esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
## 2 Methodology
### Software Setup  
Figure 2 depicts the general concept of wireless communications considered in this article. The ground station, which consists of a laptop or a desktop as the first UE, is equipped with Ardupilot software [36]. As the second UE, the drone is embedded with a Raspberry Pi, a Pixhawk flight controller (FC) [37], and an LTE modem. These UEs are connected to an LTE mobile network, which is conceptually connected in a single wide-area network (WAN).  
In this article, a cloud-based operational system was implemented and a cloud server service from UAVmatrix, known as UAVcast [35], was used. As shown in Figure 3, the drone and the ground station are connected to a virtual private network (VPN) provided by Zerotier to obtain a static IP address. With some configuration, both UEs are connected to the UAVcast cloud server using LTE mobile network. The drone will send its telemetry data and live stream video to the server, then accessed by the ground station, and vice versa. Both UEs are connected via LTE mobile network.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Product Available on the Market**} \\ \cline{2-6}  & **XB Station [31]** & **Skydrone FPV [32]** & **Botlink [33]** & **Flytpi [34]** & **Raspberry Pi [30]** \\ \hline Diagram & & & & & Real-time control & \\  & & Real-time control & & with live-feed & \\  & & with live-feed & & with live-feed & & \\  & & footage using 4G & & network & & \\  & & network & & & & \\  & & Direct API telemetry & & network & & \\ Description & & Direct API & data connection & Direct API & connection & \\  & & telemetry data & BVLoS drone control & telemetry data & & \\  & & connection & Camera gimbal & connection & control & \\  & & BVLoS drone & & Direct plug and & & \\  & & control & Special software for & & & \\  & & drone users & & & & \\  & & & & & & \\ \hline  & & LTE modem & & & LTE modem & \\  & & Flight Controller & Flight Controller & & Flight Controller & Flight Controller \\ Hardware & & Drone & Drone & Drone & Drone & Drone \\ Requirement & & Camera & Battery & Camera & Camera & Camera \\  & & Battery & & Battery & Battery & Battery \\ \hline Software & XB Firm & Sky Drone FPV & Botlink Relay & Flyt OS & UAVcast-Pro [35] \\ \hline Price & \$430 OTP & \$999 OTP & \$1400 OTP & \$399 OTP & \$39 OTP \\  & \$20/month & \$10/month & \$499/year & \$59/year \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of hardware available in the market. API, application programming interface; BVLoS, beyond the visual line-of-sight;LTE, Long Term Evolution; OTP, one time purchase.
# Realistic Simulation for Body Area and Body-To-Body Networks
## 3 Proposed Simulator
### Semi-Deterministic (Stochastic) Radio Link and Mobility Modeling
#### 3.1.3 On-Body Channel Model
 \tag{6}\]  
The intra-BAN short-term fading is implemented with a Rice distribution, whose parameters have been derived from measurements, as shown in Table 3. In addition, we assume a coherence time of 20 ms for all links, _i.e._, short-term fading is considered constant over such a period of time. Figure 6 shows a comparison between the measured and implemented belt-to-wrist on-body channel.
## 4 Results and Performance Evaluation  
To test and validate the data-fusion technique proposed in this study, precipitation fields for the case studies in Table 2 have been recreated and compared with radar data (only pixels with quality index \(\geq\) 0.6) and PR-OBS-3A rainfall estimation product. To make these three products comparable, they must be brought to the same spatial grid. This has been done with the nearest neighbor algorithm, assigning an average value to each node that has one or more points within a radius of 3 km centered on the node. The distance between each of the nodes is 5 km. The average value is computed as a weighted mean of the nearest point from each pixel inside the search radius. The weighting function used is:  
\[w(r)=\frac{1}{1+d^{2}},\ d=\frac{3*r}{s\_r} \tag{1}\]  
where \(s\_r\) is the search radius and \(r\) is the distance from the node. Furthermore, MSG SEVIRI images, weather radar rain rate and PR-OBS-3A products have the same time sampling of 15 min.  
In Table 9 the number of images processed, the number of pixels classified as rainy or not rainy (both night and day) and the number of pixels for which rain rate has been retrieved (both night and day) are presented for each case study.  
For all the case studies, two different statistical scores have been considered, continuous and dichotomous statistical scores. Dichotomous statistical scores are calculated all over the pixels classified as rainy or not. Continuous statistical scores are calculated all over the pixels classified as rainy and for which rain rate has been retrieved.
# Novel Equalization Techniques for Space Division Multiplexing Based on Stokes Space Update Rule
## 2 Space Division Multiplexing
Coupled-core fibers can support supermode multiplexing, i.e., the simultaneous propagation of modes in different cores. Supermodes can be arranged in groups transmitting the same signal, over different modes and cores, resulting in similar performance as MMF [19].
## 8 Conclusions  
We have presented a detailed description of a low-cost wide beamwidth antenna for multi-GNSS applications useful for maneuvering platforms. Wide beamwidth performance has been achieved by employing printed crossed dipoles combined with parasitic monopoles fed by a broadband quadrature hybrid coupler (QHC) and by using a ground plane of optimum diameter. The proposed antenna exhibits the following characteristics at 1.16-1.61 GHz: reflection coefficient of less than \(-\)17 dB, zenith gain of 3.9-5.8 dBic, horizontal gain of \(-\)3.3 to \(-\)0.2 dBic, zenith axial ratio of 0.6-1.0 dB, and horizontal axial ratio of 0.4-5.9 dB. In a nutshell, the gain of the proposed antenna is greater than \(-\)3.3 dBic, and the axial ratio is less than 6 dB over the entire angles of the upper hemisphere. The antenna has been realized in a dimension of 0.48 \(\times\) 0.48 \(\times\) 0.25 \(\lambda\) at the mid-frequency. The proposed antenna offers the capability of LHCP \(L\)-band satellite phone operation at 1.525-1.661 GHz. The competitive features of the proposed antenna, such as simple low-cost structure, broad bandwidth, and wide beamwidth, could develop into commercialization of a multi-GNSS antenna for maneuvering platforms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **AR (\textless{}6 dB)** & **Freq. Range** & **Gain (dBic)** & **AR (dB) Max.** & **Dimension (\(\lambda^{3}\)) \(\lambda\)** & **Dual Circular** \\  & **Beamwidth** & **(deg)** & **(GHz)** & **Min. at \(\theta\) = 90\({}^{\circ}\)** & **at \(\theta\) = 90\({}^{\circ}\)** & **at Center Freq.** & **Polarization** \\ \hline
[22] & 170 & 1.227–1.575 & \(-\)3.3 & 7.0 & 0.32 \(\times\) 0.32 \(\times\) 0.17 & No \\ \hline
[23] & 245 & 2.075 & \(-\)4.2 & 3.8 & 1.05 \(\times\) 1.05 \(\times\) 0.55 & No \\ \hline
[24] & 185 & 0.29–0.40 & \(-\)6.6 & 3.3 & 0.35 \(\times\  
**Author Contributions:** Conceptualization: S.X.; methodology: S.X., J.H., and W.S.C.; validation: B.-C.A.; formal analysis: W.S.C. and B.-C.A.; investigation: S.X., J.H., and S.-G.C.; writing--original draft: S.X.; writing--review and editing: S.-G.C. and B.-C.A.; funding: S.-G.C.; formal data curation: J.H.; supervision: B.-C.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Innovative Human Resource Development for Local Intellectualization Program through the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korean Government (MSIT) (No. IITP-2025-RS-2020-II201462, 50%) and by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. RS-2020-NR049604, 50%).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Materials and Methods
### Protein Extraction, iTRAQ Labeling, and LC-MS/MS Analysis  
The sample was ground in liquid nitrogen, then the powder was transferred to 5 mL centrifuge tubes, and sonicated three times on ice in lysis buffer (50 mmol/L EDTA, 700 mmol/L sucrose, 100 mmol/L KCl, 500 mmol/L Tris-HCl, pH 8.0). An equal volume of Tris-saturated phenol (pH 8.0) was added, then the mixture was vortexed for 5 min, and centrifuged at 4 \({}^{\circ}\)C, 5000\(\times\)\(g\) for 10 min. The supernatant was removed, mixed with a 4\(\times\) volume of chilled methanol containing 0.1 mol/L ammonium acetate, and incubated at \(-\)20 \({}^{\circ}\)C overnight. After centrifugation at 4 \({}^{\circ}\)C, 5000\(\times\)\(g\), for 10 min, the supernatant was discarded. The precipitate was washed with ice-cold methanol once, followed by ice-cold acetone three times. The remaining pellet was then air-dried, and dissolved in lysis buffer (7 M urea, 2 M thiourea, 20 mM Tris-HCl, pH 8.0-8.5), and the protein concentration was detected by the Bradford method. For digestion, the protein solution was reduced with 10 \(\mu\)L reducing reagent for 1 h at 37 \({}^{\circ}\)C and alkylated with 2 \(\mu\)L cysteine-blocking reagent for 30 min at room temperature. After digestion with trypsin, the peptides were dried by vacuum freeze centrifugation and reconstituted in triethylammonium bicarbonate buffer (TEAB). The digested peptides were labeled with iTRAQ reagents (Applied Biosystems, Foster City, CA, USA) according to the manufacturer's instructions. Common-yield samples were labeled iTRAQ tags 113, 114, and 115, and high-yield samples were labeled with iTRAQ tags 116, 119, and 121, respectively. Equal amounts of iTRAQ-labelled peptides were mixed, dried, then reconstituted in buffer A (2% acetonitrile, 98% water with ammonia at pH 10), and fractionated by HPLC (RIGOL, Beijing, China). Then, the peptides were redissolved with 0.1% formic acid (FA) and analyzed by a Q-Exactive HF mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) coupled with a nano-HPLC instrument (UtiMate 3000 LC system, Dionex, CA, USA). The mass spectrometer (MS) was operated in the data-dependent mode with positive polarity at an electrospray voltage of 2.0 kV. The m/z scan range was 300 to 1400 for the full scan, and the top 20 intense ions were detected to use for MS/MS.
## 3 Results and Discussion
### Analysis of Physicochemical Characteristics
#### 3.4.2 Fat Binding Capacity  
Chitosan cannot be digested and absorbed in the human body for a short time; it can absorb multiple times its own weight in oil, effectively prevent the digestive system from absorbing cholesterol and triglycerides, and prevent the accumulation of cholesterol and fatty acids in the body. As shown in Figure 3, the FBC of CSS (3.9 g oil/g chitosan) was the highest among the three chitosan samples, followed by CSC (2.9) and CS (2.7), which was similar to its WBC. This was explained by the higher binding ability of low M\({}_{\text{W}}\) chitosan with oil, which was consistent with the results reported by [PERSON] et al. [41]. With the increase in DD, the exposed, positively charged NH\({}_{3}\)\({}^{+}\) rise, and the oil is adsorbed via the charge action. Additionally, the decrease in M\({}_{\text{W}}\) makes the surface area of oil molecules larger, so it has greater fat binding ability. [PERSON] et al. [42] observed that for the FBC of the average M\({}_{\text{W}}\) of \(\alpha\)-chitosan (1-1800 kDa) and \(\beta\)-chitosan (1-2340 kDa), the latter had higher DD, more amino groups and stronger charge attraction. The results showed that the maximum FBC of \(\beta\)-chitosan was higher than that of \(\alpha\)-chitosan, but the regularity was distinct, indicating that chitosan FBC may be related to M\({}_{\text{W}}\), DD, and different molecular conformations also.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Molecular Structural**} & \multicolumn{2}{c}{**Physiochemical**} & \multicolumn{2}{c}{**Bio-Functional**} \\ \cline{2-9}  & **M\({}_{\text{w}}\) (kDa)** & **DD (\%)** & **WBC (g/g)** & **FBC (g/g)** & **TS** & **PS** & **SS** \\ \hline CS & 377.1 & 83.4 & 3.3 & 2.7 & 5.25 & 0.28 & 1.03 \\ CSC & 249.8 & 92.7 & 5.2 & 2.9 & 4.70 & 0.30 & 1.07 \\ CSS & 22.5 & 97.8 & 10.8 & 3.9 & 4.56 & 0.31 & 1.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Raw data of chitosan chemistry performance indexes.
## 5 GNSS Positioning Performance of Xiaomi Mi 8
### Dynamic Test Analysis
Figure 17 shows the position dilution of precision (PDOP), the IEPV status, the number of observations collected, and the number of observations used in the two kinematic circuits with GEJC satellites and single-frequency and dual-frequency observations. Here, the IEPV status describes the way the IEPV, namely \(\Delta\text{X}_{t_{i-1},t_{i}}\) in Equation (7), is computed. If the IEPV status is 1, it indicates that the IEPV is calculated using the TDCP measurements, while 0 means there are not enough usable TDCP measurements to calculate the IEPV, and it is obtained by multiplying the Doppler-derived speed with \(\Delta\text{t}\). It can be seen in Figure 17 that, between three and six observations are eliminated during gross error detection. For single-frequency GNSS solutions, the mean value of PDOP in the open environment is about 2.45, and slightly increases to 2.62 in the GNSS-degraded environment; when using dual-frequency GNSS observations, the PDOP decreases further, reaching 1.89 in the open environment and 2.10 in the adverse GNSS environment. The four IEPV status sequences in Figure 17 illustrate that it remains 1 in the open environment, regardless of the use of single-frequency or dual-frequency GNSS observations. That is to say, in the open environments, the carrier phase observations are continuous and cycle slips are rare, and the IEPV is calculated successfully from phase differences between epochs. In track 2, however, if only single-frequency observations are used, many IEPV statuses are labeled 0, which means the IEPVs are calculated from the Doppler-derived velocity whose accuracy is usually lower than that calculated by time differenced carrier phases. For example, as shown in the upper red box in Figure 17, when Xiaomi Mi 8 is just between tall buildings and dense trees (same area in the red box in Figure 16), the IEPV status remains 0 for several consecutive epochs, meanwhile the number of used observations decreases and the PDOP value increases slightly. When the dual-frequency is available, only a few IEPV statuses are 0, and the PDOP over the navigation course is relatively stable. Clearly, the L5/E5 observations play an important role in improving the performance of mobile phone navigation in the complex environment.
# Mdpi
## 1 Introduction  
With the development of web technology and the explosive growth of information, web security becomes more and more important. Web vulnerabilities such as SQL injection and XSS attacks [1] are some of the most common security problems. This kind of attack happens not only to enterprises and individuals, but also to government organizations. Attackers often exploit vulnerabilities in the system or web applications to upload a malicious file or malicious code to the webserver. This malicious file is called a webshell [2]. Once the webshell is executed, it can provide remote attackers with an interface to operate the server, including command execution, file manipulation, and database connection [3]. A webshell is often used by attackers as a backdoor tool for web server operation and management. If a webshell file is found in the web system, the attacker can exploit the vulnerability to control the server. Therefore, accurately and effectively detecting whether files stored on a web server are malicious webshell files is of great importance to the security of a web server.  
There are three traditional webshell detection methods: static detection, dynamic detection, and traffic log analysis detection [4]. To realize its functions, a webshell must have specific keywords and malicious functions in the malicious code. Feature-based detection starting from file and code is static detection. After a webshell runs, it is dynamic detection, which detects the byte-code of a webshell and its communication behavior. Log analysis detection examines the web logs generated in the network [5]. If the malicious keywords appear directly, traditional detection methods can be identified easily. Therefore, attackers use a range of methods to bypass traditional detection, including malicious function segmentation, Base64 encoding, and other techniques. These traditional webshell detectionmethods are ineffective in detecting webshells that have been obfuscated. In this paper we propose a model that improves the efficiency and accuracy of webshell detection to overcome such limitations.  
The rest of this paper is organized as follows: Sections 2 and 3 provide the background and literature review about webshell detection. Section 4 presents the methods that an attacker can use to evade detection. Section 5 proposes a new method to effectively detect webshells using a machine learning algorithm. Section 6 evaluates and discusses the new approach, before Section 7 concludes the paper including possible directions for future work.
## 2 Related Work
### Semantic Extraction
#### 2.1.2 Video Annotation  
There has also been research pertaining to video annotation. [13] proposed an event-based approach to create text annotations, which infers high-level textual descriptions of events. This method does not take into account the temporal flow or correlations between different events in the same video. Thus, the approach does not have the ability to interact or fuse multiple events into scenes or activities. As explained in the previous section, it is important to search for and retrieve continuous blocks of video, often referred to as scenes or story units.  
[PERSON] et al. [14] proposed a pipeline with keyframe-based annotation of scene descriptions, while [15] proposed a sentence-generation pipeline which provides descriptions for keyframes based on the semantic information. Even though the techniques produced acceptable results, the annotations still lacked information and faced information losses. [PERSON] et al. [16], on the other hand, proposed a solution for semantic video annotation that consists of per-frame annotations of scene tags. The per-frame annotations are computationally expensive and often redundant. Therefore, we incorporated a pipeline that takes into account the drawbacks of these previous methodologies. The pipeline obtains all possible spatial information, ranging from the location to objects and persons, in the form of textual descriptions for every \(n\)th frame of the video. This \(n\) depends on the frame rate of the video and is adjusted so that textual descriptions are obtained for a minimum of 4 frames per second.
# Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
## 2 Problem Statement  
### Event-Based NILM  
Event-based NILM involves three steps: event detection, waveform extraction, and load identification. First, the timing of appliance state changes is detected by comparing sequence differences within a sliding window [4]. Next, the current sequences \(\hat{I}^{\text{b}}\) and \(I^{\text{a}}\) and voltage sequences \(U^{\text{b}}\) and \(U^{\text{a}}\), before and after the event, are selected. Based on Kirchhoff's law, the appliance current waveform \(I^{\text{e}}=I^{\text{a}}-I^{\text{b}}\) and the voltage waveform as \(U^{\text{e}}=(U^{\text{b}}+U^{\text{a}})/2\) corresponding to this event are extracted. Finally, the load identification model \(\hat{I}^{\text{e}}(F,U^{\text{e}};W^{\text{i}})\), parameterized by \(W^{\text{i}}\), maps the event waveforms to appliance types. Specifically, \(W^{\text{i}}\) is typically learned via gradient descent on the collected dataset \(D_{0}-\{I^{\text{e}}_{0},U^{\text{e}}_{0},Y_{0}\}\), where \(Y_{0}\) denotes the corresponding appliance types.
###### Abstract  
This study evaluates the feasibility of 3D-printed polymer composite external fixator (EF) rings as a cost-effective alternative to stainless steel fixators, focusing on hybrid fixators for complex tibial fractures. Mechanical performance was assessed in three stages: (1) evaluating the initial EF-tibia configuration under axial loading and wire pre-tension conditions; (2) analyzing the stiffness evolution and weight-bearing capacity during early healing with progressive callus formation; and (3) optimizing ring designs through numerical analysis to improve structural performance under increased pre-tension. The results showed that, for the metallic EF, the axial displacement under one-leg stance reached 8.41 mm without pre-tension, reducing to 6.83 mm at 500 N pre-tension, though transverse displacement remained significant, suggesting the need for higher wire tension. Callus formation enhanced the load-bearing capacity, as expected. However, excessive displacements persisted under the one-leg stance, indicating that full weight-bearing should be delayed beyond two weeks for a fracture gap of 3 mm. A ring design assessment showed that full-ring configurations with two wires per ring improved performance. The 3D-printed full-ring design made of carbon-fiber-reinforced polylactic acid (PLA-CF) reduced stress by 85% at 500 N pre-tension compared to the initial configuration, remaining within allowable limits. While confirming feasibility, the study highlights the need for geometric refinements to accommodate higher preloads and improve transverse stiffness.  
Keywords:hybrid external fixator; Additive Manufacturing; PLA-CF; finite element analysis; Tresca criterion; callus; bone; design; stiffness +
Footnote †: journal: Applied Sciences
# High Resolution Brillouin Sensing of Micro-Scale Structures
###### Abstract  
Brillouin distributed measurement techniques have been extensively developed for structural health monitoring using fibre optic nerve systems. The recent advancement in the spatial resolution capabilities of correlation-based Brillouin distributed technique have reached the sub-mm regime, making this approach a suitable candidate for monitoring and characterizing integrated photonic devices. The small dimension associated with the short length of these devices--on the order of the cm- and mm-scale--requires high sensitivity detection techniques and sub-mm spatial resolution. In this paper, we provide an overview of the different Brillouin sensing techniques in various micro-scale structures such as photonic crystal fibres, microfibres, and on-chip waveguides. We show how Brillouin sensing is capable of detecting fine transverse geometrical features with the sensitivity of a few nm and also extremely small longitudinal features on the order of a few hundreds of \(\upmu\)m. We focus on the technique of Brillouin optical correlation domain analysis (BOCDA), which enables such high spatial resolution for mapping the opto-acoustic responses of micro-scale waveguides.  
Keywords:stimulated Brillouin scattering; distributed sensing; BOCDA; photonic waveguides +
Footnote †: journal: Applied Physics Letters