esa-sceva/satcom-chunk-collection huggingface dataset:
# Packet Injection Exploiting Attack and Mitigation in Software-Defined Networks
## 3 Packet Injection Exploiting Attack
### Threat Model  
We suppose the attacker controls at least one compromised physical or virtual host. This hypothesis is reasonable because it is compatible with the previous work [13; 17]. Besides, we also assume that the SDN controller runs in reactive mode, widely adopted by most common controllers, such as Floodlight, Ryu, and Pox. In reactive mode, the controller communicates with the switch over the secure channel. For example, the controller sends instructions and installs forwarding rules to the OpenFlow switch. The switches encapsulate and forward unknown packets to the controller. In such a case, attackers can forge lots of unknown packets and continuously trigger the matching action without needing more privileges than legitimate users [16].  
We describe a simple attack scenario shown in Figure 2, including an SDN controller, an OpenFlow switch, and two compromised hosts. The OpenFlow switch forwards unknown packets to the controller through _Packet-In_ messages, and the controller installs flow rules through _Flow-Mod_ and _Packet-Out_ messages. The attacker injects fake hosts by host to generate a ghost topology and carries out DoS attacks through host \(B\). The core attack process includes:  
(1). The attacker sends plenty of packets with forged source MAC addresses to the SDN network through host \(A\).  
(2). When the switch receives an unknown data packet from host \(A\), there is no corresponding forwarding rule in the flow table. It will encapsulate the packet into a _Packet-In_ message and forward it to the controller.  
(3). When receiving a _Packet-In_ message from the switch, the SDN controller will retrieve the Host Profile file. Since the attacker has spoofed the source MAC address, as described in Section 2, the host tracking service will think a new host has joined the network, thus adding a fake host to the SDN topology.  
(4). After injecting many fake hosts, the attacker can further construct specific packets through host \(B\), specify the destination MAC address as a forged fake MAC address, randomly generate the destination IP or port, and send it to the SDN network.  
(5). Since the fake hosts have been added to the SDN topology, the controller will not delete these unknown packets from host \(B\). This is the most significant difference from the attack by faking the destination MAC address. The controller calculates the forwarding strategy of unknown packets and installs flow table rules to the switch.  
(6). If the attacker frequently sends unknown packets through host \(B\), it will overload the controller's processing capabilities and the flow table of switches. In addition, these unmatched flows would consume excessive bandwidth of the southbound interface, and even affect the entire network.
## 2 Experimental Section  
### Field Setup  
In order to characterise the VOC profile throughout the entire decomposition process, a field trial was conducted using four pig (_Sus scrofa domesticus_) carcasses killed by captive headbolt. Pigs are considered to be suitable models for decomposition studies due to their internal anatomy, fat distribution, and gut flora [23]. While this is true, their use as human odour analogues is still not conclusive and literature does not exist providing a direct comparison of odour produced by whole body decomposition to whole pig carcass decomposition in the field. Although we are aware that minor differences in decomposition VOCs may exist between the two species [4, 12], a literature search shows that the core VOCs found from decomposing human and pig remains are representative [4, 12, 22, 24, 25].  
The pig carcasses each weighed approximately 70 kg (to mimic average human body weight) and were placed directly on the soil surface at a field research facility in Sydney, Australia. Surface deposition was used in this study due to the common occurrence of aerobic decomposition environments in homicide and missing persons' cases. Especially in the case of missing persons, death may occur during bushwalking or while lost, resulting in surface deposition of the remains. The site is classified as open eucalypt woodland and possessed sandy clay topsoil. The vegetation at the site was dominated by various eucalypt trees (_Eucalyptus punctata_, _Eucalyptus eugenoides_, _Eucalyptus fibrosassp. fibrosa), lantana shrubs (_Lantana camara_), large-leaved rush (_Lomanda longifolia_) and kangaroo grass (_Themeda australis_).  
Four additional sites with no carcasses were designated as control sites to monitor fluctuations in the natural odour profile of the area. Samples were collected periodically during a two month period (15 January-15 March 2013). Weather variables were recorded using a HOBO(r) U30 No Remote Communication (NRC) data logger (OneTemp, Marleston) in close proximity to the carcasses. Hourly measurements were taken using Hobo(r) sensors for ambient temperature (\({}^{\circ}\)C), relative humidity (%), solar radiation (W/m\({}^{2}\)), wind speed (m/s), wind direction (o) and rainfall (mm). Soil pH and volumetric water content were measured using a direct soil pH measurement kit (Hanna Instruments) and a soil moisture sensor (Vernier, LabQuest 2 Interface).
## 4 Key Technologies for MEC-Assisted Vehicular Networks  
This section reviews the key technologies necessary for effectively incorporating MEC into VNs. These technologies play a pivotal role in improving the abilities and efficiency of VNs by moving computation, storage, and networking resources closer to the network's edge. This proximity enables the development of a variety of applications and services specifically designed for vehicular environments. In the following paragraphs, we will examine these technologies and their related works, which include Software-Defined Networking (SDN) and Digital Twins (DTs).
# Indoor Vehicles Geolocalization Using LoRaWAN
## 1 Introduction
These technologies utilise different types of signals to triangulate the position of the user while indoors. Wi-Fi hot spots, Bluetooth signals and cellphone signals have all been experimented with by the various companies developing this technology. A LoRaWAN based geolocation service can be useful for less precise localisation such as for area-based location but with the advantage of requiring fewer and cheaper devices offering long-lasting batteries. Solutions based on LoRaWAN are adequate for use cases where precise location is not needed but accessibility (hence battery life) and cost are critical. LoRaWAN networks can locate devices without GPS, using fewer radio communications signals. Many vehicular applications can benefit from indoor geolocation. Geolocation can be used to locate vehicles of any type in underground car parks, keep a platoon of trucks in formation and track them as they move, or create geo-fences, for example, sending an alert if an object moves outside a defined area, like a bicycle being stolen. The routing of heavy vehicles within an industrial setting is another option. In this paper, we describe our solution based on simple client devices and on the deployment of various LoRaWAN gateways. Through the estimation of the behavior of a LoRaWAN channel and using trilateration, the localisation of a vehicle can be obtained within a 20-30 m range. The main advantage of our solution is its low cost, its simplicity and clearly the possibility to operate indoors. The paper is organised as follows: Section 2 details some related solutions available in the literature; Section 3 describes LoRaWAN and its basic characteristics; Section 4 describes the architecture proposed; Section 5 presents some preliminary results; and finally, Section 6 summarises the results obtained and outlines future work.
# Forecasting the Distortion in Solar Radiation during Midday
## 2 Hypothesis and Methodology  
We hypothesize that the distortion in the global solar radiation during the midday hours can be forecasted/expected by studying the solar radiation of the very early minutes. Studying the behavior of solar radiation in the early morning time yields good information about the behavior of solar radiation in the midday time. The aim of this approach is not to estimate the value of solar radiation but the aim is to know if there is a fluctuation in solar radiation during the time of significant power generation from PV plants or not.  
This forecasting helps in predicting the fluctuations in PV plants' generation when their impact is significant. Hence, the reliability of the integration of PV plants is increased and the negative consequences of the sudden fluctuation of solar radiation can be decreased.
# A Scalable and Consistent Method for Multi-Component Gravity-Gradient Data Processing
## 3 Synthetic Data Application  
We simulated Falcon AGG and FTG data to demonstrate the efficiency of our method for jointly processing gravity-gradient tensor components. These datasets were produced by multiple simulated geologic sources generating strong-interfering anomalies more closely to a real-world scenario. Specifically, the model consists of 302 rectangular prisms, divided into two groups. The first group represents the main geologic bodies and consists of two prisms, each with a density contrast of 1.0 g/cm\({}^{3}\), a top depth of 100 m, and dimensions of \(2000\times 1000\times 300\) m (the smaller body) and \(5000\times 2000\times 300\) m (the larger body) along the \(x\)-, \(y\)-, and \(z\)-axes, respectively. The second group, representing geologic noise, consists of 300 small, shallow cubes with side lengths and top depths uniformly distributed between 100 and 200 m, and between 0 and 10 m, respectively. These sources produce anomalies with predominantly short-wavelength spectral content.  
Both data simulations followed the flight pattern of a real airborne gravity-gradient survey over the Kauring test site, Australia [40]. To each gravity-gradient component, we introduced additive Gaussian-distributed noise, with zero mean and a standard deviation equal to 7% of the maximum absolute value of the corresponding noise-free gravity-gradient data. Specifically, the standard deviations of the noise (in etovos) for each component were the following: \(xx=2.19\), \(xy=1.94\), \(xz=4.47\), \(yy=3.83\), \(yz=4.59\), \(zz=5.46\), and \(uv=1.48\).  
The simulated synthetic data consisted of 114 flight lines each with 1275 points, totaling 145,350 observations, at vertical coordinates \(z\) (Figure 4) ranging from \(-296\) to \(-440\) m and a mean and standard deviation of \(-353.34\) m and \(23.56\) m, respectively.  
We applied our method to simulated Falcon and FTG data using a single equivalent layer with one source beneath each observation point at a constant depth of \(z_{c}=23.54\) m. Transformations between the north-east and data systems (Figure 1 and steps 1 and 5 of Algorithm 1) were computed with \(\psi=-30^{\circ}\).  
Here, we illustrate the transformation by predicting the \(\mathbf{g}_{zz}\) component and a quantity we refer to as the gradient magnitude of \(\mathbf{g}_{z}\) (i.e., \(\|\
abla\mathbf{g}_{z}\|\)), defined as the square root of the sum of the squares of the predicted \(\mathbf{g}_{xz}\)-, \(\mathbf{g}_{yz}\)-, and \(\mathbf{g}_{zz}\)-components.
# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 4 Experiment Implementation
### Evaluation Metrics  
In this study, the model accuracy was evaluated using AP\({}_{50}\) at 50% IoU, which measures precision when IoU exceeds 50%. To assess real-time performance, FPS, which indicates the number of frames processed per second, was used. This allowed for a comprehensive analysis of the balance between accuracy and speed.  
AP\({}_{50}\) is the value of AP calculated when the IoU is 0.5 or higher. In other words, only predictions with an IoU of 0.5 or higher are considered true positives (TP), and the AP for the respective class is calculated and used as a performance evaluation metric. AP\({}_{50}\) specifically refers to the AP calculated for object detection performance with the IoU threshold set at 0.5. It is calculated using Equation (6):  
\begin{table}
\begin{tabular}{c c} \hline \hline Spec & VC2050F \\ \hline Lens & 15–528 mm Megapixel HD, Zoom (x1–x35.2) \\ \hline Resolution & 1920 \(\times\) 1080, H.264 \\ \hline Detection Range & 5–7 km \\ \hline Fog Penetration & Optical filtering and AFR image processing \\ \hline Mode & B/W color mode \\ \hline Pan Tilt & Pan: 0–360\({}^{\circ}\), Tilt : –75\({}^{\circ}\)–45\({}^{\circ}\) \\ \hline Power supply & AC24V + 10\%, 50 Hz, 5 A \\ \hline Rotation Speed & Pan: 0.01–60\({}^{\circ}\)/s, Tilt: 0.01–30\({}^{\circ}\)/s \\ \hline Interface & RJ45 \(\times\) 1, AC24V \(\times\) 1 \\ \hline Dimension & 570 mm \(\times\) 200 mm \(\times\) 580 mm (L*W*H) \\ \hline Protocol & OnVIF \\ \hline \hline \end{tabular}
\end{table}
Table 2: The specifications of the cameras used for video collection.  
Figure 4: VC2050F Camera.  
\[AP_{50}=\frac{1}{N}\sum_{i=1}^{N}\int_{0}^{1}P_{i}(r)\,dr\quad\text{where}\quad IoU \geq 0.5 \tag{6}\]  
where \(N\) is the number of classes. \(P_{i}(r)\) is the precision for class \(i\) at a given recall \(r\) on the Precision-Recall curve. An IoU threshold of 0.5 means that a predicted bounding box is considered a true positive only if it overlaps with the actual bounding box by more than 50%.  
FPS is an important metric that indicates the video processing speed and is calculated as the inverse of the time taken to process a single frame. The total time for inference is calculated by summing the time spent on preprocessing, inference, and postprocessing. This total time is then divided by the number of frames in the video to determine the average time per frame. The inverse of this value gives the FPS, which reflects the model's real-time processing capability. This value varies depending on the model's complexity and the performance of the hardware used. More complex models generally take longer to process each frame, reducing FPS, while higher-performance hardware can increase FPS by processing frames more efficiently. Thus, FPS is a crucial indicator of how well the model's real-time performance aligns with the available computational resources. It is calculated using Equation (7):  
\[\text{FPS}=\frac{N_{\text{frames}}}{(T_{\text{preprocess}}+T_{\text{inference}}+T_{\text{postprocess}})\,\text{ms}}\times 1 000 \tag{7}\]
## 2 Wireless Telecommunication Networks for Critical Infrastructures and Emergencies
However, differences emerge in the specific technologies employed, ranging from private network configurations to SD-WAN solutions and LTE-based infrastructures. Moreover, each study targets distinct applications, encompassing disaster communication, business VoIP, community networking, and closed-loop control systems, indicative of diverse use cases. Lastly, while some studies prioritize performance analysis and optimization, others concentrate on practical implementations and demonstrations of proposed solutions. Despite these variations, collectively, these studies contribute significantly to advancing communication technologies and addressing multifaceted challenges across various contexts. Considering the above implementations and the history of private mobile communications, we identified the need to develop a non-commercial experimental communication platform, with the following characteristics:  
* Software-simulated. * Open source. * Easily deployed. * Portable. * Scalable. * Low cost. * Real-time voice and data transmission. * Secure.
## 1 Introduction  
Acceptance of new technology is simply a generation problem, and it is only fairly related to navigational safety. --Unknown reviewer  
The tangible development of the electronic chart systems started almost four decades ago. In the 1980s, International Hydrographic Organization (IHO) created a digital data exchange committee that laid down the foundation of the future electronic chart systems, thus enabling the beginning of a long process of PNC digitisation. One of the essential dates was 1 st of July 2012, marking the beginning of the transitional period and the time when the ECDIS system was officially recognized as a system that meets chart carriage requirements. The transitional period lasted for six consecutive years, after which it became a mandatory navigational aid for most International Convention for the Safety of Life at Sea (SOLAS) vessels [1].  
In the wake of global maritime digitalisation, traditional navigational means and the role of PNCs still appear to occupy an inevitable role. Besides recognised system- and data-centred ECDIS issues, the reasons for retaining PNCs include the feedback of navigators as central system stakeholders. The standpoints and views differ depending on experience, rank, and navigator's engagement with the system. To determine viewpoints and the way modern navigators perceive PNCs in the paperless era, the authors conducted two independent segments of research. The first survey was conducted in the period from the year 2014 to 1 st of July 2018, the date of the completion of the transitional period for most SOLAS vessels. A second research was conducted after this date in the same year. Both researches were internationally distributed among the eligible maritime navigational ranks. The answers were analysed, summarized, and subsequently presented. Both expected and debatable findings emerged from the research. The latter, together with the provided observations, represented the motivation for further activities.  
This paper is structured as follows. After the brief introduction on the topic, general features on paper and Electronic Navigational Charts (ENCs) have been presented, with an emphasis on the possible future outcomes of PNCs. Previously related research achievements are summarized, both on the research topic and the system in general. In the methodology chapter, the utilized system of research methods has been described. Research results have been presented subsequently and discussed in the following section. Based on previous findings and current research results, a rudimentary safety-related model has been defined, providing insight into potential system-related threats and possible risks which can be still considered to work in favour of traditional means. The paper concludes with summarized findings and desired future research.
# Exploiting Stacked Autoencoders for Improved Sentiment Analysis
## 4 Methodology
### Preprocessing and Building the Feature Vector  
SA can be performed at different levels, including aspect/feature, sentence, or document level. Sentiment analysis based on documents is used in our study. For this purpose, we have used eight benchmark datasets. Training data need to be cleaned before being used as an input into the classification model. To achieve this, we engaged in the process of \"cleaning\" the text by eradicating unnecessary elements such stop words, punctuation, white space, special symbols, and URLs. After cleaning the text, a feature vector is built. Two methods are currently widely used for this purpose, i.e., TF-IDF and word embedding.  
For this research, we use word embedding because of its proven supremacy over TF-IDF [14]. We use the Word2 Vec model of word embedding for building feature vectors. [PERSON] published Word2 Vec in 2013 at Google [73]. It includes the continuous bag-of-words (CBOW) and skip-gram models. In this research, we use the continuous bag-of-words (CBOW) method. One disadvantage of using Word2 Vec is that it does not support words out of its vocabulary. We dealt with this by creating a new token, [UNK], to use in place of terms that were not included in the dictionary. To eliminate the need for a unique token, we retrain Word2 Vec on our lexicon dataset that contains terms that appear more than seven times. For deep learning models, a consistent input vector is essential. We pick I, a constant length, to use across all datasets. For reviews shorter than the defined length, a zero is added to the end of the vector. For reviews longer than the fixed length I, the back is instead abbreviated. However, information may be lost during the truncation process. To minimize this loss, fixed length should be chosen to minimize truncation. Our datasets are composed of tweets and reviews, therefore, we made sure the feature vector was as long as possible without going above the limit, so we would not lose too many samples. This technique is often used in the literature [74; 75; 76]. The following are the values chosen for the fixed length in this study: As tweets consist of a maximum of 280 characters, 280 is chosen as the fixed length for tweets. For review datasets, the fixed length is between 300 and 500 for each dataset, depending on the length of the samples. If the same fixed length is chosen for tweets and reviews, it may lead to loss of information or waste of memory.
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 2 Overview of Radio Wave Propagation for 6G Systems
### Weather Effects  
In outdoor environments, many studies have been conducted to investigate the effects of weather conditions at different frequencies of the 0.1-1 THz frequency band [14; 15; 16; 17; 18; 19]. In [14], for outdoor measurement at 200 GHz along an 8 m LoS link, it was observed that the snow attenuation was approximately 2 dB. At a 0.4 km path length, the LoS measurements at 103 GHz and 120 GHz in [15; 16; 17] showed that the rain attenuation is less than 5 dB at a low rainfall rate below 10 mm/h and less than 20 dB at a high rainfall rate of 100 mm/h. Based on the conducted measurements along an 800 m LoS link at 140.7 GHz in [18], it was found that the rain attenuation is less than 8 dB and 20 dB at rainfall rate of 10 mm/h and 100/h, respectively. In [19], for the conducted measurements at 148 GHz and 156 GHz along a 325 m LoS link, it was reported that the rain attenuation is below 6 dB at a rainfall rate below 40 mm/h.
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 7 Experimental Results  
The research was performed at a swimming pool located at the military camp area in Semarang, Indonesia (Figure 12) when the disturbance of sea currents or wind waves can be neglected. In this work, the results of compass accuracy measurement are based on magnetic field data, and GPS accuracy measurement is according to satellite and (Horizontal Dilution of Precision) HDOP data. The USV performance measurement is based on trajectory and energy consumption data.