esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design Theory of Multi-Rope Friction Hoisting System with Double Cages in Vertical Shaft
### Calculation Theory and Selection of Hoisting Rope and Tail Rope  
#### 2.2.1 Initial Overhang Height of Hoisting Rope and Tail Rope  
The initial position of the cage in this system is at the bottom of the vertical shaft, and there is a certain height difference between the left and right sheave wheels. Therefore, it is necessary to calculate the initial overhang height of the hoisting ropes and tail ropes on both sides separately.  
When the heavy-load end is the left cage, the initial overhang height of the hoisting rope at the heavy-load end \(h_{1}\), the initial overhang height of the hoisting rope at the light-load end \(h_{2}\), the initial overhang height of the tail rope at the heavy-load end \(h_{3}\), and the initial overhang height of the tail rope at the light-load end \(h_{4}\) are shown in Equations (8)-(11).  
\[h_{1}=H+H_{t1}-h \tag{8}\]  
\[h_{2}=H_{t2}-h \tag{9}\]  
\[h_{3}=H_{0}+H_{g}+H_{h} \tag{10}\]  
\[h_{4}=H+H_{g}+H_{h}-h-H_{0} \tag{11}\]  
When the heavy-load end is the right cage, the initial overhang height of the hoisting rope at the heavy-load end \(h_{1}\), the initial overhang height of the hoisting rope at the light-load end \(h_{2}\), the initial overhang height of the tail rope at the heavy-load end \(h_{3}\), and the initial overhang height of the tail rope at the light-load end \(h_{4}\) are shown in Equations (12)-(15):  
\[h_{1}=H+H_{t2}-h \tag{12}\]  
\[h_{2}=H_{t1}-h \tag{13}\]  
\[h_{3}=H_{g}+H_{h} \tag{14}\]  
\[h_{4}=H+H_{g}+H_{h}-h \tag{15}\]  
where \(H\) is the target hoisting height, \(H_{t1}\) is the height of the sheave wheel at the heavy-load end, \(H_{t2}\) is the height of the sheave wheel at the light-load end, \(h\) is the height of the cage, \(H_{0}\) is the hoisting height at a certain time, \(H_{g}\) is the height of overwind, and \(H_{h}\) is the height of the tail rope ring.
## 5 Analysis of the Results
### Comparative Analysis of the Feature Extraction  
We randomly selected four channels a, b, c, and d, to compare the feature extraction capability of the proposed LSEB module with that of the SE module, and the feature extraction heat map is shown in Figure 9. It can be seen from the figure that some important features of channels b, c, and d are ignored after the convolution of the SE module, so that the model cannot efficiently recognize the different states of the blast furnace tuyere image. Following the LSEB module convolution, the model will strengthen the extraction of the local feature information, the feature information is reasonably assigned the corresponding weights, and the important information can be fully utilized. Since the original channel attention is to pool the entire channel and then do the full connection, and the weight of each part of the feature map is not the same, it should not be treated the same, so that some important features will be ignored. The LSEB module makes the regional information fully utilized and strengthens the feature extraction ability.  
Figure 9: Heat map of the feature extraction. SE-before is the feature map before the convolution of the SE module, SE-after is the feature map after the convolution of the SE module. LSE-before is the feature map before the LSE module convolution, LSE-after is the feature map after the LSE module convolution. (**a–d**) are the four randomly selected channels.
# Can Livestock Farming Benefit from Industry 4.0 Technology? Evidence from Recent Study
## 4 Results
### Poultry
#### 4.2.1 State of Digitalization of the Poultry Sector  
Digitally supported systems have found their way into poultry farming for many years now. The decreased availability of agricultural workforce and the development to more large-scale enterprises promoted the entrance of digital systems into poultry husbandry. The use of automated feeding and weighing systems and their control via the barn computer are established supporting systems in the husbandry systems. Depending on the system, so-called egg belts and also manure belts are installed in laying hen and breeder housing, which contribute to the automation of recurring processes. Sensor-supported systems can be found as standard in the area of climate control in poultry houses [38].  
The methods used so far are mainly summarized under the term Precision Livestock Farming. The increasing use of smartphone technology and the real-time transmission of barn and herd data is becoming apparent and can provide the herd manager with support in making timely decisions on controlling stock processes and checking the success of measures [39]. This no longer only includes ensuring animal health but also animal welfare. In addition to the use of proven sensor technology, digitally supported, audio-visual systems can also provide support for this in the future [39; 40]. Nevertheless, all systems support human decisions but do not substitute human knowledge and skills.  
Networking different data should definitely contribute to an improvement in the barn climate. Different interfaces should not pose a barrier here. The possibility of connecting to the internet in the stables is problematic. Alternatively, many manufacturers offer a digital barn card, which can usually only be used in-house.  
Broiler production typically happens within a well-organized food chain production system. Digital data exchange along the value chain is already common in part of the sector.
## 4 Vibration and Acoustic Sensors in Non-Destructive Testing of Port Cranes
### Vibration Sensors in Port Crane Monitoring
#### 4.1.2 Types of Vibration Sensors  
Piezoelectric vibration sensors are widely used in RNDT owing to their excellent performances [82; 83]. They are simple in structure, require no external power, and exhibit good frequency response characteristics [84; 85]. They can detect a wide range of vibration frequencies, making them suitable for various marine structures [86]. For example, piezoelectric sensors can detect vibrations caused by lifting and lowering operations in port cranes [85]. However, they are sensitive to temperature changes, which can affect the measurement accuracy. Temperature compensation techniques are often required to improve reliability.  
Capacitive vibration sensors are known to have high accuracy and stability. They have low power consumption and good linearity. They can measure low-frequency vibrations with high precision [87]. In port cranes, capacitive sensors can detect vibrations caused by operational movement [88; 89; 90]. However, they are sensitive to environmental factors such as humidity and require complex circuits for signal conditioning. Proper sealing and circuit design are required to ensure their performance.  
Strain gauge-based vibration sensors measure structural deformation by detecting resistance changes in the strain gauges [91; 92]. They can be used in complex structures to measure local vibrations and deformations [93]. They can detect localized vibrations in port cranes with complex geometries. However, their installation is relatively difficult, and they are prone to damage. High-precision measurement circuits are required to ensure accuracy.  
[PERSON] et al. [47] highlighted the advantages and limitations of different types of vibration sensors in SHM, providing valuable insights for their selection and application in port cranes.
## 3 Aloha Protocols
### Pure Aloha  
Pure Aloha (PA) is one of the simplest anti-collision protocols. It is based on TDMA [21; 22]. Whenever tags enter the interrogation zone, they randomly choose a frequency on which to transmit their data. A collision will occur if several tags transmit data at the same time, resulting in complete or incomplete collisions. A complete collision occurs when the messages of two tags fully collide; an incomplete collision, however, takes place when only part of the tag message collides with another tag message. This procedure is shown in Figure 8 and will be repeated until all tags are successfully identified.  
PA has been presented using different extra-features [8; 23] such as: muting for silencing tags after being identified; the 'Slow down' for decreasing a tag response rate after identification; the 'Fast Mode' for sending a silence message before a tag begins transmission; and combinations of these different features.
# On Microservice Analysis and Architecture Evolution:
## 5 Results and Discussion
### RQ3: Relationship between Microservices and Other Architectures  
We found that microservices share more differences than similarities with other architectures. Works considering related architectures are referenced in Table 7. The most related architectural style is Service-Oriented Architectures (SOA) because microservice architectures borrow many characteristics from SOA, such as the emphasis on scalability and the concept of service availability and responsiveness. Unlike SOA, however, individual services in microservice architectures are brought to production independently and are more granular [63]. It is also worth noting that while graph-based methods of performing root cause analysis can be used in both microservice architectures and SOA [47], many traditional methods of code analysis are not sufficient for microservice architectures [14].  
The greatest concern of much research towards static-code analysis is that microservices are distributed. Thus, it is rather common to observe static-code analysis in monolithic systems since the system is homogeneous and often comes with one codebase. On the other hand, microservices, as opposed to monoliths, are heavily distributed and heterogeneous, and thus the argument might sound valid. However, still static-code analysis is applied to individual system modules; in addition, with sufficient cross-platform support, it is possible to derive a holistic view of system architecture of microservices solely by static-code analysis [26].  
However, what seems lacking in nearly all works we assessed is consideration of other recent architectural advancements such as serverless or micro-frontends [64]. Of course, one can object that this study searched for microservices, but the argument by [PERSON] et al. [64] remains valid: if researcher and practitioner do not understand the benefits of these new architectural advancements, they are possibly decreasing productivity or increase technical debt. This could be seen as similar to using SOA to develop a new system.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Goal** & **References** & **Total** \\ \hline Microservice migration & [15, 16, 17, 18, 19, 20, 21, 23, 24, 32, 33, 48, 49, 50, 51, 56, 64] & 17 \\ \hline Technical debt identification & [29, 39, 63] & 3 \\ \hline SAR & [25, 26, 34, 57] & 4 \\ \hline Architectural analysis & [1, 14, 28, 47, 63] & 5 \\ \hline Quality attribute analysis & [12, 35, 36, 37, 38, 39, 40, 58, 59, 60] & 11 \\ \hline Evolution analysis & [2, 26, 27, 28, 29, 31, 34, 40, 57, 63, 65] & 12 \\ \hline Fault analysis & [2, 30, 38, 41, 42, 43, 54, 55] & 14 \\ \hline \(\hookrightarrow\)_Root cause analysis_ & [43, 47, 54, 55] & \(\hookrightarrow\) 4 \\ \hline Survey works & [1, 14, 45, 63, 64, 65] & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Works relevant to RQ2.
# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
## 3 Threat Model  
In 6G NTNs, protocol threats are particularly significant in low-altitude airspace; these threats often originate from malicious traffic generated by UE and man-in-the-middle (MitM) devices in remote and distributed ground areas with inadequate protection. This malicious traffic can penetrate the drone swarming CN through the distributed drones of BSs, potentially leading to paralysis of the entire low-altitude airspace network, as illustrated in Figure 2. The drones in 6G NTNs are still under discussion, protocols for both NTN terminals and the network side have been developed based on the core protocol standards established by 3 GPP 5G [56; 57]. Significant optimizations have been implemented in satellite and terminal-side protocols to facilitate seamless NTN smartphone connectivity. These improvements specifically address critical aspects such as time-frequency synchronization, precise timing relationships, efficient hybrid automatic repeat request (HARQ) configurations, and advanced mobility management strategies, as shown in Table 2.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Functions** & **3 GPP NTN Protocol** \\ \hline Beam management & Beam position planning, beam scheduling defined in 3 GPP TR 38.821 [58] \\ \hline Time-frequency synchronization & Time offset and frequency offset compensation defined in 3 GPP TS 38.211 [59] and TS 38.331 [60] \\ \hline Timing relationships & Adaptation of NTN uplink and downlink scheduling timing defined in TS 38.331 [60] and 3 GPP TS 38.213 [61] \\ \hline HARQ configurations & HARQ extension and prohibition defined in 3 GPP TS 38.331 [60], TS 38.212 [62] and TS 38.321 [63] \\ \hline Mobility management strategies & Handover based on location and time defined in 3 GPP in 3 GPP TS 38.331 [60] \\ \hline Downlink broadcasting & Broadcast message delivery for NTN information defined in 3 GPP TS 38.331 [60] \\ \hline Others & Inheriting 3 GPP 5G protocol \\ \hline \hline \end{tabular}
\end{table}
Table 2: The relationship between NTN protocol and 5G protocol.  
Figure 2: Threat model for segmenting low-altitude airspace in protocol of 6G NTNs.  
Thus, this paper presents case studies of UPF denial of service (DoS) and protocol tampering threats in 5G networks, as well as the corresponding protocol data collection process.
## 4 Data Processing and Analysis
### Squeezing Effect Analysis of Large-Diameter Tubular Piles  
In the data processing, the images are first loaded in the software, and then the control point file is established. Last, the authors run the image analysis program Geodog and post processor Post Viewer to carry out pixel and displacement solution.  
The cloud chart of soil mass displacement at different penetration depths was obtained from data processing. It can be seen from Figure 16 that, in the early penetration stages, the piles arrived at a superficial soil mass. Even though soil mass, within a certain level at the pile bottom, mainly moved downward laterally, the surrounding soil mass was mainly squeezed out laterally. With increases in the penetration depth, the rising trend of the deep soil mass was weakened, and the surrounding soil mass developed vertical displacement and deformation relative to the pile body. Radial squeezing played a dominant role in most regions. Moreover, a spherical disturbed area would be formed at the bottom of the pile. The scope of this area was related to the initial state of the soil mass, such as relative compaction (Figure 18).  
To study the spatial variation laws of the displacement deformation of the surrounding piles of the soil mass, as well as the effective sphere of influence, vertical observation belts were set at one, three, five, seven, and nine times the pile diameter away from the pile after it was penetrated completely. Moreover, horizontal monitoring belts were set at 0 cm, 8 cm, 16 cm, 24 cm, 32 cm, 40 cm, 48 cm, and 56 cm away from the soil mass surface. The variation curves of the vertical soil displacement at different distances away from the pile and soil displacement at different depths were also drawn (Figure 17).  
Figure 16: Cloud diagram of pipe pile penetration displacement when (**a**) penetrated into 1/4 pile length, (**b**) penetrated into 2/4 pile length, (**c**) penetrated into 3/4 pile length, and (**d**) penetrated into 4/4 pile lengths.  
Figure 17: Influencing factors of soil displacement around piles and response law curve for the (**a**) curve of relationship between soil body displacement and the depth along pipe pile and (**b**) relationship curve between soil body displacement and distance from pipe pile.  
Figure 17a shows that the vertical soil displacement increased first and then decreased with increases in the distance from measuring points to the central axis of the pile body. The soil displacement approached 0 at the position, which was nine times the pile diameter away. It could be viewed as the boundary of the radial squeezing zone. Figure 17b reflected that, in the penetration depth range of piles, the horizontal displacement of the soil mass followed an exponential function. The radial displacement slowly approached 0 when the penetration depth was 1.5 times the pile length, forming a hemispherical expansion area [36; 37; 38; 39]. The squeezing effect of the penetrated piles is shown in Figure 18.
# Application of Lignin for Slope Bioengineering: Effect on Soil Improvement and Plant Growth
###### Abstract  
This study aims to establish whether lignin-treated soils could result in greater soil strength and stimulate seed germination and growth, which can be essential for slope bioengineering. Three different soil types with a range of plasticity were treated with lignin solutions of 1% and 3%. The changes in soil strength and seed growth were observed for 40 days to simulate the long-term field performance. Two methods to treat the soils were employed: Method 1 involved mixing lignin solutions with the whole soil sample, while Method 2 involved spraying the lignin solutions on the already-prepared soil sample. The results indicated that the lignin concentration and the soil treatment method could affect soil strength, whereas soils treated with 3% lignin solution using Method 1 consistently produced greater soil strength values. The lignin-treated soils were able to retain more moisture at the end of the experiment than the untreated soils. Both lignin-treated and untreated soils produced similar results on seed germination and growth, suggesting that lignin does not have a negative effect on slope bioengineering.  
biopolymer; stabilization; vegetation; lignin; soil strength Article
## 3 Our LSP Sensor
### Identification of Horizontal and Vertical Sensing Radius of a Sensing Zone
#### 3.1.2 Vertical Radius  
The same experiment was repeated where the input antenna was placed 7 mm below the sensor, whereas the receiver was placed above the sensor and moved in the vertical plane, i.e., the scanning axis was along positive \(Z\)-axis. First a baseline signal was acquired and then the receiver was moved from 0 to 100 mm in steps of 10 mm as shown in Figure 4c. Figure 4d shows the RMSD curve, which becomes flat and parallel to the scanning axis after a vertical distance of 7 cm similar to the previous experiment. Once again, the graph resembles a fifth order polynomial curve with the regression, R close to unity. Thus, the vertical sensing radius can be considered as 7 cm in the air medium.  
Thus, both the horizontal and the vertical radius are equal, which indicates the presence of a spherical sensing zone around a flat sensor. However, the conductivity of timber, and the air medium are different resulting in different coefficients of \(X\) in the mathematical expressions as indicated in Figure 4b,d. Thus, the results could vary based on the type of experiment, method of application, and host material properties.
# A Metaheuristic Framework with Experience Reuse for Dynamic Multi-Objective Big Data Optimization
## 4 Usages and Test Cases
### Comparisons of Different DMBDOPs
#### 4.2.2 Experimental Results on DBigOpt2015 and BDDF Problems  
Figure 6 plots the approximate optimal PFs with the median HV value over 30 runs obtained by four algorithms in one time period. The data on the sub-figures are sampled when the optimization process starts and after changes in the problem have occurred 2, 5, and 10 times. It can be observed from Figure 6 that the position of the PFs of the problem changes as the problem is updated, and the four algorithms can adapt to the changes in the problem to obtain different approximate optimal solutions. The four algorithms perform consistently on dataset D4 and dataset D4N. Dynamic NSGA-III performs best on the first objective, Dynamic MOEA/D-DE performs best on the second objective, and Dynamic NSGA-II has the best diversity of non-dominated solution sets and performs more stably during the problem updating. In contrast, the Dynamic ABC algorithm has the least stable performance and the lowest solution quality among the four algorithms. The convergence curves corresponding to Figure 6 based on the HV indicator in Figure 7 show the convergence speed of four algorithms in solving the DBigOpt2015 problem, and reference points are set to \((1,0.1)\) on the D4 dataset and \((1.5,0.1)\) on the D4N dataset in order to calculate the HV values. It can be seen that Dynamic NSGA-III and Dynamic MOEA/D-DE perform better than the other two algorithms in terms of convergence speed comparison. Figure 8 shows the distribution of the non-dominated solutions obtained by the Dynamic ABC on BDDF1-BDDF9 problems in the objective space. Table 3 shows the MIGD and MHV obtained after 30 independent runs of the four algorithms on the BDDF family of problems. All metric values are computed at an interval \(\Delta t=0.2\) over one cycle of the problem variation. The reference fronts of all problems at time step \(t\) were sampled at 100 reference points using the method presented in the literature [57]. Then, the reference points used to calculate HV values were generated based on the maximum values of the two objectives of the reference fronts. According to the MIGD values, it can be seen that, for each problem, all four algorithms can converge well and show little difference in performance, with Dynamic MOEA/D-DE having the best convergence performance. From the perspective of MHV values, there are some differences between the diversity of non-dominated solutions obtained by the four algorithms. Dynamic ABC obtains the best diversity of non-dominated solutions, while Dynamic NSGA-III obtains the worst performance. Dynamic MOEA/D-DE and Dynamic NSGA-II perform similarly.