esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Security Vulnerabilities
### Surface-Associated Vulnerabilities
#### 3.2.1 End Device Vulnerability  
**Vulnerable Device Role**: An IoT device can have multiple roles in a smart system. A device embedded with a sensor performs as a collector. Similarly, a device with a sensor and actuator collects information as well as performs actions. It is also possible that a device has a combined role of collector, performer, and coordinator. The multiple roles of IoT nodes can make them vulnerable to identity thefts and make it easy for a malicious device to impersonate a legitimate device [78].  
**System Software Vulnerability:** IoT devices are embedded with system software, such as operating system, kernel, and firmware. The system software should be upgraded and patched regularly to avoid exploitations. However, due to the lossy nature of the constrained networks, it becomes a challenging task to apply security updates over the air to an IoT system with a large number of smart devices. As such, smart devices are at risk of being compromised if their software is not patched regularly. Moreover, the task of updating and upgrading should be performed by following proper guidelines. Otherwise, an insecure update may enable adversaries to discover security-critical information related to software and firmware versions and configurations [79; 80].  
**Storage Vulnerability:** A smart device stores security-sensitive information, such as cryptographic materials and sensor information, in its non-volatile storage. Device manufacturers may not consider storage security in the hardware design to reduce the size of a smart device or minimize the price of the device. Therefore, poor storage security can be a threat to the privacy and confidentiality of the in-device sensor information and credentials [81].
# Design and Evaluation of a Web- and Mobile-Based Binaural Audio Platform for Cultural Heritage
## 5 Evaluation  
The evaluation of PlugSonic focused on the Soundscape Create and Soundscape Experience Mobile apps, which we considered the most critical in terms of contributions to both the spatial audio and cultural heritage research communities. The purpose of the evaluation was manifold: Understand whether users with no previous experience in sound design or cultural heritage content curation, and without previous knowledge and experience of 3D audio, could easily familiarise with the apps and use the functionalities and features as they were designed for. See if users could quickly and effectively use PlugSonic to recreate a 3D soundscape that maps onto a real physical space. Find out if spatial audio technologies in general and the Experience Mobile app in particular constitute a useful and practical way to design compelling experiences and improve engagement with and understanding of cultural heritage. Specifically, we intended to answer the following questions:  
* How would users create a soundscape using PlugSonic?
* Could users easily find functionalities/features they need?
* Did users understand all functionalities/features offered in Plugsonic?
* If not, what issues did participants face?
* How long did it take them to successfully use the functionality/feature?
* How easy or difficult was it for users to recreate a 3D soundscape experienced in a real physical space?  
The evaluation was arranged in three parts. In the first part, participants created a soundscape following predefined tasks provided to them. In the second part, the same participants listened to a soundscape set up in a real physical space and were then asked to recreate it using Soundscape Create. In the third part, another group of participants was asked to explore the soundscape created for part 2 using Soundscape Experience Mobile. We discuss the details further below.  
Figure 4: PlugSonic Soundscape Experience Mobile UI.
## 2 Materials and Methods
### Trait and Ability of PGPR  
The PGPR consortium used in the field experiment consisted of the following strains of endophytic bacteria: _Bacillus subtilis_, _Bacillus amyloliquefaciens_, and _Pseudomonas fluorescens_.  
The bacterial species included in the consortium came from the collection of the Department of Soil Science and Microbiology, Poznan University of Life Sciences, Poland. They were isolated from substrates under crops, on selective media: _Bacillus subtilis_--on _Bacillus_ Chromo Select agar, _Bacillus amyloliquefaciens_--on starch agar [14], and _Pseudomonas fluorescens_--on King B agar [15]. The species were genetically identified on the basis of a fragment of the 165 rRNA gene sequence.  
The research enabled a determination of the ability of the endophytic isolates to (i) produce indole-3-acetic acid (IAA) (by spectrophotometry), (ii) produce antifungal metabolites on PDA (Potato Dextrose Agar) in interaction with the _Fusarium_, _Alternaria_, and _Botrytis_ genera, (iii) activate phosphorus on a substrate dissolving calcium phosphate Ca\({}_{3}\) (PO4)\({}_{2}\)[14], and (iv) fix molecular nitrogen by PCR, where the polF/polR primers were used to amplify a specific fragment of the _nif_H gene.  
_Bacillus subtilis_ bacteria exhibited all of the aforementioned metabolic properties. _Bacillus amyloliquefaciens_ bacteria exhibited the first (i) and second (ii) properties, whereas _Pseudomonas fluorescens_ exhibited the first (i), third (iii) and fourth (iv) properties.
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 2 Designing DaaG  
### Design Consideration  
The principal features that have to consider to implement DaaG are enumerated as follows:1. Connectivity: This enables the sensor network and actuator network to share their data when they are physically separated. Additionally, it acts as a data ferry, to deliver the data between the sensor and the actuator network, or a routing node in a multi-hop network, represented as Figure 1a, to forward the data between them. The most distinctive feature of the two methods is the mobility of the drones during the network provisioning service. In the data ferry method, represented as Figure 1b, the drones continuously travel between the sensors and the actuators. In the multi-hop network method, the drones hover and relay data between the sensors and the actuators.
2. Automated system: It can self-determine its flight plan to efficiently collect sensor data and appropriately decide the actuation.
3. Smartness: It should be smart enough to translate sensor data and actuation control between the sensor and actuator network since the latter cannot directly interpret raw sensor data that the former delivers and vice versa.
## 3 Experimental Results and Analyses
### Evolution of Accumulative Fatigue Damage of the Argillaceous Shale Samples  
To further predict the fatigue life, the correlation between the damage accumulation and the relative cycle was examined, as shown in Figure 15. The rock damage exhibited a quickly rising trend and then stabilized. Combined with the two-stage damage accumulation shown in Figure 15a-d, a new damage evolution model was proposed. It is expressed as  
\[D{=}\ 1-\left(1-\left(n/N_{f}\right)^{a}\right)^{b} \tag{7}\]  
where \(D\) is the damage variable caused by irreversible plastic deformation. \(N\) is the number of loading cycles. \(N_{f}\) is the fatigue life, and \(a\) and \(b\) are related to the material parameters. Before loading, the rock damage was defined as 0 (\(N\) = 0), and when the sample was completely damaged, it was defined as 1 (\(N\) = \(N_{f}\)).  
The fitted results for the rock samples subjected to the different frequencies are shown in Table 2. The correlation coefficients of the fitted equations for 0.5 Hz, 0.2 Hz, 0.1 Hz, and 0.05 Hz were 0.993, 0.951, 0.974, and 0.985, respectively. These correlation coefficients were high, which demonstrated how well the damage evolution equations fit the data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Loading Frequency (Hz)**} & \multicolumn{2}{c}{\(\boldsymbol{y=1-\left(1-x^{A}\right)^{B}}\)} & \multirow{2}{*}{\(\boldsymbol{\mathbf{R^{2}}}\)} \\ \cline{2-3} \cline{5-5}  & **A** & & **B** \\ \hline
0.5 & 0.73574 & 2.90574 & 0.99319 \\
0.2 & 1.01348 & 2.78921 & 0.95081 \\
0.1 & 0.82709 & 2.61645 & 0.97423 \\
0.05 & 0.74002 & 1.62622 & 0.98501 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fitted relationship between the accumulative damage and the relative cycle during the sample deformation for the different loading frequencies.  
Figure 15: **(a–d)** Relationship between the damage accumulated at the end of each cycle as a function of the relative cycle for the argillaceous shale samples subjected to the loading frequencies. The loading frequencies were 0.5 Hz, 0.2 Hz, 0.1 Hz, and 0.05 Hz, respectively.
# Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains
## 1 Introduction
The proposed approach is evaluated through simulated performance testing to assess its effectiveness and efficiency, and the results demonstrate that this approach can provide a lightweight yet robust solution for securing IoT devices and networks by leveraging the space efficiency and rapid membership checking capabilities of Bloom filters alongside the tamper-evident properties of hash chains. The rest of this paper is organized as follows: Section 2 establishes the foundation for the proposed approach. The section begins by introducing the RPL and its susceptibility to sybil and flooding attacks. Following this, we discuss two cryptographic constructs integral to our proposed solution, namely, Bloom filters and hash chains. Section 3 provides an overview of recent research efforts pertinent to our proposed solution while identifying their shortcomings. Section 4 describes our proposed solution, including its design principles and key features. Section 5 presents the results of our simulations, with a focus on evaluating the performance and effectiveness of the proposed solution. Finally, Section 6 summarizes the key findings and explores potential avenues for future research.
## 3 Proposed Solution: Modelling Components of a Communication Protocol as Resources
### Functional Entities  
* Resource: MQTT topics are modelled as UMA resources. A group of MQTT topics with the same authorization requirements can be seen as a Resource Set (RS). Given that MQTT topics form an RS, they will have a set of access policies associated. These policies must be defined by the topic owner in the AS, and will have to be evaluated to allow or deny the publishing and subscription actions, depending on who starts them.
* Resource Server Broker: This must include the MQTT Broker functionality and APIs, along with UMA-specific interfaces and APIs required by the UMA Resource Server flow.
* Authorization Server: In our proposal, this component is defined exactly as in the UMA specifications. Access control policies will be defined in this server and will need to be built considering the particularities of the resources proposed in this solution (MQTT topics).
* Client: An application that allows the IoT device owner to obtain a valid RPT through the UMA authorization flow. The IoT device owner must transmit the RPT to the device in a secure way. Typically, this application can be accessed via a Browser or using a Mobile App.
* IoT Device: Data generator or consumer that uses the MQTT protocol publish or subscribe actions to the Resource Server. All actions (to either publish or subscribe) must be accompanied by the RPT and will be defined by the IoT Device Owner.
# Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications
## 3 Method
### Phase 1--Definition of the \"What's\"
#### 4.1.1 Technology Acceptance: Theoretical Models  
Works classified under this category include extensive literature reviews whose objective was to propose theoretical models to predict and evaluate the technology acceptance or systematically analyze previous empirical research. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) developed a General Extended Technology Acceptance Model for e-learning (GETAMEL) by analyzing the external variables used in TAM extensions from the perspective of technology and user types. Similarly, [PERSON] et al. [PERSON] et al. (2019) explored the literature in the field of e-learning to identify the most commonly used external factors explaining students' acceptance. The resulting model was validated with students of five universities in the United Arab of Emirates (UAE). [PERSON] et al. (2018) organized a literature review considering each one of the constructs of the TAM2 and used the results to evaluate faculty's adoption of online teaching practices. In this same context, [PERSON] et al. [PERSON] et al. (2018) conducted a throughout literature review to deepen and synthesize TAM studies in digital technologies in education. The acceptance of everyday technologies in the context of education, such as mobile technologies, have been analyzed using TAM as in work by [PERSON] et al. [PERSON] et al. (2019) who developed a model to evaluate teachers' intention to use mobile technologies in formal education. A more profound analysis of TAM applications in M-learning is presented in the research by [PERSON] et al. [PERSON] et al. (2019). In the Virtual reality field, [PERSON] and [PERSON] (2019) adopted a qualitative approach to identify the external dimensions influencing the acceptance of a mobile augmented reality application. The results were included in an extended TAM, especially useful for virtual reality innovations within the tourism sector.
## 4 Effects of Cannabinoids on Gastrointestinal Motility
### Effects of Phytocannabinoids on Gastrointestinalal Motility
#### 4.2.2 Effect of CBD on Gastrointestinal Motility  
CBD was not observed to influence electrically induced contractility in the isolated guinea pig ileum [74]; however, in combination with THC, it inhibited the speed of transit of a charcoal meal in mice to a greater degree than THC on its own [72]. In another study, CBD normalized in vivo hypermotility induced by croton oil in mice, as measured by fluorescent marker transit speed; however, it did not influence motility in the control group. CBD did not inhibit motility in vivo in animals treated with an FAAH inhibitor. In the same study, CBD inhibited contractility induced by ACh and PGF2a in both healthy and croton-treated ileal segments in vitro, but did not inhibit spontaneous contractility [79]. A study by [PERSON] et al. from 2011 found that both cannabidiol and cannabidiolic acid (CBDA) caused a drop in resting tension of isolated intestinal segments of the Asian house shrew. They also caused a reduction in the magnitude of contractions caused by carbachol administration and reduced the tension of segments pre-contracted by KCl. These effects were not blocked by CB1 or CB2 antagonists, nor were they blocked by tetrodotoxin (TTX), which might suggest that the effect is not neuronally mediated. CBD and CBDA also inhibited electrically induced intestinal contractions [80]. One study showed significant interaction between CBD and steroidal and nonsteroidal anti-inflammatory drugs ex vivo. CBD applied as a sole agent inhibited ACh-induced motility in isolated rat colon preparations. This effect of CBD on contractility was significantly diminished by co-administration with dexamethasone. In contrast, co-administration with diclofenac caused a far greater inhibition of ACh-induced contractility than either substance applied individually. A possible mechanistic explanation of these results is that the nonsteroidal anti-inflammatory drug diclofenac caused an accumulation of arachidonic acid via cyclooxygenase pathway inhibition, thus providing more arachidonic acid for the synthesis of endogenous cannabinoids. The attenuation of the effect of CBD by dexamethasone may be explained by the inverse mechanism--dexamethasone diminishes arachidonic acid availability via phospholipase A2 inhibition. Such interactions are noteworthy, due to the high probability of these two classes of drugs being used in parallel with cannabinoid therapy [81].  
In a study using both in vivo and ex vivo models of TNBS-induced colitis in rats, pretreatment with CBD caused a reduction in TNBS-induced upper GIT hypermotility in vivo and reversed the enhancement of electrically induced contractility caused by TNBS administration in isolated colonic muscle strips ex vivo [82].
## 5 Results
### Energy Analysis  
As demonstrated in Figure 1(a), the process utilising MgCl\({}_{2}\)-KCl salts exhibits superior efficiency in comparison to solar salts under all pressure ratios. For both molten salts, there exists a specific pressure ratio that maximizes the cycle efficiency. The cycle using solar salts achieves a maximum efficiency of 41.63% at a pressure ratio of 3.025, while the cycle operating with chloride molten salts reaches a maximum efficiency of 48.86% at a pressure ratio of 3.075. Consequently, the cycle energy efficiency of MgCl\({}_{2}\)-KCl salts increased by 17.37% in relative terms and 7.23% in absolute terms. It is evident that this fact necessitates a compressor that exhibits a 1.7% increase in pressure ratio, which can be regarded as negligible in this context.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Parameter**} & \multicolumn{3}{c}{\(\boldsymbol{\eta\,(\%)}\)} \\ \hline \(T_{min}\) [\({}^{\circ}\)] & \(T_{max}\) [\({}^{\circ}\)] & \(P_{max}\) [bar] & \(PR\) [-] & \(SR\) [-] & Ref. [23] & Present Work \\ \hline
32 & 550 & 200 & 2.64 & 0.334 & 41.18 & 41.18 \\
32 & 550 & 300 & 3.86 & 0.355 & 43.32 & 42.74 \\
32 & 750 & 200 & 2.65 & 0.223 & 46.07 & 46.08 \\
32 & 750 & 300 & 3.94 & 0.281 & 49.83 & 49.83 \\
50 & 550 & 200 & 2.40 & 0.184 & 36.71 & 36.70 \\
50 & 550 & 300 & 2.80 & 0.254 & 38.93 & 38.93 \\
50 & 750 & 200 & 2.88 & 0.109 & 43.50 & 43.55 \\
50 & 750 & 300 & 3.08 & 0.175 & 45.28 & 45.28 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between the simulation results and the data reported by reference [23].  
Figure 2: Comparison of the cycle energy efficiency, overall energy efficiency and specific work at different pressure ratios.  
As can be observed in Figure 2b, the maximum overall efficiency is 23.76% for the MgCl\({}_{2}\)-KCl and 22.29% for the solar salts. The optimum pressure ratio is the same, which maximizes the cycle efficiency. The observed trends are similar to those observed in the cycle energy efficiency (Figure 2). While chloride salts enhance cycle efficiency significantly, the overall efficiency only increases by 6.6%. The higher operating temperature of the chloride salts increases the convection and radiation losses on the solar receiver. As a result, the solar block efficiency drops from 53.55% (solar salts) to 48.86% losses, which diminishes the advantages of using MgCl\({}_{2}\)-KCl salts.  
Figure 2c shows the influence of pressure ratio on the specific work. The maximum specific work value for solar salts is 92.48 kJ/kg, whereas for MgCl\({}_{2}\)-KCl salts, the value is 138.9 kJ/kg. Plants operating with MgCl\({}_{2}\)-KCl salts have demonstrated a significant increase in specific work, improving by 50.19%. Both molten salts achieve their maximum specific work at practically the same pressure ratio that corresponds to maximum efficiency.
## 3 Road Anomaly Detection and Classification Approaches through Vibration-Based Techniques
### Deep Learning-Based Methods
[45] that, according to the authors, achieved a better performance than support vector machines and multilayer perceptrons. Table 5 shows a summary of the studies that have employed deep learning algorithms for road surface anomaly detection and classification, along with their performance. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Author** & **Year** & \begin{tabular}{c} **Classified** \\ **Road Anomalies** \\ \end{tabular} & **Algorithm** & \begin{tabular}{c} **Performance** \\ **Metrics** \\ \end{tabular} \\ \hline [PERSON] et al. [42] & 2019 & \begin{tabular}{c} Crack \\ **Pothole** \\ **Smooth Road** \\ \end{tabular} & \begin{tabular}{c} Multilayer \\ **Perceptron** \\ \end{tabular} & Accuracy: 92.12\% \\ \hline [PERSON] et al. [1] & 2020 & \begin{tabular}{c} Call \\ **Door** \\ **Message** \\ **Potholes** \\ **Speed bump** \\ **Street Gutter** \\ \end{tabular} & \begin{tabular}{c} **Convolutional** \\ **Neural Network** \\ \end{tabular} & Accuracy: 93.00\% \\ \hline [PERSON] et al. [40] & 2020 & \begin{tabular}{c} Potholes \\ **Cracks** \\ **Transverse cracks** \\ **Patches** \\ **Rumble strips** \\ **Speed bump** \\ \end{tabular} & \begin{tabular}{c} **Convolutional** \\ **Neural Network** \\ \end{tabular} & Accuracy: 97.20\% \\ \hline [PERSON] et al. [3] & 2020 & \begin{tabular}{c} Pothole \\ **Bump** \\ **Gravel** \\ **Cobblestone** \\ **Broken concrete** \\ \end{tabular} & \begin{tabular}{c} **Convolutional** \\ **Neural Network** \\ \end{tabular} & Accuracy: 99.26\% \\ \hline [PERSON] et al. [41] & 2020 & \begin{tabular}{c} Good road \\ **Medium road** \\ **Bad road** \\ \end{tabular} & \begin{tabular}{c} **Convolutional** \\ **Neural Network** \\ \end{tabular} & Accuracy: 98.5\% \\ \hline [PERSON] et al. [43] & 2021 & \
\begin{tabular}{c} Asphalt road \\ **Cobblestone road** \\ **Dirt road** \\ \end{tabular} &
\begin{tabular}{c} **Convolutional** \\ **Neural Network** \\ \end{tabular} & Accuracy: 93.17\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Methods used for road anomaly detection and classification based on deep learning techniques. Although deep learning techniques can automatically extract features from raw accelerometer data and achieve relatively high performance, as depicted in the mentioned studies, typical disadvantages of deep learning techniques exist. For example, the need for large sample size, high computational power requirements, the black-box structure of these classifiers that limits their interpretability and the setting process of its parameters could be considered an art [46; 47].
# Mobility-Multihoming Duality
## 7 Discussion
### Firewalls and Middleboxes  
ILNP packets look like IPv6 packets \"on the wire\". So, for anywhere that an IPv6 is accepted by firewalls, then ILNP should also be accepted. However, there are differences in the way ILNP works that could cause difficulties with firewalls for ILNP unless those firewalls are configured to understand these differences. This might especially be the case for enterprise networks. These will typically have more restrictive firewalling compared to domestic (homed) networks, for example. The key differences in ILNP \"on-the-wire\" compared to an IPv6 are the following:  
* The ICMPv6 messages for ILNP Locator Update (LU) signalling [40] may need to be explicitly enabled for firewalls that have \"default deny\" policies for those packets types that are not required for operational use.
* The ILNP Nonce [41] implemented as Destination Option extension header for IPv6 needs to be sent from host to host for an ILNP packet flow. Some firewalls might disallow such extension headers.
* The TCP checksum computation changes when TCP is used over ILNP to exclude the L64 value, which is mutable. This is not a problem for an ILNP-capable host, but a firewall or middlebox that checks the TCP Checksum and is not ILNP-aware will see the Checksum computation as incorrect. This is a layer 4 (transport layer) packet check and so will be used where there are stricter site security policies for firewalls.  
The exact behaviour of firewalls and middleboxes varies, so it might not be possible to predict ahead of deployment where ILNP will and will not work. However, our experiments in this paper show that at the network level at least, commercial equipment can support the transmission of ILNP packets as if they are IPv6 packets.