esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Hardware and Software for Data Protection
### Types of IDS  
In this section technical securities behind IDS are explored, and how these are applied and used in the network for patrolling and detection of threats. A study produced by the National Institute of Standards and Technology (NIST) back in 2007 recognised the uses of the Intrusion, Detection and Prevention Systems (IDPS) as a way of identifying possible incidents, logging information about them, attempting to stop them, and reporting them to security administrators [31]. Businesses and organisations could also use IDPS as a means of identifying problems with security policies, documenting existing threats, and deterring individuals from violating security policies. This study gave insight into the characteristics of IDS and how to design, implement, config, secure, monitor, and maintain them plus offering the extension of a protection system. They offered a guideline and benchmark to administrators on how to maximise the usage of IDS. This IDS provides a new layer of  
Figure 3: Adaptation of the IDS framework [30].  
security in existing networks of computers and data and encouraged hardware vendors such as Cisco and HP to design fully integrated IDPS systems to do that and more [31].  
Previous technology prior to IDPS, used Firewalls as a method of controlling traffic in and out of networks. Firewalls used a set of rules and followed them. An example of a firewall that is used in the SME business in this study is the Cisco 5520 device [32]. A device like this would usually sit in network topology and provide a layer of security that works on static rules where it permits and denies connections. It prevents intrusions and limits access based on the rules it is given. It never deviates from the rules as it operates in positive and negative, unable to think outside the box. This \"normal\" firewall acts as a single point of entry, green to pass and red to deny. Figure 4 below shows the basic firewall configurations in a network topology based on the authors drawing of the SME business in this study.  
Figure 4 above shows that the internet is an external network that needs to be protected by the firewall. The firewall uses its configured rule book to allow or disallow traffic to cross it and thus providing security for resources inside the firewall such as emails, shared folders, and internal network of computers.  
Firewalls have simple rules to either allow or deny protocols, ports, or IP addresses. Some DoS attacks are too complex for today's firewalls. For example, if there was an attack on port 80 (web service), firewalls cannot prevent that attack because they cannot distinguish good traffic from DoS attack traffic [33].  
Just as the hackers have evolved in their hacking threats, firewalls have also evolved in their technology. As time evolved and threats became complicated, the introduction of IDPS has become ever more important in its role in a network topology.
###### Abstract  
This study reports the size-dependent interactions of silica nanoparticle (NP) dispersions with oil, which facilitate oil recovery from sandstone rock. Herein, we studied various 7-22 nm sized colloidal silica NPs (CSNPs; the colloidal state when dispersed in aqueous solutions) and fumed silica nanoparticles (FSNPs; the dry powder state). Interfacial tension at the oil-nanofluids interface declined with decreasing NP size in a range from 7 to 22 nm. This is because NP spatial density at the interface increased with smaller particle size, thereby, the interface area per NP decreased to approximately 1/30, and interfacial energy had reduced enough. In addition, smaller NPs more strongly were adsorbed to the rock because of improved diffusion in suspension and increased adsorption density. This caused creating a wedge film between oil and rock, which changed the oil contact angle. Due to this effect, core flooding experiments indicated that oil recovery increased with decreasing particle size. However, FSNP dispersions exhibited low recovery factor because of particle aggregation. This phenomenon may facilitate massive permeability reduction, thus causing oil trapping inside rock pore. We found that both the sizes and types of CSNPs and FSNP affected the Interfacial tension at oil-water interface and rock surface wettability, which influenced ultimate oil recovery.  
enhanced oil recovery; nanoparticle size; colloidal silica nanoparticle (CSNP); fumed silica nanoparticle (FSNP) +
Footnote †: journal: Applied Sciences
# Optimal Modeling for Dynamic Response of Energy Storage Systems
## 2 Introduction and Building of Models for Energy Storage Systems  
### Universal Model of WECC Energy Storage System  
The battery characteristics can be represented by the BATT and CBEST models among the models defined by the WECC, but the two models aim at specific types of battery energy storage modules. The manufacturers should provide the characteristic battery parameters to assist in building models as there are considerable limitations in their application. In addition, the models are affected by the residual capacity (state of charge, SOC) of energy storage systems, so they are inapplicable [3]. This study used an energy storage system model composed of REPC (generic renewable plant control model), REEC (generic renewable electrical control model), and REGC (renewable energy generator/convertor model) modules, and the relationships of these modules are shown in Figure 1. Active power control, reactive power control, and voltage control functions could be implemented by adjusting the internal control flags, and the output capacity of residual capacity was restricted. The functions and parameters of the modules are introduced below.
# A Survey of GIS and IoT Integration: Applications and Architecture
## 3 GIS and IoT Integration Use Cases
### Environmental Monitoring  
The noticeable rate of increase in the urban population has caused many problems in urban areas [83]. In recent years many organizations established environmental quality management systems [84] that cover a wide range of issues including how to face natural disasters or how to decrease the pollution of air [85; 86]. IoT can be beneficial in implementing environmental managing systems such as real-time monitoring by means of storing the data, transmission, and processing remotely. Reference [87] presents a system that monitors water quality by measuring five parameters like the turbidity and temperature of the water using IoT. The monitoring system proposed in this study diminished the time and costs in assessing water quality in reservoirs. In addition to IoT, GIS can play a significant role in monitoring systems as well [88; 89; 90]. As mentioned in [89] the ability of GIS to handle geometric, thematic, and temporal geospatial data makes it suitable for environmental data management. Therefore, the integration of GIS and IoT studies would provide more advantages at the same time in environmental monitoring systems. For example, an environmental quality management information system is designed by [32] exploiting GIS, GPS, IoT, and RFID. The proposed system benefit from the spatial data analysis capability of GIS which results in enhanced decision support for environmental quality management. Reference [31] studied the functionalities of environmental protection systems benefiting from GIS and IoT by proposing an intelligent system. Their main goal relates to the enhancement of management and decision-making level.  
The prominent purpose of these studies can be considered as to provide a better future and healthy living for all people. An air quality monitoring system is proposed by [91] to observe the air quality through a web server using IoT gas sensors to collect information about toxic and harmful gases. GIS is also beneficial in their study and that is the capability to display the areas with air pollution information. Using GIS, Remote Sensing (RS), and IoT a water quality monitoring system is presented in [92]. They concluded that the use of GIS and RS can facilitate processes such as data collection and monitoring, site selection for a source of water, and quick identification of a site that is in need of attention.
## 3 Modal Construction
### Problem Description and Model Assumptions
#### 3.2.1 Transport Time Analysis
Departure timing exhibits modal-specific characteristics: road transport permits instantaneous dispatch upon transshipment completion without queuing delays, whereas rail and maritime modes necessitate temporal synchronization with scheduled departures. For scheduled transport modes, the operational protocol involves selecting the earliest feasible service departure subsequent to cargo readiness--determined by the sum of arrival timestamp and transshipment duration--thereby establishing the definitive departure timeline through rigorous temporal optimization between resource availability and service schedules. \[t_{j}^{arrive}=\left\{ \begin{array}{cc}t^{go}&,\forall j\in O\\ \sum\limits_{i\in A}\sum\limits_{m\in M}x_{ij}^{m}\cdot\left(t_{i}^{leave}+t_ {ij}^{m}\right)&,\forall j\in N\cup D\end{array} \right. \tag{3}\]  
\[t_{i}^{leave}=\left\{ \begin{array}{cc}t_{i}^{arrive}+t_{i}^{trans},&m\in\{1 \}\\ t_{i}^{m,1}&t_{i}^{arrive}+t_{i}^{trans}\leq t_{i}^{m,1}\cup m\in\{2,3\}\\ t_{i}^{m,2},&t_{i}^{m,1}<t_{i}^{arrive}+t_{i}^{trans}\leq t_{i}^{m,2}\bigcup m \in\{2,3\}\\ &\cdot&\cdot\\ &\cdot&\cdot\\ t_{i}^{m,s},&t_{i}^{m,s-1}<t_{i}^{arrive}+t_{i}^{trans}\leq t_{i}^{m,s}\bigcup m \in\{2,3\}\\ &t_{i}^{wait}=t_{i}^{leave}-\left(t_{i}^{arrive}+t_{i}^{trans}\right)\end{array} \right. \tag{4}\]  
Conclusively, the total transit duration for cargo shipments is formally defined as the temporal differential between the arrival timestamp at the destination node and the initial departure timestamp, which is mathematically equivalent to the cumulative aggregation of three operational temporal components: inter-nodal transportation duration, transshipment processing time, and scheduled waiting intervals throughout the multimodal transportation chain. \[T=t_{D}^{arrive}-t^{go}=\sum_{i\in A}\left(t_{i}^{trans}+t_{i}^{wait}+\sum_{j \in A}x_{ij}^{m}\cdot t_{ij}^{m}\right) \tag{6}\]
# A Review of Unmanned Aerial Vehicle Based Antenna and Propagation Measurements
## 3 UAV-Based Propagation Measurements  
With advancements in technology and the demand for wireless connectivity, especially for UAVs and other applications, there is a great demand for wireless networks with low latency. These networks require a latency as low as 1 ms, which is a significant improvement over the 40 ms latency of fourth-generation (4G) networks. Transmission needs to be moved to millimeter wave (mmWave) or even terahertz (THz) frequencies to achieve such low latency. EM waves experience higher propagation losses at such high frequencies, owing to diffraction [53] and scattering from rough surfaces. Therefore, understanding the propagation environment through propagation measurements [54] is essential. The propagation of EM waves in an environment can be evaluated by using UAV-based propagation measurements. In addition, UAV-based propagation measurements enable the measurement of key performance indicators (KPIs), such as reference signal received quality (RSRQ) and reference signal received power (RSRP). In [55], a hexacopter carrying a smartphone, sensors, and guided autonomous flight paths was used to measure RSRP and RSRQ. During the transmission, the signal was attenuated by various factors  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multicolumn{2}{c}{**Frequency of Operation**} & \multirow{2}{*}{**Near–Field Distance**} & \multirow{2}{*}{**AUT**} \\ \hline
[41] & 4.65 GHz & 3.4 m &
\begin{tabular}{c} Offset reflector \\ Ground plane \\ antenna \\ \end{tabular} \\
[50; 51] & 20 MHz & 4 m &
\begin{tabular}{c} Ground plane \\ antenna \\ \end{tabular} \\
[50; 51] & 175 MHz & 15 m & SKALA \\ \hline \hline \end{tabular}
\end{table}
Table 2: Near–field measurements.  
Figure 13: UAV with monopole flying over the LPDA array.  
before it reached the receiver. The most common reasons for signal attenuation are path loss, shadowing, and multipaths. Shadowing and multipath components, such as reflection, refraction, diffraction, and scattering, are primarily caused by obstacles. In sub-Section 3.1, we discuss large-scale and small-scale propagation in detail.
## 2 Ultrasound Image Acquisition  
B-mode ultrasound imaging system, which adopts the ultrasound pulse-echo technique, is widely used. It transmits ultrasound waves \(s(t)\) into the medium with ultrasound probes. The waves are reflected, refracted and scattered when meeting objects, and then echo signals \(r(t)\) with location information are received by the ultrasound probes. Signals are processed with AD, time gain compensation (TGC), then beamforming is used to focus the multi-element signals. Finally, ultrasound images representing the distribution of objects inside the medium are obtained, which can be stored and displayed after image processing. The principle is shown in Figure 2.  
As shown in Figure 3, a UIV measurement system is proposed in the paper. For this system, an acoustic array are motivated to emit acoustic beams. Those beams point to a specific direction at every launch time [15]. After travelling in the water, echoes reflected from tracer particles or suspended sediment are received by the acoustic array. The intensity and flight duration of the acoustic echoes are recorded in the system. Echo intensity reflects the existence and material of the particles in the fluid, and flight duration reflects target distances from the array. Based on that information, the fluid morphology on a measuring line on depth-direction can be achieved. Repeat above operations, and a two-dimensional fan-shaped ultrasound image is generated.  
Figure 2: The principle of digital B-mode ultrasound imaging system.  
According to the motion characteristics of high concentration sand-bearing flow, the main influences on the motion of sediment particles are the resistance caused by relative motions between particles, the upward force cased by shearing actions of the water flow, and the additional mass force caused by variable speed motion. These types of forces will reduce the correlation of the B-mode ultrasound image sequences acquired in the presence of high concentrations of sandy water flow.  
Because of the high concentration of tracer particles, it will make the correlation map appear with multiple peaks, as shown in Figure 4. From the normalized cross-correlation formula, it is known that the cross-correlation algorithms are based on statistical calculation, and the velocity vectors obtained by those algorithms are only the displacement vectors with the highest confidence in probability.
# Application of a High-Power Reversible Converter in a Hybrid Traction Power Supply System
## 6 Conclusions  
In this paper, the high-power reversible converter, which can not only be used to recuperate regenerative braking energy, but also provide traction energy, is applied to construct a new hybrid traction power supply system for urban rail transit. A droop control method based on load current feed-forward is proposed to realize the load distribution between the reversible converter and the existing 12-pulse diode rectifiers. It is successfully verified by the field test carried out on Beijing Metro Line 10. The DC short-circuit characteristics of the reversible converter is studied, and then the relationship between the peak fault current and the circuit parameters are derived. Theoretical calculation and computer simulation shows that the peak fault current in RSC is larger, and the time to reach the peak value is also shorter. Thus, it will do greater harm to the reversible converter. The field test data indicates that the average value of energy-savings percentage for one substation is as high as 11.15%.  
This research was supported by National Key Research and Development Program 2016 YFB1200504.  
[PERSON] proposed the hybrid traction power supply system and the droop control method based on load current feed-forward. [PERSON] studied the DC short-circuit characteristics and carried out the simulation. [PERSON] analyzed the field test data.  
The authors declare no conflict of interest.
## 2 A Modified Michelson Interferometer as a Parity Selector
(**b**) A more compact interferometer by using total internal reflection. vibration-insensitive monolithic system. All of these modifications also eliminate or reduce reflection losses in mirrors, the beam splitter, the Fresnel prism and the compensator without using expensive anti-reflective coatings. Of course, the theoretical performance of the system can be improved further, at the expense of cost, with a mirror and beam splitter having all-dielectric reflecting surfaces. However, it is difficult to achieve such good values in real systems. In a previous work [14], we have achieved experimental crosstalk of around \(-14\) dB, but it can be improved as we do not balance the losses of both arms. On the other hand, [PERSON] et al. [13] have obtained crosstalk as low as \(-24\) dB with an MZI. We expect that similar values can be achieved with our Michelson interferometer.
# Analysis of Hybrid Ship Machinery System with Proton Exchange Membrane Fuel Cells and Battery Pack
## 1 Introduction
As the implementation of PEMFC stacks as the energy producer in marine systems is still in its infancy, there is very little literature regarding that field. In this paper, the research focus is on gaining detailed insight into such a system, where the novelty is that PEMFC interacts with the realistic dynamic loading conditions of a vessel in real time, forming a precise and robust carbon-neutral system with higher system efficiency and the same autonomy as a traditional system with diesel engines. An analysis was conducted on a catamaran that was previously in service, and it was considered for refitting from diesel to a PEMFC-battery hybrid propulsion system. The goal was to design an optimal hybrid system that incorporates a PEMFC stack with a battery pack for a known vessel, analyze fuel consumption, and compare it to previous systems with diesel engines, with the additional benefits of silent drive and eliminated pollution compared to previously used internal combustion engines running on diesel fuel. The proposed configuration of the main components for simulation are shown in the methodology chapter. The first goal was to develop a model conceptually and to implement it with methodology and system control using the commercial software AVL CRUISETM M 2023.1. For implementation of the conceptual model in the software, it was required to connect all parameters, such as vessel kinematics, with the power demand of electric motors and the required electricity consumption from the PEMFC stack and battery system. Additionally, the limitation of discharge on the battery system was set to 20%, with the electric motor rotation speed limitation set to 110 rpm, contrary to the maximum of 370 rpm, until the state of charge reaches 30%.
# Optimal Resource Allocation Policies for Multi-User Backscatter Communication Systems
## 3 Problem Formulation of Passive Tags  
In this section, the total goodput maximization problem of a BackCom system with \(N\) passive tags is considered. As stated before, the \(i\)-th passive tag can be activated, only when the corresponding reader transmission power is not smaller than a threshold. The threshold is given as  
\[P_{i}^{\text{th}}=\frac{P^{c}}{\eta h_{i}}+\frac{[\text{erfc}^{-1}(2 \varepsilon)]^{2}}{a_{i}},\quad\forall i.\]  
To avoid the waste of power, if the reader transmission power for the \(i\)-th passive tag is smaller than its threshold, let \(P_{i}=0\) to allocate more power to other tags with better channel conditions. Therefore, the reader transmission power for the \(i\)-th tag satisfies  
Figure 2: The working model of an activated tag.  
\[P_{i}\begin{cases}>0,&P_{i}\geq P_{i}^{\text{th}}\\ =0,&P_{i}<P_{i}^{\text{th}}.\end{cases} \tag{1}\]  
Based on the above analysis, the goodput formulation is rewritten as  
\[G_{i}(P_{i},n_{i})=\begin{cases}T\left[\frac{1}{2}+\frac{1}{2}\text{erf}\left( \sqrt{a_{i}n_{i}P_{i}}\right)\right],&P_{i}\geq P_{i}^{\text{th}}\\ 0,&P_{i}<P_{i}^{\text{th}}.\end{cases} \tag{2}\]  
Once the \(i\)-th passive tag is activated, the circuit energy constraint \(P^{\text{c}}T\leq E_{i}^{\text{th}}\) should be satisfied, from which \(n_{i}\) is rewritten as \(n_{i}\leq n_{i}^{\text{max}}=\frac{P_{i}n_{i}\eta-P^{\text{c}}}{P_{i}\eta_{i} \eta}\). Considering the QoS constraint, the minimum reflection ratio for the \(i\)-th tag is given as \(n_{i}^{\text{min}}=\frac{[\text{erfc}^{-1}(2\epsilon)]^{2}}{a_{i}P_{i}}\). Therefore, the reflection ratio for the \(i\)-th tag satisfies \(n_{i}^{\text{min}}\leq n_{i}\leq n_{i}^{\text{max}}\). The reader transmission power for each tag is also constrained by the average and peak power, which are denoted by \(P^{\text{ave}}\) and \(P^{\text{max}}\), respectively. The constraints are given as \(\frac{1}{N}\sum_{i=1}^{N}P_{i}\leq P^{\text{ave}}\) and \(P_{i}\leq P^{\text{max}},\forall i\), respectively. Therefore, the optimization problem is formulated as follows to maximize the total goodput.  
\[\begin{array}{ll}\mathbf{(P1)}&\max_{\mathbf{P,n}}&\sum_{i=1}^{N}G_{i}(P_{i},n_{i})\\ \text{s.t.}&P_{i}=P_{i}1(P_{i}\geq P_{i}^{\text{th}}),&\forall i,\\ &P_{i}\leq P^{\text{max}},&\forall i,\\ &n_{i}^{\text{min}}1(P_{i}\geq P_{i}^{\text{th}})\leq n_{i}\leq n_{i}^{\text{ max}}1(P_{i}\geq P_{i}^{\text{th}}),&\forall i,\\ &\frac{1}{N}\sum_{i=1}^{N}P_{i}\leq P^{\text{ave}},\end{array}\]  
where \(\mathbf{P}=[P_{1},...,P_{N}]\) and \(\mathbf{n}=[n_{1},\ldots,n_{N}]\), \(1(\cdot)\) is the indicator function, having the Value \(1\) if the condition \(P_{i}\geq P_{i}^{\text{th}}\) is satisfied, or the value \(0\) if the condition is not satisfied.  
Problem P1 is a non-convex problem because the constraints include the indicator function. Hence, it is difficult to find the optimal solution by solving Problem P1 directly. In the following section, the optimal solution for Problem P1 is investigated by exploiting the priority of allocated reader transmission power.
# Spectrogram Classification Using Dissimilarity Space
###### Abstract  
In this work, we combine a Siamese neural network and different clustering techniques to generate a dissimilarity space that is then used to train an SVM for automated animal audio classification. The animal audio datasets used are (i) birds and (ii) cat sounds, which are freely available. We exploit different clustering methods to reduce the spectrograms in the dataset to a number of centroids that are used to generate the dissimilarity space through the Siamese network. Once computed, we use the dissimilarity space to generate a vector space representation of each pattern, which is then fed into an support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Our study shows that the proposed approach based on dissimilarity space performs well on both classification problems without ad-hoc optimization of the clustering methods. Moreover, results show that the fusion of CNN-based approaches applied to the animal audio classification problem works better than the stand-alone CNNs.  
audio classification; dissimilarity space; siamese network; ensemble of classifiers; pattern recognition; animal audio +
Footnote †: journal: Applied Sciences