esa-sceva/satcom-chunk-collection huggingface dataset:
# Photocatalytic Degradation of Selected Non-Opioid Analgesics Driven by Solar Light Exposure
## 3 Results
### Photocatalysis and Photoelectrocatalysis--Systems with Tungsten Oxide and Iron Oxide
#### 3.2.2 Application of Hybrid WO\({}_{3}\)/Fe\({}_{2}\)O\({}_{3}\) Photocatalyst  
The efficiency of degradation of studied compounds using hybrid WO\({}_{3}\)/Fe\({}_{2}\)O\({}_{3}\) either in a photocatalytic or photoelectrocatalytic process, as presented in Figure 4, is very similar to that obtained using WO\({}_{3}\) alone. During irradiation by a solar lamp, only the content of ketoprofen was decreased gradually, but still, about 70% of its content remained in the unchanged form after 90 min. Two new peaks appeared at t\({}_{\text{R}}\) = 3.1 and 3.5 min, and the surface area under these peaks increased during prolonged irradiation time (Figure S4). The content of other studied pharmaceuticals did not change significantly. Thus, the application  
Figure 3: The changes in the surface areas of signals detected during solar lamp radiation with WO\({}_{3}\) photocatalyst (active area 4 \(\times\) 1.5 cm) for (**a**) ketoprofen and (**b**) naproxen.  
of composite WO\({}_{3}\)/Fe\({}_{2}\)O\({}_{3}\) in photocatalysis and photoelectrocatalysis processes did not increase the efficiency of their degradation. Due to the difficulties of the degradation of ketoprofen, Pt-TiO\({}_{2}\)-Nb\({}_{2}\)O\({}_{5}\) heterojunction has been proposed for the photocatalytic removal of ketoprofen under UV light irradiation [54]. It was found that the proposed composite exhibited photodecomposition of ketoprofen with a rate constant of 0.075 min\({}^{-1}\), which was higher than those of Pt-TiO\({}_{2}\) (0.0597 min\({}^{-1}\)). According to this study, elemental Pt nanoparticles could act as a bridge between TiO\({}_{2}\) and Nb\({}_{2}\)O\({}_{5}\), improving the electron-hole separation.  
Photodegradation of drug substances involves complex reaction mechanisms that yield various products throughout the degradation pathways. A representative degradation process involves oxidation-reduction, ring alteration, and polymerization, which can be catalyzed or accelerated in the presence of any form of light. Photodegradation might take place in two ways: photooxidative degradation or photolysis. Photooxidative degradation happens when the drugs react with oxidants, such as hydroxyl radicals, which are formed as a result of photolysis. It occurs when drugs absorb light, which results in a change in the conformation of drugs and, thereby, their degradation. Physicochemical properties of the drugs and wastewater, intensity, and wavelength of light control the process of photodegradation. At the same time, light exposure is essential for photodegradation to occur.
# Soil Erosion Estimates in Arid Region: A Case Study of the Koutine Catchment, Southeastern Tunisia
## 3 Materials and Methods  
### Application of the RUSLE Model for Soil Erosion  
Soil erosion modeling has shown to be a beneficial tool to reasonably describe and predict measured data under appropriate assumptions [1]. Empirical modeling, in particular, has proven to represent soil loss variabilities at the catchment scale considering simplified cause-effect relationships. Even though empirical modeling is a data-demanding approach and is usually implemented in rich in situ data studies, it is regarded as a beneficial approach when parametric models cannot be constructed and study site observations are available [39]. Empirical modeling allows the user to understand the relationships between the drivers and state variables that belong to a particular system or process based on different methodologies and approaches. In soil erosion, empirical models can support decision-makers to take the best solutions and select suitable scenarios for development plans.  
The RUSLE soil erosion quantification and assessment model is an empirical spatially distributed model based on multiplying five independent factors (Figure 2). As a result, the total annual soil loss rate (\(A\)) is calculated:  
\[A=R\times K\times C\times P\times LS \tag{1}\]  
where \(A\) is the annual rate of soil loss (t/ha/yr), \(R\) is rainfall erosivity factor, \(K\) is soil erodibility factor, \(C\) is land cover management factor, \(P\) is practice support factor and \(LS\) is topography factor.  
The use of GIS and erosion model has been integrated to estimate the degree and spatial distribution of erosion in the study area through mapping of the five different soil loss factors (\(R\), \(LS\), \(C\), \(K\) and \(P\)).
# Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
## 3 Transformer Architectures
### TransDirect-Overlapping  
The overall architecture of TransDirect-Overlapping is illustrated in Figure 3. Similar to TransDirect, the original \(I\) and \(Q\) sequences are segmented into shorter tokens of length \(l\) through the use of a sliding window method in the tokenization module. However, unlike the previous method, this technique introduces an overlap between consecutive tokens. Specifically, the step size \(w\) is configured to be \(l/2\), resulting in a 50% overlap between adjacent tokens. This overlapping strategy allows for a more comprehensive analysis through the reuse of data across tokens. The sub-sequences for the \(l\) component are, thus, initiated with \(I_{1}=[i_{1},i_{2},\ldots,i_{l}]\)  
Figure 2: TransDirect architecture. In the tokenization module of this architecture, \(IQ\) samples are segmented into shorter sequences referred to as tokens, each having a size of \(l\).  
with the following sub-sequences such as \(I_{2}=[i_{1+(l/2)},i_{2+(l/2)},\ldots,i_{l+(l/2)}]\) and continuing to \(I_{N}=[i_{1+(N-1)(l/2)},i_{2+(N-1)(l/2)},\ldots,i_{l+(N-1)(l/2)}]\), and this segmentation approach is similarly applied to the \(Q\) component. Accordingly, the total number of tokens generated by this method is calculated as \(N=\frac{n-l}{w}+2\). Despite the different tokenization techniques, TransDirect-Overlapping retains the same Transformer-encoder module and classifier as TransDirect, offering an improved method for initial data segmentation via a sliding window technique that ensures data overlap, thereby maximizing data utilization. Like the TransDirect tokenization approach, the output dimensionality of the tokenization module remains \((N,l,2)\). However, TransDirect-Overlapping sets itself apart by increasing the number of tokens to \(N=\frac{n-l}{w}+2\), while maintaining the embedding dimension similar to TransDirect. This dimension equals twice the number of samples per token \(l\), a feature unchanged from TransDirect due to the consistent number of channels (2), presenting \(l\) and \(Q\) components.
# Semi-Coprime Array with Staggered Beam-Steering of Sub-Arrays
###### Abstract  
A split-aperture array (SAA) is an array of sensors or antenna elements in which the array is split into two or more sub-arrays (SAs). Recently proposed SAAs, namely coprime and semi-coprime arrays, offer to attain a small half-power beamwidth (HPBW) with a small number of elements, compared to most conventional unified-aperture arrays, at the cost of reduced peak-to-side-lobe ratio (PSLR). To reduce HPBW and increase PSLR, non-uniform inter-element spacing and excitation amplitudes have proven helpful. However, all the existing arrays and beam-formers suffer increased HPBW, degraded PSLR or both when the main beam is steered away from the broadside. In this paper, we propose staggered beam-steering of SAs, a novel technique for decreasing HPBW. In this technique, we steer the main beams of the SAs of a semi-coprime array to angles slightly different from the desired steering angle. In conjunction with staggered beam-steering of SAs, we have utilized Chebyshev weights to suppress the side lobes. The results show that the beam-widening effect of Chebyshev weights can be mitigated considerably by staggered beam-steering of the SAs. Ultimately, the unified beam-pattern of the whole array offers HPBW and PSLR better than the existing SAAs, uniform and non-uniform linear arrays, especially when the desired steering angle is away from the broadside direction.  
Be-pattern; linear antenna array; semi-coprime array; split-aperture array; staggered steering +
Footnote †: journal: _Sensors_  
0000-0001-6580-788X]W.R.R.R.
# A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks
###### Abstract  
Clustering is an effective topology control method in wireless sensor networks (WSNs), since it can enhance the network lifetime and scalability. To prolong the network lifetime in clustered WSNs, an efficient cluster head (CH) optimization policy is essential to distribute the energy among sensor nodes. Recently, game theory has been introduced to model clustering. Each sensor node is considered as a rational and selfish player which will play a clustering game with an equilibrium strategy. Then it decides whether to act as the CH according to this strategy for a tradeoff between providing required services and energy conservation. However, how to get the equilibrium strategy while maximizing the payoff of sensor nodes has rarely been addressed to date. In this paper, we present a game theoretic approach for balancing energy consumption in clustered WSNs. With our novel payoff function, realistic sensor behaviors can be captured well. The energy heterogeneity of nodes is considered by incorporating a penalty mechanism in the payoff function, so the nodes with more energy will compete for CHs more actively. We have obtained the Nash equilibrium (NE) strategy of the clustering game through convex optimization. Specifically, each sensor node can achieve its own maximal payoff when it makes the decision according to this strategy. Through plenty of simulations, our proposed game theoretic clustering is proved to have a good energy balancing performance and consequently the network lifetime is greatly enhanced.  
wireless sensor networks (WSNs); clustering; network lifetime; game theory; equilibrium +
Footnote †: journal: Sensors
## 2 Background and Related Work
### Challenges in Applying Task Division in Ensemble Fuzzing  
The task division methods, particularly the callgraph-based task division method introduced by AFLTeam, have indeed enhanced the efficiency of parallel fuzzing. However, these task division methods are tightly coupled with the fuzzers designed specifically for them. General purpose fuzzers, as illustrated in Figure 1a, lack the capability to parse and store the tasks assigned to them, regardless of whether these tasks are based on bitmap division or callgraph division. Therefore, AFLTeam, as depicted in Figure 1b, modified the fuzzer by adding a function to parse the assigned tasks and introduced related data structures to store all of the target functions within the task. Moreover, merely modifying the fuzzer to enable the parsing and storage of assigned tasks is not sufficient. Within the main loop of fuzzing, when selecting the next seed to mutate, it is necessary to check the execution trace of the tested program when using the alternative seed as input, thus filtering out seeds that do not cover any functions belonging to the assigned task.  
Therefore, it is evident that applying the current task division method to a new fuzzer would require extensive and non-generic modifications to the fuzzer. Consequently, directly applying such a method to ensemble fuzzing frameworks entails significant modifications to each paralleled fuzzer, resulting in a massive workload and lack of scalability.  
Thus, to reduce workload and enhance scalability, there is no choice but to decouple the task division method from the fuzzer. In other words, the functionality of task parsing, seed filtering, and other related operations must be implemented on a higher level above the fuzzer, which means the task division is transparent to fuzzers. However, such an approach brings additional issues. Among them, the most significant problem is that each fuzzer would be unable to acquire the global fuzzing progress of the tested program, such as the global branch coverage, which leads to the generation of duplicate seeds or seeds that deviate from the assigned objectives of the current fuzzer. If this problem cannot be resolved, the efficiency of fuzzers will rapidly deteriorate throughout the fuzzing process, rendering the task division method ineffective.
## 3 Wind Loads on Structures  
To compute the buffeting responses of Taizhou Bridge in time domain, an important step is to integrate the wind loads on structures. In this study, the Alan G. Davenport Wind Loading Chain [30; 31] is utilized for analysis, in which the aerostatic forces, buffeting forces, and the self-excited forces are included.  
Under the framework of finite element approach, the governing equation for the buffeting analysis of Taizhou Bridge under turbulent winds can be expressed as  
\[\mathbf{M}\ddot{\mathbf{X}}(t)+\mathbf{C}\dot{\mathbf{X}}(t)+\mathbf{K}\mathbf{ X}(t)=\mathbf{F}_{\mathrm{G}}+\mathbf{F}_{st}+\mathbf{F}_{b}(t)+\mathbf{F}_{se}(t) \tag{1}\]  
where \(\mathbf{M}\), \(\mathbf{C}\), and \(\mathbf{K}\) are structural mass, damping and stiffness matrices, respectively; \(\ddot{\mathbf{X}}\), \(\dot{\mathbf{X}}\), and \(\mathbf{X}\) are acceleration, velocity, and displacement vectors; \(\mathbf{F}_{\mathrm{G}}\) = structural gravity vector; \(\mathbf{F}_{st}\) is the static wind force vector; \(\mathbf{F}_{b}\) is the buffeting force vector; \(\mathbf{F}_{se}\) is the self-excited force vector. For such a long-span cable-supported bridge, the actions of gravity and aero-static forces will heavily influence the initial  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Frequency/Hz** & **Modal Shape** \\ \hline
1 & 0.07163 & First antisymmetric lateral vibration of the main girder (AS-L-1) \\
2 & 0.08023 & First antisymmetric vertical vibration of the main girder (AS-V-1) \\
3 & 0.09512 & First symmetric lateral vibration of the main girder (S-L-1) \\
4 & 0.11489 & Second antisymmetric vertical vibration of the main girder (AS-V-2) \\
5 & 0.11758 & First symmetric vertical vibration of the main girder (S-V-1) \\
6 & 0.13709 & Second symmetric vertical vibration of the main girder (S-V-2) \\
7 & 0.17089 & Third antisymmetric vertical vibration of the main girder (AS-V-3) \\
8 & 0.18518 & Third symmetric vertical vibration of the main girder (S-V-3) \\
9 & 0.23058 & Second antisymmetric lateral vibration of the main girder (AS-L-2) \\
10 & 0.23786 & Fourth antisymmetric vertical vibration of the main girder (AS-V-4) \\
11 & 0.23983 & Fourth symmetric vertical vibration of the main girder (S-V-4) \\
13 & 0.27290 & First antisymmetric torsional vibration of the main girder (AS-T-1) \\
25 & 0.36613 & First symmetric torsional vibration of the main girder (S-T-1) \\ \hline \end{tabular}  
* AS—Antisymmetric; S—Symmetric; V—Vertical; L—Lateral; T—Torsional. The number represents the order.  
\end{table}
Table 1: Typical modes of Taizhou Bridge. 1 geometry and stiffness of the structure. Hence, the static forces must be firstly applied to the bridge structure to establish an initial structural state for the buffeting analysis.
## 2 System Model
### CoMP Set Selection  
Ideally, a UE can achieve the optimal data rate if all TPs cooperatively transmit to it. However, the corresponding power consumption and computational complexity are unaffordable. An alternative is to select a CoMP set for the UE according to channel conditions. TPs in the CoMP set can provide the UE a favorable data rate at a much lower cost.  
A UE-specific selection of the CoMP set includes three steps:  
1. TPs broadcast reference signals (RSs) periodically.
2. A UE hears the channels and measures them according to the strength of the received RSs. Based on the measurements and a given selection rule, the UE can decide its own CoMP set.
3. The UE acknowledges its decision to the CU.  
Denote \(\mathcal{M}_{k}\) as the CoMP set of UE \(k\) and \(\mathcal{M}\) as the set including all TPs in the network. UE \(k\) decides its \(\mathcal{M}_{k}\) following the rule below:  
\[\left\{\begin{array}{ll}m\in\mathcal{M}_{k}&m=\arg_{\mathcal{M}}RS\left( \text{the strongest}\right)\;\;\text{or}\\ &RS\left(\text{the strongest}\right)-RS\left(\text{TP}\;m\right)\leq\Delta\\ m\
otin\mathcal{M}_{k}&\text{otherwise}\end{array}\right. \tag{1}\]  
where \(RS\) indicates the strength of the reference signal and \(\Delta\) is a threshold in dB. UE \(k\) distinguishes the strongest RS in the first place and adds the corresponding TP to the CoMP set \(\mathcal{M}_{k}\). Other TPs will be added to \(\mathcal{M}_{k}\) only if the strengths of their RSs are no less than \(\left(RS(\text{the strongest})-\Delta\right)\) dB. As suggested in LTE releases, a rational \(\Delta\) is in the range of \(5-6\) dB [11].  
In the case where \(\mathcal{M}_{k}\) includes only 'the strongest TP', UE \(k\) is referred to as a non-CoMP UE in this paper, since no cooperation occurs during downlink transmissions towards it. On the contrary, UE \(k^{\prime}\) is referred to as a CoMP UE if its CoMP set \(\mathcal{M}_{k^{\prime}}\) includes more than one TPs. A CoMP UE is possibly is located at an overlapping area of neighboring cells where intercell interference seriously damages transmissions. To combat interference, TPs in \(\mathcal{M}_{k^{\prime}}\) are asked to conduct JT CoMP transmissions to UE \(k^{\prime}\) for strengthening transmit signals and reducing inter-cell interference.
## 5 Discussion  
As mentioned above, the advantage of the CodeTransFix approach compared to directly using large language code models (LLMCs) to generate patches and other automatic program repair (APR) methods based on neural machine translation (NMT) is the fact that it can significantly increase the number of correct fixes and generate many unique fixes.  
However, the CodeTransFix approach is constrained by the following limitations. First, this method requires perfect fault location when identifying bug statements. If the fault location is not accurate enough, its repair performance will be significantly reduced. Second, while the current definition of context (i.e., the immediate surrounding code within the same method or code block) enables efficient patch generation, it does not cover broader semantic dependencies such as inter-class method invocations, external library calls, or API usage patterns. For example, certain bugs may depend on constraints defined in other modules or libraries, which cannot be captured by local code context alone. A concrete example of this limitation is illustrated in Figure 7, which demonstrates a failed repair case for the Math 32 defect in Defects4J v1.2. In Figure 7, the buggy line is highlighted in yellow, the ground-truth fix is marked in green, and the patch generated by CodeTransFix is shown in orange. Although this patch (the first candidate among 10 generated by CodeTransFix to pass all test cases in Defects4J v1.2) passed the automated validation, manual inspection revealed semantic discrepancies between the generated patch and the correct fix. Specifically, the failure stems from CodeTransFix's inability to capture inter-class method dependencies. This case highlights how the current context definition--restricted to the immediate method scope--fails to address defects involving cross-class interactions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Projects** & **CodeTransFix** & **Recoder** & **CodeT5-Base** \\ \hline Cli & 3 & 1 & 3 \\ Codec & 3 & 2 & 2 \\ Collections & 0 & 0 & 0 \\ Compress & 1 & 1 & 1 \\ Csv & 2 & 1 & 2 \\ Gson & 0 & 0 & 0 \\ JacksonCore & 2 & 2 & 3 \\ JacksonDatabind & 1 & 2 & 2 \\ JacksonXml & 0 & 0 & 0 \\ Jsoup & 6 & 2 & 3 \\ JxPath & 0 & 0 & 0 \\ Total & 18 & 11 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of baselines on Defects4J v2.0.  
Figure 7: CodeTransFix’s failed fix for Math 32 in Defects4J v1.2.
## 2 Materials and Methods
### Random Sampling  
Each dataset was acquired three times in order to use the averaging method. To maintain a suitable image quality, 15% of k-space center lines (116 lines out of 512 for full k-space lines) were selected, and k-space periphery lines were randomly obtained to provide the final 25%, 35%, and 45% lines. In the CSAM method, the three subsets had the same number and location of k-space phase-encoding lines (193 for 25%; 268 for 35%; 346 for 45%), in which the high-frequency lines (77 for 25%; 152 for 35%; 230 for 45%) were randomly sampled, excluding the predefined center lines of 116.  
In contrast, the high-frequency k-space lines that should be randomly sampled in subsets 2 and 3 according to the undersampling ratio, were obtained in subset 1 in the CSAK method. Therefore, in subset 1, the sampling lines varied according to the undersampling ratio (291 for 25%; 448 for 35%; 579 for 45%), but in subsets 2 and 3, the same number of phase-encoding lines (i.e., 116) in the k-space center was selected (Table 1). The lines in the high-frequency region overlapped by less than 50% in each subset to maintain the averaging effect.  
Figure 1: Conceptual schemes of CSA techniques. (**a**) CSAM which has pseudo-randomly sampled high-frequency k-space lines during acquisition; (**b**) CSAK based on k-space sharing (CSAKS); (**c**) CSAK based on k-space copying (CSAKC). The numbers, #1, #2, and #3, indicate the independently acquired subsets. Different gray levels at each k-space center (i.e., 9, 10, and 11) represent different acquisitions. Boxes with different background shapes belong to the pre-allocated lines in subset 1. Gray arrows indicate the CS reconstruction process for generating the image.
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 1 Introduction  
In the last several years, computer networks have envisaged an evolution by the diffusion of smartphones, over the top (OTT) services, and cloud computing. This enforces network practitioners and researchers to a basic transformation from traditional networks to programmable networks. Software-defined networking (SDN) is a radical new idea in networking that enables innovation through network programmability. The main idea of SDN is the separation of network intelligence (control _plane_) from the forwarding device (data plane). The network intelligence is logically centralized (called _controller_), which enables the programmers to exploit global knowledge of the controller. Hence, it makes SDN proper for emerging technology like 5G, cloud data centers, and Internet exchange points (IXPs). One of the critical issues of SDN is resource reallocation in a way that meets users' quality of service requirements.  
In quality of service (QoS)-aware resource reallocation (QRA), resources (links) are assigned to each network traffic flow based on service level agreement (SLA). The selected path for each flow should satisfy the flow requirements such as maximum tolerable delay or minimum required bandwidth. There are some important issues in QRA as follows: (1) existence of burst and dynamic traffic; (2) different traffic classes; (3) big data and resource partitioning; and (4) heavy demands and limited resources. In recent networks, the traffic characteristics obey an extremely dynamic model; as an example, it can be referred to OTT services as a source of dynamics. Additionally, there are lots of burst traffic in the network, such as virtual machine (VM) migration traffic flows.  
The flow dynamics along with burst nature of network traffic makes the static resource allocation methods useless. Moreover, there are several traffic classes in the network that have different requirements, e.g., video conferences are delay sensitive while file transfer protocol (FTP) connection is bandwidth sensitive.  
In this context, several questions are arising, such as: Is it possible to practically tune the network resources with the dynamic behavior of network? How to exploit prior information of SDN controller to assign resources to the current flows while guaranteeing QoS? The goal of this paper is to shed light on these issues.