esa-sceva/satcom-chunk-collection huggingface dataset:
# Attack Categorisation for IoT Applications in Critical Infrastructures, a Survey
## 4 Attack Categorisation
### Objective Oriented
For example, attacks against DNS servers, such as DNS-Spoofing or eavesdropping methods, exploit certain characteristics, such as network protocols and the free access to the wireless medium. Finally, the Human Factor covers attacks made by the user, intentionally or not by not adhering to complicated security software or even attacks that target the user themselves, such as Social Engineering, by tricking the user into compromising their system [66]; or Phishing in an attempt to trick the target to provide the attacker with important information, generally through email communications [67]. Previously, the notion of Active or Passive attacker implication was explored, as used in [61]. However, they also extend these two categories, by listing the various attacks therein by pertaining to either _Interception_, _Interruption_, _Fabrication_ or _Modification_. These four categories all concern data-related attacks, as their goal is to impact on the efficient sharing of such information. The first category is presented as a subcategory of Passive attacks and concerns the simple _interception_ of network messages, breaching confidentiality between the two communicating parties, such as various Scanning activities. The further three categories are presented as containing Active attacks. _Interruption_ attacks cover the different methods as means to stop or impact correct network activities through DoS or an SQL Injection Attack, which takes advantage of unsanitised inputs to access database content [68]. Fabrication and Modification attacks work along the same principal, injecting false data into the network. The main difference between the two is that a Fabrication attack generates its own packets with captured or generated data, such as a Replay Attack, whereas Modification attacks change the values of communications in real time and are generally performed through MitM implementations. The final categorisation based on the overall objectives, is the separation between attacks impacting _Access Control_, _Authentication_, _Availability_, _Confidentiality_, or _Integrity_. The first set of attacks cover any method used to break or take advantage of existing _Access Control_ methods, such as a Rogue Access Point, which will grant an unauthorised back door into a secure system [69]. Following on from _Access Control_, _Authentication_ attacks aim to break authentication methods using various methods, such as Brute Force  
Next, we can find the various attacks that impact service _Availability_, reducing the capacity for users to use the various services, such as a DoS attack on a web server. Moving on, _Confidentiality_ attacks gain to gain access to private communications to extract data,or example through eavesdropping or a MitM attack. Finally, _Integrity_ attacks aim to compromise legitimate data by forging authenticated messages through FDI or Replay attacks. This categorisation is used in [70] to categorise and present various different security threats in wireless networks.
## 1 Introduction  
Squeezed vacuum states at audio-band frequencies are potentially useful for gravitational wave detection [1; 2], quantum storage and communication [3; 4; 5], all-atomic magnetometer [6; 7], and biological measurement [8]. In these application fields, since the quantum noise can be reduced by injecting squeezed vacuum states, the sensitivity or signal-to-noise ratio for measurements at audio-band frequencies can be improved. Besides four-wave-mixing technique being demonstrated to generate intensity difference squeezing at audio-band frequencies [9], most squeezed vacuum states at audio-band frequencies are generated using a subthreshold optical parametric oscillator (OPO) [10; 11; 12; 13]. Thus far, a maximum squeezing of 11.6 dB at audio-band frequencies is reported in Ref [13].  
A balanced homodyne detector (BHD) is a perfect device for the detection of squeezed vacuum states [14]. For squeezed vacuum states, the variance of a certain field quadrature is found to be squeezed below the variance of the corresponding vacuum field. Since the laser noise reaches the shot noise level (SNL) at MHz range, it is easy to measure and obtain squeezed vacuum states at high frequency range by using the BHD system. Compared with the observation of squeezing spectrum at high frequency range, the observation of that at audio-band frequencies is relatively difficult, as the intensity noise of the local oscillator (LO) for the BHD system is far beyond the SNL and actual detectors in the BHD cannot eliminate the classical noise of the LO thoroughly. The measured squeezing vacuum level at audio-band frequencies can be improved by suppressing the low frequency classical noise of the laser [15], reducing the phase jitter between the LO and squeezed vacuum fields [16] and increasing the level of the common mode rejection ratio (CMRR) of detectors in the BHD [17].  
In this study, squeezed vacuum states at audio-band frequencies were generated from a subthreshold OPO with a periodically poled KTiOPO\({}_{4}\) (PPKTP) crystal pumped by a homemade continuous wave (cw) single-frequency dual-wavelength laser. The influences of the LO power, the CMRR of detectors in the BHD, and the phase jitter between the LO and squeezed vacuum fields were considered in the measurement of squeezed vacuum states at audio-band frequencies. To improve the measured squeezing vacuum level at audio-band frequencies, the power of LO for the BHD system was optimized; the CMRR of the BHD at audio-band frequencies was improved by the design of differential fine-tuning circuit and adjustable bias voltage; and the phase jitter between the LO and squeezed vacuum fields was reduced using the coherent locking scheme.
###### Abstract  
The World Health Organisation (WHO) and others have identified, as a priority, the need to improve antimicrobial stewardship (AMS) interventions as part of the effort to tackle antimicrobial resistance (AMR). An international health partnership model, the Commonwealth Partnerships for Antimicrobial Stewardship (CwPAMS) programme, was established between selected countries in Africa (Ghana, Tanzania, Zambia and Uganda) and the UK to support AMS. This was funded by UK aid under the Fleming Fund and managed by the Commonwealth Pharmacists Association (CPA) and Tropical Health and Education Trust (THET). The primary aims were to develop local AMS teams and generate antimicrobial consumption surveillance data, quality improvement initiatives, infection prevention and control (IPC) and education/training to reduce AMR. Education and training were key components in achieving this, with pharmacists taking a lead role in developing and leading AMS interventions. Pharmacist-led interventions in Ghana improved access to national antimicrobial prescribing guidelines via the CwPAMS mobile app and improved compliance with policy from 18% to 70% initially for patients with pneumonia in one outpatient clinic. Capacity development on AMS and IPC were achieved in both Tanzania and Zambia, and a train-the-trainer model on the local production of alcohol hand rub in Uganda and Zambia. The model of pharmacy health partnerships has been identified as a model with great potential to be used in other low and middle income countries (LMICs) to support tackling AMR.  
Keywords: antimicrobial stewardship; antimicrobial resistance; CwPAMS; AMR +
Footnote †: journal: Journal of the American Journal of  
antimicrobial stewardship are discussed in more detail in this paper, focusing on the impact of the local pharmacists taking a leadership role within these projects.  
To further develop the global health leadership skills of pharmacists in the UK, Health Education England and CPA developed a new global health fellowship programme for pharmacists within the projects: The Chief Pharmaceutical Officers Global Health Fellowship [9]. This was set up to provide education on global health/the international development of global AMS, leadership skills and additional project management skills to enhance their own personal development.
# A Cyclic Graywater Treatment Model for Sustainable Wastewater Management Applied in a Small Scale
###### Abstract  
Water scarcity presents a critical challenge to global sustainability, exacerbated by population growth, climate change, and environmental pollution. In this context, graywater reuse has emerged as a promising solution, offering substantial water savings with significant potential for agricultural applications. However, efficient treatment methods are essential to ensure safe reuse, as contaminants vary depending on the source. This study introduces a cyclic graywater treatment system that integrates both mechanical and biological filtration processes. A key feature of this system is the inclusion of _Chenopodium quinao_, a resilient plant known for its phytoremediation potential, which enhances filtration efficiency and facilitates contaminant removal. The study examines the impact of treated graywater on soil and quinoa properties, focusing on its suitability for irrigation. The results show that the cyclic treatment system significantly improves graywater quality, enhancing the removal of biological and microbiological contaminants, such as BOD, with a significant decrease ranging from 31.33 mg O\({}_{2}\)/L to 15.74 mg O\({}_{2}\)/L is observed after treatment. For COD, the average values decreased from 102.64 mg O\({}_{2}\)/L to 54.19 mg O\({}_{2}\)/L after treatment, making the treated graywater compliant with Tunisian regulation NT 106.03 and WHO guidelines. Cyclic treatment significantly reduced the microbial load of graywater. For example, for _E. coli_, the average decreased from 0.87 log 10/100 mL in RGW to 0.58 log 10/100 mL in GWT3. The results demonstrate that the cyclic treatment process can predict the graywater quality beyond the three tested stages. This study highlights the potential of plant-based cyclic graywater treatment systems as an eco-friendly and scalable approach for sustainable water management in agriculture.  
non-conventional water; treatment; valorization; reuse; soil; quinoa
# Dynamic Response Analysis of Mega-Sub Isolated Structures under Multiaxial Earthquakes
## 4 Analysis of the Influence of the Vertical Parameters of the Mega-Sub Isolated Structure  
### Analysis of the Influence of the Vertical Damping Ratio of the Seismic Isolation Layer  
The vertical dynamic analysis was performed on the seismically isolated structure. The input ground shaking acceleration peaks are the Vert-El Centro seismic wave in the X and Z directions of 0.31 g, with the acceleration peaks scaled by 1:0.65. The vertical damping ratios were taken to be the same for both seismic isolators and ranged from (0.05 to 0.5).  
Figure 5: Variation of main frame dynamic response: (**a**) Displacement change curve of the top floor of the main frame; (**b**) Absolute acceleration change curve of the top layer of the main frame.  
Figure 6: Variation of subframe dynamic response: (**a**) Acceleration change curve of the top layer of the sub-frame; (**b**) Displacement variation curve of the seismic isolation layer.  
Influence of Vertical Damping Ratio on the Response of Seismically Isolated Subframes  
The variation of vertical acceleration and seismic isolation layer displacement with a vertical damping ratio of the sub-frame under the vertical earthquake action is shown in Figure 7.  
As seen from the above graph, after the vertical damping ratio is greater than 0.2, the subframe acceleration remains constant, and the damping ratio is no longer a factor affecting the dynamic response. For the vertical displacement of the seismic isolation layer, its value decreases as the damping ratio increases, and the change becomes smaller when the damping ratio is greater than 0.15.  
For the seismic isolation subframe, the vertical damping ratio has a certain influence on its response, and the control effect does not change much when the vertical damping ratio is greater than 0.2. Therefore, it is recommended that the vertical damping ratio of the seismic isolation layer be adjusted to approximately 0.2 in the design.
## 2 Optimization Models
### Wind Condition Model  
The wind scenario is typically depicted using wind speed and wind direction. In the literature, the simplified wind scenario has been extensively employed for the wind farm layout optimization, which is mainly used for testing the effectiveness of newly proposed methods and techniques, and it mostly incorporates the discrete wind speed/direction description. Realistic description of wind scenario is established on the basis of the measured wind condition data with continuous wind speed/direction variation, and it is commonly accepted that the wind condition variation can be described by the Weibull distribution [32]:  
\[f(v)=\frac{k}{c}\left(\frac{v}{c}\right)^{k-1}\exp\left(-\left(\frac{v}{c} \right)^{k}\right) \tag{9}\]  
where \(f(v)\) is the probability frequency of wind speed \(v\), \(c\), and \(k\) are the scale parameter and shape parameter, respectively. The cumulative Weibull distribution \(F(v)\) is given by:  
\[F(v)=1-\exp\left(-\left(\frac{v}{c}\right)^{k}\right) \tag{10}\]  
The wind condition in the real wind farm layout optimization study in this paper is shown in Table 2. In the current work, 360\({}^{\circ}\) wind directions are equally divided into 24 sections with 15\({}^{\circ}\) interval. In each of these 24 sections, the wind speed variation is represented by the Weibull distribution, where the probability of occurrence of wind blowing amongst the section is indicated by \(\omega\). Obviously, the predominate wind direction is between 75\({}^{\circ}\) and 105\({}^{\circ}\). Notice that, although the indicated wind condition is employed for wind farm layout optimization, a finer 3\({}^{\circ}\) wind direction division (angular resolution) will be applied for power output evaluation analysis due to the reported remark of potentially favorable wind power performance with a resolution coarser than 3\({}^{\circ}\) in reference [33; 34].
## 3 Measurement Techniques of the Electro-Optic Coefficient
### Temperature Scanning Technique  
In order to retrieve preliminary information on the stability of the induced electro-optic coefficient, temperature scanning measurements were performed. This type of measurement consists of the continuous measurement of the electro-optic coefficient of a poled sample while the temperature is increased from room temperature up to temperatures above \(T_{g}\) at a fixed rate, typically ranging between 1 \({}^{\circ}\)C/min to 5 \({}^{\circ}\)C/min.  
During the temperature scanning procedure, the polymer electro-optic coefficient quickly relaxes and it is not stable enough to allow the use of the complete ellipsometric technique or the complete Maker fringe technique. In this case, the faster measurements shown previously (at the end of the Section 3.1, 3.2 paragraph) are preferred. The sample must be poled as usual and cooled down to room temperature, then the sample is gradually heated to the glass transition temperature (\(T_{g}\)), while the electro-optic coefficient is continuously monitored (by using either fast TMT or fast SHG). At the end of the measurements, the \(r_{33}\) is evaluated as a function of the temperature. Typical graphs are given by normalizing the \(r_{33}\) value with respect to the \(r_{33}\) coefficient measured at the beginning in room temperature condition. As an example, Figure 5 reports the measurement performed on a fluorinated electro-optic polymer [62], the HFIP-DR1 AF that we will investigate more extensively in the fourth chapter. It is clear that the electro-optic coefficient remains stable up to a certain temperature, then decreases down to zero when the sample is heated close to \(T_{g}\). It is possible to define a depolarization temperature (\(T_{dep}\)) as the temperature at which \(r_{33}\) gets half of the initial maximum value at room temperature. In this case, \(T_{dep}\) represents an indication of the stability of chromophore orientation in the polymer matrix.  
It is evident that it is possible to compare different polymer types with this technique only if all the measurements are performed under the same conditions, _i.e.,_ with the same temperature scanning rate.
# Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm
## 2 Materials and Methods
### Problem Complexity  
While the models of PV used in the paper, in particular the SDM and DDM, are relatively simple in structure, the multi-objective nature of the parameter optimization problem makes it really challenging, in fact [39]. Among the important factors that the PSA needs to consider in a balanced manner are the accuracy of the parameter extraction, computational efficiency, and also robustness for different population sizes and iteration counts.  
Each of the parameters in SDMs and DDMs affects the accuracy of the model, and its calibration should be performed as precisely as possible to reach minimum RMSE. However, the complexity of this task is compounded by the interdependencies between these parameters:adjusting one parameter can impact the behavior of others, potentially leading to a less accurate solution or longer computational time. Moreover, to achieve optimum performance, different types of PV cells and panels add extra complexity. Each cell type has different characteristics that can differently affect the efficacy of the algorithm. For this, a solution is required that will be able to adapt dynamically to changing conditions and avoid well-known challenges such as local minima and computationally expensive execution times [40].  
In regard to this, PSA dynamically adjusts PID factors and a Levy flight mechanism in terms of population sizes and iteration counts, balancing needs for exploration with the goal of efficient convergence. The following section presents the PSA with design features to address these challenges in PV parameter extraction.
## 4 Conclusions  
After the theoretical study, computer simulation and initial experiments, the LASM algorithm has been proven efficient for large scale wireless sensor networks. The main results are listed as follows:  
The localization algorithm based on spring model (LASM) has the advantage of low complexity in computation, communication and time consumption since the calculation steps are almost constant while the node size increases. Three patches are launched to improve the algorithm performances to avoid the local optimum, remove bad nodes and adapt to dynamic changes of the active node. The simulation results of a sensor network with 40 to 800 nodes show that the calculation steps (_l_) are 150\(\pm\)50 for different node numbers. The localization accuracy is about 10% with the variety of sensor nodes. Hardware of a small scale sensor network has been developed to test the algorithm and the positioning accuracy in initial experiments. Future work includes optimizing the positions of anchor  
Figure 8: Experiment design. (a) The proposed sensor node architecture (b) the sensor node designed by us (c) experiment field to test the localization algorithm (d) the result of experiment.  
nodes to obtain the performance of no reinitializing in LASM(P), and increasing the network size for lunar rover localization and navigation.
# Performance Analysis in Heterogeneous Networks with Spatiotemporal Traffic and Scheduling
###### Abstract  
In this work, we consider interference performance under direct data transmission in a heterogeneous network. The heterogeneous network consists of \(K\)-tier base stations and users, whose locations follow independent Poisson point processes (PPPs). Packet arrivals of users follow independent Bernoulli processes. Two different scheduling policies, round-robin (RR) and random scheduling (RS), are employed to all the Base Stations (BS). The universal frequency reuse mode is adopted to reveal actual spectrum reuse. By leveraging stochastic geometry and queueing theory, the interference interactions of the proposed network are accurately modelled. Accurate expressions for the mean packet throughputs of the network under universal frequency reuse mode are derived. The simulation results explore the optical bias factors in heterogeneous networks to maximize the mean packet throughput. Under a given user density, by changing BS densities, we achieved a certain mean packet throughput level.  
Keywords:stochastic geometry; queueing theory; universal frequency reusing; scheduling schemes; bias association; mean packet throughput +
Footnote †: journal: Applied Sciences
## 3 Results
#### Comparison  
Accuracy and recall metrics were used to evaluate the effectiveness of the proposed model. These criteria provide a comprehensive assessment of the model's classification performance:  
* Accuracy represents the model's ability to correctly identify P waves among all detected seismic waves, reflecting the algorithm's positive predictive value.
* Recall measures the proportion of correctly detected P waves relative to the total number of actual P waves, indicating the sensitivity of the model.  
The formulas for these metrics are defined as follows:  
\[accuracy=TP/(TP+FP), \tag{1}\]  
\[recall=TP/(TP+FN), \tag{2}\]  
here: TP (true positive): Correctly detected P waves. FP (false positive): False alarms (incorrectly detected P waves). FN (false negative): Missed detections (P waves not recognized by the model).  
The performance of the proposed CNN-based model was compared to traditional and deep learning-based P-wave detection methods, including STA/LTA, PhaseNet, and GPD. The results are summarized in Table 3.  
Figure 11: Error matrix for P-wave detection. 0—noise (P waves absent); 1—P waves detected.  
Figure 12: Seismograms with a highlighted time window and the moment of P-wave detection: (**a**) for the event on 4 March 2024. (**b**) on 3 March 2024.  
According to Table 3, the STA/LTA method achieved the lowest recall (0.64) and accuracy (0.75), as expected as it is a classical approach that was introduced in 1978 and serves as a fundamental baseline method [4]. More advanced deep learning-based approaches were also evaluated, namely, PhaseNet, a deep neural network algorithm designed to recognize both P and S waves (recall = 0.857 and accuracy = 0.939) [23], as well as GPD, a model based on the convolutional neural network (ConvNet), which demonstrated a recall of 0.8038 and an accuracy of 0.8796 [38]. The CNN model proposed in this study achieved a recall of 0.891 and an accuracy of 0.941, outperforming the other methods regarding P-wave detection accuracy.