esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
## 1 Introduction
### Study Background
Additionally, some studies using graphics processing units have been presented for solving large concert hall models [14; 15]. Other time-domain room acoustic solvers are based on the finite element method framework. They are called the time domain finite element method (TD-FEM). Actually, TD-FEM can accommodate complex geometries naturally. Therefore, it has attracted considerable attention in recent years [16; 17; 18; 19; 20; 21; 22]. In TD-FEM, the weak form of a wave equation is spatially discretized using finite elements (FEs). Then the resulting semi-discretized equation is discretized temporally using a time integration method such as Newmark \(\beta\) method [23]. In general, the resulting time marching scheme becomes implicit as opposed to the explicit scheme in FDTD method. It is also adversely affected by dispersion error, but it offers attractive capabilities for room-shape modeling and offers stability in computations. Similar to the FDTD method, two efficient room acoustic TD-FEMs exist. They use low-dispersion FEs, a highly accurate time integration method, and Krylov subspace iterative solvers for large linear systems. In several reports of the relevant literature [17; 18], an efficient room acoustic solver with a high-order spline TD-FEM has been used for acoustic simulations in existing rooms such as a multi-purpose hall and a reverberation room. A further improved method has been developed recently for high frequencies [20]. Another solver uses fourth-order accurate linear TD-FEM in both space and time [19]. Its applicability has been demonstrated via acoustic simulations at kilohertz frequencies of a simply shaped concert hall and of reverberation room absorption coefficient measurements [22]. Although the two solvers offer some promising potential for their ability to predict sound fields in actual-sized rooms accurately, they are still time-consuming methods because of their implicit formulation. Therefore, recent studies have included some attempts at developing a room acoustic solver using explicit TD-FEM.
# Reduced Overhead Routing in Short-Range Low-Power and Lossy Wireless Networks +
## 4 Reduced Overhead Routing Protocol
### Enhanced P2P Routing and Forwarding
#### 4.1.2 Proposed Enhancement
The figures demonstrate that ERPL can achieve the same functionality with reduced control overhead. A comparison of Figure 3 with Figure 1 reveals that ERPL can achieve the same functionality with fewer transmissions and lower control overhead. To develop a better understanding of ERPL's potential benefits, we carried out computer-based simulation experiments. In experiments, the number of nodes in the network is varied from 500 to 2000. In our simulated network topologies, any node can have at most 8 direct neighbors, and the maximum number of hops between a node in a network and the root node is no more than 6 hops. In general however, a node can have an arbitrary number of neighbors using ERPL, and similarly a node can have an arbitrary number of nodes along the forwarding path to the gateway. In our simulation, each node generates 1000 P2P packets, and the destination peer node for each packet is selected randomly. Each simulation was repeated 30 times. P2P transmission comparison for ERPLand RPL is shown in Figure 4a. The figure reveals that ERPL demonstrates statistically significantly lower number of P2P transmissions to deliver P2P data packets compared to RPL. The difference between both protocols' P2P transmissions is shown in Figure 4b. The figure shows that in all cases RPL requires over 100,000 more transmissions compared to ERPL. Hence, ERPL can help to reduce a node's energy consumption. Moreover, ERPL requires a lower number of transmissions to deliver P2P data packets, hence it has the potential to positively impact the performance of MP2P and P2 MP communications due to lower contention in a network. Figure 4: P2P Transmissions Comparison. Figure 3: P2 MP and P2P Route Construction in Enhanced RPL (ERPL).
## 2 Seawater Channel Modelling Elements
### UOWC System Layout
#### 2.1.2 Seawater Propagation Channel
Also, changes in water scattering and absorption characteristics lead the optical signal to be attenuated. Additionally, objects in the water, including suspended particles or marine life, can scatter and absorb an optical signal. The size and density of these objects influence light transmission through the water. Larger objects may produce more substantial attenuation and dispersion, resulting in higher power loss and reduced communication performance. As a result, underwater channels are regarded as nature's most complicated and severe channels [47]. The water propagation CIR (\(h_{c}(t)\)) is a primary function in assessing the propagation impairments' impact on the overall system performance by causing spatial and temporal dispersion and attenuation, ultimately dropping the received optical power and leading to a reduction in the communication receiver's SNR [45]. Various researchers have focused on the \(h_{c}(t)\) evaluation to complete the overall description of the UOWC system [48]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Water Type** & \(\mathbf{a}(\lambda)\)**m\({}^{-1}\)** & \(\mathbf{b}(\lambda)\)**m\({}^{-1}\)** & \(\mathbf{c}(\lambda)\)**m\({}^{-1}\)** & \(\mathbf{w_{0}}\) \\ \hline Clear & 0.114 & 0.037 & 0.151 & 0.25 \\ \hline Coastal & 0.179 & 0.219 & 0.398 & 0.55 \\ \hline Harbor & 0.287 & 0.813 & 1.100 & 0.74 \\ \hline Turbid & 0.366 & 1.824 & 2.190 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Seawater absorption, scattering, and attenuation coefficients [49].
## 4 Implementation and Experiments
### Results and Evaluation  
The experiments were executed in an iMac with a 3 GHz Intel Core i5 processor (Victoria, Tamps, Mexico), 16 GB of RAM and macOS Mojave as an operating system. The implemented application was developed in Java 8.  
Figure 7: Breadth-first URL extraction process (**A**) and queue URLs (**B**) after adding the extracted URLs. LV denotes the level of each node.  
The experiments were conducted over three different corpuses, built from the Wikipedia categories of computer science, politics, and diabetes. A KRS was constructed to represent the content of each corpus. The relevance of a web page content in a given topic was computed using a similarity measure based on the statistical measure IDF and a threshold defined by the arithmetic mean and the standard deviation. Table 3 shows the statistics of the three Wikipedia categories. The number of Wikipedia pages retrieved for each category corresponds to the first level of the category hierarchy. For example, the root level of computer science category contains 19 subcategories and 51 Wikipedia pages. For each subcategory, the corresponding Wikipedia pages are extracted, resulting in 1151 documents (second column in Table 3). The third column presents the total number of enriched nouns extracted and the average enriched nouns per document. The fourth column shows the total number of enriched nouns with a URL associated with a KB of the SW and the average value per Wikipedia page.  
The results from experiments were analyzed qualitatively and quantitatively. The first one is focused on the number of downloaded web pages related to a topic. The second one is focused in the quality of the results from the quantitative experiments.
# Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs
## 1 Introduction
This is particularly useful when dealing with a learning problem with limited training data, which is often the case for niche classification problems such as Arabian camel breed classification. Secondly, through further training (fine-tuning) of pre-trained CNNs on a domain-specific dataset, namely our Arabian camel dataset, our  
Figure 1: The six Arabian camel breeds are: (**A**) Waddeh, (**B**) Majaheem, (**C**) Homor, (**D**) Sofor, (**E**) Shaele, and (**F**) Shageh. approach is able to effectively capture the distinctive and subtle features of different Arabian camel breeds and improve our system's classification accuracy. Finally, our approach is scalable and can be easily extended to classify other animal breeds or even other types of objects after fine-tuning of our model on a domain-specific niche dataset. The main contributions of our work presented in this paper are as follows:  
* We introduce a new image dataset for Arabian camel breed classification, consisting of 1073 images of 6 different breeds of Arabian camels collected from various sources. The breeds are: Majaheem, Honor, Shaele, Shageh, Sofor, and Waddeh. To the best of our knowledge, this is the first and only dataset of its kind;
* We propose a machine learning application framework for image-based camel breed classification based on transfer learning. We use pre-trained CNNs as feature extractors, fine-tune them on our new dataset, and retain only the best performing CNN in our final application system;
* We evaluate our model on our new dataset and compare it with several baselines and state-of-the-art methods. We show that our model achieves high accuracy and good generalization across different breeds and image conditions;
* We integrate our selected best-performing CNN into a mobile application for further validation and actual use in a real-world scenario. The rest of this paper is structured as follows. Section 2 gives an overview of previous related works that utilize machine learning to solve animal breed classification. Section 3 provides the details of the proposed solution for Arabian camel breed classification. Section 4 presents the implementation details and our experimental results. Finally, Section 5 concludes the paper and points out the directions of our future work.
# Improving Very High Plastic Clays with the Combined Effect of Sand, Line, and Polypropylene Fibers
## 5 Conclusions  
This research aimed to improve the geotechnical properties of very high plasticity clay using three agents: sand, lime and PP fibers. The outcomes of this research can be summarized as follows:  
* Sand as an additive reduces the plasticity of clay, but its effect on strength is limited (i.e., 10% improvement), while sand content should be limited to 20 wt% (for the clay tested), since further increases in sand content reduces the UCS strength. In addition, the use of sand reduces the demand in lime from 8.7% to 3.4% (i.e., 155% decrease).
* Adding PP fibers to lime-stabilized-clay-sand mixtures, results in a slight decrease in the MDD, due to the relative difference in density between fibers and soil; it is also difficult to achieve the same level of densification in the reinforced soil with the same  
Figure 12: Load-penetration CBR curves for different fiber concentrations.  
Figure 13: Effect of fiber addition on CBR value (**a**) and Secant modulus (**b**).  
compaction energy. In terms of OMC, it is observed that PP fibers have an insignificant effect; any such change observed was due to the presence of lime. The latter consumes water for its pozzolanic reactions.
* Using sand, lime, and PP fibers simultaneously significantly improves the UCS strength. For 0.6% of fiber addition, the UCS strength reached 2864 and 4593 kPa after 28 and 90 days of curing respectively (please compare with the 361 kPa of the untreated clay, which is 7.9 and 12.7 times lower respectively).
* An analogous improvement was observed in the CBR value and secant modulus for 0.9% of fiber addition in the lime-stabilized-clay-sand mixtures. Further addition of fibers results in a decrease of the CBR value. The above findings clearly show that, the simultaneous use of sand, lime, and PP fibers is very advantageous in improving the engineering properties of very high plastic clays. In addition to that, using these additives together reduces the lime demand and corrects some other technical limitations commonly appear when using each one additive separately (brittleness, limited effect on strength).  
Conceptualization, A.M. and L.P.; methodology, A.M. and L.P.; validation, A.M. and L.P.; formal analysis, A.M. and L.P.; investigation, A.E.G.; resources, A.E.G.; data curation, A.M. and A.E.G.; writing--original draft preparation, A.M. and L.P.; writing--review and editing, A.M. and L.P.; visualization, A.M.; supervision, A.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data availability StatementData are available on request from the corresponding Author.  
The authors declare no conflict of interest.
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 4 Discussion
### Comparisons with Previous Methods
In addition to the difference in the correct rate of epileptic detection, there is a gradual trend of smaller segments of input epileptic EEG data from traditional machine learning to deep learning algorithms [36, 43, 44, 45, 46], and the time of input data segment is set to 1 s, which is worse than most deep learning epileptic detection algorithms. Even if the recognition accuracy of the proposed model is individually smaller than existing algorithms, it does come close to the highest-performing classifier without using hand-crafted features, and it outperforms most other comparative methods. In addition, clinicians have endorsed the epilepsy detection method and associated research outcomes [47, 48]. Overall, the micro-capsule network framework can effectively enhance the weakly epileptic EEG features and improve the classification effect in epileptic states by reducing the capsule network layer, which can solve the problem of a small amount of seizure data in clinical applications to some extent. This facilitates real-time detection of epilepsy patients and further prompts patients, their families, and doctors to take necessary preventive measures.
# Trends and Challenges in Network Covert Channels Countermeasures
## 4 Covert Channel Detection
### VoIP  
Owing to its ubiquitous availability and the potential capacity when used as a carrier, many works concentrated on the detection of covert channels targeting VoIP traffic. Alas, VoIP streams can be exploited in different parts, e.g., in the vocal payload or in the resulting network traffic. Specifically, many techniques use some form of audio steganography or digital watermarking for blending an arbitrary message within the audio of the talker. In general, a common approach for detecting the presence of secret information requires the warden to compute some audio quality metric to reveal the hidden data [37]. However, this could not be a simple task, as the attacker may take advantage of specific features of the used codec, or transcode the conversation to free space in packets to embed information while maintaining the same level of quality [38]. Therefore, the performance of the detection can be influenced by the original codec both in terms of performance and scalability. A possible improvement could exploit information provided by the network traffic in order to reveal \"misuse patterns\" [39]. For instance, the warden can identify a covert channel within a VoIP flow by using statistical metrics like the variance of inter-arrival time of packets composing the Real-time Transport Protocol (RTP) stream. The idea of using simple metrics and calculations for spotting the presence of the channel has not been considered only to optimize performances and extendability of a warden. Specifically, computing metrics on the audio signal could be unfeasible due to encryption or to not break the privacy of users. In this vein, lower-level and simpler approaches should be carefully evaluated. As an example, in [40] a scheme exploiting a matrix containing sketches of packets is presented. Another challenge concerns the ability of detecting short communications or channels transferring a restricted amount of information. In this case, the adoption of some form of artificial intelligence looks very promising [41] but at the price of being able to collect suitable data for training the detection framework.  
Lastly, if the VoIP stream exploits some form of standard technologies like the Session Initiation Protocol (SIP), the warden can try to detect the hidden communication by analyzing anomalous distributions of values in the headers, bursts of methods or deviation in the traffic statistics of the resulting RTP and SIP flows [31; 42]. A major challenge concerns the development of a suitable warden able to perform detection without disrupting the real-time, interactive nature of VoIP communications and SIP signaling. Emerging approaches exploit the use of application-agnostic parameters (e.g., the size of the protocol data units) and deep the detection at the codec or audio level only if packets are considered potentially suspicious. Moreover, algorithms used for traffic coloring can offer an effective foundation to reveal hidden data within RTP and Realtime Transport Control Protocol streams [34].
# Limitations on the Use of Recycled Asphalt Pavement in Structural Concrete
## 4 Mechanical Behaviour of Concrete Containing RAP  
Many studies (see Table 1) were conducted on concrete with RAP aggregates and with different percentages of substitution. It is well known that a higher substitution of natural aggregate with RAP aggregate influences the strength of concrete [19]. In the production of concrete for constructions, the RAP aggregates used are more likely to be coarse, since fine aggregates affect workability [21].  
The results on fresh concrete show that both air content and unit weight of concrete mixes with RAP do not vary significantly with the RAP content percentage; slump slightly increases with increasing RAP content. In terms of workability, RAP aggregate mixes are easily mixed compared to natural aggregates ones and this is probably due to the presence of oil traces [3].  
Hardened concrete characteristics were analysed in terms of compressive strength, flexural strength, split tensile strength and elasticity modulus, and the main results are shown in the following paragraphs.
## 5 Results and Discussion
### Evaluation of Dichotomous Estimates/Forecasts
#### 5.4.1 Evaluation of Dichotomous Estimates/Forecasts for Precipitation below 15 mm  
As can be seen in Figure 17a-c and Table 17, in the Guilan area, ERA-Interim performs better than IMERG and 3B42 for precipitation below 15 mm/day which might be due to high amount of moisture in the atmosphere in this area that the satellites observed, although precipitation did not occur. Whereas, IMERG yields better results in Tehran and Bushehr areas in comparison to the other products. Moreover, in Kermanshah, IMERG and ERA-Interim indicated rather similar behavior, while in all study areas, 3B42 indicated low values of indices for precipitation below 15 mm/day.  
Figure 17: (**a–c**) POD, FAR and CSI in four study region for precipitation below 15 mm/day.
# SNR-Based GNSS-R for Coastal Sea-Level Altimetry
## 4 Port-au-Prince Tide Gauge Station  
The GNSS station at the Port-au-Prince tide gauge station (POPR) was installed in March 2019 at the Haiti Coast Guard Base in Port-au-Prince, with a collaboration between the University of Luxembourg, the Faculty of Sciences of the State University of Haiti, and the Haiti National Center for Geospatial Information (CNIGS). The GNSS station was equipped with a PolaR\(\times\)5 receiver at 1 Hz sampling rate and a VP6050 high-precision full-spectrum GNSS antenna (Figure 1).  
The antenna was installed about 3.23 m above the mean sea level and has clear reflections from the sea surface towards the north direction (Figure 2). SNR observations from GPS and GLONASS for the two-year period from 1 March 2019 to 26 March 2021 are processed to retrieve sea levels in the Caribbean Sea. An azimuthal mask and elevation mask of 180\({}^{\circ}\) (90-270\({}^{\circ}\)) and 20\({}^{\circ}\) (5-25\({}^{\circ}\)) are used at the POPR.  
In addition, the retrieved reflector heights are corrected for offset of the APC relative to the APR [30]. The retrieved reflector heights at the Port-au-Prince tide gauge station are shown in Figure 4. The seasonal tidal sea-level variation and a reflector height envelope of 1.03 m is clear at the POPR.  
The tide gauge station at Port-au-Prince (PTPR) is used to assess the performance of GNSS-R sea level altimetry. The PTPR, which is operated by the Maritime Service and Navigation of Haiti (SEMANAH) is a part of the Global Sea Level Observing System (GLOSS) network. The GLOSS network, with 287 tide gauge stations around the word, is for long-term climate change and oceanographic sea level monitoring [31]. The PTPR was equipped with a S12C hydrostatic submersible level transmitter having a piezo-resistive ceramic pressure sensor [32] and an OTT radar level sensor [33]. The S12C SDI-12 was retired at the beginning of 2017, and the OTT RLS was offline until 9 October 2020 due to the GPS week number rollover bug. The radar level sensor, with pulse radar technology that reports the sea level every minute with an accuracy of \(\pm\)3 mm, was installed 61.6 cm vertically below the GNSS antenna ARP.  
Figure 4: Relative reflector height retrieved by GNSS-R at Port-au-Prince tide gauge station.
## 3 Approach
### Modulation  
If the phase of the modulation signal is changed by 180\({}^{\circ}\), i.e., the signal and the resulting stripe pattern are inverted, between two consecutive frames of a captured video, the receiving camera can detect if the state has changed since the last recorded frame. This state change always appears, independent of the position of the transmitting light source in the frames, given that the modulated light source did not move a significant amount between the two frames. The UDPSOOK modulation method by [PERSON] et al. [3] uses this characteristic to encode information with those phase shifts between two consecutive frames. If there was no phase shift, which means the state of the modulated light source is  
Figure 4: Stripe pattern occurs when capturing a flickering LED with a rolling-shutter camera.  
Figure 5: Car with modulated taillights at various distances from close-up to farther away [1].  
Figure 3: Plane with moving propeller captured by high-speed camera (left) and rolling-shutter camera (right) [20].  
the same in both frames, a logic 0 was transmitted, if the state changed because of a phase shift between the frames, a logic 1 was transmitted.
# IoT Firmware Emulation and Its Security Application in Fuzzing: A Critical Revisit
###### Abstract  
As IoT devices with microcontroller (MCU)-based firmware become more common in our lives, memory corruption vulnerabilities in their firmware are increasingly targeted by adversaries. Fuzzing is a powerful method for detecting these vulnerabilities, but it poses unique challenges when applied to IoT devices. Direct fuzzing on these devices is inefficient, and recent efforts have shifted towards creating emulation environments for dynamic firmware testing. However, unlike traditional software, firmware interactions with peripherals that are significantly more diverse presents new challenges for achieving scalable full-system emulation and effective fuzzing. This paper reviews 27 state-of-the-art works in MCU-based firmware emulation and its applications in fuzzing. Instead of classifying existing techniques based on their capabilities and features, we first identify the fundamental challenges faced by firmware emulation and fuzzing. We then revisit recent studies, organizing them according to the specific challenges they address, and discussing how each specific challenge is addressed. We compare the emulation fidelity and bug detection capabilities of various techniques to clearly demonstrate their strengths and weaknesses, aiding users in selecting or combining tools to meet their needs. Finally, we highlight the remaining technical gaps and point out important future research directions in firmware emulation and fuzzing.  
IoT devices; MCU; firmware; vulnerability detection; peripheral modeling; firmware emulation; fuzz testing; hardware-in-the-loop Review