esa-sceva/satcom-chunk-collection huggingface dataset:
# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 6 Experimental Results
### Expanded Statistical Evaluation  
In addition to the standard accuracy metrics reported earlier (with the DIKWP-based system achieving high anomaly detection accuracy of -95%), we evaluated several additional performance metrics to more comprehensively assess the system's responsiveness, efficiency, and user experience. These additional metrics include the following:* Detection Latency per Event: The time delay between the occurrence of a health-related event (e.g., an arrhythmia onset) and its detection/alert by the system. This measures the system's real-time responsiveness.
* Average Energy Consumption per Inference: The energy required to run the AI inference (e.g., classification or anomaly detection) for each sensor input. This gauges the efficiency and battery impact of continuous monitoring on IoT devices.
* User Satisfaction Rating for Alert Relevance: A qualitative metric obtained via user feedback (on a 5-point scale) indicating how relevant and helpful the alerts were, which reflects the perceived usefulness of the system's notifications.  
Table 4 summarizes the results for these additional metrics. The detection latency was low--on average about 2.1 s from event onset to system alert. This short delay indicates the framework operates in near real-time, which is crucial for timely interventions in health emergencies. The energy consumption per inference was measured at roughly 45 mJ (millijoules) on a typical smartphone, which is very economical. Even on a resource-constrained wearable, this level of energy use would translate to only a small fraction of a 300-500 mAh smartwatch battery per hour, suggesting the system can run continuously without draining devices prematurely. Finally, the user satisfaction with alert relevance was high: on average 4.3 out of 5. This score was obtained from a pilot study with test users who rated each alert; it indicates that most alerts were considered helpful and appropriate. The high satisfaction correlates with a low false-alarm rate in the system. Notably, minimizing false positives is vital--in clinical contexts up to 80-99% of monitor alarms can be false or non-actionable, leading to alarm fatigue. Our system's purposeful DIKWP-driven reasoning (filtering out noise and insignificant events) likely contributed to fewer spurious alerts, thereby improving user trust and acceptance.  
The DIKWP-based system exhibits low-latency detection, energy-efficient operation, and high user-perceived usefulness, which are all critical for a dependable real-time health monitoring solution.
# Vulnerability Analysis of LTE-R Train-to-Ground Communication Time Synchronization
## Abbreviations  
The following abbreviations are used in this manuscript:  
ARP  
Address Resolution Protocol  
BSC  
Base station controller  
BTS  
Base transceiver station  
CTMC  
Continuous-time Markov chain  
DoS  
Denial of service  
eNodeB  
evolved Node B  
EPC  
Evolved Packet Core  
E-UTRAN  
Evolved Universal Telecommunication Radio Access Network  
GPRS  
General Packet Radio Service  
HSS  
Home Subscriber Server  
IMS  
Information Management System  
LTE-R  
Long-term evolution for railway  
MC
Markov chain  
MME  
Mobility Management Entity  
MIMO  
Multiple-input multiple-output  
MITM  
Man-in-the-middle  
NTP  
Network time protocol  
OBC  
On-board controller  
OFDM
Orthogonal frequency division multiplexing  
P-GW  
PDN GateWay  
PTP  
Precision time protocol  
S-GW  
Serving GateWay  
SPN  
Stochastic Petri net  
VS  
Vehicle station
# Extracting Sentence Embeddings from Pretrained Transformer Models
## 5 Conclusions
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **T0** & **T4** & **BERT** & **BERT + Avg.** & **Avg.** & **B2S** & **B2S-100** & **RE** \\ \cline{2-9}
**Layer** & \multicolumn{3}{c}{**Last**} & \multicolumn{3}{c}{**First + Last**} & \multicolumn{3}{c}{**-**} \\ \hline avg. & 60.4 & 61.0 & 59.0 & 59.9 & 54.2 & 56.1 & 60.7 & 46.5 \\ + normalize & 60.4 & 61.0 & 59.0 & 59.9 & 54.2 & 56.1 & 60.7 & 46.5 \\ + quantile-u & 67.7 & 70.9 & 63.6 & 65.2 & 61.7 & 56.9 & 61.1 & 52.4 \\ + quantile-u\({}^{W}\) & 62.8 & 65.0 & 61.3 & 63.5 & 59.7 & 54.7 & 60.4 & 50.5 \\ + whiten & 68.2 & 70.0 & 68.7 & 70.0 & 69.6 & 66.7 & 64.6 & 68.1 \\ + whiten\({}^{W}\) & 62.1 & 65.8 & 62.6 & 67.3 & 69.4 & 65.1 & 64.1 & 67.5 \\ + zscore & 68.7 & 71.1 & 65.0 & 66.7 & 63.0 & 58.8 & 62.5 & 54.6 \\ \hline idf\({}^{W}_{t}\) & 69.0 & 68.2 & 70.3 & 69.6 & 66.4 & 68.0 & 66.9 & 69.8 \\ + normalize & 69.0 & 68.2 & 70.3 & 69.6 & 66.4 & 68.0 & 66.9 & 69.8 \\ + quantile-u & 69.4 & 68.6 & 69.3 & 69.6 & 67.8 & 66.7 & 64.7 & 64.4 \\ + quantile-u\({}^{W}\) & 69.4 & 68.8 & 70.2 & 70.3 & 68.1 & 67.0 & 65.3 & 65.7 \\ + whiten & 65.6 & 65.0 & 67.9 & 68.0 & 67.2 & 65.8 & 64.1 & 66.5 \\ + whiten\({}^{W}\) & 67.0 & 66.6 & **71.2** & **72.1** & **71.5** & **69.8** & **68.2** & **70.4** \\ + zscore & 70.7 & 69.9 & 71.0 & 71.7 & 70.1 & 69.0 & 67.8 & 70.0 \\ \hline idf\({}^{I}_{t}\) & 68.8 & 70.7 & 69.6 & 69.3 & 67.3 & 56.1 & 60.7 & 67.0 \\ + normalize & 68.8 & 70.7 & 69.6 & 69.3 & 67.3 & 56.1 & 60.7 & 67.0 \\ + quantile-u & **72.4** & **75.1** & 69.2 & 69.3 & 67.7 & 56.9 & 61.1 & 64.2 \\ + quantile-u\({}^{W}\) & 70.4 & 73.2 & 69.7 & 69.6 & 67.7 & 54.7 & 60.4 & 65.0 \\ + whiten & 69.6 & 72.2 & 68.4 & 68.4 & 67.7 & 66.7 & 64.6 & 67.0 \\ + whiten\({}^{W}\) & 67.8 & 71.0 & 69.6 & 69.9 & 68.7 & 65.1 & 64.1 & 67.1 \\ + zscore & 72.4 & 74.7 & 70.1 & 70.3 & 68.6 & 58.8 & 62.5 & 67.4 \\ \hline -biases & 64.7 & 65.4 & 70.2 & 71.0 & 67.8 & 52.9 & 57.3 & 66.6 \\ + normalize & 64.7 & 65.4 & 70.2 & 71.0 & 67.8 & 52.9 & 57.3 & 66.6 \\ + quantile-u & 70.1 & 73.4 & 69.9 & 71.0 & 68.9 & 54.2 & 58.1 & 62.8 \\ + quantile-u\({}^{W}\) & 66.1 & 69.2 & 70.3 & 71.5 & 69.0 & 51.7 & 57.1 & 63.9 \\ + whiten\({}^{W}\) & 66.1 & 68.5 & 69.6 & 71.3 & 70.9 & 61.8 & 60.8 & 68.8 \\ + whiten & 68.1 & 69.9 & 69.5 & 69.9 & 69.3 & 64.6 & 62.4 & 66.5 \\ + zscore & 71.1 & 73.2 & 70.2 & 71.1 & 68.9 & 56.1 & 59.4 & 67.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Spearman correlation dependence on token aggregation and post-processing techniques for semantic textual similarity STS-B task. The best result for each model is bolded, while underlined result is the best across all the models.
## 2 Materials and Methods
#### 2.1.3 Data Acquisition (DAQ)  
The data acquisition was carried out with a compactRIO DAQ system (cRIO-9040 with three expansion cards NI 9210, NI 9207 and NI 9216) from National Instruments and with the pump software (Qmix Elements) supplied by Cetoni. The flow rate (dosed volume) was recorded using the Cetoni software. Since the two pressure sensors were connected directly to the pump with the help of the Cetoni I/O port splitter, the values of the two pressure sensors were also logged directly in the programme of the Cetoni pump. The measurement data was later exported as a single Comma Separated Values (CSV) file (including absolute time stamps, dosed volume and both pressure values) for further evaluation. The temperature of the test liquid was indirectly measured with the help of type K thermocouples (TC) with 2-pin mini-TC connectors attached at four different spots in the measuring setup: (1) at the outlet of the syringe; (2) at the upstream pressure sensor; (3) at the downstream pressure sensor and (4) at the needle. All thermocouples were connected directly to the DAQ system via the NI 9210 expansion card. The measuring conditions in the laboratory (air pressure, room temperature and air humidity) were measured separately by means of a Vaisala PTU300 and logged on the RISE internal server (EXOscada). The Vaisala device has an internal data logger that can be read out via different interfaces. In addition, there is the possibility to connect the Vaisala device directly to the DAQ system. The Vaisala device has current and voltage signal outputs for air temperature and air humidity. However, for the measurements shown in the following, the unit was not directly connected to the DAQ system.
## 5 Lemmas and Proofs
Moreover, vertex \(\gamma(v)\) was added to set \(D_{2}\) instead of \(\beta(v)\), which means the following:  
1. \(\gamma(v)\) dominates a larger (in terms of inclusion) set of vertices not dominated by \(D_{1}\) than \(\beta(v)\), or
2. \(\gamma(v)\) dominates the same set of vertices not dominated by \(D_{1}\) as \(\beta(v)\), but \(\gamma(v)\) has a smaller identifier than \(\beta(v)\). Since \(v\) added vertex \(\gamma(v)\) to set \(D_{2}\) instead of \(\beta(v)\), we find that \(\beta(v)\) must have been added to set \(D_{2}\) by another vertex, \(u\
eq v\), in the second phase of the algorithm. However, note that since vertex \(v\) chose \(\gamma(v)\) instead of \(\beta(v)\) in phase 2 of the algorithm, it follows from satisfying conditions (1) or (2) that vertex \(u\) would also choose \(\gamma(v)\) instead of \(\beta(v)\). We obtain a contradiction. Therefore, \(\beta(v)\
otin D_{1}\cup D_{2}\). **Lemma 12**. : _Let \(m\in M\) be such that \(\deg(m)\geq 4\). Then, \(c_{IN}(m)\leq 2\)._  
Proof. : Let us assume, by contradiction, that \(c_{IN}(m)\geq 3\) and the assumptions of the lemma are satisfied. Then, by the definition of the counting function \(c_{IN}(m)\), there exist at least two vertices \(u_{1},u_{2}\in\Gamma_{1}^{IN}\cup\Gamma_{2}^{IN}\) chosen by neighbors of vertex \(m\); let us denote them as \(v_{1},v_{2}\). Since \(\deg(m)\geq 4\), we have \(\deg(u_{1}),\deg(u_{2})\geq 4\). First, let us assume that \(v_{1}\
eq u_{1}\) and \(v_{1}\) belongs to some bunch \(B\) with endpoints \(m\) and \(m^{\prime}\). Then, \(v_{1}\) is adjacent to \(m\). Therefore, from Lemma 9, we know that there exists a vertex \(z\in B_{IN}\) such that \(\deg(z)=2\) and \(z\) is adjacent to \(m^{\prime}\). Since vertex \(z\) in phase 1 of the algorithm adds either \(u_{1}\) or \(m^{\prime}\) to set \(D_{1}\), vertex \(v_{1}\) is dominated and, thus, cannot choose vertex \(u_{1}\) as \(\beta(v_{1})\). We obtain a contradiction. Therefore, we have \(v_{1}=u_{1}\). Similarly, there must exist a vertex \(z\) of degree 2, but in this time it is adjacent to \(m^{\prime}\). Then, vertex \(z\) in the first phase of the algorithm chooses vertex \(u_{1}\) or \(m^{\prime}\) as \(\beta(z)\). However, since \(u_{1}\) chose itself in phase 2 of the algorithm, we find that \(\beta(z)=m^{\prime}\). Note that all neighbors of vertex \(m^{\prime}\) are dominated. Thus, according to Lemma 10, we find that either \(m\in D\) or \(u_{1}\in D\). Hence, we find that \(c_{IN}(m)\leq 2\). **Lemma 13**. : _For a triangle-free graph, the number of external vertices selected by the algorithm is bounded by_  
\[|\Gamma_{1}^{OUT}\cup\Gamma_{2}^{OUT}|\leq 4|M|\]  
Proof. : From Lemma 8, we know that for triangle-free graphs, \(|T|\leq 4|M|\). Since the external vertices selected by the algorithm are a subset of all the external vertices, we have  
\[|\Gamma_{1}^{OUT}\cup\Gamma_{2}^{OUT}|\leq|T|\leq 4|M|\]
# TARCS: A Topology Change Aware-Based Routing Protocol Choosing Scheme of FANETs
## 4 Evaluations and Results  
Simulation experiments are designed to evaluate the scheme. It is assumed that all nodes can receive signals from neighboring nodes within one hop in each direction, the node energy is sufficient, and the transmission distance is constant. The speed of the nodes, the distance between nodes, and the direction of motion of the nodes are known. Nodes move only in two dimensions.  
In order to more clearly reflect the highly dynamic network topology, the Chain scenario [22] is selected to simulate the highly dynamic scene, which combines RWP, RPGM and Pursue model to simulate nodes to complete a series of tasks such as search/exploration, reconnaissance/patrol and target tracking/rescue. The duration is 600 s. From 0 s to 200 s nodes move with the RWP model, then they move with the RPGM (g = 5) model from 200 s to 400 s, and finally nodes move with the Pursue model.  
The simulation tool is NS-3.25 [23] (Network Simulator 3, Version 3.25). The region is a square of 2000 m \(\times\) 2000 m. The number of nodes is 50 with 50 m/s as the lower speed and the 500 m/s as the higher speed. The Free space propagation model [23; 24] is selected as the propagation mode and the constant speed propagation delay model is selected as the delay model. The transmission mode is constant bit rate (CBR) with a bit rate of 16 Kbps and a packet size of 1024 bytes. The MAC (media access control) protocol is IEEE 802.11g. The alternative routing protocols involved are AODV, OLSR, and DSDV. A detailed description of each mobility model and routing protocol can be found in the references. Table 2 lists the experimental parameter items and parameter values.  
In the first experiment, the network performance is compared with or without the TARCS scheme. The second experiment is to compare the impact on the network performance with different TARCS strategies.
## 3 Results and Discussion
### Adsorption Kinetics  
Figure 8 displays adsorption kinetic curves of anilines on the CDM-HCP versus the contact time at 20 \({}^{\circ}\)C. As shown in Figure 8, \(q_{e}\) increased significantly within 200 min and then increased slowly in the subsequent step until reaching equilibrium after 600 min. A further increase of contact time was insignificant for the equilibrium \(q_{e}\) of adsorbate. This may be due to the fact that the active adsorption sites were sufficient at the beginning of adsorption, and decreased with the increment of adsorption time. Therefore, the adsorption time of 600 min was sufficient to attain equilibrium for the CDM-HCP in the aniline adsorption, which was selected for the further experiments.  
The fit of the experimental data to the pseudo-first-order, pseudo-second-order, and intraparticle diffusion models for the CDM-HCP was performed in Figure S4, and the calculated kinetic parameters are summarized in Table 1.  
Figure 8: Adsorption kinetics of anilines on the CDM-HCP.  
Figure 7: Effect of pH on the adsorption of anilines by the CDM-HCP.  
The results indicated that the correlation coefficient of the pseudo-second-order kinetic model (\(R^{2}>0.99\)) was higher than that of the pseudo-first-order kinetic model (\(R^{2}<0.99\)), implying that the adsorption process was more consistent with the second-order kinetic model. The results showed that the adsorption process may involve the mechanism of chemisorption. Furthermore, the theoretical calculated values (\(q_{\rm e,cal}\)) obtained from the pseudo-second-order model were in good agreement with the experimental values (\(q_{\rm e,exp}\)), indicating the validity of the model to the adsorption system. According to the intraparticle diffusion model, \(q_{t}\) to \(t^{1/2}\) is not a straight line through the origin, showing that internal diffusion is not the nail-limiting mechanism for the aniline adsorption. Adsorption of anilines onto the CDM-HCP is divided into three steps as follows: diffusion of anilines to the external surface of the CDM-HCP; diffusion of anilines into the pores of the CDM-HCP; and adsorption of anilines onto the active sites of the CDM-HCP. Similar results have been reported for the adsorption of organic pollutants [38].
## 3 Results and Discussion
### Comparison with Other Methods  
In the following section, Table 12 compares the results of our proposed methods with other methods that used the same datasets. We specifically compared RFFMDS v1.0 to contrast the partial face features of the eyes and eyebrows and the masked face images. The best accuracy result from RFFMDS v1.0 [32] was 87.50%, while using the eyebrow images from the same dataset with the proposed method yielded a higher accuracy of 95%. This result demonstrated that the eyebrows could be discriminative enough and employed when other facial parts were occluded.  
Using face images from the ABD, [PERSON] [67] produced the best accuracy result of 74.83% for an unbalanced dataset using 10-fold cross-validation. Unfortunately, our eye and eyebrow images in this dataset failed to match this result. This poor result was due to more uncontrolled conditions in the ABD that affected the performance of our model. Using face images from the EYB dataset with limited data, our proposed method and idea of using only eyebrow images produced higher results than [PERSON]'s study [68]. Using face images from the whole EYB dataset, [PERSON] [69], [PERSON] [70], [PERSON] [71], and [PERSON] [72] displayed different accuracy results. The highest accuracy was 97.70% from [PERSON] [72], and we successfully achieved a similar accuracy result of 96.88%, but only when using the eye images.
## 4 Discussion
### Strengths  
Biomechanical Insights: All the reviewed studies offer valuable biomechanical insights into the effects of different lacing techniques. They contribute to our understanding of how lacing patterns impact foot mechanics, pressure distribution, and tendon loading.  
Diverse Study Populations: The studies encompass diverse study populations, including healthy participants and individuals with specific conditions like diabetes. This diversity enhances the generalizability of the findings.  
Objective Measurements: The studies employ objective measurements, such as pressure insoles and cadaveric models, to quantify the effects of lacing patterns. This objective approach ensures the reliability of the results.  
Clinical Relevance: The findings have clinical relevance, particularly in advising individuals with foot health concerns. They offer practical recommendations for optimizing lacing techniques to reduce the risk of injuries and discomfort.  
Although this review provides important insights into the biomechanical effects of lacing techniques, future studies should incorporate biomechanical models and quantitative data, such as finite element analysis and pressure mapping, to validate the effects on plantar pressure and tendon loading. Integrating these approaches will help in developing more robust, data-driven guidelines for lacing techniques in injury prevention [64; 65].  
While this review highlights the biomechanical benefits of lacing techniques, it is essential to compare these effects with other common interventions, such as the use of orthotics and specialized footwear. Orthotics has been shown to redistribute plantar pressure and stabilize the foot but often lacks the flexibility that lacing customization can provide. Future studies should explore how these methods can be used complementarily to enhance foot health outcomes [16; 66].
## 5 Numerical Examples  
In this section, we will analyze the parameter estimator bias by simulation and measurement experiments. For each test, the bias is estimated by Gauss-Newton method. Being close enough to the least-squares solution is one of the sufficient conditions for convergence to occur. The well-conditioned initial values for the iteration are set to ensure the correct convergence. We should note that if the measurement error is small, it can happen that the final estimator is closer to the true position. For each fixed configuration with one specific standard deviation, the parameter estimator bias is obtained by the Equation (10). To check if the bias is significant, the hypothesis test is performed to compare the Mahalanobis distance \(D_{M}\) and the threshold \(\sqrt{\gamma}\) which is 1.96.
# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## 2 Trends in Malware Detection
### Malware Detection with Primitive Methods (Statistical Analysis Based Methods)
[25] concentrated on the solution for detecting malicious activity which should be low cost and should not be using any third-party software so that in less time and low budget detection can be done. Secondly, since some malware behavior can overcome the virtual environment, therefore, running malware in a virtual machine for dynamic analysis can compromise some of the triggering scenarios. The authors manipulated windows audit logs into interpretable features and presented a linear classification model for detecting malicious behavior using the windows audit log as a feature set with high accuracy. This approach explored some new malware behaviors. For performing validation, six different experiment sets were designed. One of the experiments for validation involved a dataset that had malware a year or two older than the malware presented in training. Second experiment for validation was performed based on malware families. Secondly, the same trained classifier was run in a virtual environment as well as in an enterprise environment to cater to the variable of the environment. The experimental dataset consisted of 32,078 samples out of which 17,399 were benign samples and 14,679 malicious samples. 6,898,593 unique features were extracted, and 20,362 audit logs were collected from binaries executed in a cuckoo sandbox. Figure 3 shows the performance metrics used by the surveyed papers that fall in the category of statistical based methods.
## 4 Experimental Results and Discussion
### Performance Analysis of Clustering and Routing  
There are multiple sensor nodes in a WSN that can be alive or dead. The proposed clustering technique identifies distances between nodes by regularly monitoring the nodes and forming clusters based on distances. The K-medoids algorithm is used to group nearby nodes into clusters, and the selection of the cluster head is based on the SN's energy usage. As CH, the energy-efficient SN is chosen, which can be changed anytime. Figure 2 depicts the transmission of data from a sensor node to the cluster head. According to the diagram, six cluster heads manage all the SNs in a cluster.  
Compared to existing methods, the K-medoids algorithm and E-CERP suffer from high packet loss, delivery delays, and a lack of stable power when using existing processes to transmit data. The proposed K-medoids clustering approach is compared with the K-means [18] and Fuzzy C-means approaches, which include particle swarm optimization and krill herd optimization algorithms [19; 20; 21; 22]. The introduced ASFO method is compared with two existing optimization approaches: the krill swarm optimization algorithm [23] and the whale optimization algorithm (WOA) [24]. The proposed results were compared with cross-layer-based adaptive thresholding (CLAT) and cross-layer fuzzy logic (CLFL).