esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Deep Learning Approach for Possible VLC System Enhancement
### Proposed Initial BLSTM-Based VLC System  
From our VLC perspective, since the distortion occurs in the transmitter, it would be reasonable to conduct the predistortion on the transmitter instead of the receiver. Figure 7 shows that the predistortion module must be placed right before the diode. In the predistortion module, our BLSTM models are double in parallel, and each BLSTM structure is designed with two BLSTM layers and a dense layer. For the correction purpose, the output length of the BLSTM should be equivalent to the input length. Note that, due to the system complexity and hardware capability, we designed the OFDM system with an FFT size of 32.  
Several properties must be carefully considered for BLSTM design and training, such as batch size, epoch, and data size. Since these parameters can impact the predistortion correction performance, our investigation extended these parameters into our scope. Once the BLSTM models are completed to train, the models are placed in the transmitter for continuous use. When the VLC system conditions and environments are changed, we need to retrain the BLSTM model, which is highly unlikely in the in-building environmental case.  
Figure 6: Bidirectional LSTM structure. Each LSTM unit in Figure 5 lines up in series in forward and backward layers in this structure. Note that the length of BLSTM is the same as the number of LSTM in the forward and backward layers.
# Efficient Task Scheduling Using Constraints Programming for Enhanced Planning and Reliability
## 3 Methodology
### Justification for Selecting Constraints Programming  
The choice of constraint programming (CP) for solving the maintenance, repair, and overhaul (MRO) task scheduling problem was primarily driven by the nature and complexity of the problem. Unlike large-scale problems commonly addressed by machine learning (ML) or metaheuristic methods, MRO scheduling typically involves a manageable number  
Figure 1: Visualization of mathematical model.  
of tasks and constraints, making CP an efficient and optimal choice. Below, we outline the key reasons for selecting constraint programming and compare it to alternative methods.
# OneMap Drought Monitoring Analysis Based on Statistical Models
## 2 Materials and Methods
### Study Area and Data
#### 2.1.2 Remote Sensing Data  
In this study, satellite data-based information, such as precipitation, evaporation, soil moisture, and vegetation data, were extracted to calculate various drought indices. The latest available MODIS, PERSIANN-CDR, TRMM, and GPM IMERG data from Aqua satellites were utilized (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Product** & **Resolution** & **Time** \\ \hline \multirow{4}{*}{MODIS} & MOD11A1 & Land Surface Temperature & 1 km–daily & \\  & MOD13A3 & Vegetation Indices & 1 km–monthly & \\  & MOD16A2 & Evapotranspiration & 0.5 km–8 days & \\  & MCD43B2 & Albedo & 1 km–8 days & \\ \hline PERSIANN-CDR & PERSIANN-CDR & Precipitation & 25\({}^{\circ}\)–daily & 1983–1997 \\ \hline TRMM & TRMM3B42 & Precipitation & 25\({}^{\circ}\)–3 h & 1998–2014 \\ [PERSON]hline GPM & GPM IMERG & Precipitation & 10\({}^{\circ}\)–30 min & 2015–2020 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Satellite data information used in this study.  
Figure 1: Study area, encompassing 167 government administrative districts.
# Destructive M6.2 Petrinja Earthquake (Croatia) in 2020--Preliminary Multidisciplinary Research
## 2 Petrinja Earthquakes in December 2020  
The last week in 2020, for the citizens of Petrinja and its surroundings, along with Zagreb, began violently. On Monday, 28 December 2020 at 05:28 (UTC), a strong M5 earthquake occurred with an epicentre 5 km southwest of Petrinja. The earthquake disturbed the population in the epicentral area and caused extensive damage, which was aggravated by the series of earthquakes that followed. Namely, in the hour and a half after this earthquake, two stronger events followed with magnitudes of 4.5 and 3.8. Everyone assumed that a period of calming would follow.  
However, a day later (after more than 40 weaker and stronger earthquakes), on 29 December 2020 at 11:19 (UTC), the wider area of Petrinja was hit by a destructive M6.2 earthquake, with its epicentre at almost the same location as the epicentre of the M5 foreshock. The tremor occurred at a depth of approximately 6 km, and strong to moderate shaking was felt in northern Croatia. Light to moderate shaking was likely felt across Croatia, Bosnia and Herzegovina; far northwestern Serbia; western Hungary; far southwestern Slovakia; southern Austria; Slovenia and far northeastern Italy. The first reports indicated significant damage to buildings, seven casualties and many injuries.  
A day later, it was readily observable that the material damage was enormous, with many destroyed or damaged buildings and structures in towns near the epicentre, especially Petrinja, Glina and Sisak. In those areas, the ground shaking reached intensities of almost VIII-IX EMS (European Macroseismic Scale). Hundreds of people who had evacuated from their homes were transferred to makeshift shelters, hotels and other temporary accommodations in the wider area.  
In less than three days after the main quake, almost 2000 aftershocks followed. Until 15 January 2021, there were nine M4+ aftershocks, of which the strongest was of magnitude 5.0 (Figure 1).  
Figure 1: (**a**) The spatial distribution of earthquakes in Croatia (373 BC-2019, according to the Croatian Earthquake Catalogue (CEC), of which an updated version was first described in [4], with the Pokupsko-Petrinja epicentral area indicated (blue rectangle). Thick, black-dashed lines mark regional active faults: PKBL=Pokuplje-Kostajnica-Banja Luka right-lateral fault, SPGT=Sisak-Petrinja-Glina-Topusko left-lateral fault and OVBL=Orljava-Vrbas-Banja Luka left-lateral fault; and (**b**) Pokupsko-Petrinja epicentral area with the distribution of the epicentres of the 28 to 30 December 2020 seismic sequence.
# On-Machine Precision Form Truing and In-Situ Measurement of Resin-Bonded Spherical Diamond Wheel
###### Abstract  
The resin-bonded spherical diamond wheel is widely used in arc envelope grinding, where the demands for form accuracy are high and the form truing process is challenging. In this paper, on-machine precision form truing of the resin-bonded spherical diamond wheel is accomplished by using a coarse-grained diamond roller, and in-situ measurement of the form-truing error is conducted through a laser scan micrometer. Firstly, a novel biarc curve-fitting method is proposed based on the in-situ measurement results to calculate the alignment error between the diamond roller and the spherical diamond wheel. Then, on-machine precision form truing of a D46 resin-bonded spherical diamond wheel is completed after alignment error compensation. The in-situ measurement results show that the low-frequency form-truing error is approximately 5 \(\upmu\)m. In addition, the actual form-trued diamond wheel has been employed in grinding a test specimen, and the resulting form accuracy is approximately 1.6 \(\upmu\)m without any compensation. The ground surface profile shared similar characteristics with the roller-trued diamond wheel profile, confirming that the diamond roller truing and in-situ measurements methods are accurate and feasible.  
spherical diamond wheel; diamond roller; form truing; in-situ measurements +
Footnote †: journal: Applied Physics Letters  
17 January 2020; Accepted: 18 February 2020; Published: 21 February 2020
# PCCNoC: Packet Connected Circuit as Network on Chip for High Throughput and Low Latency SoCs
## 4 The NoC Workflow
### Short Packet  
As mentioned above, PCCNoC is designed to support relatively fixed task flows which usually have a large amount of data to transfer. In order to improve the ability of transmitting small amount of data, the proposed design provides a small packet transmission function. This kind of message is aimed at small data transmissions which can be packed in a single route packet. The packet can only be transferred from the routing source to the routing target through packet switching, and does not lock the circuit along the way. The packet structure is specified in Figure 7b.
# Gypsum-Dependent Effect of NaCl on Strength Enhancement of CaO-Activated Slag Binders
## 3 Results and Discussion
### Mercury Intrusion Porosimetry (MIP)  
The pore size distributions of hardened paste samples at 7 and 28 days are presented in Figure 5. In hardened cementitious pastes, pores ranging from 0.01 to 1 \(\upmu\)m are known as capillary pores, and they are significantly detrimental to strength due to their angular pore shapes.  
Larger sizes of capillary pores generally result in reduced strength, while strength tends to increase as total porosity decreases, pore size distribution is more influential than the total porosity [7; 8; 58; 59; 60]. Given these conditions, the MIP results of this study elaborate on the compressive strength testing results provided in Figure 2.  
[PERSON] et al. [12], reported that the strength of CaO-activated slag was significantly improved by adding gypsum mainly due to the effect of pore size refinement through substantial ettringite formation, and the use of 10% gypsum achieved the greatest compressive strength. Similar result was obtained in the Ca-N0-G5 samples of this study.  
When no gypsum was present, at 7 days, the total porosity of Ca-N1-G0 was slightly smaller than that of Ca-N0-G0, which is consistent with the observation that more C-S-H formed in Ca-N1-G0 than in Ca-N0-G0 at 7 days, as shown in the TG/DTG results. However, the use of NaCl did not beneficially affect the pore size distribution for increasing strength at 7 days because, as shown in the gray highlighted shades for Ca-N1-G0 in Figure 5a. Although relatively small pores were reduced, larger sizes of pores increased when compared to that of Ca-N0-G0. At 28 days, while Ca-N1-G0 still contained a large portion of original big pores (ranging from 0.02 to 0.04 \(\upmu\)m), the use of NaCl hindered the refinement of pore sizes, which consists of the process of reducing total porosity and overall pore size. Meanwhile, Ca-N1-G0 also experienced slight reduction of total porosity from 7 to 28 days. These observations are fairly consistent with their corresponding strength results.  
When gypsum was present, the influence of NaCl showed a substantially positive influence on pore size distribution. Although the average pore sizes of Ca-N0-G5 and Ca-N1-G5 were similar at 7 days, relatively large pores (indicated by the diagonal line pattern of Ca-N0-G5 in Figure 5a) were removed in Ca-N1-G5 after adding NaCl in the presence of gypsum, exhibiting the greatest strength of Ca-N1-G5 among all samples at 7 days.  
At 28 days, although the difference in pore size distribution mostly disappeared between Ca-N0-G5 and Ca-N1-G5, the Ca-N1-G5 still showed slightly smaller overall pore sizes than that of Ca-N0-G5.
## 3 Operating Characteristics Analysis and Parameter Design
### Parameter Design  
#### 3.3.1.  
Inductor Design  
According to Figure 4, in modes 3 and 4, the inductor current \(i_{L}\) increased linearly from its minimum value, with a total time of \(DT\). In modes 1 and 2, \(i_{L}\) decreased linearly from its maximum value, with a total time of \((1-D)T\). Assuming that the inverter has no power loss, its output power \(P_{o\_inv}\) and input power \(P_{in}\) should be equal. When the inverter operates in a steady state, capacitor \(C\) should meet the ampere-second-balance principle, meaning that its average current \(I_{C}\) should be zero. Therefore, the average inductor current \(I_{L}\) can be calculated as  
\[I_{L}=\frac{P_{o\_inv}}{U_{dc}} \tag{16}\]  
According to Figure 4, the ripple of \(i_{L}\) can be calculated as  
\[\Delta i_{L}=\frac{U_{dc}DT}{L} \tag{17}\]  
Then, the ripple coefficient of \(i_{L}\) is  
\[r_{iL}=\frac{\Delta i_{L}}{I_{L}}=\frac{U_{dc}^{2}DT}{P_{o\_inv}L} \tag{18}\]  
The maximum allowable ripple coefficient of the inductor current is defined as \(r_{iL\_max}\). When the inverter outputs the maximum power \(P_{o\_inv\_max}\) and the voltage boost duty cycle reaches \(D_{\max}\), the condition that \(r_{iL\_max}\geq r_{iL}\) should be met. Therefore, the range of values for inductor \(L\) can be calculated as  
\[L\geq\frac{U_{dc}^{2}D_{\max}T}{P_{o\_inv\_max}r_{iL\_max}} \tag{19}\]
# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 2 Current Research Status
### Prediction Model Based on Intelligence Theory  
The intelligent theory prediction model is the establishment of machine learning theory, neural network theory, and deep learning theory applied to a short-term traffic flow prediction model. [PERSON] et al. [10] used an improved particle swarm algorithm to optimize the relevant parameters of the support vector machine, aiming at the problem that noise in the original traffic flow data led to the degradation of model accuracy, which proved that the prediction effect can still be good when the traffic data is noisy. [PERSON] et al. [11] used principal component analysis (PCA) to extract the historical data of core traffic parameters of traffic flow data in Baltimore, USA and then applied the KNN algorithm to predict the state values of core traffic parameters, including traffic flow and traffic speed in the near future. [PERSON] et al. [12] proposed a stochastic forest algorithm that combines multi-node splitting to solve the problem of the poor applicability of traditional decision trees due to the single-split node. [PERSON] et al. [13] proposed using the firefly algorithm (FA) to optimize the weights and biases in the BP neural network and constructed the FA-BPNN model. [PERSON] et al. [14] proposed a parallel radial basis neural network (RBFNN) model to predict traffic flow in a certain area of the city. [PERSON] et al. [15] used genetic algorithms (GA) to perform the hyperparameter optimization of long short-term memory neural network (LSTM) models, and the GA-LSTM model had good predictive performance. [PERSON] et al. [16] considered the spatiotemporal correlation of the data and proposed a new deep learning framework called the attention map convolutional sequence-to-sequence model.
# Compensation Method for Polarization Mixing in the Homodyne Interferometer
## 3 Compensation for the Polarization Mixing of PBS\({}_{1}\)
The Jones matrix of the \(P_{2}\) and the \(P_{3}\) with an extinction ratio of 10,000:1 can be rewritten as follows:  
\[P_{2}=\left[ \begin{array}{cc}\varepsilon&0\\ 0&1\end{array} \right]\qquad P_{3}=\left[ \begin{array}{cc}1&0\\ 0&\varepsilon\end{array} \right] \tag{17}\]  
Figure 3: Homodyne interferometer with a compensation part. P1, P2 and P3 are the polarizers; PBS0, PBS1, PBS2 and PBS3 are the polarizing beam splitters; Mr is the mirror; Mm and M0 are the right-angle prisms; QWP1, QWP2 and QWP3 are the quarter-wave plates; HWP is the half-wave plate; NPBS is the non-polarizing beam splitter; and D1, D2, D3 and D4 are the photoelectric detectors. where \(\varepsilon\) is the extinction ratio and is equal to 0.01 (i.e., \(\varepsilon^{2}\) = 0.0001). The extinction ratio \(\varepsilon\) of the polarizer before and after the correction of the Jones matrix is shown in Table 2.  
The real matrix Equation (17) of the P\({}_{2}\) and the P\({}_{3}\) is substituted into Equation (8). Then, the two intensity signals can be obtained by adopting the gain coefficients of the four detectors according to Equation (12) as follows:  
\[\frac{I_{y}}{I_{x}}=\frac{r_{s}^{2}t_{p}^{2}\sin(\varphi)}{2\varepsilon\cdot t _{p}\cdot r_{s}\cdot r_{p}\cdot t_{s}+r_{s}^{2}t_{p}^{2}\cos(\varphi)} \tag{18}\]  
Equations (18) and (14) represent the results with and without the optical compensation method, respectively. To compare the nonlinear errors in the two cases, the nonlinear errors are simulated and compared when Equations (14) and (18) are substituted into Equation (4). The comparison results are illustrated in Figure 4. The blue dotted curve shows the nonlinear error when only gain adjustment is adopted, and the peak-to-peak value is approximately 4.5 nm. The red solid curve shows the nonlinear error when high-quality polarizers are combined with gain adjustment, and the peak-to-peak value is approximately 0.045 nm. The nonlinear error is reduced by two orders of magnitude after adopting the high-quality polarizer, and polarization mixing caused by the PBS\({}_{1}\) in the interferometer part is also evidently decreased. A residual error of 0.045 nm is observed, which is attributed to the high-quality polarizer not being completely ideal. If the extinction ratio is increased further, then this error will be reduced further.
# From Recruitment to Retention: AI Tools for Human Resource Decision-Making
## 8 Discussion of AI Models in Recruitment  
Artificial intelligence has revolutionized recruitment practices through the deployment of advanced models that enhance efficiency and decision-making accuracy [114]. Among these, machine learning algorithms such as random forest, extreme gradient boosting (XGBoost), and neural networks play pivotal roles [115]. For example, random forest and XGBoost are frequently utilized in candidate screening and ranking processes, where they analyze vast datasets to identify patterns and predict outcomes, such as candidate suitability for specific roles. Neural networks, particularly those enhanced with natural language processing (NLP) techniques, contribute to resume parsing and candidate sentiment analysis by extracting key information from unstructured data formats, including resumes and cover letters. These models automate repetitive tasks such as resume screening and interview scheduling, allowing human resource professionals to focus on strategic activities [116].  
Moreover, AI-based applicant tracking systems (ATSs) integrate these models to evaluate candidates based on predefined criteria, such as experience, skills, and educational background [117]. These tools not only streamline hiring processes but also reduce the potential for human biases, as they rely on data-driven methodologies. The inclusion of conversational AI, such as chatbots, further enhances candidate experience by offering real-time interaction and feedback, ensuring a seamless recruitment journey [118].  
The accuracy of AI models in recruitment is a critical factor in their adoption and effectiveness. Various studies highlight promising results for these models. For instance, random forest and XGBoost classifiers have demonstrated accuracy rates exceeding 95% in predicting candidate suitability based on past hiring data [119]. Similarly, predictive analytics models are used to rank candidates, achieving high levels of precision by matching job requirements with candidate attributes. Accuracy metrics such as precision, recall, and F1 scores are often employed to evaluate the performance of these models in different recruitment contexts [120].  
However, despite their high accuracy, it is important to acknowledge the limitations and challenges of AI models. For example, the quality of predictions heavily depends on the training data provided to the models. If the data is biased or incomplete, the models may perpetuate these biases, leading to potential ethical concerns. Consequently,organizations adopting AI tools for recruitment must implement robust validation and monitoring practices to ensure the models remain reliable and unbiased. By improving the accuracy and transparency of these models, companies can maximize their utility while fostering trust among stakeholders.
## 2 Patients and Methods
### Data Acquisition  
For clinical data (PROMISE-CL), 7,257 patients with PCa treated with radical prostatectomy were included from each participant hospital's EMR data. In addition, we used the multicenter Korean Prostate Cancer Database (K-CaP) and the Asian Prostate Cancer (A-CaP) to refer to select data fields [8,9]. The K-CaP database is an observational longitudinal database of Korean patients with biopsy-proven PCa enrolled from five hospitals throughout Korea. The K-CaP provides 220 items for PCa. We set up the rules for exclusion and inclusion prior to collecting data from all of the PCa patients of each participating hospital. Final data were collected by discussions with physicians [10]. We are collecting prospective data, and the number of data groups continues to grow.  
In PROMISE-I, a total of 610 patients with PCa treated with radical prostatectomy were included, and clinical data for all patients were collected. 610 patients' multiparametric MRI images obtained from 3.0 T MRI were collected.  
In PROMISE-P, 39,160 previously diagnosed PCa glass slides were de-identified and scanned into SVS file whole slide images at 400\(\times\) magnification (Aperio AT2). A total of 39,160 whole slide images (10-12 needle biopsy cores per patient) were then annotated according to criteria determined by four independent experienced pathologists.  
Before loading these data into the research areas, each data group in PROMISE-CL, PROMISE-I, and PROMISE-P was reviewed by clinicians, radiologists, and pathologists. Participant companies are working on developing diagnosis and predictive models using these confirmed data.  
PROMISE-CL, PROMISE-I, and PROMISE-P were connected by research identification numbers (RIDs), which were generated by de-identification tools developed by participant development companies (Figure 2). For instance, pathology slide de-identification SW removes pathologist's marks and meta-information on the slide scanned data. When it was necessary to review the original details of research data, for security reasons to protect private information, only an authorized clinician accessed the RID and patient ID mapping information to verify the data.  
For the external validation of diagnosis and predictive models, we separated training and validating data groups by hospital. We are collecting prospective data to compare to the retrospective model.  
Collected data are available only for participants of the PROMISE CLIP project. SW developed from these data is going to be used by participant hospitals for the initial implementation test followed by spreading across the entire country.
## 3 Use Case. CEDER-CIEMAT Microgrid
### Characterising the Efficiency of the Elements of the Microgrid
#### 3.3.2 Lead-Acid Battery PEPAI: 120 Cells of 2 V Each, Capacity of 1080 Ah per Cell  
To calculate the performance of the different CEDER-CIEMAT battery systems, a discharge from 100% to 20% at different power levels was carried out, followed by a complete recharge.  
The energy obtained during the discharge process was calculated and compared with the energy required to recharge the battery to its initial state. With these values and using Equation (1), the efficiency at each power was calculated. The values obtained are shown in Table 4.  
The efficiency of these batteries is practically the same for all power ratings, at 83%.