esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Pain Assessment  
Postoperative pain measurements were performed using different methods, such as the visual analog scale (VAS) and the amount of analgesics taken in the postoperative period (up to 7 days after surgery) [91; 92; 93; 94].  
The authors of the various studies also considered the VAS and the intake of analgesics among the main methods used to compare the intensity of postoperative pain.  
The studies included in the reviews analyzed in this review demonstrated conflicting results on postoperative pain management.  
Many studies have analyzed pain control during the first week but also in the first 14 days after FGG surgery which is also the most delicate week for postoperative pain control and wound healing [66].  
The application of cyanoacrylate tissue adhesive appears to reduce postoperative morbidity after an FGG or a CTG thanks to the protection of the palatal wound during the healing. Cyanoacrylate tissue adhesive maintains its adhesive qualities in the presence of moisture. Furthermore, it has bacteriostatic, biodegradable, and hemostatic properties that have been related to cyanoacrylate tissue adhesive [60; 95; 96; 97; 98; 99; 100].  
Additionally, cyanoacrylate tissue adhesive with platelet-rich fibrin suggested less pain than cyanoacrylate and wet gauze alone. It is important to remember that cyanoacrylate tissue adhesive alone caused less pain than wet gauze, just as cyanoacrylate tissue adhesive combined with suture demonstrated less pain than suture alone [30].  
It seems that the use of PRF has better results for pain levels compared to other control groups (oxidized regenerated cellulose, wet gauze compression, gelatine sponge, and untreated) 7 days after surgery [64].  
[PERSON] has demonstrated that PRF associated with cyanoacrylate obtained better analgesia results up to day 6 of follow-up. However, there were no significant differences between the PRF and cyanoacrylate groups [82].  
[PERSON] demonstrated that hemostatic sponges associated with cyanoacrylate give the best postoperative pain comfort from the 1 st to the 14 th day with the lowest use of analgesics compared to the control group compared to sutures alone [70].  
Palatal coverage seems to give better results regarding postoperative pain management regardless of the protection of the palatal wound [62; 63; 64; 65; 30; 66].
## 4 Room Acoustic Parameter Analysis
### Reverberation Time  
Reverberation time is the duration it takes for the sound pressure level to decay by 60 dB after the source ceases. From the Schroeder curve generated through backward integration of the squared RIR, we calculate the reverberation time (\(T_{20}\)) by extrapolating the least-squares fit between \(-5\) dB and \(-25\) dB following the ISO 3382 recommendation [31; 32].  
Figure 5a,b present the \(T_{20}\) values across frequency in octave bands, derived from RIRs measured by the Rode NT-SF1 microphone and em32 Eigenmike, respectively. The two environments with felt and wood panels show similar \(T_{20}\) values from 125 to 250 Hz. However, from 500 Hz to 8 kHz, the wood panel environment consistently demonstrates higher \(T_{20}\) compared to the felt environment. The average \(T_{20}\) for the felt and wood panel environments is approximately 0.74 s and 0.84 s, respectively. The faster sound decay in the felt panels aligns with their higher sound absorption capabilities compared to the wood panels. The overall trend remains consistent between the NT-SF1 and Eigenmike measurements, but the latter shows slight variations in \(T_{20}\) values among different source positions beyond 500 Hz.  
Figure 6a,b illustrate 2D heatmaps of \(T_{20}\) values across receiver locations for both paneling environments and all three source locations, calculated from the RIRs measured by the NT-SF1 microphone and em32 Eigenmike, respectively. In these visualizations, higher \(T_{20}\) values are noticeably closer to the front and right walls, particularly in the front-right corner of the room (refer to Figures 1 and 3 for orientation). This observation suggests the influence of the overhang in this corner, a phenomenon consistent with similar effects observed in other rooms with such structures [33; 34]. Additional acoustic treatment around this corner can help to achieve a more uniform distribution of  
Figure 5: Reverberation time (\(T_{20}\)) across frequency in octave bands for the felt and wood environments calculated from the RIRs measured by (**a**) Rode NT-SF1 microphone, (**b**) em32 Eigenmike.  
Figure 6: Reverberation time (\(T_{20}\)) in seconds at different receiver locations for each of the three source locations and the two wall paneling types calculated from the RIRs measured by **(a)** Rode NT-SF1 microphone, **(b)** em32 Eigenmike. The black arrowhead indicates the source location and orientation. The white areas represent spaces where no receivers were positioned. Note that colormap limits are different for felt and wood panels.  
In Figure 6a, we can see lower \(T_{20}\) values for a few receiver points directly in front of the source. However, this observation is moderated in Figure 6b for the Eigenmike measurements. Moreover, the local variations in \(T_{20}\) are distinct between NT-SF1 (Figure 6a) and Eigenmike (Figure 6b). This discrepancy warrants deeper analysis, considering that the Eigenmike captures finer spatial details of the reflected sound compared to NT-SF1.
## 2 Methodology  
Figure 1 depicts a flowchart of the overall CA process proposed in this study. The following subsections explain each process and the interactions among them.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Dynamic**} & \multicolumn{2}{c}{**Dynamically**} \\
**Approach** & **Local Minima** & **GNRON** & **Obstacles** & **3D Space** & **Feasible** \\ \hline [PERSON] \& [PERSON] [15] & O & O & X & X & X \\ \hline [PERSON] et al. [16] & O & O & X & X & X \\ \hline [PERSON] et al. [17] & O & O & X & X & X \\ \hline [PERSON] et al. [14] & O & O & X & X & X \\ \hline [PERSON] et al. [21] & O & O & O & X & X \\ \hline [PERSON] et al. [22] & O & O & O & X & X \\ \hline [PERSON] et al. [23] & O & O & X & O & O \\ \hline [PERSON] \& [PERSON] [18] & O & X & X & O & O \\ \hline [PERSON] et al. [12] & O & X & O & X & X \\ \hline [PERSON] et al. [25] & O & X & X & X & O \\ \hline [PERSON] et al. [26] & O & X & X & X & O \\ \hline [PERSON] \& [PERSON] [27] & O & O & X & X & O \\ \hline [PERSON] et al. [28] & O & O & X & X & O \\ \hline [PERSON] et al. [29] & O & O & X & X & O \\ \hline [PERSON] \& [PERSON] [30] & O & O & X & X & O \\ \hline [PERSON] et al. [19] & O & O & X & X & X \\ \hline [PERSON] et al. [20] & O & O & X & X & X \\ \hline [PERSON] et al. [24] & O & O & \(\Delta\) & O & X \\ \hline [PERSON] & O & O & O & O & O \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison of the collision avoidance (CA) approaches using the artificial potential field (APF) (O: Solved, X: Unsolved, and \(\Delta\): Limited case only).
## 2 Problem Description
### The Principle of Choosing Prediction Horizon  
In this paper, the length of the prediction horizon is depended on the wind power spectrum density and the type of energy storage. We explain the reason as following:  
First, for fly wheel storage, it mainly deals with the turbulence due to its fast response speed and low power characteristics. Pumped storage handles macro meteorological fluctuation due to its slow response speed and high power characteristics. Turbulence belongs to minute-level fluctuation, and macro meteorological fluctuation is hour-level or day-level fluctuation. Therefore, the length of the prediction horizon should be hour-level, if the MPC controller is controlling the pumped storage.  
Second, the wind farm power spectrum density curve was shown in Figure 2. The macro meteorological fluctuation is relatively low frequency and huge energy, therefore, it should belong to the left part of the curve. The turbulence is relatively high frequency and low energy, so it should belong to the right part of the curve. In Figure 2, \"One hour window\" means that the length of the MPC prediction horizon is one hour, and \"Two hour window\" means that the length of MPC prediction horizon is two hours. \"One hour window\" contains much turbulence and little macro  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Power** & **Energy** & **Energy** & **Power** & **Efficiency** & **Respond** & **Life (year** \\  & **(MW)** & **(MWh)** & **Density** & **Density** & **(\%)** & **time** & **or cycle)** \\ \hline Pumped hydro & 0–1800 & \(>\)200 & 0.5–1.5 & - & 75 & 10–600 & 50 y \\ Compressed air & 0–300 & 0–105 & 30–60 & 10–100 & 64 & 1–600 & 30 y \\ SMES & 0–10 & 0–1 & 30–100 & 10\({}^{4}\)–10\({}^{5}\) & 95 & 0.005 & 30 y \\ Fly wheel & 0–5 & 0–10 & 5–10 & 10\({}^{2}\)–10\({}^{3}\) & 93 & 0.05 & 20 y \\ Super cap & 0–0.3 & 0–10 & \(<\)50 & 0–4000 & 98 & 0.05 & 10\({}^{5}\) c \\ Battery & 0–50 & 0–100 & 30–200 & 0–500 & 70 & 0.02 & 3000 c \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of typical energy storage types.  
meteorological fluctuation; on the contrary, the \"Two hour window\" includes much turbulence and some macro meteorological fluctuation. According to these observations, we decided to choose two hours as the MPC prediction horizon.  
Finally, we undeniably agree that the three hour solution is also feasible, perhaps it is better than the two hours solution because it contains more macro meteorological fluctuations. However, the prediction accrual of three hours is worse than two hours, which is not beneficial for the real time scheduling. Thus, to balance the prediction accrual and macro meteorological fluctuations, we choose the two hour solution.
# A Capacity Enhancement Method for Frequency-Hopping Anti-Jamming Communication Systems
## 3 Results
### Comparison with OFDM Signals  
OFDM is a multicarrier modulation with high frequency utilization and good resistance to multipath fading [28]. It is generally used in scenarios where frequency resources are limited and frequency resource efficiency is prioritized, such as terrestrial mobile communications. In mobile communication 5G systems, OFDM signals are divided into two categories: OFDM access signals and OFDM transmission signals.  
FH capacity-boosting signals with high power utilization are generally used in scenarios where power is limited and transmission reliability is a priority, such as anti-jamming communications, satellite signaling beams, and private network communications.
## 2 Materials and Methods
### Equipment  
To measure the impact force, a force plate positioned as the target was utilized. The force plate, an AMTI model MC12-2K from the 2000 series (AMTI, Watertown, MA, USA), was affixed to a stable structure and covered with a training shield to safeguard participants from direct contact. The dimensions of the aluminum force plate were 305 \(\times\) 406 \(\times\) 79 mm. Its maximum force capacity was 4450 N for both F\({}_{\mathrm{x}}\) and F\({}_{\mathrm{y}}\), and 8900 N for F\({}_{\mathrm{z}}\). The force plate was synchronized in both time and space with Noraxon (MR 3.18, Scottsdale, AZ, USA). To capture acceleration data, there were employed 3 wireless IMU sensors, specifically the Ultium model by Noraxon, synchronized with the force plate. The IMU sensors had a sampling rate of 2000 Hz and were designed for acceleration measurements up to 4000 g. These sensors were strategically placed with Velcro straps tape on the back of the hand, the upper forearm, and on the arm just below the shoulder (refer to Figure 1).
## 3 Operational Context of Proof-of-Concept Scenarios
### Use Case  
The verification scenario for our Proof-of-Concept demonstration is based on a fictional Internet Service Provider (ISP) that wants to extend their NFV portfolio and their data centre resources. The ISP located in country A, named ISP-A, wants to lower their costs on their Customer Premise Equipment (CPEs) by virtualising them and consequently more efficiently extending their service delivery. They have limited resources in their data centre and want to offload parts of their services to remote data centres. They have found two cooperative partners in country B (ISP-B) and country C (ISP-C) that can provide them with data centre resources. They want all data centres to contribute to delivering and extending their virtual CPE (vCPE) services. They are aiming to provide this by chaining SFs across all data centres by the use of the SFC protocol NSH.  
The IETF has defined a variety of SFC use cases [28], but, for our Proof-of-Concept demonstration, we limit the SFC use case to the following: ISP-A aims to provide three SFs to their customers. Two of the SFs are mandatory, while one additional SF is optional for the end-users to choose. The basic SFs are a vCPE (SF-1) and a firewall (SF-3), while the optional SF is a video caching service (SF-2). Due to the cost of data centre resource consumption and SF security policies, the ISP-A policy is defined to require that the vCPE runs at ISP-A, the video caching service at ISP-B and the firewall at ISP-C. The vCPE is the first element in the SFC. The additional video caching service is placed in the middle of the SFC in order to let the first two services be protected by the last element in the SFC, which is a virtual firewall (SF-3) (Figure 2). Hence, from a service plane perspective, the firewall is protecting the inner SFs and the end-user from the outside world.  
The data centres operate with multi-tenants where ISP-A has interconnected all their tenant instances in an overlay network. In this setup, ISP-A is concerned about the privacy of their customers and they do not know if ISP-B is eavesdropping the end-user traffic traversing them. Neither are they sure whether ISP-B is malicious. ISP-C is, on the other hand, a trusted partner. In order not to let ISP-B being capable of eavesdropping all end-user traffic, all traffic that is traversing ISP-B, except video streaming traffic, must be encrypted. Note: for Proof-of-Concept purposes, the video caching service is categorised as a non-privacy sensitive service.  
We simplified the SFC isolation problem by omitting a full mesh topology of the interconnected data centres in the Proof-of-Concept scenario. However, the components in a full mesh topology are also vulnerable to eavesdropping. Corrupt intermediate virtual switches or faulty SFs can modify, intercept and manipulate SFC traffic inside an overlay network. Hence, the protection of the Virtual Link (VL) [2] is relevant both between the Compute Nodes in one data centre and for the VLs between multiple data centres.
# A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS
## 3 Systematical Design of the Receiver
### Gain and Gain Control Range  
As mentioned above, the in-band thermal noise power levels for GPS and BDS-3 are \(-111\) and \(-108\) dBm, respectively. The minimum power level arriving at the antenna port is lower than the in-band thermal noise. Therefore, thermal noise dominates the input power level of the receiver [4; 5]. It means that noise determines the cascade gain and analog-digital conversion (ADC) quantization threshold [5]. Assuming that the optimal input signal power of ADC is 0 dBm, the needed cascade gain of the proposed receiver is at least 111 dB. The practice has proved that an RF circuit with a gain of 40\(\sim\)55 dB is reasonable [5; 7; 8; 14]. The gain of the RF circuit is tentatively set at 51 dB, where the signals can be normally received. A gain control range (Gr) larger than 45 dB needs six-bit control words to be implemented in IF circuits because the corresponding binary of 45 is 101 101. To avoid wasting any resources, the Gr is tentatively set at 63 dB, whose corresponding binary is \(111\,111\). This means that the gain range of IF circuits is between 0 and 63 dB. In all, the maximum cascade gain is tentatively set at 114 dB, considering a safety margin of 3 dB.
# Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing
## 4 Classification Methodology Based on Linear Unmixing  
In the following sections, we propose two strategies for classifying the six in vivo brain tissue intraoperative HS images based on BLU by EbEAE. As previously described, in each HS image, some pixels were manually labeled by the clinical expert as four classes: NT, TT, HT, and BG, as ground-truth datasets. In fact, the BG class has a wide spectral range since it considers different materials or substances in the surgical scenario [27; 29; 30]. Hence, the rubber ring markers placed by the surgeon were part of the BG class. However, these rings had flat spectral signatures, which were pretty distinctive from the NT, TT,and HT classes. Therefore, to reduce the variability in the linear unmixing process by EBEAE, the rubber ring markers were segmented initially based on their flat spectral signatures (especially in the lower frequency bands) [29] and to avoid classification errors due to the rubber material, humidity, and light scattering. For this purpose, the energy in the initial twenty spectral signatures was collected, and the value was raised to the power of \(3/2\) to enlarge the magnitude differences. The resulting image was normalized to grayscale tones. Finally, the Otsu method was applied to the grayscale image to segment the areas assigned to the rubber ring markers [40]. Next, two classification methodologies were explored in this work after this initial segmentation. The rationale behind these two methodologies was to use BLU to identify the most representative spectral signatures/end-members of the ground-truth datasets. Once these representative end-members were estimated, they were used to characterize each class studied at a lower dimension. To reduce the computational cost, each pixel was labeled by the minimum distance to the classes' sets, which was feasible by the low dimension of these sets. The main difference from the second methodology was the assumption that an initial segmentation of the binary classes BG vs. no-BG (NT, TT, and HT) could improve the overall accuracy, without largely increasing the computation time. Next, these methodologies are explained in detail.
# Controller-Pilot Data Link Communication Security
## 4 Threat Model
### Possible Attacks against CPDLC Technology  
CPDLC's current lack of security puts it at risk of multiple possible threats, as summarized in Table 1.  
**Eavesdropping** is performed when an unauthorized party listens to the data traffic without the permission of the communicating parties [17]. Eavesdropping is widely considered the easiest form  
Figure 2: Example of a blank flight plan. (EET: estimated elapsed times.)  
of attack as it does not involve any active actions by the attacking party other than some necessary equipment to receive the signal, and the equipment needed to decrypt any encrypted information. In case of CPDLC this is made even easier as there is no current encryption in use. This attack does not directly influence any data, yet it is a straightforward way to map the usage of the CPDLC in the local area of the attack.  
**Jamming** intends to deny the victim of the attack access to the service [18]. By jamming the receiver, the attacker intends to block the access to the service by reducing the channel's capacity. This is done by filling the channel with enough noise to make it impossible to get any comprehensible data through to a receiving party. All users connected through a jammed node in a closed circuit network would be affected by a jamming attack, and the bottleneck connections become extra vulnerable to this kind of attack. In general, a wireless jamming attack is easy to detect using a directional receiver to probe for the direction of the jamming signal.  
**Flooding** is very similar to jamming. Flooding aims to deny the victim's access to the service. It is usually enforced by sending multiple packets of data to the same receiving party instead of filling the channel with noise. Incoming queries will start to queue up if the destination receives more queries than it can handle per given time frame. Other actions will likely be left unattended as the queue is preventing valid data from being processed in a timely manner. Ultimately, a system might time out under the stress and completely come to a standstill due to overload.  
**Injection** is done by sending unauthorized messages. The information is said to be injected into the network by not originating from an authorized source. This type of attack could possibly be very severe and is difficult to detect if the system lacks necessary protection to check from what source the information is originated.  
**Alteration** attack is defined when attacker alters the legitimate data. It manifests itself as data being modified, redirected to another recipient, or delayed. Despite the fact that the data is altered, it keeps its original validity which makes this type of attack hard to detect.  
**Masquerading** means that the attacker impersonates an authorized user, be it an aircraft or ATC, and gains unauthorized privileges. Unless detected, the masquerading attacker has the means to commit to a full conversation with its victim. In case of CPDLC, an outsider may be capable of issuing instructions to the aircraft [17].
## 3 Discussion
Our study therefore supports the concept that reduced immunosuppression leads to prolonged inflammation at the organ sites, ultimately resulting in a long duration of diabetes with complications. Interestingly, in our study, _CD200R1_ was connected to the down-regulated monocyte function markers CD86 and _TLR4_ (Figure 4B), implicating that the _CD200-CD200R1_ immunosuppressive pathway is responsible for the decreased monocyte-mediated T cell activation (Figure 6). In this study, a satellite network identified the up-regulated _TRPV1_ gene in individuals with diabetes-related complications. Earlier studies have coherently reported that blockade of _TRPV1_ in rodents with type 2 diabetes halted disease progression [30], thus indicating that a _TRPV1_ antagonist may be a therapeutic target for the disease progression of diabetes by improving glucose intolerance and correcting dysfunctional insulin secretion [30]. A limitation of the current study is the small sample size. Additionally, despite generating extensive data on various aspects of cellular functions, we restricted the differential gene analysis to areas of particular interest for this study. While we have comprehensively profiled the RNA transcriptome and surface marker phenotype of CD163+ monocytes in the context of diabetes complications, further research is needed to uncover the causative mechanisms underlying this distinct cell profile. Future studies will focus on identifying the genomic mechanisms involved in lipid, insulin, or glucose dysregulation in CD163+ monocytes. Furthermore, we will employ the weighted gene co-expression network analysis to thoroughly explore the dataset and expand the sample size to facilitate a more comprehensive investigation. In conclusion, this study is the first to comprehensively characterize the RNA profile and phenotype of CD163+ monocytes in individuals with long-term diabetes and complications, presenting new implications for the role of CD163+ monocytes in diabetes-related complications. It identified specific enriched genes in CD163+ monocytes that are involved in centrosome cycle regulation, and miRNAs that are associated with cell death, consistent with the decreased CD163+ cells. Additionally, decreased expression of genes and cell surface markers involved in monocyte activation, recruitment, and immune regulation was observed. These results suggest that CD163+ monocytes have unique functions in diabetes-related complications, and it is their loss of function and programmed death that leads to the increased and persistent tissue complications in diabetes. Prospective research is now needed to build upon our data and define the role of these cells in the development of complications in diabetes.