esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Variable-Beam Divergence Angle FSO Communication Systems
## 4 Challenges and Future Work  
This paper provides a comprehensive overview of various VBDA methods used in FSO communication, focusing on the advantages and disadvantages of different methods. It also sorts out the research on the use of VBDA methods for different link types. Despite significant advances in VBDA technology, several challenges still remain. To address these challenges, we propose several promising research directions and provide potential measures for overcoming these challenges, which will be discussed below.
## 4 Security Analysis
### Key Definition, Encryption and Decryption  
The proposed security protocols generate a key value used to encrypt identification parameters through the challenge-response process. The key used to encrypt IMSI and GUTI is defined as the value not used in the challenge-response process during the numerical progression generated through the f() function of the secret sharing between the UE and the MME. The relevant key value is defined only in the UE and MME, but not transmitted through the communication process to the outside. Therefore, an attacker can find the identification parameters only through the attack on the encryption algorithm, like AES-256, to know the encrypted IMSI and GUTI.  
The key value encrypting the RNTI is transmitted through the challenge-response process, but the location of the bit continues to change for the transmission. Even if an attacker collects the bit string through an attack, like tapping, the probability of finding the key value with a total of \(n\) bits is (1/4)\({}^{n}\).
## 3 Saline-Based One Lung Filling (OLF)
### Discussion  
It was found that saline-based OLF results in unimpaired visualization of lung in the absence of remaining gas content using B-Mode sonography. The limited visualization during OLF using cadavers indicates the importance of perfusion for gas resorption [39]. Saline as an acoustic matched liquid to parenchyma is an ideal medium where effects of refraction and scattering as well as attenuation of ultrasound waves are minimized in lung as a liquid-tissue compound. OLF has been investigated consecutively, starting with physiological and safety aspects via acoustic modelling to clinical applications. It required two decades to pave the way path from the idea towards evaluation of its clinical use for diagnostic and therapeutic ultrasound applications.  
The high sonographic nodule detection rate and clear demarcation from vascular and bronchial structure is valuable, not requiring contrast agents or fusion techniques. During OLF, anatomic orientation is given due to visualization of adjacent organs. For complementation of the sonographic findings, modern imaging modalities such as elastography or contrast agents should be investigated in future work, improving visualization of bronchoalveolar cell carcinoma.  
It is likely that OLF will not be applicable for a diagnosis requiring full sedation and insertion of a DLT. Its use for sonographic guidance of therapeutic interventions might justify the invasivity. Favorable for interventions during OLF, are the high nodules detection rate, with the demarcation of vascularity and reduction of ventilation associated motion [40]. The reduction of intrapulmonary perfusion in the flooded lung [28] reduces the risk of hemorrhage during puncture and eliminates possible perfusion associated heat flux when performing thermal ablation. In combination with real-time sonography providing high resolution, low costs, and absence of radiation, LUS guiding during OLF is more favorable than CT or MRI.  
Most promising for the clinical use of OLF is the combination of diagnostic lung ultrasound for guidance of therapeutic ultrasound ablation (HIFU) [41]. From such a non-invasive ablation of central lung malignancies the field of pulmonary oncology might benefit from. However, the translation to human subjects is hindered due to the unavailability of clinical HIFU systems capable of trans-costal focusing. Several authors have proposed methods to focus HIFU behind the rib cage [42], but these methods have not been implemented in clinical systems to date.  
A limitation of OLF, arises from the procedure duration. This operating window was investigated so far only up to 60-90 min, which is sufficient for sonographic guided tissue sampling, MWA or catheter-based ablation (typical 10-15 min). During HIFU ablation the procedure time might exceed these limits depending on ablation speed and tumor size. Future work should investigate methodological optimization to expand OLF duration without the impairment of acoustic access.
## 6 Conclusions  
In this paper, MG stability with high penetration level of CPLs is examined and investigated. An autonomous MG including CPLs is modeled using MATALB. Current, voltage, and power controllers are optimally designed to enhance the transient response of the autonomous MG considering CPLs. A power droop controller is utilized to share the DGs output powers in the presence of high penetrated CPLs. PLL is employed to synchronize the involved CPLs with the MG and track the MG frequency. Based on error curtailing in the DC voltage of the CPL and measured active power of the DG, an optimal control design for the controllers of DGs, CPLs, and PLL is presented. Several disturbances are applied to assess the optimal parameters impact on the MG stability. The impact of the high penetration level of CPLs on MG stability is investigated. Additionally, the dynamic stability of the autonomous MG is examined under these disturbances. CPLs are a potential instability source for the MG system. It can be observed from the different applied disturbances in DGs or CPLs sides that the MG system is highly stable with no oscillatory behavior. The simulation results confirm the effectiveness of the proposed method to improve the performance of the MG dynamic stability in the presence of highly penetrated CPLs. The proposed controller provides a fast and accurate power control, an efficient damping characteristic, and satisfactory performance. However, the classical linear control methods such as a PI controller have stability limitations around the operating points. Future work will focus on hardware implementation, testing and verifying the proposed controller effectiveness. Additionally, different MG topologies can be used to study the destabilization effects of the high penetrated CPLs on the AC MGs.  
[PERSON] and [PERSON] initiated the idea, formulated the problem, performed the simulation, and analyzed the results. [PERSON] and [PERSON] participated in the paper revision stage, contributed to enhancing the simulation results, and shared in paper writing. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the support provided by the Center for Engineering (CER), Research Institute at King Fahd University of Petroleum & Minerals. [PERSON] would like also to acknowledge the funding support by King Abdullah City for Atomic and Renewable Energy (K.A. CARE), Energy Research & Innovation Center (ERIC) at KFUPM through the Direct Funded project #DF191004.  
Not applicable.  
The authors declare no conflict of interest.
# Bistatic Forward ISAR with DVB-T Transmitter of Opportunity
## 5 Numerical Simulation Experiment
The lower range resolution is due to the narrow frequency bandwidth of the DVB-T waveform, \(\Delta F=7.61\) MHz, corresponding to a range resolution equal to 20 m. In addition, the DVB-T waveform such as a stepped frequency modulation waveform is characterized with a comparably high level of side lobes and a narrow main lobe width that can be noticed in Figures 5 and 6. The low level of scattering points' intensities is due to the high levels of side lobes and sparse structure of the DVB-T coefficients, whose absolute values randomly accept zero values. This phenomenon is the reason the azimuth resolution to be not very satisfactory. The impact of the noise on the object's image was not considered [43]. This important issue is the subject of future study.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 4 Experimental Evaluation
### Datasets and Experimental Setup
#### 4.1.3 Evaluation Metrics  
We evaluated our method using standard metrics for object detection and zero-shot learning. Mean Average Precision (mAP) was calculated at an IoU threshold of 0.5 (mAP@50) for both ZSD and GZSD settings, providing a comprehensive measure of detection accuracy. We also measured Recall@100, which captures the recall of the top 100 detections per image at an IoU threshold of 0.5, indicating the model's ability to find all relevant hazards in an image. For GZSD specifically, we reported the Harmonic Mean (HM) of seen and unseen class performance, calculated as \(\text{HM}=\frac{2\times\times\cup\text{U}}{5+\text{U}}\) where S and U are the mAP or Recall@100 for seen and unseen classes, respectively. The harmonic mean provides a balanced evaluation metric that penalizes methods that perform well on seen classes but poorly on unseen classes, or vice versa.
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 3 Method
### The CTC Alignment Algorithm
#### 3.3.2. \(p(\mathbf{I}|\mathbf{x})\)  
Computation via Dynamic Programming  
The reasoning is similar to that followed to obtain the recurrence equation, Equation (6). As shown on Figure 9, if the optimal path between two sequences passes through the point \((i,t)\), it necessarily passes through one of the points \((i-2,t-1)\), \((i-1,t-1)\), or \((i,t-1)\). It is thus enough to know the probability of these 3 predecessor to deduce the probability on the point \((i,j)\).  
Let \(\alpha(i,t)\) denote the total probability to have the prefix \(\mathbf{a}_{1;i}\) corresponding to the prefix \(\mathbf{b}_{1;t}\), and assume that \(\alpha(i-2,t-1)\), \(\alpha(i-1,t-1)\), and \(\alpha(i,t-1)\) are already known. Given the permitted path transitions depicted on Figure 9, it is straightforward to observe that:  
\[\alpha(i,t)=\begin{cases}[\alpha(i,t-1)+\alpha(i-1,t-1)]p(a_{i},t)&\text{if }a_{i}= \epsilon\text{ or }a_{i}=a_{i-2}\\ [\alpha(i,t-1)+\alpha(i-1,t-1)+\alpha(i-2,t-1)]p(a_{i},t)&\text{otherwise}\\ \forall(i,t)\in\{3:2|\mathbf{I}|+1\}\times\{2:T\}\end{cases} \tag{15}\]  
The initialisation step is given by:  
\[\begin{cases}\alpha(0,t)=0&\quad\forall t\geq 0\\ \alpha(i,0)=0&\quad\forall i\geq 0\end{cases} \tag{16}\]  
Finally, an extension can terminate with \(\epsilon\) or the last label \(l_{|\mathbf{I}|}\); thus, the probability \(p(\mathbf{I}|\mathbf{x})\) is the sum of the total probability of \(\mathbf{a}\) with or without the last \(\epsilon\):  
\[p(\mathbf{I}|\mathbf{x})=\alpha(|\mathbf{a}|-1,T)+\alpha(|\mathbf{a}|,T) \tag{17}\]  
In their seminal paper, [PERSON] et al. introduced \(\boldsymbol{\alpha}\) to compute \(p(\mathbf{I}|\mathbf{x})\), i.e., the CTC loss of Equation (13), with the objective to differentiate it with respect to the network weight for back-propagation training. For our present purpose, it is enough to compute \(\boldsymbol{\alpha}\). We refer the interested reader to the original paper for details about CTC loss differentiation.
## 3 Results
### Effect of Contact Time, pH, Adsorbent Dose and Lactic Acid Concentration on Lactic Acid Adsorption
\begin{table}
\begin{tabular}{c|c|c|c|c} \hline
**Kinetic Model** & **Differential Form** & **Linear Form** & **Plot** & **Equation** \\ \hline Pseudo-First & \(\frac{dq_{e}}{dt}=K_{1}(q_{e}-q_{t})\) & \(log(q_{e}-q_{t})=log\;q_{e}-\left(\frac{K_{a}}{2.303}\right)t\) & \(log(q_{e}-q_{t})\) vs. \(t\) & (A) \\ Order & \(\frac{dq_{t}}{dt}=K_{2}(q_{e}-q_{t})^{2}\) & \(\frac{t}{\phi}=\frac{1}{K_{\text{diff}}}+\frac{1}{q_{t}}t=\frac{1}{R_{0}}+\frac{1} {q_{t}}t\) & \(\frac{t}{q_{t}}\) vs. \(t\) & (B) \\ \hline Elovich & \(\frac{dq_{t}}{dt}=\alpha e^{-\beta t_{t}}\) & \(q_{t}=\beta ln\;a\beta+ln\;t\) & \(q_{t}\) vs. \(ln\;t\) & (C) \\ \hline \multicolumn{4}{l}{Notes: \(K_{1}=\text{pseudo-first order rate constant (min}^{-1}\)), \(q_{t}=\text{amount of adsorbate in the adsorbent at equilibrium (mg/g), }t=\text{time (min), }q_{t}=\text{ adsorption at any given point of time (mg/g), }K_{2}=\text{ pseudo-second order rate constant (mg/g/min), }h_{0}=\text{ initial adsorption rate (min}^{-1}\)), \(\beta=\text{desorption constant (g/mg) and }\alpha=\text{ initial adsorption rate (mg/g/min).}\)} \\ \end{tabular}
\end{table} 
Table 2: List of differential and linear forms of adsorption kinetic model equations. acid recovery efficiency from 41.7 (\(\pm\)1.4)% to 73.1 (\(\pm\)1.1)%. The experimental results with different adsorbent dose showed that 10-15% (\(w/v\)) adsorbent dose has a good lactic acid recovery (%), and thus, 10% (\(w/v\)) adsorbent dose was selected for all the batch adsorption experiments. The binding capacity of lactic acid was studied by varying the initial lactic acid concentration in the range of 1.5 to 36.3 g/L as shown in Figure 1d. The lactic acid recovery efficiency decreased from 83.2 (\(\pm\)0.9)% to 38.2 (\(\pm\)0.2)% for GAC when the lactic acid concentration increased from 1.5 g/L to 36.3 g/L. However, the adsorption capacity (\(q_{e}\)) with GAC increased sharply from 12 mg lactic acid/g adsorbent (at 1.5 g/L lactic acid) to 162 mg lactic acid/g adsorbent (at 36.3 g/L lactic acid). Similarly, for RLX425, the lactic acid recovery efficiency decreased from 41.9 (\(\pm\)6.6)% (at 1.5 g/L lactic acid) to 19.1 (\(\pm\)0.5)% (at 36.3 g/L lactic acid) with a maximum \(q_{e}\) value of 79 mg lactic acid/g adsorbent (at 36.3 g/L lactic acid). The recovery efficiency for AMB400 decreased from 58.8 (\(\pm\)2.6)% to 20.2 (\(\pm\)0.1)%, with an increase in the lactic acid concentration from 1.5 g/L to 36.3 g/L and the maximum \(q_{e}\) value was found to be 73 mg lactic acid/g adsorbent at a lactic acid concentration of 36.3 g/L.
## 2 Methods
### Simplified Static-Equivalent Model
#### 2.1.3 Wave Loads  
Wave loads were chosen based on Airy linear wave theory, in which a wave profile is represented by a sine function. Firstly, the horizontal particle velocity, \(w\), and horizontal particle acceleration, \(\dot{w}\), were calculated (see, e.g., [6]). At each instant \(t\), the distributed wave force acting over a submerged element was obtained as the sum of two components: the drag force, \(dF_{D}\), and the inertia force, \(dF_{I}\):  
\[dF_{D}(z,t)+dF_{I}(z,t)=\frac{1}{2}\,\rho_{w}\,D_{S}\,C_{D}\,|w(z,t)|+C_{m}\, \rho_{w}\,A_{S}\,\dot{w}(z,t) \tag{7}\]  
where \(C_{D}\) and \(C_{m}\) are the drag and inertia coefficients, whose values are defined in DNV-RP-C205 [23].  
The static-equivalent distributed wave load at each level \(z\) is assumed to be the sum of the maximum drag and inertia forces that occur in a wave cycle (as the most unfavorable scenario [6]) multiplied by a Dynamic Amplification Factor (DAF) to include its dynamic nature:  
\[dF_{\text{wave}}(z)=\left(\max_{t\in[0,T_{\text{w}}]}\,dF_{D}(z,t)+\max_{t\in[ 0,T_{\text{w}}]}\,dF_{I}(z,t)\right)\cdot\text{DAF} \tag{8}\]  
where \(T_{\text{w}}\) is the wave period, and the DAF is defined as  
\[\text{DAF}=\frac{1}{\sqrt{(1-(\frac{f_{\text{w}}}{f_{\text{w}}})^{2})^{2}+(2 \cdot\xi\cdot\frac{f_{\text{w}}}{f_{\text{n}}})^{2}}} \tag{9}\]  
where \(f_{\text{w}}=1/T_{\text{w}}\) is the wave frequency and \(\xi\) is the system damping ratio. In this study, a global damping ratio equal to 1% is considered as a characteristic value of the material damping of steel. The DAF indicates the increment in the dynamic response of the system with respect to the static one derived from the proximity of the excitation and fundamental frequencies. Note that, in the simplified model, the DAF is applied solely to wave loads because the low-frequency wind loads can be directly assumed to be static.
## 3 Safety Analysis and Environmental Modeling
### Environmental Modeling  
The Electronic Nautical Chart has been widely used in maritime navigation. However, applying traditional path search algorithms is complicated due to the complex geometric figures that characterize the marine environment's geographical information data. Therefore, solving the environmental modeling problem and simplifying the complex chart data into an environmental model with transparent geometric relationships is necessary. Implementing path planning with the center of all grids as a node allows path search algorithms to search and plan more efficiently across these grids. This helps improve the efficiency of the path search algorithm during analysis and processing. Use the grid method to design the appropriate grid size, coding method, and representation method according to the navigation area and ship characteristics. Divide the marine environment into navigable and non-navigable areas by setting a state for each grid that reflects its navigability in the actual environment. Doing so helps optimize the navigation routes of the USV and ensures its safe navigation in the marine environment. Environment modeling is an essential foundation for route planning using the A* algorithm and the quality of a route is closely related to the size of the grid. Using a too-small grid may shorten the path distance but significantly increase the search time. Conversely, a too-large grid may decrease the accuracy of path planning and increase navigation risks. Therefore, the selection of grid size has a crucial impact on the quality of route planning.  
When modeling the chart environment, obstacles in the water are treated as follows: first, obstacles smaller than the grid size are inflated to fill the entire grid to ensure accuracy. Second, suppose the distance between adjacent obstacles is smaller than the safe radius of the USV. In that case, the obstacles will be classified as the same obstacle to avoid local dead-lock and collision risk, ensuring the connectivity and feasibility of the obstacles. Through these processes, the chart environment can be established more accurately, improving the integrity and reliability of the environmental model. The grid environment model is shown in Figure 11.  
Figure 11: Grid environment model.  
Figure 10: Safe water depths processing diagram.
## 3 Long-Term Durability of Sensor Coupling and Mounting and of the Measurement Equipment
### Durability of Sensors or Transducers under Service Loads and Environmental Exposure
Long-term monitoring operations of salt rock with AE have been performed in Germany since 1995, and partial results have been published in, e.g., [109]. A total of twenty-four sensors were installed in boreholes between three and twenty meters deep. Five sensors were pressed pneumatically against the borehole walls and the remaining nineteen by springs against the borehole faces. Even though they were not noted in the publication, there were failures in the measurement chain [110]. These occurred in the preamplifiers due to leakage of saltwater into the casing. Therefore, in corrosive media or environments, as far as possible, use of corrosion-resistant materials is recommended, and the long-term sealing of the entire measurement system, including plugs and electric contacts, is essential. For checks performed with automatic sensor tests, the result combines sensor and mounting device performance, including the quality or degree of the coupling to the test object.
## 3 Influence of Hardware Configuration  
In the shared memory environment, the delivery performance of the algorithms is primarily affected by memory size, storage access, and CPU. However, both KMC3 and CHTKC do not involve complex arithmetical operations; therefore, the CPU is not a critical factor. Both algorithms read and write storage sequentially in block mode to utilize the available bandwidth. The block size of KMC3 varies from 8 MB to 64 depending on the available memory, and CHKTC's block size is 4.8 MB. Out tests showed that both algorithms access the storage with an average rate close to the peak bandwidth. Taking counting 32-mers in dataset T-GC30 (Table 1) as an example, KMC3 writes 32 GB data to storage B with an average write speed of 290 MB/s (peak 350 MB/s), and the storage access has the characteristics of long burst without random access. Therefore, we mainly analyze the influence of memory size and IO bandwidth in this section.
# Chinese Named Entity Recognition Based on Multi-Level Representation Learning
## 2 Related Work
### Span-Based Methods  
Span-based NER methods identify named entities by detecting the start and end positions of entities within the text, thereby avoiding the errors that may arise from word-by-word labeling, offering greater flexibility and accuracy.  
Compared to sequence labeling methods, span-based approaches often perform better in handling nested entities, as they can directly capture the full span of an entity, making them more suitable for the complexities of nested entity recognition. TCSF [21] combines a token context network for token learning with a deep residual CNN and span relation network for span learning. The model employs a novel head-inside-tail operation to expand internal features within the span, allowing for fixed-length representations. LB-BMBC [22]injects lexical information into the Transformer layers of BERT, leveraging the model's lexical learning capability to observe spatial relationships between adjacent spans. These relationships are then modeled using a dual affine decoder and CNN. GlobalPointer [23], a span-based decoding method, eliminates boundary recognition ambiguity through the use of an upper triangular matrix. It processes input feature vectors via complex matrix multiplication and adjusts dimensions to generate an entity boundary score matrix. The score matrix records the confidence levels of each category at various boundary positions, allowing GlobalPointer to accurately identify entities and their categories by analyzing these confidence scores. While GlobalPointer outperforms sequence labeling methods like CRF in NER tasks, it still faces challenges in handling contextual information, making it less ideal for long-distance dependencies and complex sequence data.  
From the above studies, it is evident that span-based methods offer certain advantages in NER tasks. However, when dealing with Chinese texts containing complex contexts and polysemous words, models often exhibit limitations. To address these issues, this study proposes using GlobalPointer as a decoder to process entity representations. However, GlobalPointer lacks the ability to effectively utilize contextual semantic information. Therefore, this paper introduces a Chinese Named Entity Recognition model based on multi-level representation learning: WP-XAG. The model utilizes the word-based pre-trained model WoBERT, and during training, incorporates PGD to introduce perturbations, improving the model's generalization ability. Considering that using only the XLSTM network in the feature fusion layer may lead to suboptimal performance in handling complex sequences and that the fixed weights of multi-head attention restrict the model's adaptability to different contextual features across varying scenarios, this paper proposes adaptive multi-head attention (ADMHA) to enhance semantic connections. Finally, GlobalPointer is employed as the decoder to construct an entity matrix, capturing global entity information to achieve accurate entity recognition.