esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Thermal Evaluation of Overhead Electrical Lines  
The ampacity of overhead transmission lines is defined as the maximum electrical current that the lines can carry without reducing their electrical and mechanical properties. The most used conductors for overhead transmission lines are ACSR conductors, with aluminium forming the conductive layer. Manufacturers of these conductors state their maximum operating temperature in the range of 90 to 110 \({}^{\circ}\)C. Prolonged exposure to temperatures beyond this range makes the material more brittle and reduces its lifespan. Overloading of electrical conductors appears as a recurring topic in many studies on various subjects, often either as a primary focus or as a limitation on transmission capacity due to issues with current overload [7; 8; 9].  
Similarly, it is imperative to avoid exceeding its extension or maximum sag, which, if surpassed, would violate the minimum safe distances from the ground, objects, or other conductors beneath the overhead lines. To ensure in practise that the material strength of the electrical conductors is not compromised, nominal values are assigned to transmission lines in the design process, i.e., limits on energy transmission [7,8].  
The minimum safe height of the conductors is determined in standards for various types of environments, which are designed for the most critical circumstances and with a high level of reliability [7,8]. The mentioned factors influencing the ampacity of the lines are described in Figure 1[10].  
The determination of ampacity is carried out through thermal evaluation, which is categorized into the following [11]:  
* Static ampacity (probabilistic approach, static line rating--SLR),
* Dynamic ampacity (deterministic approach, dynamic line rating--DLR) with direct methods and indirect methods.  
The ampacity of long lines is often defined by stability limits or voltage constraints, whereas the ampacity of short lines is determined by temperature limitations [11].
## 7 Future Perspectives and Sustainability Goals
### Digital Technologies for Optimization  
The integration of digital technologies, such as artificial intelligence, machine learning, and 3D printing, is set to revolutionize how secondary resources are used in construction [294]. These technologies can optimize material formulations, enhance production efficiency, and allow for precise material application, reducing waste and energy consumption. In particular, 3D printing offers the potential to create customized, efficient structures using materials derived from waste, thus minimizing environmental impact. Emerging digital technologies, like blockchain, the internet of things (IoT), and digital twins, are further enhancing the integration of secondary resources into construction. Blockchain cancreate secure and transparent records of material quality and origin, increasing confidence in the use of secondary materials. The IoT connects physical devices and systems, enabling better data exchange and automation in material processing and construction, while digital twins simulate construction processes to optimize the use of secondary materials by predicting their performance under different conditions. These future perspectives highlight the significant potential of secondary resources to contribute to a more sustainable construction industry.
## 5 Performance Evaluation
### Simulation Results
#### 5.2.8 Traffic Load vs. Network Lifetime  
A comparison of the network lifetimes of the protocols with varying packet rates and traffic loads is shown in Figure 14. The network lifetime performances for 100, 300, 600, and 900 nodes are shown in Figures 14a, 14b, 14c, and 14d, respectively. When the network traffic increased, the lifetime logically decreased. However, FCM-OR exhibited slower changes with increasing traffic load and density compared with existing protocols. Furthermore, its network lifetime was longer than those of the ORW and ORR protocols.
# Cyber4 Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones
## 6 Drone Forensics
### Drone Forensic Framework
#### 6.1.1 Collection  
In the forensic procedure, it is the initial stage. The devices or components that operate in the subsequent steps are identified and marked. Both pictures and notes of the scene are recorded. What device (or evidence) is present, where it is situated, how it is kept (in which format), and finally, safely isolating them are the primary determinants of the collection phase. Preventing tampering with digital evidence also involves prohibiting access to the collected devices. Seized devices such as memory cards and hard drives are isolated and forensically imaged to preserve and prevent data alteration of original media.  
In terms of drone forensics, the relevant devices are the linked mobile device, memory card, drone chip, and Ground Station Controller (GSC). The accessible gadgets found and confiscated at the crime scene are identified and collected. Data stored in the device of interest is collected using different extraction methods. To be used for subsequent analysis, the data must be collected without affecting or affecting the source. A poor approach may make the evidence in court inadmissible. Thus, techniques for gathering evidence from a device should be reliable and forensically sound. The acquisition can be physical or logical. First, the logical extraction is performed by connecting the device to a forensic operating system. This provides quick access to the accessible data on the device's file system. Write blocks are employed wherever possible to maintain evidence integrity. After logical extraction, physical extraction is performed if required by employing JTAG, ISP, and Chip Off extractions.  
Joint Test Action Group (JTAG), a manufacturing industry standard for testing printed circuit boards (PCBs), was created to test PCBs that had just come off an assembly line. Connecting the Test Access Ports (TAPs) on a PCB is a step in the procedure known as JTAG Forensics. The technique known as \"chip-off forensics\" entails removing a memory chip from a device and preparing it so that a chip reader may gather the raw data to produce a physical data dump. When used in forensics, In-System Programming (ISP) is the procedure of connecting to a flash memory chip with the goal of obtaining a device's complete memory contents. Before performing a chip-off, examiners first use a non-destructive technology such as JTAG or ISP.
## 3 Design Process
### Planned Study Population for Testing DAid(r) Smart Socks-Based Biofeedback System  
The study included eighty youth soccer players, divided evenly into two groups (an active comparator group and experimental group) of forty participants each. The sample size was calculated using G*Power version 3.1.9.7, aiming to detect an effect size greater than 0.5, with an alpha level of 0.05 and a study power of 0.95. All participants were recruited through the Latvian Football Federation, and confidentiality was ensured by assigning each participant a unique identification number instead of using names.  
Inclusion and Exclusion Criteria  
Inclusion Criteria:  
* Players actively participating in the Youth Soccer League.
* Age between 14 and 18 years.
* Parental consent is required for participants under eighteen.
* Players who have completed at least three months of the FIFA 11+ program before the study.
* Agreement to participate in the study and ability to understand and speak Latvian. Exclusion Criteria:
* Knee or ankle pain rated above 5 on the VAS (Visual Analog Scale) for pain or an increase of more than two points during a single-leg squat.
* History of significant musculoskeletal injury within the past six months.
* Concurrent participation in other injury prevention programs or research trials.
* Medical conditions that contraindicate physical activity.
* Recent lower limb surgery (within the past nine months).
* Lower limb injuries within the last six months.
* Vestibular disorders, metallic implants in the sensor or head-mounted display (HMD) application areas, current eye infection, or photosensitivity (including risk of epilepsy).
# Mechanical Properties of Concrete with Bamboo Chips
## 2 Experimental Program
### Specimen Preparation and Test  
A pan mixer was used to mix the concrete. Regarding the fresh concrete properties, the value of the slump loss was measured after slump tests conducted in accordance with ASTM C143 [12]. For each mixture, a total of twelve 10 mm (diameter) \(\times\) 20 mm (height) cylinders were cast in steel molds. These specimens were then cured in a temperature-controlled water bath at \(20\pm 2\)\({}^{\circ}\)C until the time of testing.  
Compressive strength tests and splitting tensile strength tests were performed, in accordance with ASTM C39 and ASTM C496, respectively, on three 7-day- and 28-day-cured specimens. The axial deformations occurring during the compressive tests were measured using two linear variable differential transformers (LVDTs) mounted on opposite sides of the specimen (gage length: 50 mm), in accordance with JSCE-SF5 [13; 14; 15].  
\begin{table}
\begin{tabular}{c c c c} \hline
**TEST ID** & **Curing Age (day)** & **Bamboo Chip Type** & **Bamboo Chip Content, BCC (\%)** \\ \hline NB-7 & & No bamboo & 0 \\ \cline{2-3} DB10-7 & & & 10 \\ DB20-7 & & & 20 \\ DB30-7 & & & 30 \\ \cline{2-3} CB10-7 & & & 10 \\ CB20-7 & & Coated & 20 \\ CB30-7 & & & 30 \\ \cline{2-3} WB10-7 & & & 10 \\ WB20-7 & & Wetted & 20 \\ WB30-7 & & & 30 \\ \hline NB-28 & & No bamboo & 0 \\ \cline{2-3} DB10-28 & & & 10 \\ DB20-28 & & & 20 \\ DB30-28 & & & 30 \\ \cline{2-3} CB10-28 & 28 & & 10 \\ CB20-28 & & Coated & 20 \\ CB30-28 & & & 30 \\ \cline{2-3} WB10-28 & & & 10 \\ WB20-28 & & Wetted & 20 \\ WB30-28 & & & 30 \\ \hline \end{tabular}
\end{table}
Table 2: Summary of concrete specimen with bamboo chips.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**W/C (\%)**} & \multirow{2}{*}{**S/a (\%)**} & \multicolumn{3}{c}{**Unit Weight (kg/m\({}^{3}\))**} \\ \cline{3-5}  & & **OPC** & **S** & **G** \\ \hline
0.45 & 50 & 467 & 804 & 777 \\ \hline \end{tabular} Note: W/C = water to cement ratio, S/a = sand to coarse aggregate ratio, OPC = ordinary Portlandcement, S = sand, G = Gravel.  
\end{table}
Table 3: Mixture proportion of plain concrete.
# Design and Test of Seeding-Picking Mechanism of Fully Automatic Transplanting Machine
## 3 Theoretical Calculation  
### Calculation of Seeded Inclination Angle Design  
To ensure the stability and accuracy of the hole tray conveyance throughout the entire seedling conveying operation, while also maintaining a relatively compact structure that aligns with the dimensions of the subsequent transplanting frame, it is essential to design the overall structure of the seedling bed and determine the appropriate angle of inclination [19]. This design will guarantee that the hole trays remain secure during the conveying process and do not disengage during seedling topping and picking. Additionally, analyzing the forces acting on the seedling substrate within the hole grid is necessary to ascertain the angle between the lower surface of the hole tray and the horizontal plane, which is the inclination angle between the seedbed of the seedling delivery mechanism and the horizontal plane \(\alpha\) as shown in Figure 4[20].  
\[F_{\alpha}=G\times\cos\beta \tag{1}\]  
where \(F_{\alpha}\) denotes represents substrate gravity force along the horizontal direction of the inner wall of the cavity grid (N); \(G\) denotes the gravity of the substrate (N); and \(\beta\) denotes represents the angle between the gravitational force and its component force along the horizontal direction of the cave lattice.  
Figure 4: Diagram of the forces on the substrate in the cavity compartment.  
Figure 3: Schematic diagram of the fetch and drop process. (**a**) Prefetched seedling status. (**b**) Extraction condition. (**c**) Seedling delivery condition. (**d**) Seeding state. (**e**) Falling condition. (**f**) Complete state.  
Due to the cavity tray substrate gravity \(G>0\), in order to ensure that the cavity tray seedling does not slide along the direction of the large mouth of the cavity grid to fall, \(F_{a}\leq 0\) should be satisfied, i.e., \(\cos\beta<0\), and the cavity tray seedling substrate should not fall easily, establishing the equation:  
\[\alpha=180^{\circ}-\arctan\left[\frac{l}{n-m}\right] \tag{2}\]  
where \(m\) is the substrate bottom side width (mm); \(n\) represents the width of the top edge of the substrate (mm); and \(l\) denotes the substrate height (mm).  
Bringing in the values of \(m\), \(n\), and \(l\) for the calculations yields that the angle of inclination \(\alpha=102^{\circ}\), when the angle between the bottom surface of the burrowing tray and the horizontal plane is greater than or equal to 102 degrees, there is no tendency for the seedlings in the burrowing tray to slide or fall toward the large opening of the burrowing compartment. Taking into account the limitations of the actual assembly space, machine operation vibrations, and other relevant factors, this paper adopts the specified value of \(\alpha\) as \(120^{\circ}\).
# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
## 3 Results
### Forecasting Model Results  
In the forecasting model ForecastBySsa, 400 tests were conducted, with the value of windowSize iteratively adjusted from 1 to 400. Various performance metrics were calculated for each iteration, focusing on the \(R^{2}\), which measures the model's accuracy. Figure 13 illustrates the evolution of \(R^{2}\) concerning different windowSize values. The analysis determined that the optimal \(R^{2}\) value was achieved when windowSize was set to 325. This indicates that the model performs best with this specific window size. The employed C# script is available as a Supplementary Material (ForecastingScript.txt).  
In Figure 13, the red value indicates the best \(R^{2}\) value for the windowSize variation. The indicator corresponds to the windowSize of 325. The AQIF model with this indicator achieved the highest \(R^{2}\) of 0.7162, indicating moderate accuracy. Low errors are reflected by an \(MAE\) of 0.4, \(MedAE\) of 0.4, and \(MAPE\) of 0.9%, demonstrating its ability to forecast \(AQI\) with minimal deviation.  
Negative \(R^{2}\) values observed for other windowSize values indicate suboptimal parameter tuning. Inappropriate windowSize choices lead to overfitting or underfitting, resulting in poor predictions and negative \(R^{2}\).  
Expanding the dataset can improve the forecasting component. The authors introduced this component into the research with a demonstrative purpose, aiming to illustrate the concept of urban-level information dissemination.  
Figure 12: Comparison of predicted and actual \(AQI\) values between 1 March 2024 and 1 July 2024.
## 4 Repetition-Based Cooperative Broadcasting Scheme
### Helper Selection
```
1:\(\mathcal{H},\mathcal{D}_{p}\)
2:\(\mathcal{D}_{p}^{1}\), \(\mathcal{D}_{p}^{2}\), \(\mathcal{D}_{p}^{2}\), \(\cdots\), \(\mathcal{D}_{p}^{|\mathcal{H}|}\)
3:\(d_{1-num},d_{2-num},\cdots,d_{\{\mathcal{H}\}-num}\)
4:Initialization: \(\mathcal{H}_{1}^{1}=\phi,\mathcal{H}_{o}^{2}=\phi,num=\left|\mathcal{H}\right|\)
5:while\(num>0\)
6:for\(h\in\mathcal{H}\)do
7:if\(\left|\mathcal{D}_{p}^{1}\right|\) is equal to \(num\)then
8:\(\mathcal{H}_{o}^{1}=\mathcal{H}_{o}^{1}\cup h\)
9:endif
10:endfor
11:if\(\mathcal{H}_{o}^{1}\
eq\phi\)
12:for\(i\in\mathcal{H}_{o}^{1}\)
13:if\(d_{i-num}\) is smallest
14:\(\mathcal{H}_{o}^{2}=\mathcal{H}_{o}^{2}\cup i\)
15:endif
16:endfor
17:else
18:\(num=num-1\)
19:endif
20:endwhile
21:\(\mathcal{H}_{o}^{2}\)
```  
**Algorithm 1** Selection of the best helper
## 2 MHT for Data Authentication
### MHT Overview
Hence, it can compute \(V_{18}\), \(V_{9}\), \(V_{4}\), \(V_{2}\), and \(V_{1}\) as follows: \(V_{18}=h(s[2])\), \(V_{9}=h(V_{18}||V_{19})\), \(V_{4}=h(V_{5}||V_{9})\), \(V_{2}=h(V_{4}||V_{5})\), and \(V_{1}=h(V_{2}||V_{3})\). A user receiving a segment \(s[i]\) of data can check the integrity of \(s[i]\), the publisher of, \(s[i]\), and whether \(s[i]\) is a piece of the data as follows:  
(1) The integrity of the \(s[i]\): NDN does not tell the hash value of the original \(s[i]\) to the user. Therefore, even if the user computes the hash value of the received \(s[i]\), it cannot confirm whether the calculated hash value is correct. However, NDN provides the digital signature for the valid \(V_{1}\) of the MHT. If the received \(s[i]\) is changed for some reason, so that the calculated hash value of the received \(s[i]\) is different from the hash value of the original \(s[i]\), the user must compute an incorrect \(V_{1}\) and fail to verify the digital signature. Hence, the user can check the integrity of the received \(s[i]\). (2) The publisher of the \(s[i]\): If the user checks the digital signature, the user can authenticate the publisher of the \(s[i]\). (3) Whether \(s[i]\) is a piece of the requested data: If the received \(s[i]\) is a segment of data other than the requested data, the user can check the verification of the computed \(V_{1}\), even if \(s[i]\) itself is valid. Since the MHT is uniquely built for each data point, the computed \(V_{1}\) must be different from the \(V_{1}\) of the MHT of the requested data. Hence, the user can confirm whether \(s[i]\) is a correct piece of the requested data.
## 2 Materials and Methodology
### Effect of CO\({}_{2}\) Concentration (10%, 18%, and 25%)
#### 3.2.1 Mixed Recycled Aggregates (MRAs)  
For MRA-RP1, the maximum amounts of CO\({}_{2}\) captured per cement paste were 1.8% at 10% [CO\({}_{2}\)] and 12.6% at 25% [CO\({}_{2}\)]. Interestingly, in the tests at 18% [CO\({}_{2}\)], carbonation  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**CO\({}_{2}\) Maximum Uptake (\%)/ Carbonation Period (h)**} \\ \cline{3-8}  & & **MRA-RP1** & **MRA-RP2** & **MRA-RP3** & **RCA-RP1** & **RCA-RP2** & **RCA-IW1** & **CA-L** \\ \hline \multirow{3}{*}{[CO\({}_{2}\)]} & 10\% & 1.8\%/10 h & 7.5\%/12 h & 10.3\%/24 h & 9.3\%/5 h & 9.5\%/12 h & 14.4\%/5 h & 18.6\%/12 h \\ \cline{2-8}  & 18\% & - & 10.0\%/5 h & 2.1\%/24 h & 8  
was very small (0.7%). For MRA-RP2, the maximum CO2 uptake values were 7.5%, 10.0%, and 22.5%, at 10%, 18%, and 25% [CO2], respectively. Lastly, for MRA-RP3, the maximum values of CO2 captured were 10.3%, 2.1%, and 12.3% at 10%, 18%, and 25%, respectively. Therefore, it is concluded that increasing the CO2 concentration from 10% to 25% promoted a higher CO2 capture by the mixed recycled aggregates. When the CO2 concentration is increased within the reaction environment, it stimulates the progression of the reaction, resulting in a more accelerated and thorough conversion of CO2 into carbonate compounds. In this sense, lower concentrations lead to longer periods of carbonation. These results are consistent with those of [PERSON] et al. [7], who also found that increasing CO2 concentration led to an increase in the rate and degree of carbonation. Figure 7 presents the variation in weight loss from calcium carbonate decarbonation in the period of maximum CO2 capture, before and after forced carbonation, indicating an increase in CO2 in the aggregates.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 4 Experimental Evaluation and Analysis
### Phase 3
#### 4.4.2 Improved Detection Capability  
The UA-DES mechanism refines DBN training for ransomware detection, enhancing precision and reliability. The process involves initializing the DBN with specific parameters and setting early stopping criteria based on performance, uncertainty, and calibration quality, with epoch-based training and validation set evaluation guiding stopping decisions. UA-DES incorporates uncertainty estimation via dropout simulations and calibrates predictions to ensure reliability. Active learning prioritizes informative samples, promoting training efficiency, while a dynamic stopping criterion mitigates overfitting, resulting in a robust DBN model.  
For UA-DES-DBN model validation, we assembled a comprehensive dataset comprising 8152 crypto-ransomware samples from virusshare.com, representing diverse families like Cerber, TeslaCrypt, CryptoWall, Petya, and WannaCry, alongside 1000 benign programs from informer.com. Both ransomware and benign samples underwent analysis in a sandbox environment. Python libraries, including Sklearn, Pandas, NumPy, and SkFeature, facilitated data analysis and evaluation.  
The API call features used as model input, a sample of which is shown in Table 7, are highly indicative of ransomware behavior. Notably, cryptographic APIs like CryptEncrypt and CryptGenKey play a crucial role in file encryption. File access APIs such as CreateFileand DeleteFile indicate unauthorized file manipulation. Network APIs like WinHttpConnect and WinHttpOpenRequest suggest potential data exfiltration or command-and-control communication. These features collectively provide a robust foundation for the model's detection capabilities.
## 3 Materials and Methods: Workforce Capacity and Workload
### Computing the Required Workforce
\[TP_{k}=\frac{TWu_{k}}{TWu}.AT \tag{14}\]  
Next, we want to assign some of the 12 operators to operation X (\(w_{\text{XA}}\)) and the rest to Y (\(w_{\text{YA}}\)). The values of the unit workload (\(Wu^{*}_{i\text{A}}\)) have to be computed again (Equation (15)) because the time devoted to model A is \(TP_{\text{A}}\) instead of \(AT\). \[w_{ik}=Wu^{*}_{ik}=\frac{TWu}{TWu_{k}}.Wu_{ik}=TWu.\frac{Wu_{ik}}{TWu_{k}} \tag{15}\]  
Since the unit workload (\(Wu^{*}_{i\text{A}}\)) is 9 for product A (according to Equation (15)), operation X requires nine operators (\(w_{\text{XA}}=9\)) during \(TP_{\text{A}}\), and three operators will be assigned to operation Y. If \(Wu^{*}_{ik}\) is not a whole number, one of the operators in charge of one operation might help with the other operation for a while, as described by [PERSON] [5]. Otherwise, the company should consider several alternatives: (i) that task would be assigned to the fastest workers; (ii) overtime; (iii) as lean manufacturing is based on continuous improvement and problem solving [33], this would be an opportunity for re-designing that task; and (iv) another operator might be added. If Equation (15) is now applied to model B, three people are assigned to operation X and nine to operation Y. As the number of operators performing X or Y depends on the model, at least six cross-trained people would be necessary to complete the task with a constant workforce of 12 people. Before attempting the design of a multi-model assembly line in Section 4, let us rewrite Equation (15) in terms of capacities and total unit workload of the system (Equations (4) and (10)), as shown in Equation (16). Then, the capacity provided by the operators is adjusted to the workload (Equation (17)), with the same number of operators (\(w\)) for all models, as desired [41]. \[Wu^{*}_{ik}=Wu_{ik}.\frac{TWu}{TWu_{k}}=TWu.\frac{\frac{q_{k}}{CP_{ik}}}{\sum_{ i}\frac{q_{k}}{CP_{ik}}}=TWu.\frac{\frac{1}{CP_{ik}}}{\sum_{i}\frac{1}{CP_{ik}}}= \frac{TWu}{\sum_{i}\frac{1}{CP_{ik}}}.\frac{1}{CP_{ik}} \tag{16}\]  
\[w=CP^{*}_{k}=\sum_{i}[PERSON]^{*}_{ik}=\sum_{i}\left(TWu.\frac{Wu_{ik}}{TWu_{k}} \right)=TWu.\frac{\sum_{i}Wu_{ik}}{TWu_{k}}=TWu\;\forall k \tag{17}\]  
Taking into account the changes in \(Wu^{*}_{ik}\), Equation (4) may be rewritten as Equation (18). This allows computing the amount of units of model \(k\) that can go through operation \(i\) per time unit (\(q^{*}_{k}\)). We can rename \(q^{*}_{k}\) as \(P_{ik}\) (for daily production rate) and then isolate it in Equation (18) and combine it with Equations (4) and (15) in order to relate the daily production capacity of that model (\(P_{ik}\)) with its scheduled production (\(q_{k}\)) (Equation (19)). Equation (19) is the expression of a correctly balanced system, with a common rate for all operations, because it does not depend on \(i\). \[Wu^{*}_{ik}=\frac{q^{*}_{k}}{CP_{ik}} \tag{18}\]  
\[P_{ik}=Wu^{*}_{ik}.CP_{ik}=TWu.\frac{Wu_{ik}}{TWu_{k}}.\frac{q_{k}}{Wu_{ik}}= \frac{TWu}{TWu_{k}}.q_{k}\;\forall i \tag{19}\]