esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment 2: Deployment of Biofeedback System during Online Academic Examinations
### Discussion  
SEM analysis was applied in order to examine the relationship between the students' Technology Readiness (TR) personality traits, anxiety, and academic performance. The  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Estimate** & **S.E.** & **C.R.** & \(p\) \\ \hline INSAVG & \(<\leftarrow\) & OPTAVG & \(-0.569\) & 0.126 & \(-4.502\) & *** \\ Performance & \(<\leftarrow\) & INSAVG & \(-1.667\) & 0.422 & \(-3.949\) & *** \\ Performance & \(<\leftarrow\) & OPTAVG & 0.870 & 0.393 & 2.213 & 0.027 \\ Anxiety & \(<\leftarrow\) & Performance & \(-4.498\) & 0.453 & \(-9.935\) & *** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relationships between optimism, insecurity, anxiety, and performance. Sign (***) indicates the statistical significant relationships.  
Figure 7: The resulting path models.  
results showed that optimism has a significant negative influence on insecurity. This result is in line with [PERSON] and [PERSON] [63], who support that \"insecurity is negatively associated with mental health and this relation is mediated by optimism\". Moreover, insecurity has a significant negative effect on performance, whilst optimism has a positive but not significant effect on performance. Furthermore, optimism is a positive psychological construct [64]. Optimism is a control belief, which represents general positive outcome beliefs, rather than identifying specific routes through which one can achieve specific outcomes, such as academic achievement [65].  
Moreover, the derived path model showed that insecurity causes academic performance to decline, which in turn has a significant negative effect with increasing anxiety.  
[PERSON] [20] states in his book \"Test Anxiety, The State of the Art\", that the relationship between test anxiety and performance is of a cyclical nature. In our case, the derived path model shows one part of the cyclical relationship between performance and test anxiety. More analytically, the student's realization of his/her poor current performance during the examination causes an increase in test anxiety. In this case, the realization of poor performance precedes the test anxiety increment and thus we could support that this is in line with [PERSON] [20], who claims that test anxiety can very possibly occur as a result of previous performance. Moreover, this is in line with another study, which claims that poor performance can lead to additional anxiety over time [66].  
Apart from research in learning environments, there is a similar and very interesting study applied in the job environment, dealing with personality traits, organizational anxiety, and job performance. In more detail, the results of this article showed that job insecurity has a negative impact on job performance and the organizational anxiety mediates the effect of insecurity on performance [67].
# Design and Modeling of a Parent Big STAR Robot Platform That Carries a Child RSTAR
## 1 Introduction
In Section 3 we cover the kinematic and dynamic analysis of the collaboration between the two robots in different scenarios, including their combined working range and scouting areas. In Section 4 we present multiple experiments in which the two robots collaborate to overcome obstacles. Figure 1: The parent BSTAR robot can carry RSTAR robots and is capable of deploying and recovering them using its actuated tail. The size ratio of the characteristic lengths of the BSTAR to the RSTAR is 6:1.
## 3 Methodology
### Deep Learning Backbone  
In this study, two types of artificial neural network (ANN) models were employed. The first model follows the conventional feed-forward neural network architecture. This network comprises 14 inputs to match the input features of the dataset. The model is structured with seven layers, encompassing both the input and output layers. Driving style is a binary classification problem, with each input data instance is categorized as either aggressive or normal driving style. Thus, the Sigmoid function is employed with one neuron in the output layer to generate the probability of each class. The sigmoid function yields a value ranging from 0 to 1, indicating the probability of the input data belonging to a specific class. On the other hand, in road traffic prediction, the scenario involves three classes: low, medium, and high traffic. Consequently, the Softmax function and three neurons are utilized in the last layer to produce the probability of each class. The softmax function is utilized in multi-class problems because it normalizes the output of a set of multiple neurons into a probability distribution over multiple classes [36]. This normalization property ensures that the sum of the probabilities for all classes equals one, making it suitable for interpreting the output as probabilities. Figure 2 shows the ANN architecture used in the classification problems of driving style and road traffic.  
We also employed an ANN model using Bootstrap Aggregating, commonly known as bagging. In this method, the training dataset is split into five subsets, each of which is used to train a separate ANN model. The final prediction is obtained by averaging the predictions from these five models. Bagging is employed to mitigate the risk of overfitting, particularly given the imbalanced nature of our dataset. Further elucidation on the bagging technique is provided in the results section. Figure 3 illustrates the architecture of the ANN  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Size** & **Input Features** & **Traffic Conditions Labels** & **Driving Style Labels** & **Road Conditions Labels** \\ \hline
24,957 data array & 14 features & 18,769 low traffic & 22,089 normal driving & 3249 full of holes \\  & & 3171 normal traffic & 2868 aggressive driving & 15,242 smooth \\  & & 3017 high traffic & 6466 even conditions \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset size, feature numbers, and number of labels for each class.  
Figure 2: The ANN architecture of the models utilized in the classification of driving style and road traffic.  
model incorporating the bagging approach. The displayed five ANN models in Figure 3 have the same architecture of the ANN described in Figure 2.
# New Remote Care Models in Patients with Spinal Cord Injury: A Systematic Review of the Literature
## 1 Introduction
The use of telecommunications is viewed as a beneficial, easy, and efficient way of healthcare delivery [19], representing a potential solution for providing assistive technology services, especially in rural and remote areas [1]. The evolution of telehealth over recent decades has revealed innovative options in telerehabilitation to address unresolved challenges for patients who need rehabilitation [20]. For all these reasons, people with spinal cord injuries (SCIs) represent a group that could benefit from telehealth services [21]. Despite the extensive literature on e-health in healthcare/assistive settings, the landscape of remote care models for SCI patients has not been comprehensively described in terms of characteristics and approaches. It is important to have an overview of these tools to know how to select, further develop, and improve them [2]. The aim of this work was to conduct a literature review to describe care models supported by innovative technology for long-term assistance on SCI. Particularly, two research queries (RQs) guided the study:RQ1: Is there evidence in the literature about alternative remote nursing models to standard/in-person care for spinal cord injury patients? What is the role of the nurse in such settings and how does the nurse interface with the multidisciplinary team? RQ2: From a clinical and psychosocial point of view, do remote care models show differences compared to traditional in-person assistance?
## 4 Case Study- Business Process Innovation and Improvement
### Help-Desk Process  
The help-desk process for our petrol service station case study has been adapted from the literature due to the similarity of the case scenario [14]. A service request could be related to an incident such as a fault in the petrol service station equipment, customers' after-sales issues or an IT-related problem that requires the help desk department to process the incident and resolve it. Service requests need to be handled according to their type and their priority that comes with three priority levels: \"critical\", \"urgent\" or \"normal\". In the current process, a client (customer) calls the help desk or sends an e-mail in order to make a service request. The help desk is staffed with \"Level-1\" support staff who typically possess a junior role with less than 12 months' experience but are capable of resolving known problems and simple requests. When the Level-1 staff does not know the resolution to a request, the request is forwarded to a more experienced \"Level-2\" support staff. When a Level-2 staff receives a request, he/she evaluates it and assigns a priority level to it. The job tracking system assigns the request to the same or another Level-2 staff depending on the assigned priority level and the backlog of requests. Once the request is assigned to a Level-2 staff, the request is investigated by the staff and a resolution is developed, which is sent back to the Level-1 staff. Eventually, the Level-1 staff forwards the resolution to the client who verifies the resolution. The client notifies the outcome after verifying the resolution to the Level-1 employee via an e-mail. If the client states that the request is fixed, it is marked as complete and the process ends. If the request is not fixed, it is then resent to Level-2 support for further action and goes through the process again. Requests are registered in a job tracking system, which allows the help desk staff to record the details of the request, the priority level and the name of the client who generated the request. When a request is registered, the status of the job is marked as \"open\". When it is moved to Level-2 staff, it is marked as \"forwarded to Level-2\" and when the resolution is sent back to \"Level-1\", the request is marked as \"returned to Level-1\". When a service request cannot be resolved by the help desk staff, a third-party solution is sought from an outside company which serves as a contractor to complete the job. Finally, when a request is resolved, it is marked as \"closed\". Every request has a unique identifier (ID) assigned by the job tracking system when it is registered, and an e-mail is sent to the client. The e-mail includes a \"request reference number\" (job ID), which the customer needs to quote for all queries about the request.
# Early Results on GNSS Receiver Antenna Calibration System Development +
## 2 Materials and Methods  
### The Receiver Antenna Phase Center Correction Model  
To fulfill the requirements of high-accuracy positioning applications, the receiver antenna PCCs must be determined. In line with the IGS convention, and as depicted in Figure 1, PCC is divided into the phase center offset (PCO) vector and the azimuth- and elevation-dependent phase center variation (PCV) [4; 6; 10; 11; 12; 14]:  
\[\begin{split} PCC\left(\alpha^{i},z^{i}\right)=-\,\mathbf{e}^{ \mathbf{T}}\left(\alpha^{i},z^{i}\right)\cdot\mathbf{PCO}+PCV\left(\alpha^{i},z^{i}\right)+r.\end{split} \tag{1}\]  
where \(\mathbf{e}\left(\alpha^{i},z^{i}\right)\) is the line-of-sight unit vector from the receiver to the satellite \(i\), and \(r\) is the constant part equal in all directions present due to the relative characteristics of GNSS measurements [4; 6]. The PCO is a vector from the antenna reference point (ARP) to an arbitrarily defined mean phase center (MPC). The PCV is the direction-dependent range correction function, i.e., the difference between the real and ideal phase wavefront. The PCCs, and all corresponding antenna-related points, vectors, and scalars, are defined in a 3D antenna-fixed left-handed coordinate system (antenna frame-AF).  
A set of PCCs of an antenna are transformed to PCO and PCVs using a least squares (LSQ) adjustment and by simultaneously fulfilling two conditions. Firstly, the PCV at the antenna zenith are constrained to zero, i.e., zero-zenith constraint, by \(PCV\left(\alpha^{i},z^{i}=0^{\circ}\right)=0\). Secondly, the PCO is determined such that the sum of the squared PCVs is minimal, i.e., \(\sum\left[PCV\left(\alpha^{i},z^{i}\right)^{2}\right]\rightarrow\min\).
# Comparing the Evolutionary Trajectories of Industry 4.0 and 5.0: A Management Fashion Perspective
## 3 Research Background
### Key Differences between the Two Concepts
#### 3.2.2 Customers  
There is also a shift from mass customization (I4.0) to mass personalization (I5.0). Mass personalization refers to how the needs of individual consumers can be met [64]. Although some argue that I4.0 also enables mass personalization [65; 66], the focus is arguably even stronger in I5.0. [PERSON] [9] calls I5.0 the \"human touch\" revolution and argues that customers increasingly demand mass personalization, which is only possible if the \"human touch\" is brought back into manufacturing through I5.0 [53]. In achieving this, the use of collaborative robots (cobots) plays an important role. Therefore, the view of the role of robots differs slightly between the two concepts. Proponents of I5.0 point out that robots should be used for repetitive, mundane, and labor-intensive tasks and not with the sole aim of replacing employees and making them obsolete [67].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Relative Emphasis** & **Industry 4.0** & **Industry 5.0** \\ \hline Human vs. technology & Technology & Humans (man–machine cooperation) \\ \hline Customers & Mass customization & Mass personalization \\ \hline Productivity vs. sustainability & Automation & Sustainability, resilience, and human–robot interaction \\ \hline Shareholders vs. stakeholders & Shareholders & Stakeholders \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the main differences between I4.0 and I5.0.
# Competing Reverse Channels' Performance with Sustainable Recycle Innovation Input
## Appendix A Proofs for Theorems, Lemmas and Propositions  
### Proof for Theorem 1  
Proof for Theorem 1.: In model \(B\), we solve for the collector's profit function first.  
\[\Pi_{C}^{B}(\tau)=b\tau D(p)-A\tau D(p)-\frac{H}{2}\tau^{2}\]  
The first order condition of \(\Pi_{C}(\tau)\) on \(\tau\) is  
\[\frac{d\Pi_{C}(\tau)}{d\tau}=(b-A)(\alpha-\beta p)-H\tau\]  
The second order condition of \(\Pi_{C}(\tau)\) on \(\tau\) is  
\[\frac{d^{2}\Pi_{C}(\tau)}{d\tau^{2}}=-H<0\]  
Thus the collector's profit function is strictly concave in \(\tau\).  
We assume the first order condition equation above is \(0\), then we get  
\[\tau(p,b)=\frac{(b-A)(\alpha-\beta p)}{H}\]  
Solving for the manufacturer's profit function  
\[\Pi_{M}^{B}(p,b) =\left[p-c_{m}+(c_{m}-c_{r}-b)\;\tau\right]D(p)\] \[=\left[p-c_{m}+(\Delta-b)\tau\right]D(p)\]  
We substitute the value of \(\tau\) into the above equation and derive  
\[\Pi_{M}(p,b)=\left[p-c_{m}+(c_{m}-c_{r}-b)\;\frac{(b-A)(\alpha-\beta p)}{H} \right](\alpha-\beta p)\]  
The first order condition of \(\Pi_{M}(p,b)\) on \(b\) and \(p\) are  
\[\frac{\partial\Pi_{M}(p,b)}{\partial b}= (\Delta+A-2b)\frac{(\alpha-\beta p)^{2}}{H}\] \[\frac{\partial\Pi_{M}(p,b)}{\partial p}= \alpha-2\beta p+\beta c_{m}-2\beta(\alpha-\beta p)\frac{(\Delta-b )(b-A)}{4H}\]  
The second order condition of \(\Pi_{M}(p,b)\) on \(b\) and \(p\) are  
\[\frac{\partial^{2}\Pi_{M}(p,b)}{\partial b^{2}}= -2\frac{(\alpha-\beta p)^{2}}{H}<0\] \[\frac{\partial^{2}\Pi_{M}(p,b)}{\partial p^{2}}= -2\beta\left[\frac{4H-\beta(\Delta-A)^{2}}{4H}\right]<0\] \[\frac{\partial^{2}\Pi_{M}(p,b)}{\partial p\partial b}= (\Delta+A-2b)\frac{2(\alpha-\beta p)(-\beta)}{H}\]  
So the determinant is  
\[\frac{4\beta\left[4H-\beta(\Delta-A)^{2}\right](\alpha-\beta c_{m})^{2}}{ \left[4H-\beta(\Delta-A)^{2}\right]^{2}}>0\]  
So we assume \(4H-\beta(\Delta-A)^{2}>0\).  
For the Hessian H is negative definite. Thus, the manufacturer's profit function is jointly concave in \(b\) and \(p\).  
We assume the first order condition equations above are 0, then we get  
\[p^{B*}= \frac{\alpha+\beta c_{m}-2\alpha\beta\frac{\left(\Delta-A\right)^{2 }}{4H}}{2\beta-2\beta(\beta)\frac{\left(\Delta-A\right)^{2}}{4H}}=\frac{\alpha }{\beta}-\frac{2H\left(\alpha-\beta c_{m}\right)}{\beta\left[4H-\beta(\Delta- A)^{2}\right]}\] \[b^{B*}= \frac{\left(\Delta+A\right)}{2}\]  
The optimal collection rate is obtained as  
\[\tau^{B*}=\frac{\left(\Delta-A\right)\left(\alpha-\beta c_{m}\right)}{4H-\beta \left(\Delta-A\right)^{2}}\]  
From the above equilibrium values we derive the collector's profit, manufacturer's profit and the supply chain's profit.  
Proof for Lemma 1.: \[\tau^{B*}= \frac{\left(\Delta-A\right)\left(\alpha-\beta c_{m}\right)}{4H- \beta(\Delta-A)^{2}}>0\Rightarrow 4H-\beta(\Delta-A)^{2}>0\] \[\tau^{B*}= \frac{\left(\Delta-A\right)\left(\alpha-\beta c_{m}\right)}{4H- \beta(\Delta-A)^{2}}<1\Rightarrow H>\frac{\beta(\Delta-A)^{2}+\left(\Delta-A \right)\left(\alpha-\beta c_{m}\right)}{4}>0\]
###### Abstract  
Designing antennas suitable for generating highly directive electromagnetic signals has become a fundamental task. This is particularly relevant for the development of efficient and sustainable point-to-point communication channels, and for energy transfer. Indeed, these are nowadays expanding areas of research. In order to deal with said particular wave phenomena, an extension of the electrodynamics equations is taken into account, where exact solitonic type solutions are admitted. These waves may have compact support and travel along a straight line, without dissipation, at the speed of light. The result suggests the design of biconic type antennas having specific properties that are numerically examined in this paper. The cones, supplied with an oscillating source, are embedded in a dielectric material of suitable shape, with the purpose of driving the signal in the proper direction. The computations based on the extended model are aimed toward simulating the possibility of generating peculiar wave behaviors, in view of practical implementations in the framework of point-to-point communications or wireless power transmission.  
b
## 3 Results and Discussion
### Texture Analysis  
The results of the texture profile analysis are depicted in Figure 6. Due to software issues during the measurement, the TPA data for batch '15% gluten-1% pectin-55% dry matter' was lost and there was not sufficient sample material left to repeat the measurement in statistically relevant repetition numbers. Hence, it was not included in the assessment.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Batch Composition** & \multicolumn{2}{c}{**a\({}_{\textbf{w}}\) Values (-)**} \\ \hline  & **In Raw Batter** & **At 55\% Dry Matter** & **At 65\% Dry Matter** \\ \hline
15\% glut–1\% pec. & 0.971 \(\pm\) 0.001 & 0.933 \(\pm\) 0.003 & 0.922 \(\pm\) 0.002 \\
15\% glut–3\% pec. & 0.968 \(\pm\) 0.001 & 0.931 \(\pm\) 0.001 & 0.920 \(\pm\) 0.001 \\
15\% glut–5\% pec. & 0.967 \(\pm\) 0.001 & 0.927 \(\pm\) 0.001 & 0.916 \(\pm\) 0.002 \\
10\% glut–1\% pec. & 0.979 \(\pm\) 0.000 & 0.937 \(\pm\) 0.003 & 0.926 \(\pm\) 0.000 \\
10\% glut–3\% pec. & 0.977 \(\pm\) 0.001 & 0.935 \(\pm\) 0.001 & 0.926 \(\pm\) 0.002 \\
10\% glut–5\% pec. & 0.966 \(\pm\) 0.001 & 0.932 \(\pm\) 0.000 & 0.922 \(\pm\) 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Water activity values of plant-based raw-fermented salami analogs, all with identical base masses, varying only in binder composition (pectin/gluten), at different time points throughout the drying and ripening process.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Batch** & **Water** & **Protein** & **Ash** & **Fat** & **Carbohydrates** \\
**Composition** & **Content (\%)** & **(\%)** & **(\%)** & **(\%)** & *** (\%)** \\ \hline
15\% glut–1\% pec. & 59.90 \(\pm\) 0.31 & 19.39 \(\pm\) 1.52 & 2.89 \(\pm\) 0.02 & 10.69 \(\pm\) 0.03 & 7.13 \(\pm\) 1.87 \\
15\% glut–3\% pec. & 58.06 \(\pm\) 0.33 & 19.68 \(\pm\) 0.33 & 2.90 \(\pm\) 0.03 & 10.14 \(\pm\) 0.03 & 9.22 \(\pm\) 0.72 \\
15\% glut–5\% pec. & 56.18 \(\pm\) 0.07 & 19.34 \(\pm\) 0.39 & 2.85 \(\pm\) 0.01 & 10.56 \(\pm\) 1.25 & 11.07 \(\pm\) 1.72 \\
10\% glut–1\% pec. & 65.94 \(\pm\) 0.23 & 15.11 \(\pm\) 0.78 & 2.58 \(\pm\) 0.02 & 9.94 \(\pm\) 0.56 & 6.43 \(\pm\) 1.59 \\
10\% glut–3\% pec. & 63.15\(\pm\)0.28 & 14.89 \(\pm\) 0.55 & 2.61 \(\pm\) 0.02 & 10.21 \(\pm\) 0.11 & 9.14 \(\pm\) 0.96 \\
10\% glut–5\% pec. & 60.66 \(\pm\) 0.19 & 14.96 \(\pm\) 0.48 & 2.65 \(\pm\) 0.04 & 10.48 \(\pm\) 0.13 & 11.25 \(\pm\) 0.84 \\ \hline \hline \end{tabular}  
* Calculated.  
\end{table}
Table 3: Chemical analysis (water, protein, ash, fat and carbohydrate contents) of the different plant-based raw-fermented salami analogs, all with identical base masses, varying only in binder composition (pectin/gluten), measured in the raw batter.
### Organization of the Paper  
Section 2 delves into the related work, providing an overview of prior research in the field. Sections 3 and 4 focus on the system model and the secure authentication scheme, offering comprehensive explanations of these components. Section 5 is dedicated to the formal analysis, where rigorous examination and evaluation take place. Section 6 presents the findings derived from the analysis, offering insights and observations. Finally, in Section 7, this paper draws its conclusions, summarizing the key takeaways, and contributions.