esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Reactive Ultra-Fine Fly Ash and Sulfoaluminate Cement in Self-Leveling Mortar
## 4 Conclusions  
Based on the above results, the following conclusions are drawn:  
* Regarding the properties of fresh self-leveling mortar, a greater amount of PUFA provides a denser microstructure, a higher workability value, and a lower viscosity value. When the PUFA volume percentages of total cementitious materials are 10% and 15%, the mortar can meet the self-leveling mortar fluidity test standards, with no segregation observed. As the SAC volume percentage of total cementitious materials increases, it significantly shortens the setting time, resulting in a faster hardening performance of the mortar.
* Regarding 1-day compressive strength, it is found that the higher the PUFA volume percentage, the worse the strength. However, as the curing time reached 28 days, the compressive strength of the mixes with higher PUFA volume percentages showed an improved strength.
* A higher PUFA volume percentage results in lower porosity, higher compressive strength, and better resistance to drying shrinkage, abrasion, and restrained shrinkage cracking.
* Increasing the SAC volume percentage increases the porosity of self-leveling mortar as well as its early compressive strength but decreases its late-stage strength. At a 10% volume percentage level, SAC achieves an ideal balance among drying shrinkage, abrasion, and shrinkage cracking.
* A comprehensive evaluation of the relevant data shows that a 10% SAC volume percentage of cementitious materials and a 10% or 15% PUFA volume percentage of cementitious materials meet the self-leveling mortar flowability requirements. The results from the relevant tests align with expectations, demonstrating optimal engineering performance (mechanical properties, bleeding, pore structure, and shrinkage).  
Conceptualization, W.-C.Y. and P.-M.C.; methodology, P.-M.C., R.H. and W.-C.Y.; validation, P.-M.C.; formal analysis, W.-C.Y., P.-M.C. and J.-J.C.; investigation, P.-M.C. and W.-C.Y.; resources, W.-C.Y.; data curation, W.-C.Y. and P.-M.C.; writing--original draft preparation, P.-M.C.; writing--review and editing, W.-C.Y.; visualization, W.-C.Y. and P.-M.C.; supervision, W.-C.Y. and R.H.; project administration, W.-C.Y.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data can be provided under request via the email address of the corresponding author.  
**Acknowledgments:** The authors would like to express their gratitude to Triaxis Co., Ltd., (Changsha, China) for kindly supplying reactive ultra-fine fly ash (RUFA) and SAC.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
## 3 Results
### Model Performance Evaluation  
#### 3.4.1 Convergence  
Figure 9 presents the training and validation loss curves of MD-Autoformer across four datasets. The model shows good convergence on all datasets, with both training and validation losses decreasing during training and then stabilizing, indicating effective feature learning and fitting on datasets.  
For MD-Autoformer, when trained on small datasets (e.g., Apparel Sales and ILI), it needs more training epochs to converge. This is because it is harder to learn the data's underlying patterns and the model usually has smaller hidden dimensions. In contrast, when trained on larger datasets (e.g., Exchange and Weather), the abundance of data allows the model to quickly learn sequence patterns. Meanwhile, higher hidden dimensions help faster convergence, so fewer training epochs are needed.  
During model training, to prevent overfitting, we used several effective strategies. First, we applied Dropout technology, randomly deactivating some neurons in fully connected layers. This enhanced the model's generalization ability. Second, we adopted early stopping, closely monitoring the validation set loss. Training was terminated early when the loss stopped decreasing, preventing overfitting to training data. Last, we properly adjusted the hidden layer dimensions during training to avoid overfitting caused by too many parameters. Using these methods together ensured the model could learn useful data infor  
Figure 9: Loss curves of MD–Autoformer on various datasets. (**a**) Apparel Sales; (**b**) ILI; (**c**) Exchange; (**d**) Weather.  
mation fully while effectively avoiding overfitting. This resulted in better generalization and prediction accuracy in practical applications.
## 5 Results and Discussion  
The performance of MINDS is evaluated in this section according to four metrics: ETE, DAR, PHC and TTR. It is compared with ZMA and TBID to understand the superiority and inferiority of the proposed approach. ZMA and TBID were selected, as there are available on OMNET++.
# A C-Based Framework for Low-Cost Real-Time Embedded Systems
## 6 System Configuration
### Proxy Access  
The last configuration file to be discussed is the one used to configure proxy access. As described in Section 4.5, proxy tasks may be granted access to a subset of the contents of shared memories [B] and [C]. Access configuration, exemplified in Figure 9, consists of one  
Figure 8: Example of (output) shared memory configuration.  
invocation of the macro PROXY_VARIABLES, whose argument contains zero or more invocations of the macro PROXY_VARIABLE. Configuration takes place in two hierarchical steps:  
1. The first step determines whether or not a shared variable is accessible to proxy tasks in general. More specifically, only variables mentioned as a first argument of a PROXY_VARIABLE macro invocation are made accessible.
2. The second step specifies which proxy tasks get access, and provides additional mapping and conversion information specific to each access method. Namely, the second argument of PROXY_VARIABLE is a sequence of mapping macro invocations, one for each enabled access method.  
For Modbus-CAN and Modbus-TCP the mapping macro is named PROXY_MB_MAPPING. Its first two arguments specify the range of Modbus registers that serve the variable to external nodes, the third is the name of the converter to be used on the variable while serving it, and the fourth contains the converter's arguments. In the example shown in Figure 9, the shared variable br_shared is served, without conversion, when an external agent reads Modbus registers 1000-1003.
## 1 Introduction
[25] proposed an application to multi-sensor remote-sensing images with a new unsupervised feature learning method for efficient urban image clustering. Within the described research scenario, this work presents a methodology for the extraction of cloud points (representing the buildings of the area) from datasets of satellite DInSAR deformation time series relative to a chosen area, by applying the AI techniques referred to as clustering methods, allowing to identify groups of similar instances in a multivariate dataset (the so-called \"clusters\"). Then, the retrieved deformations are processed and exploited for the identification of critical constructions of an investigated area that could need further and more in-depth investigations. In particular, the implemented methodology is applied to three different areas in the city of Rome (Italy), imaged by the X-band COSMO-SkyMed SAR satellite constellation. The generated DInSAR products (deformation time series and mean displacement velocity maps relevant to the coherent points, referred to as Persistent Scatterers--PS) are used as input data, and the DBSCAN clustering method is applied in order to identify the buildings existing in the areas. In this context, an attempt to properly set the DBSCAN hyper-parameters and to integrate the clustering method by means of additional steps that take into account the real distribution of the measuring points, is presented. Then, for each cluster, the deformation condition in the monitored period is analyzed and plotted in maps. This is very useful for the identification of the relative stability among the buildings in the area. The results of the experiments show that the clusters (buildings) are successfully detected through the proposed approach, and the achieved deformation maps of the areas are effective for the identification of the critical buildings needing further and in-depth investigations. Thereafter, a more advanced structural monitoring for the buildings in critical situations is suggested. For example, in situ inspections and traditional monitoring could be carried out, and on-site information could be integrated with DInSAR measurements. The paper is organized as follows. Section 2 describes the materials and the methods of the proposed methodology. In particular, Sections 2.1 and 2.2 highlight, respectively, the key aspects of the DInSAR and DBSCAN techniques and their specific features for civil engineering applications; moreover, Section 2.3 is devoted to the description of the proposed methodology for the preliminary identification and ranking of the critical constructions in urban areas, based on the joint exploitation of the satellite DInSAR measurements and DBSCAN-based techniques. In Section 3, the application of the proposed methodology to three different study areas relevant to the city of Rome (Italy), imaged by the COSMO-SkyMed SAR constellation from ascending and descending orbits during the 2011-2019 time interval, is presented, deeply discussed, and validated. Section 4 contains the discussions on potential and limits of the proposed methodology, whereas Section 5 reports the conclusive remarks.
# Forecast Analysis of Pollutant Emissions of Cruise Ship Routes in Western Mediterranean
## 4 Results
### Estimation of the Gas Emission Inventory  
The vessel(s) that sailed on each route is (are) identified. Then, the number of connections that each route has and the sailing distance are calculated. The distance of each cruise route was determined using website tools, such as [[https://sea-distances.org/](https://sea-distances.org/)]([https://sea-distances.org/](https://sea-distances.org/)) (accessed on 24 May 2023) or [[http://www.searates.com/reference/portdistance/](http://www.searates.com/reference/portdistance/)]([http://www.searates.com/reference/portdistance/](http://www.searates.com/reference/portdistance/)) (accessed on 29 May 2023). To avoid an overestimation of the sailed distance by the ship on each route, the distance was calculated following the shortest sailing sea path between the pair of ports. Next, the estimation of the gas emission inventory is obtained through the combination of the three databases generated: namely, the technical characteristics of the vessels, the cruise routes, and the sailing distance. The cruise activity of the 292 cruise routes analysed emitted 26,026 t of NO\({}_{\text{x}}\), 4504 t of SO\({}_{\text{2}}\), and 1.47 Mt of CO\({}_{\text{2}}\) during 2019. Table 7 summarises the results obtained for the 15 routes with the highest number of connections, and the results of the remaining routes are grouped by range of number of connections. The 15 routes with the highest number of connections are associated with 27.3% of total emissions. Among them, the route Palma de Mallorca-Barcelona stands out, with 350 connections and the highest CO\({}_{\text{2}}\) emissions at 72,035 t; see Table 7. For this set of routes, the number of connections rises as the main factor that motivates the level of emissions. Moreover, two different types of routes can be identified. Those where the two ports of the route are in the Western Mediterranean can be called \"regular routes\", and those where one of the ports of the route is situated outside the Western Mediterranean can be referred to as a \"repositioning route\", characterised by high sailing distance with a low
## 3 DRL-Based Solution
### RL Framework-Based Optimization Problem  
As illustrated above, the main purpose of this study is to maximize a long-term average channel capacity. Every relay selection will have an impact on the capacity in the following TSs. As a result, long-term cumulative rewards should be maximized in each TS. This agrees with the fact that RL approaches can maximize cumulative rewards. We defined the discounted cumulative capacity from \(k\)-th TS as follows:  
\[\begin{array}{l}G(k)=\sum\limits_{t=k}^{\infty}\gamma^{t-k}\mathbb{C}_{k}\\ =\sum\limits_{t=k}^{\infty}\gamma^{t-k}\big{(}1-\tau_{N_{k},N_{k-1}}\big{)} \cdot\min\bigg{\{}\log_{2}\bigg{(}1+\Big{(}\eta Ph_{k}^{N_{k}}\Big{)}^{2}/ \sigma_{r}^{2}\bigg{)},\log_{2}\bigg{(}1+\Big{(}\eta Pg_{k}^{N_{k}}\Big{)}^{2} /\sigma_{d}^{2}\bigg{)}\bigg{\}}\\ \text{where }\gamma\text{ represents the discount factor ranging from 0 to 1.}\end{array} \tag{5}\]  
As a result, we formulate the cumulative capacity maximization problem as  
\[\begin{array}{l}\max_{\{\Omega_{M}\}}\mathbb{E}[G(k)]\\ =\max_{\{\Omega_{M}\}}\mathbb{E}\Bigg{\{}\left[\sum\limits_{t=k}^{\infty}\gamma ^{t-k}\max\bigg{\{}\big{(}1-\tau_{N_{k},N_{k-1}}\big{)}\cdot\min\bigg{\{}\log_{2 }\bigg{(}1+\Big{(}\eta Ph_{k}^{N_{k}}\Big{)}^{2}/\sigma_{r}^{2}\bigg{)},\log_{ 2}\bigg{(}1+\Big{(}\eta Pg_{k}^{N_{k}}\Big{)}^{2}/\sigma_{r}^{2}\bigg{)}\bigg{\}} \bigg{\}}\right]\bigg{\}}\\ \text{s.t. }1\leq N_{0},N_{1},\cdots,N_{M-1}\leq N\end{array} \tag{6}\]
## 3 Results and Discussion
### Precipitation Indices
#### 3.2.1 Total Annual Precipitation  
The analyses of the variability of the total annual precipitation for the RCP4.5 scenario in the horizon until 2030 show an increase in the value of the index for cities located in the eastern part of Poland by 5-10% (43 mm) (Figure 6). In the southern and central parts of the country, these increases are smaller and reach a maximum of 5% (23 mm). The smallest increase is expected in locations in the western part of the country--below 3% (15 mm). In the 2050 horizon, increases of between 5% and 10% (45 mm) are expected in most locations in the country. Smaller changes are expected in the central and southern parts of the country, where the values of this indicator do not exceed 5% (32 mm) of the base value for 2010.  
In the RCP8.5 scenario for the 2030 horizon, the most significant changes in the value of this indicator are expected, especially in the western and southern parts of Poland, where the rates of increase range are expected to be from 5% to 10% (42 mm). For the central and northern parts of the country, smaller increases in annual precipitation are expected, up to 5% (28 mm) compared to 2010. In the 2050 horizon, the forecasts for the entire country are high. In most cities, simulations show an increase in the indicator value equal to between 10% and 15% (76 mm). This increase in the country's southwestern part is also expected to be greater than 15% (88 mm). The smallest increase is expected for the northern and northwestern parts of the country, where it is expected to increase by a maximum of 10% (44 mm) compared to the base year.  
Figure 6: _Cont._
## 3 Results
### Carcass Performance and Meat Quality  
The results of carcass performance are shown in Table 3. The dressing percentages of the PGZ and PGZ + RES groups were higher than that in control group (\(p<0.05\)). Moreover, the semi-eviscerated yield was significantly increased (\(p=0.045\) and \(p=0.037\), respectively) and the abdominal fat was decreased in PGZ, RES, and PGZ + RES supplemental groups (\(p<0.05\)).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{4}{c}{**Treatment**} & \multirow{2}{*}{**SEM**} & \multirow{2}{*}{**\(p\)-Value**} \\ \cline{2-2} \cline{5-6}  & **Control** & & & & & \\ \hline IBW (g) & 1623.6 & 1624.2 & 1623.7 & 1624.1 & 8.24 & 0.303 \\ FBW (g) & 2112.3 b  & 2163.1 a  & 2119.6 a  & 10.32 & \(<\)0.05 \\ ADFI (g) & 93.4 & 94.2 & 94.8 & 94.9 & 2.01 & 0.647 \\ ADG (g) & 17.4 b  & 19.2 a  & 17.7 b  & 19.4 a  & 17.7 b  & 19.4 b  & 17.7 b  & 19.4 b  & 17.7 b  & 19.4 b  & 16.71 b  & 19.4 b  & 10.13 b  & 10.12 b  & 9.4 b  & 4.89 b  & 14.84 & 14.12 & 0.78 & 0.520 \\ Thigh muscle yield (\%) & 17.46 & 16.71 & 16.88 & 16.2 & 0.56 & 0.793 \\ Abdominal fat (\%) & 11.15 a  & 10.12 b  & 9.40 b  & 0.08 & \(<\)0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effects of dietary supplemental with PGZ and RES on serum biochemical indices of broilers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{4}{c}{**Treatment**} & \multirow{2}{*}{**SEM**} & \multirow{2}{*}{**\(p\)-Value**} \\ \cline{2-2} \cline{5-6}  & **Control** & & & & & & \\ \hline Dressing percentage (\%) & 90.96 b [FOOTNOTE:b]Footnote b: Values with different letters within the line mean significantRelative to the control treatment, RES and PGZ + RES supplementation reduced the cooking loss and drip loss of breast muscle (Table S3) and thigh muscle (Table 4) (\(p<0.05\)). In addition, the thigh muscle of broiler chickens fed RES and PGZ + RES exhibited an increased muscle \(A^{*}\) (red) (\(p<0.05\)) and pH (\(p<0.05\)) at 24 h after slaughter. Furthermore, the breast muscle of broiler chickens fed RES and PGZ + RES displayed a tendency toward an improved pH (\(p=0.08\)) at 24 h after slaughter. As expected, increases in the IMF values of breast muscle and thigh muscle were observed in broilers fed PGZ or PGZ + RES-supplemented diet (\(p<0.01\)), as evidenced by Oil Red O staining (Figure 1).
# A More Efficient Conditional Private Preservation Scheme in Vehicular Ad Hoc Networks
## 4 Preliminaries
### Weak Chosen Message Attacks  
In this paper, we will prove the scheme existential unforgeability under a weak chosen message attack [26], which need the adversary to submit all messages in advance and then are provided the public key and signatures. This notion is defined using the following game between a challenger and adversary \(A\):  
**Query:** A list of \(q_{s}\) messages \(M_{1},...,M_{q_{s}}\in\{0,1\}^{*}\) were sent to challenger by the adversary \(A\).  
**Response:** The challenger runs algorithm _KeyGen_ to generate a public key _PK_ and private key \(SK\) and then give \(A\) the public key _PK_ and signatures \(\sigma_{i}=Sign(SK,M_{i})\) for \(i=1,...,q_{s}\).  
**Output:** Algorithm \(A\) wins the game if a pair \((M,\sigma)\) is output, where:  
1. \(M\) is not in \((M_{1},...,M_{q_{s}})\), and \(Verify(PK,M,\sigma)=true\)  
**Definition 2**.: _A forger \(A(t,q_{s},\varepsilon)\)-weakly breaks a signature scheme if \(A\) runs in time at most \(t\), \(A\) makes at most \(q_{s}\) signature queries, and has an advantage at least \(\varepsilon\). A signature scheme is \((t,q_{s},\varepsilon)\)-existentially unforgeable under a weak chosen message attack if no forger \((t,q_{s},\varepsilon)\)-weakly breaks it._
# Correlation of Microstructure and Mechanical Properties of Metal Big Area Additive Manufacturing
## 2 Methods
### Microstructural Characterization  
Samples for microstructural examination were machined at seven locations parallel to the build direction in the wall where the tensile specimens were machined (see Figure 4a). The samples were mounted in phenolic resin and prepared using standard metallographic techniques. Hardness mapping was conducted after a polish of 1 \(\upmu\)m and was conducted on a Leco LM100 AT microhardness machine. A final polish was achieved with a 6 h interval using 0.05 \(\upmu\)m colloidal silica on a Buehler Vibromet II(r). For the microstructural investigation, the samples were etched with 2% Nital. Optical imaging was conducted using a Zeiss Axioscope 5 light optical microscope. Scanning Electron Microscopy (SEM) of the microstructure and fracture surfaces was conducted using a JEOL 6500 FEG-SEM at 20 kV equipped with EDAX Electron Back Scatter Diffraction (EBSD).  
Figure 4: Pictorial representation of the (**a**) metallographic, tensile (572 mm tall by 470 mm long) and (**b**) Charpy v-notch specimens (305 mm tall by 362 mm long).
# Traceability of Surgical Instruments: A Systematic Review
## 4 Results
### Short-Term Benefits: Patient Safety and Logistical Ergonomics  
#### 4.1.1 Patient Safety  
Traceability systems for surgical instruments significantly enhance patient safety by enabling precise tracking and monitoring of individual instruments. This reduces human error and ensures compliance with safety protocols. Surgical trays, containing up to 188 instruments per procedure, present substantial inefficiencies, as 78-87% of these instruments often go unused [18]. Large hospitals managing over 100,000 surgical trays and 2.6 million instruments annually [32] face amplified challenges in optimizing resources and improving patient safety. Addressing these inefficiencies through robust traceability systems can lead to safer and more efficient surgical environments.  
* At Shimane University Hospital in Japan (2013) [27; 30], the implementation of an RFID system reduced the counting time for approximately 130 instruments by more than 50%, thereby minimizing the likelihood of errors caused by time pressure.
* Similarly, Duke University Hospital (2022) [18] demonstrated high sensitivity (93.8%) and specificity (80.8%) in measuring surgical instrument usage, reducing risks associated with missing or defective tools.
* Surgical instrument errors, such as forgotten instruments or use of unsterilized tools, can lead to severe outcomes, including post-operative infections or patient death [13; 33; 34]. According to Public Health France [35], nosocomial infections affect 1 in 18 patients, with an estimated 4200 deaths annually. Addressing these errors through traceability is critical to improving patient outcomes and strengthening health security.