esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### XAI Evaluation
#### 3.8.1 Qualitative  
All but one XAI method using QL evaluation performed visual inspections of the XAI output, i.e., saliency maps and segmentation masks, to determine XAI method performance. Visual inspections generally consisted of examining AI-generated saliency maps overlaid on original images to ensure that the highlighted regions in the saliency maps corresponded to medically significant areas, qualitatively validating the model's focus areas and their alignment with medical knowledge. In the study of [PERSON] et al. (2024), the objective was to confirm that the CAMs correctly focused on lesion regions rather than the surrounding tissues to determine reliable diagnosis [50]. Some studies compared visual inspections to previous methods or manual segmentations. For example, [PERSON] et al. (2023) showed that their model's Grad-CAMs consistently and accurately identified thyroid nodules, outperforming previous studies' Grad-CAMs, which had scattered focus areas [37]. [PERSON] et al. (2022) compared LIME-generated explanations with radiologist-annotated regionscontaining malignancy indicators, confirming the model's predictions [42]. The degree of improvement attributed to applying XAI was not explicitly expressed with a (qualitative) performance metric.  
In most studies, clinician involvement within the visual inspections was not mentioned, with the evaluation primarily focused on the technical processes of generating and inspecting the XAI methods' computational means. However, manual segmentations [39] or ROI annotations [38; 42] by experienced radiologists during the dataset preparation phase indicated some clinical input in multiple studies. Formalized evaluations of XAI contributions with standardized questions or metrics were often lacking. [PERSON] et al. (2024) analyzed how the model's attention shifted during different diagnostic stages, comparing this to the step-by-step approach of human experts [53]. Experts provided qualitative feedback on the Grad-CAM heat maps' clarity and relevance, assessing whether these visual explanations improved their understanding of the model's decisions and clinical decision making. Interpretability scores were given, although specific details on the scoring were not mentioned.
## 2 Theoretical Background
### Continuity Conditions
#### 2.5.2 Semi-Rigid Joint Model  
Regarding the semi-rigid joint model (\(m=2\)), the continuity is still valid since displacements are not permitted. This can be expressed as:  
Figure 3: Free-body diagram of a sub-laminate system (**a**) and crack-tip equilibrium of different joint models (**b**).  
\[w^{(21)}=w^{(22)},\ \ u^{(21)}-\frac{h^{(1)}}{2}\phi^{(21)}=u^{(22)}+\frac{h^{(2)} }{2}\phi^{(22)} \tag{11}\]  
However, allowing rotations implies a change in the condition of the relative rotation. The relative displacements (\(\Delta u^{(2)},\ \Delta w^{(2)}\)) and rotation (\(\Delta\phi^{(2)}\)) are given by:  
\[\Delta u^{(2)}=u^{(22)}-u^{(21)}=0,\ \ \Delta w^{(2)}=w^{(22)}-w^{(21)}=0,\ \ \Delta\phi^{(2)}=\phi^{(22)}-\phi^{(21)}\
eq 0 \tag{12}\]
## 5 Applications of THz-FSO Communication  
The innovation in the field of THz-FSO communication has transformed many industries by providing high data rates, low latency, and high security. The following examples include space communication and integration in seventh-generation networking, like 6G.
## 3 Worst-Case Integrity Risk and Sensitivity Determination  
To investigate the effect of the improved \(P_{\text{sat}}\) and _URA_ on the integrity risk, the first group of parameters, i.e., the fault-induced bias \(b_{i}\), should be strictly determined. The concept of worst-case bias (WCB) was used to acquire the worst-case integrity risk. The minimum hazardous bias (MHB) and the minimum detectable bias (MDB) can be calculated according to the requirements of \(P_{\text{fa}}\) and \(P_{\text{md}}\). MHB and MDB can be defined as [22]  
\[MHB_{i}=\frac{l-k_{\text{md}}\sigma_{q}}{\left|A_{q,i}\right|},MDB_{i}=\sigma \sqrt{\frac{\lambda}{\mathbf{S}_{i,i}}} \tag{6}\]  
where \(k_{\text{md}}\) is the quantile of Gaussian distribution that meets the requirement of \(P_{\text{md}}\); \(\lambda\) is the non-central parameter determined by \(P_{\text{fa}}\) and \(P_{\text{md}}\); \(MHB_{i}\) is the minimum bias leading to integrity risk; and \(MDB_{i}\) is the minimum detectable bias of RAIM. When bias \(b_{i}\) is within \([MHB_{i},MDB_{i}]\), the fault-induced bias cannot be detected by RAIM and yields the integrity risk. Under the different fault modes, \(WCB\) is defined as the bias with maximum integrity risk. Based on the worst-case protection concept, the worst-case integrity risk can be computed as the maximum integrity risk under different fault modes,  
\[ \begin{split}& WIR=\sum\limits_{i=1}^{n}\left\{\underset{b_{i} \in[MHB_{i},MDB_{i}]}{\arg\max}\left\{\left[Q\big{(}z_{i}^{+}\big{)}+Q\big{(}z _{i}^{-}\big{)}\right]P(T,n-m,\lambda_{i})\right\}P_{\text{sat},i}\prod \limits_{k=1,k\
eq i}^{n}(1-P_{\text{sat},k})\right\}\\ &+2Q(z_{0})P(T,n-m)\prod\limits_{k=1}^{n}(1-P_{\text{sat},k}) \end{split}  \tag{7}\]  
_WIR_ is a conservative metric to estimate the integrity risk given the three groups of parameters, and acceptable integrity parameters can be obtained by comparing WIR with the required integrity risk. The integrity loss introduced by inaccurate integrity parameters can be strictly characterized. By assuming the probability of different satellite faults is identical, the worst-case integrity risk error with respect to the second group of parameters can be analytically derived as  
\[dWIR=\frac{\partial(WIR)}{\partial URA}dURA+\frac{\partial(WIR)}{\partial P _{\text{sat}}}dP_{\text{sat}} \tag{8}\]where \(d\)_WIR_ represents the error of _WIR_, i.e., the integrity loss, and \(d\)_URA_ and \(dP_{\text{sat}}\) represent the error of _URA_ and \(P_{\text{sat}}\), which are the difference results of the selected parameters minus the true parameters. If the results are greater than zero, the selected parameters are conservative. In contrast, the selected parameters are optimistic if the results are smaller than zero. \(\frac{\partial(WIR)}{\partial URA}\) and \(\frac{\partial(WIR)}{\partial P_{\text{sat}}}\) represent the partial derivatives of _WIR_ with respect to _URA_ and \(P_{\text{sat}}\):  
\[
## 1 Introduction
In the form of offloading, the computing task can immigrate from the central cloud far from humans to the distributed edge (e.g., access points). This directly decreases the latency. However, depending on service distribution and characteristics, deciding where a computing task should be executed remains still a problem. In a FiWi network for tactile internet services, offloading computing workload to edge nodes via MEC is feasible. This offloading can reduce the data transmission path. Therefore, the latency is declined. The offloading destination place, i.e., gateway/ONU, OLT, and the internet, provide different kinds of latency reductions. The decision to offload a tactile internet service to gateway/ONU, OLT, and the internet, becomes the point. To solve this problem, a latency-aware offloading strategy over FiWi infrastructures for tactile internet services is proposed in this paper. The contributions of our work are listed as follows. Figure 1: The FiWi architecture. * The FiWi network with MEC is modeled, including the wireless link, the fiber link, the data rate, and the offloading. Note that the multiple-hop communication, not directed, one-single-hop communication, and three kinds of offloading are taken into consideration. The interference of all the other users for a specific user is modeled. * To obtain total minimum latency, a mixed-integer non-linear programming (MINLP) problem is formulated. This MINLP is further converted to a linear programming (LP) problem using the constraint condition relaxation. The Lagrange multiplier method is used to solve the converted LP problem. * Through numerical simulation, our work is compared with the previous work regarding aspects of the data transmission rate, the signal strength, and the total latency. The rest of this paper is organized as follows. In Section 2, works on low-latency tactile services regarding FiWi infrastructures are analyzed. Section 3 provides a systematic modeling of the wireless link, the fiber link, the data rate, and the offloading, and proposed a minimized total latency MINLP problem. A two-step solution to the proposed problem is represented in Section 4. Section 5 is devoted to presenting the characteristics of our work via numerical simulation. Finally, Section 6 concludes our work and looks ahead to future research.
## 4 Mixed-Flow Load-Balanced Scheduling (MFLBS)
#### Mixed-Flow Load-Balanced Scheduled Network Algorithm  
Below, we outline the Algorithm 3 of single-flow \(f_{k}\) scheduling in the case of MFLBS.  
```
Input:\(\lambda\), \(\mu\), V(\(\alpha\)), Output: load-balanced scheduling
1. flow \(f_{k}\) arrives at mixed-flow load-balanced scheduled network;
2. while (\(f_{k}\) does not end the transmission)
3. if (\(t_{f_{k}}<\lambda\)&& rerouting times \(<\mu\))
4. \(f_{k}\) enters the dynamic routing network;
5. else
6. \(f_{k}\) enters the static routing network;
7. end while
8. flow \(f_{k}\) leaves mixed-flow load-balanced scheduled network;
```  
**Algorithm 3**. **MFLBS Algorithm**  
When the flow \(f_{k}\) enters the network, it first enters the dynamic routing network for scheduling. When some of its properties reach the threshold, such as the number of scheduling times greater than the set value \(\mu\) or the transmission time exceeds \(\lambda\) within this period, it will be transferred to the static routing network for scheduling until all transmissions are completed.  
In addition, every 600 s, we update \(\lambda\), \(V(\alpha)\), \(\mu\), and other information based on the historical information in the network. It is worth noting that we only focus on the transport flow in the network and ignore the control flow, and we believe that the global control signal has no delay.
## 4 Alteration of Gut-Brain Axis and Neurodegeneration  
The gut microbiota plays an important role in the development of brain regions, and it affects human behavior, as well as brain function. Recent evidence points to a causative link between alteration of the gut microbiota and neurodegenerative/neuroinflammatory diseases. Several psychiatric and neurological disorders were found to be associated with changes in the gut microbiota. Studies showed that the gut-brain axis may have a significant impact on anxiety, stress, depression, chronic pain, autism, and cognitive function. The central, autonomic, and enteric nervous systems together with the immune system and the endocrine system constantly ensure the proper functioning of the gut-brain axis.  
Dysregulation of the gut-brain axis through a complex bidirectional communication system is associated with the pathogenesis of several neurodegenerative diseases [50].  
Gut microbiome dysbiosis is responsible for the development of local and systemic inflammation, resulting in disintegration of the gut epithelial membrane, hyperpermeability, invasion of bacteria and viruses within the brain parenchyma, and, ultimately, neuroinflammation and dysfunction of neuronal cells. Several studies using GF animal models were conducted to investigate the interaction between gut microbiota and brain, and many of them showed increased BBB permeability compared to animals with normal gut flora [51; 52; 53].  
Bacteria secrete and consume a plethora of neuromodulators and neurotransmitters, including 5-HT, dopamine, gamma-aminobutyric acid (GABA), epinephrine, and norepinephrine, which are identical to those produced by humans. Accumulating evidence suggests that manipulation of these neurotransmitters by bacteria may have an impact on host physiology, and preliminary human studies showed that microbiota-based interventions can also alter neurotransmitter levels involved in synaptic plasticity, such as BDNF, as well as modify the activity of \(N\)-methyl-d-aspartate (NMDA) and 5-HT receptors [54].  
Although the gut microbiota produces neuroactive compounds, other toxins and metabolites can be released. These molecules act through the ENS by modulating the brain signaling pathways that regulate mood, cognition, social behavior, and memory [55; 56; 57; 58; 59; 60].  
A key factor of the most neurodegenerative diseases is the formation of insoluble protein aggregates within neurons. Gut dysbiosis can result in the accumulation of toxic misfolded proteins with \(\beta\)-sheet conformation, leading to cellular cell dysfunction, loss of synaptic connections, and neurodegenerative disorders [61]. A representative scheme is illustrated in Figure 2.
# The Use of Educational Games in Programming Assignments: SQL Island as a Case Study
## 5 Discussion
Apart from the aforementioned features that are considered necessary for a game utilized in the context of a programming assignment, the following features will provide a better player experience and potentially better learning outcomes:  
* The game should allow users to register in order to _save their progress_ in the game. This is considered important for students that face difficulties with the underlying programming concepts and need more sessions for finishing the game or for games with many programming tasks. * Saving students' progress in the game along with other data, such as time of playing, number of sessions, and erroneous attempts, would provide invaluable capabilities for instructors. These include _automatic assessment_ of each individual student and presentation of _class statistics_ utilizing the power of learning analytics. * _Background music_ can enhance the player experience. * _Sound effects_ that denote the correctness of the players' attempts in the tasks can help them keep informed about the outcomes of their efforts in the tasks of the game. * _Providing brief and concise theory_ at the side of the game can be helpful. * Presenting details about the statement being executed at the side of the screen, an ability utilized successfully in programming microworlds [35] and known as _explanatory visualization_[36], can support students in comprehending the semantics of the underlying statements.
# Unveiling Monoterpene Biosynthesis in _Taiwania cryptorneioides_ via Functional Characterization
## 2 Results and Discussion
### Characterized Monoterpene Biosynthetic Pathway in Taiwania cryptomerioides  
Pinenes are major volatile terpenoids in _T. cryptomerioides_, and in addition to \(\alpha\)-pinene and \(\alpha\)-pinene/terpinolene synthase, we functionally characterized a \(\beta\)-pinene synthase (_Tc_TPS16) in this study, which allowed us to explore pinene biosynthesis in _T. cryptomerioides_ (Figure 6) [7; 9; 11]. In _Tc_PinS and _Tc_TeoS mutagenesis studies, the slight exchange of residue would result in the ratio of monocyclic and bicyclic products [21]. Compared with _Tc_PinS and _Tc_TeoS, _Tc_TPS16 was closer to pinene synthase from _C. formosensis_, indicating that the phylogenetic relationship of Cupressaceae monoterpene synthases was dominated by species instead of enzymatic products.  
Meanwhile, _Tc_TPS13, 14, and 15 were identified as geraniol and linalool synthases, although monoterpene alcohol was only present in trace amounts in extracts, volatiles, and essential oils of _T. cryptomeoides_ needles and woods. In fact, monoterpene alcohols have been commonly observed in various plants, and their derivatives often exhibit excellent bioactivity that may be beneficial for plants to overcome biotic and abiotic stress [29; 30; 31]. The ecological and physiological functions of geraniol, linalool, and their unknown derivatives in _T. cryptomerioides_ need to be further studied.  
These results showed that transcriptome mining allowed us to discover novel transcripts for exploring specialized metabolism, and could be useful for further metabolite engineering and breeding.  
Figure 5: The protein structure of _Tc_TPS13 (model template ID: 2 onh.1. A) and catalytic residue alignment. Helixes A to K and their related locations are illustrated in the left panel. The full alignment is illustrated in Figure S1.
###### Abstract  
Thulium fiber laser with a wavelength of 1.94 \(\upmu\)m is widely used in urology for lithotripsy. This paper studies the mechanism of lithotripsy and evaluates its clinical efficiency using the superpulse thulium fiber laser with a wavelength of 1.94 \(\upmu\)m and a peak power of 500 W. An experimental setup was developed to study the mechanism of lithotripsy. The superpulse thulium fiber laser (TFL) with a wavelength of 1.94 \(\upmu\)m with a peak power of 500 W (FiberLase U2 from \"IRE-POLUS\" Ltd., Fryazino, Moscow Region, Russia) was used for the lithotripsy of stone phantoms (BegoStone). The processes were recorded with a high-speed camera. The acoustic signals registered during lithotripsy were studied with wideband and needle hydrophones. The main mechanism of lithotripsy performed by using superpulse TFL was thermal cavitation in the water-filled pore space and thermal destruction of the phantom. During the clinical application of the superpulse thulium fiber laser, the high efficiency of laser lithotripsy was established. The performed optical and acoustic studies showed that the mechanism of the destruction of stones was based on the synergic effect of the explosive boiling of water in the pore space of the stone, and its thermal destruction is associated with the heating of the stone to several hundred degrees with laser radiation.  
superpulse thulium fiber laser; laser lithotripsy mechanism; thermal cavitation; thermal destruction 2020
# Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques
## 4 Results and Discussion
### Validation of Results  
The results obtained through the association rule mining validate the risky locations identified through the cluster analysis. For cluster analysis, in the example discussed in Section 4.1.1 of record 1, it has been identified that _Crime Type_ 'Fraud' is expected to happen on the 'Multan Chungi' location based on the values of the attributes  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\#} & **Crime Event** & \multirow{2}{*}{**Crime Time**} & \multirow{2}{*}{**Day Type**} & **Distance of Crime** & **The direction of Crime Event from Police Station** & **Crime Type** & **Risk of Possible Criminal Activities** \\ \cline{4-9}  & & & & **Station** & & & & **Activities** \\ \hline
**1** & Multan Chungi & After-Noon & Weekdays & 3 km & SOUTH & Robbery &
\begin{tabular}{c} Gambling, Fraud, \\ Traffic Violation \\ Robbery, Possession \\ \end{tabular} \\
**2** & Wahdat Road & Morning & Weekdays & 2 km & NORTH & Kidnapping &
\begin{tabular}{c} of Weapon \\ Drug Traffic King, \\ Kidnapping, Theft \\ \end{tabular} \\
**3** & \begin{tabular}{c} BOR Main \\ Road \\ G1 Market \\ \end{tabular} & \
\begin{tabular}{c} Morning \\ Night \\ Weekdays \\ \end{tabular} & 1 km & WEST & Theft &
\begin{tabular}{c} Possession of \\ Weapon, Kidnapping \\ Fraud, Price \\ Voidation \\ \end{tabular} \\
**6** & \begin{tabular}{c} Mangarh Bazar \\ Chowk \\ \end{tabular} & \
\begin{tabular}{c} Morning \\ Night \\ Weekend & 1 km & WEST & Gambling &
\begin{tabular}{c} Assaults, Drug \\ Trafficking, Theft \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sample list of identified criminal activities that can also happen in particular locations from cluster 0.  
_Crime Time_ = 'After-Noon', _Crime Day_ = 'Weekdays', _Distance of Crime Event from Police Station_ = '3 km', and _Direction of Crime Event from Police Station_ = 'SOUTH'.  
Similarly, the Apriori algorithm result discussed in Section 4.2, according to rule #6, shows that if _Crime Time_ is 'After-Noon', _Day Type_ is 'Weekdays', _Direction of Crime Event from Police Station_ is 'SOUTH, WEST' and _Distance of Crime Event from Police Station_ is '3 km', then there is a high probability of occurring of 'Fraud' (_Crime Type_). Hence, the same result of the occurrence of _Crime Type_, 'Fraud' has been identified through the cluster analysis as well as association rule mining. Cluster analysis is performed to identify the risk of occurrence of possible criminal activities in a particular location while association rule mining gives frequent patterns of criminal activities with the strength of the association between two events. The results yielded from both analyses support each other which implies that the identified similar criminal activities and crime patterns are expected to happen.
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 2 Literature Review and Related Work
### Existing Transportation Services with SDN  
According to [8], traffic management involves lightweight data, which may be around 40 bytes. Lightweight admission control and traffic management with SDN allow the service providers to improve the traffic of lightweight data, such as monitoring at the data plane. The measurement and classification of smart systems during the data traffic over 5G mobile networks should be considered carefully with the efficient algorithms [9]. Existing transportation services use a legacy system with the basic concept of SDN. When SDN technology grew with a new programmable control layer, transportation service providers enhanced the services through efficient network configurations.  
According to [10], traffic engineering (TE) solutions with SDN provide efficient traffic management according to the situations. Instead of using a fixed weight setting of routing algorithms, a dynamic weight setting allows the service providers to improve the traffic performance and network management. Traffic measurements considered in the network applications are a prerequisite for traffic management. Despite many traffic models, traffic measurements are dynamically monitored using the SDN concept, which enhances the monitoring facilities and manages and controls the traffic systematically. Anteater's status detection tool [10] provides traffic measurement facilities through the traffic prediction of SDN static analysis.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
###### Abstract  
One of the challenges in the field of remote sensing is how to automatically identify and classify high-resolution remote sensing images. A number of approaches have been proposed. Among them, the methods based on low-level visual features and middle-level visual features have limitations. Therefore, this paper adopts the method of deep learning to classify scenes of high-resolution remote sensing images to learn semantic information. Most of the existing methods of convolutional neural networks are based on the existing model using transfer learning, while there are relatively few articles about designing of new convolutional neural networks based on the existing high-resolution remote sensing image datasets. In this context, this paper proposes a multi-view scaling strategy, a new convolutional neural network based on residual blocks and fusing strategy of pooling layer maps, and uses optimization methods to make the convolutional neural network named RFPNet more robust. Experiments on two benchmark remote sensing image datasets have been conducted. On the UC Merced dataset, the test accuracy, precision, recall, and F1-score all exceed 93%. On the SIRI-WHU dataset, the test accuracy, precision, recall, and F1-score all exceed 91%. Compared with the existing methods, such as the most traditional methods and some deep learning methods for scene classification of high-resolution remote sensing images, the proposed method has higher accuracy and robustness.  
convolutional neural network; ResNet; semantic information; remote sensing images; scene classification; TensorFlow +
Footnote †: journal: Applied Sciences