esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix B Molecular Dynamics Simulation  
The molecular dynamics simulation employed in this work calculates the ion trajectory of a single ion in a multipole trap by solving [PERSON]'s equations of motion. The electric field vectors for the calculation of the force acting on the charged particle are obtained from a COMSOL simulation. The radio-frequency field is modulated with a sine-function and is superimposed with constant electric field vectors forming the static voltages. The ion is propagated using the well-known Verlet algorithm since it is time efficient to compute and is symplectic--i.e., it conserves phase space. Since a discrete amount of electric field vectors are loaded into the simulation, the exact force acting on a particle is determined by linear interpolation of the nearby vectors. The collisions with a buffer gas are modeled as random events. After each time step, it is calculated by drawing a number from a random distribution, representing whether a collision occurred or not. In the case where a collision is detected,the ion and the buffer gas are moved into the center-of-mass frame, with the buffer gas velocity vector components being drawn from 1D Maxwell-Boltzmann distributions. A hard sphere collision is calculated, and the velocity vector components of the ion are changed accordingly. The position and velocity of the ion is recorded after the time-average of 25 buffer gas collisions. In case the ion is lost from the trap, a new ion is added in the center of the trap and initialized by waiting for 500 collisions.  
After half a million recordings (for the 2D simulations), or 1 million recordings (for the 3D simulations), the simulation is terminated and the data is evaluated by binning the velocities. The time between two collisions with the buffer gas is 10 us, the time-step size of the simulation is 7 ns, and the resolution of the electric field grid is 1000 per mm. All the three parameters were checked for convergence, and sweeps of different time steps, electric field grid resolution, and buffer gas collision rate were performed in order to investigate a change in temperature for the result. Note that the time between two buffer gas collision was checked between 1 ms and 0.1 us, but no significant change in temperature was detected for the trap frequencies \(f\) of 5 MHz and 6.8 MHz used in the simulation. The simulation step sizes were checked between 0.7 ns to 50 ns. Stable trapping and convergent energy distributions were achieved for 10 ns step size, thus a step size of 7 ns was chosen for time efficiency.
###### Abstract  
This study analyzed the changes of odor and taste in Daokou braised chicken during processing by GC-MS, GC-IMS, e-nose and e-tongue. The 75 and 55 volatile compounds identified in Daokou braised chicken by GC-MS and GC-IMS, respectively, included hydrocarbons, aldehydes, alcohols, terpenes, ketones, heterocyclics, esters, acids and phenols; among them, aldehydes, alcohols and ketones were the most abundant. The number and proportion of volatile compounds in Daokou braised chicken changed significantly (\(p<0.05\)) in the process. The proportion of volatile compounds with animal fatty odor, such as aldehydes and alcohols, decreased, while that of esters, ketones and terpenes from spices with fruity fragrance increased, especially in the braising stage. An e-nose showed that the odor intensities of sulfur-containing and nitrogen oxide compounds were higher (\(p<0.05\)) after the braising stage, but weakened after 2 h braising. An e-tongue showed that saltiness and richness increased significantly (\(p<0.05\)) after braising. The results of these four techniques showed that braising promoted the release of flavor compounds, and was beneficial to salt penetration and umami release. However, long braising could lead to weakened flavor intensity and the introduction of bitterness and astringency. This study also found that GC-IMS and e-nose were more sensitive to trace compounds such as sulfur-containing and nitrogen oxide compounds, esters, acids and phenolics in Daokou braised chicken than GC-MS. The use of multiple technologies could provide more comprehensive flavor profiles for Daokou braised chicken during processing. This study provides insights into the control of flavor of Daokou braised chicken, and may be of practical relevance for the poultry industry.  
Daokou braised chicken; odor; taste; GC-MS; GC-IMS; e-nose; e-tongue Article
## 4 Protocol Description
### Initialized Routing Structure
_delay\({}_{qmax}\)(sink_, _i_) is the delay experienced from sink to \(i\) with the maximal delay on queuing. Then our delay estimate and realistic delay on the route path \(T\) satisfy: _delay_ (_sink_, _i_) \(\leq\)\(T\)\(\leq\)\(delay_{qmax}\)(_sink_, _i_). We can use _delay_ (_sink_, _i_) as a \"bound\" to guide the real-time routing forwarding selection. If the delay and _slack_ time (defined as time left for routing) meets the estimated delay time for data delivery, the packet has a high probability to arrive before deadline and thus ensures real-time communications. _Periodic Sink Update_:  
With the HEIGHT message broadcast process, an initial neighborhood is formed by each sensor for which it records neighbor ID, height, state, estimated delay, residual energy of all neighbors, as well as the transmission power that the node uses to communicate with its neighbor on the path to the sink. Each sensor records its own ID, state, and residual energy. In addition, each node maintains sink ID with its minimal-delay sink. In a fire scenario, the sink may become disabled and the network's topology will be changed by the fire. To ensure robust connectivity, each sink will periodically send out a HEIGHT message to refresh the network. The refresh rate is a protocol design parameter that trades off overhead for increased robustness to lost HEIGHT messages and path changes. In a fire situation one would expect to decrease the period, although the impact on network traffic load must also be examined.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
###### Abstract  
Future wireless communication systems are facing with many challenges due to their complexity and diversification. Orthogonal frequency division multiplexing (OFDM) in 4G cannot meet the requirements in future scenarios, thus alternative multicarrier modulation (MCM) candidates for future physical layer have been extensively studied in the academic field, for example, filter bank multicarrier (FBMC), generalized frequency division multiplexing (GFDM), universal filtered multicarrier (UFMC), filtered OFDM (F-OFDM), and so forth, wherein the prototype filter design is an essential component based on which the synthesis and analysis filters are derived. This paper presents a comprehensive survey on the recent advances of finite impulse response (FIR) filter design methods in MCM based communication systems. Firstly, the fundamental aspects are examined, including the introduction of existing waveform candidates and the principle of FIR filter design. Then the methods of FIR filter design are summarized in details and we focus on the following three categories--frequency sampling methods, windowing based methods and optimization based methods. Finally, the performances of various FIR design methods are evaluated and quantified by power spectral density (PSD) and bit error rate (BER), and different MCM schemes as well as their potential prototype filters are discussed.  
multicarrier modulation; prototype filter design; frequency sampling methods; windowing based methods; optimization based methods +
Footnote †: journal: Electronics
# An On-Demand Scheduling-Based MAC Protocol for UW-WiFi Networks
## 6 Conclusions  
For constructing an actual UIoT, the UW-WiFi network architecture is introduced. Taking into consideration the application requirements, in this paper, we propose a time sequence-based dynamic on-demand assignment MAC protocol with low complexity for the real UW-WiFi network scenario, namely SDDA, enabling anchored and mobile nodes to access the network flexibly. This protocol employs the amount of data to be transferred by each node and the propagation delay to optimize channel resource allocation. Simulations are conducted to evaluate the performance of the SDDA protocol. The simulation results demonstrate that the SDDA protocol has much better performance with respect to channel utilization, end-to-end delay, and packet delivery ratio. Moreover, this protocol has no time synchronization required compared to TDMA. Additionally, the design reduces the number of control messages exchanged and accomplishes non-conflict transmissions to achieve energy efficiency. Furthermore, we implement the SDDA protocol in the embedded Linux operating system and carry out ocean field trials. Results from the ocean trials agree with the simulations. In addition, different classes of real ocean data collection experiments validate the practicability and fairness of this protocol. All in all, both simulation and ocean experimental results justify that the SDDA protocol is a promising solution for the UW-WiFi network.  
In the future, we will optimize this protocol to adapt to the varying channel and design a more effective MAC protocol based on this scheme for the hierarchical UW-WiFi network.  
Figure 14: Image, CTD and AUV data from different terminal nodes.  
**Author Contributions:** Conceptualization, X.P. and J.Z.; methodology, X.P.; software, X.P. and J.Z.; validation, X.P., J.Z., M.L., and X.W.; formal analysis, X.P.; investigation, X.P.; resources, J.C. and J.L.; data curation, X.P. and J.C.; writing--original draft preparation, X.P.; writing--review and editing, X.P. and Z.P.; visualization, X.P. and M.L.; supervision, Z.P.; project administration, J.C.; funding acquisition, J.C. and J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant 61971206, Grant 62101211, and Grant U1813217; in part by the National Key Basic Research Program under Grant 2018 YFC1405800; in part by Overseas Top Talents Program of Shenzhen under Grant KQTD20180411184955957, and Grant LHTD20190004; and in part by the National Key Research and Development Program under Grant 2021 YFC2803000.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this paper are available after contacting the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# TrojanDetector: A Multi-Layer Hybrid Approach for Trojan Detection in Android Applications
## 3 TrojanDetector Approach
### Trojan Detector  
To mitigate the threats found in the threat model, we propose TrojanDetector. Our approach to malware analysis starts with the training process at three levels in parallel; as shown in Figure 1, TrojanDetector installs an Android phone application and monitors its analysis features at the application, package, and user levels in parallel. The TrojanDetector uses four phases to detect a Trojan.  
* **Phase 1:** TrojanDetector performs static analysis at the application level features, e.g., meta-data and critical API, and package level features, e.g., system calls and the energy level.  
Figure 1: TrojanDetector: Multi-Layer Hybrid Trojan Detection.  
* **Phase 2:** TrojanDetector applies dynamic analysis at the user level features (activity/network, emails, SMS logs). Once triggered, it performs app evaluation, ranks lists, and extracts behavioural patterns.
* **Phase 3:** TrojanDetector extracts energy, email, SMS, and activity features.
* **Phase 4:** TrojanDetector feeds all the features extracted from all the stages to the chosen classifiers to apply classification.  
Algorithm 1 shows the underlying TrojanDetector Algorithm. It elaborates how TrojanDetector collects meta-data from the source code package, collects the features, evaluates the meta-data and then ranks them. Afterwards, the model observes the behavioural patterns of the Trojan and extracts the user-level features. After getting all the features, it is given to the Prediction ML models, which then predict the Trojan and give the results.  
```
1 procedureDetect Trojan(DATA)
2 Meta Data-set \(\leftarrow\) application is installed permissions, ratings
3 CollectFeatures \(\leftarrow\) GetFeaturesFromMata (Meta Data-set[])
4 PackageLevelAppEvaluation \(\leftarrow\) EvaluateMeta (CollectFeatures[])
5 PackageLevelRank \(\leftarrow\) RANK (PackageLevelAppEvaluation)
6
7 Check Known Behavioral Patterns:
8 BPrediction \(\leftarrow\) PredictB (BehavioralPatterns[])
9
10 Check User Level Features:
11 Activity Data-set \(\leftarrow\) MonitorActivity (app)
12 SMS Data-set \(\leftarrow\) MonitorSMS (app)
13 Energy Data-set \(\leftarrow\) MonitorEnergy (app)
14 Network Data-set \(\leftarrow\) MonitorNetwork (app)
15 Email Data-set \(\leftarrow\) MonitorSmall (app)
16
17 Predict Trojan Using AI Models Based on Features:
18 Predictions \(\leftarrow\) AIModel (PackageLevelFeatures+ BehavioralPatterns+UserLevelFeatures)
19 IsTwjan \(\leftarrow\) Classifier (Predictions)
```  
**Algorithm 1** Detect Trojan
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 2 Background  
### TSCH, 6 TiSCH, and IPv6  
The IEEE802.15.4e amendment [12] first standardized the TSCH mode. It uses a sparse time-slotted schedule, depicted in Figure 1, combined with channel hopping over 16 distinct frequencies to provide robust communication in multihop networks. Each TSCH device maintains its schedule. The schedule synchronizes the device's access to the wireless transmission medium and prevents collisions. Channel hopping provides frequency diversity in the crowded 2.4 GHz ISM band. The schedule consists of a repeating structure, called \"slotframe\". A slotframe, in turn, consists of a group of cells. The individual cells describe when the device should wake up to communicate with its neighbors and when it should sleep to save battery. There are three active cell types: transmission slots (Tx), reception slots (Rx), and shared slots (TxRx). A single cell in the schedule is long enough to send a maximum length IEEE802.15.4 frame (127 B) and receive a short acknowledgment frame. While the exact duration of a cell is implementation-specific, 10 ms, 15 ms, and 20 ms are commonly used.  
To support dynamic traffic patterns on top of the TSCH schedule, the 6 TiSCH Working Group (WG) designed the 6 TiSCH Operation Sublayer (6 top) Protocol (6P) [13]. 6P is a pairwise negotiation protocol that enables TSCH neighbors to allocate and delete cells in their schedules [14]. The protocol defines seven commands: _ADD_, _DELETE_, _RELOCATE_, _COUNT_, _LIST_, _SIGNAL_, and _CLEAR_. A device uses these commands to manipulate its neighbor's schedule and requests an increase or decrease of the available bandwidth. The Scheduling Function (SF), a separate module of the 6 TiSCH specification, uses the 6P protocol to implement a specific TSCH cell allocation policy. The policy depends on the traffic patterns of the upper-layer protocols and applications. The 6 TiSCH WG defined a default scheduling policy called Minimal Scheduling Function (MSF). MSF identifies two types of cells in the schedule: autonomous cells and managed cells. Nodes install autonomous cells to provide minimal bandwidth to their neighbors and managed cells to respond dynamically to a varying traffic load in the network.  
6 TiSCH networks are IPv6 compliant thanks to the IETF 6 LoWPAN adaptation layer. The 6 LoWPAN layer optimizes the limited IEEE802.15.4 payload space by compressing the headers of upper layers. It also defines mechanisms for the support of operations required in IPv6, such as neighbor discovery and address autoconfiguration. Since IPv6-compliant networks must be capable of handling IP packets with a payload of at least 1280 B [15], 6 LoWPAN specifies a fragmentation mechanism. The algorithm iterates over the IPv6  
Figure 1: A typical TSCH schedule shared between device A and B, containing a Tx, an Rx, and a TxRx slot for communication.  
packet and slices it into fragments according to the maximum frame size at the link layer, 127 B for IEEE802.15.4.
## 2 Generation of F-F Image
Thus, when planning a measurement experiment, the sampling frequency (Nyquist condition) and the frequency resolution of the spectrum (duration of the measurement signal is directly connected with the steady-state of the device operation) should be properly selected so as not to lose important information about the frequency components of the signal, and to minimize the effects of DFT approximation and, consequently, obtain an F-F image representative of all processes taking place in the diagnosed system. A separate problem while creating F-F images based on the spectrum of the experimentally obtained signal is the elimination of the background noise from the useful signal. For the purpose of selection, only those frequency components carrying useful information and removing the remaining noise frequency components, a simple amplitude criterion can be used.
## 3 Results and Discussion
### Effect of Pomelo Cultivars on Color and Viscosity of Juices  
The color parameters (\(L\), \(a\), \(b\)) of different pomelo juices were also detected. The color of the juice was decisive in juice acceptance by the consumers as it reflected the quality, safety, and nutritional values [36]. The results are presented in Table 4. The colors of the pomelo juices from different cultivars were markedly different. In general, the juices had a high brightness when the \(L\) value above 30 [1]. The \(L\) values of all pomelo juices were higher than 30, and the \(L\) values of most HP pomelo juices were higher than those of corresponding LP pomelo juices, revealing that all pomelo juices had a strong brightness and most HP pomelo juices showed a higher brightness. Furthermore, the juice color was affected by the fruit characteristics, such as cultivar, ripening, pulps and growth conditions [38]. The \(a\) and \(b\) values of grapefruit juices, as well as _cv._ Pingshanyu juices, had positive value, indicating that the color of them were strong reddish yellow, while the \(a\) and \(b\) values of _cv._ Guanxiyu juices showed negative values, which revealed that the yellow-green color was found in _cv._ Guanxiyu juices. Hence, it could be concluded that the difference in the b values of the guava juices were mainly dependent on the cultivar. The results were consistent with the report about sugarcane juice [39]. The viscosity of the juices could reflect the stability of the juices [40]. The viscosity of pomelo juices of different cultivars is shown in Figure 3. As the shear rate increased the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types** & **Cultivars** & **L** & \(a\) & \(b\) \\ \hline \multirow{6}{*}{LP juices} & Grapefruit & 33.73 \(\pm\) 0.05 h & 1.64 \(\pm\) 0.02 h & 3.43 \(\pm\) 0.02 a \\  & Guanxiyu & 33.24 \(\pm\) 0.05 i & \(-\)0.42 \(\pm\) 0.02 e & \(-\)0.82 \(\pm\) 0.02 f \\  & Wendanyu & 37.10 \(\pm\) 0.02 f & \(-\)1.55 \(\pm\) 0.02 j & 0.42 \(\pm\) 0.02 e \\  & Liangpinyu & 37.92 \(\pm\) 0.02 e & \(-\)2.13 \(\pm\) 0.02 k & 1.68 \(\pm\) 0.03 b \\  & Pingshanyu & 31.86 \(\pm\) 0.02 k & 1.52 \(\pm\) 0.03 c & 0.98 \(\pm\) 0.04 d \\  & Shatianyu & 33.24 \(\pm\) 0.01 i & \(-\)0.5 \(\pm\) 0.01 f & 1.48 \(\pm\) 0.01 c \\ \hline \multirow{6}{*}{HP juices} & Grapefruit & 38.40 \(\pm\) 0.02 c & 4.31 \(\pm\) 0.02 a & 7.55 \(\pm\) 0.03 a \\  & Guanxiyu & 39.47 \(\pm\) 0.02 b & \(-\)1.23 \(\pm\) 0.02 h & \(-\)0.053 \(\pm\) 0.01 f \\ \cline{1-1}  & Wendanyu & 35.81 \(\pm\) 0.02 b & \(-\)1.48 \(\pm\) 0.03 i & 1.56 \(\pm\) 0.02 d \\ \cline{1-1}  & [PERSON] & 32.84 \(\pm\) 0.02 l & \(-\)0.73 \(\pm\) 0.02 b & 2.66 \(\pm\) 0.04 b \\ \cline{1-1}  & [PERSON] & 37.98 \(\pm\) 0.01 d & 0.85 \(\pm\) 0.01 d & 1.89 \(\pm\) 0.01 c \\ \cline{1-1}  & [PERSON] & 45.27 \(\pm\) 0.01 a & \(-\)1.57 \(\pm\) 0.02 b & 0.93 \(\pm\) 0.02 e \\ \hline \end{tabular} Data are expressed as means \(\pm\) standard deviation (\(n=3\)); different letters indicate significantly different means at \(p<0.05\).  
\end{table} 
Table 4: Effects of cultivars on color indexes of pomelo juices.
## 3 Results and Discussion
### Diffuse Reflection FTIR (DRIFTS)  
Using diffuse reflectance FTIR (DRIFT) we compared the PLA spool material and the PLA material after printing. The FTIR spectrum collected from the PLA spool material, Figure 6, is of somewhat lower intensity as compared to the other spectra, likely due to difficulties in analytical sampling configuration for the filaments. It also shows a number of differences as compared to spectra from post-printed surfaces.  
Peaks of interest from the PLA spool material include: a C-COO peak at 875 cm\({}^{-1}\) (which can also be attributed to CaCO\({}_{3}\) as found in the Raman spectroscopic analysis); a CH\({}_{3}\) peak at 1053 cm\({}^{-1}\); and CH\({}_{3}\)/CO rocking vibrations with overlapping C-O-C stretching at 1113, 1145, and 1234 cm\({}^{-1}\). Other CH\({}_{3}\) deformation vibrations occur at 1389 and 1463 cm\({}^{-1}\). There is a peak associated with the C=C bond at 1685 cm\({}^{-1}\) and a sharp, strong C=O peak at 1784 cm\({}^{-1}\). There is also a small OH peak at 3616 cm\({}^{-1}\) which, over time, becomes more pronounced, as PLA spool material was discovered to be hygroscopic in nature.  
A number of chemical changes were observed as the PLA spool material is heated to 230 \({}^{\circ}\)C, is extruded, and then cools and recrystallizes, significantly changing bonding at least in the near surface region of the printed PLA. Some characteristic peaks in printed PLA test samples (Figure 7) are: the C-O-C deformation vibration at 668 cm\({}^{-1}\); the CH\({}_{3}\) deformation with overlapping C-O-C stretching as 1077 cm\({}^{-1}\); and the C-COO peak at 880 cm\({}^{-1}\)--which can be associated with CaCO\({}_{3}\) (which is also indicated by the peak at 2503 cm\({}^{-1}\)). This shows that the calcium carbonate-based additive indicated by the Raman spectra and FTIR of the spool material was retained after the printing  
Figure 6: Diffuse Reflectance FTIR (DRIFTS) spectroscopy of PLA spool material as received.  
process. Finding calcium carbonate in the PLA is significant due to the fact that PLA can also be used as support structure material for growing cells [25], and calcium is a required element for the growing cells. Other peaks in printed PLA include: the CH\({}_{3}\) stretching vibrations at 2829 and 2932 cm\({}^{-1}\) and OH stretching vibrations at 3501 and 3667 cm\({}^{-1}\) due to the hygroscopic nature of PLA (although these features can also be attributed to the C=O overtone stretching vibration at 3501 cm\({}^{-1}\)). Additionally, there is an increase in CO\({}_{2}\) possibly due to a partial breakdown on calcium carbon to CaO and CO\({}_{2}\) which becomes bound the printed PLA material (see XPS of 3D printer PLA). There are two vibrations at 1965 and 2081 cm\({}^{-1}\) that may be possible M-CO vibrations. These two M-CO vibrations may be due to metal contamination formed during the extrusion process.