esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Protocols Performance on 6 LoWPAN IoT Networks
## 1 Introduction
They performed analyses to determine how different topologies, and the addition of more motes affect the dynamics of energy usage. They used the Powertrace tool to confirm their findings, which show that an increase in node count is correlated with an increase in energy usage. [PERSON] et al. [7] and [PERSON] and [PERSON] [8] tried to assess the efficacy of different protocols. Consistently, research results showed that using Powertrace, the number of motes added incrementally across both protocols was accompanied by a proportionate increase in power consumption. The actual results indicate that motes interacting with the LOADng protocols used more energy than motes interacting with RPL, mostly because of continuous transmission modalities. Additionally, conducting thorough and systematic analysis poses inherent challenges, especially when evaluating routing protocols. The difficulties linked to network deployments and rapid router implementations can introduce significant costs and logistical obstacles. Moreover, designing and testing real-time networks, developing network protocols, and overseeing numerous variables present complex obstacles for researchers, requiring meticulous planning and execution to navigate these challenges effectively [9]. Simulations provide the capability to accurately control various parameters, such as radio medium, transmission range, and other configurable settings within the simulators. Achieving this level of precision in real-world environments is inherently challenging due to the unpredictable variables that can impact results. The presence of these unpredictable elements causes the difficulty of experimental replication, potentially leading to inconsistent outcomes. Consequently, conducting simulations becomes necessary to evaluate performance metrics thoroughly before deploying routing protocols in real-world settings and integrating them into IoT devices. Hence, this study analyses the performance and power consumption of three routing protocols which are RPL, AODV, and LOADng within the context of 6 LoWPAN using simulation techniques. This study utilizes simulation tools to gather data on power consumption metrics, such as CPU Power, Listen Power, Transmit Power, and Total Power. The findings indicate that RPL is the most power-efficient protocol compared to AODV and LOADng. It also contributes new insights into their performance in specific simulated environments. Additionally, the study presents new comparative simulation data and visualized insights into the routing protocols, informing future testbed implementations, and potential improvements for the routing protocols in 6 LoWPAN environments. The following section presents related work to this study followed by the methodology in Section 3. The results and analysis are presented in Section 4, and finally Section 5 concludes and provides recommendations for future studies in this area.
# Investigation into Influences of Hydraulic Fracturing for Hard Rock Weakening in Underground Mines
## 2 Numerical and In Situ Testing Programs  
To investigate hydraulic fracturing weakening technology for hard roofs in underground coal mines, one of the coal mines that experienced hard roof issues in Shannxi province was selected for numerical simulation and in situ tests. Different layers, including the sandy mudstone and medium-grained sandstone, were observed on the strata, but only the harder medium-grained sandstone layer was investigated for hydraulic fracturing. For the examination of the mechanical parameters of the hard roof, a relatively intact portion of the drilled rock cores was selected for the preparation of specimens. Three different sizes of specimens were prepared for uniaxial compression tests, rock shear tests, and Brazilian splitting tests. The physical and mechanical parameters of the hard roof rock layer are displayed in Table 1. The experimental results show that the hard roof of the working face belongs to a hard roof. The tensile strength of the hard roof of the medium sandstone in the working face is 7.8 MPa, with a uniaxial compressive strength of 71.3 MPa, elastic modulus of 46.4 MPa, cohesive force of 9.1 MPa, and internal friction angle of 38\({}^{\circ}\).
## 4 Role of sRNA in Antibiotic Resistance
### Activation of Efflux Pumps
#### 4.3.3 SdsR and RyeA  
Small RNAs (sRNAs) SdsR and RyeA in _E. coli_, transcribed from opposite strands of the same genomic locus, exemplify a unique sRNA-mediated toxin-antitoxin (T/A) system [210]. The ectopic expression of SdsR during exponential growth caused Hfq-dependent cell death and filamentation, with RNA-seq analysis revealing the altered expression of 209 genes, including the repression of _yhcB_, encoding an inner membrane protein critical for survival. The overexpression of RyeA mitigated SdsR-induced toxicity, highlighting an antagonistic interplay between the two sRNAs. These findings uncover a novel regulatory role for SdsR in bacterial stress responses and emphasize its functional relationship with RyeA in maintaining cellular homeostasis [210].  
Importantly, SdsR is essential for the posttranscriptional regulation of the AcrAB-TolC efflux system, directly base pairing upstream of the TolC ribosomal binding site to repress its expression, thereby reducing TolC protein production [211]. This repression leads to increased susceptibility to antibiotics such as novobiocin, indicating that SdsR negatively impacts the bacterium's ability to expel toxic compounds. Similarly, the overexpression of SdsR has been shown to heighten susceptibility to quinolone antibiotics, including levofloxacin, nalidix acid, and norfloxacin, in multi-drug-resistant strains of _E. coli_ and _Salmonella_.  
Additionally, SdsR has been linked to broader stress responses and genetic regulation. For instance, SdsR appears to influence bacterial mutagenesis, as its interaction with _mutS_ has been associated with an increased mutation rate under subinhibitory ampicillin treatment [212]. Furthermore, SdsR overexpression was found to reduce biofilm formation in _E. coli_, suggesting that it may influence bacterial surface attachment and persistence.  
These findings collectively highlight SdsR as a key modulator of antibiotic susceptibility and stress adaptation, though its precise physiological roles warrant further investigation.
# Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform
## 6 Conclusions  
In this work, a solution for traffic monitoring was proposed with an unsupervised adaptation, which runs with a speed of 9.1 frames per second on the Jetson Tx2 platform. It was demonstrated that a supervised training application may lead to satisfying vehicle detection performance, even in the case of limited computing power being available. However, a significant drop in accuracy was observed when testing the model on data from our camera, that is, when the test data were from different distribution that the training. This shows that it is very important to test the algorithms in out-of-distribution setting, because, in the real-world, gathering annotated data from deployment environment is not always possible. Nevertheless, we have shown that it is possible to obtain training examples automatically that can be used for further fine-tuning of the model, bringing, in turn, an increase in vehicle detection efficiency when the system is already deployed. The fine-tuned model may improve relative accuracy by 33%. However, it is difficult to design a system that works in a fully autonomous mode, since adding even a small amount of human work in the whole process can significantly improve its performance.  
The proposed system consists of many interacting components (for detection, background subtraction, tracking, and labelling), and it requires careful design and testing. The main limitation of the presented method results from the application of a single-stage detector, and a background subtraction algorithm for background modelling. Consequently, the method may not work efficiently in some dense traffic scenes or during the night. This is why detectors that can be trained from sparse labels and a more advanced background modelling technique could bring a further enhancement of the proposed system. In future work, we also plan to test our algorithms on more cameras where the model is fine-tuned on data acquired from several locations at the same time.  
[PERSON] designed and implemented the algorithm, prepared the dataset and carried out the experiments and drafted the manuscript. [PERSON] suggested the scope of research, gave suggestions on the structure of article and participated in modifying the manuscript. All authors have read and agreed to the published version of the manuscript.  
Project financed by the Polish National Centre for Research and Development (NCBR) from the European Regional Development Fund under the Operational Programme Innovative Economy No. POIR.04.01.04-00-0089/16 entitled: \"INZNAK: Intelligent Road Signs with V2X Interface for Adaptive Traffic Controlling\". This work has been partially supported by Statutory Funds of Electronics, Telecommunications and Informatics Faculty, Gdansk University of Technology.  
The authors declare no conflict of interest.  
Figure 10: Examples of inaccurate annotations.
# Message Action Adapter Framework in Multi-Agent Reinforcement Learning
## 2 Related Work
### Communication in MARL  
Modeling communication with neural networks is essential to make robust, efficient MARL. Several works model social intersection in MARL to improve MARL algorithms. Communication components are either explicit, like language and discrete symbols, or implicit, like differential communication. Differential communication makes inconsistent messages unlike explicit components, also called _common grounding_. Ref. [PERSON] et al. [37] utilizes anautoencoder to make a compact representation from pixels to shape the common grounding. Ref. [PERSON] et al. [25] models causal inference, which maximizes the mutual information between agent actions. Another way to model intersection is opponent modeling. Ref. [PERSON] et al. [38] models the Bayesian inference on the opponent modeling. A recent work utilizes large language models to learn intersections in social settings [39]. Another research branch studies neural network modules for communication. TarMAC (Targeted Multi-Agent Communication) [11] models attention-based communication. I2C (Individually Inferred Communication) [40] further improves communication by limiting communication channels. CommFormer [41] introduces a learnable communication graph to optimize inter-agent coordination, allowing agents to update communication pathways through bi-level optimization dynamically. IMGS-MAC (Information Maximizing Gated Sparse Multi-Agent Communication) [42] applies an information bottleneck approach to achieve sparse communication, enabling agents to communicate effectively under limited bandwidth constraints. CAMA (Complementary Attention for Multi-Agent reinforcement learning) [43] uses complementary attention mechanisms to enhance agent cooperation in variable team sizes, addressing issues of distracted attention. Although these works shed light on message flow, the communication channels are highly entangled with observations, making it difficult to verify the exact contribution of messages in determining actions. We solve this problem by modeling explicit action branches for observation and messages.
# Quasi-Wireless Capacitive Power Transfer for Wire-Free Robotic Joints +
## 2 Materials and Methods
\[[\frac{-j}{\omega x_{1}^{\prime}C_{1}}+j\omega x_{1}L_{1}]i_{1}+[\frac{j}{ \omega x_{1}^{\prime}C_{1}}-\omega x_{2}M_{12}]i_{2}+[-j\omega x_{1}L_{1}]i_{ L}=V_{S}, \tag{2}\]2  
Note that the \(\gamma_{2}\) terms cancel in Equation (22). From Equation (22), it can be seen that at resonance, \(\gamma_{1}\) acts to subtract the inductance of the QWR below the tap location (\(x_{1}\) section). This causes the non-linear distributions of the \(\mu\)cT-line to start at the tap location and not at the connection of the supply. This is an important result, as it shows that the non-linear effects of the \(\mu\)cT-line have been greatly decoupled from the load branch, such that variations in the load and associated matching network will not greatly change the electrical length of the QWR once connected. When \(X_{C}=X_{L}\), the most current will flow through the load branch, improving the efficiency of transmission. The current of the QWR can be approximated by assuming that the non-linear voltage distribution occurs only in the upper portion of the QWR, above the tap, allowing the portion below the tap to be a paralleled equivalent of \(Z_{LR}\) and \(Z_{L}\). \[[\frac{-j}{\omega x_{1}^{\prime}C_{1}}+j\omega x_{1}L_{1}]i_{1}+[\frac{j}{ \omega x_{1}^{\prime}C_{1}}-\omega x_{2}M_{12}]i_{2}+[-j\omega x_{1}L_{1}]i_{ L}=V_{S}, \tag{2}\]3 The new addition of a capacitance in series to the transformer and in parallel to the \(\mu\)cT-line seen in [25]. \[[\frac{-j}{\omega x_{1}^{\prime}C_{1}}+j\omega x_{1}L_{1}]i_{1}+[\frac{j}{ \omega x_{1}^{\prime}C_{1}}-\omega x_{2}M_{12}]i_{2}+[-j\omega x_{1}L_{1}]i_{ L}=V_{S}, \tag{2}\]4  
It is important to validate the above theory. The current along the QWR is difficult to directly measure since probes placed very near the QWR will alter the stray capacitance, drastically changing the parameters of the \(\mu cT\)-line. Using thermal imaging, it is possible to approximate the distribution of current in the system. Referring to Figure 2, the heat in the image is from the \(i^{2}R\) losses in the QWR; by plotting the thermal distribution from the image, the square root of this thermal distribution yields an approximate current along the QWR. The measured input current (\(I\)) was used to properly scale the data extracted from the thermal image. The predicted versus experimental measurements are shown in Figure 3, where the predicted current was plotted from Equation (23). The plots are in decent agreement but are not exact since the thermal distribution is not a direct, one-to-one measurement of current--only an approximation. Thermal conduction within the copper windings of the QWR will spread with time. This causes error when trying to approximate the value of current from the thermal image.
# A Thermopile Device with Subwavelength Structure by CMOS-MEMS Technology
## 2 Research Preliminary  
Based on the cantilever beam structure floating over an etched silicon cavity, the properties of SWS were investigated and measured for the properties of infrared absorption. The working principle of infrared absorption on the front-side of a thermopile is depicted in Figure 1[16], and the dynamic thermal behavior is governed by the heat equation including the exchange of thermal radiation between the infrared source and sensor, and solid conduction and convective conduction, which are described in Equation (1). The spectral absorption of thermal radiation for SWS is more important to be highlighted as \(R(\lambda)\). The absorption of the rest area of cantilever beam structure is denoted as \(R_{m}\). The temperature of hot junction, ambient environment and radiation source are denoted as \(T_{h},\ T_{a}\) and \(T_{b}\), respectively. \(S_{o}\) is used to describe the geometrical factor of the light path between the radiation source and sensor. \(\varepsilon_{b}\) and \(\varepsilon_{a}\) are the emissivity of radiation source and the sensor, respectively. H is the heat capacitance of thermopile membrane and \(A_{a}\), \(A_{m}\) are the active area and the rest area of cantilever beam structure, respectively. h is the convective heat transfer coefficient and \(A_{o}\) is the total area of cantilever beam structure.  
\[\begin{array}{c}H\frac{dT_{h}}{dt}+G_{s}(T_{h}-T_{a})+\int_{0}^{\infty}S_{o} \varepsilon_{b}\alpha A_{a}\frac{2\hbar c}{\lambda^{3}}\frac{R(\lambda)}{ \varepsilon^{\mu/\lambda t\varepsilon_{b}}-1}d\lambda\\ +\int_{0}^{\infty}S_{o}\varepsilon_{b}\alpha A_{m}\frac{2\hbar c}{\lambda^{3} }\frac{R_{m}}{\lambda^{3}}\frac{R(\lambda)}{\varepsilon^{\mu/\lambda t \varepsilon_{b}}-1}d\lambda+\varepsilon_{a}\alpha A_{o}T_{a}^{4}+hA_{o}(T_{h}- T_{a})=P_{e}\end{array} \tag{1}\]  
Without an external heat source \(P_{e}\), Equation (1) can be simplified and manipulated as Equation (2) and the infrared radiation exchange plays the major role of heat source in the heat equation. \(\varepsilon\) is the emissivity of absorption area and plays the major role of the infrared absorption for our proposed SWSs. \(G_{o}\) is the total thermal conductance including the solid and heat convection, which conduct the heat to the environment at the ambient temperature \(T_{a}\) i.e., \(G_{o}=G_{s}+G_{c}\).  
\[H\frac{dT_{h}}{dt}+G_{o}(T_{h}-T_{a})=\varepsilon\sigma A(T_{b}^{4}-T_{a}^{4}) \tag{2}\]  
\[\tau=\frac{H}{G_{o}}=\frac{1}{\omega_{c}}=\frac{1}{2\pi f_{c}} \tag{3}\]  
The response time and bandwidth of frequency response, \(f_{c}\) for thermal sensor are determined by \(H\) and \(G\), which can be estimated and measured. The frequency response curve through the modulation of infrared radiation with mechanical chopper is preceded and then it can conduct the thermal time constant, which is expressed as the following Equation (3).
# Ten Years of TerraSAR-X Operations
## 5 Radar Experiments with TerraSAR-X
### Wrapped Staring Spotlight  
Wrapped Staring Spotlight [28] is a new method to extend the azimuth steering capability of phased array SAR antennas in order to achieve improved azimuth geometric and/or radiometric resolution. It extends the steering to directions with very low signal contribution. Point and extended  
Figure 8: Bidirectional synthetic aperture radar (BiDi) acquisition geometry example with simultaneous fore and aft acquisitions.  
Figure 9: Color composite of fore (red) and aft (green) zoom images of a TerraSAR-X BiDi acquisition of Singapore. The azimuth direction is on the left, from bottom to top. Time lag between fore and aft image is 7.2 s. Further zooms of a small fast and a large ship in the white frames.  
targets in experimental TerraSAR-X acquisitions were evaluated up to \(\pm\)4.4\({}^{\circ}\) steering, i.e., gains up to 45 dB below the grating lobes. It was found that, for TerraSAR-X, an extension of up to about \(\pm\)3.9\({}^{\circ}\) improves the image quality although the cost-benefit ratio decreases with increasing steering angles.  
One innovation developed is the Wrapped Commanding of steering angles. Figure 10 shows in green the lobes into desired steering direction and in red the undesired grating lobes. Up to \(\pm\)2.2\({}^{\circ}\), the on-board available angles can directly be used. Beyond, larger steering angles are obtained--without the need of additional on-board stored azimuth patterns--by re-accessing available angles, and later extraction of the desired steering directions in the SAR processing.  
Figure 11 shows wrapped corner reflector measurements. The azimuth resolution is steadily improving with increasing processed bandwidth up to 0.14 m, i.e., 68 kHz or \(\pm\)3.9\({}^{\circ}\) in the case of sidelobe suppression weighting. For comparison, the staring spotlight mode of TerraSAR-X exploits only 38 kHz (\(\pm\)2.2\({}^{\circ}\)). Two statistical approaches were developed [28] that improve the radiometric performance when integrating signal directions with low SNR, i.e., the space invariant Look-Normalized Pattern Compensation (LNPC) and the space variant \(\Omega\)-weighting. It was demonstrated that in wrapped TerraSAR-X imaging up to \(\pm\)4.4\({}^{\circ}\), the contrast for manmade targets was improved by 20%, and for extended targets, an improvement in the equivalent number of looks from two to four looks was measured.  
Figure 11: Corner reflector measurements in a TerraSAR-X wrapped staring spotlight image.  
Figure 10: Desired steering lobes (green) and undesired grating lobes (red). TerraSAR-X on-board available angles up to \(\pm\)2.2\({}^{\circ}\). Steering angles beyond are obtained by re-accessing available steering angles.
## 1 Historical Overview of Antibiotic Use
### Beta-Lactamases  
Gram-positive bacteria, much like _S. aureus_, have developed resistance mechanisms against antibiotics over time. These mechanisms began emerging well before the discovery of penicillin. For instance, _E. coli_ strains capable of producing penicillin, a type of \(\beta\)-lactamase, were identified early on, and this ability spread rapidly to other bacterial species [35; 36]. The introduction of cephalospor in clinical practice initially expanded treatment options. However, this development was soon accompanied by the rise of more potent \(\beta\)-lactamases, such as _TEM_ and _SHV_, which were capable of breaking down early cephalosporins, significantly reducing their effectiveness. By the early 1970s, the prevalence of \(\beta\)-lactamase-producing _E. coli_ isolates became widespread. This phenomenon continued with the advent of \(\beta\)-lactamase-resistant cephalosporins, particularly third-generation cephalosporins. The increased use of these antibiotics led to the identification of Extended-Spectrum Beta-Lactamases (ESBLs), a more potent class of \(\beta\)-lactamases capable of hydrolyzing a broader spectrum of \(\beta\)-lactam antibiotics, including third-generation cephalosporins [22; 37]. As the use of these cephalosporins grew, the emergence of new, more effective \(\beta\)-lactamases, such as ESBLs, became more prominent, and these enzymes were detected in increasing numbers of clinical isolates [25; 38].  
The spread of ESBLs is primarily driven by mutations in the \(\beta\)-lactamase gene, which results in the formation of new, more potent enzymes. These mutations have led to a rapid increase in the prevalence of ESBL-producing bacteria. Among the most prevalent types are the _CTX-M_ group, along with _TEM_ and _SHV_ types. The dissemination of these enzymes occurs mainly through the transfer of plasmids and other mobile genetic elements, facilitating the horizontal spread of resistance across different bacterial strains [24; 25].  
In the Euregion Meuse-Rhine, notable ESBL types, such as TEM-52, _CTX-M-14, SHV-12, CTX-M-1_, and _CTX-M-15_, have been identified in _E. coli_ isolates. One prominent example of the global spread of ESBLs is the _E. coli_ O25:H4-ST131 clone, which is a major cause of urinary tract infections (UTIs). This strain is particularly notable for its ability to acquire new resistance traits, including a high prevalence of _CTX-M-15_, and has been detected across various European countries. The genetic makeup of _E. coli_ ST131 strains includes not only ESBL genes, but also resistance to fluoroquinolones, as well as a newly identified carbapenemase gene, which further complicates treatment options for infections caused by this strain [39; 40].