esa-sceva/satcom-chunk-collection huggingface dataset:
# Continuous-Variable Quantum Key Distribution Robust Against Polarization-Dependent Loss
## 3 The PDL-Involved CVQKD System
### The Effect of PDL on Orthogonal Components  
Due to the effect of PDL, an angle between two channels decreases with the increasing loss. Therefore, there is a component projection on the corresponding channel, eventually leading to the crosstalk between channels. Figure 3a reveals the effect on components of the electric field. The right panel shows the state of the electric field vectors before entering the component with PDL, while the left panel shows these vectors after propagating through the component with PDL.  
When two polarizations enter the optical devices, \(a\) and \(b\) are the angles of the electric field with the corresponding outputs \(a^{\prime}\) and \(b^{\prime}\). For \(\tau\in\{a,b\}\) and \(\tau^{\prime}\in\{a^{\prime},b^{\prime}\}\), we have [25]  
\[\tau^{\prime}=\tan^{-1}(\tan(\tau)\times 10^{\frac{\mathrm{[PDL]}}{20}}), \tag{1}\]  
where \(0^{\circ}\leq a\leq 90^{\circ}\) and \(0^{\circ}\leq b\leq 90^{\circ}\). The relative angle of two nonorthogonal polarizations is given by \(\varphi=|a^{\prime}-b^{\prime}|\), which experiences a periodic variation dependent on \(a\) and \(b\).  
Let \(\mu\) be the relative PDL factor between two polarizations and \(\zeta\) is the incident angle, which is the angle between one of these channels and the \(X\)-axis, then, we have  
\[\varphi=\arccos\frac{1-\mu^{2}}{\sqrt{\mu^{4}+\mu^{2}(\tan^{2}\zeta+\cot^{2} \zeta)+1}}. \tag{2}\]  
With the variable relative loss factor \(\mu\), the orthogonality between two originally orthogonal channels may be lost, where \(\mu\in\{0.1,0.5,1.0\}\). As shown in Figure 3b, for the decreased \(\mu\), the orthogonality becomes worse.  
Figure 2: (Color online.) **(a)** Schematic PDL diagram. After transmission, two polarization states lose their orthogonality, with the different loss in their polarization directions. **(b)** PDL distributed model. PDLE: PDL emulator. ASE: amplified spontaneous emission.
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 1 Introduction  
Nowadays, smartphones have become necessary devices in our daily lives. With their widespread use, every day applications are using our location. For instance, we can quickly navigate using an online service such as Google Maps. However, indoor navigation is limited, since Global Navigation Satellite System (GNIn addition, the error accumulation is suppressed by collaborative indoor localization. Specifically, we propose a method to exchange estimated localization using Bluetooth Low Energy (BLE)-enabled smartphones when users encounter each other. As shown in Figure 1, either of the phones can estimate a more accurate location, and the other can correct its location using the encounter place detected by BLE. There exists methods using encounter communication [8; 9]. However, these methods do not fully utilize the estimated locations, as explained in Section 2.2. The proposed method compares the accuracy of the estimated locations using a machine learning model. Then, the localization performance is improved by the comparison result.  
The contributions of this paper are described in the following three points:  
* A reliable encounter communication architecture using BLE-enabled smartphones.
* A model trained on synthesized data for comparing the localization of two users.
* A collaborative localization method by adjusting the localization parameters according to the comparison results.  
The experiments were carried out at eight facilities. The results showed that the proposed localization comparison method achieved 83.0% localization accuracy. Furthermore, the proposed collaborative localization improved the baseline method by 13.0%.
# A Static Detection Method for SQL Injection Vulnerability Based on Program Transformation
## 2 Background
### Database Extensions  
Database extensions refer to the extension libraries provided by PHP for various types of databases, which enable PHP developers to interact with databases through their programs. A database in PHP may have one or more extensions, both official and third-party. For example, MySQLi commonly uses native MySQL libraries, enhanced MySQL extensions, or PHP Data Objects (PDO) for connectivity and manipulation. Different extensions provide similar basic operational approaches but may have some new features and differences in operational performance. The OODBE connects to and manipulates the database through some key statements, which are all in the object-oriented programming style.  
In PHP, there are two main OODBE: MySQLi and PDO. MySQLi is an enhanced version of MySQL that provides an object-oriented interface, prepared statements, and transaction processing.  
To connect to a database in an object-oriented manner, a MySQLi object representing the connection to the database needs to be created first. For example \(\$conn=new\)\(\;\;\)_mysqli_ (_'localhost'_, _'username'_, _'password'_, _'mydb'_). After a successful connection, the methods of the \(\$conn\) can be called to operate on the database. For example, the select query statement:\(\$result=\$conn\to query(\$query);\$query=\"SELECT*FROM\)\(\mathit{mytable}\"\). Or insert query statement: \(\$this\to db\to query(\"INSERT\)\(\mathit{INTO}\)\(\mathit{system\_settings}\)\(\mathit{set}\"\). \(\$data)\). PDO is a lightweight, scalable OODBE that provides a unified API that makes it easy for developers to work with different types of databases. When using a PDO to connect to a database, parameters such as database type, hostname, user name, and password need to be specified. For example \(\$pdo=new\)\(\mathit{PDO}(\"\mathit{mysql}:\mathit{host}=\mathit{localhost};\mathit{dbname}= \mathit{mytdatabase}\"\), \"\(\mathit{username}\"\), \"\(\mathit{password}\")\). The PDO provides a function for the prepared statement. By using parameterized queries, the risk of SQL injections can be effectively avoided, and the security and reliability of code can be improved. When executing a query, a prepared statement using the prepare() method needs to be created first, and then the bind-Value() method to bind parameters is used, and the execute() method is called. For example: \(\$stmt=pdo\to prepare(\"SELECT*FROM\)\(\mathit{user}\)\(\mathit{WHERE}\)\(\mathit{id}=?\");\$stmt\to bindValue(1,1);\$stmt\to execute()\).
## 3 Experiment Design
### Driving Scenarios  
The AV first drove on an urban road at 30 km/h for approximately 1 min, as shown in Figure 2a, while participants were instructed to read a CNN news article of their choice with their own smartphones to moderately engage their attention in an NDRT [45] as illustrated in Figure 1a. Task design was based on a previous study that identified the most desired activities in an AV is using a personal digital assistant [2,7]. We allowed participants to choose their own news article to read, as we assumed they would choose the article they found most interesting and thus engaging. Our experiment scenarios were designed following previous studies that focused on scenarios where drivers assist the AV in decision-making [13,14]. Participants were asked to rate their preferences about each AV actions in two scenarios, Slower car ahead and Accident ahead, via IESM displayed on the HUD where it was introduced as the most preferred display in AVs and provides a higher situation awareness [46], Figure 2d. In the Slower car ahead scenario, the AV would either (a) slow down or (b) overtake the car ahead. In the Accident ahead scenario, the AV would either (a) change lanes or (b) change the route, as shown in Figure 1b. In each scenario, the AV notified drivers about which action it was performing and why as shown in Figure 2b,c.  
To transfer the driver's attention away from a smartphone and toward the studied scenario, a notification sound was generated, followed by the appearance of a HUD message to illustrate information about the current scenario and the AV's actions, as in Figure 2b,c, and then the IESM questionnaire was conducted after the AV completed the action as in Figure 2d. For example, after the notification, the AV would first display the message \"A slower car ahead\" to explain the driving situation, followed by the message \"Overtake the car ahead\" to notify drivers about its action. After the messages were displayed, the IESM questionnaire was shown. Subjects were required to respond to the IESM after each AV action using the randomly assigned input interaction considering the counterbalancing. The detailed experiment flow is illustrated in Figure 3.  
Figure 2: Illustration of our experiment scenarios sequence. (**a**) The main road view where the AV starts moving; (**b**) scenario alert (notification message shown on HUD to explain the current scenario to the participant before it starts performing the action); (c) AV scenario action (a notification message shown on HUD while the action occurs); (**d**) two immediate experience sampling questions were shown on HUD immediately after the completion of each action.
## 4 Anaerobic Digestion Process
### Acetogenesis  
At this stage, the reactions are reversible with a release of hydrogen. Volatile fatty acids, specifically acetic acid and butyric acid, are converted into carbon dioxide gas, hydrogen, and acetate, as shown in Equations (8)-(10). The active bacteria in this stage are _Clostridium_, _Syntrophomonas wolfeii_, and _Syntrophomonas voliniii_[55]. This conversion of volatile fatty acids is enabled by the presence of water molecules (acting as electron sources) from the previous stages of anaerobic digestion. Equation (9) converts the phase product to acetate and hydrogen, used in the next stage [56]. This stage is equally important since it reflects the biogas production efficiency, given that the reduction of the acetate ion forms about 70% of methane. Acetate is a primary intermediary product of this phase and it accounts for 25% of the products formed together, with approximately 11% of hydrogen [57].  
\[\text{CH}_{3}\text{CH}_{2}\text{COO}^{-}+3\text{H}_{2}\text{O}\ \leftrightarrow\ \text{CH}_{3}\text{COO}^{-}+\text{H}^{+}+\text{HCO}_{3} ^{-}+3\text{H}_{2} \tag{8}\] \[\text{C}_{6}\text{H}_{12}\text{O}_{6}+2\text{H}_{2}\text{O}\ \leftrightarrow\ 2\text{CH}_{3}\text{COOH}+2\text{CO}_{2}+4\text{H}_{2}\] (9) \[\text{2 CH}_{3}\text{CH}_{2}\text{OH}+2\text{H}_{2}\text{O}\ \leftrightarrow\ \text{CH}_{3}\text{COO}^{-}+2\text{H}_{2}+\text{H}^{-} \tag{10}\]
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## 3 Artifacts Correction by Deep Learning
### Reflection Artifacts Correction Model  
Assume that \(I_{\text{IS}}\) is the simulated initial source without any artifacts and \(I_{\text{RA}}\) is a PAT image including reflection artifacts, the relationship between them can be expressed as follows:  
\[\mathbf{I}_{\text{RA}}=\mathcal{F}(I_{\text{IS}}), \tag{2}\]  
where function \(\mathcal{F}\) denotes the acoustic-reflection-induced process due to the hyperechoic structures or the special setting of making measurement such as in reverberant-field PAT. The reflection artifacts correction model is to seek an approximate inverse, \(\mathcal{G}\approx\mathcal{F}^{-1}\), to reduce the reflection artifacts from \(I_{\text{RA}}\), i.e.,  
\[\mathbf{I}_{\text{est}}=\mathcal{G}(\mathbf{I}_{\text{RA}})\approx\mathbf{I}_{\text{IS}}. \tag{3}\]  
Next, we introduce the proposed network to learn the approximate inverse function \(\mathcal{G}\).
# DLMFCOS: Efficient Dual-Path Lightweight Module for Fully Convolutional Object Detection
## 3 Proposed Method
### Proposed Feature Pyramid Structure
#### 3.1.2 Lightweight Spatial Module  
Figure 6 illustrates the structure of the proposed LSM, which efficiently extracts spatial information with a small amount of computation. With a conventional convolution, the kernel processes the spatial and channel dimensions simultaneously to extract features. However, the LSM extracts spatial information between each channel through the depth-extended convolution, which expands the receptive field with minimized computation. For the extracted spatial information, channel information is extracted by compressing and expanding the channel 0.5 times through convolutions. Unlike general convolution modules, the proposed method improves the convergence speed of the network by extracting spatial information and performing batch normalization without using batch normalization or activation functions after convolution. Finally, when the channel is compressed and expanded, the feature map is activated. Residual learning is used to minimize the feature loss that otherwise occurs during compression and expansion. Equations (2)-(4) detail the dilated [21], depth-wise, and dilated depth-wise convolutions, respectively:  
\[y[i]=\sum_{k}x[i+r\cdot k]\omega[k], \tag{2}\]  
\[DwConv(W,y)_{(i,j)}=\sum_{k,l}^{K,l}W_{(k,l)}y(i+k,\ j+l), \tag{3}\]  
\[DilatedDwConv(W,y)_{((i,j)}=\sum_{k,l}^{K,l}W_{(k,l)}y(i+k+r,\ j+l). \tag{4}\]  
where \(i\), \(j\) represent the elements of the feature map, \(x\) is the input feature map, \(y\) is the output feature map, \(w\) is the convolution kernel, \(r\) is the expansion ratio, \(k\), \(l\) is the kernel size, and \(m\) is the channel.  
Figure 5: Proposed dual-path lightweight module structure. BatchNorm: batch normalization; (C): concatenation; Conv: convolution; CA: channel attention; H \(\times\) W: (H: height, W: width) input feature map size; LSM: lightweight spatial model; SiLU: sigmoid linear unit.
# Hybrid MU-MIMO Precoding Based on K-Means User Clustering
## 4 User Grouping
To further reduce the computational burden, once the sets are created, the algorithm will schedule in the same time-frequency resource a subset of users in each cluster of the set through a non-linear precoding scheme. The user selection within each cluster is random; however, this could result in co-scheduling users with bad channel conditions, which would degrade the overall system performance. Employing more complex selection methods like semi-orthogonal or greedy user selection, as detailed in [4; 20; 22], would have severely increased the convergence time and the complexity of the algorithm, but was not in the scope of this article. In the context of a hybrid precoding scheme, in order to perform intra-cluster user selection, we would require knowing the effective channel matrix (including the inter-cluster linear precoding). This is conditioned on the users selected in other clusters. As such, the number of combinations is much higher and requires a search over the entire set of users, not only over those of the same cluster. Note that after spatial clustering, the number of users in each cluster is not the same. Users that have fewer neighbors within their clusters could get higher throughput as they will be co-scheduled more often. For that, this approach is an overlapping user scheduling algorithm [22]. To evaluate the complexity of the spatial clustering algorithm compared to the state-of-the-art solutions, the number of FLOPs is represented for various distance metrics and clustering algorithms, in Figure 6. It can be noted that the complexity of the algorithms depends on the number of users \(K\), the number of antennas \(N_{T}\), the number of clusters \(N\), and the chosen distance metric. For a fixed number of clusters, \(N=4\) and \(N_{T}=16\) antennas, the proposed algorithm has better performance than the two-stage precoder with chordal distance (two-stage chordal), which is \(\mathcal{O}\left(K^{N+1}\times N\right)\)[23], compared to \(\mathcal{O}\left(K^{N+1}\times N\times\left(N_{T}^{2}+2N_{T}^{3}\right)\right)\)[5]. In addition, as the proposed metric remains in the Euclidean space, the K-means implementation based on Lloyd's algorithm is optimized and has an \(\mathcal{O}(KNI)\) complexity [24]. Fixing the number of iterations needed until convergence at \(I=50\), the optimized K-means also outperforms the ZF precoding with User Selection (ZF-US), which has an \(\mathcal{O}\left(KN_{T}^{5}\right)\) complexity [22]. The number of iterations will further decrease when the users already have a cluster structure, which is the case of a typical user distribution, as shown in Section 6.
## 3 Current Applications
### Retina
#### 3.2.1 Age-Related Macular Degeneration
Notably, the study did not investigate drusen regression, and the structural analysis did not support positive outcomes reported in earlier studies. Limitations encompassed the absence of a control arm, potential selection bias, and a relatively small sample size ([PERSON] _et al._, 2018). [PERSON] and collaborators published the results of a pilot study in which the Valeda System with 3 different wavelengths (590 nm; 4 mW/cm\({}^{2}\), 660 nm; 65 mW/cm\({}^{2}\), 850 nm; 0.6 mW/cm\({}^{2}\)) was employed. The study aimed at assessing the effects of PBM on visual acuity and morphological changes in patients with large soft drusen and/or drusenoid pigment epithelial detachment associated with dry AMD. Twenty eyes underwent PBM treatment with 2 sessions per week for 5 weeks. The results showed a significant improvement in visual acuity, drusen volume, and thickness at the 6-month follow-up visit [46]. Despite all the positive effects described in the above-mentioned studies, treatment-related adverse effects were reported. [PERSON] and co-authors described a slight increase in geographic atrophy area and a drusenoid pigment epithelial detachment rupture in a patient [46]. In addition, in late 2023, [PERSON] and colleagues documented a case involving a 63-year-old woman with a subfoveal drusenoid pigment epithelium detachment (D-PED) that collapsed two weeks after the last session of (PBM). The patient received three PBM sessions per week for three weeks using the Valeda Light Delivery System by LumiThera Inc. This is the first report of acute macular atrophy development. While the imaging characteristics of this case may not strongly suggest a PBM-related complication, the brief duration between the last visit and atrophy emergence implies that PBM might expedite the natural progression of D-PED, leading to atrophic changes. This case report suggests that caution is recommended when employing PBM in the treatment of eyes with D-PED exhibiting outer retinal layer thinning or posterior hypertransmission, as this therapeutic approach may accelerate macular atrophy [47]. Currently, the ELECTROLIGHT study (NCT04522999) is ongoing and aims at evaluating the impact of PBM therapy by recording electroretinograms in patients with dry AMD using the Valeda System. A total of 23 eyes from 15 patients with intermediate dry AMD receiving PBM treatment have been enrolled and various visual function parameters were assessed. Another prospective multicenter randomized controlled trial named DRUSEN (NCT06046118) employing the Eye Light device is currently recruiting patients. Treatment consists of two phases: (i) 300 s of continuous yellow light with eyes closed + 60 s of pulsed yellow light with eyes opened; (ii) 300 s of continuous red light with eyes closed + 60 s of pulsed red light with eyes opened. Patients undergo two cycles of treatment: the first one consisting of 8 sessions (2 per week for 4 weeks) and the second one consisting of 6 sessions (2 per week for 3 weeks).
## 3 Results  
### SEM Analysis  
The SEM observation showed that the different types of roughening protocol exhibited no substantial differences among samples of the same group.  
Comparisons between groups showed different surface morphologies, displayed in Figure 1.  
The surface of the control group samples (group A) was homogeneous, without irregularities and without microcavities. Etching with 35% orthophosphoric acid for 30 s or 60 s (groups B and C) did not cause any observational modification of the composite surface. The surface roughened by diamond bur (group D) showed evident parallel streaks resulting from the bur action. The images of the sandblasted samples (group G) show greater irregularity and a high number of microcavities. Etching using 35% orthophosphoric acid for 30 s and 60 s (groups E, F, H and I) as an additional treatment to milling and sandblasting caused no change on the sample surface.  
In all experimental groups, the optical confocal microscope analysis shows three-dimensional topographic images similar to the SEM observation (Figure 2).  
Figure 1: SEM images (2100\(\times\)). Voltage: 5 kV. Type of electron: secondary electron (SE). Groups A, B and C show a surface without irregularities; groups D, E and F show evident parallel streaks resulting from the bur action (arrows indicates the parallel streaks); groups G, H and I, the sandblasted samples, show the highest number of microcavities (arrows indicate microcavities).
# Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management
## 4 Artificial Intelligence and Machine Learning in EMS of Power Systems
### Key Performance Indicators (KPIs)  
KPIs are indispensable metrics that evaluate the efficacy and efficiency of ML models in EMS within power systems. These indicators are essential for assessing the effectiveness of these models in predicting, optimizing, and managing energy consumption and distribution. For example, the predictive performance of models utilized in demand forecasting is evaluated using metrics such as precision, recall, accuracy, and F1-score. Operational efficiency can be assessed by comparing the ratio of energy output to input, which assists in identifying areas where energy usage can be optimized. The power supply is guaranteed to remain stable and uninterrupted by reliability indicators, including system availability and failure rates. Moreover, KPIs that are associated with cost, such as cost savings from optimized energy use, present tangible financial benefits. By consistently monitoring these KPIs, stakeholders can make informed decisions to improve the performance of EMS, resulting in more sustainable and cost-effective energy management. KPIs, including minimum absolute error (MAE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), root mean square percentage error (RMSPE), F1-score, accuracy, recall, precision, computational complexity, and ranked probability score (RPS), are essential for assessing the performance of ML models in EMS. The average magnitude of prediction errors is quantified by the MAE, which has a clear indication of accuracy. RMSE and MSE accentuate larger errors as a result of their squared nature. RMSE is more interpretable as it is expressed in the same units as the target variable. MAPE and RMSPE are percentages that convey prediction errors, providing information on the relative size of errors. The F1-score is a critical metric for classification tasks within EMS as it balances precision and recall, indicating the model's ability to identify true positives. Accuracy is a metric that quantifies the percentage of correct predictions, whereas recall and precision are concerned with the totality and accuracy of positive predictions, respectively. The model's  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **Robustness** & **Dynamics** & **Data Handling** & **Cloud-Based Control** & **Computational Complexity** \\ \hline ANN & \(\tt\tt\tt\tt\tt\tt\tt\tt\tt\tt\tt\tt\tt\tt\tt\tt\tt\tt\tt\tt\tt\tt\tt \tt\tt\tt\tt\tt\tt\tt  
efficacy in terms of time and resources is evaluated by computational complexity, which ensures the feasibility of real-time applications. In ambiguous scenarios, the RPS enhances decision-making by assessing the accuracy of probabilistic predictions. Consequently, the mentioned KPIs are mathematically defined in [244; 245; 246; 247].