esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Collection
#### Data Pre-Processing  
The success of an anomaly detection model depends significantly on the quality of the input data. This study performed a series of data pre-processing steps to ensure that the data are both representative of real patterns and prepared for effective model training. This operation is described below:  
Noise Reduction (NR): In this study, a Moving Average Filter (MVF) was used to reduce noise [36]. This method refines the dataset by averaging a predefined number of consecutive data points in a specified window, effectively smoothing out short-term fluctuations and increasing the clarity of underlying patterns. By filtering out unwanted noise, we ensure that the data fed into our deep learning model represent actual plant operating patterns, increasing the accuracy and reliability of any resulting insight or diagnosis. Figure 4 displays an output from applying an MVF on the member of the collected dataset. The MVF window size of 10 was considered.  
Figure 4: The result of applying an MVF on the collected data.  
Figure 3: An example of data collected from the VIBRO-METER monitoring system installed in the Kirkuk gas power plant.  
Handling Missing Data (HMD): Maintaining the seamless flow and validity of this time-series dataset is critical, as any disruptions or gaps can potentially distort its inherent temporal sequences. For this purpose, we chose the linear interpolation technique to adeptly tackle and rectify such inconsistencies. This method estimates missing values based on adjacent data points and maintains the dataset's flow. Especially for time-sensitive readings such as vibrations, linear interpolation assumes a constant change between consecutive data points and provides a smooth transition for missing values. However, it is important to consider the root cause of any data omissions. If they are caused by specific events, such as equipment failure, more specific treatment may be required. After imputation, the dataset was checked to ensure that the introduced values did not distort the true patterns.  
Anomalies were initially labeled based on historical maintenance records and confirmed by experienced engineers at the plant. The dataset was divided into 80% for training and 20% for testing, which ensures sufficient data for learning and also reserves an unbiased subset for evaluation each time.
## 2 Materials and Methods  
### Experimental Design and Sample Processing  
Olive oil was obtained from 13-year-old 'Arbequina' olive trees cultivated in a super-high density (4.0 m \(\times\) 1.5 m) orchard located in Carmona (37.49\({}^{\circ}\) N, \(-\)5.67\({}^{\circ}\) W, Seville, Spain). Four irrigation treatments were randomly distributed in blocks (Figure 1) with 60 trees per plot. Olives were mechanically harvested when presented a maturity index of 1.9 [13]; the grape straddle harvester (New Holland Braud Olive) selectively collected samples for each treatment (central line of each plot) and emptied the harvested olives into individual bags; then, the samples were manually cleaned and prepared for the production of the olive oil.  
Figure 1: Distribution of deficit irrigation treatments (2018 and 2019 seasons) at a super-high density ‘Arbequina’ olive trees orchard.  
Olive oil was obtained using an olive mill model Frantoino Bio (Toscana Enologica Mori, Florence, Italy) at 40-50 kg h\({}^{-1}\), with an aqueous two-phase oil extraction system. In total, 100 kg of olive samples was cleaned, grounded (T \(<\) 28 \({}^{\circ}\)C, 20 min) with 1% (_w:w_) talc ore and 2% (_w:w_) water.  
A pressure chamber (PMS Instrument Company, Albany, OR, USA) was used in 4 trees per treatment to determine the _midday stem water potential_ (\(\Psi_{stem}\)). The water stress integral (_SI_) was calculated (Equation (1)) to describe the accumulative effect of deficit irrigation strategies, from the beginning of pit hardening to harvest (19 June-14 October in 2018 (117 days) and 14 June-24 October in 2019 (132 days)):  
\[SI=\Sigma\left[\Psi\ -\ (-0.2)\right]\times n \tag{1}\]  
where _SI_ is the stress integral, \(\Psi_{stem}\) is the average midday stem water potential for any interval and \(n\) is the number of the days in the interval.  
Four irrigation treatments were established and conditions were monitored and controlled using the pressure chamber technique and the threshold values of midday stem water potential before and after the pit hardening period:  
* Control (T\({}_{0}\)): full irrigated conditions [14].
* RDI (T\({}_{1}\)): trees had no water restriction along phases I and III (the beginning of olive fruit growth and fruit maturation with oil accumulation, respectively, while regulated irrigation was applied at phase II (pit hardening), with 37% and 54% of water reduction in the 2018 and 2019 seasons, respectively.
* Confedation RDI (T\({}_{2}\)): same conditions established in T\({}_{1}\) (deficit irrigation applied at phase II), but with a water restriction set up at donation of Guadalquivir hydrographic confederation (67% of water reduction in 2018 and 72% of water reduction in 2019).
* Confedation SDI (T\({}_{3}\)): sustained deficit irrigation with the same water restrictions as T\({}_{2}\) but during the whole cycle of the olive tree.
# Stabilization Methodology in Foundation Soils by ERT-3D Application in Estepona, South Spain
## 3 Case Study
### Inverted Resistivity Image  
The second phase of the proposed procedure (Figure 5) intended to obtain uniformity of chemical and physical features in the stabilized subsoil [65; 70; 86] until the desired goal was reached. As shown in Figure 10, the contrast tomography gave a favorable result after the cement grout injections were applied. The resistivities up to 3000 \(\Omega\) m that caused distortions (cavities), affecting the stability of the building in Figure 8, were generally reduced drastically, even at values below 55 \(\Omega\) m. This means the anomalies and stability issues of the building were resolved. In this way, the affected building could be recovered, and demolition avoided.  
Figure 8: Volume plot rendered for the subsoil model of the building analyzed with topography included results of 3D inversion of electrical resistivity tomography over the whole survey area. On the left, view from the south, and, on the right, view from the north.  
Figure 9: Example of a 3D survey using the offset dipole–dipole: (**a**) resistivity contour plot by anomalies. Scheme obtained through ERT-3D. (**b**) Resistivity contour plot by anomalies. Isolation of areas to be treated with soil consolidation injections grout using RES3 DINV software.  
The proposed intervention methodology is, therefore, highly effective in buildings damaged by settlements in shallow foundations, due to a deficient stress capacity of the subsoil, in this case by a removed anthropic fill. In this case study, a building on the verge of collapse, was recovered. Figure 10, showing the tomography performed after consolidation injections, indicates that up to 3000 \(\Omega\) m resistivity anomaly areas have disappeared and left the building's subsoil consolidated and stabilized.  
Four beneficial effects of our combined interactive methodology (tomography-injection) have been observed on subsoil where a building foundation rests:  
1. [label=(0)]
2. Fills existing gaps improving its bearing capacity.
3. 100% subsoil compaction.
4. Reduces and/or eliminates interstitial water in subsoil gaps.
5. Recovery of severely damaged buildings with hardly any collateral effects.
## 3 Results and Discussion
### Length of Nails  
#### 3.1.1 Lateral Movement of the Soil Nailing Slope  
The lateral movement of the slope face with and without the nailing system during the loading stages is shown in Figure 5. The figure depicts that, in general, the lateral movement of the slope in the middle third is higher than the top and bottom third and it has the lowest value at the toe of the slope. The maximum lateral displacement of the slope is in the top upper portion when the length is more than the slope height or at least equal to the slope height, but it shifts to the middle of the slope when the nail length is reduced to the slope height. The maximum displacement with different nail lengths is observed at the middle third of the slope, except with the nail length of 1.20 H wherein the maximum horizontal movement is at the upper third of the slope.  
Figure 6 shows the relation between the nail length and horizontal movement at different nail lengths at the center of the slope (at point B). In general, the presence of nails decreases the lateral movement. The installation of nails mobilizes friction stresses around the sand and the surface of the nail. These friction stresses prevent lateral movement. By increasing the length of the nail, the total value of the shear stresses between the sand and nail increases. The lateral movement decreases rapidly when the nail length increases from 0.3 H to 0.75 H; however, further increases in the nail length, for example, up to 1.2 H, will not help in decreasing the lateral movement of the slope. This may be attributed to the fact that an increase in the nail length is effective up to a certain limit, i.e., when the nail length lies in a stable zone (inside the surface of failure).
## 5 Improving Reactor Performance
### Operating at a Higher Solid Content  
The increase in solid content as a way of increasing organic loading, and therefore digester productivity, may cause a detrimental effect, which is linked to the delicate balance  
Figure 3: Techniques for improving anaerobic digestion performance.  
between VFA, ammonia release, pH and the lower dilution capacity of the system. High-solid anaerobic digestion and solid-state anaerobic digestion are two forms of carrying out the process at high organic loadings. This strategy has as a main advantage the downsizing of the biological system, but adverse effects may result from the lower water content. Solid-state digestion is a term used to describe digestion carried out at a solid content greater than 15% [193]. The term high-solid digestion is used when carrying out the fermentation at a solid content greater than 6%, a threshold for the appearance of diffusion limitations [194,195].  
[PERSON] et al. [196] reviewed the performance of digestion reactors treating sewage sludge under high-solid conditions. These authors reported that the main limitation of these systems was associated with process instabilities, high viscosity and high concentration of ammonia and acid intermediaries. The increase in solid content causes the accumulation of VFAs and ammonia, thus leading to a decrease in methane production [197]. [PERSON] et al. [198] studied the digestion of the OFMSW under a high-solid configuration reactor, obtaining 40% less methane yield at a total solid (TS) content of 15.0% and an NH\({}_{3}\) concentration greater than 2.3 g N-NH\({}_{3}\)/kg. The performance of the process could be improved by adding an inert material to exert a diluting effect and decrease the high localized nitrogen levels. It becomes evident that any attempt to increase reactor treatment capacity will need to deal with the attenuation of inhibitory conditions.
###### Abstract  
It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy, and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.  
Biochemical oxygen demand (BOD); cuckoo search algorithm (CSA); extreme learning machine (ELM); soft sensor; wastewater treatment process Article
## 5 Experimental Results
### Model Performance and Evaluation  
RMSE as a metric in the experimental results section serves to assess the accuracy of statistical forecasts made by the developed models. RMSE calculates the square root of the average of the squared differences between predicted and actual values. This measure is critical for evaluating how closely the model's predictions match the real-world data, providing insight into the model's predictive performance and its applicability for energy production forecasting and market supply planning.  
\[RMSE=\sqrt{\sum_{i=1}^{n}\frac{\left(\hat{a}_{i}-a_{i}\right)^{2}}{n}} \tag{2}\]  
When evaluating a developed model using RMSE, a lower value is desirable, indicating closer alignment between predictions and actual values. Conversely, a high RMSE suggests significant discrepancies, signaling poor model performance. Thus, lower RMSE values denote more accurate predictions, reflecting better model effectiveness.
###### Abstract  
The edge computing paradigm, allowing the location of network services close to end users, defines new network scenarios. One of them considers the existence of micro data centers, with reduced resources but located closer to service requesters, to complement remote cloud data centers. This hierarchical and geo-distributed architecture allows the definition of different time constraints that can be taken into account when mapping services into data centers. This feature is especially useful in the Virtual Network Function (VNF) placement problem, where the network functions composing a Service Function Chain (SFC) may require more or less strong delay restrictions. We propose the ModPG (Modified Priority-based Greedy) heuristic, a VNF placement solution that weighs the latency, bandwidth, and resource restrictions, but also the instantiation cost of VNFs. ModPG is an improved solution of a previous proposal (called PG). Although both heuristics share the same optimization target, that is the reduction of the total substrate resource cost, the ModPG heuristic identifies and solves a limitation of the PG solution: the mapping of sets of SFCs that include a significant proportion of SFC requests with strong low-delay restrictions. Unlike PG heuristic performance evaluation, where the amount of SFC requests with strong low-delay restrictions is not considered as a factor to be analyzed, in this work, both solutions are compared considering the presence of 1%, 15%, and 25% of this type of SFC request. Results show that the ModPG heuristic optimizes the target cost similarly to the original proposal, and at the same time, it offers a better performance when a significant number of low-delay demanding SFC requests are present.  
edge computing; micro data centers; VNF placement; time restricted NF +
Footnote †: journal: Applied Sciences
# Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator
## 6 Performance Evaluation
### Settings of Simulation Analysis  
The simulation study was carried out with different settings of \(p\) and \(n\). In more detail, the values of [PERSON]'s interception density are as follows:  
\[p=\{\ 0,\ 0.2,\ 0.4,\ 0.6,\ 0.8,\ 1\ \}\,,\]  
and for each of them, four different settings of \(n\) were considered:  
\[n=\{\ 1024,\ 1536,\ 2048,\ 2048\}\,.\]  
The first three values refer to scenarios where BB84 is used to generate a symmetric key with the same security level as today's standard symmetric key algorithms: AES-128,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **aer_simulator** & **fake_brooklin** \\ \hline Number of qubits & 29 & 65 \\ \hline Maximum shots & 1,000,000 & 8192 \\ \hline Noise modeling & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the main features and settings of the simulators used.  
AES-192, and AES-256, respectively [32]. Indeed, about half of the \(n\) bits are lost due to the random choice of the bases by [PERSON] and [PERSON], and another half of the remaining bits must be discarded because of the key-checking procedure (see Section 4.2). Consequently, the average number of bits useful for the key is about \(\frac{n}{4}\). Furthermore, once quantum computers can implement [PERSON]'s algorithm, the complexity of a symmetric key search will be reduced from \(\mathcal{O}(2^{n})\) to \(\mathcal{O}(2^{\frac{n}{2}})\)[1]. This means that, to have an equivalent post-quantum security level, the key lengths must be multiplied by a factor of 2. In summary, for example, \(n=1024\) permits having the same security level of AES-128. Finally, the value \(n=4096\) was chosen to provide sufficient data to accurately model the noise of the simulator and analyze the evolution of the model's performance under increased safety requirements.  
For each scenario characterized by the pair \((p,n)\), 50 independent runswere conducted to estimate the average value and the corresponding 95% confidence interval (CI).
## 4 Discussion
### Moisture Retention Tests
#### 4.1.1 Effect of Biopolymer Type  
Medium-grained sand.The water-treated control group exhibited a higher moisture retention than several biopolymer treatments, which is not consistent with the existing literature [17; 46; 49; 53]. It is assumed that the relatively low viscosity of water allowed it to penetrate deeper into the medium-grained sand, making it less prone to evaporation. By contrast, the more viscous biopolymer solutions did not infiltrate as deeply into the sample, resulting in a higher evaporation rate.  
Some biopolymer treatments significantly increased moisture retention (Figure 5), which is consistent with the findings of other studies examining the ability of biopolymers to improve soil moisture retention [17; 25; 28; 46; 49; 54; 55]. This increase is likely due to the high water-absorption capacity of the biopolymers, as well as the effects of the solution viscosity, bio-clogging, and crust formation. Low solution viscosity allows water molecules to infiltrate deeper into the soil, making them less susceptible to evaporation. Bio-clogging occurs when hydrated biopolymers clog the soil's pore space, reducing capillary conductivity and, thus, the evaporation rate [52; 56]. Additionally, biopolymer-induced surface crusts may act as barriers between moisture and air, slowing the evaporation rate [47; 57]. Biopolymers such as XG, PP, POS, and CMC did not significantly affect moisture retention (Figure 5), which does not correspond with the published literature [17; 28; 49]. Conversely to the biopolymer treatments that increased the moisture retention of medium-grained sand, it is likely that a low water-absorption capacity (PP and POS) or a relatively high viscosity (XG and CMC) contributed to a faster evaporation rate and thus lower moisture retention.  
_Fine-grained silica sand_. Similar trends in moisture retention were found on fine-grained silica sand as on medium-grained sand, with some biopolymers decreasing it, some having no effect, and some increasing it significantly compared to the water-treated control group (Figure 6). In addition, as found for medium-grained sand, the control did not have the lowest moisture retention. The same reasons as identified for medium-grained sand likely caused these results.  
_Comparison of polysaccharides and proteins_. On average, the well-performing proteins, such as FBPC, HEA, WP, and HG, achieved a higher moisture retention capacity than the well-performing polysaccharides, such as CS, NLS, and WS (Figures 5 and 6). It is likely that this trend has been caused by the lower viscosity exhibited by most protein solutions and potentially a higher water-absorption capacity.
## 2 Basic Theories
### Efficiency Calculation of SBG
#### 2.1.2 Loaded Tooth Contact Analysis  
The loaded tooth contact analysis (LTCA) is a simulation method of gear contact performance under load, which has close proximity to actual working conditions. LTCA of SBG are mostly based on the finite element method (FEM) or its variants or modifications because of the complexity of SBG tooth surface [25; 26]. A mixed finite element-mathematical programming method is used for LTCA simulation, which could obtain load distributions, deformations, transmission errors, stresses, and some other performance indexes at a fast computing speed. This method will support input parameters for lubrication and efficiency simulation and its computing speed means it would be suitable for iterative operation and optimal design. The main steps are as follows: (1) initial tooth surface clearance calculation; (2) flexibility matrix calculation; (3) mathematical programming model building and solving; (4) discrete point load and tooth deformation calculation; (5) postprocessing and outputs. The details could be found in reference [27; 28] and is omitted here for brevity.
# Marble and Glass Waste Powder in Cement Mortar
## 3 Results and Discussion
### Durability Properties
#### 3.3.3 Porosity  
Figure 11 presents the porosity performance of mortar containing MGWP at different curing ages (3, 7, 28, 56, and 91 days). Porosity decreases with increasing MGWP replacement up to 15% relative to the control mix across all curing ages. As expected, porosity declines with age due to continuous hydration, which also explains the parallel trend observed in water absorption results [63].  
The lowest porosity was observed at 10% MGWP replacement, attributed to micro-filling effects of glass and marble waste powder [46; 61; 62]. Additionally, enhanced hydration promotes C-S-H gel formation, reducing pore volume within the mortar matrix [45; 64]. Reduced porosity also correlated with long-term strength development. However, porosity  
Figure 11: Effect of MGWP on the porosity of mortar.  
Figure 10: Effect of MGWP on water absorption of mortar.  
increases beyond 15% MGWP replacement, likely due to reduced cementitious content and decreased hydration reaction efficiency [50]. Specifically, porosity reductions of 21.5% and 11.9% were observed for 10% and 15% MGWP replacement at 28 days, with further improvements at 56 and 91 days. Conversely, porosity increased by 28.9%, 25.0%, and 33.8% at 28, 56, and 91 days of curing, respectively, at 30% replacement, reinforcing the notion that excessive MGWP substitution adversely impacts mortar densification.
## 3 Datasets and Methodology
### Datasets
#### 3.1.3 Other Data  
Groundwater level data were obtained from two long-term observation wells in Dangshan County, designated JC01 and JC02, both monitoring shallow unconfined aquifers within the depth range of 5.0-50.0 m. Subsurface strain data were collected using a distributed fiber-optic sensing system installed in a borehole near the InSAR observation area, covering the same depth range of 5.0-50.0 m with high spatial resolution. In this study, the strain data was used to identify subsurface responses that precede the onset of surface subsidence.