esa-sceva/satcom-chunk-collection huggingface dataset:
# Cybersecurity for AI Systems: A Survey
## 3 AI Attacks and Defense Mechanisms
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Attacks** & **AI System Development** & **Vulnerabilities** & **Defense Mechanisms** \\ \hline Model poisoning attacks [41, 42, 43, 44] & During the training stage & Trust ability of the trainer, based on a privately held validation dataset. Use of pre-trained models that are corrupted. & Securely hosting and disseminating pre-trained models in virtual repositories that guarantee integrity to preclude benevolent models from being manipulated. Identifying backdoors in malevolently trained models acquired from untrustworthy trainers by fine-tuning untrusted models. & Securely hosting and disseminating pre-trained models in virtual repositories that guarantee integrity to preclude benevolent models from being manipulated. Identifying backdoors in malevolently trained models acquired from untrustworthy trainers by fine-tuning untrusted models. & Securely hosting and disseminating pre-trained models from being manipulated. Identifying backdoors in malevolently trained models acquired from untrustworthy trainers by fine-tuning untrusted models. \\ \hline Transfer learning attacks [42, 44, 45, 46] & During the training stage & Similarity of the model structures. & Obtain pre-trained models from trusted source. Employ activation-based pruning with different training examples. \\ \hline Model poisoning in federated learning [41, 45, 47, 48] & During the training stage & Obstruct the convergence of the execution of the distributed Stochastic Gradient Descent (SGD) algorithm, & Robust aggregation methods, robust learning rate. \\ \hline Model inversion attack [49, 50, 51, 52] & During Inference and/or testing stage & Models are typically trained on rather small, or imbalanced, training sets. & L2 Regularizer [49], Dropout and Model Staking [50], MemGuard [51] and Differential privacy [52]. \\ \hline Model extraction attack [53, 54] & During Inference and/or training stage & Models having similar characteristics (parameters, shape and size, similar features etc.) & Hiding or adding noises to the output probabilities while keeping the class label of the instances intact. Suppressing suspicious queries or input data. \\ \hline Inference attack [55] & During Inferencing, Training, and Testing & Model Leaking information leading to inferences being made on private data. & Methods proposed in [55] have leveraged heuristic correlations between the records of the public data and attribute values to defending against inference attacks. Modifying the identified k entries that have large correlations with the attribute values to any given target users. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._(2) Post-deployment. This includes the intrinsic and extrinsic reasons for AI technologies to be malicious, such as design flaws, intentional activities, or environmental factors.
## 6 Discussion and Remarks
On the contrary, it has to be taken into account when selecting the shortest spacing \(d\) between the elementary antennas of the array, because the approach for inverting multi-channel visibilities may vary from one sub-band to the other. As an illustrative example, consider the L-band radiometry case with observations in a bandwidth of 24 MHz centered on 1413.5 MHz. With a short spacing \(d=148.5\) mm, the fraction \(d/\lambda\) is equal to 0.7 in the very center of the bandwidth, but it varies from 0.694 for the lowest frequency of the bandwidth to 0.706 for the highest one. Referring back to Figure 6, the corresponding condition number \(\kappa(\widetilde{G})\) varies from 4300 to 320. As sub-band processing raises many other issues (sampling grids, useful swath, angular resolution, radiometric sensitivity, etc., that may vary from one sub-band to another) in addition to this conditioning problem, it deserves a dedicated study that will be addressed in a forthcoming paper.
## 1 Introduction
### Continua Health Alliance Design Guidelines  
Continua Health Alliance's design guidelines are focused on the Interface to Personal Area Network health devices (PAN-IF) and the Interface between Disease Management Services (DMS), Wireless Area Network (WAN) devices (xHR Senders), and Electronic Health Record (EHR) devices (xHR Receivers) (xHRN-IF). Continua follows the Open Systems Interconnection (OSI) model to establish the protocols to use for interoperability in the PAN-IF. The protocols used in the different OSI layers can be seen in Figure 1[15].  
Continua has constrained the lower-level protocol standards for Bluetooth and USB communications with the use of specific profiles for health devices. For the higher layers, Continua uses the ISO/IEEE 11073 standards family, concretely the IEEE 11073-20601-Optimized Exchange Protocol and ISO/IEEE 11073-104 xx-device specializations to provide application-level interoperability.  
The ISO/IEEE 11073 series of standards is based on an object-oriented system management paradigm. The system model is divided into three principal components: the domain information model (DIM), the service model, and the communication model.  
The DIM describes an agent as a set of objects with attributes, which represent the behavior of the data that can be sent to the manager. The communication between the agent and the manager is defined by the ISO/IEEE 11073-20601 standard. The service model defines the messages that an agent and a manager interchange following Abstract Syntax Notation One (ASN.1). These messages are encoded in Medical Device Encoding Rules (MDER), described in the ISO/IEEE 11073-20101 standard. The communication model supports the topology of one or more agents communicating over logical point-to-point connections to a single manager. A connection state machine, specified in ISO/IEEE 11073-20601, defines the system behavior for each connection.  
Above the IEEE 11073-20601 exchange protocol, are the device specializations that describe specific details about how a type of agent works and the objects and attributes that it has. For the system implemented in this paper, the pulse oximeter (IEEE 11073-10404) and the recently basic ECG (IEEE 11073-10406) specializations were used.
# Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
## 7 Conclusions  
As more and more subscribers connect their devices to wireless networks in the present and the future, the industry of wireless communication systems is faced with a growing challenge: how to maximize spectral efficiency and throughput. Consequently, users have high expectations for the new networks in terms of the energy economy, communication reliability, and data transmission rates. It was suggested that non-orthogonal multiple-access technology be used to address these issues (NOMA). Recently, a significant amount of effort has been into studying how to make 5G networks more efficient by incorporating NOMA technology. Many different types of non-orthogonal access (NOMA) have been proposed as part of the 3 GPP standardization process, but no final decision has been taken on the regulation of the usage of any particular NOMA schemes. The 3 GPP technical analysis demonstrates that the high complexity of implementing algorithms for the generation and processing of signals, especially on the receiving side, is the primary challenge with the application of NOMA technology in 5G networks. Not only that but there are also novel techniques for modulation and multi-user reception, efficient algorithms for encoding and generating code, additionally, efficient strategies for integrating NOMA systems with orthogonal access technologies. This technology would be useful for a variety of uplink and downlink application scenarios in both current and future 5G and 6G systems; it would allow for multiple accesses for subscribers in a variety of settings. This led those involved in 3 GPP standards to decide to delay the implementation of NOMA technology in 5G systems and instead keep exploring NOMA technology with code division, perhaps for use in novel scenarios.  
The authors of this paper worked together to construct it. The idea and framework for the article process were proposed by [PERSON], [PERSON], [PERSON] and [PERSON], [PERSON] and [PERSON] were responsible for the literature search, journal articles, data extraction, data analysis, and manuscript writing. [PERSON] and [PERSON] did a thorough literature search, analyzed the data, and edited the early proposal. [PERSON] gathered the information, prepared the article, and analyzed and organized the data. The paper was proofread by all authors. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Polytechnic University of Bucharest [PUB Art] grant number [HG 37/19.6.2019] and the APC was funded by [PUB Art].  
Not applicable.  
The authors declare no conflict of interest.
# A Novel Ranging Technique Based on Optical Camera Communications and Time Difference of Arrival
## 2 Brief Literature Review  
This section tries to contextualize the presented work introducing the state-of-the-art VLP solutions. The main contributions in the field have been organized with respect to the type of receiver: photodiode or camera. Photodiode-based VLP has been extensively studied, especially at the beginning of Visible Light Communication (VLC). However, camera-based approaches, which use OCC for both ID detection and position estimation are currently being profoundly studied.
## 2 IDC Prototype Details and Fast Thermal Reconstruction Method  
### Experimental Setup of IDC Prototype  
A scale-reduced prototype of IDC at the laboratory level is developed experimentally, as shown in Figure 1, which includes a computer room air conditioner (CRAC) and six IT equipment servers. The IDC prototype has a total length of 52 cm, a width of 46 cm, and a height of 34 cm. The core unit of CRAC is installed between the CRAC cabinet and the outside environment. Six IT servers are placed in 2 rows \(\times\) 3 columns with heating cores inside each server. The floor is raised with a height of 4.4 cm, which allows the cold air from the CRAC unit to flow beneath the floor. Two outlets of the air flow are located at the left and right side of the room, respectively, constituting the cold aisles of the IDC with the cold air rising and flowing past the heating servers.  
For the IDC prototype materials, polymethyl methacrylate (PMMA) is employed as the wall, which provides thermal insulation between IDC and the surrounding environment. Copper plates and PTC heating elements are considered to represent the heating servers in the data center. Semiconductor cooling sheets in the CRAC prototype are associated with the two fans during air cooling, as shown in Figure 1f.  
To evaluate the heating performance of the PTC elements and the cooling capability using the semiconductor cooling sheets, the temperature measurement of six servers using  
Figure 1: Experimental setup of IDC prototype. (**a**) Virtual model. (**b**) IDC placement from vertical view. (**c**) Distribution of IDC prototype. (**d**) Heater elements. (**e**) Positions of thermocouples. (**f**) Colling plate and fans.  
thermoouples is conducted, as shown in Figure 2. Six thermocouples are installed between the walls and each of the servers to record the temperature during the heating and cooling process. The test during the heating process is performed with all the PTC heating elements in the six servers turned on. Subsequently, all cooling units in the CRAC are turned on during the cooling process. It can be observed from Figure 2 that, although there are some discrepancies among the six thermocouples, a similar temperature rises with 4-5 \({}^{\circ}\)C during the heating process, followed by a decrease of 2-3 \({}^{\circ}\)C during the cooling process. According to this temperature evaluation test, a specific temperature of 33 \({}^{\circ}\)C is considered as a hotspot criterion of IDC, which leads to the automatic operation of CRAC units using the following DQN decision-making strategy.
# A Deterministic Methodology Using Smart Card Data for Prediction of Ridership on Public Transport
## 3 Methodology  
When a new line is added to a public transport network, candidates affected by the new line are extracted from the smart card data. The candidates represent potential users of the new line. Then, in the updated network, a search is performed for the optimal route of each candidate. Among the candidates, passengers with the new line included in their optimal route become users of the new line.  
Figure 1: An example of a simple network.  
Figure 2: Data tables of a public transport network.  
Figure 3 shows the updated public transport network and part of the schedule table. The star-shaped nodes represent nodes included in the catchment area of the new line. These nodes include not only nodes through which the new line passes but also nodes connected to those nodes by walk links. The candidates are determined by extracting passengers with a check-in record from nodes included in the catchment area of the new line from smart card data.  
A search is performed for the optimal route of each candidate using the RAPTOR algorithm [29]. The RAPTOR algorithm takes the passenger's O-D and departure time as inputs, and it searches for a route that takes the minimum travel time. The minimum travel time includes in-vehicle time, waiting time, walking time, and transfer penalties. The transfer penalty is a value converted into time for the psychological resistance caused by the transfer. In the proposed methodology, a penalty of 5 min per transfer [30] is given.  
Figure 4 displays an example of allocating the optimal routes to the passengers in Table 1, assuming that line No. 5000 has been added to the public transport network. Passengers 1, 2, and 3 are all included candidates because they have check-in records at the node through which the new line passes. Before the line was added, passenger 2 arrived at node 300 with one transfer. Using the new line, passenger 2 can arrive at their destination 10 min earlier without transferring. Similarly, passenger 3 can arrive 15 min earlier without transferring by walking to node 250. As a result, the ridership on line No. 5000 includes passengers 2 and 3.  
Figure 4: \(Cont.\)  
Figure 3: An example of an updated network: (**a**) before; (**b**) after.
## 1 Introduction
ComK induces the expression of more than 100 genes, including all the genes required for transformation, and most of these genes are directly regulated by the binding of ComK to recognition sites within their promoters [22; 23]. In Gram-negative organisms such as _Haemophilus influenzae, Vibrio cholerae_, and others, competence is induced by activation of the required genes through cyclic AMP (cAMP) bound to the receptor protein CRP (cAMP receptor protein). Complex formation of these two molecules is not sufficient to generate competence. In _H. influenzae_, the co-regulator Sxy has been proposed to work as an activating cofactor for CRP, and in _V. cholera_, the Sxy orthologogue Tfox presumably works in a similar manner [24; 25]. As it has been reported before [16], genes for many of the known proteins involved in DNA uptake and competence acquisition were not found in the _M. luteus_ genome, pointing to substantial differences in this organism and maybe in the other members of the actinobacterial phylum. In a recent study, it was proposed that amino acids act as nutritional factors and that the stringent response alarmone (p) ppGpp plays a role in the regulation of natural transformation in _M. luteus_[26]. Our aim in this work was to identify new elements related to the regulation of the co-transcribed genes _comEA_ and _comEC_ in _M. luteus_. Considering how certain main elements of the regulation of the late _com_ genes have been found in _B. subtilis_ and _S. pneumoniae_[27; 28; 29], we decided to take a similar approach by using ethyl methanesulfonate (EMS) to generate random mutations in the genome of _M. luteus_, followed by a search for mutants with altered _com_ gene expression. Similar to _B. subtilis_, _M. luteus_ expresses competence (and late _com_ genes) under nutritional stress and specifically in a particular physiological state, the late exponential phase [15]. Using a reporter strain that has the _comEA_ and _comEC_ ORFs replaced by the _Escherichia coli lacZ_ gene, we screened for mutants that have high _comEA/EC_ promoter activity under repressing conditions (M-mutants) and others that have the promoter completely or partially repressed in competence-inducing minimal medium (R-mutants). After sequencing of selected mutants, we sought to identify mutated regions in the genome that apparently cause the observed phenotypes and thus may have a role in _comEA/EC_ promoter regulation. We anticipated that by sequencing of the mutants' genomes, analysis of the mutations' distribution and reinsertion of the most interesting ones into the _M. luteus_ reporter strain chromosome, it should be possible to identify certain causative mutations and genes that are involved in the regulation of _com_ gene expression and natural transformation in this model representative of the Actinobacteria.
# Dynamic Deflection Measurement on Stiff Bridges with High Piers by Preloaded Spring Method
## 2 Preloaded Spring Method and the Related System  
Figure 1(a) shows the scaffolding section process for a bridge with a pier height of 4 m using the SFM to measure the dynamic deflection, while Figure 1(b) shows a bridge with a pier height of 6 m using the SHM.  
When a vertically suspended wire is unstressed, its lateral stiffness is almost zero. However, when the wire is subjected to a large tensile force \(f_{P}\), its lateral stiffness increases significantly. As shown in Figure 2(a), when the section stiffness \(EA\) and axial preload force \(f_{P}\) of the wire tend to be infinite, the vertical displacement at site A is the same as that at site B. In practice, the preloading of the wire requires only tens of N, and the vertical displacements of A and B are very close even under the disturbance of the horizontal load \(q_{k}\). The PSM is proposed to incorporate the above stress-stiffening phenomenon and dynamics. The corresponding system is shown in Figure 2(b) and is referred to as the stretched-wire-spring system (SWSS). The SWSS consists of a stretched wire (steel-wire is generally used), a pull bar, a compression spring, a dial gauge, a bracket, heavy objects, and a data acquisition system. One end of the stretched wire is suspended at the bottom of the bridge, and its material requires a high tensile strength and low elongation. The lower end of the stretched wire is connected to the upper end of the pull bar, and the lower end of the pull bar is connected to the compression spring. Inside a sleeve, the compression spring, pull bar, and connector form a closed system. The components in the above closed system are fixed to the bracket by combining them with a dial gauge. Some heavy objects, such as bricks or stones, are placed at the bottom of the bracket to prevent the entire system from failing during testing. Before testing, the initial preload force of the compression spring is applied by adjusting the length of the stretched wire. After that, the stretched wire is kept in tension during the test. Data are acquired by a signal acquisition system, which may be wireless or wired. Both static and dynamic deflections of the bridge (site A) can be obtained by measuring the displacement of the pull bar (site B) in real time. The principles of static and dynamic deflection measurement by the PSM are described in Sections 2.1 and 2.2, respectively.  
Figure 3: Dynamic deflection measurement of the stiff bridge by the preloaded spring method (PSM): (**a**) stress-stiffening phenomenon of a stretched-wire and (**b**) stretched-wire-spring system (SWSS). \(EA\) denotes the axial stiffness of a wire; \(f_{P}\) represents the preloading in the axial direction of a wire; \(q_{k}\) is the horizontal load perpendicular to the axial direction of a wire.  
Figure 2: Conventional contact type methods for bridge dynamic deflection measurement: (**a**) layout of the SFM and (**b**) layout of the SHM.
## 4 Applications
### Research Limitations and Future Work  
While the proposed constitutive model has been validated with high-cycle fatigue experimental data in axial compression and flexural tension, it is important to note that several limitations should be addressed in future work. This section underscores the ongoing nature of our research and the need for continuous improvement.  
Good agreement between predicted results and observed behavior depends on whether the selected monotonic stress-strain curve could reasonably predict the development of maximum fatigue strains: the loading branch considers the initial loading, and the unloading branch predicts the maximum fatigue strain just before failure. The method of determining fatigue residual strain could also be improved. Since the damage evolution equation is derived from uniaxial fatigue test data, only its application to particular cases of axial compression and flexural tension has been confirmed. Whether the proposed method can be applied to analyze more general cases of multi-axial fatigue and variable amplitude fatigue problems needs further investigation.  
Furthermore, it is crucial to consider scatters in monotonic constitutive curves and fatigue damage evolution curves between specimens tested under the same conditions. Therefore, a reliability analysis that considers the probability of failure is desirable in the future. A similar constitutive relation should be constructed for reinforcement before the proposed model can be successfully applied to real-world scenarios for fatigue response analysis of reinforced concrete structures.