esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 4 CenTri: Reinforcement Learning Model and Algorithm
### Reinforcement Learning Models
#### 4.1.2 Distributed Reinforcement Learning Model
Since routes \(k_{3}\) and \(k_{4}\) are occupied by PUs, route \(k_{2}\) is selected. At time \(t_{1}\), both routes \(k_{2}\) and \(k_{4}\) are occupied; therefore, route \(k_{3}\) is selected. At time \(t_{2}\), all D2D routes \(k_{2}\), \(k_{3}\), and \(k_{4}\) are occupied, so the source node uses the backbone route through MC BS. At time \(t_{3}\), route \(k_{3}\) is occupied by PUs, but both routes \(k_{2}\) and \(k_{4}\) are available to the source node. The source node selects route \(k_{2}\) as it has a higher priority due to a smaller number of intermediate hops and nodes compared to route \(k_{4}\). At time \(t_{4}\), route \(k_{2}\) is occupied by PUs, and both routes \(k_{3}\) and \(k_{4}\) are available to the source node. In this case, the source node selects route \(k_{3}\) as it has a higher priority over route \(k_{4}\). During the communication between the source node and the destination node through D2D routes, the source node learns about the appearance of PUs and the availability of D2D routes. This makes route selection more accurate as time goes by. For instance, both D2D routes \(k_{3}\) and \(k_{4}\) have the same number of hops, but the source node has learned that route \(k_{3}\) has a higher channel capacity, and so it has a higher Q-value compared to route \(k_{4}\). Therefore, route \(k_{3}\) is selected. \begin{table}
\begin{tabular}{c l} \hline \hline State & \(s_{t}^{k_{n}}\in S=\{k_{1}^{MC},k_{2}^{fc_{1}},k_{3}^{fc_{2}},...,k_{|K|}^{ MC,fc_{4}}\}\) represents the given route from the CC at the time instant \(t_{1}\). \\ \hline Action & \(a_{t}^{k_{n}}\in A=\{k_{1}^{fc},k_{1}^{fc},...,k_{t_{n}}^{fc}\}\) represents a set of actions in which a source node \(fc_{s}\) selects a D2D route \(k_{n}\in K\) with the highest priority level at the time instant \(t_{1}\). \\ \hline Reward & \(r_{t+1}^{k_{n}}(s_{t}^{k_{n}},a_{t}^{k_{n}})\) represents the traffic intensity \(\Psi_{n}^{k_{n}}\) of the selected route when PU is in the OFF state at the time instant \(t+1\). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The RL model for the route selection embedded in the BS of the small cell and its corresponding nodes in the data plane.
## 4 Foundations of HWSNs
### Nodes Basic Roles
These properties of the filters also determine the level of coupling between them. The HWSNs consider six basic types of roles for the nodes, and also any composition among them. Next, we explain each role. Figure 1: Structure of the presence awareness service. Regular Sensor (RS).This role allows a node to measure a particular variable on-demand. The measured variable can be continuous or discrete. A smartphone can play this role if its peripherals are used to capture audio or video (_i.e.,_ sensing) of the environment. This role can also be played by a weather station that informs on-demand the wind speed and direction to software applications. This role considers only two actions (or services) that are performed in a sequential way, as defined in Equation (7): the reception of an order indicating the measurement request (8), and the delivery of the measurement results (9). Therefore we can expect that a regular sensor has a passive input and an active output. However, a sensor can simulate an active behavior if we add a processing unit that controls the regular sensor. Nodes playing only this role have no capability to detect other nodes in the HWSN:  
\[\text{regular\_sensor}::=\text{<retrieve\_request}\mid\text{ deliver\_result}> \tag{7}\]  
\[\text{retrieve\_request}::=\text{<message, gather\_request\_pa, return\_request}> \tag{8}\]  
\[\text{deliver\_result}::=\text{<request, measure\_variable\_pa}\mid\text{ return\_result}> \tag{9}\]  
Human Input Mechanisms (HIM).This is another mechanism to input information to the HWSN. Devices playing this role allow people to add information or trigger commands through the network. Several peripherals (e.g., a virtual or physical keyboard) or devices (e.g., a pushing button) can play this role (Figure 2). In this case, the data input is performed on-demand (_i.e.,_ the input to this filter is passive). Using human inputs the user can also trigger an alarm or make a request to other nodes, e.g., to ask for information about a situation or a process that is being managed through the network. As shown in Equation (10), this role performs one action and it does not receive information from other nodes; only a person (or robot) can invoke the service provided by this role. Once received an invocation, the service receives the input (data or command) and deliver it through the network:  
\[\text{human\_input\_mechanism}::=\text{<invocation, data\_gathering\_pa,message}> \tag{10}\]  
Witness Unit (WCU).This role provides two independent services: storing and retrieving shared information on-demand, as shown in Equation (11). This role follows a producer/consumer paradigm and it considers a limited storage capability for the nodes. Typically, servers or public temporal repositories play this role. Mobile computing devices could also play such a role for short time periods:  
\[\text{witness\_unit}::=\text{<storing\_information or retrieving\_information}> \tag{11}\]  
\[\text{presence\_awareness}::=<\text{s\_picture\_capture}\mid\text{p\_face\_recognition}> \tag{4}\]0 Examples of human input mechanisms.
## 3 System Architecture and Attack Surface
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CVE ID** & **Affected Component** & **Severity** & **Impact Summary** & **Affected** \\ \hline CVE-2023-45853 & Zlib & Critical & \begin{tabular}{c} Heap-based buffer overflow during \\ decompression \\ \end{tabular} & viewer, CRUD, ETL \\ \hline CVE-2024-26462 & Kerberos (GSSAPI) & High & \begin{tabular}{c} Memory leak in KDC GSAPI; may \\ lead to DoS or auth issues \\ \end{tabular} & gateway, CRUD \\ \hline CVE-2023-7104 & SQLite & Critical & \begin{tabular}{c} Heap overflow triggered by \\ malformed SQL parsing \\ \end{tabular} & CRUD, viewer \\ \hline CVE-2024-45490 & libexpat & Critical & Integer overflow in XML parser & ETL, gateway \\ \hline CVE-2024-45491 & libexpat & Critical & \
\begin{tabular}{c} Memory corruption via recursive \\ entity expansion \\ \end{tabular} & ETL, gateway \\ \hline CVE-2024-45492 & libexpat & Critical & Heap exhaustion leading to crash & ETL \\ \hline CVE-2024-5535 & OpenSSL & Medium &
\begin{tabular}{c} TLS buffer overread; risk of session \\ leakage \\ \end{tabular} & all \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Critical and High-Risk Vulnerabilities Identified in Container Images. Figure 2 illustrates the BLE communication channel between the Galaxy Watch 7 and the Samsung Galaxy A55 smartphone, emphasizing the data flow, connection phases, and associated risk points. The pairing process, periodic data transmission, and GATT-based interactions are all visualized, with specific annotations for attack vectors such as passive sniffing, unauthorized characteristic access, and spoofed notification triggers. This diagram serves to map the real-world BLE behavior observed during our audit and supports a layered threat model that begins at the wearable interface. As the visual mapping in Figure 2 reveals, the BLE layer, often overlooked due to its constrained protocol design, plays a pivotal role in shaping the end-to-end security posture of mHealth platforms. Unsecured GATT characteristics and permissive connection policies introduce latent vulnerabilities that can be exploited without physical compromise of the host device, a risk previously underestimated in real-world deployments [48].
# Deposition of 3 YSZ-TiC PVD Coatings with High-Power Impulse Magnetron Sputtering (HiPTMS)
###### Abstract  
Optimized coating adhesion and strength are the advantages of high-power impulse magnetron sputtering (HiPIMS) as an innovative physical vapor deposition (PVD) process. When depositing electrically non-conductive oxide ceramics as coatings with HiPIMS without dual magnetron sputtering (DMS) or mid-frequency (MF) sputtering, the growing coating leads to increasing electrical insulation of the anode. As a consequence, short circuits occur, and the process breaks down. This phenomenon is also known as the disappearing anode effect. In this study, a new approach involving adding electrically conductive carbide ceramics was tried to prevent the electrical insulation of the anode and thereby guarantee process stability. Yttria-stabilized zirconia (3 YSZ) with 30 vol.% titanium carbide (TiC) targets are used in a non-reactive HiPIMS process. The main focus of this study is a parameter inquisition. Different HiPIMS parameters and their impact on the measured current at the substrate table are analyzed. This study shows the successful use of electrically conductive carbide ceramics in a non-conductive oxide as the target material. In addition, we discuss the observed high table currents with a low inert gas mix, where the process was not expected to be stable.  
coating characterization; disappearing anode effect; electrically conductive additives; HiPIMS parameters; yttria-stabilized zirconia +
Footnote †: journal: Applied Sciences
###### Abstract
The limitations of traditional anxiety monitoring methods, such as self-reported questionnaires, necessitate more dynamic and quantitative measures [28]. These measures should be capable of capturing real-time changes across various contexts [29; 30]. The focus has shifted towards understanding the autonomic nervous system's role in anxiety, leading to the development of diverse tools for anxiety assessment. These tools range from cardiac activity analysis using HRV to innovative non-contact telemetry methods [31; 32]. Peripheral biophysiological measures, including electromyography (EMG) [33], respiration patterns (RESPs) [34], and EDA [35], offer additional insights into anxiety states. Behavioral measures have also emerged as crucial tools in detecting anxiety, utilizing physical and interactive markers to reveal cognitive and affective states related to anxiety [30]. Various methods have been employed in laboratory settings to induce anxiety, each tailored to specific research objectives. These methods range from tasks like the Trier Social Stress Test [36], evoking emotions through experienced stimuli [37], to physiological manipulations like the Cold Pressor Test [38]. The choice of method is contingent upon the research question, whether it is examining anxiety's effects on cognitive performance or other aspects of behavioral performance. The primary objective of this study was to rigorously evaluate the effectiveness and accuracy of ML and deep learning (DL) models to detect anxiety under various levels of noise. Specifically, the study sought to understand the extent to which these models can maintain accuracy in identifying and classifying anxiety-related patterns amidst noise interference. Furthermore, this research aimed to explore the potential benefits of employ ing deep learning end-to-end models in improving the robustness of anxiety detection systems against noisy conditions. To this end, this study utilized the Wearable Stress and Affect Detection (WESAD) dataset [8], a publicly available resource, as the basis for experimentation. This dataset was augmented with synthetic noise to simulate real-world noisy environments, thereby providing a platform to assess model performance as a function of noise interference. In pursuit of replicating and benchmarking against the state-of-the-art, this study adhered closely to established literature [5; 8; 39] in terms of filtering parameters, data processing techniques, and methodologies for feature selection and extraction. This approach ensured that the findings were grounded in current best practices [40; 41; 42; 43], allowing for a meaningful contribution to the field of anxiety detection.
## 2 Materials and Methods
### OCO-2 SIF Products  
We used the OCO-2 SIF data, which is provided freely from ([[https://disc.gsfc.nasa.gov](https://disc.gsfc.nasa.gov)]([https://disc.gsfc.nasa.gov](https://disc.gsfc.nasa.gov))) and available from September 2014 to present. The OCO-2 instrument is a three-channel grating spectrometer recorded the high resolution spectra of the O2 A-band (757-775 nm) and the other two bands. Fraunhofer lines at 758.8 and 770.1 nm can be used for the fluorescence retrieval via the FLD (Fraunhofer Line Discrimination) algorithm. Generally, the former is referred as the 757 nm window, with the latter as the 771 nm window [24; 26; 28; 36]. Therefore, OCO-2 provides mid-day SIF retrievals at both 757 and 771 nm. The mid-day SIF products were converted to daily SIF by applying the daily correction factor that was included in the SIF Lite product. The OCO-2 system observed data by three modes, which included nadir, glint, and target. Typical OCO-2 alternately measured between the nadir and glint viewing mode. The repeat frequency of the special target observation mode is approximately 16 days. In the nadir mode, the instrument views the ground directly below the spacecraft. In the glint  
Figure 1: The trajectory of Orbiting Carbon Observatory-2 (OCO-2) passed by cropland from June to August 2015 in China. The land cover map is from the moderate resolution imaging spectroradiometer (MODIS) land cover type product (MCD12Q1) based on the IGBP (International Geosphere-Biosphere Programme) classification scheme.  
mode, the instrument tracks near the location with direct sunlight being reflected [29]. Besides, a target mode is infrequently turned on when the satellite overpasses the ground validation sites. In this mode, a large number of temporally continuous measurements at different viewing zenith angles (VZA) are made [37]. Details of the retrieval that is based on the IMAP-DOAS (Iterative Maximum A Posteriori-Differential Optical Absorption Spectroscopy) preprocessor, as well as the OCO-2 SIF product can be found in [PERSON] et al. [26; 28], which is also efficient in removing low and thick clouds. In this study, we used the SIF Lite product (V8r) aggregated as daily files with 1.29 \(\times\) 2.25 km\({}^{2}\) spatial resolution.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 4 Joint Digital Signal Processing Schemes for Carrier Recovery  
In this section, we discuss how the phase coherence of the channels in a frequency comb-based system can be utilized by joint processing in the digital domain. In contrast to the methods described in Section 3, this section focuses on DSP-based algorithms and methods for systems with free-running LO-combs without pilot tones. It is partly based on the work presented in [66]. As previously discussed, the principles of joint processing are similar regardless of the physical principles of the phase locking. We therefore review the previous work on joint carrier recovery also for SDM systems.  
The most basic system where joint carrier recovery is possible is in fact a standard dual-polarization signal, which was demonstrated early in the development of digital coherent receivers [98]. More recently, joint-polarization phase-noise estimation was studied for a pilot-base algorithm and shown to be able to both increase phase-noise tolerance and reduce the pilot rate [99]. In more advanced SDM systems, joint phase-tracking has been demonstrated in multicore [100] and multimode [101; 102] fiber. Both of these demonstrations focused on reducing the DSP complexity.  
For comb-based spectral superchannels, joint phase tracking was first proposed by [PERSON] et al. [103]. Using simulations they showed that the phase noise tolerance increased when processing three channels together. However, before that, [PERSON] et al. [104] demonstrated joint processing of two subchannels generated electrically and modulated on a single laser line. The subchannels were detected in a single coherent receiver and jointly processed to achieve a higher phase noise tolerance. In the system proposed in [82], an optical frequency comb was used to generate a spectral superchannel, which was detected in a single ultra-broadband coherent receiver using a single LO line. The subchannels were then jointly processed using a pilot-aided phase estimation algorithm. For comb-based superchannels with a comb as LO, we demonstrated joint carrier recovery in [66].
# Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication +
## 3 Broadcast Feedback on Packet Collisions
### Feedback Operation  
Each vehicle executes two sides of the feedback logic:
# An Improved YOLO Model for UAV Fuzzy Small Target Image Detection
## 3 Improvement of YOLOv5s Algorithm
### Improved Loss Function  
The loss function of the YOLOv5s network model is mainly composed of three parts. Its calculation method is as follows:  
\[\psi=\psi_{obj}+\psi_{bbox}+\psi_{cls} \tag{10}\]  
where \(\psi_{obj}\) is the loss of object confidence, which is calculated using the binary cross-entropy loss. \(\psi_{bbox}\) is the classification loss of the object, which is calculated using the cross-entropy loss. \(\psi_{cls}\) denotes the loss for the predicted bounding box positions. This study uses Alpha-IoU to optimize the calculation of box loss. The loss function for the predicted bounding box positions is calculated as shown in the formula.  
\[\mathcal{L}\alpha-CIoU=1-IoU^{\alpha}+\frac{\rho^{2\alpha}\left(b,b^{gt} \right)}{c^{2\alpha}}+\left(\beta v\right)^{\alpha} \tag{11}\]  
here, \(IoU\) represents the intersection over the union between the predicted bounding boxes (\(b\)) and ground-truth bounding boxes (\(b^{gt}\)). Alpha (\(\alpha\)) is a hyperparameter that can be adjusted to control the accuracy of the predicted bounding boxes. \(\rho\) represents the Euclidean distance between the centers of the computed predicted bounding boxes and ground-truth bounding boxes. \(C\) represents the diagonal length of the minimum bounding box, which encloses the predicted bounding box and ground-truth bounding box. \(v\) is a parameter that measures the similarity of the aspect ratio between the predicted bounding box and ground-truth bounding box. Its calculation formula is shown in Equation (12).  
\[v=\frac{4}{\pi}\left(arctan\frac{w^{gt}}{h^{gt}}-arctan\frac{w}{h}\right) \tag{12}\]  
where (\(w^{gt}\),\(\mu^{gt}\)) and (\(w\),\(h\)) are the width and height, respectively, of the ground-truth bounding box and predicted bounding box.  
\[\beta=\frac{v}{(1-IoU)+v} \tag{13}\]  
The original YOLOv5 model used the _CIoU loss_. It is calculated using the following formula:  
\[L_{CIoU}=1-I+\frac{\rho^{2}\left(b,b^{gt}\right)}{c^{2}}+\theta v \tag{14}\]  
The advantage of the CIoU Loss is that it considers the intersection over union (IoU), center point distance, and aspect ratio between the predicted bounding box and ground-truth bounding box. However, the disadvantage is that the parameter \(v\) in the formula does not consider the differences in width and height relative to their confidence. This fact can reduce the model optimization speed and decrease the convergence rate of the gradient when training on a large number of images with small objects. Using the Alpha-IoU loss function and increasing the exponent of alpha, the gradient convergence speed is increased to address the limitations of the CIoU loss, improving the model's detection capability with small objects.  
The network architecture of the improved YOLOv5s after the four aforementioned improvements is presented in Figure 9.  
Figure 8: Network architecture of YOLOv5s after the addition of transposed convolution module.
# _Arthrospira platensis_ as a Feasible Feedstock for Bioethanol Production
## 3 Environmental Stresses for Glycogen Accumulation
### Phosphorus Depletion  
The glycogen synthesis in _Arthospira platensis_ is strictly regulated by the amount of phosphorous in the cell, and the enzyme ADP-glucose pyrophosphylase is responsible for producing ADP-Glc, which is the glucosyl donor for the elongation of the \(\alpha\)-1,4-glucosidic chain to synthesize glycogen [71]. The enzymatic reaction of the ADP-glucose pyrophosphylase is ATP + Glc-1-phosphate \(\leftrightarrow\) ADP-Glc + inorganic pyrophosphate (Pi); this enzymatic reaction in cyanobacteria is regulated positively by the 3-phosphoglycerate (3-PGA) and inhibited by Pi. If the concentration of inorganic phosphorus is low, the synthesis of glycogen is stimulated, as shown by [32], in phosphorus starvation conditions. This has also been observed by [PERSON] et al. [72] with mixed cyanobacterial cultures, for which the glycogen accumulation was enhanced, obtaining up to 47% of total carbohydrate content. In addition, [PERSON] et al. [62] observed that the amount of biomass and glycogenproduced by _Arthrospina platensis_ in different scenarios of starvation (NO3-, PO43-, and SO42-). In this case, comparing phosphorous starvation with the other scenarios, it did not present a significant difference in the biomass production compared to the control, while an increase in the production of glycogen was observed, reaching up to 30% DW of glycogen content after 18 h of culture. This was also predicted by the simulations and was consistent with the results obtained by experiments where the simulated fluxes had no significant differences. Another example of the enhancement in the production of glycogen via phosphorus deprivation is in Monshupanee and Incharoensakdi (2018), where the total glycogen content increased from 15.1 to 28.9% DW. A similar result was also observed with nitrogen deprivation. Nevertheless, the biomass production was not compromised. Another interesting result was obtained by [PERSON] et al. (2016), who increased the total carbohydrates of _Arthrospina platensis_ by 300%, when the phosphate concentration was half-reduced during the culture (from 0.5 g/L to 0.25 g/L). In summary, the reduction of phosphorous to enhance the glycogen content of _Arthrospina platensis_ seems like a good strategy, assuring both high glycogen content and biomass production. Therefore, this approach would increase the overall yield of the bioethanol production.