considering model-based control algorithms, such as the linear-quadratic regulator (LQR) and back-stepping (BS) techniques, the use of differential-algebraic equations of motion typical of the multibody approach is challenging. These heavily rely on model accuracy, which hinders their application to real platforms [7]. Therefore, the development of controllers for nonlinear systems is extremely challenging, time-consuming, and in many cases, an infeasible task. The traditional engineering approach consists of analytically derivingthe system governing equations and manually adjusting the parameters of the control system to fit some measured physical parameters. Advanced expertise in applied mathematics, dynamical systems theory, computational methods, mathematical modeling, optimization frameworks, and operator-assisted algorithmic tuning of control parameters is then required.
# Electrodegradation of Selected Water Contaminants: Efficacy and Transformation Products
## 3 Results and Discussion
### Removal Efficiency, Kinetic Analysis, and Energy Consumption of Electrooxidative Treatment  
In the following subsections, the removal efficiency, kinetic analysis, and energy consumption of EO treatment are presented and discussed for DBP and for t-OPPE with its main TP, t-OP. For the EO of BTA, the results were published in a previous paper [19].  
The applied current densities were based on our previous experience and earlier research [19]. Special attention was given to use the lowest possible current density that still ensured satisfactory removal efficiency. High current densities typically result in a lower energy efficiency, which is a critical limitation for the practical application.
## 3 Background  
### Trust Management in IoV  
Trust is described as the probability that an individual will anticipate the behavior of another peer based on the behavior of the peer and the individual's well-being. This definition comes from the field of psychology [18]. It is worth noting that trust is significantly influenced by the subject's viewpoint. As a result, trust indicates the trustor's confidence that the trustee will act in his or her best interests [19]. According to sociological research, reciprocity and cooperation among persons in social interactions are believed to build trust among individuals [20]. In the computer science field, trust is defined as a trustor's conviction in the reliability of the target node, with the goal of achieving a trust objective under particular conditions [21]. Trust can also be viewed as an assessment made by an evaluator, which is influenced by past experiences with a particular entity and the perspectives of other trustworthy sources [22]. Trust management strategies are commonly employed to secure many network environments. To comprehend trust management approaches, it is also important to understand the basic definition of the trust concept. There is a distinction between trust and trustworthiness, which is a trait that describes reliability. Trust and reputation can be used interchangeably and at times can cause confusion. The reputation of a specific node in the network environment is defined as the opinion of that node that has been built based on recommendations of other network nodes that are either direct or indirect neighbors of the node in question. Trust concepts demonstrate the relationship existing between a trustor and trustee and that the trustor believes the trustee will fulfill its obligations. The success of trust relationships result in security and optimistic feelings, while their failure result in mistrust and insecurities. In the computer science community, the trust concept has been proven to be ideal for the protection of networks [23; 24; 25].
###### Abstract  
Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important. There are two key research gaps for missing sensor data imputation in buildings: the lack of customized and automated imputation methodology, and the difficulty of the validation of data imputation methods. In this paper, a framework is developed to address these two gaps. First, a validation data generation module is developed based on pattern recognition to create a validation dataset to quantify the performance of data imputation methods. Second, a pool of data imputation methods is tested under the validation dataset to find an optimal single imputation method for each sensor, which is termed as an ensemble method. The method can reflect the specific mechanism and randomness of missing data from each sensor. The effectiveness of the framework is demonstrated by 18 sensors from a real campus building. The overall accuracy of data imputation for those sensors improves by 18.2% on average compared with the best single data imputation method.  
missing data; data imputation; ensemble method; pattern recognition; machine learning; building sensors +
Footnote †: journal: Sensors
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### Outage Management and Reliability in Operation
#### 3.4.2 Automated Switchgear and Reclosers for Improving Outage Response  
The deployment of automated switchgear and reclosers plays a crucial role in enhancing outage management within distribution networks. The research in [31] underscores a digital strategy utilizing coordinated recloser-fusesaver systems, notably improving outage management by minimizing the frequency, duration, and customer impact of outages.  
Further analysis in [32] explores a model assessing the impact of automated fault location, isolation, and service restoration on system resilience, particularly under disaster conditions, leveraging digital simulations for performance metrics. Study [124] investigatesthe strategic allocation of protective devices, including digital reclosers, adopting an MILP methodology to boost reliability in networks with DGs.  
Additionally, Ref. [189] highlights the importance of strategically placing auto-reclosers to improve system reliability and efficiency, which is especially pertinent in network re-modeling, using network modeling and simulation for optimal placement decisions.  
Together, these studies illustrate the significant benefits of incorporating automated switchgear and reclosers in power distribution networks, underscoring a shift towards more resilient and efficient outage management.
## 2 VSI-Derived CSI SVM Modulator
### Overlap Time Consideration  
In analogy with the exclusive dead time for the VSI, the CSI requires an overlapping time (inclusive time) of the upper and lower switches, as indicated below. Figure 3 also shows the detailed implementation of the overlap control mechanism for the CSI MOSFETs. The overlap time is very important to guarantee the switching sequence integrity and good switching state transitions, thus improving the generic reliability and performance of that CSI. This configuration ensures safe and efficient switching, mitigating the risk of an open circuit (voltage spikes) for a current source during the overlap period.  
Figure 4 presents the simulation results for a CSI using a VSI-derived CSI SVM. The figure displays waveforms for the output filter's capacitor voltage, the output current before the low-pass filter, and the load current. It demonstrates the effectiveness of the modulation technique in generating almost pure sinusoidal output currents from the CSI.  
\begin{table}
\begin{tabular}{c c c c} \hline
**VSI** & **CSI Upper (S1, S3, S5)** & **CSI Lower (S4, S6, S2)** & **CSI Vectors** \\ \hline
**abc** & **a+, b+, c+** & **a–, b–, c–** & \\ \hline
001 & \(1,0,0\) & \(0,1,0\) & S1S6 \\
011 & \(1,0,0\) & \(0,0,1\) & S1S2 \\
010 & \(0,1,0\) & \(0,0,1\) & S3S2 \\
110 & \(0,1,0\) & \(1,0,0\) & S3S4 \\
100 & \(0,0,1\) & \(1,0,0\) & S5S4 \\
101 & \(0,0,1\) & \(0,1,0\) & S5S6 \\
000 & \(1,0,0\) & \(1,0,0\) & S1S4 \\
111 & \(0,1,0\) & \(0,1,0\) & S3S6 \\ \hline \end{tabular}
\end{table}
Table 1: VSI analog input as variables to the CSI vectors.
## 4 Results
### Reduction of Power Losses in Peak Hours
#### 4.1.1 Distribution System of 33 Nodes  
For the 33-node system, when there is no type of generation by a PV system, the power loss at the rush hour is 210.9876 kW. Considering the inclusion of three PV systems that inject active power into the system for this value, the power loss reduction is quantified for each method, as shown in Table 3. The value of power losses is directly affected by the location and capacity of each of the PV systems. The power demanded in the nodes where the PV systems are installed decreases thanks to the renewable generation provided; thus, the maximum loss reduction can be found through optimization techniques.  
From the results obtained from the numerical validation carried out with the methods presented in Table 3, it states that:  
* The best method for solving the power loss reduction problem through the location and optimal generation size is MOHTLBOGWO, whose solution is the discrete vector of \(\{13,\ 24,\ 30\}\) as the location at the node of the generation and \(\{0.9960,\ 0.9380,\ 0.8520\}\) MW of installed capacity for a reduction in percentage losses of 65.8226%.
* The results obtained by the DCVSA demonstrate the effectiveness of the proposed algorithm since it achieves the reduction of losses of the CBGA-VSA and DSCA-SOCP methods with a solution vector of nodes \(\{13,\ 24,\ 30\}\) and installed power of \(\{0.8018,\ 1.0913,\ 1.0536\}\) MW to achieve a percentage reduction of 65.5026% in power losses.
* The DCVSA method proposed in this document presents a reduction in power losses of 65.5026% for the peak hour, with a total injection of the active power of 2.9476 MW. This result obtains a higher reduction in power losses than 86% of the methods presented in Table 3.
* The power loss reduction of the proposed algorithm concerning the lower power loss reduction presented by HSA is higher by 29.8144%. Likewise, it represents that the DCVSA power loss reduction is 0.32% lower than the method with the highest power loss reduction presented by MOHTLBOGWO.
## 2 Materials and Methods
### Olfactometer Assays
  
* *  
When testing a botanical oil treatment with intact (non-damaged) citrus plants, individual Valencia sour orange plants (20-30 cm height) were enclosed within custom made two-port glass domes (40 cm height, 15 cm ID). The upper and lower portions of each plant were separated by a PTFE guillotine (2.5 cm width), so that the same amount of tree canopy was exposed to the airflow entering each arm of the olfactometer as described in [21]. The guillotine can be opened to insert the plant into the glass dome chamber without damage. The plants were positioned under a 150 W high-pressure sodium grow light (Hydrofarm, Petaluma, CA, USA). The glass dome was equipped with an inlet for entry of airflow from the air delivery system and an outlet for pushing air to the connected chamber containing either ethylene glycol or the repellent odor-treated cotton wick (treatment). For initial screening, we tested each concentration of fir, litesa, and citronella oils against ethylene glycol. In addition, we tested the repellency of fir oil with crushed citrus against crushed citrus alone, as well as fir oil with an intact citrus plant against an intact citrus plant alone. We assayed between 57 and 119 individual _D. citri_ per treatment for each of the two experiments. Pools of approximately 30 individuals were tested per treatment combination per day. Only one treatment combination was tested daily and chosen at random. For some treatment combinations more than 30 _D. citri_ were assayed when adults were abundant.
## 3 Methodology
### Evaluation Metrics
#### 3.9.2 ROUGE (Recall-Oriented Understudy for Gisting Evaluation)  
The study uses ROUGE to assess the model's ability to recover meaningful content from reference translations, particularly in Twi, a low-resource language, focusing on recall rather than n-gram precision, unlike SpBLEU, which focuses on n-gram precision. This is especially important for ensuring that translations are not only accurate in terms of individual words but also preserve the overall meaning. ROUGE includes several variants:  
* ROUGE-1: Measures unigram recall, i.e., the overlap of individual words;
* ROUGE-2: Measures bigram recall, focusing on consecutive word pairs;
* ROUGE-L: Measures the longest common subsequence (LCS), capturing longer sequences of words that preserve sentence structure [35,36].  
ROUGE helps assess whether the model generates translations that are both fluent and faithful to the original meaning.  
\[\text{ROUGE-N}=\frac{\sum_{s\in S}{Recall\ (N,s)}}{\sum_{s\in S}{Reference_{-}Ngrams\ (s)}} \tag{14}\]  
where \(S\) is the set of sentences in the predicted translation. Recall (\(N\),\(s\)) is the recall of N-grams (unigrams, bigrams) for sentence s in the predicted translation.
# Watermarking Tiny MLCommons Image Applications Without Extra Deployability Costs
## 3 Related Works
### Watermarking Applied to NNs by Modifying Layers and Weights  
This category did not focus on data but specifically on NNs themselves.  
Ref. [26] proposed a new framework called PreGIP to mark graph neural network (GNN) encoder pre-training, safeguarding IP while preserving the quality of the embedding space. This framework incorporated a task-free watermarking loss to mark the embedding space of the pre-trained GNN encoder. Furthermore, it implemented fine-tuning resistant watermark injection, ensuring similar representations for each pair of different watermark graphs during pre-training. This approach ensured that if the representations of two paired WMd graphs were close enough, they would receive the same predictions from the classifier. In contrast, an independent model trained on unlabeled data would produce distinct representations for each pair of labeled graphs, leading to inconsistent results.  
Ref. [27] examined the watermarking capability of NNs from an information theory perspective. A new definition of watermark capacity for NNs was proposed, similar to channel capacity. The study analyzed its properties and defined an algorithm to estimate its upper limit in cases of adversarial overwriting. Furthermore, a method was proposed to safeguard the transmission of identity messages through multiple rounds of ownership verification. Ref. [28] applied watermarking for IP protection of NN models against an extraction model called MEA-Defender. The watermark was achieved by combining samples of two original input classes.  
Moreover, exploration involved a taxonomy of various watermarking techniques for ML models, introducing a threat model to compare various methods in different scenarios [29]. This taxonomy outlined the desired requirements and attacks against watermarking models for ML.  
Ref. [30] investigated watermark removal and highlighted the limitations of existing watermarking methods.  
Ref. [31] proposed incorporating the concept of \"identity bracelet\" into DNNs as a means of protecting IP. This approach extended existing trigger set watermarking techniques by embedding a post-cryptography style serial number directly into DNNs, where both the input data and the watermark were processed within the same network.  
The related works' studies suggested that none of the research mentioned used a watermarking algorithm without affecting the pre-trained NN.  
Additionally, regarding integrating watermarks into ML models, there were two main approaches: training from scratch and fine-tuning pre-trained models, which are addressed in the following sections.
## 2 Radioactive Characteristics of Irradiated CANDU Pressure Tube  
By using the ORIGEN-2 code, activation simulation was performed for the CANDU pressure tube. ORIGEN-2 could calculate the radioactivity, concentration, and decay heat of activation products in the irradiated CANDU pressure tube [21]. It was assumed that the pressure tube was irradiated by neutron activation for 30 years, based upon actual CANDU operating experience [22]. The neutron flux was set to 1 \(\times\) 10\({}^{14}\) n/(cm\({}^{2}\) s). The composition of nuclear grade Zr-2.5 Nb was referred to from the ASTM B353-12 [23].  
Figure 1 shows the ratio of radioactivity in the irradiated CANDU pressure tube to the activity concentration limit for LLW disposal in the ROK with cooling time. The ratio higher than 1 means the activity of radionuclides exceeded the LLW criteria. It was revealed that three radionuclides, Nb-94, C-14, and Co-60, had higher radioactivity than the limit. Co-60 is produced by the neutron activation of Co-59 and rapidly undergoes radioactive beta decay with a short half-life of 5.27 years. For this reason, the radioactivity of Co-60 in the discharged pressure tube can decrease below the concentration limit after a cooling period of 10 years. Based on the NPP decommissioning plan in the ROK, decommissioning will begin after 10 years from the reactor shutdown, planning, and administrative period. Therefore, Co-60 is not a significant radionuclide in terms of pressure tube decontamination.  
C-14 is mainly generated by the neutron activation of N-14 and decays with a long half-life of 5730 years. Nb-94, generated by the neutron activation of Nb-93, has the longest half-life, of 20,300 years, among the major radionuclides. After 10 years from the discharge, it was shown that C-14 and Nb-94 had 4.4 and 7.5 \(\times\) 10\({}^{4}\) times higher radioactivity than the limits, respectively. In addition, the half-life values of C-14 and Nb-94 are so long that the radioactivity cannot rapidly decrease as depicted in Figure 1. Considering that metal structural wastes generated from the NPP decommissioning require long-term disposal in the underground facility, C-14 and Nb-94 are regarded as the most important isotopes for the management of the irradiated CANDU pressure tube.  
During the electrorefining process, it is expected that C-14 is detached as a particle form from the anode material, the CANDU pressure tube, without any ionization. This can enable C-14 to be easily retrieved within the molten salt system. Nb-94 which exists in the metal phase is dissolved from the anode by electrolytic methods. Therefore, we mainly focused on the separation between Zr and Nb in the Zr-2.5 Nb alloy by electrorefining.
## 1 Introduction
In the utilization of image processing techniques for crack location estimation, the lack of feature points can lead to instability in the results of spatial information construction using methods such as image-stitching and Point Cloud techniques that rely on feature points. A lack of reference objects can degrade the performance of algorithms used in tasks and increase errors, which can affect the accuracy and reliability of the results. Therefore, in this study, we attempted to estimate the location of concrete cracks in facilities lacking reference objects and feature points, and we propose a methodology for this purpose. In addition, this study is expected to be novel in developing a crack localization technology in a situation where most studies are focused on crack detection and identification.
# Assessment of Skin Deep Layer Biochemical Profile Using Spatially Offset Raman Spectroscopy
## Materials and Methods
### Raman Measurements  
The signal was collected using five accumulations of 5-10 s depending on the CCD saturation levels. Dark noise was recorded prior to each measurement and automatically removed by the software (Enlighten Spectroscopy software, Wasatch Photonics). The system was calibrated using Raman bands of an aspirin tablet (acetylsalicylic acid). All samples were measured with full laser power (300 mW on the sample) in an extended spectral range (0-3656 cm\({}^{-1}\)). Reference skin spectra were acquired in the conventional configuration. For SORS measurements, the spatial offset used was in a span of 0-10,000 \(\upmu\)m (with a 500 \(\upmu\)m increment) for the phantoms and 0-200 \(\upmu\)m (with a 10 \(\upmu\)m increment) for the skin tissue. Visual examination of the tissue revealed no heat degradation due to the laser beam as (i) the sample was kept hydrated between measurements with saline and (ii) the large volume of each tissue sample (compared to tissue sections) allowed for efficient heat dissipation.  
Figure 2: Raman setup in a conventional (excitation indicated with red solid line) and a SORS (excitation with red dashed line) configuration. In the SORS configuration, a 785 nm laser was delivered at a 45\({}^{\circ}\) angle with regards to the collection path. A translational xyz stage was used to move the collection away from the external excitation path.
## 4 Results and Discussion
### Robustness of Deep-Learning Models under Various Communication Systems  
In this section we discuss the robustness of DL models on images recovered from different communication systems presented in Section 4.2. We briefly describe the CNN models utilized in the experiment and the motivation of the choice, followed by the robustness analysis of the individual models with and without augmentation techniques under the influence of the different communication systems.
# Error-Function-Based Penalized Quantile Regression in the Linear Mixed Model
## 2 Model and Algorithm
This alternating optimization procedure is repeated until convergence, which is determined when \(\|\hat{\beta}^{(k+1)}-\hat{\beta}^{(k)}\|_{2}\leq\varepsilon\). ```
1:Input:\(X\in\mathbb{R}^{m\times p},Y\in\mathbb{R}^{n},Z\in\mathbb{R}^{m\times q},\tau,\varepsilon>0\), maxiter
2:Initialize:\(\hat{\beta}_{i}^{(0)}=0,k\gets 0\)
3:\(\hat{\mathbb{A}}^{(0)}=\arg\min_{\mathbb{A}}L\big{(}\hat{\beta}^{(0)},\alpha \big{)}\)
4:while\(k<\text{maxiter}\) and \(\|\hat{\beta}^{(k+1)}-\hat{\beta}^{(k)}\|_{2}>\varepsilon\)do
5:\(\hat{\beta}^{(k+1)}=\arg\min_{\beta}L\big{(}\hat{\beta},\hat{\mathbb{A}}^{(k) }\big{)}\)
6:\(\hat{\mathbb{A}}^{(k+1)}=\arg\min_{\mathbb{A}}L\big{(}\hat{\beta}^{(k+1)},\alpha \big{)}\)
7:\(k\gets k+1\)
8:endwhile
9:return\(\hat{\beta}^{(k)}\)
```  
**Algorithm 2** Two-step DERF-QR algorithm. To proceed further, it is critical to select appropriate penalty parameters. The Schwarz information criterion (SIC) and the generalized approximate cross-validation (GACV) are common selection criteria for quantile regression [21; 22; 23]. In this paper, we utilize the extended Bayesian information criterion (EBIC) to select penalty parameters [24]:  
\[\text{EBIC}=\ln(S_{M})+|M|+\gamma_{p}\ln(p), \tag{11}\]  
where \(S_{M}=\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{m}\rho_{\tau}\big{(}Y_{ij}-x_{ ij}\bar{\mathbb{A}}-z_{ij}\bar{\mathbb{A}}_{i}\big{)}\), and \(|M|\) are the nonzero parameters in the model. \(p\) denotes the dimensionality of the variables. The criteria are extended from BIC to EBIC by adding a penalty term to the dimensionality of the variables [24]. The regularization term \(\gamma_{p}\ln(p)\) with parameter \(\gamma_{p}>0\), which is imposed on the model by restricting the dimensionality of the variables, improves EBIC in the high-dimensional cases. Specifically, each iteration in the two-step DERF-QR algorithm can be viewed as a univariate penalized quantile regression problem, in which only one parameter is updated at a time. In this paper, we take \(\varepsilon=10^{-4}\) and \(\varepsilon_{1},\varepsilon_{2}=10^{-4}\) in Algorithms 1 and 2. Figure 1: _Cont._