esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study on the Application of Near-Infrared Temperature Distribution Measurement of HIFU
## 2 Theoretical Models
### Basic Theory
#### Heat Transfer Equation  
Ultrasound is a mechanical vibration that, when propagated within the human body, causes vibration displacement and the vibration rate of tissue particles. As long as the heat absorption rate of biological tissue exceeds the heat dissipation rate, the temperature inside the tissue will increase. The prevailing heat transfer model is the biological heat transfer model, known as the Pennes equation, proposed by [PERSON] in 1948 [16]. The expression of the Pennes equation is given below.  
\[\rho C_{\text{t}}\frac{\partial T}{\partial t}=k\
abla^{2}T+W_{\text{b}}C_{ \text{b}}(T_{\text{b}}-T)+Q \tag{2}\]  
In the given formula, \(T\) represents the tissue temperature. \(C_{\text{t}}\) and \(k\) represent the specific heat and thermal conductivity of biological tissues, respectively. \(W_{\text{b}}\) represents the perfusion rate of blood flow for non-living tissues. The perfusion rate of blood flow can be ignored in the model by putting \(W_{\text{b}}=0\). \(C_{\text{b}}\) represents the specific heat capacity. \(T_{\text{b}}\) is the temperature of the blood. \(Q\) is the ultrasonic power loss. \(Q\) can be expressed as follows:  
\[Q=\frac{2\alpha_{n}}{\omega^{2}\rho c}\bigg{\langle}\big{(}\frac{\partial p}{ \partial t}\big{)}^{2}\bigg{\rangle} \tag{3}\]  
In the above formula, \(\alpha_{n}\) is the absorption coefficient corresponding to the nth harmonic component, \(\omega\) is the angular frequency of ultrasound, and the symbol \(<\) \(>\) represents the time average.
## 3 Model and Background
### Key Generation  
After the formation of network structure, we use the DGHV scheme to encrypt the source sensor data, evaluate the encrypted data and decrypt the aggregated encrypted data. The parameters (all polynomial in the security parameter \(\lambda\)) used in our scheme are as follows: \(\gamma\) is the bit-length of the integers in the public key, \(\eta\) is the bit-length of the secret key (which is the hidden approximate-gcd of all the public-key integers), \(\rho\) is the bit-length of the noise (_i.e._, the distance between the public key elements and the nearest multiples of the secret key), and \(\tau\) is the number of integers in the public key. Let \(\kappa=\gamma\eta\)/\(\rho\)', \(\theta=\lambda\), and \(\Theta=\omega(\kappa\cdot\log\lambda)\). For a secret key \(sk^{*}=p\) and public key \(pk^{*}\) from the original SWHE scheme \(\varepsilon^{*}\), we add to the public key a set \(\mathbf{y}=\{y_{1}\),...,\(y_{\Theta}\}\) of rational numbers in [0,2) with \(\kappa\) bits of precision, such that there is a sparse subset \(S\)\(\subset\)\(\{1\),..., \(\Theta\}\) of size \(\theta\) with \(\sum_{i\in S}y_{i}\)\(\approx\)\(1/p(\bmod 2)\). We also replace the secret key by the indicator vector of the subset \(S\).  
For the key generation phase, BS respectively generates the secret key \(sk\) and public key \(pk\) as follows:  
\[sk=s \tag{1}\]  
\[pk=(pk^{*},\mathbf{y}) \tag{2}\]  
Then BS informs the source nodes the public key \(pk\) for them to encrypt the plaintexts which are sensed by the source nodes.
## 7 Architecture Validation
### Throughput  
Modern systems must be able to provide the needed data efficiently and in a fast and consistent way. Measuring the average response time of an API might provide valuable data about the system's performance, but it is a metric that is easily affected by very high and very low values and does not provide insights about the encountered user experience.  
Throughput analysis offers a more realistic way of understanding metrics like request time. This form of analysis focuses more on the distribution of data rather than the average value and aims to examine what experience (good or bad) a percentage of users will have.  
This analysis method begins by gathering a comprehensive dataset, which is then subject to a data-cleansing process to remove or correct anomalies. Then, the data are organized and sorted in ascending order. A histogram can be created to visualize the data distribution, which helps in understanding the spread and central tendencies.  
Percentiles are calculated as specific values that correspond to a given percentage of the data falling below them. For example, the 50 th percentile (median) is the value below which 50% of the data points occur.  
Other valuable percentiles are the 90, 95, and 99 th percentiles. Those percentiles can further be translated into statements like \"What response time will have 95% of the users?\" or \"What is the worst-case scenario users can experience in terms of response time?\" (Figure 36).  
Figure 34: GraalVM Test 3—CPU consumption evolution.  
Figure 35: GraalVM Test 3—RAM consumption evolution.  
Starting from the ordered histogram that showcases the frequency of each response time, the percentile line can be computed as follows:  
\[P_{x}=\frac{P}{100}*(N+1)\]  
where \(P\) represents the desired percentile (e.g., 50, 90, 95, and 99) and \(N\) the number of samples in the dataset. To analyze the throughput of the system, the previously obtained results of the 3 rd test case (1 h stress test with 100 active users) were subject to the described histogram analysis process. The response time values were aggregated because this analysis is more suited for the general system throughput distribution.
## 2 Materials and Methods
### Methods
#### 2.3.3 Standardized Yield Residual Series (SYRS)  
Over the last century, agricultural productivity has increased significantly due to innovations, such as developing new cereal cultivars, fertilizer applications, more efficient machinery, and improved water management techniques [5, 32]. To accurately isolate the effects of climate change, it is necessary to account for non-climatic factors, like varietal changes, fertilizer use, mechanization advancements, and water management practices [5, 32]. Previous research achieved this by converting the chronological data of cereal yields into a Standardized Yield Residual Series (SYRS) [5]. The residuals, denoted as \(y_{i}^{T}\) and detailed in [5], help assess agricultural drought risk by focusing on variations unexplained by farming practices, innovations, or favorable environmental conditions, such as fertile soil or topography. The SYRS highlights the impact of climate on yields,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SPEI Values** & **SPEI Classification** \\ \hline SPEI \(\geq\) 2 & Extremely wet \\ \(1.5\leq\) SPEI \(<\) 2 & Severely wet \\ \(1\leq\) SPEI \(<\) 1.5 & Moderately wet \\ \(-1<\) SPEI \(<\) 1 & Near Normal \\ \(-1.5<\) SPEI \(\leq-1\) & Moderate drought \\ \(-2<\) SPEI \(\leq-1.5\) & Severe drought \\ SPEI \(\leq-2\) & Extreme drought \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification of SPEI values [21].  
which is crucial for decision-makers, researchers, and farmers in managing agricultural drought risk.  
\[y_{i}^{T}=y_{i}^{0}-y_{i}^{(\tau)} \tag{4}\]  
The variable \(y_{i}^{0}\) represents the measured cereal yield, and \(y_{i}^{(\tau)}\) represents the value of the fitted quadratic polynomial regression model. We calculated the SYRS using the following equation:  
\[SYRS=\frac{y_{i}^{(T)}-\mu}{\sigma} \tag{5}\]  
where \(\mu\) is the mean of the yield residuals, and \(\sigma\) represents their standard deviation. Table 4 presents the yield categories based on the SYRS [5; 32].  
We analyzed the relationship between drought conditions and cereal yields using the SYRS data and the monthly CHIRPS-based SPEI. The SYRS provides aggregated provincial-level cereal yields, while the SPEI was averaged across each province to match the spatial scale of the SYRS. Pearson correlation analyses were performed between the SYRS and the monthly SPEI at 1-, 3-, 6-, and 12-month timescales. To align with the agricultural year, we used the SPEI data from January to August of the current year (2003-2018) and from September to December of the previous year (2002-2017), ensuring temporal and spatial coherence between the datasets. Additionally, we conducted significance tests at the 5% level (\(p\)-value \(<\) 0.05) to determine the statistical robustness of these correlations.
# Global IoT Mobility: A Path Based Forwarding Approach
## 2 Mobility Management Solutions in Different Access Technologies
### 5G Networks  
3 GPP specifies the General packet radio service (GPRS) Tunneling Protocol (GTP) [24] to support mobility in cellular networks by anchoring user plane and control plane traffic at specific core entities. Figure 3 shows the overall 5G network architecture, including the network elements and the standardized interfaces. In 5G networks, user plane traffic is anchored at the User Plane Function (UPF) and control plane traffic at the Access and Mobility Management Function (AMF) [25]. Handover is performed: (a) via the Xn interface, which provides a direct connection between gNBs, or, (b) via the N2 interface between the gNB and the AMF, when Xn handover is not possible (e.g., new radio conditions, load balancing, no Xn connectivity to the target gNB, etc.). The serving gNB decides when to trigger a handover process, which consists of three phases (handover preparation, execution, and completion) based on measurement reports received from the MN that include among other information, indicators of the radio signal strength of the serving and neighbor cells as received by the MN. When handover is performed over the Xn interface, downlink packets are delivered over the interface from the serving gNB to the target gNB during handover execution phase in order to prevent packet loss [26].  
In both wireless and cellular mobility solutions described above, anchoring is used to overcome the location dependent end-to-end communication paradigm by tracking the address of a moving host, and maintaining a tunnel to its new location for packet delivery. This form of \"dog-leg\" routing introduces tunnelling overhead and sub-optimal routes with an overall increase in traffic and latency.  
Figure 2: Seamless handover in PFMIPv6.
# Damage Detection Method Based on Continuous Wavelet Transformation of Lamb Wave Signals
## 2 Damage Estimation Method Based on CWT
### Time Compensation  
The value of T\({}_{D}\) in Equation (1) represents the total ToF of excitation signal from actuator to damage and then from damage to sensor. This is the time in the dynamic response signal when the sensor starts receiving the damage-scattered signal [21]. Consider a normalized damage-scattered signal presented in Figure 2 as an example, the traveling time from actuator to sensor, T\({}_{D}\), should be from the beginning to the point when damage-scattered signal starts to become non-zero as shown in Figure 2a. However, this non-zero point is very difficult to identify in practice. The envelope of the coefficient of CWT of this signal, based on Equation (7), is presented with red line in Figure 2b. The excitation signal used throughout this research was a 5.5-cycle sinusoidal tone burst modulated by Hann window at central frequency 383 kHz, as shown in Figure 3. For the central frequency of 383 kHz and sampling time of 0.02 us, the scale parameter \(a\) was calculated as 130.5 using Equation (3). If T\({}_{\mathrm{e}}\) is the total acting time of the excitation signal as shown in Figure 3a, a good time-frequency analysis feature of the CWT can depress the dispersion of Lamb wave effectively to make the signal contain the same duration of time during wave propagation.  
The maximum value 1 in the CWT envelope in Figure 2b appears at the center of the normalized damage-scattered peak, which is half of the time \(\rm T_{e}\) ahead of the time \(\rm T_{D}\) when sensor actually received the non-zero point of the damage-scattered signal. Therefore, a time lag was introduced in all the CWT signals by subtracting \(\rm T_{e}/2\) as time-compensation \(\rm T_{c}\), shown with blue line in Figure 2b. If \(N\) is the total number of cycles and \(f_{e}\) is the central frequency of the excitation signal, \(\rm T_{c}\) can be expressed as  
\[\rm T_{c}=\frac{N}{2f_{e}}. \tag{8}\]
_J. Sens. Actuator Netw._**2015**, \(4\), 189-207; doi:10.3390/jsan4030189  
_Journal of Sensor and Actuator Networks_  
**ISSN 2224-2708**  
www.mdpi.com/journal/jsan  
_Article_  
**A Long-Range Directional Wake-Up Radio for Wireless Mobile Networks \({}^{\dagger}\)**  
**[PERSON] \({}^{1,*}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\), [PERSON] \({}^{4}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Institute of Information Science, Academia Sinica, 115 Taipei, Taiwan;  
E-Mail: [EMAIL_ADDRESS]; [EMAIL_ADDRESS]  
\({}^{2}\) Commonwealth Scientific and Industrial Research Organisation, Pullenvale, 4069 QLD, Australia;  
E-Mail: [PERSON]ESS]  
\({}^{3}\) Commonwealth Scientific and Industrial Research Organisation, Marsfield, 2122 NSW, Australia;  
E-Mail: [PERSON]]  
\({}^{4}\) Department of Computer Science, University of California, Irvine, 92697-2625 CA, USA;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) This paper is an extended version of our paper published in [PERSON]; [PERSON]; [PERSON]; [PERSON] Improving the throughput of sensor network wakeup radios using block orthogonal codes. In Proceedings of the Annual International Conference on Mobile Communications, Networking and Applications (MobiCONA), Singapore, 2011.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +886-2-2788-3799; Fax: +886-2-2782-4814.  
Academic Editors: [PERSON], [PERSON] and [PERSON]  
_Received: 23 May 2015 / Accepted: 14 July 2015 / Published: 3 August 2015_  
**Abstract:** This paper describes a long-range directional wake-up radio (LDWuR) for wireless mobile networks. In contrast to most wake-up radios (WuR) to date, which are short range, ours is applicable to long-range deployments. Existing studies achieve long distance by using modulation and coding schemes or by directional antennas, though the latter require exploring the direction of the transmitter. To address this issue, our LDWuR adopts both static and dynamic antennas, where the static ones are directional, while the dynamic ones are omnidirectional for beamforming. We present our LDWuR prototype and design principle. Simulation results show that our LDWuR and event-driven MAC protocol suppress the idle-listening of Wi-Fi stations in a wireless network, thereby enhancing the Wi-Fi power savings.
## 2 Theoretical Background  
### Organic Polymers in Silicon Photonics  
A guest-host polymer system consisting of the azo benzene dye Disperse Red 1 (DR1) in a poly[methyl methacrylate] (PMMA) matrix is employed as the electro-optical medium in this work and is called PMMA+DR1 in the following. The chemical structure of both components is presented in Figure 1. DR1 is a non-centro-symmetric organic chromophore, where the donor and acceptor molecules are bonded through a conjugated \(\pi\)-bridge. NO\({}_{2}\) acts as an acceptor, while the conjugated \(\pi\)-bridge allows the transport of charges, which are donated by the alkyl-amino-group. The weakly bound \(\pi\)-electrons in this system already offer a charge displacement which leads to a high polarizability through applied electrical fields. Large dipole moments and therefor a high nonlinear optical efficiency are clear advantages of this medium, considering a photonic modulation purpose [12; 13]. Additionally, the usage of EO polymers shows low free-carrier dispersion, which usually is the fundamental speed limitation in silicon photonics modulators [14]. Moreover, an intrinsic coupling of amplitude and phase of the EO material can be achieved, making advanced modulation formats such as 16-state quadrature amplitude modulation (16 QAM) or quadrature phase-shift keying (QPSK) possible [3; 15].  
Figure 2 shows the dispersion curves for PMMA+DR1, measured by ellipsometry. The values are given for two different mass concentrations (weight percentages) \(C\) in wt% of the DR1 chromophore within the polymer matrix PMMA. The extinction coefficient \(\kappa\) is zero over the entire wavelength range for \(C=0\) wt%, while it has an absorption peak of \(\kappa=0.105\) at \(\lambda=497.8\) nm for \(C=10\) wt%. However,  
Figure 1: Chemical structure of the organic chromophore DR1 (**left**) and the polymer PMMA (**right**) employed as the EO medium.  
for applications in silicon photonics, the 1500 nm to 1600 nm range (red-striped box) is from great importance, due to the transmission window of silicon [16]. At the optical telecommunication C-band wavelength \(\lambda=1550\) nm, the measured refractive indices are \(n(0\,\text{wt}\%)=1.478\) and \(n(10\,\text{wt}\%)=1.517\), respectively, showing a normal dispersion behavior.
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 2 Families of Sequence-Specific Endoribonuclease TA Toxins
### HicA  
HicA from _E. coli_ digests mRNA, as well as tmRNA, independent of translation [2]. The induction of HicA reduces the rate of translation by up to 50% within 10 min [2]. Studies with primer extension did not yield a consensus cleavage sequence, but the cleavage of tmRNA at two A^AAC sites was observed [2]. Further research is needed to decipher the exact target sequence specificity and to determine whether a requirement for a specific secondary structure exists. The antitoxin HicB is degraded by Lon under amino acid starvation [2]. The _hicB_ deletion mutant of _E. coli_ is viable, indicating either that HicA is not a potent toxin and/or an additional protein in the genome can neutralize HicA.
# Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality
## 3 Materials and Methods
### System Architecture

Table 2: Overview of the hardware and software components in the experimental setup and their most important specifications. Moreover, the object transform interpolation was activated for each object whose position and orientation are shared across the network, following the default configuration of Unity NGO. This entails a gradual transition of objects to their server-reported positions over time, rather than immediate and abrupt adjustments. Employing this technique, or similar ones that are common in game development, enhances user experience by ensuring smoothness. This approach helped prevent the introduction of negative bias towards the game quality, thereby allowing us to assess the impact of network disruptions in a manner akin to real-world experiences. In addition, to guarantee the synchronization of environments between users, an authoritative server model was adopted, as advised by the Unity NGO documentation, to regulate the transformations and orientations of objects. This framework ensures that any modifications within the game's environment must receive prior verification and authorization by the server upon a client's request. Following such validation, the server broadcasts the approval of the changes to all clients, thereby preserving uniformity among users.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 3 Architecture of the Network Emulation Platform
### Physical Implementation Model
#### 3.2.4 Sdn Switch  
According to assigned network parameters, each emulation node is linked to the corresponding SDN switch by the main controller. The SDN software switch is responsible for the connection of virtual nodes. Meanwhile the SDN hardware switch is in charge of the connection of physical nodes, and provides access to external networks or links for the emulation platform. SDN software switch and SDN hardware switch are connected with each other to form an interconnected emulation network. Once the emulation experiment starts, the main controller sends real-time flow tables to each SDN switch to simulate the dynamic topology of the target network model. SDN switches can also collect and send their own states back to the Web UI for display, or save these states for subsequent analysis.
## 5 Conclusions  
Four main conclusions can be drawn as a result of the herein presented analysis.  
First, optical sensors provide relevant information for wall-to-wall mapping of forest disturbance over large areas. Indeed, in this work, we used Landsat data and automatic procedures to monitor about 11 Mha of forests for the period 1985-2019, predicting almost two million disturbances.  
Second, the GEDI sensor retrieves information on forest height and biomass--information not captured by optical sensors such as Landsat--and allows the identification of spatial and temporal trends regarding forest biomass changes that can be used to inform questions related to forest management and climate change. This study demonstrated that GEDIdata--if properly aggregated--provide reliable information, as a good relationship (\(r^{2}=0.75\)) with a large ALS dataset was identified.  
Third, using the about two million forest disturbances we predicted, we demonstrated that the GEDI sensor provides useful information for monitoring biomass changes due to forest disturbance and, consequently, may assist in the future as a relevant data source for forest disturbance characterization.  
Finally, while in this study it was not possible to use GEDI to statistically rigorously compare biomass change caused by different drivers of forest disturbance, future research should focus on this topic, which is presently needed. Indeed, forests represent a crucial component of the global terrestrial carbon cycle [85] and must take a part in the climate change fight. Due to climate change, forest disturbances are expected to increase and their capacity to absorb carbon may be altered. For these reasons, forest monitoring programs must consider a more in-depth characterization of forest disturbance in terms of biomass as well [86], which, as we showed in this paper, may require the integration of optical and space photonic sensors.  
Conceptualization, S.F. and G.C.; data curation, E.V., G.D. and S.F.; formal analysis, S.F., G.D. and C.B.; methodology, S.F, G.D. and E.V.; project administration, G.C.; writing--original draft preparation, S.F. and C.B.; writing--review and editing, G.D., C.B., E.V. and G.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
For the data used please refer to: [66] for 3l3D dataset, [68] for reference dataset, [37] for GEDI data in Italy, [40] for Italian ALS data.  
The authors declare no conflict of interest.