esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Formulation
### Mathematical Model Building  
Based on the problem description and underlying assumptions, we establish a mathematical model for the FJSP. First, we define and describe the parameters used in the model as presented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Definitions** \\ \hline \(i\) & Index set of the jobs, \(i=1,2,\ldots,n\) \\ \(j\) & Index set of the operations, \(j=1,2,\ldots,o\) \\ \(k\) & Index set of the machines, \(k=1,2,\ldots,m\) \\ \(p_{ijk}\) & Processing time of operation \(O_{ij}\) on machine \(M_{k}\) \\ \(C_{ij}\) & Completion time of operation \(O_{ij}\) \\ \(C_{max}\) & Makespan (maximum completion time across all jobs) \\ \(S_{ij}\) & Start time of operation \(O_{ij}\) \\ \(x_{ijk}\) & Binary decision variable : \(x_{ijk}=\) \\ \(x_{ijk}\) & \(1\) if operation \(O_{ij}\) is processed on machine \(M_{k},0\) otherwise \\ \(y_{ijij^{\prime}jk}\) & Binary decision variable : \(y_{ijij^{\prime}jk}=\) \\ \(D_{i}\) & \(1\) if operation \(O_{ij}\) precedes \(O_{ij}\) on machine \(M_{k},0\) otherwise \\ \(Pr_{i}\) & Priority of job \(J_{i}\) based on due date urgency \\ \(t\) & Current time, representing a specific time point in the scheduling process \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definition of the model parameters.  
To minimize the production cycle and achieve more efficient scheduling, the objective is set to minimize the maximum completion time across all jobs. The objective function is formulated as follows:  
\[\min\ C_{max}=\min\bigl{(}max\{C_{ij}\}\bigr{)} \tag{1}\]  
The constraints of the mathematical model are defined as follows:  
\[\sum_{k=1}^{m}x_{ijk}=1\ \forall i,j \tag{2}\]  
\[\sum_{i-1}^{n}\sum_{j-1}^{J}x_{ijk}\leq 1,\forall k \tag{3}\]  
\[S_{ij}+x_{ijk}\cdot p_{ijk}\leq C_{ij},\forall i,j,k \tag{4}\]  
\[C_{ij}\leq S_{i(j+1)},\forall i,j \tag{5}\]  
\[C_{ij}\leq C_{max},\forall i,j \tag{6}\]  
\[Pr_{i}=\frac{1}{D_{i}-t},\forall i \tag{7}\]  
Equation (2) ensures that each operation is uniquely assigned to one machine. Equation (3) maintains that each machine handles only one operation at a time. Equation (4) guarantees that the completion time of each operation accounts for its processing duration. Equation (5) enforces the sequential processing of operations within the same job. Equation (6) ties all operations to the overarching objective of minimizing the maximum completion time. Equation (7) establishes a dynamic priority system for jobs based on their proximity to due dates.
## Results  
### Windthrow Detection  
For all three classification methods, the damage was concentrated on the western side of the _Setana North_ study area (Figure 5). In a comparison of damage area, the SVM method classified 312.8 ha of windthrow, while the NDVI filtering method and SAM method classified 234.0 ha and 203.3 ha, respectively (Table 1).  
Figure 5: Windthrow classification results in _Setana North_: PlanetScope RGB image, NDVI filtering method results, spectral angle mapper (SAM) method results and support vector machine (SVM) method results. The detected windthrow areas are expressed in red.  
Based on the overall accuracy in the error matrices, the SAM method was superior (overall accuracy of 92.0% with a standard error of 0.0234, followed by the other two methods' overall accuracies: 84.0% for the NDVI filtering method with a standard error of 0.0159 and 83.0% for the SVM method with a standard error of 0.0098). The kappa values of the SVM and NDVI filtering methods were over 0.65, which was considered a \"substantial level of agreement\", while the SAM method had a value of 0.82, which was considered an \"almost perfect level of agreement\" [39]. Despite the highest overall accuracy, the SAM method presented the lowest producer accuracy value for damaged areas (82.4% : 56/68), while the NDVI filtering method and the SVM method presented higher numbers (95.6% : 65/68 and 98.5% : 67/68, respectively). The lowest producer accuracy for the SAM method reflected the largest number of false-negative points (the classified map defined the area as not damaged, but the reference map showed that the area was damaged). On the other hand, the SAM method delivered the highest user accuracy of damaged areas (93.3% : 56/60), elevating its overall accuracy.  
In terms of false positives, the SAM method misclassified only 4 points (forest areas), while the NDVI filtering method and the SVM method misclassified 29 and 33 points, respectively, especially in grassland areas (Table 2). The misclassification from both methods occurred basically at the same points. The spectral signatures between all misclassified points and windthrow area (Figure 6) presented similar shapes. The variation in each band of the misclassified points was wider than the variation in the windthrow area, mainly in the near-infrared bands (B4 and B8).
## 1 Introduction
[13] showed that mass loss rate, surface hardness, point load strength, and porosity changed to different degrees with the increase of freeze-thaw times, among which hardness was closely related to point load strength, and it was believed that point load strength could be inferred based on rock surface hardness. [PERSON] et al. [14] used nuclear magnetic resonance to analyze the damage law of sandstone under freeze-thaw action according to the size, distribution, number, and connectivity of pores, and concluded that the microstructure changed significantly, and the changes in each index were significantly different. [PERSON] et al. [9] evaluated the deterioration degree of five types of sandstone under freeze-thaw conditions by using indexes such as porosity, wave velocity, and com-pressive strength and believed that the deterioration law of strength could be predicted by a function. To sum up, scholars have carried out in-depth studies on the damage process and mechanism of rock under the action of freezing and thawing considering different factors, and the research ideas and methods are becoming increasingly mature. However, there is no exact standard for freeze-thaw cycle tests of stone cultural relics at present, and experimental design is usually carried out according to the environment of cultural relics. It is worth noting that even in the same cultural relic, due to the different environments of rocks in different parts, they are subjected to different freezing and thawing modes, as shown in Figure 1. Under different freezing and thawing conditions, the changes in weathering process and damage characteristics of stone cultural relics are worth exploring. Because of this, this paper designed three environmental models for freeze-thaw tests of sandstone cultural relics and then used a variety of analysis and detection methods to analyze the variation law of various physical indexes of sandstone in the freeze-thaw weathering processes. Finally, this paper further discusses the relationship between relevant mechanical indexes and physical properties indexes. The results of this paper can provide theoretical support and a scientific basis for the protection of sandstone cultural relics.
## 3 Experimental Data and Evaluation Methods
### Assessment Criteria  
In order to evaluate the performance of the proposed method, the matching strategy proposed by [PERSON] et al. (2018) is used to pair the reference tree (true value) with the estimated tree (predicted value), as shown in Equation (10):  
\[D=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}}+k\cdot \left(z_{i}-z_{j}\right)^{2}, \tag{10}\]  
where \(D\) is the relative distance between the reference tree and the estimated tree; \(\left(x_{i},y_{i},z_{i}\right)\) and \(\left(x_{j},y_{j},z_{j}\right)\) represent the treetop coordinates of the reference tree and the estimated tree, respectively. The estimated tree \(i\) and the reference tree \(j\) match successfully when the two three-dimensional spatial points representing the location information of the tree are closest. \(k\) is the weight for the height difference (0.5 by default).  
Three metrics, recall (\(r\)), precision (\(p\)), and F-score (\(F\)), are used to evaluate the performance of the proposed method, as shown in Equations (11)-(13):  
\[r=\frac{\text{MT}}{\text{MT}+\text{OE}}\, \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Plot IDs** & **Number of Individual Trees** & **RCD (\%)** & **Number of Sub-Plots** \\ \hline U1 & 132 & 61.58 & 16 \\ U2 & 186 & 69.09 & 16 \\ \hline G1 & 108 & 93.25 & 12 \\ G2 & 261 & 96.40 & 16 \\ \hline \end{tabular}
\end{table}
Table 1: A detailed description of the test set.  
Figure 6: Schematic representation of the test plots.  
\[p= \frac{\text{MT}}{\text{MT}+\text{CE}}\, \tag{12}\] \[F=2\times\frac{r\times p}{r+p}\, \tag{13}\]  
where MT represents the one-to-one matching relationship between the reference tree and the estimated tree, that is, the correct segmentation; OE represents that some reference trees are not matched to the estimated tree, that is, an individual tree is considered as a branch belonging to other trees, so it is not detected, resulting in under-segmentation; CE indicates that the reference tree and the estimated tree have a one-to-many relationship; that is, an individual tree is divided into multiple trees, resulting in over-segmentation; \(r\), \(p\), and \(F\) are recall, precision, and F-score, respectively.
# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
## 4 Implementation of DRX Power Savings with Adaptive SPS
### UE State Transitions with Adaptive SPS  
In our proposed scheme, the UE only performs uplink transmissions periodically in the adaptive SPS allocations defined for it. Apart from the initial grant, the UE does not seek any PDCCH grants, therefore, it does not send any SR in case of new packet arrivals. So the process is much simplified. Figure 6 shows how maximum DRX power saving can be achieved by synchronizing the ON-period of the DRX cycle with the adaptive SPS transmission opportunities from the UE.  
Initially, the UE is in the active state. When a new packet is generated by the UE, it sends an SR to the eNodeB and continuously monitors the PDCCH for uplink grants. Unlike the dynamic scheduling, the eNodeB responds with an adaptive SPS grant and DRX parameters. Accordingly, the UE starts an inactivity timer and transmits in the designated PUSCH resources. Upon the inactivity timer expiry (as no other PDCCH grants are due),  
Figure 6: DRX state transitions with adaptive SPS.  
the UE enters sleep state. During the sleep period, the UE does not wake up in case of new packet arrivals. When the sleep-timer is expired, the UE enters active state and makes the adaptive SPS transmission in its allocated PRBs and reports the amount of data left in the buffer (if any) to the eNodeB in the BSR. Upon the ON-timer expiry, the sleep period is started again. If the eNodeB requires to modify or cancel the adaptive SPS allocations, it sends a PDCCH signal during the ON-period of the UE and the UE acts accordingly. In this scheme, the randomness of packet arrivals and unpredictable PDCCH grants do not affect the entering/remaining in the sleep state of the UE. Thus the UE can save power by lengthening its sleep period, subject to matching with the delay tolerances of the buffered packets.
# Fault-Tolerant Tripod Gait Planning and Verification of a Hexopod Robot
###### Abstract  
In some hazardous or inaccessible applications, such as earthquake rescue, as a substitute for mankind, robots are expected to perform missions reliably. Unfortunately, the failure of components is difficult to avoid due to the complexity of robot composition and the interference of the environment. Thus, improving the reliability of robots is a crucial problem. The hexopod robot has redundant degrees of freedom due to its multiple joints, making it possible to tolerate the failure of one leg. In this paper, the Fault-Tolerant Tripod (F-TT) gait dealing with the failure of one leg is researched. The Denavit-Hartenberg (D-H) method is exploited to establish a kinematic model for the hexopod robot, the Jacobian matrix is analyzed, and it is proved that the body can be controlled when three legs are supported. Then, an F-TT gait phase sequence planning method based on a stability margin is established, and a method to improve stability is proposed. The trajectory for the center of gravity (COG) and foot is studied. Finally, a simulation model and prototype robot experiments are developed, and the effectiveness of the proposed method is verified.  
fault-tolerant gait; leg failure; locomotion planning; gait generation 2020
###### Abstract
Active faulting in areas with strong earthquakes has always been a key problem threatening the safety of tunnel construction and operation [33; 34]. [PERSON] et al. believe that the seismic toughness of underground structures should include two parts: resistance toughness and recovery toughness [35]. Based on the probabilistic analysis method [36], the two-stage design method has been widely used in the seismic design of underground structures such as tunnels [37; 38]. Therefore, it is highly necessary to consider the earthquake resistance level for tunnel structural design. Although extensive research has been conducted on the stability of surrounding rocks and supporting structures in tunnels under various excavation methods, studies specifically addressing single-bore four-lane large-cross-section tunnels are relatively limited. Furthermore, the deformation mechanisms of super large section tunnels under different excavation methods remain insufficiently understood, particularly regarding the selection and optimization of construction schemes for these tunnels. In response to this gap, this paper presents a pioneering study using the Xiaoyuan Tunnel Project in Jiangxi Province, which is the first to feature a single-bore four-lane large-cross-section tunnel. This study establishes a three-dimensional finite element model for the tunnel using ABAQUS 6.14.4 finite element analysis software, applying three different excavation methods: the CRD method, the three-benching seven-step method, and the double-side drift method. By assessing the internal forces and deformation indices of the surrounding rock and supporting structure, this research summarizes the qualitative laws of tunnel force and deformation under various excavation conditions. The findings aim to provide valuable insights for the design and construction of excavations and supports for similar super-large-cross-section tunnels.
# Fractal Contact Mechanics Model for the Rough Surface of a Beveloid Gear with Elliptical Asperities
###### Abstract  
Understanding the contact mechanics of rough tooth surfaces is critical in order to understand phenomena such as tooth surface flash temperature, tooth surface wear, and gear vibration. In this paper, the contact behavior between the meshing tooth surfaces of beveloid gear pairs with elliptical asperities is the focus. The contact area distribution function of the elliptical asperity was proposed for the point contact of curved surfaces by transforming the elastic contact problem between gear meshing surfaces into the contact between elastic curved surfaces with an arbitrary radius of curvature. In addition, a fractal contact mechanics model for the rough surface of a beveloid gear with elliptical asperities was established. The influence of tooth surface topography on the contact load and contact stiffness under different fractal parameters was investigated, and the results demonstrated that the real contact load and the contact stiffness of curved surfaces increase with the increase in the fractal dimension \(D\) and the contact coefficient \(\lambda\). Conversely, the real contact load and normal contact stiffness decrease with the increase in the fractal roughness \(G\) and eccentricity \(e\).  
mechanics model; surface topography; elliptical asperity; beveloid gears +
Footnote †: journal: Applied Sciences
# Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments
## 3 Results
### Results Analysis
#### 3.2.1 Simulation and Analysis of High Dynamic Signal
The signal's dynamic situation is shown in Table 1.  
In Figure 7(a), Tracking error results of carrier phase under varying acceleration jerks (a), the Y coordinate represents the carrier phase error of the vector loop. Unlike the tracking result of FLL-PLL, carrier phase tracking is very stable regardless of the magnitude and direction of the acceleration jerk change. Figure 7(a) Tracking error results of carrier phase under varying acceleration jerks (b) shows the carrier frequency tracking results of the vector tracking under the dynamic condition in Table 1. The X-axis is the time of signal tracking, and the Y-axis is the carrier Doppler frequency. The red and blue curves represent the real carrier and estimated Doppler frequencies, respectively. The results of the frequency tracking are also stable enough. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Time: (ms)** & **0–300** & **300–700** & **700–1000** \\ \hline Initial velocity: (m/s) & & 5060 \\ Initial acceleration: (g) & & 0 \\ Acceleration jerk: (g/s) & 100 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of two tracking structures under different dynamics. Figure 7: Stable tracking results of carrier phase under different acceleration jerks. Figure 9a,b provide the comparison results between the first derivative, the second derivative of the carrier Doppler frequency, and the true value, respectively. Both red curves are the true first derivative and the second derivative of the carrier Doppler frequency. The higher the derivative order is, the worse the jitter and the slower the convergence are because of the weak constraint of the observation equation. Despite this, the cascading vector loop can still stably track the dynamic signals with the acceleration jerk changes. Figure 8: (**a**) Tracking error results of carrier phase under varying acceleration jerks, (**b**) Contrast of carrier frequency under varying acceleration jerk. Figure 9: Contrasts of the first and second derivatives of carrier frequency with varying acceleration jerks.
# A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis
###### Abstract  
The observation of mobility tests can greatly help neurodegenerative disease diagnosis. In particular, among the different mobility protocols, the sit-to-stand (StS) test has been recognized as very significant as its execution, both in terms of duration and postural evaluation, can indicate the presence of neurodegenerative diseases and their advancement level. The assessment of an StS test is usually done by physicians or specialized physiotherapists who observe the test and evaluate the execution. Thus, it mainly depends on the experience and expertise of the medical staff. In this paper, we propose an automatic visual system, based on a low-cost camera, that can be used to support medical staff for neurodegenerative disease diagnosis and also to support mobility evaluation processes in telehealthcare contexts. The visual system observes people while performing an StS test, then the recorded videos are processed to extract relevant features based on skeleton joints. Several machine learning approaches were applied and compared in order to distinguish people with neurodegenerative diseases from healthy subjects. Real experiments were carried out in two nursing homes. In light of these experiments, we propose the use of a quadratic SVM, which outperformed the other methods. The obtained results were promising. The designed system reached an accuracy of 95.2% demonstrating its effectiveness.  
computer vision; machine learning; mobility test; neurodegenerative disease detection Article
# The Impact of Prompting Techniques on the Security of the LLMs and the Systems to Which They Belong
## 2 Domain Overview
### Large Language Models  
To avoid or at least diminish these risks, we need a clear understanding of what a large language model is, how it behaves, and what are the factors that have the greatest impact on its performance and behavior.  
Prior to discussing the concept of a large language model, it is essential to first comprehend what constitutes a language model. A language model serves as a probabilistic representation of a natural language [10]. The functionality of a language model is based on the principle that the likelihood of the subsequent word in a sequence is determined solely by a specific number of preceding words, which is referred to as context. To facilitate this, language models utilize methodologies such as N-grams [10], Hidden Markov models [11], or neural networks to identify patterns and interrelations among words within a text corpus.  
A large language model (LLM) is nothing more than a language model, but being larger allows it to gain the ability to perform different kinds of natural language processing (NLP) tasks. Architecture-wise, except for the size, the difference between the two lies in the fact that the underlying model of an LLM is an artificial neural network using a transformer-based architecture. The transformer architecture is based on the \"Attention is all you need\" [12] paper that we will not cover in this paper, but we recommend checking it out. The ability of LLMs to perform general purpose NLP tasks makes them flexible, turning them into perfect components for our systems, since they are useful on almost every level, from interacting with our end user to doing different kinds of task under the hood. This strength can also be a weakness, because once we find a use case in our system for an LLM, we would like to be very good at it and do nothing other than what it is supposed to do. Another problem that arises is that LLMs do not have any kind of internal memory; therefore, they are strongly dependent on the context they are fed. Of course, these downsides can be overcome at least partially using different prompting techniques, some of which are covered in the papers we will discuss in Section 2.2. Looking at these techniques, we will observe that good prompting can achieve in some cases even better performance than fine tuning the model for a specific task. This highlights once again the fact that by feeding LLMs the right context, we can make the model perform the way we want and obtain the expected results without changing its internal structure. But the following question should pop up in our head. If you can change the context in such a manner that the behavior of the model changes, can someone from outside of our system do the same? Suddenly the task becomes harder, because we do not have only to tweak the model's context to perform the task correctly, but we also have to ensure somehow that the behavior of our model cannot be tweaked or changed by outsiders to serve a different purpose than the initial one.
## 5 Evaluation  
In this section, we evaluate the performance of STUN implemented using reinforcement learning. The following are the main points we wanted to confirm through the evaluation:  
* **STUN performance based on number of learning iterations**. We analyzed the results by changing the values of the episode count N and Max_T, which are factors that determine the total number of iterations in reinforcement learning.
* **Performance with micro-benchmark**. To confirm the detailed performance improvement of STUN, we chose a micro-benchmark, i.e., hackbench, and compare its performance under the optimized scheduler parameters by STUN with that under the default scheduler environment.
* **Performance under real workload**. To evaluate the performance impact of STUN for a real workload, we ran a face detection application using Haar Cascades and compare the execution time and frames per second of the application between the cases with default and optimized settings.
* **Improvement based on the number of CPU cores**. To confirm whether the number of CPU cores affects the performance of STUN, we compared the performance improvement rates by optimizing Sysbench in 4-core, 44-core, and 120-core machines.