esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Developing an OAE Monitoring Protocol for ISSNHL  
No two studies in this review used the same OAE protocol. Obviously, further investigation to develop the most sensitive OAE protocol to cochlear ISSNHL and its recovery will be needed. We recommend universally screening for the presence of TEOAE and DPOAE as part of the initial site of lesion assessment. OAE protocols used to monitor noise- and ototoxicity-induced hearing loss have been refined over time to identify the most sensitive frequency regions: for ototoxic agents,monitor the highest frequencies with pre-exposure responses [42]; for noise-exposure, monitor the OAE frequency region \(\frac{1}{2}\) to 1 octave lower than the frequency with significant threshold shift [8]. Refining an ISSNHL OAE monitoring protocol will be hampered by the variety of audiometric configurations that occur, and the unlikelihood of having baseline measurements. With these factors in mind, we recommend measuring both TEOAE and DPOAE over as wide a frequency region as possible, with the hopes that future systematic reviews may identify the most sensitive frequency region to monitor. For all serial OAE monitoring, we would caution against using OAE SNR as a criterion for significant change, as was used by [PERSON] and [PERSON] [7]. DPOAE amplitude changes were reported to be the best predictor of pure tone threshold changes in patients receiving potentially ototoxic chemotherapy [25; 43]. [PERSON] et al. suggest establishing a clinic test-retest amplitude criterion [8]; we suggest using the patient's own unaffected ear to establish the test-retest amplitude criterion for unilateral ISSNHL.
# Link Budget Analysis with Laser Energy for Time Transfer Using the Ajisai Satellite
## 2 TLTT Link Budget
### Link Budget Equation for TLTT
#### 2.1.1 Free Space Loss and Atmospheric Effects for TLTT  
The geometric configuration consisting of one satellite and two SLR stations for the TLTT technique changes depending on the time. Free space loss and atmospheric effect in the laser link budget equation are affected by changes of geometric configuration, which are functions of slant range and zenith angle between satellite and ground station.  
In the case of the free space loss term, we can simply replace the two slant ranges in Equation (1) with the slant ranges between the transmitting station and satellite (\(R_{t}\)) and the slant range between the remote receiving station and satellite (\(R_{r}\)), respectively. Atmospheric attenuation and cirrus cloud cover are the main effects that decrease the intensity of electromagnetic waves propagating through the Earth's atmosphere, and have to be considered separately for the transmitting and receiving stations when using the TLTT technique because these values are dependent on the zenith angle of each station.  
The atmospheric attenuation at the altitude of the ground station above sea level (\(h_{sta}\)) is given approximately by [11]:  
\[T_{a}(\lambda,\ V,\ h_{sta})=exp\left[-\sigma_{atm}(\lambda,\ V,\ 0)h_{scale}\sec \theta_{z}\ exp\left(-\frac{h_{sta}}{h_{scale}}\right)\right] \tag{2}\]  
where \(\sigma_{atm}(\lambda,\ V,\ 0)\) is the sea-level atmospheric attenuation, which is the function of wavelength (\(\lambda\)) and the sea-level atmospheric visibility (\(V\)), \(h_{scale}\) is a visibility scale height, and \(\theta_{z}\) is the zenith angle of the satellite as observed at the station. The mean cirrus transmission achieved from the experiment with assumption of no cirrus clouds 50% of the time is given by [11]:  
\[T_{c}=\exp\Bigl{(}-0.14(t_{cc}\sec\theta_{z})^{2}\Bigr{)} \tag{3}\]  
where \(t_{cc}\) is the cirrus cloud thickness.  
Therefore, the link budget equation for TLTT can be introduced from the ordinary SLR link budget equation with slight modification to account for the effect of geometric configuration, as in:  
\[n_{p}=\left(E_{t}\frac{\lambda}{\Omega c}\right)\eta_{t}\ \frac{G_{t}}{4\pi R_{t} ^{2}}\ \frac{\sigma}{4\pi R_{t}r^{2}}\ A_{r}\eta_{r}\ \eta_{q}\ T_{at}T_{ct}T_{ar}T_{cr} \tag{4}\]  
where \(T_{at}\), \(T_{ar}\), \(T_{ct}\), and \(T_{cr}\) are the atmospheric attenuation and the mean cirrus transmission at the transmitting (_t_) and receiving (_r_) stations, respectively.
## 4 Results and Discussions
### Measurement Analysis of the Coordination Degree
The remaining provinces were at the primitive level of coordination development. In 2018, eleven provinces, including Beijing, Tianjin, and Zhejiang, reached the intermediate or higher level of coordination development, of which Beijing reached the level of high-quality coordination development. By contrast, Gansu, Xinjiang, Hebei, and Shanxi were still at the very low coordination development stage. The rest of the provinces were all at the primitive level of coordination development. Overall, provinces with a high level of coordination development in terms of carbon emissions, economic development, and environmental protection were mainly distributed around the eastern coastal regions, and provinces with a low level of coordination development were mainly distributed in the underdeveloped areas within the central and western regions, with an increase in the inter-regional gap. Figure 2: A diagram of China’s coordination level in regard to carbon emissions, economic development, and environmental protection.
# Crop Disease Diagnosis with Deep Learning-Based Image Captioning and Object Detection
## 3 Materials and Methods
### Structure of the Crop Disease Diagnosis Solution
#### 3.3.2 Object Detection Model  
(a) YOLOv5  
There have been a number of models dedicated to the Object Detection, and we decided to use a pretrained model to reduce the training time. Among them, our model employed the YOLO algorithm released in 2016. YOLO algorithm outperforms its competitors, such as Fast R-CNN, in accuracy and speed. We use the YOLOv5 model among other YOLO family models since YOLOv5 showed improved processing time when in deeper networks. Our Object Detection model employed YOLOv5m, one of the subvariants of YOLOv5, considering our situation.  
YOLOv5 made features from input images, and the features passed into a prediction system to create a rectangle to form a boundary around the detecting object for prediction using class labels.  
As illustrated in Figure 9, Backbone, Neck, and Head are three main parts of YOLOv5. The Backbone collects and creates image features, while the Neck passes those features to the Head for the prediction. Then, bounding boxes and class predication were made in the Head part.  
Figure 8: Structure of Image Captioning Model.
## 4 Proposed Approach
### Maintenance
#### 4.3.2 Cluster Joining  
When a node \(i\) decides to join a cluster, it executes the initialization phase and broadcasts a join request to the nearest neighbors. These neighbors reply by sending an \(LSV_{i}\) beacon that contains the clustering information. The new node saves the received information in its \(CLR_{i}\) list and computes its weight value \(W_{i}\). If it has the largest weight among all the \(CRL_{i}\) members, then it elects itself as the new CH and broadcasts an announcement message, so that the surrounding neighbors can perform the necessary changes (set the new node as their new CH). Otherwise, the new node joins the cluster of the weighiest CH in its k-hop neighborhood. Next, \(i\) selects the closest node to the CH (node with the lowest RSSI value) as its new parent to relay the collected data.  
In the case where no reply packet is received, i.e., the new node is inside an isolated region, the new node \(i\) elects itself as a cluster head (to avoid isolated node scenario), updates its local state (\(Mych_{i}=ID_{i},\ Dist(i,Mych_{i})=0\)) and broadcasts a \(CH\_Announcement\) packet, since other nodes may have joined this region while \(i\) was executing the joining procedure. The cluster joining process is shown in Figure 8.  
Figure 7: The cluster head leaving process.
## 2 Materials and Methods
### E-Field Computation  
The T1-weighted isotropic (1 mm \(\times\) 1 mm \(\times\) 1 mm voxel) magnetic resonance image (MRI) data of participants were used to generate their personalized anatomical head models in order to numerically calculate the induced EFs. The anatomical head models, consisting of white matter, grey matter, CSF and ventricles, bone and scalp, were initially automatically segmented by the _headreco_ tool of the SimNIBS software package (SimNIBS Developers. SimNIBS 3.2.4) and then visually inspected, and manually customized when required, through the iSeg toolkit (ZMT Zurich MedTech & IT'IS foundation. iSeg).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subject ID** & **Age** & **Sex** & **Handedness** & **LT(\%MSO)** & **Available SIs** \\ \hline S01 (H) & 26 & F & Right & 32\% & 25–35\%, 38\% (120\%LT) \\ S02 (H) & 31 & M & Right & 30\% & 25–35\%, 36\% (120\%LT) \\ S03 (H) & 30 & M & Right & 30\% & 26–35\%, 36\% (120\%LT) \\ S04 (P) & 20 & M & Right & 28\% & 22–35\%, 34\% (120\%LT) \\ S05 (P) & 23 & F & Right & 27\% & 20–35\%, 32\% (120\%LT) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Subject demographics.  
Each head model, with its tissues assigned to standard electrical conductivities [38], was stimulated by the model of H7 coil. For the numerical computations of the electric field distribution inside the head, as induced by the TMS coil, we used the quasi-static low frequency solver of the computational life sciences software platform Sim4 Life (ZMT ZurichMedTech, Zurich, Switzerland). This solver was used in the past for TMS calculations of various human anatomical models as implemented in another software platform, i.e., SEMCAD-X (SPEAG, Zurich, Switzerland) [39]. Recently, it was used in conjunction with a highly detailed human model to perform numerical simulations of TMS [40]. The equations and the physics of the solver are described in both [39; 40], whereas a validation of it is included in [40].
## 2 Background and Related Work
### Maintenance Strategy  
1. Regular maintenance (time-based maintenance): Regular maintenance is a basic maintenance strategy that involves conducting scheduled maintenance according to a predetermined maintenance plan. This approach is convenient to implement and is particularly suitable for components whose risk of failure increases with extended usage over time.
2. Predictive maintenance: Through real-time monitoring and data analysis, predictive maintenance anticipates the potential time of failure for equipment or systems, allowing for proactive maintenance measures to be taken before issues arise. The objective of this strategy is to minimize unplanned downtime, enhance equipment reliability and performance, and reduce maintenance costs.
3. Energy-oriented maintenance: Emphasizing the enhancement of equipment performance, reliability, and efficiency through the management and optimization of energy usage. This strategy incorporates energy efficiency into the scope of maintenance considerations. When certain devices and systems exhibit abnormal operating conditions, energy efficiency indicators can undergo significant changes. By monitoring energy efficiency, rational energy management measures can be implemented, leading to prolonged equipment lifespan, reduced energy wastage, and an overall improvement in equipment operational performance.
# Oceania's 5G Multi-Tier Fixed Wireless Access Link's Long-Term Resilience and Feasibility Analysis
## 2 Materials and Methods
### Functional Split in 5G Networks
The topologies in Figure 4 illustrate examples of centralized radio access networks (CRAN) with partially centralized RAN and fully centralized vRAN, as well as distributed radio access networks (DRAN) with the BBU either fully integrated in the tower or at a distribution site at or near the building. These architectures are band-independent and can operate in numerous bands from 6 GHz to mmWave. Figure 4: Architectural designs for centralized radio access networks (CRAN) and distributed radio access networks (DRAN) in which the BBU is completely integrated at the tower or integrated at a distribution station at or near the tower. A \(4\times 4\) antenna array with three sectors using a single carrier would need \"around\" a fronthaul capacity of about 34 Gbit/s with an option 7 split, while an \(8\times 8\) array would need about 67 Gbps, assuming a bandwidth of 100 MHz and a subcarrier 60 KHz. Further, 100G Silicon Photonics CWDM4 QSFP28 extended currently offers up to 100 \(\times 10^{9}\)b/s of bandwidth, which is the perfect choice for the fronthaul network. The capacity required for the option two split fronthaul is significantly lower, so a lower capacity optical link is required. The split option used in this link is 7 (xRAN). With Intel Silicon photonics optical transceivers, the split option 7 DU to RRH connection of this design performs admirab. The extended-temperature or commercial-temperature variants of these transceivers can be used, depending on the implementation. The use basis specimens earlier provided and reviewed in Table 1 have demonstrated that the fronthaul rate requirements for 5G will be severe, even after considering some greater efficiency over CPRI. Silicon Photonics incorporates silicon's light-emitting capability at the wafer level in the market's only hybrid laser design. This approach eliminates the need for optical alignment and results in a production technique that makes use of high-volume silicon fabrication capabilities. This method offers significant scale, economic, and performance advantages. Additionally, Silicon Photonics optical transceivers enable software-defined infrastructure and network function virtualization, support several industry standards, and result in flexible network architecture and simple network maintenance [45].
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## Appendix A  
Given a vector space \(\mathbf{X}\) over a subfield \(\mathbf{F}\) of the complex numbers \(\mathbb{C}\), a norm on \(\mathbf{X}\) is a real-valued function \(\|\cdot\|\): \(\mathbf{X}\rightarrow\mathbb{R}\) with the following properties, where \(|a|\) denotes the usual absolute value of a scalar \(a\):  
1. Triangle inequality: \(\|\mathbf{x}+\mathbf{y}\|\leq\|\mathbf{x}\|+\|\mathbf{y}\|\) for all \(\mathbf{x},\mathbf{y}\in\mathbf{X}\).
2. Absolute homogeneity: \(\|a\mathbf{x}\|=|a|\cdot\|\mathbf{x}\|\) for all \(\mathbf{x}\in\mathbf{X}\) and all scalar \(a\).
3. Positive definiteness: for all \(\mathbf{x}\in\mathbf{X}\), \(\|\mathbf{x}\|\geq 0\) and \(\|\mathbf{x}\|=0\Leftrightarrow\mathbf{x}=0\).  
Then, the validation of the new norm \(\|\cdot\|_{\text{new}}\) defined in Equation (13) is derived as follows. According to the definition, \(\|\mathbf{x}\|_{\text{new}}\) can be calculated by  
\[\|\mathbf{x}\|_{\text{new}}=\sqrt{\left\langle\mathbf{x},\mathbf{Q}^{-1} \mathbf{x}\right\rangle}=\sqrt{\sum_{n}q_{nn}^{-1}\cdot\overline{x}_{n}x_{n}} \tag{16}\]  
where \(x_{n}\) is the \(n\)th element of \(\mathbf{x}\), and \(q_{nn}\) is the \(n\)th row and \(n\)th column element in \(\mathbf{Q}\). Since \(\overline{x}_{n}x_{n}>0\) and \(q_{nn}^{-1}>0\), the positive definiteness of \(\|\mathbf{x}\|_{\text{new}}\) is easily proved. Then, according to Equation (16), we have  
\[\begin{split}\left\|\mathbf{x}+\mathbf{y}\right\|_{\text{new}}& =\sqrt{\sum_{n}q_{nn}^{-1}\cdot(\overline{x}_{n}+\overline{y}_{ n})(x_{n}+y_{n})}\\ &=\sqrt{\sum_{n}q_{nn}^{-1}\cdot\overline{x}_{n}x_{n}+\sum_{n}q_{ nn}^{-1}\cdot\overline{x}_{n}y_{n}+\sum_{n}q_{nn}^{-1}\cdot\overline{y}_{n}x_{n}+ \sum_{n}q_{nn}^{-1}\cdot\overline{y}_{n}y_{n}}\\ &\leq\sqrt{\sum_{n}q_{nn}^{-1}\cdot\overline{x}_{n}x_{n}+\sum_{n}q _{nn}^{-1}\cdot\overline{y}_{n}y_{n}+2\sqrt{\sum_{n}q_{nn}^{-1}\cdot\overline{ x}_{n}x_{n}}\sqrt{\sum_{n}q_{nn}^{-1}\cdot\overline{y}_{n}y_{n}}}\\ &=\left\|\mathbf{x}\right\|_{\text{new}}+\left\|\mathbf{y}\right\| _{\text{new}}\end{split} \tag{17}\]  
and  
\[\left\|a\mathbf{x}\right\|_{\text{new}}=\sqrt{\sum_{n}q_{nn}^{-1}\cdot \overline{x}_{n}ax_{n}}=\left|a\right|\cdot\sqrt{\sum_{n}q_{nn}^{-1}\cdot \overline{x}_{n}x_{n}}=\left|a\right|\cdot\left\|\mathbf{x}\right\|_{\text{ new}} \tag{18}\]  
So \(\|\cdot\|_{\text{new}}\) satisfies property 1 and 2. Then, the validation of \(\|\cdot\|_{\text{new}}\) is proven.
## 2 Results and Discussion
### Antioxidant Activity
#### 2.3.2 Fe\({}^{2+}\)-Chelating Ability  
Figure 7 showed that four isolated APs (RSHP-A-D) chelated Fe\({}^{2+}\) in a dose-effect manner at concentrations that ranged from 0 to 1.0 mg/mL, and RSHP-C showed the highest chelating ability on Fe\({}^{2+}\) among four isolated APs (RSHP-A-D). Transition metals, such as Fe\({}^{2+}\) and Cu\({}^{2+}\), can serve as catalysts that accelerate the generation of ROS, which further oxidize unsaturated lipids and initiate the oxidative chain reaction [52]. In recent years, some metal chelating peptides have been identified from different food protein resources, such as oyster, anchovy, whey, soybean, and mungbean [53]. [PERSON] and [PERSON] reported that side chains, including carboxyl (Glu, Asp) and amino groups (Lys, His, Arg), can serve as metal chelators to reduce the available amount of transition metals, causing the inhibition of radical-mediated oxidative chain reactions [42]. [PERSON] et al. have reported that Asp, Glu, and Pro were the most abundant amino acids in the iron-binding peptides from whey protein [54]. GPAGPHGPPKDRGR, AGPHGPPKDRG, and AGPAGAGAGAR from gelatin tryptic hydrolyses of pacific cod skin exhibit high affinity to ferrous ions. Among the groups of the three peptides, the amino and carboxylate terminal groups and peptide bond from peptide backbone, as well as the amino and imine from Arg side chain, are involved in the complexation. In addition, several amino acid side chain groups of GPAGPHGPPKDRG and AGPHGPPGKDRG, including amino (Lys), imine (His), and carboxylate (Asp), supplied additional iron binding sites [53]. The four isolated APs (RSHP-A-D), especially RSHP-B-D, are rich in acid and basic amino acids. Therefore, they showed higher Fe\({}^{2+}\)-chelating ability than the positive control of GSH.  
Figure 7: Fe\({}^{2+}\)-chelating ability of the four isolated APs (RSHP-A–D) from protein hydrolysate of red stingray (_D. akajei_) cartilages. All data are presented as mean \(\pm\) SD (\(n=3\)).
# Large-Language-Model-Enabled Text Semantic Communication Systems
## 5 Discussion
### Complexity Considerations  
The computational complexity of LLM-SC and DeepSC is compared in Table 5 in terms of the average demodulating runtime per character. It is evident that the runtime of LLM-SC significantly exceeds that of DeepSC, primarily due to the vast number of parameters in LLMs. The assumptions in this paper presume unlimited computational power at both the transmitter and receiver. However, LLM-SC introduces a novel paradigm for semantic communication systems. With the exponential growth in computational power, real-time demodulation by both transmitter and receiver becomes achievable, potentially mitigating the complexity concerns. Moreover, comparing the runtime of machine learning-based semantic communication methods on GPUs is inherently biased, as practical communication devices are unlikely to integrate high-performance GPUs at both ends.
# ABORt: Acknowledgement-Based Opportunistic Routing Protocol for High Data Rate Multichannel WSNs
## 3 Acknowledgement-Based Opportunistic Routing Protocol
### Channel Allocation Scheme  
When multiple channels are used in the same network for communication, each node has to know the reception channel of its neighbors to be able to communicate with them. This requires sharing information on channel allocation among neighboring nodes. As demonstrated in [20], channel reuse in the 3-hop neighborhood leads to collisions when MAC layer acknowledgement messages are enabled. In order to prevent these kinds of collisions, nodes avoid selecting already used channels in their 3-hop neighborhood when possible.  
Nodes select channels in an order based on their _ids_. A node _id_ is a 2-byte integer that is allocated to each node in a unique manner at the start-up phase. The node with the smallest _id_ in its 3-hop neighborhood selects its channel first. A node does not select a channel as long as its predecessor has not announced its reception channel in the beacon. A predecessor of a node is its 1-hop, 2-hop or 3-hopneighbor with an _id_ that immediately precedes its own _id_. For example, in Figure 1, 1-hop, 2-hop and 3-hop neighbors of node 8 are nodes 4, 5, 11, 12, 14, 15, 16, 17, 21, 25 and 27. Thus, the predecessor of node 8 is node 5. Node 8 cannot select its reception channel before node 5 chooses its channel and announces it in a beacon frame. Each node searches for a free channel in its 1-hop, 2-hop and 3-hop neighborhoods. If no free channel is available, it searches for a free channel in its 1-hop and 2-hop neighborhoods. In case all channels are already taken in its 1-hop and 2-hop neighborhood, it tries to find a free channel in its 1-hop neighborhood. Finally, if no free channel is found, the less used channel in the 1-hop neighborhood will be selected.  
At the end of the channel selection process, each node has its reception channel and this choice is also known by its neighbors. In Figure 1, each node channel is labelled. The sink node has three reception channels (\(f11\), \(f12\) and \(f13\)) while other nodes have one channel. When a node has a frame to send to a neighbor node, it switches to the reception channel of this neighbor and sends the frame using unslotted CSMA/CA of IEEE 802.15.4. When the node finishes sending the frame, it switches back to its reception channel.
## 4 Proposed Resource Allocation Scheme
### Optimal Problem Formulation
Assuming that the execution process of any request, \(r\in R\) is non-preemptive and all the virtual base stations (VBSs), \(v\in V\), in a BBU will be created on demand of the incoming request to the BBU Pool. Computational resources are allocated for the incoming requests with the help of a certain number of scheduling intervals according to the available BBUs and resource requirements. Let \(\Gamma_{r,b}\) denote the number of scheduling intervals required for a request, \(r\in R\), to be assigned to a BBU, \(b\in B\). This value is incremented by one as the scheduling interval number increases until a BBU is allocated for a request. The objective function of this resource allocation problem is formulated as:  
\[\max_{r,b}\sum_{r\in R}\sum_{b\in B}(\alpha\times\hat{\rho}_{r,b}-\beta\times \hat{\tau}_{r,b}-\frac{\gamma}{\Gamma_{r,b}}), \tag{6}\]  
which is subject to some constraints that are discussed elaborately in Equations (8)-(13). Equation (6) reveals that the total QoE offered by the system to the end user for all the requests will be maximized through minimization of the total time to get a response. It also ensures the earlier execution of the requests that promise higher profit to the service provider. Furthermore, we also minimize the starvation of any specific request by ensuring a higher chance of execution of requests with a higher number of waiting slots. Here the greater the difference of these considered values, the greater the maximization of Quality-of-Experience (QoE) and profit is achieved without having long starvation on the request queue. Here, \(\alpha\), \(\beta\), and \(\gamma\) are the system parameters which can be dynamically changed according to the system environment satisfying Equation (7):  
\[\alpha+\beta+\gamma=1 \tag{7}\]