esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This work proposes a novel signal called Pulse-OTFS. We have derived analytical ACF and PSD expressions for OTFS modulation, which are also applicable to the proposed signal. The results of the navigation performance evaluation show that the acquisition complexity decreases with the increase in the Doppler number \(N\). The acquisition complexity of Pulse-OTFS is at least 89.4% lower than that of the continuous GNSS signals where \(N\) is not less than 10, which effectively reduces the acquisition time of LEO signals, and at the same time, the acquisition probability of the proposed signal is about 1 dB higher than that of the latter with a total false alarm probability of \(10^{-3}\). In terms of measurement precision, Pulse-OTFS has a steep ACF main peak and a large Gabor bandwidth; its delay measurement accuracy is about 8 dB higher than that of BPSK and Pulse-BPSK signals. The analysis of the S-curve demonstrates that the proposed signal is capable of unambiguous tracking. Moreover, Pulse-OTFS has a smaller SSC value and lower multipath error envelope than that of BPSK and Pulse-BPSK signals, which suggests that it has good compatibility with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Doppler Frequency** & **Pulse-OTFS(10,1023)** & **Pulse-** & **OTS(10,1023)** & **BPSK(5)** \\
**Offset (kHz)** & & **BPSK(5)** & & \\ \hline
0 Hz & 0.015 & 0.017 & 0.142 & 0.144 \\
40 kHz & 0.016 & 0.016 & 0.144 & 0.147 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average running time of acquisition programs for different signals (s).  
Figure 16: Correlator peak magnitude for different signals.  
other navigation signals in the same frequency band and better theoretical anti-multipath performance. The experimental results validate the advantages of fast acquisition and high-precision measurements of the proposed signal. Pulse-OTFS is easy to integrate with communication signals and can be used as a navigation signal from broadband LEO satellites as an effective complement to the GNSS.  
Conceptualization, D.F., H.L. and G.O.; methodology, D.F. and M.Y.; software, D.F. and M.Y.; validation, M.M. and G.O.; formal analysis, D.F. and H.L.; investigation, D.F.; resources, H.L.; data curation, D.F.; writing--original draft preparation, D.F.; writing--review and editing, H.L. and M.M.; visualization, D.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2023 YFC2205400, and the National Natural Science Foundation of China, grant number U20A0193.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
###### Abstract  
The widespread popularity of smartphones makes it possible to provide Location-Based Services (LBS) in a variety of complex scenarios. The location and contextual status, especially the Indoor/Outdoor switching, provides a direct indicator for seamless indoor and outdoor positioning and navigation. It is challenging to quickly detect indoor and outdoor transitions with high confidence due to a variety of signal variations in complex scenarios and the similarity of indoor and outdoor signal sources in the IO transition regions. In this paper, we consider the challenge of switching quickly in IO transition regions with high detection accuracy in complex scenarios. Towards this end, we analyze and extract spatial geometry distribution, time sequence and statistical features under different sliding windows from GNSS measurements in Android smartphones and present a novel IO detection method employing an ensemble model based on stacking and filtering the detection result by Hidden Markov Model. We evaluated our algorithm on four datasets. The results showed that our proposed algorithm was capable of identifying IO state with 99.11% accuracy in indoor and outdoor environment where we have collected data and 97.02% accuracy in new indoor and outdoor scenarios. Furthermore, in the scenario of indoor and outdoor transition where we have collected data, the recognition accuracy reaches 94.53% and the probability of switching delay within 3 s exceeds 80%. In the new scenario, the recognition accuracy reaches 92.80% and the probability of switching delay within 4 s exceeds 80%.  
machine learning; quickly switching; GNSS measurements; indoor/outdoor detection; seamless indoor and outdoor navigation and positioning; smartphone +
Footnote †: journal: Sensors
# Emerging Technologies for Degradation of Dichlorvos: A Review
## 3 Physicochemical Transformation of DDVP
[52] reported an efficient way for catalytic degradation of DDVP using protein-capped zero-valent iron nanoparticles, which removed the pesticide in 1 h. Moreover, the degradation mechanisms of DDVP during oxygen plasma treatment have been successfully detailed [53], so several active materials (high-energy electrons and free radicals) in oxygen plasma can thoroughly degrade DDVP within a short exposure time. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Study Sample/** & **Physicochemical** & **Medium** & **Specific Statement** & **Reference** \\  & **Sample Sources** & **Method Used** & & & pH 3 conditions increased dichloros & \\
1 & Sunlight/UV & Photocatalysis & Water & photodegradation up to 32\%, with degradation & [44] \\  & & & & rate constant of 0.064 h\({}^{-1}\) & \\ \hline \multirow{2}{*}{2} & Hydrodynamic & Advanced & & & \\  & cavitation & oxidation & Water & 91.5\% dichloros was degraded in 1 h & [45] \\  & reactor/Fenton & processes (AOPs) & & & \\ \hline
3 & Activated carbon & Adsorption & Water & Average removal rate of dichloros was 95.1\% & [46] \\ \hline
4 & O\({}_{2}\) plasma & AOPs & Air & Most of the dichloros was removed in 120 s & [47] \\ \hline
5 & Fe ZSM-11 & Photocatalysis & Water & Dichlorovos was degraded in 120 min (6\% Fe ZSM-11) & [51] \\ \hline
6 &
\begin{tabular}{c} Zero valent iron \\ nanoparticles \\ \end{tabular} & Photocatalysis & Water & Pesticide was removed in 1 h & [52] \\ \hline
7 & Fenton/H\({}_{2}\)O\({}_{2}\) & AOPs & Water &
\begin{tabular}{c} In acidic and saturated dissolved oxygen \\ conditions, it took nearly 90 min to push \\ degradation ratio up to 98\% \\ \end{tabular} & [49] \\ \hline
8 & H\({}_{2}\)O\({}_{2}\) & AOPs & Air &
\begin{tabular}{c} 80.7\% of dichloros vapor was decontaminated \\ by 110–130 mg/m\({}^{3}\) of H\({}_{2}\)O\({}_{2}\) aerosol in 60 min \\ \end{tabular} & [48] \\ \hline
9 & O\({}_{3}\) & AOPs & Water & Ozone plays an important role in dichloros degradation & [50] \\ \hline \multirow{4}{*}{10} & Dielectric barrier & & &
\begin{tabular}{c} At lower initial concentration, the \\ disappearance rate of dichloros followed \\ first-order rate law; at higher initial \\ concentration, the disappearance rate of \\ dichloros shifted to zero-order rate law \\ \end{tabular} & [54] \\ \hline
11 &
\begin{tabular}{c} Fresh frozen \\ plasma \\ \end{tabular} & AOPs & Air & Dichlorovos half-life is 17.9 min & [55] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Physical and chemical methods used to degrade dichloros from environments. Figure 2: Proposed physical degradation pathways for dichloros decomposition in water and soil system.
## 5 Transfer Learning Actor-Critic Framework for Data Protection in Cognitive Radio Networks
### Transfer Learning Actor-Critic Algorithm
#### 5.2.2 Case 2  
The sensing result indicates that the PU is absent from the primary channel. There are two possible occurrences:  
1. The SU decides to stay idle to save energy for the next time slot.
2. The SU transmits encrypted information to the fusion center.  
In the first occurrence, there is no reward: \(R(s(t),ID)=0\). The probability that the PU is truly inactive in the current time slot is updated using Bayes' rule as follows:  
\[\rho^{*}(t)=\frac{\rho(t)(1-P_{f})}{\rho(t)(1-P_{f})+(1-\rho(t))(1-P_{d})}. \tag{24}\]  
The belief and the remaining energy for the next time slot are calculated by using Equations (22) and (23), respectively.  
Regarding the second occurrence, the SU uses \(e_{tr}(t)\) packets of energy to transmit the encrypted data to the FC. The remaining energy of the SU for the next time slot is calculated as follows:  
\[e_{r}(t+1)=\min(e_{r}(t)+e_{h}(t)-e_{tr}(t)-E_{s}-E_{Nk},E_{ca}), \tag{25}\]  
where \(E_{Nk}\) is the energy consumption for the encryption process, which is dependent on the key length \(Nk\) of the encryption algorithm. If the SU does not receive an acknowledgement (ACK) from the FC, which means the transmission was unsuccessful, there is no reward: \(R(s(t),TR_{Nk}|\overline{ACK})=0\). The probability that the channel will be free of the PU signal in the next time slot is given as  
\[\rho(t+1)=P_{A\bar{A}}. \tag{26}\]  
On the other hand, if the SU receives ACK from the FC, indicating that transmission was successful, the reward is  
\(\bullet\) \(R(s(t),TR_{Nk}|ACK)=10\) if \(Nk=128\), \(\bullet\) \(R(s(t),TR_{Nk}|ACK)=12\) if \(Nk=192\), \(\bullet\) \(R(s(t),TR_{Nk}|ACK)=14\) if \(Nk=256\).  
The belief that the PU will be absent from the channel in the next time slot is given by  
\[\rho(t+1)=P_{\bar{A}\bar{A}}. \tag{27}\]  
Thereafter, the value function and the new policy are updated based on the received reward and the new state. This process repeats until it converges into the optimal solution that maximizes the long-term reward of the system, which means that value function \(V(s)\) and policy \(\pi(s)\) will finally converge to \(V^{*}(s)\) and \(\pi^{*}(s)\) as \(k\rightarrow\infty\)[37].
###### Abstract  
Existing research on truck location prediction focuses on direct trajectory prediction and ignores the link between activity patterns and predictability, whereas the mode of operation is an important factor in the difference between activity trajectories, and analyzing the mode of operation can help to develop the next-location prediction algorithms to improve the efficiency of matching truckloads and to reduce costs. Our empirical study, based on 562,071 truck trip data in China, employs Fuzzy c-means (FCM) for clustering operational patterns in space, intensity, and stability dimensions. K-nearest neighbors (KNN), Back Propagation neural (BP) network, and Long Short-Term Memory (LSTM) predict the next truck locations in different modes. The results indicate that range-of-motion stability significantly influences predictability. Truckers with stable spatial activity exhibit the highest predictability, with 45% nearly achieving 100% predictability. Through cluster analysis of driving characteristics, we found that truck clusters are the most predictable because of their relatively small and low-intensity activities, with the percentage of samples with prediction accuracy above 90% reaching over 80%. This research not only characterizes the freight truck community but also aids algorithm optimization by revealing predictability factors for real-world applications.  
p _Applied sciences_ Article
## 1 Introduction  
Sarcopenia was initially defined by [PERSON] as age-related loss of muscle mass and function [1; 2]; however, sarcopenia caused by various factors, such as chronic liver diseases including liver cirrhosis (LC) and hepatocellular carcinoma (HCC), has recently become an important concern [3]. As the definition of sarcopenia varies among various regions, such as Europe and Asia [3; 4], the specific definition should be applied to each region. In 2015, the Japan Society of Hepatology (JSH) proposed sarcopenia criteria for patients with chronic liver disease [5], which consists of measurements for handgrip strength and skeletal muscle mass by computed tomography (CT) or bioelectrical impedance analysis (BIA). Sarcopenia has been reported to be associated with the prognosis of patients with LC or HCC [6; 7; 8]. For HCC patients receiving sorafenibib, the absence of skeletal muscle depletion is a favorable predictor of survival [9; 10; 11; 12]. Another report showed that muscle depletion predicted the occurrence of sorafenibib-related toxicities [13]. However, the measurement of skeletal muscle mass using CT alone without the assessment of handgrip strength has been used as the definition of sarcopenia in most previous reports [6; 7; 8; 9; 10; 11; 12; 13].  
The recent success of a clinical trial, phase III REFLECT study, has changed the treatment strategy in advanced HCC patients [14]. The REFLECT study demonstrated that lenvatinib was non-inferior to sorafenibib regarding overall survival (OS) and had a strong objective response (OR) and different adverse events (AEs) compared with sorafenibib [15]. Therefore, lenvatinib is also considered as a first-line agent in addition to sorafenibib [16; 17; 18; 19]. Recently, a subanalysis of the REFLECT study demonstrated the efficacy of sequential therapy (most common being sorafenibib) [20]. Therefore, it is important to use lenvatinib for a longer duration in responders and to switch to another molecular targeted agent (MTA) earlier in non-responders to obtain a favorable OS. Although previous studies have reported prognostic factors for HCC patients receiving lenvatinib [21; 22; 23; 24; 25; 26], no studies have evaluated the effects of both handgrip strength and skeletal muscle mass on the clinical outcomes. Therefore, this retrospective study aimed to investigate the individual effect of handgrip strength, skeletal muscle mass, and sarcopenia on clinical outcomes of HCC patients treated with lenvatinib.
## 2 Related Work  
[PERSON] et al. [6] classified existing DTN routing protocols according to relay selection strategies in forwarding messages to the destinations. The protocols are grouped into social-based routing and pure opportunistic routing categories and are discussed in terms of source injection (i.e., single-copy or multi-copies schemes) and communication strategies based on the number of receivers (e.g., unicast, multicast, or anycast). Only a few multicast routing protocols, up until 2014, are listed in their paper.  
[PERSON] et al. [20], however, presented a more comprehensive survey on multicast routing for DTN. The following four categories of multicast routing protocols are discussed: Flooding, tree-based, probabilistic, and intelligent protocols. Multicast messages can be flooded in the network or forwarded along a multicast tree to reach the destinations. Probabilistic approaches select the best relays based on the encounter history. Intelligent approaches combine flooding and forwarding techniques for better use of the available resources. A brief performance comparison between different multicast routing approaches is also provided in the survey.  
[PERSON] and [PERSON] [21] discussed the following three models related to multicast DTNs: The single-node model, the multiple-copies model, and the single-copy model. In the first, a ferry node that moves around holds message bundles intended for all destinations and delivers them to the destinations upon contact. The multiple copies model replicates the destination set when it encounters a node that satisfies certain conditions. In the single-copy model, a single copy of each destination is maintained, in which destinations can be scattered at different nodes. Here, the copy is forwarded to the encountered nodes whenever there is a higher delivery probability.  
[PERSON] et al. [22] provided an overview of multicast models for DTNs. They divided multicast in DTNs into the following four categories: Unicast-based, broadcast-based, tree-based, and group-based multicast. [PERSON] et al. [23] presented a general overview of multicast routing schemes in DTN, but the performance evaluation is done using an old algorithm. Lastly, in our previous work [24],a taxonomy of DTN multicast routing strategies is introduced, and we discussed some of the common protocols briefly.
## 6 Characterization of Surface Wave Modes
### Characterization Parameters of Surface-Wave Mode
A number of eigen-mode simulations with different phase delays are required in order to get the phase delay \(\phi_{0}\) at target frequency \(f_{0}\) through interpolation. Once \(|\vec{k}_{t}|\) is determined from \(\phi_{0}\), \(Z_{\text{surf}}\) can be computed through Equations (48) or (49). Since eigen-mode solver requires parametric sweep of phase delay to determine the propagation characteristics, the simulation-based characterization method for surface-wave mode is usually very time-consuming.
## 5 Ethical Considerations and Patient-Centric Focus
### Data Privacy and Security  
In the era of big data and AI, the protection of patient information is paramount. Pediatric data are especially sensitive due to the vulnerability of the population and the potential long-term implications of data breaches [47]. Regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in the European Union set stringent standards for data privacy. Collecting and utilizing pediatric health data for AI applications require robust measures to ensure data security [48]. This includes the encryption of data during transmission and storage, access controls to limit data handling to authorized personnel, and regular audits to detect and prevent unauthorized access. Data de-identification or anonymization is essential to protect patient identities, but care must be taken to prevent re-identification through data triangulation [49].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Traditional Approach** & **AI-Driven Approach** \\ \hline Predictive Accuracy & Low; relies on clinical judgment & High; utilizes data-driven models \\ \hline Early Detection & Challenging; often reactive & Facilitated; proactive identification \\ \hline Personalized Treatment & Limited; generalized protocols & Enhanced; tailored interventions \\ \hline Resource Utilization & Variable; may lead to over-/under-treatment & Optimized; efficient allocation \\ \hline Clinician Workload & High; manual assessments & Reduced; automated analysis \\ \hline Patient Outcomes & Variable; risk of complications & Improved; potential reduction in LLDs/deformities \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of traditional and AI-driven approaches to pediatric fracture overgrowth management.
## 5 Experimental Results
### Evaluation of the CGFace, IF-CGFace Model on the PCGAN Dataset
#### 5.4.2 Imbalanced Scenario  
In the imbalanced experiment, a new dataset is created from PCGAN dataset with the balancing ratio of 1/10 indicating that the number of real images are 10 times more than the number of computer-generated images (the real class contains 1000 images, whereas the computer-generated  
Figure 8: Generated images from the trained BGAN model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Classifier** & **Accuracy (\%)** & **AUC** \\ \hline VGG16 & Softmax & 76 & 80.5 \\ CGFace & Softmax & 98 & 81 \\
**ADA-CGFace** & **AdaBoost** & **97.3** & **89.4** \\ XGB-CGFace & XGB & 92.6 & 84.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance summarization of different models on PCGAN dataset.  
class contains only 100 images). After that, 4-fold cross-validation is conducted on three different models: the CGFace model and IF-CGFace models (including AdaBoost and XGB). We follow the transfer learning procedure in deep learning for imbalanced dataset by extracting output features map from the hidden layer before the final dense layer. Then, the features are trained in AdaBoost classifier to form ADA-CGFace model and XGB classifier to form XGB-CGFace. As we can observe in Figure 9, in the most imbalanced case, in which the number of computer-generated images is a minority compared to the majority of real images, the CGFace model gets the lowest AUC, which also means that it worked poorly on the imbalance scenario because it misclassified many CG images. In contrast, for fold 4, IF-CGFace, and especially ADA-CGFace performed well at 90.1% AUC. The results prove the effectiveness of AdaBoost and XGB classifiers on the imbalanced environment.
# Electromagnetic Soil Characterization for Undergrounded RFID System Implementation
## 1 Introduction
In the first part of this paper, thorough analysis of seven soil samples is conducted for soil electrical properties, i.e., relative permittivity, electrical conductivity, and tangential losses. However, in this study, a simpler method based on a ready-made dielectric assessment probe is utilized. The measurement probe, represents a coaxial cavity where, its resonance frequency response can be modified according to the backed material (the material under test). The Vector Network Analyzer (VNA) is utilized to record the frequency response of the measurement probe. A ready-made software package, provided by SPEAG, is used for estimating the various electrical properties of the soil samples. The second part shows a practical test, carried out on a system level, for a metal backed buried tag antenna. The input impedance of the proposed tag antenna is measured using the method mentioned in [8]. The tag antenna can be localized under 40 cm of yellow sand accurately, and the stored information in the chip can be collected properly.
# Analysis of Multi-Level Simultaneous Driving Technique for Capacitive Touch Sensors
## 4 Results and Discussion
### \(z_{x}\) Variation with Respect to \(n\)
Figure 8 shows the change in the modulated external noise with respect to \(n\) and the coupling capacitance \(c_{ext}\) that is proportional to the size of the noisy area. A higher \(n\) requiring a decrease in \(|A_{v}(f)|\) reduces the incident noise. \(z_{ext}\) increases entirely when \(c_{ext}\) covers a larger part of conducting channels ranging from \(c_{ext}=1\) pF to \(c_{ext}=8\) pF. However, a higher \(n\) requiring a low \(|A_{v}(f_{c})|\) still reduces the external noise. The result shows that \(z_{ext}\) in a fixed \(c_{ext}\) is proportional to \(\alpha\), which was set by the one of configuration sets in Figure 7a. In summary, \(z_{ij}\) with a relatively small \(z_{drv\_kj}\) can eventually be decreased due to an obligate use of lower \(\alpha\) at a higher \(n\). However, unlike the overall relation between \(z_{ij}\) and \(\alpha\), \(z_{amp\_j}\) for CA would be increased according to (4). As shown in Figure 9, increasing \(n\) results in an excess of \(z_{amp\_j}\) when even higher frequency components are limited by post-stage low-pass filters. The dotted line and solid line show the frequency responses of \(n=16\) with and without a low-pass filter, respectively. Below a frequency of 10 MHz, \(z_{amp\_j}\) is inversely proportional to \(\alpha\). This clearly shows \(z_{ij}\) can increase in a low \(\alpha\), if \(z_{amp\_j}\) dominates \(z_{ij}\). Theoretical SNRs for simultaneous driving derived in (13) and (16) must be degraded in that case. Therefore, to improve SNR, it would be advantageous to decrease the maximum column sum of the \(H\) matrix satisfying \(M\) and eventually not to decrease \(\alpha\) at a higher \(n\). Figure 8: Reduction in the external noise with respect to the number of TX channels and for different gain sets in \(f_{c}=380\) kHz, \(e_{ext}(f)=\delta(f-200\)k).