esa-sceva/satcom-chunk-collection huggingface dataset:
# Anti-Bacterial Action of Plasma Multi-Jets in the Context of Chronic Wound Healing
###### Abstract  
This work is a contribution to the development and implementation of non-thermal plasma technology for decontamination in the perspective of nosocomial and chronic wound innovative therapies. Multi jets devices based on Plasma Gun(r) technology in static and scanning operation modes and bacterial lawns inoculated with resistant and non-resistant bacterial strains were designed and used. A pilot toxicity study exploring plasma treatment of wound bearing patients, performed with a low voltage plasma applicator, is documented as a first step for the translation of in vitro experiments to clinical care. Bacterial inactivation was demonstrated for _Staphylococcus aureus_, _Pseudomonas aeruginosa_ and drug resistant _S. aureus_, _P. aeruginosa_ and _Escherichia Coli_ strains collected from patient wounds at Orleans (France) hospital. A few square centimeter large contaminated samples were inactivated following a single plasma exposure as short as one minute. Samples inoculated with a single but also a mix of three resistant pathogens were successfully inactivated not only right after their contamination but for mature lawns as well. Similar bactericidal action was demonstrated for antibiotic-resistant and non-resistant _P. aeruginosa_. The time exposure dependent increase of the inhibition spots, following multi jets exposure, is discussed as either the accumulation of reactive species or the likely combinatory action of both the reactive species and transient electric field delivery on inoculated samples.  
plasma jets; non-thermal plasma; plasma decontamination; resistant pathogen; wounds; plasma medicine; _Escherichia Coli; Staphylococcus aureus; Pseudomonas aeruginosa_; multidrug -resistant bacteria
## 3 Results
### Results and Analysis
#### 3.3.4 Training and Testing of MFasNetV1  
Based on the multidimensional feature fusion network described in Section 2.2.5, the training and validation sets from the target task sample library were utilized for training. During the training process, the weight parameters of each feature extraction network were  
Figure 16: Validation curves for various feature extraction networks.  
Figure 17: Relationship between accuracy and RHOA.  
kept fixed, while the feature fusion classifier was trained independently. The validation curve obtained during the training process is presented in Figure 18. After three steps of training, the classifier achieved convergence, with a validation accuracy of approximately 99.0%. After the completion of training, a multidimensional feature fusion recognition network capable of prediction was developed, as illustrated in Figure 12.  
Finally, the proposed multidimensional feature fusion recognition network MFsNetV1 was evaluated for inference performances using the target task test set, obtaining an accuracy rate of 97.1%. The corresponding confusion matrix is presented in Table 4.  
As shown in Table 4, three interference samples were misclassified as volume targets, while three volume targets were misclassified as interference. Compared to single features, the fusion of spatial, time-frequency, and Doppler features significantly enhances target recognition capabilities. The P3 SF, ETFF, and DFSDF features extracted in this study, along with the MFsNetV1 information fusion network, are validated as effective solutions. Furthermore, they significantly address the adaptability challenge associated with the target's relative horizontal orientation angle in the few-shot scenario.
###### Abstract  
Fused deposition modeling (FDM) is widely employed in prototyping due to its cost-effectiveness, speed, and ability to produce detailed and functional prototypes using a variety of materials. Simultaneously, consideration for the use of biodegradable polymers and a general reduction in their usage while enhancing the production of polymer-based products is at the forefront of sustainable practices and environmental consciousness. This study investigates the impact fracture resistance of FDM models fabricated from Polylactic Acid (PLA), examining the influence of infill density (50% and 100% infill) and sample thickness (2 mm, 3 mm, and 5 mm). Optical microscopy, FTIR spectroscopy, and SEM analysis of PLA filament and fractured FDM PLA surfaces in impacted samples were conducted to ascertain the influence of process parameters on impact damage and failure mechanisms. The results indicate that a 100% infill profile with a 2 mm thickness should be avoided due to unpredictable behavior under impact. Conversely, a 5 mm thickness demonstrates significantly higher durability in comparison to a 50% infill profile. Optimal impact strength is observed in samples with a 3 mm thickness, suggesting potential material savings with 50% infill without compromising mechanical properties. The findings contribute valuable insights for refining FDM parameters and advancing the understanding of material behaviors in sustainable manufacturing practices.  
Fused deposition modeling; polylactic acid; impact strength; fracture resistance; surface characterization; infill density; sample thickness; SEM; FT-IR; optical microscopy
# A Roadside and Cloud-Based Vehicular Communications Framework for the Provision of C-ITS Services
## 3 Roadside and Cloud Architecture
### Roadside Architecture
#### 3.1.2 Vehicle and On-Board Unit (OBU)  
Any type of road vehicle with an OBU providing vehicular communications, is able to exchange vehicular information in the architecture. This includes, private vehicles, such as cars, motorcycles and trucks, as well as public entities and authorities, such as public buses, police vehicles, and other emergency vehicles. A cellular module providing access to the Internet is usually included in the OBU for PKI-related certificate operations or other applicational purposes. A Wi-Fi module can also be included to provide Internet to the vehicle's passengers. The described protocol stack used by the RSU is also employed by the OBU.  
To assist the driver with the vehicular information exchanged in the vehicular network, a smartphone application was developed [43] in the current architecture to act as an HMI. The application is continuously connected to the OBU through the Wi-Fi connection provided by the latter. Information is exchanged through the MQTT protocol, where the broker (server) runs locally on the OBU. Messages received by the OBU (CAMs, DENMs, CPMs, etc.) are forwarded to the broker where they are published. The application, subscribed to the respective topics, fetches these messages and provides a viewable translation of the message data, such as the positions of perceived objects in a CPM or the event position and type in a DENM. A driver can also create events in the application that are associated with DENMs, such as accidents or roadworks. These generated events are published in the OBU's broker and then forwarded and broadcast through ITS-G5 to neighboring ITS-Ss.
# High-Precision Combined Tidal Forecasting Model
## 2 Related Concepts
### Arima  
The ARIMA model is a typical time-series analysis and prediction model. From historical time-series data, we can build a dynamic model and predict the future trend of the data [20]. The ARIMA model is based on the autoregressive and moving average model, which is represented by the following formula:  
\[Y_{t}=\mu+\sum_{i=1}^{p}r_{i}y_{t-i}+\varepsilon_{t}+\sum_{i=1}^{q}\theta_{i} \varepsilon_{t-i} \tag{8}\]  
and it adds the following difference operator:  
\[\left\{\begin{array}{l}\Delta Y_{t-i}=Y_{t-i}-Y_{t-i-1}=Y_{t-i}-LY_{t-i}=(1 -L)Y_{t-i}\\ \Delta^{2}Y_{t-i}=\Delta Y_{t-i}-\Delta Y_{t-i-1}=(1-L)Y_{t-i}-(1-L)Y_{t-i-1}= (1-L)^{2}Y_{t-i}\\ \Delta^{d}Y_{t-i}=(1-L)^{d}Y_{t-i}\end{array}\right. \tag{9}\]  
In these expressions, \(Y_{t}\) is the predicted value at time \(t\); \(\varepsilon_{t-i}\) and \(\varepsilon_{t}\) denote the errors at times \(t-i\) and t, respectively; and \(Y_{t-i}\) is the measured value at time \(t-i\). \(\mu\) is a constant; \(r_{i}\) is the autocorrelation coefficient; \(\theta_{i}\) is the is the moving average coefficient, which is different from the initial phase of the tidal components \(\theta_{i}\) mentioned in Formula (7); \(d\) is the differential term; and \(\Delta^{d}y_{t-i}\) is the time series after adding the \(d\)-order difference. \(p\) is the order of the autoregressive model, which represents the lagged rank of the time series. \(q\) is the order of the moving average model, which represents the lagged rank of prediction errors. The model must determine three parameters \((p,d,q)\). If the sequence is unstable, it should be transformed into a stable sequence by the \(d\)-order difference (Equation (9)), and \(y_{t-i}\) in Equation (8) is replaced with \(\Delta^{d}y_{t-i}\). If the original sequence is a stationary, \(d\) is set to 0.
## 3 Design of a General Relay-Node Selection on Intersection
### Exponent-Based Partition on General Intersection
#### 3.3.1 Intersection Phase  
Since Relay\({}_{1}\) is expected to be closer to the center than H, the partition range \(R_{\rm P}\) is the circle region centered at C with the radius equal to the distance between H and C. In addition, the functionality defined for \"Communication Range\" and \"Border\" in Figure 1 is now employed for the \(R_{\rm P}\) and C, respectively. The \(R_{\rm P}\) is partitioned into \(N_{\rm part}\) circular or ring regions for \(N_{\rm iter}\) iterations to find a thinnest no-empty segment in each branch closest to C, and the thinnest no-empty segment is referred to as final segment. Clearly, the inner segment has higher priority of getting selected over the outer segment. And in each iteration, the segment closer to C has a smaller width. Once the final segment in each branch is determined, nodes located in these segments enter the CTB-contention phase [13]. Finally, one of them is elected as Relay\({}_{1}\), and the Relay\({}_{1}\) starts the branch phase.
## 4 Incorporation of Wearable Sensors to Measure OA Risks
### Inertial Measurement Units (IMUs)  
IMUs are one of the most widely recognised, practical and affordable wearable sensors that, depending on specifications, can record acceleration and joint angles [52; 53]. The simplest application is the direct attachment of IMUs to body segments, such as the shank and knee, to record vibration transferred from the foot [54]. This concept has been reported by [PERSON] et al. [32] in which a progressive reduction in vibration between the shank and head were considered to reflect the amount of the shock absorbed by the body.  
Measurements of lower limb joint kinematics have also been used to predict highly associated kinetic information. Knee abduction-adduction angle can be indicative of adduction moment, and maximum knee flexion is related to power absorption as described in Section 2.3. Joint angles coupled with acceleration data can be used to estimate kinetic values at particular events and, as Table 1 describes, various parameters can be considered for risk assessment. As technology stands, IMUs are, therefore, the essential wearable sensors to enable an active exoskeleton system to predict knee OA risks [55].
# Demonstration of Triband Multi-Focal Imaging with Optical Coherence Tomography
## 1 Introduction  
Optical coherence tomography (OCT) is an imaging modality based on low coherence interferometry [1]. Compared to other optical methods such as multiphoton and confocal microscopies, a typical OCT system operates with reduced resolution but offers larger imaging fields and deeper penetration depth in biological samples. The axial and transverse resolutions of OCT are set by the spectral bandwidth of the light source and the numerical aperture (NA) of the imaging lens, respectively. While higher NA lenses can be used to increase transverse resolution in OCT, this results in a limited depth of focus (DoF). Therefore, most systems operate with moderate transverse resolutions that approximately match DoF to the relatively long penetration depth of OCT.  
Various strategies have been introduced to address the trade-off between transverse resolution and DoF. Most straightforward is the serial acquisition of imaging data at discrete focal planes followed by a merging/fusion of these imaging data into a single image or volume [2; 3]. This approach, however, slows imaging speed and imposes requirements on sample stability. Systems that illuminate the sample with multiple beams, each with a distinct focal plane, allow simultaneous acquisition of multi-focal data. While simple in design, these systems have the disadvantage of reducing the power in eachbeam [4; 5] and requiring lateral registration of the non-coaxial beams [6]. More complex approaches based on shaped (e.g., Bessel) beams have also been demonstrated [7; 8; 9; 10; 11; 12; 13]. For endoscopic imaging, shaping the axial profile of the sample beam at the end of a miniaturized fiber-optic probe [14; 15; 16; 17] to create more axially uniform irradiance can extend the focal range of the imaging probe in a similar manner. In addition to needing more complex optics to generate the shaped beam, these methods negatively affect signal collection efficiency and generate higher side-lobes in the transverse point-spread function. Approaches involving digital refocusing of the complex field [18; 19; 20] have been reported. While effective and powerful, these methods require a sample phase-stability that is difficult to achieve in many settings.  
In this manuscript, we demonstrate an approach for multi-focus imaging using triband illumination. Chromatic aberration has been used previously to extend DoF in high-resolution microscopy [21] and has applied to the fiber-optic probe design of the subcellular imaging in OCT [14]. Our system is based on the same principle; we perform simultaneous imaging in three spectral bands centered at 1060 nm, 1310 nm, and 1550 nm. We engineered the chromatic aberration of the microscope optics to cause a displacement in the focal plane of each band such that imaging data across all three bands can be merged to create a single extended-focus acquisition. This approach has the advantage of maintaining a single-fiber microscope with Gaussian beam illumination and detection, while also simplifying the fusion of the imaging data by using coaxial beams.
# Cellulose Nanofibers and Other Biopolymers for Biomedical Applications. A Review
## 3 Alternative Biopolymers
### Lignin  
Lignin is a highly biocompatible biopolymer, which is found in most terrestrial plants in the approximate range of 15 to 40% dry weight, conferring its rigid property to the cell walls [102; 103], Lignin is a complex aromatic heteropolymer synthesised mainly from three primary monolignols, \(p\)-coumaryl alcohol, coniferyl alcohol, and sinapyl alcohol. During the lignification, each of these monolignols gives rise to a different type of lignin subunit called \(p\)-hydroxyphenyl (H), guaiacyl (G), and syringeyl (S) units, respectively [104; 105] (Figure 11). This biosynthesis process creates a unique and distinctive lignin polymer depending on the plant species and even, depending on the type of tissue of the same specimen [106]. Thus, the structure and composition of lignins could vary depending on the plant origin, cell type, and even with the climate conditions. Lignin is produced in large quantities as a by-product in paper manufacturing and new biorefineries that convert cellulosic biomass into liquid transportation fuels [103]. In most cases, it is simply used as an energy source or just discarded as waste, and less than 2% of the total amount of lignin produced is reused to transform it to value-added products [24; 103]. Therefore, extensive efforts in different fields of knowledge are being made to find alternative uses for lignin [107; 108; 109; 110; 111; 112; 113]. Moreover, lignin possesses antimicrobial and antioxidant properties. Because of this, and due to its high availability, lignin has a potential for biomedical applications.
# SpecTalk: Conforming IoT Implementations to Sensor Specifications
## 1 Introduction
At the smart application level, model-based software tests cannot be conducted due to inconsistent data formats. Therefore, acceptance tests of intelligent systems (smart applications) are difficult because there are no globally accepted standards to specify these systems. At present, intelligent system-related standards, including building automation and energy-based air conditioning [12; 13], have gradually involved advanced networking and IoT technologies. Since multiple systems with different communication protocols [14; 15; 16] and data formats [17; 18] are often developed in a building, the matter of how to integrate the various systems and collect the required information is a key factor in managing building intelligence. At this stage, the market has a variety of intelligent energy management systems, independent intelligent building subsystems, and data format interfaces. It is difficult to incorporate various independent subsystems for overall monitoring and management, which affects the open and common development for future intelligent networking to heterogeneous services in smart buildings. In light of the application situation and demand for intelligent building energy management systems, the Taiwan Association of Information and Communication Standards (TAICS) specifies the data format standard of intelligent building [13], which is applicable to the data exchange between the subsystems and the devices under the integrated system monitoring platform or the integrated operation center (IOC). However, most vendors are unable to study TAICS specifications carefully and provide Application Programming Interface (API) software that conforms to TAICS specifications. When customersrequest to follow TAICS standards, vendors may significantly increase the price, causing more controversy. Therefore, we design SpecTalk, an interface code generation system that makes it easy for the vendors to automatically interwork their intelligent systems to the TAICS specification-compliant platform for acceptance tests. SpecTalk does not involve data exchange within subsystems or devices. Vendors can easily integrate the APIs of their systems to the TAICS test system by using SpecTalk's Graphical User Interface (GUI). The paper is organized as follows. Section 2 introduces TAICS; Section 3 proposes SpecTalk; Section 4 describes how SpecTalk is used to conduct three types of acceptance tests; Section 5 proposes an analytic model to select the frequency of the control signals in the input pattern of an IoT device test.
## 3 The OpenFlow Protocol
### OpenFlow Specifications
#### 3.2.1 OpenFlow 1.0  
The OpenFlow 1.0 specification [22] was released in December, 2009. As of this writing, it is the most commonly deployed version of OpenFlow. Ethernet and IP packets can be matched based on the source and destination address. In addition, Ethernet-type and VLAN fields can be matched for Ethernet, the differentiated services (DS) and Explicit Congestion Notification (ECN) fields, and the protocol field can be matched for IP. Moreover, matching on TCP or UDP source and destination port numbers is possible.  
Figure 3 illustrates the packet handling mechanism of OpenFlow 1.0 as described in Section 3.1. The OpenFlow standard exactly specifies the packet parsing and matching algorithm. The packet matching algorithm starts with a comparison of the Ethernet and VLAN fields and continues if necessary with IP header fields. If the IP type signals TCP or UDP, the corresponding transport layer header fields are considered.  
Several actions can be set per flow. The most important action is the forwarding action. This action forwards the packet to a specific port or floods it to all ports. In addition, the controller can instruct the switch to encapsulate all packets of a flow and send them to the controller. An action to drop packets is also available. This action enables the implementation of network access control with OpenFlow. Another action allows modifying the header fields of the packet, e.g., modification of the VLAN tag, IP source, destination addresses, _etc_.  
Statistics can be gathered using various counters in the switch. They may be queried by the controller. It can query table statistics that contain the number of active entries and processed packets. Statistics about flows are stored per flow inside the flow table entries. In addition, statistics per port and per queue are also available.  
OpenFlow 1.0 provides basic quality of service (QoS) support using queues, and OpenFlow 1.0 only supports minimum rate queues. An OpenFlow-compliant switch can contain one ore more queues, and each queue is attached to a port. An OpenFlow controller can query the information about queues of a switch. The \"Enqueue\" action enables forwarding to queues. Packets are treated according to the queue properties. Note that OpenFlow controllers are only able to query, but not to set, queue properties. The OF-CONFIG protocol allows one to modify the queue properties, but requires OpenFlow 1.2 and later. We present OF-CONFIG in Section 3.2.7.
# A Systematic Revision of the NFIP Claims Hazard Data in Florida for Flood Risk Assessment
## 3 Results and Discussion
### Further Analyses for Flood Risk Assessments
#### 3.2.3 Historical Reconstruction  
In general, observations of flood elevations and wave conditions are not available or very limited. To obtain estimates of these data, the authors used the FPFLM hazard models and other data to reconstruct select historical flood events. For storm surges, the Coastal Estuarine Storm Tide (CEST) model [14] was used. The CEST is forced by estimated observed winds from the H*Wind hurricane wind analyses [15] where available; otherwise, modeled winds are used. Wave conditions were determined by a wave model from the FPFLM that was based on the STWAVE (steady-state spectral wave) model [16]. For inland flooding, a simple two-dimensional modeling framework [17] was used. The modeled hazard data was interpolated to the claim locations, taking into account very-high-resolution 5 m DEM (Digital Elevation Model) elevation data. Based on the modeled results, each claim in the revised NFIP portfolio could be assigned to one of four hydrological states: inland flood with no waves; coastal flood with minor waves; coastal flood with moderate waves; and coastal flood with severe waves. A better set of flood vulnerability curves can be developed based on these hydrological states and can be validated using the modified NFIP claim data for the FPFLM project [9,10].
## Appendix 1: Derivation of the Isochronous Condition (General Case)--Equation 16
### Calculation of \(B\)  
Using [PERSON]'s law in prism 1, one has:  
\[\sin\theta_{4}=n\sin(\beta-\theta_{2}) \tag{125}\]  
Differentiation of Equation 125 leads to:  
\[\begin{split}\begin{bmatrix}\dfrac{\partial\theta_{4}}{\partial \omega}=\dfrac{\sin\beta}{\cos\theta_{4}\cos\theta_{2}}\dfrac{\partial n}{ \partial\omega}\\ \dfrac{\partial\theta_{40}}{\partial\omega}=\dfrac{\sin\beta}{\cos \theta_{40}\cos\theta_{20}}\dfrac{\partial n_{0}}{\partial\omega}\end{split} \tag{126}\]  
This gives an expression for B:  
\[\begin{split} B=&\frac{\omega\sin\beta}{\cos\theta_{2 }}\big{(}a\times\tan\theta_{4}-b\big{)}\dfrac{\partial n}{\partial\omega}- \dfrac{\omega\sin\beta}{\cos\theta_{20}}\big{(}a\times\tan\theta_{40}-b\big{)} \dfrac{\partial n_{0}}{\partial\omega}\\ =&\omega\Bigg{(}x_{30}\,\dfrac{\partial n_{0}}{ \partial\omega}-x_{3}\,\dfrac{\partial n}{\partial\omega}\Bigg{)}\end{split} \tag{127}\]  
where \(x_{3}\) is the light pass in prism 2 when the electric field is applied:  
\[x_{3}=\frac{\sin\beta}{\cos\theta_{2}}\big{(}b-a\times\tan\theta_{4}\big{)} \tag{128}\]