esa-sceva/satcom-chunk-collection huggingface dataset:
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 4 The Proposed Data Caching and Sharing Security (DCSS) Protocol
### Data Caching and Data Sharing Authorization  
The DCSS protocol intends to provide authorization to the UE to cache and share the data accessed using the service authorization protocols such as NS-FId protocol [4]. After accessing the data, the UE will have the option to cache or share data or do both, hence, the need for cache and share security. The proposed protocol is divided into caching and sharing authorization phases, uses cache token \(ChT\) and share token \(ShT\) for caching and sharing requests, respectively comprising claims, labels, and access rights delegation. The token structure similar defined in [4], the tokens however have some additional claims and scope to achieve their objectives. Both cache and share tokens should include the UE and data object capabilities, delegation ability in form of label bitstring, and the security context such as nonce key ID. The nonce key could be used to extend data security with encryption and decryption options. The use of labels enable the caching and sharing of the data even when the SP involvement is minimal or direct authorization is no existent, for example, in areas with no network coverage.  
The UE uses the access token and security context from federated authorization protocol [4] such as the keys to request caching authorization of the data accessed. The security token is timestamped, has an expiry date that deems the token invalid, enforcing the UE to request a new token or refresh the expired token [37]. It also includes an identifier, hence a security token will have the following tuple, similar to those in [4]: Token = ID, label, timestamp, expiry date, so the cache token is \(ChT=(fid,sid,d1,label,Ts_{1},Exp_{1})\). The use of timestamps and the expiry date reduces the risk of replay attacks. The security token is assigned to the respective entities \(UE,SPAAA,SS\) and digitally signed by the \(SPAAA\) securely to provide the integrity and authenticity of the token. The data caching security provides authorization to UE to cache the accessed data.  
After Caching the data, the UE uses the cache token and security context such as \(ACK\_1\) strings to request sharing authorization of the data accessed and now cached. The data sharing security provides authorization for the UE to share the accessed data. The UE in HN or VN might want to share the cached data with another UE, to be able to do so the UE must request permission from the SP to grant the UE access to data. The Share Token is \(ShT=(fid,d1,label,Ts_{2},Exp_{2})\).  
Figure 2: 5G FIdM Model.  
The security token can be deemed invalid if it used after the expiry date and revoked if it is misused or the attributes do not match the requested data attributes in the SS database. This is followed with negative acknowledgement (NACK) to the UE from the SS.
# Route Prefix Caching Using Bloom Filters in Named Data Networking
## 3 Route Prefix Caching  
### Naive Approaches to Prefix Caching  
In a route caching scheme, the matching result from the FIB is cached to be looked up faster for subsequent reuse. If the matching result is one of the leaf prefixes in the FIB, that result can be safely cached without causing any coherency problem. However, in the case of non-leaf prefixes, it causes incomplete prefix caching as mentioned in Section 2.2. Such a problem occurs when a non-leaf prefix is cached, but not all of its descendant prefixes are cached. In Figure 3, suppose that prefix \(p\) is cached, but its descendant prefixes \(q\), \(r\), \(s\), and \(t\) are not cached. Now, an interest packet arrives with a name, /ride/bike/ha/xo. Obviously, \(q\) is the longest matching prefix for the name; however, \(q\) was not cached and \(p\) is misleadingly matched with it in the cache. To avoid misleadingly caching, we could consider two simple approaches: any non-leaf prefix is not allowed to be cached, or all the descendant prefixes should be cached when a non-leaf prefix is cached. The benefit of the first idea is restrictive. As shown in Figure 3, the non-leaf prefixes \(p\) and \(r\) cannot be cached. Furthermore, in the second approach, when \(p\) is cached, its descendants \(q\), \(r\), \(s\), and \(t\) should be cached together. This results in a huge burden to cache all the descendant prefixes, in the aspects of cache replacement and miss penalty. Thus, both approaches are considered not to be viable in practice.
# A Study on Crowd Evacuation Model Considering Squeezing Equilibrium in Crowded Areas
###### Abstract  
A new crowd evacuation model is established to solve the stagnation problem of traditional social force models in a complex and dense scene. In the proposed model the acting forces between pedestrians, and between pedestrians and obstacles in the traditional social force model, are improved to find out the relationship in the two cases which are within the influence range and are not intersected, and those which are intersected and not greater than the maximum degree of squeezing, and to solve it for parameter optimization. The simulation platform built is used to compare the performance of the traditional social force model and the improved model, and to deeply analyze the relationship between the evacuation time and the degree of squeezing. The results show that as the evacuation time increases, the crowd in the emergency exit area is getting denser, the optimized model is distributed more evenly, and the probability of squeezing is lower. The optimized model has better stability in terms of the ability to control the intersection without exceeding the maximum degree of squeezing. Due to less squeezing, the optimized model can reduce the time of passing through the exit to a large extent. Therefore, the way to resolve the disorderly evacuation of pedestrians caused by excessive crowd density in the evacuation process is to solve optimization parameters.  
crowd evacuation; dense areas; social force models; degree of squeezing 2022
## 4 Results and Discussion
### Evaluation of Model Quantization
Although the differences in accuracy and F1-score among the three models were marginal, YOLOv8-small INT8 demonstrated the highest normalized computational efficiency due to its quantized architecture. The INT8 precision significantly reduced model size and inference latency, making it particularly suitable for real-time deployment on mobile or embedded devices. This efficiency, combined with its strong predictive performance, makes YOLOv8-small INT8 the optimal model, effectively balancing high detection quality with the practical requirements of mHealth applications. Therefore, considering these factors, the INT8 quantized version of the YOLOv8-small (YOLOv8s) variant was chosen as the final model for use in the mHealth application.
###### Abstract  
From a public health perspective, the monitoring of water quality intended for human consumption belongs to the operational and audit management of the supply zones. Our study explores the spatial and temporal patterns of the parameters of drinking water in Sibiu County, Romania. We related the relevant physical-chemical parameters (ammonia, chlorine, nitrates, Al, Fe, Pb, Cd, Mn, pH, conductivity, turbidity, and oxidizability) and radioactivity (gross alpha activity, gross beta activity, and radon-222 content) from a 5-year survey to the water source (surface water and groundwater, which may be of subsurface or deep origin), space (sampling locality) and time (sampling month and year). We conducted a combined evaluation using the generalized linear mixed models (GLMMs), Pearson correlation analysis of the physical-chemical parameter, multivariate linear redundancy analysis (RDA), t-value biplots construction, and co-inertia analysis. The obtained regional model shows that the source, locality, and month of sampling are significant factors in physical-chemical parameters' variation. Fe and turbidity have significantly higher values in surface water, and nitrates and conductivity in groundwater. The highest values are recorded in January (nitrates), March (Cl, ammonia, pH) and August (Fe, turbidity). The RDA ordination diagram illustrates the localities with particular or similar characteristics of drinking water, two of which (rural sources) being of concern. The water source is the best predictor for radioactivity, which increases from surface to ground. The gross alpha and beta activities are significantly and positively correlated, and are both correlated with conductivity. In addition, the gross alpha activity is positively correlated with nitrates and negatively with pH, while the gross beta activity is positively correlated with Mn and negatively with Fe; these relationships are also revealed by the co-inertia analysis. In conclusion, our model using multilevel statistical techniques illustrates a potential approach to short-term dynamics of water quality which will be useful to local authorities.  
gross alpha activity; gross beta activity; Rn-222; drinking water; physical-chemical parameters; multivariate statistical techniques +
Footnote †: journal: Applied Sciences
## 3 The Proposed Method and Scenarios
### Initial Authentication Phase  
The protocol for the authentication phase comprises numerous distinct stages. These stages are delineated as the communication between the vehicle and the Authentication Server (AS), the communication between the vehicle and the TGS, the phase dedicated to storing authentication messages on the blockchain, and the communication stages between the vehicle and the RSU. The illustration of the signaling process during this authentication phase can be observed in Figure 6.  
Figure 5: Maps for the scenario of (**a**) suburban and (**b**) urban with 1 TAS and (**c**) urban with 2 TASs.
# On Connectivity and Energy Efficiency for Sleeping-Schedule-Based Wireless Sensor Networks
## 3 Connectivity and Energy Efficiency Algorithm  
A collection of the serial numbers for \(N\) sensor nodes is represented as \(M=\{1,2,3,\ldots\ldots,N\}\). The sensor nodes are distributed uniformly in the detection region [12; 13]. According to graph theory, set up \(D_{ij}\) as the communication distances between node \(i\) and node \(j\). The node communication radius is \(R\). If \(D_{ij}\leq R\), a communication relationship can be established between sensor node \(i\) and node \(j\), so there is a communication path between node \(i\) and node \(j\). There are multiple paths \(L\) existing in the wireless sensor networks, where \(0\leq L\leq\frac{N(N-1)}{2}\). If and only if \(L=\frac{N(N-1)}{2}\) the WSN is called a fully-connected WSN [14].  
According to the characteristics of WSN, the number of sensor nodes in the monitoring area is enormous, and the density is high, so the number of sensor nodes is \(N>>10\). We set the node communication radius as \(R\), where \(R\) is generally 10 to 300 m [15], and set the area of the detection area as \(S\), where \(S>>\pi R^{2}\). Thus, the ratio of the communication area of a node to the area can be obtained, that is, the node's communication probability \(P=\frac{\pi R^{2}}{S}<<0.1\).
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
### Related Works
In turn, the work of [PERSON] and [PERSON] [45], highlighted the intricacies of video streaming in MPTCP environments with no definite answer within the current multipath protocol solutions. A major obstacle seems to be the Head-of-Line (HoL) blocking phenomenon [44], examined in more depth in [46]. Also, in a recent publication, they proposed a new Scheduler for MPTCP, specifically targeting the protocol delay and stream reassembly challenges in multipath traffic scenarios [47]. Clearly, the domain of MPTCP CC studies is vast and multilayered. Researchers have examined both the performance of the already established solutions in new application areas, as well as sought new targeted ones tailored to the particular needs of a given domain. The objective of the study reported in this work is to explore the potential of key popular MPTCP CC algorithms, namely LIA, OLIA, BALIA, and wVegas, in the context of streaming applications. In order to gauge their performance in the communication scenarios relevant for end-users, the algorithms are rigorously tested in the open Internet, rather than a closed laboratory environment or simulations, similarly to many previous works. Both qualitative and quantitative analysis is performed, employing delay- and throughput-related metrics.
## 4 Materials and Methods
### Detection of ROS (H\({}_{2}\)O\({}_{2}\) and O\({}_{2}\)\({}^{\bullet-}\)) Signals  
Approximately 1-2 mm sections of alfalfa root tips were cut and rinsed with deionized water. The ROS-specific fluorescence probe was prepared using 10 mM Tris-HCl buffer (pH 7.4). The root tips were incubated at 37 \({}^{\circ}\)C for 30 min in the dark with 10 \(\upmu\)M dihydroethidium (DHE) and then gently rinsed with the same buffer. Reactive oxygen species (O\({}_{2}\)\({}^{\bullet-}\)) signals were detected at excitation and emission wavelengths of 488 nm and 520 nm, respectively. The same initial procedure was followed for H\({}_{2}\)O\({}_{2}\) detection, but in this case, a specific probe, 2\({}^{\prime}\),\({}^{\prime\prime}\)-dichlorofluorescein diacetate (DCF-DA, Sigma-Aldrich), was used. The samples were incubated with 25 \(\upmu\)M DCF-DA for 30 min. Afterward, the root samples were washed with the same buffer, and the excitation and emission were recorded at 480 nm and 530 nm, respectively [35]. The probe-treated root tips were washed twice with DEPC-treated water and mounted on a glass slide, and H\({}_{2}\)O\({}_{2}\) signals were detected using a fluorescence system (CLS-01-00076, Logos Biosystem, Anyang, Kyonggi-do, Republic of Korea).
# Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks
## 5 Experimental Setup
### Experimentation Scenarios
UAV 2 flied over 187.24 m with a 30 m/s maximum speed; UAV 3 had one waypoint  
Figure 4: Experiment scenario-1 (3 UAV). Figure 5: Experiment scenario-2 (5 UAV). at a 71.14 m distance, flying at a 10 m/s maximum speed; UAV 4 flew 176.45 m to make a return flight to the waypoint; and UAV 5 flew 98.85 m with a 20 m/s maximum speed. As in scenario-1, the vehicles reached the intermediate waypoint and returned to the starting point. Vehicles designated with more than one waypoint flew at a speed of 30 m/s, vehicles that presented a trajectory of up to 70 m flew at 10 m/s, and the other vehicles flew at 20 m/s. Scenario-3 is illustrated in Figure 6. The flight area was increased to circa 5000 m\({}^{2}\) and more UAVs (8 nodes) were used in this scenario. The total distance traveled was 560.68 m. The aim of this scenario was to include a larger number of UAVs and cover a larger area to allow nodes to reach farther distances in relation to each other in spatial topology using a single waypoint. The goal was to evaluate the performance when the limit of the interfaces range is slightly exceeded, i.e., evaluating the interface manager and the interfaces used separately in situations where the nodes exceed by a few meters the theoretical wireless signal coverage limit. In all scenarios, the purpose of this variation in the number of waypoints and speeds was to simulate different distances to be covered by different types of UAV, standardizing the longer distances to be covered for faster UAV, as shown in Figures 4-6. A complete trajectory of the mission was defined by the start waypoint, intermediate waypoints, and return to the start waypoint. Table 2 presents the relevant configuration parameters used by all scenarios. The Friis free-space propagation model was used in the simulation as a signal attenuation model, without obstacles in the line-of-sight (LOS). However, to include signal fading effects,  
Figure 6: Experiment scenario-3 (8 UAV). the Nakagami statistical shading model was applied, once the nodes were in non-line-of-sight (NLOS). The occurrences of NLOS in these scenarios were caused by the long distances between UAV in parts of the trajectory, where the fading coefficient was applied. The Nakagami model was employed using a Rayleigh distribution, considering variations in signal strength due to multi-path fading. The frequency channel adopted by Interface A (IEEE 802.11n 2.4 GHz) was composed of the sum of channels 5 (2.432 GHz) and 6 (2.437 GHz), channels of 20 MHz each, over the Industrial Scientific and Medical (ISM) band at 2.4 GHz, allowing a coverage area of 300 m. Interface B was composed of the IEEE 802.11p 5 GHz standard using channel 172 in the frequency of 5.860 GHz with a 10 MHz width band over the 5.9 GHz (5.850-5.925 GHz) frequency spectrum of the Unlicensed National Information Infrastructure (U-NII) band, with a coverage area from 350 m to 1 km (nominal range, according to the standard).
# Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest
## 3 Materials and Methods
### Feature Extraction and Fusion
#### 3.4.4 Feature Fusion  
In the field of machine learning, multimodal fusion is a technique that integrates information from multiple modalities, including early, later, and hybrid fusion. Among them, early fusion, also known as feature-based multimodal fusion, refers to the connection of features from different modalities before model training [28].  
In this study, the ECG and SpO2 signals collected by different sensors can be considered as two modalities. In order to combine the information from different modalities, we fused the above three feature sets using an early fusion strategy with the following steps: let \(In\) be the feature vector of RR intervals, let \(Rn\) be the feature vector of RAMP, and let \(Sn\) be the feature vector of SpO2; then, the concatenation of these three representations \(In\), \(Rn\), and \(Sn\) produced a feature vector of which the dimension is 24.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 5 Architecture and Implementation
### Recording Component  
The acquisition of the necessary data is not possible with a stock Android device, since the security features of the operating system restrict the collection of, for example, touch input data to the application, which is requesting the feature. To circumvent this issue, the recording software exploits the capabilities of rooted device access, by reading data made available through the getevent ([[https://source.android.com/devices/input/getevent](https://source.android.com/devices/input/getevent)]([https://source.android.com/devices/input/getevent](https://source.android.com/devices/input/getevent)), accessed on 4 December 2021) kernel-level tool, which can provide live data of kernel input events from the corresponding input devices. The implemented application is recording timestamped touch information, as well as timestamped accelerometer data.  
In order to use the getevent tool, the application has to issue manual shell commands, since no system library for accessing this information is available for the Android operating system. After acquiring the standard output produced by the tool, usable touch information can be obtained by parsing the event representations generated to a proper format. They consist of identifiers written in capital letters, and associated values. The \(ABS\_MT\_POSITION\_X\) and \(ABS\_MT\_POSITION\_Y\) events return the pixel coordinates of the corresponding input in hexadecimal format. A whole touch includes the data between two \(BTN\_TOOL\_FINGER\) events, starting with the tracking id obtained from \(ABS\_MT\_TRACKING\_ID\). Such a touch event can report a single, but also more than one, \(x\) and \(y\) position, resulting in a traceable swipe gesture. Combined data simultaneously generated by multiple fingers may allow even more precise predictions, but has not been considered in the given proposal yet for simplicity reasons.  
Figure 1: High Level Architecture of the System.
## 4 Method: Optimization Using the GD + GR Method
### Formulation of the Gradient Descent Method
\begin{array}{l}j\
eq i\\ \wedge\\ k\
eq i\end{array} \right\}\Rightarrow\frac{\partial\overline{\delta P_{j\to k}} }{\partial x_{i}}=0 \tag{39}\]it yields  
\[\frac{\partial U}{\partial x_{i}}=\sum_{j\
eq i}\frac{\partial\overline{\delta P}_{ j\to i}}{\partial x_{i}}+\sum_{k\
eq i}\frac{\partial\overline{\delta P}_{i \to k}}{\partial x_{i}}=2\sum_{j\
eq i}\frac{\partial\overline{\delta P}_{j \to i}}{\partial x_{i}} \tag{40}\]  
and analogously for \(y\),  
\[\frac{\partial U}{\partial y_{i}}=2\sum_{j\
eq i}\frac{\partial\overline{ \delta P}_{j\to i}}{\partial y_{i}} \tag{41}\]  
If \(\hat{e}_{x}\)\(\hat{e}_{y}\) are the unitary Cartesian vectors, then we can define  
\[\
abla^{i}\cdot\equiv\frac{\partial}{x_{i}}\hat{e}_{x}+\frac{\partial}{y_{i}} \hat{e}_{y} \tag{42}\]  
and (40) and (41) turn into  
\[\
abla^{i}U=2\sum_{j\
eq i}\
abla^{i}\overline{\delta P_{j\to i}} \tag{43}\]  
From (31), it also results that  
\[\
abla^{i}\overline{\delta P}_{i}=\sum_{j\
eq i}\
abla^{i}\overline{\delta P _{j\to i}}\quad\Rightarrow\quad\
abla^{i}U=2\
abla^{i}\overline{\delta P}_{i}. \tag{44}\]  
For the coordinates of turbine \(i\), given only by \(x_{i}\) and \(y_{i}\), it yields  
\[\
abla^{i}U=2\
abla^{i}\overline{\delta P}_{i}=2\
abla\overline{\delta P}_{i} \tag{45}\]  
Since \(\
abla^{i}U\) is parallel to \(\
abla\overline{\delta P}_{i}\) (in fact, twice), the previous expression together with the method of GD dictates that the displacement direction of turbine \(i\) which maximizes the decrease in \(U\) and is given by \(-\
abla\overline{\delta P}_{i}\). This is the key idea of the proposed gradient-based method because it decouples and drastically simplifies the process, turning the problem of optimizing in \(\mathbb{R}^{2\,nt}\) into \(nt\) problems in \(\mathbb{R}^{2}\). This means that, at each iteration \(k\), the algorithm moves each turbine \(i\) to reduce its power deficit \(\overline{\delta P}_{i}\), using the GD method through (45). Applying this for every turbine leads to the overall minimization of the farm power deficit. This way, the method of GD expressed in (37) turns into \(nt\) optimization problems in the form  
\[\mathbf{t}_{i}^{k+1}=\mathbf{t}_{i}^{k}-\gamma^{k}\
abla\overline{\delta P}_ {i}^{k} \tag{46}\]  
where \(\mathbf{t}_{i}^{k}\) is the position of turbine \(i\) at step \(k\).