esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Y-Branch Coupler
### Polarization Dependent Loss of the Y-Branch Coupler  
Polarization of mode keeps unchanged if SMF is completely axisymmetric. However, if SMF bends, polarization of mode changes immediately and then coupling efficiency of fiber-to-waveguide coupler changes too [24]. Polarization-dependent loss refers to the decrease in coupling efficiency when mode in the SMF changes from TE mode to TM mode, and it is used to quantitatively evaluate the influence of mode polarization on the coupling efficiency. Polarization-dependent loss \(\eta_{\mathrm{p}}\) can be calculated as follow:  
\[\eta_{\mathrm{p}}=\left|\eta_{\mathrm{y}}(\mathrm{TE})-\eta_{\mathrm{y}}( \mathrm{TM})\right| \tag{4}\]  
where \(\eta_{\mathrm{y}}(\mathrm{TE})\) denotes the coupling efficiency between the SMF and Y-branch coupler when TE mode is in the SMF, \(\eta_{\mathrm{y}}(\mathrm{TM})\) denotes the coupling efficiency when TM mode is in the SMF. Coupling efficiency \(\eta_{\mathrm{y}}(\mathrm{TE})\) and \(\eta_{\mathrm{y}}(\mathrm{TM})\) are 0.24 and 0.13 respectively when width \(W\) is 0.10 \(\upmu\)m, gap \(G\) is 0.86 \(\upmu\)m, and length \(L_{\mathrm{b}}\) is 10 \(\upmu\)m. Therefore, polarization-dependent loss of the Y-branch coupler (\(\eta_{\mathrm{p}}\)) is 0.11.  
Figure 6: (**a**) Alignment of the single mode fiber (SMF) and waveguide RWS; (**b**) Influence of the deviation between the SMF center and waveguide center in the \(x-y\) plane on the coupling efficiency \(\eta_{\mathrm{y}}\).
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 2 Related Work
### Swahili Natural Language Processing Efforts  
Efforts to advance Swahili Natural Language Processing (NLP) have gained momentum in recent years as researchers recognize the need to support underrepresented languages [20]. Early work in this area focused on traditional machine learning methods for tasks like text classification and sentiment analysis. For example, algorithms like logistic regression and support vector machines were used to classify Swahili documents into categories or detect sentiment in social media posts. These models, while easy to implement, often required extensive manual feature engineering to achieve reasonable performance [21]. However, their limited ability to understand complex language patterns restricted their effectiveness. Recent advancements in multilingual models, such as multilingual BERT (mBERT) and SwahBERT, have provided new opportunities for Swahili NLP. These models are trained on large multilingual datasets, allowing them to process Swahili alongside other languages. Tasks like machine translation and context understanding have benefited from these models, especially when fine-tuned on Swahili-specific data [22]. However, challenges remain, particularly with regional dialects, idiomatic expressions, and the scarcity of parallel corpora [23]. RAG has further advanced these efforts by introducing a mechanism to integrate external knowledge into the modeling process. For Swahili, RAG-based systems can dynamically retrieve relevant text snippets from databases or knowledge repositories, supplementing the limited training data available. By doing so, these systems overcome the challenges posed by linguistic diversity and insufficient data, enabling models to handle complex conversational tasks with greater accuracy and fluency. This approach has proven effective in enhancing Swahili conversational AI, particularly for generating responses to queries that require detailed knowledge or contextual awareness [24]. These advancements highlight the importance of integrating modern techniques like RAG to overcome resource constraints in Swahili NLP. While traditional and multilingual models have laid the foundation for progress, RAG represents a significant step forward, offering a scalable and efficient way to improve Swahili conversational systems. This approach not only addresses the immediate challenges of data scarcity but also sets the stage for future innovations in NLP for other low-resource languages, contributing to a more inclusive digital landscape [25].
## 4 Discussions
### Human-Readable Vectors and Human-Generated Queries  
In Section 2, we have discussed the fact that all similarity measures are in [0, 1] range and that they can be interpreted by a human operator as a percentage similarity according to a specific feature. Whereas this interpretability is useful for analysis purposes, it can also be used to construct feature vectors while not having an actual image. The possibility to conduct search for an object without query images was investigated in the early days of re-ID [38]. In addition, it can be further improved by using our proposed approach, which includes human parsing.  
Constructing feature vectors can be performed as follows: let us assume that colors of the clothes of a person are known. The RGB values of the colors can be converted into Lab in order to instantly obtain triplets (\(I_{L}\), \(I_{a}\), \(I_{b}\)), as discussed discussed in Section 2.3.  
By using same triplets, binarized histograms can be generated by assigning several \"ones\" in a region surrounding the main color's intensity. Moreover, the LBP texture features are less intuitive, such that a look-up table of vectors in respect of typical textures can be used instead.  
Now, let us consider a situation in which a query is a vague verbal description. Comparing such a query with a database would be impossible for methods that rely on convolutional operations, i.e., all DL models, due to the fact that the image is missing. Generating vectors corresponding to a given verbal query is also impossible, as the feature vectors generated by DL models have no clear interpretation. This would require one to generate an artificial image, which is a very complex task and would require making assumptions of numerous unknown parameters [65].  
On the contrary, it is possible to search for matches regarding such a query using our proposed method. Figure 6 illustrates an example of a search for a dark hair person wearing a red shirt, black pants, and black shoes. For the purposes of this search, color descriptions are converted into vectors; in addition, texture features are generated using values typical for clothes in the CUHK03 dataset. Figure 7 illustrates another set of search results for a person wearing a white shirt. Hence, an operator needs to describe only relevant classes without the need to specify other class properties, e.g., the color of the shoes, if it is unknown.
## 2 Structure of Chitosan  
One of the major sources of chitin occurs in nature as crystalline microfibrils that form structural elements of exoskeletons of crustaceans, arthropods, and fungal cell walls. The structure of chitin resembles the structure of cellulose, but it consists of monomers of N-acetyl D-glucosamine units which are linked to each other via \(\beta\) (1\(\rightarrow\)4) linkages. The major difference between cellulose and chitin structures is the substitution of the hydroxyl group (OH) on the glucose molecule in cellulose with an amyl functional group in chitin Figure 1a. The presence of the acetyl amine group in the structure of chitin is responsible for increasing its capacity to form hydrogen bonds between adjacent polymers which imparts increased structural strength [5]. The chitin molecule can be converted into chitosan either by the action of enzymes or by the chemical hydrolysis method [6]. Chitosan is an N-deacetylated derivative of chitin, which is formed by the conversion of the acetamide groups in the chitin into a primary amine group in chitosan (Figure 1b). The molecular weight of chitosan is around \(5.3\times 10^{5}\) Daltons, with the nitrogen content varying from 5-8% [7,8]. To date, two polymorphs of chitosan have been discovered. The crystal structure of polymorphs can be either hydrated (tendon) form or anhydrous (annealed) form [9]. The degree of deacetylation of chitin governs the chemical reactivity of the chitosan molecule as the deacetylation process of chitin is never complete. The chitosan molecule is insoluble in water, which limits its usage, but it is soluble in dilute organic acids, and thus chitosan-acid salts are used extensively in industrial applications [10].  
Figure 1: (**a**) Difference between cellulose and chitin polymers. (**b**) Structure of chitosan polymer.
# Empirical Study on 5G NR Cochannel Coexistence
## 5 5G Performance for Industrial Control  
Industrial automation and control applications are typically characterized by mission-critical traffic, where data from sensors to controllers and command messages from the controllers to actuators are to be delivered with low latency and high reliability [4; 6]. The data traffic is thus in both UL and DL directions. In our empirical performance evaluation, we have studied the impact on the UL and DL latency and throughput of the non-public indoor network. The results have been analyzed with different TDD patterns, UE locations and interference directions in UL and DL from the outdoor network. The results presented in this article contain mission-critical traffic with a low to medium load, at which the overall throughput impact is insignificant. This is due to the inherent retransmission  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Scenario** & **Indoor UE Location** & **Outdoor UE Location** \\ \hline
1. Worst-case interference & 50 cm across from the window (red) & 50 cm across from the shopfloor window (yellow) \\
2. Robot cell & 10 m away from the window (blue) & 50 cm across from the shopfloor window (yellow) \\
3. Interference far away & 50 cm across from the window (red) & 15 m away from the shopfloor (green) \\
4. Robot cell—far away & 10 m away from the window (blue) & 15 m away from the shopfloor (green) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of indoor and outdoor UE deployment locations for each of the scenarios.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Network** & **5G Architecture** & **5G Frequency Band** & **4G Frequency Band** & **5G Bandwidth** \\ \hline Indoor & NSA & 3.7–3.8 GHz (n78) & 2300 MHz (B40) & 100 MHz \\ Outdoor & NSA & 3.7–3.8 GHz (n78) & 2300 MHz (B40) & 100 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Indoor and outdoor 5G network characteristics.  
Figure 2: (**a**) Outdoor campus network deployment in Industry Campus Europe (Aachen) and location of indoor test factory, Fraunhofer IPT. (**b**) Indoor and outdoor UEs deployment location for coexistence performance evaluation around the test factory hall [2]. The figure is based on Google Maps © 2021.  
and error control mechanisms in the 5G NR protocol stack, and hence we focus only on the effect on the latency performance. Moreover, for mission critical traffic, latency characteristic is an important performance metric instead of throughput. Please note that the throughput impact due to interference becomes visible only at high traffic conditions of the indoor network, as there are not enough system resources available due to the error control mechanisms.
# MeLa: A Programming Language for a New Multidisciplinary Oceanographic Float
## 2 A Programming Language Based on Models
### Mermaid Float Architecture  
The instrument (Figure 4) is made of a glass sphere that resists until a depth of 5000 m. A hydraulic circuit transfers oil between a tank inside the sphere and an outside bladder. When the bladder deflates, the instrument volume decreases and its density increases, allowing it to dive. Up to eight sensors can be installed on the instrument, although currently we have only experimented with a hydrophone and a Conductivity Temperature Depth (CTD) sensor to monitor water temperature, salinity and density. The hydrophone has two outputs to monitor sounds at low and high frequencies, between 0.1 Hz to 100 Hz and between 10 Hz to 10 kHz. The sampling frequency of each output can be chosen by the developer. A satellite antenna at the top of the instrument is used for positioning with the GPS and data transmission with the Iridium Router-Based Unrestricted Digital Internetworking Connectivity Solutions (RUDICS) protocol. Batteries with a total capacity of 4 kW.h, equivalent to four hundred times those of a smartphone (~10 W.h), power the float for years or months, depending on the power consumption of the applications. For the Mermaid floats currently operating to monitor the seismic activity, the expected lifetime is five years.  
The float contains two electronic boards. The pilot board manages the hydraulics (i.e., depth regulation) and communications (i.e., GPS, and Iridium). The acquisition board manages the sensors and processing of data. It has 512 kB of programmable memory, 8 MB of Static Random Access Memory (SRAM) and 128 GB of flash (SD card). The microcontroller is based on a Cortex-M4 core that integrates a Digital Signal Processor (DSP) and works at a frequency of 32 MHz. This is a very limited configuration when compared to a smart phone, but it has a low power consumption that is adapted for long-term operation.  
Both electronic boards are programmed with C languagel which allows writing software with efficient execution time and low memory usage. Both have a Real Time Operating System (RTOS) allowing them to execute several tasks concurrently. The pilot board software can be configured with several parameters, such as the dive duration, depth, and other ones more technical, such as the time interval between each depth correction. The acquisition board has access to the sensors and can be programmed by scientists with MeLa, which generates C code. Both of the boards can communicate with each other, for example, the acquisition board can ask to the pilot board for the ascent of the float.  
Figure 4: Mermaid float.
# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 3 Materials and Methods
### Comparison  
The correctness of the TFSW algorithm was tested using a number of statistical techniques, including the Pearson correlation coefficient (\(R\)), the root mean square deviation (\(RMSD\)), and bias. The equations for these statistical metrics are as follows:  
\[R=\frac{\sum_{i=1}^{n}(x_{\mathrm{i}}-x)(y_{\mathrm{i}}-y)}{\sqrt{\sum(x_{ \mathrm{i}}-x)^{2}}\;\sqrt{\sum(y_{\mathrm{i}}-y)^{2}}} \tag{15}\]where \(x_{i}\) are the estimated data, \(y_{i}\) is the MODIS LST product from the ith time, \(x\) is the overall average of the estimated data, \(y\) is the average of the MODIS data over the study period, and \(R\) is the Pearson correlation. In this scenario, \(R\) is between minus and plus 1.  
\[RMSD=\sqrt{\frac{\sum_{i=1}^{N}(Estimated_{i}-MODIS_{i})}{N}} \tag{16}\]  
\[\text{{Bias}}=\frac{\sum_{i=1}^{N}(Estimated_{i}-MODIS_{i})}{N} \tag{17}\]  
where \(N\) represents the number of pixels. \(RMSD\) was performed to see how well the particular variable correlation explained the observed variance. The difference between the actual and estimated variables is referred to as bias.
## 3 Software Platform and Simulation Tools
### Network Protocols  
The Network layer protocol's stack on the nodes list in Table 3 is among the most widely used in WSN. We use the feature of a collect-view application available in Contiki OS to allow data to be aggregated and categorized by a node in the network. It basically involves a sink node and one (or more) sender nodes. Data such as sensor, power, and network information is sent from the sender node to the sink node, and the collect-view (a java-based application available in Contiki OS Cooja) displays them in a graphical form [40]. In this work, the IPv6 Routing Protocol for LLNs (RPL) is set as a proactive routing protocol. RPL automatically forms a tree topology by exchanging ICMPv6 control packets to find a path to the root. 6 LowPAN (i.e., IPv6 over Low -Power Wireless Personal Area Networks) is an adaptation layer protocol that allows the transport of IPv6 packets over 802.15.4 links. Carrier sense multiple access with collision avoidance (CSMA/CA) adopted for Mac layer as the IEEE 802.15.4 standard, and finally, ContikiMAC are used as the RDC layer for control 802.15.4 radio based on Low Power Listening (LPL). The RDC layer in the sink is set to Null_RDC, so the sink does not sleep during network life.
## 3 Results
### Optical Properties
#### 3.2.1 UV-Vis Absorption Spectra  
As shown in Figure 3 for **5 mT** and **5 nT** in CH\({}_{3}\)CN (see Figures S23 and S24 of Supporting Information for spectra of all compounds in CH\({}_{3}\)CN and CH\({}_{2}\)Cl\({}_{2}\)), the compounds exhibited three maxima, \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\). The \(\lambda_{1}\) abs was not visible in CH\({}_{2}\)Cl\({}_{2}\) for all compounds due to the solvent cut-off. The -CN substituent in position 6,6\({}^{\prime}\) moved the \(\lambda_{3}\) absorption to the highest wavelengths with respect to -COOMe. The compounds with fluorophenyl substituents **5 mF** and **5 nF** displayed blue shift of the \(\lambda_{2,3}\) abs maxima of around 50 nm compared with the corresponding compounds with thiophenyl substituents. The compounds with phenothiazze **5 mP** and **5 nP** exhibited the intense signal at 250 nm due to the phenothiazze substituent (Figure S25, Supporting Information, the Phenothiazze UV spectrum) and the \(\lambda_{3}\) was difficult to visualize due to the low intensity, similar to the corresponding tetraphenyl fulvenes unsubstituted on the phenyl rings in a previous study [41].
# Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
## 3 Validating TL for Bridge Damage Detection Using Simulated Experiments
### FE Modeling  
Numerical models of the bridge were developed using SAP2000 [35]. The Open Application Programming Interface (OAPI) was used to assist with algorithm development that integrates MATLAB [36] with SAP2000 to automatically initialize numerical simulations. Model performance was validated against field data. To investigate potential damage at locations of interest identified in Figure 1, stringer connections were modeled using rotational springs, and connection damage levels, termed DI, were simulated by reducing their rotational stiffness [22].  
Structural response to the recorded train loads in a simulated healthy bridge was obtained in the form of strain time histories and validated against the measured data. For each train that traversed the bridge, the strain time histories at each sensor location were stored in the snapshot matrix. The Root Mean Square (RMS) means of the snapshot matrix was used to sort the train loads. The train events were then divided into four groups based on their RMS and the last group with a higher RMS (heavier trains) was involved in this study [14; 22]. Validated numerical models were used to examine structural response to several modeled damage scenarios representing fatigue cracks at stringer-to-floor beam connections [37]. Stringers connections were modeled using rotational springs and deficiency due to fatigue cracks was simulated by a gradual decrease in rotational stiffness at the connections. Rotational stiffness reductions were in 10% increments, starting with 0% (uncracked) to 100% (completely cracked). POMs were developed for multiple field recorded loading events for each damage scenario. At total of 20 DL and 24 train load configurations were considered, resulting in 4800 damaged and 24 healthy scenarios being modeled.  
Figure 1: (**a**) Isometric view, truss span; (**b**) plan and elevation views and strain transducer locations.
# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
## 4 Experiments
### GIS Filtering and Its Effectiveness
In contrast to the more minor horizontal errors in Table 5, the attitude errors in Table 6 are affected significantly by the application of the GIS filter, especially for the university campus area (see Figure 11), where features from building tops and the ground have different heights. The predicted altitude and reference altimeter readings are plotted as a function of time shown in Figure 12. When the platform flies over high buildings, most matching features are from the building tops. Without a GIS filter, those building features are mistakenly assumed on the ground, thus increasing the positioning error. On average, without the GIS filter, the MSE and maximum errors of altitude estimation are, respectively, 6.22 and 29.50 m. When the GIS filter is activated, the MSE decreases to 1.89 m with a 69.6% improvement, and the maximum error decreases to 6.40 m with a 78.3% improvement. This is visible in regions 1, 2, and 3, shown in Figure 13, where the UAS flies over tall buildings such as the Thompson Library or the RPAC building at the Ohio State University Campus. In multiple experiments at the UAS flying altitude of 110 m, we observed that buildings below 15 m do not violate the constraints for the pseudo-perspective and affine transformations, and the GIS filter is unnecessary. Our experiment results from the _parking_ and _Farm1_ columns in Table 6 validate this.
# Latency-Aware Accelerator of SIMECK Lightweight Block Cipher
## 3 Proposed 2-Stage Pipelined Architecture for SIMECK32/64  
The block design of our proposed SIMECK accelerator architecture is shown in Figure 1. It takes a 64-bit key and 32-bit data as input and results in 32-bit data as output. Moreover, a singular bit mode signal determines the encryption/decryption operation to be executed on the proposed design. For modes equal to 0 and 1, encryption and decryption operations are needed to compute. Furthermore, the internal architecture of our SIMECK latency-aware crypto core consists of (i) a data expansion, (ii) key expansion, and (iii) control unit blocks. The key and data expansion blocks determine the datapath of the SIMECK architecture while the control block produces the control signals that determine the operation of the circuit. The datapath is responsible for the execution of the encryption/decryption logic. Consequently, the corresponding details for these blocks is described in the next sections, i.e., Sections 3.1-3.3.
# Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System
## 2 Overall Design
### Genetic Algorithm Design
#### 2.3.2 Data Results and Verification  
In Figure 5a, the black square represents the slat angle value in the test set, the red circle represents the slat angle value predicted by BP, and the blue triangle represents the slat angle value predicted by GA\(-\)BP. It can be seen that the fitting degree of the GA\(-\)BP predicted value is higher. In Figure 5b, the red circle represents the error between the slat value predicted by GA\(-\)BP and the true value of the slat angle in the test set. The black squares represent the error between the slat value predicted by BP and the true value of the slat angle in the test set. It can be found that the GA\(-\)BP error value is small, the maximum is 0.5\({}^{\circ}\), and the optimized network has a higher accuracy.  
Figure 5: **(a)** The true value of the slat angle is in the test set, the angle value of slats is predicted by BP, and the angle value of slats is predicted by GA\(-\)BP; and **(b)** the error of BP and GA\(-\)BP in predicting the angle value of slats and the true value.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Time** & **Sun Altitude Angle** & **Sun Azimuth Angle** & **The Angle of Slats** \\ \hline
4 February—Beginning of Spring & 10:48 a.m. & 31.2\({}^{\circ}\) & 148.1\({}^{\circ}\) & 32.8\({}^{\circ}\) \\
6 May—Beginning of Summer & 10:48 a.m. & 61.0\({}^{\circ}\) & 126.7\({}^{\circ}\) & 50.8\({}^{\circ}\) \\
8 August—Autumn begins & 10:48 a.m. & 68.7\({}^{\circ}\) & 155.9\({}^{\circ}\) & 50.2\({}^{\circ}\) \\
8 November—Beginning of Winter & 10:48 a.m. & 37.4\({}^{\circ}\) & 175.9\({}^{\circ}\) & 33.7\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sun altitude angle and Sun azimuth angle data on different dates in 2023.