esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Achieving neighbor discovery for a directional flying ad hoc network (FANET) with multiple channels poses challenges for media access control (MAC) protocol design, as it requires simultaneous main lobe and channel rendezvous while dealing with the high UAV mobility. In order to achieve fast neighbor discovery for initial access without coordination or prior information, we first establish the theoretical supremum for the directional main lobe. Then, to achieve the supremum, we introduce the BR-DA and BR-DA-FANET algorithms to respectively establish the supremum on rendezvous between a pair of UAVs' main lobes and rendezvous of main lobes for all UAVs in the FANET. To further accelerate the neighbor discovery process, we propose the neighbor discovery with location prediction protocol (ND-LP) and the avoiding communication interruption with location prediction (ACI-LP) protocol. ND-LP enables quick main lobe rendezvous and channel rendezvous, while ACL-LP enables beam tracking and channel rendezvous together with the avoidance of communication interruptions. The simulation results demonstrate that the proposed protocols outperform the state-of-the-art works in terms of neighbor discovery delay.  
beam rendezvous; channel rendezvous; directional antenna; flying ad hoc network (FANET); medium access control (MAC); neighbor discovery +
Footnote †: journal:  
Article  
A Multichannel MAC Protocol without Coordination or Prior Information for Directional Flying Ad hoc Networks
## 2 Materials and Methods
### Data Analysis  
#### 2.5.1 PTR-ToF-MS Spectra Processing  
Dead time correction, internal calibration of PTR-ToF-MS data, and subsequent peak extraction steps were performed according to the procedures described elsewhere [15; 16; 17] to reach a mass accuracy (\(\leq\)0.001 \(m/z\)) which is sufficient for sum formula determination, and a dynamic range extending above 1 ppmV. Mass spectra baseline removal was carried out after averaging the whole measurement, and a modified Gaussian was used to fit the data for the purpose of peak detection and peak area extraction. VOC concentration was calculated directly in ppbV (parts per billion in volume) starting from the number of detected ions, using the model developed by [PERSON] and co-workers [18]. A constant reaction rate coefficient (k\({}_{R}\) = 2.0 \(\times\) 10\({}^{-9}\) cm\({}^{3}\)/s) was assumed for all ionisation reactions. Mass spectra recording was set in the mass-to-charge ratio range of 15-300 \(m/z\). During sample measurement, no significant decrease in the primary ion amount, as estimated by monitoring ion \(m/z\) 21.022, was observed, which indicates that no primary ion depletion took place at any given moment.
## 3 Home Area Network
### Smart Home HAN Requirements
#### 3.2.3 Dependability  
Reliability  
Reliability can be defined as the probability that a network functions continuously and properly in a time period interval. A reliable network is a network that is capable to unceasingly deliver an accurate service. Reliability can be categorized into different levels: event reliability, packet reliability, Hop-by-Hop reliability, and End-to-End reliability.  
Both packet and event reliability levels operate with the required quantity of information to notify the sender of the occurrence of an event within the network environment. End-to-End and Hop-by-Hop reliability levels are related with the successful recovery of the event information. Still, all of them rely on redundancy and retransmission mechanisms.
## 4 Experimental Results
### Evaluation  
For evaluation, we measured the performance of our proposed model on training data and test data. In order to verify the performance of the proposed model, we analyzed the number of parameters, confusion matrix and measurement parameters such as accuracy, recall, and F-score.  
Use the above TCDCN model and CNN-LSTM, CNN-CTC, and XGBoost models to train the noise-free training set. The test set also uses noise-free audio. The network configuration of each comparison model is introduced below:  
For the audio event classification task, we used the previously proposed model architecture and optimizer, trained for 40 epochs. Figure 7 shows the accuracy variation of the four networks on the validation set, while saving the weights of the networks for the period with the largest F1 values in the validation set. Table 3 shows the overall detection results.  
Figure 8 shows the prediction results for each classification model for the Urban-Sound8K dataset. One of the larger errors is the partial similarity between the sounds of children playing and the street music scene, leading to a reduction in classification accuracy. Overall, compared with the traditional model structure and the speech model based on attention mechanism, the structure of the TCDCN model is far more complex than other models. Through the multi-layer hole convolution, the perception field can be expanded, and the fitting of context relevance is strong, so the final topic recognition rate is raised. To further validate the classification performance of the TCDCN hybrid model, we chose the publicly available audio scene dataset AudioSet for testing, and the test results are shown  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Architecture** & **F1 (\%)** \\ \hline CNN-CTC & 3 conv + 8 batch norm + 3 max-pooling layers & 71.4 \\ CNN-LSTM & 4 conv + 2 Maxpooling + LSTM & 73.0 \\ TCDCN & transformer encoder and decoder + dilation & 89.8 \\ XGBoost & 3 conv + 2 Maxpooling + XGBoost & 78.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result of Accuracy VALUES on the test set.  
Figure 7: Variation of the accuracy value in the validation set for each model.  
in Table 4. From Table 4, we can conclude that the classification performance of the hybrid model is significantly better than that of the individual model.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### General IoT Applications
#### 3.4.2 WaterGrid-Sense  
WaterGrid-Sense [33] is a LoRa-based sensor more targeting the IoT in industrial set-ups, especially smart water management systems. The availability of sensor interfaces enables its usage in different applications. The processor and memory specifications of the mode are not specified by the authors. The LoRa radio module operating at 868 MHz was adopted for long-range communication. In addition, the WaterGrid-Sense mode houses the pressure and pulse sensor interfaces. The pressure sensor is installed on a water pipe while the pulse sensor is installed on a water meter. The pressure and water meter readings are transmitted to users via the LoRa gateway.  
Concerning the mote's power supply, it relies on a 1000 mAh, 3.7 V Li-ion battery and is capable of scavenging solar energy by means of an external solar panel. The charge limit of the battery is 4.2 V at 500 mA. The node consumes 0.1 mV, 23 mV and 73 mV in the sleep, sensing and the transmission states, respectively. The node draws approximately 0.52 mA, 0.16 mA, 0.1 mA during the transmission, in idle and sleep states, respectively. The power consumed during transmission, sensing/idle and sleep states is around 190 mW, 52 mW and 0.1 mW, respectively. On average, a battery voltage of around 0.067 V is consumed per day.
## 1 Introduction  
Society is continuing to age, and as it does so, the number of cancer patients is increasing worldwide. Surgery is a primary method used for cancer therapy; however, in the elderly population there are a large number who take anticoagulants to treat thrombosis, which leaves them at risk of severe bleeding during any surgical procedure.  
Photodynamic therapy (PDT) is a cancer therapy that uses laser irradiation and a photosensizer that has tumor-specific accumulation, such as porphyrins [1]. Irradiation with low energy of a specific wavelength tuned to the photosensitizer initiates a photochemical reaction that produces a reactive oxygen species (ROS), namely, singlet oxygen, which leads to the death of cancer cells via apoptosis or necrosis [2]. Thus, PDT is a non-invasive and promising therapy for the treatment of patients with cancer. However, the mechanism underlying the tumor-selective accumulation of porphyrins has not been completely elucidated.  
In order to address this, I focused on a proton-coupled folate transporter, referred to as heme carrier protein 1 (HCP1), which has been identified as a heme transporter. It has previously been reported that both heme and porphyrins are transported into cells via HCP1 [3, 4, 5]. HCP1 is also reported to be regulated by ROS derived from mitochondria [6]. ROS, especially superoxide, are generated from mitochondria, as well as by the actions of NADPH oxidase (NOX) and xanthine oxidase. However, mitochondria are the main producers of superoxide because electrons that leak from the mitochondrial electron transport chain regularly react with oxygen. It is well known that increased oxidative stress can act as a signaling event that leads to the development of a variety of chronic diseases [7]. Overgeneration of ROS has been observed in cancer cells, and it has been shown to be related to cancer progression [8]. In recent years, macrophages have been reported to infiltrate tumor tissues, where they are referred to as tumor-associated macrophages (TAMs) and are associated with increased tumor malignancy [9]. Activated macrophages express inducible nitric oxide synthase (iNOS), which produces nitric oxide (NO) [10], a reactive compound that mediates many types of signaling events. In addition to activating signaling pathways, NO is able to react with superoxide to produce peroxynritrite, a highly reactive nitrogen-containing compound [11]. In this study, I used the murine macrophage cell line RAW264 treated with lipopolysaccharide-LPS and irradiated with X-rays to mimic activated TAMs and examined the relationship among peroxynritrite levels, HCP1 expression, and cellular porphyrin uptake. LPS is an activator of macrophages and can induce iNOS expression [12]. In addition, cells irradiated with X-rays often overproduce ROS, especially superoxide derived from mitochondria [13]. Therefore, these systems were used to generate peroxynritrite, and the effect of porphyrin accumulation was studied.
# State-Sensitive Black-Box Web Application Scanning for Cross-Site Scripting Vulnerability Detection
## 2 Background
### XSS Vulnerability  
Cross-Site Scripting (XSS) vulnerabilities arise from flaws in how web applications handle user-generated content. The core principle behind XSS vulnerabilities revolves around the ability for malicious actors to inject unauthorized code or scripts into trusted websites [9]. Utilizing XSS vulnerabilities can obtain user privacy information, spread webworms and cause denial of service. XSS vulnerabilities can be categorized into three primary types [10; 11]: reflected XSS, stored XSS, and DOM-based XSS. The primary objective of this paper is to detect reflected and stored XSS vulnerabilities.  
* **Reflected XSS** occurs when a malicious script, injected into an HTTP request, is directly included in the response without appropriate sanitization, leading to its execution by the web browser. This vulnerability commonly manifests in scenarios where malicious scripts are embedded within search content, thereby becoming integral components of search results or error messages. Reflected XSS is also referred to as non-persistent XSS since the malicious scripts are not permanently stored on the server.
* **Stored XSS** refers to the practice of storing malicious scripts on the server. When a user requests relevant content, the malicious script is included as part of the response, leading the browser to execute it. A common instance of this vulnerability involves injecting malicious scripts into message boards. Upon viewing the content within this message, the injected script is executed. Since the malicious scripts persistently reside on the server, Stored XSS is alternatively referred to as persistent XSS.
* **DOM-based XSS** occurs when the client-side script alters the Document Object Model (DOM) using a malicious script. Unlike stored and reflected XSS, the malicious script associated with DOM-based XSS can be inserted into the URL as the value of a particular DOM object or HTML element, without ever reaching the server.
## 2 Principles of Structured Light Measurement and Residual Neural Networks  
### Interference Factors in Structured Light Measurement Systems  
The structured light method is based on the triangulation principle. As shown in Figure 3, the system generally consists of a camera, a projector, and a computer. The coded patterns are projected onto the object, and the patterns modulated by the object are captured by a camera [27]. Based on the triangulation principle, the 3D information can be calculated according to the modulated patterns in the computer. There are three types of structured light patterns, which are point, line, and surface structured light. The measurement steps of the structured light system are shown in Figure 4.  
Figure 2: Flow Chart of the Solution.  
The projection and shooting of the surface structured light image are shown in Figure 3, the image containing the three-dimensional information of the object under test is captured and input into the computer for data processing, and, finally, the three-dimensional information of the object under test is obtained.  
Since the 3D information is obtained from the modulated stripes, the location of the stripes in the image is very important for the accuracy of measurement. Most of the stripe-centering methods are based on the stripe grayscale distribution. However, structured light stripes and experimental environments are complex and diverse. Many factors, such as image noise, color, and shape of the object to be measured; projecting angle; and shooting angle, degenerate the stripe gray distribution, which in turn decreases the accuracy of stripe centering [28]. As shown in Figure 5, the factors induce a low signal-to-noise ratio and asymmetric distribution of stripe grayscale, which decrease the accuracy of stripe locating. The large error data is introduced into the measurement results.
## 3 Results  
### Evaluation of DBF_SOSat with Phenological Observations of Bud Break  
Comparisons of DBF_SOSat with bud break observations including all site-years resulted in an RMSE of one week and an early bias of 0.13 days (R\({}^{2}\) = 0.75, probability \(p\) < 0.001, Number of observations \(N\) = 48) (Figure 2a). The site-wise correlation between DBF_SOSat and in situ observations were not significant (Table 2), but the number of observations (4-7 per site) was also very small. RMSE and bias ranged between 4 to 10 days and 0 to 5 days, respectively. The smallest RMSE and bias were obtained for northern sites, Saariselka and Kevo, in Lapland. The largest RMSE (10 days) and bias (5 days early) were both found for Kannus in western Finland. Large deviations (\(>\)two weeks) between satellite and in situ observations were observed for Vesijako in 2005 (15 days) and Kannus in 2007 (19 days).  
For the southern boreal zone, including sites Aulanko, Vesijako and Koli, the correlation of DBF_SOSat with bud break observations was weak (R\({}^{2}\) = 0.32, \(p\) = 0.012, \(N\) = 19). The RMSE was 6.4 days and the bias 1.8 days (early). For the four sites in northern Finland: Varrio, Saariselka, Koli and Kevo, the correlation was higher than for the sites in the southern boreal zone (R\({}^{2}\) = 0.62, \(p\) < 0.001, \(N\) = 22), but the RMSE (6.9 days) and the bias (2.8 days late) were larger. The correlation between interannual anomalies of DBF_SOSat and bud break was significant (Figure 2b), but could explain only less than 30% of the interannual variation in the field observations.  
Figure 2: Relationship of the start of season in deciduous broad-leaf forests (DBF_SOSat) with phenological observations of birch bud break for the period from 2001 to 2008: (**a**) for all site-years and (**b**) for the year-to-year anomalies.
# Multi-Body Dynamics Modeling and Simulation of Maglev Satellites
## 2 Modeling of Three Body Dynamics of Maglev Satellite
### Platform Subsystem Dynamics Equation  
Substitute Equation (20) into the second Lagrangian equation:\[\frac{d}{d\Gamma}\left(\frac{\partial L_{pf}}{\partial\dot{q}}\right)-\frac{ \partial L_{pf}}{\partial\mathbf{q}}=\mathbf{Q} \tag{21}\]  
where \(L_{pf}=T_{pf}-U_{pf}\) is the Lagrangian function, where \(U_{pf}=0\), \(\mathbf{q}\) is the generalized coordinate, and \(\mathbf{Q}\) is the generalized force corresponding to the generalized coordinate.  
The expansion of Equation (21) can be written as the following components to obtain the translational combined external force of the platform subsystem:  
\[\frac{d}{dt}\left(\frac{\partial L_{pf}}{\partial\dot{\mathbf{X}}_{pf}}\right)- \frac{\partial L_{pf}}{\partial\mathbf{X}_{pf}}=\mathbf{F}_{pf} \tag{22}\]  
By substituting \(L_{pf}\) into the above equation, we can obtain a translational equation for the platform:  
\[M_{pf}\ddot{\mathbf{X}}_{pf}=\mathbf{F}_{pf} \tag{23}\]  
where \(\mathbf{F}_{pf}\) refers to the projection of \(\overline{\mathbf{F}}_{pf}\) in the inertial frame. \(\overline{\mathbf{F}}_{pf}\) including orbital control forces \(\overline{\mathbf{F}}_{k}\), space environment interference forces \(\overline{\mathbf{F}}_{d}\), and the reaction of electromagnetic forces \(\overline{\mathbf{F}}_{m}\) applied by the MM to the PM, \(\mathbf{F}_{pf}\) can be expressed as:  
\[\mathbf{F}_{pf}=\mathbf{F}_{k}+\mathbf{F}_{d}-\mathbf{A}_{b}\mathbf{C}_{r}(\mathbf{F}_{ur}+\mathbf{F}_{ur} +\mathbf{F}_{lr}) \tag{24}\]  
where \(\mathbf{F}_{k}\) is the projection of \(\overline{\mathbf{F}}_{k}\) in the inertial frame, \(\mathbf{F}_{d}\) is the projection of \(\overline{\mathbf{F}}_{d}\) in the inertial frame, and \(\mathbf{F}_{ur},\mathbf{F}_{ar},\mathbf{F}_{lr}\) are the electromagnetic forces applied to the load compartment by the upper OLFMB, the LLFMB, and the lower OLFMB, respectively.  
The expansion of Equation (21) can be written as the following components to obtain the driving torque of the MM:  
\[\frac{d}{dt}\left(\frac{\partial L_{pf}}{\partial\dot{\theta}_{r}}\right)- \frac{\partial L_{pf}}{\partial\theta_{r}}=\tau_{r} \tag{25}\]  
By substituting \(L_{pf}\) into the above equation, we can obtain the driving equation for the MM:  
\[\left(J_{rx}-\frac{M_{r}^{2}}{M}\mathbf{D}_{r}^{\mathsf{T}}\widetilde{\mathbf{x}}_{ r}\widetilde{\mathbf{x}}_{r}^{\mathsf{T}}\mathbf{D}_{r}\right)\ddot{\theta}_{r}+\dot{ \mathbf{\omega}}_{b}^{\mathsf{T}}\mathbf{R}_{r}\mathbf{D}_{r}+\mathbf{\omega}_{b}^{\mathsf{T} }\dot{\mathbf{R}}_{r}\mathbf{D}_{r}=\tau_{r} \tag{26}\]  
where \(\tau_{r}\) is the moment of force exerted on the MM.
## 3 Design Challenges of MAC Protocol for IBSN
### Requirements of MAC Protocol Design for IBSN  
The fundamental task of a MAC protocol is to avoid collision of data packets and to prevent simultaneous transmissions while preserving maximum throughput, minimum latency, communication reliability and maximum energy-efficiency [25]. QoS is also an important factor representing a good MAC protocol. In medical applications, a latency of only 125 ms is allowed, whereas in consumer electronics latency can be less than 250 ms [7]. Other important features include adaptability to a dynamic physical medium, maximum achievable throughput in different network scenarios, low jitter in heterogeneous traffic, efficient bandwidth utilization with high payload, safety and security. Table 5 presents major features of MAC protocols for IBSN and their values suggested by the IEEE 802.15.6 [23]. In general, a good BSN MAC protocol should be energy-efficient, reliable even in the presence of heterogeneous traffic, safe and secure, in addition to being QoS-aware [27]. The closed-loop mode of operation of medical devices does not only require these features but also additional features, such as ability to intelligently access the medium in case of emergency events, establishing reliable and low latency communication between nodes and medical devices, and ability to respond quickly within the specific time-frame for a given medical device.  
The features of the MAC protocol specified by the IEEE 802.15.6 are common for the in-body and on-body BSNs, however they need to be better specified to suit the requirements of closed-loop medical devices.
# Liquid Crystal-Embedded Hollow Core Fiber Temperature Sensor in Fiber Ring Laser
## 4 Conclusions  
A fiber ring laser temperature sensor is presented experimentally. The liquid crystal filled MZ interferometer of single-mode-no-core-hollow-core-no-core-single-mode structure is embedded into the fiber ring laser as sensing element and filter. Consequently, the laser possesses the ability of temperature detection. The characteristics of temperature is tested and the sensitivities is \(-1.181\) nm/\({}^{\circ}\)C and \(-1.318\) nm/\({}^{\circ}\)C, respectively. Additionally, the good signal-to-noise ratio of 35 dB, narrow 3 dB bandwidth of 0.15 nm are achieved for temperature measurement. In addition, the fiber ring laser sensor has the characteristics of small size, low cost, and convenient manufacture, which makes it a good choice in the practical application of fiber sensor.  
Conceptualization, W.L. and S.Z.; methodology, W.L.; validation, Y.L.; formal analysis, L.S.; investigation, M.I.V.; resources, S.Z.; data curation, W.L.; writing--original draft  
Figure 9: Test for time stability of wavelength shift and power fluctuation.  
preparation, S.Z.; writing--review and editing, W.L.; supervision, L.S.; project administration, L.S.; funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the startup fund from Southern University of Science and Technology and Shenzhen government.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Materials and Methods  
We used X-ray image data obtained by [PERSON] et al. [13] in the experiment. The dataset was already organized by [PERSON] et al. [13] into three folders (train, validation, and test). Each folder contained sub-folders for each image category (Normal/ Pneumonia).  
The train folder consists of 5216 X-ray images (1341 images labeled as Normal and 3875 images labeled as Pneumonia). There are 16 X-ray images in the validation folder, and 624 X-ray images in the test folder. It is evident that the data in the train folder are imbalanced, with the majority of the data labeled as Pneumonia, and training a deep neural network to classify the data among two categories will over-fit the data. This experiment augmented the Normal chest X-ray images by Deep Convolutional Generative Adversarial Networks (DCGAN). Figure 2 shows the architecture of the DCGAN.  
First, because of GPU memory limitations, the images were resized to 128 \(\times\) 128 pixels. Second, the images were scaled to a [\(-\)1,1] pixel value range to match the generator's output as it uses the Tanh activation function. In this architecture, the generator network takes a 100 \(\times\) 1 noise vector as input. There are then four convolutional-transpose layers with batch norm layers applied with the ReLU activation function interlaced in-between to scale to the appropriate 128 \(\times\) 128 \(\times\) 3 image size. A 128 \(\times\) 128 \(\times\) 3 image is taken as input by the discriminator network, followed by five strided convolution layers with batch norm layers and leaky ReLU as an activation function. A sigmoid activation function follows the last strided convolution block's output to output whether the image is real (original data) or fake (generated data).